Choice Of Inputs Research Articles

Dynamic analysis and application of network structure control in risk conduction in the industrial chain

According to control theory, a dynamical system is controllable if, with a suitable choice of inputs, it can be driven from any initial state to any desired final state within a finite time. Most dynamic characteristics of real networks are nonlinear, so achieving target control is more practical and necessary. The network’s control energy is also a problem that must be considered. Whether and how to control the complex system of the industrial chain has high theoretical and practical significance. In this study, we use the GARCH model, DCC model, and network structure control theory comprehensively to study the price fluctuation risk of the mining stock market from the perspective of the industry chain and network control dynamics and obtain interesting results. (1) Risk conduction among stocks has a prominent industry-driving effect, and the risk conduction ability of upper and middle stocks is stronger. (2) The risk regulation cost, time cost, and node number cost of the whole-industry chain are all higher than those of the two-tier chain, which indicates that the correlation complexity of the network has a positive relationship with risk control. (3) Key risk nodes play an essential role in risk control, so monitoring key stocks from the industrial chain perspective is necessary to control risks in time. This work can provide valuable suggestions for market regulators and policy-makers in terms of risk management and control.

Using ChatGPT and determinologisation to enhance understanding of lung cancer information

Abstract Lung cancer is associated to high mortality rates and has a large impact on the quality of life of patients and families, who therefore need suitable information to deal with the situation. However, information provided by health services is often not adapted to lay users and retains a considerable number of technicalities that impair comprehensibility. Accessibility, a concept receiving increasing attention, not only involves physical access but also understanding relevant information when it comes to the medical setting. One of the main intralingual translation procedures used to adapt specialised text is determinologisation, which comprises strategies like using common-speech synonyms, explanations, examples, etc. Recent artificial intelligence generative models offer a promising tool to produce texts at different specialisation levels. In this study, we evaluated the capacity of ChatGPT for determinologisation of terms extracted from a corpus of patient-oriented lung-cancer texts and compared the results with reliable patient-oriented online sources. ChatGPT produced definitions and context information similar to those of the online sources in a very short amount of time. However, both the choice of suitable input prompts and the post-edition process necessary to produce quality final texts on lung-cancer information still required the supervision of human experts.

Assessing the Impact of Rainfall Inputs on Short-Term Flood Simulation with Cell2Flood: A Case Study of the Waryong Reservoir Basin

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

Data-driven blood glucose level prediction in type 1 diabetes: a comprehensive comparative analysis

Accurate prediction of blood glucose level (BGL) has proven to be an effective way to help in type 1 diabetes management. The choice of input, along with the fundamental choice of model structure, is an existing challenge in BGL prediction. Investigating the performance of different data-driven time series forecasting approaches with different inputs for BGL prediction is beneficial in advancing BGL prediction performance. Limited work has been made in this regard, which has resulted in different conclusions. This paper performs a comprehensive investigation of different data-driven time series forecasting approaches using different inputs. To do so, BGL prediction is comparatively investigated from two perspectives; the model’s approach and the model’s input. First, we compare the performance of BGL prediction using different data-driven time series forecasting approaches, including classical time series forecasting, traditional machine learning, and deep neural networks. Secondly, for each prediction approach, univariate input, using BGL data only, is compared to a multivariate input, using data on carbohydrate intake, injected bolus insulin, and physical activity in addition to BGL data. The investigation is performed on two publicly available Ohio datasets. Regression-based and clinical-based metrics along with statistical analyses are performed for evaluation and comparison purposes. The outcomes show that the traditional machine learning model is the fastest model to train and has the best BGL prediction performance especially when using multivariate input. Also, results show that simply adding extra variables does not necessarily improve BGL prediction performance significantly, and data fusion approaches may be required to effectively leverage other variables’ information.

