Terms Of Input Research Articles

Nonlinear Adaptive Back-Stepping Optimization Control of the Hydraulic Active Suspension Actuator

The displacement tracking performance of the electro-hydraulic servo actuator is critical for hydraulic active suspension control. To tackle the problem of slow time-varying parameters in the existing actuator dynamics model, a nonlinear adaptive back-stepping control (ABC) approach is adopted. Simultaneously, the parameters of the nonlinear ABC are difficult to configure, resulting in a poor control effect. An enhanced particle swarm optimization (PSO) approach integrating crazy particles (CP) and time-varying acceleration coefficients (TVAC) is suggested to optimize the controller settings. Furthermore, in order to obtain satisfactory dynamic characteristics of the transition process, the absolute value of the error time integral performance index is used as the minimum performance index function of parameter selection, and the square term of the control input is added to the performance index function to prevent excessive controller energy. Finally, it can be observed from the simulation results of the highest value emax of the displacement tracking error, the average value eμ of error, and the standard deviation eσ of error that the performance of the ABC parameters optimized by PSO+CP+ATVC is superior to the manually given ABC parameters. Therefore, this control method significantly improves the stability and speed of the control system. It provides a new research idea for the parameter optimization of controllers.

CNN-based classification of phonocardiograms using fractal techniques

Deep Learning based heart sound classification is of significant interest in reducing the burden of manual auscultation through the automated detection of signals, including abnormal heartbeats. This work presents a method for classifying phonocardiogram (PCG) signals as normal or abnormal by applying a deep Convolutional Neural Network (CNN) after transforming the signals into 2D color images. In particular, a new methodology based on fractal theory, which exploits Partitioned Iterated Function Systems (PIFS) to generate 2D color images from 1D signals is presented. PIFS have been extensively investigated in the context of image coding and indexing on account of their ability to interpolate and identify self-similar features in an image. Our classification approach has shown a high potential in terms of noise robustness and does not require any pre-processing steps or an initial segmentation of the signal, as instead happens in most of the approaches proposed in the literature. In this preliminary work, we have carried out several experiments on the database released for the 2016 Physionet Challenge, both in terms of different classification networks and different inputs to the networks, thus also evaluating the data quality. Among all experiments, we have obtained the best result of 0.85 in terms of modified Accuracy (MAcc).

CLASSIM: A relational database driven crop model interface

Crop models are valuable tools for examining the interactions of cultivar characteristics, environment, and management practices, and how they affect crop growth and development. The difficulty in finding all the data needed to set up a simulation can often deter potential users from utilizing a crop model. Model interfaces are necessary to make these complex tools accessible to end-users who may lack the expertise needed to work with the models directly, but who would benefit from the information generated by the models. As crop models vary in terms of input and output structures, there is no one universally compatible interface, so different crop model suites require their own interface. CLASSIM is a graphical user interface (GUI) for a suite of models developed by USDA-ARS. The interface provides a simplified tool to assemble input data for a model simulation. Web access to online databases for soil and weather data simplifies finding site-related data. The user is guided step by step to build the necessary input files. A database structure facilitates data entry, storage, and retrieval. The use of Structured Query Language (SQL) for data retrieval allows for advanced analysis and visualization outside of the interface. We describe the features of the GUI and provide examples of various operations. Model outputs are provided in tables and graphs to allow for visualization and analysis. Two-dimensional contour plots of soil processes are provided to visualize output from the two-dimensional finite element soil model. CLASSIM also allows users to set up single- and multiple-season runs for maize, cotton, soybean, potato, and fallow treatments, and is expected to be expanded to more crops in the future. A user-friendly GUI is essential for making simulation tools accessible to the broad range of researchers and practitioners working to solve the agricultural system challenges.

