W. Penny
W. Penny
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This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by m...
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This paper investigates models of working memory in which memory traces evolve according to stochastic attractor dynamics. These models have previously been shown to account for response-biases that are manifest across multiple trials of a visual working memory task. Here we adapt this approach by making the stable fixed points correspond to the multiple items to be remembered within a single-trial, in accordance with standard dynamical perspectives of memory, and find evidence that this multi-item model can provide a better account of behavioural data from continuous-report tasks. Additionally, the multi-item model proposes a simple mechanism by which swap-errors arise: memory traces diffuse away from their initial state and are captured by the attractors of other items. Swap-error curves reveal the evolution of this process as a continuous function of time throughout the maintenance interval and can be inferred from experimental data. Consistent with previous findings, we find that empirical memory performance is not well characterised by a purely-diffusive process but rather by a stochastic process that also embodies error-correcting dynamics.
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Posted 2 days ago
Rimantas Knizikevičius
Rimantas Knizikevičius
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The chemical etching of germanium in Br2 environment at elevated temperatures is described by the Michaelis–Menten equation. The validity limit of Michaelis–Menten kinetics is subjected to the detailed analysis. The steady-state etching rate requires synergy of two different process parameters. ...
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The chemical etching of germanium in Br2 environment at elevated temperatures is described by the Michaelis–Menten equation. The validity limit of Michaelis–Menten kinetics is subjected to the detailed analysis. The steady-state etching rate requires synergy of two different process parameters. High purity gas should be directed to the substrate on which intermediate reaction product does not accumulate. Theoretical calculations indicate that maximum etching rate is maintained when 99.89% of the germanium surface is covered by the reaction product, and 99.9999967% of the incident Br2 molecules are reflected from the substrate surface. Under these conditions, single GeBr2 molecule is formed after 30 million collisions of Br2 molecules with the germanium surface.
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Posted 2 days ago
Muhammad Moin,
Muhammad Moin
Institution: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu China; Department of Physics, University of Engineering and Technology, Lahore, Pakistan
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Abdul Anwar,
Abdul Anwar
Institution: Department of Physics, University of Engineering and Technology, Lahore, Pakistan
Email:
Mehrunisa Babar,
Mehrunisa Babar
Institution: Department of Physics, University of Engineering and Technology, Lahore, Pakistan
Email:
Udayabhaskararao Thumu,
Udayabhaskararao Thumu
Institution: Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu China
Email:
Anwar Ali
Anwar Ali
Institution: Department of Physics, University of Engineering and Technology, Lahore, Pakistan
Email:
A first principle study intense on the density functional theory with Heydscuseria-Ernzerhof screened hybrid functional hybrid function (HSEO6) is used to assess the structural, Electronic, elastic, mechanical and optical responses of LaXOlt;subgt;3lt;/subgt; (X = Al, In, Ga) perovskite materials. T...
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A first principle study intense on the density functional theory with Heydscuseria-Ernzerhof screened hybrid functional hybrid function (HSEO6) is used to assess the structural, Electronic, elastic, mechanical and optical responses of LaXOlt;subgt;3lt;/subgt; (X = Al, In, Ga) perovskite materials. The compressive investigation under the external static isotropic pressure (P= 0 to 80GaP), phase stability, band structure and their important impact on the optical response of LaAlOlt;subgt;3lt;/subgt;, LaInOlt;subgt;3lt;/subgt; and LaGaOlt;subgt;3lt;/subgt;. Electronic band structure shows that LaXOlt;subgt;3lt;/subgt; (X = Al, In Ga) semiconductor with indirect band gap and an optically inactive response up to 20GPa, while the band gap becomes direct at 80GaP. There are gamma points (G-X-Q) at 80GPa and the band gap changes from indirect to direct nature. Under main desperation physical parameters of perovskite materials are well explained the response of TDOS, PDOS and EPDOS contour plots have been well understood for the full description of the band gap. It is further observed that the external pressure enhanced upto 40GPa both materials are significantly more mechanically stable compared to pristine LaXOlt;subgt;3lt;/subgt; (X = Al, In, Ga) at 0GPa. The optical properties of LaAlOlt;subgt;3lt;/subgt;, LaGaOlt;subgt;3 lt;/subgt;and LaInOlt;subgt;3lt;/subgt;, dielectric coefficient lt;igt;(εlt;subgt;1lt;/subgt;, iεlt;subgt;2lt;/subgt;)lt;/igt; have been employed along with the optical responses like absorption, energy loss function, reflectivity and reflective index are obtained in the energy scale from 0 to 60 eV. It was observed that static dielectric constant decreases with the decrease in optical band gap. The optical tunings under the effect of pressure which are good candidates in practical optoelectronic applications are extensively used and interpreted by the calculation of the dielectric function.
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Posted 2 days ago
Elin Kjelle,
Elin Kjelle
Institution:
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Ingrid Øfsti Brandsæter,
Ingrid Øfsti Brandsæter
Institution:
Email:
Eivind Richter Andersen,
Eivind Richter Andersen
Institution:
Email:
Bjørn Morten Hofmann
Bjørn Morten Hofmann
Institution:
Email:
Abstract
Background
An intervention to reduce low-value magnetic resonance imaging (MRI) was designed and implemented in private imaging centres in Norway in October 2022. The intervention used return letters for poor referrals of MRI of the lower back, brain and kn...
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Abstract
Background
An intervention to reduce low-value magnetic resonance imaging (MRI) was designed and implemented in private imaging centres in Norway in October 2022. The intervention used return letters for poor referrals of MRI of the lower back, brain and knee at private imaging centres in Norway. The study aimed to investigate key stakeholders’ experiences and assessment of the intervention and the specific research questions were:
• How many return letters were sent during the study period?
