Error Approach Research Articles

The role of institutional quality on the impact of Chinese foreign direct investments and human capital development on macroeconomic performance in the CEMAC zone

This paper investigates the role of institutional quality in terms of governance on the impact of Chinese foreign direct investments and human capital development on the macroeconomic performance of the CEMAC zone between 2003 and 2020. The data were analyzed using descriptive statistics and the Correlated Panels Corrected Standard Errors (PCSEs) Approach. The findings indicated that poor governance performance in the CEMAC zone deteriorates the effects of Chinese foreign direct investments and human capital development on the macroeconomic performance of the CEMAC zone. At the same time, poor governance performance also deteriorates the impact of foreign aid and personal remittances received on the macroeconomic performance of the CEMAC zone. This study strongly recommends measures to improve institutional quality such as increased training on ethical thinking in all forms of education, meritocratic recruitment to the civil service, and auditing of public finances and services.

Comprehensive analysis of vitamin D3 impurities in oily drug products using supercritical fluid chromatography-mass spectrometry.

Vitamin D3, an essential micronutrient, often requires supplementation via medicines or food supplements, which necessitate quality control (QC). This study presents the development of a method for detecting and quantifying seven impurities of vitamin D3 in oily drug products using supercritical fluid chromatography-mass spectrometry (SFC-MS). Targeted impurities include two esters of vitamin D3 and five non-esters including four that are isobaric to vitamin D3. Firstly, a screening study highlighted the Torus 1-AA column and acetonitrile modifier as adequate for the separation, followed by optimization of the SFC conditions. Secondly, make-up solvent composition and MS settings were optimized to reach high sensitivity. For both the separation and MS response, the screening design of experiments proved useful. Lastly, a fast saponification and liquid-liquid extraction method was developed, enabling efficient sample cleanup and impurities recovery from the complex oily matrix. The SFC-MS method suitability was assessed in two validation studies. The first study employed the ICH Q2 guideline for impurity limit test to demonstrate method specificity and establish a limit of detection (LOD) and a limit of quantification (LOQ) at 0.2% and 0.5%, respectively, for ester impurities. The second study conducted a comprehensive quantitative assessment for three non-ester impurities using a total error approach, determining method validity through accuracy profiles. The validated method exhibited reliable performance across impurity concentrations from 0.1% to 2.0%, with estimated LODs ranging from 2 to 7ng/mL. This study further promotes SFC-MS as a valuable, versatile, and green tool for routine pharmaceutical QC.

Aerodynamic shape optimization based on proper orthogonal decomposition reparameterization under small training sets

Complex shape aerodynamic optimization frequently encounters the problem of dimensionality curse. Extracting geometric features from the original design space proves effective in this issue by reconstructing shapes with lower-dimension. While deep learning methods possess strong capabilities in dimensionality reduction, they suffer from longer training times and higher computational resource requirements. However, Proper Orthogonal Decomposition (POD) presents a more practical algorithm that can be effectively utilized for complex high-dimensional shapes. In this study, we propose a combination of the POD-based method and Gaussian Process Regression with an automatic kernel construction algorithm (AKC-GPR) to optimize complex shapes in a lower-dimensional reparameterization space using small training sets. This approach aims to explore the potential for reducing optimization cost. By reducing the design parameters from 56 to 18 with an error rate of 1.28 %, we establish surrogate models for optimization using AKC-GPR with progressively decreasing training sets. For samples below 100, the Mean Absolute Percentage Errors (MAPEs) remain below 2 %, while the Mean Relative Percentage Errors (MRPEs) approach 10 %. It emphasizes the proficiency of POD in geometric dimensionality reduction. The optimized shapes exhibit smoother contour, resulting in a 10 % improvement in lift and a relatively modest reduction in drag of less than 3 %. The lift-to-drag ratio experiences an increase of over 14 %. The shapes optimized by various training sets possess minimal differences in geometric shapes and aerodynamic characteristics. This confirms the AKC-GPR algorithm's high-precision fitting capability in effectively handling complex aerodynamic coefficients of multi-parameter shapes during small-sample shape optimization.

Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes

Abstract. Part 1 (Wan et al., 2024) of this study discusses the motivation and empirical evaluation of a revision to the aerosol-related numerical process coupling in the atmosphere component of the Energy Exascale Earth System Model version 1 (EAMv1) to address the previously reported issue of strong sensitivity of the simulated dust aerosol lifetime and dry removal rate to the model's vertical resolution. This paper complements that empirical justification of the revised scheme with a mathematical justification leveraging a semi-discrete analysis framework for assessing the splitting error of process coupling methods. The framework distinguishes the error due to numerical splitting from the error due to the time integration method(s) used for each individual process. Such a distinction results in a framework that provides an intuitive understanding of the causes of the splitting error. The application of this framework to the dust life cycle in EAMv1 confirms (i) that the original EAMv1 scheme artificially strengthens the effect of dry removal processes and (ii) that the revised splitting reduces that artificial strengthening. While the error analysis framework is presented in the context of the dust life cycle in EAMv1, the framework can be broadly leveraged to evaluate process coupling schemes, both in other physical problems and for any number of processes. This framework will be particularly powerful when the various process implementations support a variety of time integration approaches. Whereas traditional local truncation error approaches require separate consideration of each combination of time integration methods, this framework enables evaluation of coupling schemes independent of particular time integration approaches for each process while still allowing for the incorporation of these specific time integration errors if so desired. The framework also explains how the splitting error terms result from (i) the integration of individual processes in isolation from other processes and (ii) the choices of input state and time step size for the isolated integration of processes. Such a perspective has the potential for the rapid development of alternative coupling approaches that utilize knowledge both about the desired accuracy and about the computational costs of individual processes.

Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.

Designing Drug Delivery Vehicles based on N-(2-Hydroxypropyl) Methacrylamide.

The development of polymeric-based drug delivery has seen faster growth in the past two decades. In polymers, copolymers as drug carriers are increasing to decrease the drug compounds' side effects and dosage-related toxicity. The study's primary objective is to utilize computational resources to design drug molecules and perform in silco physicochemical property analysis. In our study, we designed new copolymers based on N-(2-Hydroxypropyl) methacrylamide (HPMA) as backbone along with polyethylene glycol (PEG) and lauryl methacrylate (LMA). Different functional groups were selected for attaching to the side chain of the copolymers through a random trial and error approach. In order to predict the pharmacokinetic properties (absorption, distribution, metabolism, excretion, and toxicity), the designed copolymer molecules were evaluated utilizing ADME and PkCSM pharmacokinetics servers. Molecular interaction between the designed copolymer molecules and human serum albumin (HSA) was performed using AutoDock Vina and PatchDock server. The designed molecules are shown to be soluble in water and have high gastrointestinal absorption. Only one molecule is predicted to pass through the blood-brain barrier. Two designed molecules have been shown to have carcinogenic properties. Lethal dose 50 (LD50), cytochrome P450, and permeability glycoprotein Enzyme's substrate formation were also analyzed for toxicity and metabolism. Our study will provide insight for designing new drug compounds or carriers and analyzing their physicochemical properties to help further optimize compounds for clinical studies.

Robust adaptive iterative learning control for high-speed trains under non-strictly repeated conditions

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis.

Analytic evaluation of the thermal conductivity at the conditions of the powder bed fusion with electron beam (PBF-EB) additive manufacturing (AM) process

In the additive manufacturing powder bed fusion process with electron beam (PBF-EB) process, the thermal conductivity of the powder bed controls the interaction of the electron beam with the material, the heat transfer during the process, the cooling rate and, consequently, the final component microstructure. This property requires to be optimized, and the desired value is achieved at the present day only with a trial and error approach. This work proposes a numerical method that considers the powder bed’s geometrical characteristics, including the neck formation among the powder particles. Examples of model applications are reported, considering the Ti6Al4V alloy processed with the PBF-EB process.

Estimation of reference evapotranspiration using some class-A pan evaporimeter pan coefficient estimation models in Mediterranean-Southeastern Anatolian transitional zone conditions of Turkey.