A systematic review on open-set segmentation

Open-set semantic segmentation remains yet a challenging task, not only due to the inherent challenges of pixel-wise classification but also the precise segmentation of categories not seen during training. The pursuit of that task is rapidly growing in the Computer Vision community, urging the need to organize the literature. In this paper, we extend our previous work by conducting a more comprehensive systematic mapping of the open-set segmentation literature between January 2001 and January 2023 and proposing a novel taxonomy. Our goal is to provide a broad understanding of current trends for the open-set semantic segmentation (OSS) task defined by existing approaches that may influence future methods. By characterizing methodologies in terms of open-set identification strategies, data inputs, and other relevant aspects, we present a structured view of how researchers are advancing in the field of open-set semantic segmentation. To the best of the authors’ knowledge, this is the first systematic review of OSS methods. Moreover, we apply the proposed taxonomy to selected methods for open-set recognition, outlining important similarities and differences of such a closely related field.

All Along the Watch Tower: The European Central Bank and Legitimation Strategies in Times of Crisis

AbstractHow does the European Central Bank (ECB) legitimise its decisions and monetary policy programmes in the period from 2009 to 2021 in terms of input, throughput and output legitimacy? This article contains an answer to this important question based on a content analysis of speeches made by the presidents of the ECB. Ideally, the institution is founded on its independence and therefore cannot maintain legitimacy through public influence. This article proves that there are clearly most references on output legitimacy in the ECB's legitimation strategy, showing that it highlights its results as an independent institution to compensate for its lack of democratic channels in terms of legitimacy. Generally, in times of crises, there is an increase in frequency of references to legitimacy. During these periods, ECB actors also more frequently refer to throughput legitimacy in speeches, showing that the institution is in fact sensitive to public criticism.

An Evaluation of Selo Kencono Futsal Club (SKNFC) Kebumen Coaching Program

This study aims to evaluate the futsal club program using the CIPP model (context, input process & product) with a qualitative approach and an interactive analysis model from Miles & Huberman. This research uses a descriptive qualitative approach method. The object of evaluation is SKN FC Kebumen. Six participants were deliberately selected as interview participants. Data collection was carried out as follows: (1) making indirect observations; observing news about the object of evaluation, direct observation; observing activities in the mess and team exercises (2) conducting documentation, (3) conducting interviews with six participants. The instrument was validated by using ‘expert judgment’. Success criteria evaluate each CIPP variable with three categories: good, fair, and poor. With the CIPP analysis combined with a comprehensive approach, the evaluation results in terms of context include vision and mission and the club’s goals in living the league is included in th e “fair” category. In terms of input, which includes recruiting players and coaches is in the “good” category. Regarding the process, which includes implementing training programs, provision, use, maintenance of facilities and infrastructure, and funding is included in the “fair” category. In terms of products, which include positive and negative results, alignment of goals with costs and achievements generated, program success, and programs that can be continued is in the “good” category.

Feasibility of cold wire gas metal arc welding AA5052 H-32

The feasibility of applying cold wire gas metal arc welding (CW-GMAW) is evaluated by comparison to standard gas metal arc welding (GMAW) 5 mm-thick AA5052 butt-joints. The results show that the variability in porosity (and potentially overall total) is decreased by feeding 20 or 40 % additional cold wire in CW-GMAW welds, and with weld metal microstructures comparable to standard GMAW welds. In terms of nominal heat input, CW-GMAW offers lower values with increased deposition rates, and thermal distortion could be decreased by feeding 20 or 40 % additional cold wire in CW-GMAW welds. The microstructures in CW-GMAW appear to be more refined compared to standard GMAW.

A data-driven framework to model the organism-environment system.