Smallholder access to purchased seeds in the presence of pervasive market imperfections and rainfall shocks: panel data evidence from Malawi and Ethiopia

Seed purchasing enables farmers to respond to adverse events that may cause chronic and temporary seed insecurity by allowing them to exploit opportunities associated with accessing new seeds. However, as with other inputs, seed purchasing is complicated by pervasive market imperfections and climate risk common in Sub-Sahara Africa. This study uses balanced household panel data for Malawi (2010–2018) and Ethiopia (2012–2016) and applies dynamic random effects Probit and Tobit models to assess how seed purchase decisions are affected by earlier participation in the market, lagged rainfall shocks, and historical climate variables. Our findings show that there are nonlinear effects of lagged seed purchase decisions on subsequent decisions with strong initial effects (weakening over time). For instance, initial maize seed purchase decisions are associated with about 11 and 22% higher probability of purchase and 1 and 2% higher shares of seed volumes purchased in later rounds in Malawi and Ethiopia, respectively. Seed purchase decisions also respond to climate variability and shocks. For instance, lagged drought shocks enhance subsequent maize purchase decisions in both countries. Historical average rainfall and temperature enhance maize seed purchase decisions in both countries. Overall, results point to state dependency on the demand side of the seed market, leading to selective access to purchased seeds. Also, seed purchase in smallholder farming is a liquidity and risk-dependent input choice. Policy efforts need to continue targeting reducing transaction costs and other barriers to entry into seed markets to enhance access to off-farm seed and support adaptation to rainfall shocks.

Ultrafast jet classification at the HL-LHC

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O(100) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

How do dental practices respond to changes in scope of practice regulations?

Regulations that restrict the tasks that credentialed workers are allowed to perform may affect a firm's input choices, output, and which part of the market the firm serves. Using dental practice survey data from 1989 to 2014 and a stacked difference-in-differences design, this paper examines the effects of state-level scope of practice regulations on the behavior of dental practices. Results suggest that scope of practice deregulation in regards to dental hygienists' ability to administer nitrous oxide or local anesthesia is associated with fewer dentist visits per week in the short-term, lower patient wait times, and an increased likelihood of treating lower revenue generating publicly insured patients. There is weak evidence that scope of practice deregulation alters a practice's labor inputs.

Efficient Sleep–Wake Cycle Staging via Phase–Amplitude Coupling Pattern Classification

The objective and automatic detection of the sleep–wake cycle (SWC) stages is essential for the investigation of its physiology and dysfunction. Here, we propose a machine learning model for the classification of SWC stages based on the measurement of synchronization between neural oscillations of different frequencies. Publicly available electrophysiological recordings of mice were analyzed for the computation of phase–amplitude couplings, which were then supplied to a multilayer perceptron (MLP). Firstly, we assessed the performance of several architectures, varying among different input choices and numbers of neurons in the hidden layer. The top performing architecture was then tested using distinct extrapolation strategies that would simulate applications in a real lab setting. Although all the different choices of input data displayed high AUC values (>0.85) for all the stages, the ones using larger input datasets performed significantly better. The top performing architecture displayed high AUC values (>0.95) for all the extrapolation strategies, even in the worst-case scenario in which the training with a single day and single animal was used to classify the rest of the data. Overall, the results using multiple performance metrics indicate that the usage of a basic MLP fed with highly descriptive features such as neural synchronization is enough to efficiently classify SWC stages.

Energy Tax Exemptions and Industrial Production

Abstract This paper investigates the impact of a large electricity tax exemption on production levels, employment and input choices in the German manufacturing industry. For two policy designs, we show that exempted plants increase their electricity use. This effect is larger under a notched exemption policy, where passing an eligibility threshold yields infra-marginal benefits, compared to a policy without such benefits. We detect no significant impact of the exemptions on production levels, export shares and employment. Using counterfactual simulations, we document that notched policies substantially distort firms’ production input choices when financial stakes are high and compliance costs are low.

Using the inverse finite‐element method to harmonise classical modal analysis with fibre‐optic strain data for robust population‐based structural health monitoring