Reference evapotranspiration (ETo), which is used as the basic data in many studies within the scope of hydrology, meteorology, irrigation and soil sciences, can be estimated by using the evaporation (Epan) measured from the class-A pan evaporimeter. However, this method requires reliable pan coefficients (Kp). Many empirical models are used to estimate Kp coefficients. The reliability of these models varies depending on climatic and environmental conditions. Therefore, they need to be tested in the local conditions where they will be used. In this study, conducted in Kahramanmaraş, which has a semi-arid Mediterranean climate in Turkey during the July-October periods of 2020 and 2021, aimed to determine the usability levels of six Kp models in estimating daily and monthly average ETo. The Kp coefficients estimated by the models were multiplied with the daily Epan values, and the daily average ETo values were estimated on the basis of the model. The daily Epan values were measured using an ultrasonic sensor sensitive to the water surface placed on the class-A pan evaporimeter. The ultrasonic sensor was managed by a programmable logic controller (PLC). To enable the sensor to be managed by PLC, a software was prepared using the CODESYS programming language and uploaded to the PLC. The daily average ETo values determined by the FAO-56 Penman-Monteith equation were accepted as actual values. The ETo values estimated by the Kp models were compared with the actual ETo values using the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2) statistical approaches. The Wahed & Snyder outperformed the other models in estimating daily (MAE = 0.78 mm day-1, MAPE = 14.40%, RMSE = 0.97 mm day-1, R2 = 0.82) and monthly (MAE = 0.32 mm day-1, MAPE = 5.88%, RMSE = 0.32 mm day-1, R2 = 0.99) average ETo. FAO-56 showed the nearest performance to Wahed & Snyder. The Snyder model presented the worst performance in estimating daily (MAE = 2.09 mm day-1, MAPE = 37.53%, RMSE = 2.36 mm day-1, R2 = 0.82) and monthly (MAE = 1.83 mm day-1, MAPE = 31.82%, RMSE = 1.87 mm day-1, R2 = 0.99) average ETo. It has been concluded that none of the six Kp models can be used to estimate the daily ETo in Kahramanmaraş located in the Mediterranean-Southeastern Anatolian transitional zone, and only Wahed & Snyder and FAO-56 can be used to estimate the monthly ETo without calibration.

Investigating the impact of GDP, energy mix, energy intensity, and the service sector on environmental pollution in MENA countries: An application of Driscoll-Kraay standard error approach

The goal of this study is to explore the stochastic impacts of population, GDP, renewable energy, fossil fuels, energy intensity, and the service sector on environmental pollution through regression analysis. To achieve this goal, this study employed the Driscoll-Kraay standard error approach for the “Middle East and North African (MENA)” countries. The study assured a balanced panel of data from 1997 to 2021 and applied the STIRPAT model to test which variables are responsible for environmental pollution in the MENA region. The study's findings show that factors such as population size, gross domestic product, the use of fossil fuels, and energy intensity all contribute to environmental pollution. On the other hand, using renewable energy and strengthening the service sector can reduce long-term environmental pollution. The study's findings have proposed various policy implications for MENA countries to reduce environmental pollution, including carbon and greenhouse gas (GHG) emission mitigation, considering sustainable development goals 7 and 13.

Evaluation of 12-lead electrocardiogram at 0.55T for improved cardiac monitoring in magnetic resonance imaging