Organisms modify their development and function in response to the environment. At the same time, the environment is modified by the activities of the organism. Despite the ubiquity of such dynamical interactions in nature, it remains challenging to develop models that accurately represent them, and that can be fitted using data. These features are desirable when modeling phenomena such as phenotypic plasticity, to generate quantitative predictions of how the system will respond to environmental signals of different magnitude or at different times, for example, during ontogeny. Here, we explain a modeling framework that represents the organism and environment as a single coupled dynamical system in terms of inputs and outputs. Inputs are external signals, and outputs are measurements of the system in time. The framework uses time-series data of inputs and outputs to fit a nonlinear black-box model that allows to predict how the system will respond to novel input signals. The framework has three key properties: it captures the dynamical nature of the organism-environment system, it can be fitted with data, and it can be applied without detailed knowledge of the system. We study phenotypic plasticity using in silico experiments and demonstrate that the framework predicts the response to novel environmental signals. The framework allows us to model plasticity as a dynamical property that changes in time during ontogeny, reflecting the well-known fact that organisms are more or less plastic at different developmental stages.

Effectiveness of Implementation of the Child-Friendly School Program

The purpose of this research is to identify the alignment or misalignment between the implementation of the Program on CFS (Child-Friendly School) according to the guidelines outlined in the Child-Friendly School Handbook, as stated in the Minister of Women Empowerment and Child Protection Regulation No. 08 of 2014 concerning Child-Friendly School Policies, with the actual practices in schools that have already implemented the CFS program. The research methodology employed in this study is descriptive qualitative. Data collection methods include document verification, interviews, and observations. Based on the research findings, it is evident that thorough and meticulous preparation and planning are crucial for the successful implementation of the program. Additionally, strong commitment from all parties involved, comprehensive understanding, intensive training, and strict supervision are essential for program execution. The evaluation indicates that in terms of Context, Input, and Process aspects, the average results fall under the category of "Adequate," while in the Product aspect, the average results are categorized as "Good." Therefore, the overall evaluation yields an "Adequate" rating for the implementation of the Child-Friendly School Program in the public junior high schools in Depok City, West Java. This research implies that it can enhance awareness and understanding of child-friendly school policies and contribute to the development of guidelines and best practices.

A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

PurposeThe purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.Design/methodology/approachThe authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”FindingsThey synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.Research limitations/implicationsThe main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.Originality/valueThere is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

Sterilize Methods Comparison for Soils: Cost, Time, and Efficiency

Soil sterilization is generally used to eliminate or reduce microbial activity in studies involving microbial inoculations, soil enzymes, among others. Achieving an adequate sterility condition is not straightforward due to the variety of resistance structures that are generated in soil microbial ecosystems and the reservoirs that can form between soil aggregates. This is why finding an effective method to achieve good sterilization is important in methodological terms, so the present work aims to compare the effectiveness of three widely used methodologies to sterilize soil and to evaluate their cost/benefit in terms of time and inputs invested. Four treatments were tested: gamma irradiation, sterilization cycles at different times: three cycles of 1 h each and four cycles of 15 min each, and chloroform vapors. The evaluation and comparison of all samples sterilized by the different methodologies were based on the total aerobic heterotrophic bacterial count. The results of this study suggest that it is more efficient to use autoclaving methods because the process is more accessible in terms of equipment and methodologies, and the final results are the same. In the case of this work, sterilization with chloroform vapors had to be rejected. While the use of gamma radiation may be more efficient in terms of time, it can be a costly and inaccessible service for some laboratories that do not have the equipment. Therefore, the most viable options in terms of time, cost, and benefit are those using autoclaves. Among these, shorter treatment times mean a reduction in the cost of using the equipment, so the option of 15-minute cycles is desirable.

Exploring how EU agri-food SMEs approach technology-driven business model innovation

The EU agri-food sector, which is mainly composed of small and medium-sized enterprises has become more demanding in terms of technological inputs to reduce costs, improve the added value of food products, achieve sustainability issues, or address new market opportunities. The introduction of new technologies poses challenges for SMEs that lack the resources and time to cope with the technological transformation, which involves not only the assimilation of new technology in organizational processes but also business model innovation. In this context, this research aims to empirically explore the paths of agri-food SMEs engaged in the technological transformation process, based on the drivers and barriers, as well as the strategies used by these companies to overcome these barriers. In-depth semi-structured interviews with 14 EU agri-food SMEs were conducted and analysed using a combination of deductive pattern-matching and inductive approach. Based on similarities in terms of drivers, barriers, and strategies, seven main groups were identified to draw the paths of the technological transformation process for agri-food SMEs. Findings reveal that there is one common strategy that is adopted by all 14 companies, namely the employment of different types of partnerships and collaborations that allow companies to successfully enter new markets. The study contributes to the literature on technology transformation related to the agri-food sector, by proving insights into how EU agri-food SMEs cope with this process.