AbstractVibration‐based approaches to structural health monitoring (SHM) gained increasing significance for assessing the behaviour of existing structures because of their non‐intrusive nature and high sensitivity to damage. However, data availability often limits the application of SHM approaches. The population‐based structural health monitoring (PBSHM) theory addresses this challenge, enhancing diagnostic inferences by sharing knowledge across a population of similar structures. In real‐life scenarios, sharing data from distinct structures requires dealing with results obtained with different experimental setups, multiple sensors, input choices and acquisition systems. Therefore, it is crucial to harmonise various features to achieve accurate and reliable results. The present study presents the results of a classic experimental modal analysis (EMA) using scanning laser Doppler vibrometer (SLDV) measurements and a strain‐based EMA conducted using high‐definition distributed fibre‐optic strain sensors. The experimental case study of a laboratory‐scale steel aircraft subjected to specific operating and damage conditions is introduced, allowing for a comprehensive discussion of the features extracted from the two EMA techniques, which can also be generalised to structures within different domains. This research highlights the advantages and limitations of fibre‐optic‐based EMA compared to classic methods, as fibre‐optic strain sensors offer a cost‐effective alternative to accelerometers or SLDV for dynamic testing. Furthermore, the feasibility of employing the inverse finite‐element method (iFEM) in the dynamic domain is investigated. This method can estimate the whole displacement field of a structure from a limited number of strain values, thus harmonising strain measurements with the SLDV measurements. By analysing the features extracted from different EMA techniques within the PBSHM framework, this study contributes to advancing the understanding and application of the PBSHM approach in diverse experimental scenarios, laying the foundation for further investigation of features and adequate methods for sharing damage‐state knowledge across a population of structures.

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.

Multi-criteria decision analysis: technique for order of preference by similarity to ideal solution for selecting greener analytical method in the determination of mifepristone in environmental water samples

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment.

Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984-2022.

This study reviews scholarly publications on data envelopment analysis (DEA) studies on acute care hospital (ACH) efficiency published between 1984 and 2022 in scholarly peer-reviewed journals.We employ systematic literature review (SLR) method to identify and analyze pertinent past research using predetermined steps. The SLR offers a comprehensive resource that meticulously analyzes DEA methodology for practitioners and researchers focusing on ACH efficiency measurement. The articles reviewed in the SLR are analyzed and synthesized based on the nature of the DEA modelling process and the key findings from the DEA models. The key findings from the DEA models are presented under the following sections: effects of different ownership structures; impacts of specific healthcare reforms or other policy interventions; international and multi-state comparisons; effects of changes in competitive environment; impacts of new technology implementations; effects of hospital location; impacts of quality management interventions; impact of COVID-19 on hospital performance; impact of teaching status, and impact of merger. Furthermore, the nature of DEA modelling process focuses on use of sensitivity analysis; choice of inputs and outputs; comparison with Stochastic Frontier Analysis; use of congestion analysis; use of bootstrapping; imposition of weight restrictions; use of DEA window analysis; and exogenous factors. The findings demonstrate that, despite several innovative DEA extensions and hospital applications, over half of the research used the conventional DEA models. The findings also show that the most often used inputs in the DEA models were labor-oriented inputs and hospital beds, whereas the most frequently used outputs were outpatient visits, followed by surgeries, admissions, and inpatient days. Further research on the impact of healthcare reforms and health information technology (HIT) on hospital performance is required, given the number of reforms being implemented in many countries and the role HIT plays in enhancing care quality and lowering costs. We conclude by offering several new research directions for future studies.

Myths and methodologies: Assessment of dynamic cerebral autoregulation by the mean flow index.

The mean flow index-usually referred to as Mx-has been used for assessing dynamic cerebral autoregulation (dCA) for almost 30years. However, concerns have arisen regarding methodological consistency, construct and criterion validity, and test-retest reliability. Methodological nuances, such as choice of input (cerebral perfusion pressure, invasive or non-invasive arterial pressure), pre-processing approach and artefact handling, significantly influence mean flow index values, and previous studies correlating mean flow index with other established dCA metrics are confounded by inherent methodological flaws like heteroscedasticity, while the mean flow index also fails to discriminate individuals with presumed intact versus impaired dCA (discriminatory validity), and its prognostic performance (predictive validity) across various conditions remains inconsistent. The test-retest reliability, both within and between days, is generally poor. At present, no single approach for data collection or pre-processing has proven superior for obtaining the mean flow index, and caution is advised in the further use of mean flow index-based measures for assessing dCA, as current evidence does not support their clinical application.