The 12-lead electrocardiogram (ECG) is a standard diagnostic tool for monitoring cardiac ischemia and heart rhythm during cardiac interventional procedures and stress testing. These procedures can benefit from magnetic resonance imaging (MRI) information; however, the MRI scanner magnetic field leads to ECG distortion that limits ECG interpretation. This study evaluated the potential for improved ECG interpretation in a "low field" 0.55T MRI scanner. The 12-lead ECGs were recorded inside 0.55T, 1.5T, and 3T MRI scanners, as well as at scanner table "home" position in the fringe field and outside the scanner room (seven pigs). To assess interpretation of ischemic ECG changes in a 0.55T MRI scanner, ECGs were recorded before and after coronary artery occlusion (seven pigs). ECGs was also recorded for five healthy human volunteers in the 0.55T scanner. ECG error and variation were assessed over 2-minute recordings for ECG features relevant to clinical interpretation: the PR interval, QRS interval, J point, and ST segment. ECG error was lower at 0.55T compared to higher field scanners. Only at 0.55T table home position, did the error approach the guideline recommended 0.025mV ceiling for ECG distortion (median 0.03mV). At scanner isocenter, only in the 0.55T scanner did J point error fall within the 0.1mV threshold for detecting myocardial ischemia (median 0.03mV in pigs and 0.06mV in healthy volunteers). Correlation of J point deviation inside versus outside the 0.55T scanner following coronary artery occlusion was excellent at scanner table home position (r2 =0.97), and strong at scanner isocenter (r2 =0.92). ECG distortion is improved in 0.55T compared to 1.5T and 3T MRI scanners. At scanner home position, ECG distortion at 0.55T is low enough that clinical interpretation appears feasible without need for more cumbersome patient repositioning. At 0.55T scanner isocenter, ST segment changes during coronary artery occlusion appear detectable but distortion is enough to obscure subtle ST segment changes that could be clinically relevant. Reduced ECG distortion in 0.55T scanners may simplify the problem of suppressing residual distortion by ECG cable positioning, averaging, and filtering and could reduce current restrictions on ECG monitoring during interventional MRI procedures.

Investigating the link between green finance, environmental orientation, and carbon neutrality: A panel study of the metal extraction sector

In the natural resources exploitation sector, metal industry is considered as one of the major carbon emitters. In this context, this study aims to explore the role of green finance and environmental orientation in improving the carbon neutrality of the firms operating in this sector. The data was collected from two large economies' metal sectors: the People's Republic of China (PRC) and India. Five years of data from 2015 to 2019 was collected from 98 firms from financial statements and other sources. Given the nature of the data, panel cointegration was employed to analyze the developed model. Keeping in view the findings of these three diagnostic tests, it was clear that our model did not fully meet the assumptions to apply a normal panel. It led us to apply a panel-corrected standard error (PCSE) approach, which accounts for the violations of assumptions (autocorrelations, heteroscedasticity, and cross-sectional dependence) as identified in the results of diagnostic tests. The PCSE approach the can preserve the OLS parameter estimates while adjusting the standard errors (SE) to counter the issues found in OLS estimation. These results explain that the provision of green finance can help improve the carbon neutrality of firms in the metal sector of the PRC. Further, the results on the impact of environmental orientation on carbon neutrality are also significant, showing environmental orientation's instrumental role in improving carbon neutrality. Further, the interaction role of environmental orientation in the association between green finance and carbon neutrality is tested creating an interaction term by multiplying both variables. The results show a statistically significant role of environmental orientation in the association between green finance and carbon neutrality. These findings coefficient that, in the case of the PRC, the joint effect of green finance and environmental orientation is statistically significant. The results on the impact of environmental orientation are also significant, confirming its instrumental role in improving carbon neutrality. In short, our findings, suggest that by embracing such an environmental orientation, organizations can position themselves as leaders in carbon neutrality and contribute to a more sustainable future.

Bipartite leaderless synchronization of fractional-order coupled neural networks via edge-based adaptive pinning control

This paper introduces the signed graph into fractional-order coupled neural networks (FCNNs) and the bipartite synchronization is investigated for leaderless FCNNs. Instead of formulating leader’s state or isolated node’s state as the synchronization reference target, the bipartite synchronization of leaderless FCNNs is discussed by developing a direct error approach. First, an important fractional-order inequality is rigorously proved by contradiction. By virtue of fractional-order inequality, gauge transformation and several analytical tools, the criteria of bipartite leaderless synchronization are obtained for FCNNs with heterogeneous and homogeneous coupling weights. Specially, for the two types coupling weights, the adaptive pinning schemes are adopted which just rely on partial network information based on the spanning tree and connected dominating set, respectively. Eventually, the theoretical analysis is verified by two numerical simulations.