Co-designed Practical Use of Probabilistic Climate Advisories among Smallholder Farmers: A Balance between Confidence and Caution

Especially for smallholder farmers with limited land and financial resources, farming in arid and semi-arid lands (ASALs), where season-to-season rainfall fluctuation dictates production, is a risky business. Through participatory approaches, this study compares deterministic and probabilistic interpretations of climate forecasts and their use by smallholder farmers through a crop-growing season. The study revealed that deterministic advisories are good for smallholder farmers only when formulated from forecasts with higher accuracy than the historical climatological distribution. Otherwise, they cause farm loss in terms of labor and inputs. On the other hand, probabilistic advisories help farmers spread the risk to cater to all the uncertainty and in so doing bring out a balance between confidence and caution. However, farmers must be supported with enough sensitization to comprehend forecast probability, translate it into probabilistic advisories and use that to plan and manage farm activities. The findings support the hypothesis providing packaged climate products in transparent probabilistic terms in place of deterministic form can overcome inherent credibility challenges. The study’s conclusion highlights important takeaways and new understandings of the advantage of using probabilistic advisories among resource-poor smallholder farmers.

Off-Policy Risk-Sensitive Reinforcement Learning-Based Constrained Robust Optimal Control

This article proposes an off-policy risk-sensitive reinforcement learning (RL)-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment. The risk-aware value function, constructed using the pseudo control and risk-sensitive input and state penalty terms, is introduced to convert the original constrained robust stabilization problem into an equivalent unconstrained optimal control problem. Then, an off-policy RL algorithm is developed to learn the approximate solution to the risk-aware value function. During the learning process, the associated approximate optimal control policy is able to satisfy both input and state constraints under disturbances. By replaying experience data to the off-policy weight update law of the critic neural network, the weight convergence is guaranteed. Moreover, online and offline algorithms are developed to serve as principled ways to record informative experience data to achieve a sufficient excitation required for the weight convergence. The proofs of system stability and weight convergence are provided. The Simulation results reveal the validity of the proposed control framework.

EVALUATION OF STUDENT CHARACTER EDUCATION PROGRAM KATOLIK WIDYA MANDIRA UNIVERSITY

This study aims to analyze and determine the effectiveness of the implementation of student character education programs at Widya Mandira Catholic University in terms of context, input, process and product variables. This research includes evaluative research, which evaluates the implementation of learning programs. In this study, analyzing the effectiveness of implementing learning with the CIPP model (context, input, process and product). Respondents in this study were 150 students. Data was collected using questionnaires, interviews, observations, documents and APKG. Data were analyzed by descriptive analysis. To determine the effectiveness of implementing the student character education program, raw scores were transformed into Z then into T-Scores then verified into the Glickman prototype. In connection with the findings of this evaluative study, the results of the analysis of the context, input, process and product variables respectively show a value of (+ + +-), and if converted into the Glickman quadrant, the quality of the effectiveness of implementing student education programs at Widya Catholic University Mandira in 2021, classified as effective. To ensure that this program is more effective, the substance that needs to be refined is the product factor

Marine Biodegradability and Ecotoxicity of MWool® Recycled Wool Fibers: A Circular-Economy-Based Material