Relation on Fermatean Neutrosophic Soft Set with Application to Sustainable Agriculture

The need for cutting-edge tools for decision-making to address the difficulties of sustainable agriculture is the driving force behind this study. We aim to integrate soft relations into fermatean neutrosophic hypersoft sets, improving the management of uncertainty. Using this approach, we provide a novel Multi-Criteria Decision-Making (MCDM) algorithm that evaluates agricultural techniques holistically by considering environmental, social, and economic variables. We highlight the significance of taking uncertainty into account when making decisions by demonstrating the algorithm’s efficiency in optimizing the choice of agricultural inputs through a real-world case study. Our solution handles such uncertainty better than conventional ways. In the future, we hope to improve the algorithm even further and investigate integrating cutting-edge technology for real-world use in changing agricultural circumstances.

Survey based assessment of sustainable agricultural practices: Evidence from Indian plots

AbstractIn recent years concerns have been raised regarding the environmental consequences of over‐use of nitrogen fertilizers on a global level. However, the balance between sustainability and agricultural productivity, a central concern for policy makers in developing countries, has not been sufficiently addressed. In this paper, I evaluate farmers’ fertilization practices and their effect on yield using unique plot level data from India. I estimate quadratic crop response functions for different crops and cropping systems. To address endogenous input choices, I use input prices and cost shifters from the fertilizer industry as instrumental variables for the fertilization practice. I find that a large share of Indian cultivators overuse nitrogen relative to the other two nutrients, and could benefit from simply reducing the amount of nitrogen used while keeping the other nutrients fixed. This suggests a potential win‐win situation where both productivity and sustainability can be improved by changing fertilizer application. The widespread “nitrogen‐only” fertilization pattern is rejected as optimal in most cases. [EconLit Citations: Q12, Q15, Q16, E23, C26, C14].

Effect of Common Vetch (Vicia sativa L.) Green Manure on the Yield of Corn in Crop Rotation System

Regenerative farming systems are gaining increasing attention in crop production worldwide. The challenge of the future is to find and apply farming methods that not only reduce the carbon footprint of cultivation, but also produce sustainably through an optimal choice of inputs. We set up our crop rotation experiment in 2019 in order to evaluate the role of common vetch (Vicia sativa L.) as a green manure crop in a crop rotation system. The results were compared with nitrogen fertilizer (80 kg ha−1 N) and control (no green manure and no fertilizer) treatments. Based on the three years of results, it can be concluded that the biomass production capacity of common vetch sown in August is determined by the amount of precipitation in October under continental climatic conditions. In an optimum year for corn, common vetch as a forecrop was found to be equivalent to the effect of fertilizer application at all three applied seed rates, but under stress conditions in a drought year, significantly higher corn yields were obtained when common vetch green manure was applied. Our results suggest a justified role for the use of common vetch green manure in crop rotation systems.

Application of data envelopment analysis to IT project evaluation, with special emphasis on the choice of inputs and outputs in the context of the organization in question

ABSTRACT Data Envelopment Analysis is applied to a sample of IT projects. The application is based on a discussion concerning the choice of inputs and outputs in the case when the units being evaluated are projects. This discussion takes into account various understandings of project success and thus project desired outputs, as well as the ambiguity and a strong situation dependence concerning the choice of project inputs, understood as variables that should be minimized. The sample consists of over 80 IT projects implemented in Poland. The applied DEA model has permitted to identify the projects that clearly stand out from the rest of the sample as less efficient. Their analysis made it possible to draw conclusions on the choice of inputs and outputs. A set of indications on the context-specific choice of inputs and outputs were formulated.

Joint encoding of stimulus and decision in monkey primary visual cortex.

We investigated whether neurons in monkey primary visual cortex (V1) exhibit mixed selectivity for sensory input and behavioral choice. Parallel multisite spiking activity was recorded from area V1 of awake monkeys performing a delayed match-to-sample task. The monkeys had to make a forced choice decision of whether the test stimulus matched the preceding sample stimulus. The population responses evoked by the test stimulus contained information about both the identity of the stimulus and with some delay but before the onset of the motor response the forthcoming choice. The results of subspace identification analysis indicate that stimulus-specific and decision-related information coexists in separate subspaces of the high-dimensional population activity, and latency considerations suggest that the decision-related information is conveyed by top-down projections.

PM2.5-attributable mortality burden variability in the continental U.S.

Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ∼3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.