(Keynote) Competition of Oxygen and Chlorine Evolution on Oxide Surfaces – a Soft X-Ray and Dems Approach to Identification of Active Site

Anodic gas evolving reactions, e.g. oxygen evolution and chlorine evolution, are of fundamental importance both in energy storage as well as in electrolytic production of chemicals. Both reactions are of electrocatalytic nature, proceed in the same potential range and are catalyzed by the same types of electrode materials. While the oxygen evolution is thermodynamically favored the two electron chlorine evolution is kinetically preferred, namely at acid and neutral pH. This kinetic preference is industrially explored in chlor-alkali processes as well in chlorate electrolysis. The growing interest in electrolytic hydrogen production fueled by the popularity of the hydrogen as the likely replacement of fossil fuels brings up the question of the tolerance of the industrial anodes to presence of chlorides in the water intended for electrolytic hydrogen production. Such a tolerance can be best achieved by steering the selectivity of the anode between oxygen and chlorine evolution aiming eventually at the possibility of the sea water use in hydrogen producing water electrolysis. So far existing attempts at control the anode selectivity in presence of chlorides explore two principal approaches – pH control and surface engineering. While the pH related approach is rationalized by different pH sensitivity of each of the competing reaction the surface engineering related activities so far rely on trial and error approach. Qualitatively the selectivity may be controlled by either selectively promoting/suppressing each of the contributing reactions. While the literature lists attempts when the overall selectivity towards OER is apparently achieved by selective suppression of CER [1] more recent reports suggest that the actual selectivity is in fact controlled by the ability of the catalysts to support a lattice oxygen activating mechanism of the oxygen evolution process [2]. While these assignments are based on correlation of selectivity measurements with local structure and DFT modelling and a direct evidence of such a behavior is so far missing. This paper will document here the capability of the real-time and operando soft X-ray absorption spectroscopy [3] to provide this missing evidence. Anodic behavior of two type of ruthenium oxide electrodes in chloride free and chloride containing solutions will be documented by comparison of the nature and potential dependent population of the oxygen related intermediates formed at anode surface in the potential range of oxygen and chlorine evolution in acid media (pH=1) and combined with DEMS data to identify the active sites for each contributing electrode reaction.

Temperature Influence on the Staging Process in Carbon Materials during Anionic Intercalation

A typical set-up of a dual-carbon battery consists of a pair of carbon electrodes and an organic-based electrolyte with a broad window of electrochemical stability. When such a cell is being charged to a reasonably high voltage, two processes occur: intercalation of cations at the electrode with low potential, and intercalation of anions at the one with high potential [1-2]. Although there are significant limitations for practical application of such systems, mostly related to electrolyte decomposition and exfoliation of carbon, the concept appears to be rather inspiring for investigations, as the materials required for dual-carbon batteries would be significantly cheaper compared to those for state-of-the-art electrochemical energy storage solutions.The general idea of using carbon as a cathode material is known for many decades, however, most of the studies dedicated to it pursue the improvement of specific electrochemical characteristics through variation of the cell materials, mostly by trial and error approach [3-4]. Clearly, the results of such research are highly important, but the topic itself lacks the knowledge of electrochemical kinetics for the process of various anions being inserted into graphitic materials, which are typically used as dual-carbon battery cathodes.In our work, we attempted to overcome the limiting factor of electrolyte decomposition by selecting a relatively unusual organic sulfones as solvents. Addition of a salt reduces the melting point of the sulfone, allowing to consider the system as a classic liquid electrolyte. Such cells are known to sustain the high voltages long enough to conduct the studies on structural evolutions which occur in the graphite cathode [4]. We selected two commercial graphitic materials and subjected them to anionic intercalation in various alkali-based electrolytes. Galvanostatic cycling with intermittent titration was conducted to evaluate the diffusion coefficients of anion insertion. The best-performing system was also studied in operando X-ray diffraction cells using the synchrotron radiation facilities, so anionic diffusion was coupled to different stages of intercalation. The staging process itself appears to be completely different at evaluated temperature. Thus, the experiment with KPF6 electrolyte at 298 K showed similar results with the work [5], with subsequent formation of intercalated stages down to stage 2 (see the figure), whereas at 358 K, multiple stages are present together almost from the beginning of intercalation process.The results of the current study show a drastic influence of temperature on the structural behavior of graphite during anionic intercalation, and by electrochemical methods, the kinetics of these changes is characterized, which is necessary for further development of dual-carbon battery topic. R. T. Carlin, H. C. De Long, J. Fuller, and P. C. Trulove, J. Electrochem. Soc., 141, L73, 1994.R. Santhanam and M. Noel, J. Power Sources, 76, 147, 1998.L. Zhang, J. Li, Y. Huang, et al., Langmuir, 35(11), 3972-3979, 2019.M. Tebyetekerwa, T. T. Duignan, Z. Xu, et. Al, Adv. Energy Mater. 12, 2202450, 2022.J. A. Seel and J. R. Dahn, J. Electrochem. Soc. 147, 892, 2000. Figure 1