Pollution of the marine environment by microfibers is considered a problem for ecosystem conservation. The amount of microplastic, localization of sources, and associated ecotoxicity are well known in the literature. Wastewater from washing machines is the main source of microplastic fibers in the aquatic environment, and fabrics made from recycled plastic are widely reused. The circular economy also promotes recycling of dyed natural wool materials as a basis for making new clothing, but in this case, less research has been conducted on the behaviour and effects of recycled wool microfibers in marine ecosystems. MWool® (MW) and MWool® carded (MWc) products made from recycled wool fibers were tested in mesocosms to investigate the biodegradation of wool fibers over a 260-day period and the effects of this process on marine ecosystems in terms of microfiber inputs and the ecotoxicological effects of by-products and chemicals released during degradation. The early degradation process was associated with the loss of artificial pigments from the dyed wool, particularly pink and red, which occurred within 30–90 days of exposure. Mean release of microparticles into contact water is significantly different from control (T0, p < 0.01) at 90 days MWc (36.6 mg/L) and 180 days MW (42.9 mg/L). The biodegradation process is accompanied by swelling of wool fibers, which is associated with a significant increase in mean wool thickness (p < 0.05, 18.8 ± 2.1 µm at T0 vs. 24.0 ± 7.1 µm). In both cases, the contact water was not associated with signs of ecotoxicity for the marine species tested in this study (Phaeodactylum tricornutum, Brachionus plicatilis, and Paracentrotus lividus).

AI enhanced collaborative human-machine interactions for home-based telerehabilitation.

The use of robots in a telerehabilitation paradigm could facilitate the delivery of rehabilitation on demand while reducing transportation time and cost. As a result, it helps to motivate patients to exercise frequently in a more comfortable home environment. However, for such a paradigm to work, it is essential that the robustness of the system is not compromised due to network latency, jitter, and delay of the internet. This paper proposes a solution to data loss compensation to maintain the quality of the interaction between the user and the system. Data collected from a well-defined collaborative task using a virtual reality (VR) environment was used to train a robotic system to adapt to the users' behaviour. The proposed approach uses nonlinear autoregressive models with exogenous input (NARX) and long-short term memory (LSTM) neural networks to smooth out the interaction between the user and the predicted movements generated from the system. LSTM neural networks are shown to learn to act like an actual human. The results from this paper have shown that, with an appropriate training method, the artificial predictor can perform very well by allowing the predictor to complete the task within 25s versus 23s when executed by the human.

Pulsed electric fields-assisted extraction of valuable compounds from red grape pomace: Process optimization using response surface methodology.

The application of Pulsed electric fields as a mild and easily scalable electrotechnology represents an effective approach to selectively intensify the extractability of bioactive compounds from grape pomace, one of the most abundant residues generated during the winemaking process. This study addressed the optimization of the pulsed electric fields (PEF)-assisted extraction to enhance the extraction yields of bioactive compounds from red grape pomace using response surface methodology (RSM). The cell disintegration index (Z p ) was identified as response variable to determine the optimal PEF processing conditions in terms of field strength (E = 0.5-5 kV/cm) and energy input (WT = 1-20 kJ/kg). For the solid-liquid extraction (SLE) process the effects of temperature (20-50°C), time (30-300min), and solvent concentration (0-50% ethanol in water) on total phenolic content (TPC), flavonoid content (FC), total anthocyanin content (TAC), tannin content (TC), and antioxidant activity (FRAP) of the extracts from untreated and PEF-treated plant tissues were assessed. The phenolic composition of the obtained extracts was determined via HPLC-PDA. Results demonstrated that the application of PEF at the optimal processing conditions (E = 4.6 kV/cm, WT = 20 kJ/kg) significantly enhanced the permeabilization degree of cell membrane of grape pomace tissues, thus intensifying the subsequent extractability of TPC (15%), FC (60%), TAC (23%), TC (42%), and FRAP values (31%) concerning the control extraction. HPLC-PDA analyses showed that, regardless of the application of PEF, the most abundant phenolic compounds were epicatechin, p-coumaric acid, and peonidin 3-O-glucoside, and no degradation of the specific compounds occurred upon PEF application. The optimization of the PEF-assisted extraction process allowed to significantly enhance the extraction yields of high-value-added compounds from red grape pomace, supporting further investigations of this process at a larger scale.

Simulating Meteorological and Water Wave Characteristics of Cyclone Shaheen

The Bay of Bengal and Arabian Sea are annually exposed to severe tropical cyclones, which impose massive infrastructure damages and cause the loss of life in coastal regions. Cyclone Shaheen originally generated in the Bay of Bengal in 2021 and translated a rare east-to-west path toward the Arabian Sea. Although the cyclone’s wind field can be obtained from reanalysis datasets such as ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts), the wind speed cannot be reproduced with realistic details in the regions close to the center of cyclone due to spatial resolution. In this study, to address this problem, the high-resolution advanced Weather Research and Forecasting (WRF) model is used for simulation of Shaheen’s wind field. As a critical part of the study, the sensitivity of the results to the planetary boundary layer (PBL) parameterization in terms of the track, intensity, strength and structure of the cyclone Shaheen is investigated. Five experiments are considered with five PBL schemes: Yonsei University (YSU); Mellor–Yamada–Janjić (MYJ); Mellor–Yamada–Nakanishi–Niino level 2.5 (MYNN); Asymmetric Convective Model version 2 (ACM2); Quasi-Normal Scale Elimination (QNSE). The track, intensity, and strength of the experiments are compared with the wind fields obtained from the Joint Typhoon Warning Centre (JTWC) dataset. The results imply the high dependency of the track, intensity, and strength of the cyclone to the PBL parameterization. Simulated tracks with non-local PBL schemes (YSU and ACM2) outperformed those of the local PBL schemes (MYJ, MYNN, and QNSE), especially during the rapid intensification phase of Shaheen before landfall. The YSU produced highly intensified storm, while the ACM2 results are in better agreement with the JTWC data. The most accurate track was obtained from the ERA5 data; however, this dataset overestimated the spatial size and underestimated the wind speed. The WRF model using either YSU or ACM2 overestimated the wind speed compared to that of the altimeter data. The YSU and ACM2 schemes were able to reproduce the observed increase in wind speed and pressure drop at in situ stations. The wind data from EAR5 and cyclone parametric model were applied to the SWAN model to simulate the wave regime in the Arabian Sea during the time that Shaheen was translating across the region. Janssen formulation for wind input and whitecapping dissipation source terms in combination with both ERA5 and hybrid wind were used and the minimum combined error in the prediction of significant wave height (Hs) and zero up-crossing wave period (Tz) was examined. The maximum significant wave height for hybrid wind is higher than that of ERA5, while the cyclone development was successfully inferred from the wave field of the hybrid data.

Advanced Kalman Filter-Based Backstepping Control of AC Microgrids: A Command Filter Approach

The stability of alternating current microgrids (ac MGs) is significantly affected by the procedure of collecting precise and sufficient information of the system and tightly controlling power inverters. Whereas using many sensors increases ac MG ripples and cost, integrating cost-effective and low number of sensors is preferred. Furthermore, assuring the stability and tracking issue of ac MGs in different operating modes and in the presence of unknown time-varying loads is a hard task. Aiming at these issues, this article proposes an improved augmented-Kalman filter to estimate the state vector and disturbance inputs and a nonlinear backstepping controller with a command filter to design the control law. Compared to the conventional Kalman filters, the developed approach is able to estimate the external disturbances, which improves the state estimation performance and provides extra information about the power system. The proposed command filter-based backstepping has the key feature of avoiding the calculation of time-derivatives of desired references of virtual inputs, which is a common drawback of conventional approaches. Whereas the dynamics of the disturbance time-varying load are not available, the command filter is utilized to avoid the time derivatives terms of the disturbance inputs. Simulation results illustrate the estimation performance of the augmented Kalman filter and the tracking performance of the command filter-based backstepping controller.