• What were the medical directors’ and managers’ experiences with and reflection on success factors for the intervention implementation and using return letters?
Methods
The number of return letters sent was collected directly from Norway’s two main private imaging providers. Two semi-structured individual interviews were conducted with the medical directors of the imaging providers, as well as two focus group interviews with nine managers from the various private imaging centres operated by the two imaging providers.
Results
In total, 1,182 return letters were sent for patients undergoing one of the three types of MRI examinations, and the number of return letters was highest at the beginning of the intervention. The interview analysis resulted in five categories: general experience, anchoring, organisation, return letter procedure and outcome. Sufficient information, anchoring and support were identified as crucial success factors.
Conclusions
This study provides insights into the practical and crucial details of implementing interventions to reduce low-value imaging. The intervention was generally well received, and the high initial number of return letters decreased rapidly over the course of the study. Several key success factors were identified.
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Posted 2 days ago
Chen Liu
Chen Liu
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In this study, we used unidirectional and bidirectional long short-term memory (LSTM) deep learning networks for Chinese news classification and characterized the effects of contextual information on text classification, achieving a high level of accuracy. A Chinese glossary was created using jieba—a word segmentation tool—stop-word removal, and word frequency analysis. Next, word2vec was used to map the processed words into word vectors, creating a convenient lookup table for word vectors that could be used as feature inputs for the LSTM model. A bidirectional LSTM (BiLSTM) network was used for feature extraction from word vectors to facilitate the transfer of information in both the backward and forward directions to the hidden layer. Subsequently, an LSTM network was used to perform feature integration on all the outputs of the BiLSTM network, with the output from the last layer of the LSTM being treated as the mapping of the text into a feature vector. The output feature vectors were then connected to a fully connected layer to construct a feature classifier using the integrated features, finally classifying the news articles. The hyperparameters of the model were optimized based on the loss between the true and predicted values using the adaptive moment estimation (Adam) optimizer. Additionally, multiple dropout layers were added to the model to reduce overfitting. As text classification models for Chinese news articles, the Bi-LSTM and unidirectional LSTM models obtained f1-scores of 94.15% and 93.16%, respectively, with the former outperforming the latter in terms of feature extraction.
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Posted 2 days ago
Junyan Tian
Junyan Tian
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The coronavirus disease 2019 (COVID-19) pandemic has affected vulnerable households’ livelihoods in developing countries. Using high-frequency phone survey data from the World Bank, we assess rural Indian households’ vulnerability and poverty status during the pandemic. Results reveal that over ...
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The coronavirus disease 2019 (COVID-19) pandemic has affected vulnerable households’ livelihoods in developing countries. Using high-frequency phone survey data from the World Bank, we assess rural Indian households’ vulnerability and poverty status during the pandemic. Results reveal that over three-fifths of Indian rural households are vulnerable to poverty in the context of COVID-19, despite India’s evident progress in mitigating poverty in the pre-pandemic era. Poverty plays a major role in accounting for variations in household vulnerability; however, the impact of risks on household welfare is not negligible. On average, households with more members, older household heads, and more outmigrants are more vulnerable to poverty during the pandemic. The impacts of the gender of the household head, access to masks, consumption loans, and COVID-related information are nevertheless insignificant. Results stress the urgent necessity of deploying concerted interventions to strengthen household vulnerability in rural India.
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Posted 2 days ago
Dongjie Gao
Dongjie Gao
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With the rapid development of biotechnology, gene sequencing methods are gradually improved. The structure of gene sequences is also more complex. However, the traditional sequence alignment method is difficult to deal with the complex gene sequence alignment work. In order to improve the efficiency...
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With the rapid development of biotechnology, gene sequencing methods are gradually improved. The structure of gene sequences is also more complex. However, the traditional sequence alignment method is difficult to deal with the complex gene sequence alignment work. In order to improve the efficiency of gene sequence analysis, D2 series method of k-mer statistics is selected to build the model of gene sequence alignment analysis. According to the structure of the foreground sequence, the sequence to be aligned can be cut by different lengths and divided into multiple subsequences. Finally, according to the selected subsequences, the maximum dissimilarity in the alignment results is determined as the statistical result. At the same time, the research also designed an application system for the sequence alignment analysis of the model. The experimental results showed that the statistical power of the sequence alignment analysis model was directly proportional to the sequence coverage and cutting length, and inversely proportional to the K value and module length. At the same time, the model was applied to the system designed in this paper. The maximum storage capacity of the system was 71 GB, the maximum disk capacity was 135 GB, and the running time was less than 2.0s. Therefore, the k-mer statistic sequence alignment model and system proposed in this study have considerable application value in gene alignment analysis.
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Posted 2 days ago
Pavithra Mahesh,
Pavithra Mahesh
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Rajkumar Soundrapandiyan
Rajkumar Soundrapandiyan
Institution:
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A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector an...
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A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R<jats:sup>2</jats:sup> values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%.
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Posted 2 days ago
Marziyeh Jafari,
Marziyeh Jafari
Institution:
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Mohammad Hosein Daneshvar
Mohammad Hosein Daneshvar
Institution:
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Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning ...
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Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Posted 2 days ago
Abdurrahman Coskun
Abdurrahman Coskun
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Highlights
• Prediction interval has a great potential to be used in laboratory medicine
• It is a powerful tool for computing personalized reference interval and reference change value
• It can be used to assess the stability of analytical systems
• It can be used in monitoring the accu...
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Highlights
• Prediction interval has a great potential to be used in laboratory medicine
• It is a powerful tool for computing personalized reference interval and reference change value
• It can be used to assess the stability of analytical systems
• It can be used in monitoring the accuracy and reproducibility of analytical systems
Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients’ data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an “interval” based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients’ data and analytical systems.
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Posted 2 days ago