Nurse Leader Attitudes and Beliefs Regarding Medical Errors.

This study aimed to explore nurse leader attitudes and beliefs about medical errors. The perfectibility model suggests errors are avoidable if nurses are trained and follow safety guidelines. This impacts how nurse leaders approach medical errors. Nurse leaders (NLs) may be the first person to whom a nurse reports an error. Leaders support nurses through the investigation process and subsequent recovery. A cross-sectional quantitative study was conducted to determine NL attitudes toward medical errors using the Medical Error Attitude Scale (MEAS). MEAS scores were high in all categories. Medical error perception was significant for NLs with institutional support programs and when compared with job titles. Medical error approach and perception were significant for NLs with direct reports who had committed a medical error. This study provides insight into NLs' views on medical errors, awareness of NLs who have had nurses involved in medical errors, and formal caregiver support programs.

Mono-objective Optimization of Retaining Wall Using Genetic Algorithm

Abstract This paper examines the importance of geotechnical optimization techniques for soil engineering applications, with a particular emphasis on evaluating geotechnical structures. Due to its prevalence in civil engineering, the complex interplay of geotechnical, structural, and financial considerations necessitates a trial and error approach. The study focuses on design elements, geometric dimensions, and volume considerations in order to highlight the economic viability of reinforced concrete retaining walls. Three code files are created using MATLAB to analyze their impact on active and passive thrusts and, as a result, the structure’s volume. The slope angle, backfill overload, and friction angle are varied. The results demonstrate the effectiveness of using evolutionary algorithms to precisely optimize a single goal and demonstrate that this approach can enhance the design of retaining walls in reinforced concrete. This method demonstrates the ability to improve design procedures in this crucial area, which makes it an invaluable resource for structural engineering researchers and civil engineers.

Trials and tribulations: Developing an artificial intelligence for screening malaria parasite from peripheral blood smears

BackgroundDetection of malaria parasite from blood smears remains the gold standard for confirmation of diagnosis. Screening blood smears for malaria parasite has a sensitivity of 75 %, and requires intensive training of the laboratory technician. In the present study, we have attempted to develop an artificial intelligence to automate the process of malaria parasite detection. MethodsWe acquired 352 images of Leishman–Giemsa-stained peripheral blood smears, containing either normal red blood cells (RBCs) or parasitised RBCs. With a trial and error approach, we developed five deep learning models: (A) Naive deep convolutional neural network (DCNN) for trophozoites, (B) Modified Inception V3 pretrained neural network (C) Combination of model A and B, (D) Segmentation of cells from the images through Watershed Transform and naive tri-class DCNN (normal RBCs, parasitised RBCs, WBC/platelets), and (E) A naive DCNN model to detect ring forms. The images were randomly split into training and test sets and training was imparted on all the models. After completion of training, performance of each model was assessed on the test set. ResultsOverall, the best combination of sensitivity and specificity was seen in model D (85 % and 94 %, respectively) in detecting parasites; in addition to trophozoites, model D could also detect ring forms. The performance of model A, B & C suffered from lack of either sensitivity or specificity. ConclusionThe present study represents the first step towards development of a complete module for screening malaria parasites from automated microphotography/whole slide images.

Unsupervised learning of stationary and switching dynamical system models from Poisson observations

Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning. Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules. Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods. Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.

Closed-Loop Output Error Approaches for Drone's Physics Informed Trajectory Inference

The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or off-line learning algorithms, radical innovation in on-line trajectory inference is required. In this paper, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: i) full states measurements and ii) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach.