Determination Of Threshold Research Articles

Zinc Water Prevents Autism-Like Behaviors in the BTBR Mice

This study aims to explore the effects of zinc water on autism-like behavior, convulsion threshold, and neurogenesis in ASD model animals. This study used the young BTBR ASD mouse model to explore the effect of a 6-week zinc water supplementation on ASD-like behaviors such as repetitive behavior and social communication disorder, seizure threshold, and the correlation with excitability regulation. The mice were divided into four groups of normal controls (B6) and models (BTBR) who did and did not receive zinc supplementation in water (B6, B6 + zinc, BTBR, and BTBR + zinc). For morphological changes in the hippocampus, we selected two indicators: hippocampal mossy fiber sprouting and neurogenesis. ASD-like behavior testing, seizure threshold determination, Timm staining, and neurogenesis-related assays—represented by Ki67 and DCX—were performed after 6 weeks of zinc supplementation. Our results show that zinc water can prevent autism-like behavior, reduce susceptibility to convulsions, and increase the proliferation of hippocampal progenitor cells in BTBR mice but has less effect on mossy fiber sprouting and neural progenitor cell differentiation. Zinc water reduces autism-like behavior in a partially inherited autism model mice—BTBR—which may be associated with hippocampal neural precursor cell proliferation and reversed hyperexcitability.

Solid oxide electrolysis cells – Interplay between operating conditions, fuel electrode overpotential and degradation

With the integration of increased share of energy from renewable, but fluctuating, energy sources there will be an increased demand for large-scale flexible conversion of electrical energy into easily storable chemical fuels. Electrolysis will be a key component in the future of energy conversion and storage solutions. Solid oxide cells (SOC) can provide the most efficient electrolysis technology. Furthermore, solid oxide electrolysis cell (SOEC) technology has improved tremendously in terms of initial performance, long-term durability, and up-scaling of cell and stack sizes; and with respect to commissioning and operation of SOEC plants. Nevertheless, it is evident that further improvement of electrochemical performance and especially long-term durability is crucial for the coming up-scaling and integrated SOEC systems as part of our energy grid. This calls for research within 1) thorough mapping of degradation mechanisms, 2) determination of thresholds for the onset of irreversible degradation processes, 3) safe operating conditions and means to mitigate known degradation processes, etc. However, degradation is not a simple singular property and there is often a puzzling interplay between parameters such as: the chosen electro-catalysts, the manufacturing of cell components, obtained microstructures, the resulting initial electrochemical performance, operating conditions, and the nature and extent of different degradation processes. This work will present11This work has been presented as invited keynote speak at symposium E01.07 as part of the 23rd International Conference on Solid State Ionics, MRS Meeting, Boston (US), July 2022. an overview of the puzzling interplay between initial electrochemical performance and long-term durability. It will then provide examples of multiple complementary characterization techniques applied for the analyses of these features and processes (e.g., SEM, TEM, Raman spectroscopy and electrochemical impedance spectroscopy). Furthermore, strategies to mitigate and/or limit specific degradation processes and insight into data required to establish degradation wise safe operating conditions are discussed. The present work will touch upon multiple performance characteristics and degradation processes: 1) triple-phase-boundaries (TPB) and critical micro/nano-structural parameters of the porous nickel/yttria-stabilized-zirconia (Ni/YSZ) electrode; 2) the perception of electrode overpotential and why this parameter is more important than the set current density or operating cell/stack voltage, 3) the effect of impurities, 4) the interplay between impurities and carbon deposition and 5) the degradation caused by Ni migration and actions to mitigate this degradation process.

Determination of Consumer Color and Discoloration Thresholds for Purchase of Retail Ground Beef When Evaluating Multiple Days of Display Simultaneously

Objective: The objective of this study was to identify the threshold for color and discoloration for consumers to purchase ground beef in a simulated retail display and to determine the best objective measurement to predict consumer purchase intent. Study Description: For this study, 180 1-lb 80% lean/20% fat ground beef packages were assigned to a day of retail display (day 0–9). Consumers (n = 216) and trained descriptive panelists evaluated ground beef samples from each day of display simultaneously. Instrumental L* (lightness), a* (redness), and b* (yellowness) values were collected, and spectral data were recorded. Logistic and simple linear regression models were calculated for consumer likelihood to purchase and appearance ratings. Pearson correlation coefficients were calculated for all measurements. The Bottom Line: The models generated from this study provide the ability to predict consumer willingness to purchase ground beef of varying days of retail display and provide ground beef producers an indication of potential consumer purchasing behaviors based upon objective values that are easy to measure.

Electron-induced ionization and cationic fragmentations of the isolated molecule of 2,4-imidazolidinedione (hydantoin): a study of the relaxing path thresholds.

In this work, our new experimental setup has been used to study the ionization and fragmentation of a prebiotic molecule, hydantoin, by electron impact. Scanning of the incident electron energy allows the determination of the appearance thresholds of the cations. The vertical ionization potential was found to be in good agreement with previous data. Dissociation thresholds for the main fragmentation patterns were also measured. In parallel, thanks to quantum chemical calculations, reaction schemes compatible with the experimental results are given.

Influence of alkali metal cations on the formation of the heterobimetallic actinide tert-butoxides [AnM3(OtBu)7] and [AnM2(OtBu)6] (AnIV = Th, U; MI = Li, Na, K, Rb, Cs).

Heterobimetallic tert-butoxides of alkali metal cations with tetravalent actinide centers exhibit two distinctive structural motifs, [AnM2(OtBu)6] and [AnM3(OtBu)7] (AnIV = Th, U and MI = Li, Na, K, Rb, Cs), evidently governed by the size of the alkali metal ions. Both [AnM3(OtBu)7] AnM3 (AnIV = U, MI = Li; AnIV = Th, MI = Li, Na) and [AnM2(OtBu)6] AnM2 (AnIV = U, MI = Na-Cs; AnIV = Th, MI = K-Cs) compounds are obtained in nearly quantitative yields by reacting actinide and alkali metal silyl amides with an excess of tert-butyl alcohol. The AnM3 complexes form a cubane-type coordination motif, whereas the AnM2 complexes display a geometry resembling two face-shared bipyramids. The sodium derivatives of thorium and uranium (ThNa3 and UNa2) allow the determination of the structural transition threshold as a function of the ratio of the ionic radii ri(AnIV)/ri(MI). The AnM3 complexes are formed for ratios above 0.92 and the AnM2 type is formed for ratios below 0.87. All compounds are unambiguously characterized in both solution and solid states by NMR and IR spectroscopic studies and single crystal X-ray diffraction analyses, respectively.

Clinical Practice Guidelines for the Therapeutic Use of Repetitive Transcranial Magnetic Stimulation in Neuropsychiatric Disorders.

Clinical Practice Guidelines for the Therapeutic Use of Repetitive Transcranial Magnetic Stimulation in Neuropsychiatric Disorders.

PREDICTING MECHANICAL LEG ALIGNMENT FROM KNEE X-RAY IMAGES BY UTILIZING DEEP-LEARNING NEURAL NETWORKS

Several studies have linked leg malalignment to early onset and progression of knee OA. Detecting abnormalities in mechanical alignment before radiographic evidence of knee OA is present, might increase the rate of early detection. Hence, conservative joint preserving interventions or even osteotomies could reduce the need or delay full joint replacement. In addition, medical trials often face high dropout rates, consequently, AI based support could improve the recruitment process. Detecting mechanical leg malalignment: 1) before the malalignment is immediately obvious to the clinician and 2) excluding/including patients recruited for medical trials. This study utilized a dataset of 8,878 digital radiographs ((I) 6,181 AP/PA full-leg x-rays, (II) 2,292 AP/PA knee x-rays with SynaFlexer positioning frame, (III) 405 AP/PA knee x-rays captured without SynaFlexer). The images were obtained from a diverse range of sources, including the OAI study, the University for Continuing Education Krems (AT), and five additional sites in Austria and the US. All knee images in the dataset were accompanied by their corresponding full-leg images, obtained on the same day. To determine the ground-truth hip-knee-ankle angle (HKA), the full-leg images were processed using IB Lab LAMA (ImageBiopsyB Lab, Vienna, Austria), a commercial software that automatically determines clinical measurements from full-leg radiographs. These images were used to train the AI algorithm. The algorithm produced binary outputs for HKA threshold values of 5° (clinically relevant) and 7.5° (clinical trial relevant). The algorithm reached MAE rates of 0.98° (cropped full-leg), 1.56° (SynaFlexer knee) and 2.10° (non-SynaFlexer knee). Summarizing all image modalities, the overall performance for the 5° threshold was 74-86% Sensitivity and 79-90% Specificity. Following, 7.5° threshold performance reached 81-92% Sensitivity and 85-92% Specificity. While this study primarily focused on absolute HKA threshold values of 5° and 7.5° for varus/valgus classification, this threshold determination process is highly adaptable to accommodate specific use cases, such as the desired angle threshold for classification or the preferred balance between sensitivity and specificity. In addition, overestimated HKA have an elevated angle above the normal range and could proceed towards or above mentioned thresholds. Chen K. PhD funding “LowerAustria Society for Scientific Research” (LSC20-020). Salzlechner C. and Lepenik C. are employees of ImageBiopsy Lab. Ljuhar R. is a shareholder of ImageBiopsy Lab. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators or the NIH. Lepenik C. thanks Avelar C. for comments on an early version of this article. CORRESPONDENCE ADDRESS: [email protected]

Condition Monitoring of Wind Turbines With Implementation of Interactive Spatio–Temporal Deep Learning Networks

Condition monitoring of wind turbines (WT) is a crucial task to ensure efficient and safe operations. This paper proposes a novel model, interactive spatio-temporal network (IST-Net), to extract features from supervisory control and data acquisition (SCADA) data effectively. The proposed model utilizes an interactive learning structure that combines uniform refine long and short-term memory network (URLSTM) and convolutional neural network (CNN) to extract spatio-temporal features. Firstly, the SCADA data is preprocessed by utilizing the missing values filling, the feature selection, and the normalization. Secondly, an IST-Net model is trained by the data of healthy state. The IST-Net can extract spatio-temporal information to represent the operation status of WT by the interactive learning process of two isometric sequences split from the original sequence. Finally, the trained model receives online data and calculated the residual between true value and predicted value. The root mean squared error (RMSE) of the residual is calculated as the monitoring indicator, and the exponentially weighted moving average (EWMA) control chart is utilized for threshold determination. Experimental results reveal that the proposed model has better capability in precise anomaly detection compared to existing methods.

Determination of Monitoring Control Value for Concrete Gravity Dam Spatial Deformation Based on POT Model

Deformation can directly reflect the working behavior of the dam, so determining the deformation monitoring control value can effectively monitor the safety of dam operation. The traditional dam deformation monitoring control value only considers the single measuring point. In order to overcome the limitation, this paper presents a new method to determine the monitoring control value for concrete gravity dam based on the deformations of multi-measuring points. A dam’s comprehensive deformation displacement is determined by the measured values at different measuring points on the positive inverted vertical line and the corresponding weight of each measuring point. The projection pursuit method (PPM) combined with the grey wolf optimization (GWO) algorithm is used to determine the weight of each measuring point according to the spatial correlation distribution characteristics of dam deformation. The peaks over threshold (POT) model based on the extreme value theory is adopted to determine the monitoring control value with the obtained dam comprehensive deformation displacement. In addition, the POT model is improved with the automatic threshold determination method based on the criterion in probability theory and the GWO algorithm, which can avoid subjectivity and randomness in determining the threshold. The results of the engineering application show the feasibility and applicability of the proposed method.

DAE-ConvBiLSTM: End-to-end learning single-lead electrocardiogram signal for heart abnormalities detection.

The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection. As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, and F1 score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities. The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.

Correlation of symptoms registered during blood collection from drivers who tested positive for Δ9-tetrahydrocannabinol (THC)

The impact of marijuana on road safety has been the subject of many debates over the years. These debates have intensified in recent years due to initiatives carried out in several jurisdictions aimed at marketing medical cannabis. According to Polish forensic toxicologists, road safety is a key issue, especially following the conferences in November 2012 in Kraków and in 2013 in Augustów (Gieroń et al., 2013), Poland, on the determination of appropriate concentration thresholds. These thresholds, defining the limit for the ‘after use’ and ‘under the influence’ conditions, are still not unambiguous or easy to determine for the Polish government. An unambiguous assessment of the impact of a given concentration of blood Thc on psychomotor performance is very difficult, and will remain so in the foreseeable future. This study used 107 randomly selected blood collection protocols, with 10 protocols for each THC concentration ranging from 1 to 10 ng/ml, on the basis of which thirteen features related to the external appearance and behaviour of the tested individuals were analysed. These features were: facial skin (pale, normal or red), slurred speech, mood and behaviour (cheerful, talkative, rowdy or reticent), heart rate, pupils (normal, dilated or contracted), reaction of pupils to light, the romberg test, the finger-to-nose test, picking up objects from the ground and awareness of time and place. Five of the thirteen features related to the appearance and behaviour of the respondents showed no abnormalities (gait, the romberg test, the finger-to-nose test, picking up objects from the ground, and awareness of time and place). The study found that the symptoms observed and recorded in the protocols are insufficient to make a decision about the impact of Thc on psychomotor performance. The characteristic symptoms (cheerfulness and talkativeness) occurred no more frequently than in 3 out of 10 of the concentrations tested.

A deep learning process anomaly detection approach with representative latent features for low discriminative and insufficient abnormal data

Anomaly detection in industrial processes is vital for yield improvement and cost reduction. With the development of sensor system and information technology, industrial big data provide opportunities to detect the abnormalities of processes and raise alarms by using operating parameters. However, the slight deviations in operating parameters and the insufficient abnormal data may hinder the effectiveness of existing anomaly detection models. To cope with the above problems, a more effective process anomaly detection framework combining shallow feature fusion learning with unsupervised deep learning is constructed. Specifically, the extracted statistical features that can reflect the slight deviations of operating parameters and the original measured features are firstly concatenated to enrich the available information. Then, a combined feature selection method of SMOTE & Tomek Links and random forest is developed to further discover the abstract features closely relevant to the quality characteristics of finished products with imbalanced data. After that, an unsupervised anomaly detection method is developed, where only normal process data are available for training the stacked denoising autoencoder. The utilized autoencoder can alleviate the effect of imbalanced data as the reconstruction error would be larger when the abnormality occurs. Lastly, the anomaly discrimination criteria, which consist of the monitoring index construction and the threshold determination, are formulated to detect the state of the production process. The experimental results demonstrate that the proposed method can detect the abnormalities effectively and achieves better performance than other state-of-art anomaly detection methods in commutator spot welding of a practical motor manufacturing process.

Diffraction separation and imaging using ensemble empirical mode decomposition and multichannel singular spectrum analysis

AbstractSeismic diffracted wavefield has substantial potential for high‐resolution subsurface imaging of discontinuous geological structures; however, they are often masked by higher amplitude reflection, thus requiring separation. According to the low‐rank nature of a seismic wavefield, the diffracted wavefield can be extracted using the rank‐reduction method. The traditional low‐rank diffraction separation suffers from a threshold selection problem, especially for field data. To improve threshold accuracy, we propose a method in the common‐offset or poststack domains based on the f–x ensemble empirical mode decomposition and multichannel singular spectrum analysis. We demonstrate that such decomposition allows the determination of the diffraction threshold according to the difference between the singular values of the data before and after it. Synthetic and field data examples prove that the decomposition can effectively predict and suppress the horizontal reflected signal, while attenuating the energy of the dipping reflected signal. The following multichannel singular spectrum analysis suppresses the dipping reflection and separates the diffraction according to the precise threshold obtained earlier. The proposed method effectively improves the accuracy of the diffraction threshold selection, enhances diffraction and attenuates reflection, resulting in an enhanced image of small‐scale geological structures.

ARASsort: A new sorting based multiple attribute decision‐making algorithm

AbstractMultiple attribute decision‐making (MADM) tools can effectively support the decision analysts in selecting the best alternative among many, ranking the alternatives in decreasing or increasing order of preference, or allocating the alternatives into pre‐defined ordered classes/categories. Even though the literature provides the analyst with precious sorting‐based MADM tools such as PROMSORT, UTADIS, AHPSort, TOPSISsort, and so forth, the majority of the methods can be found complex and hard to be understood by the researchers and practitioners who are not familiar with the mathematical notions and computations of MADM (distance calculation, threshold and preference function determination, and so on). To provide a simpler but powerful MADM tool aiming at sorting the alternatives into classes, this study proposes a sorting‐based additive ratio assessment algorithm which is called ARASsort. For limiting (interval‐based) and central (reference‐based) profiles describing the categories, we have developed two algorithms: ARASsort‐lp and ARASsort‐cp, respectively. Their applicability was shown in two examples: green supplier evaluation and economic freedom evaluation of countries. The validity of algorithms was presented by demonstrating the class assignment similarities between the results obtained by ARASsort, VIKORsort, and TOPSISsort. The findings show that ARASsort works well because it shows a higher level of class assignment similarities with the other methods.

A matched-filter technique with an objective threshold

We propose an objective threshold determination method for detecting outliers from the empirical distribution of cross-correlation coefficients among seismic waveforms. This method is aimed at detecting seismic signals from continuous waveform records. In our framework, detectability is automatically determined from Akaike’s Information Criterion (AIC). We applied the method of seismic signal detection to continuous records collected over two years. The results show that the maximum value of network cross-correlation coefficients sampled from each constant interval can be approximated by the theory of extreme value statistics, which provides a parametric probability density function of maxima. By using the function, outliers can be considered with a reasonable criterion.

Methods for detecting heat stress in hutch-housed dairy calves in a continental climate

Dairy calves exposed to solar radiation, elevated ambient temperature, and humidity are at risk of impaired welfare and productivity. Initial detection of thermal discomfort requires determination of optimal heat stress indicators and thresholds. Such values have recently been established in calves in chronic, subtropical, and acute continental environments but not in continuous, temperate conditions. Herein, the objectives were to determine associations between animal-based and environmental heat stress indicators and establish environmental breakpoints for hutch-raised dairy calves during a continental summer. From June to August, dairy calves (n = 63; 14 to 42 d of age) were individually hutch-housed and managed according to the dairy standard operating procedures in Arlington, Wisconsin. Calf respiration rates (RR), rectal temperatures (RT), shaved or unshaved skin temperatures (ST), and hutch internal and external air speed were measured thrice weekly at 0700 and 1400 h after a 15 min hutch restriction. Environmental indices including dry bulb temperature (Tdb), black globe temperature, and relative humidity were measured every 15 min, averaged hourly, and used to calculate temperature-humidity index (THI) using 8 different equations (THI1-8). Correlation and linear regression models were used to determine relationships within and between animal-based and environmental indicators. Environmental breakpoints were established using segmented regression models to estimate THI and Tdb thresholds for abrupt changes in animal responses. There were strong, positive correlations between animal-based indicators and Tdb or THI1-8, with the strongest association observed between unshaved ST and Tdb (r = 0.80). The linear regression of animal-based indicators with the best fit included Tdb or Tdb plus relative humidity and air speed. The threshold at which RR and RT began to rise was at a THI of 69 for both or at a Tdb of 21.0 or 21.5°C, respectively. No threshold was established for ST. Together, these outcomes indicate that Tdb is an appropriate measurement to detect thermal discomfort for calves in a temperate summer climate and individual hutch housing. Monitoring of calves is warranted before ambient temperature reaches 21.0°C, corresponding to RR of 40 breaths per minute and RT of 38.5°C, to promote calf comfort and reduce the risk of hyperthermia-related welfare and productivity consequences.

A Novel Cephalometric Approach Aiming to Quantify a Normal Range of Bony Chin Protrusion.

Cephalometric analysis is an essential tool in the diagnostics and planning of orthognathic surgery. No objective criterion exists to facilitate decision making regarding genioplasties. Differing opinions amongst clinicians therefore leads to wide variability amongst treatment options offered to potentially suitable patients. This study has three aims. The first was to quantify the distribution of chin morphology amongst the average population using cephalometric analysis. Secondly, we sought to determine whether cephalometric parameters could be used to predict overlying soft tissue changes. Lastly, we consider the use of a new cephalometric angle, BNPg, for pre- and post-operative assessment of genioplasty patients. This study retrospectively analysed 231 lateral cephalograms. The angle between the landmarks: B point, Nasion and Pogonion was measured to generate 'BNPg' a novel unit to quantify bony chin protrusion. The mean BNPg from all 231 samples was 1.12 degrees with a standard deviation of ± 1.35. Comparison between sexes showed no significant differences between male and females (P = 0.108). Furthermore, bony chin protrusion was found to strongly positively correlate with soft tissue chin appearance (r = 0.731), however, BNPg was found not to correlate with skeletal malocclusion (ANB, r = 0.085). The novel unit BNPg may serve as a useful tool in contributing to the determination of treatment thresholds in osseous genioplasty for desirable aesthetic outcomes and may be used post-operatively to assess outcomes also. As this is a pilot study, further clinical studies would be required to validate this parameter in genioplasty patients, both pre- and post-operatively. The online version contains supplementary material available at 10.1007/s12663-022-01784-5.

The effect of acute heat exposure on the determination of exercise thresholds from ramp and step incremental exercise.

The aim of this study was to examine how respiratory (RT) and lactate thresholds (LT) are affected by acute heat exposure in the two most commonly used incremental exercise test protocols (RAMP and STEP) for functional evaluation of aerobic fitness, exercise prescription and monitoring training intensities. Eleven physically active male participants performed four incremental exercise tests, two RAMP (30W·min-1) and two STEP (40W·3min-1), both in 18°C (TEMP) and 36°C (HOT) with 40% relative humidity to determine 2 RT and 16 LT, respectively. Distinction was made within LT, taking into account the individual lactate kinetics (LTIND) and fixed value lactate concentrations (LTFIX). A decrease in mean power output (PO) was observed in HOT at LT (-6.2 ± 1.9%), more specific LTIND (-5.4 ± 1.4%) and LTFIX (-7.5 ± 2.4%), compared to TEMP, however not at RT (-1.0 ± 2.7%). The individual PO difference in HOT compared to TEMP over all threshold methods ranged from -53W to +26W. Mean heart rate (HR) did not differ in LT, while it was increased at RT in HOT (+10 ± 8bpm). This study showed that exercise thresholds were affected when ambient air temperature was increased. However, a considerable degree of variability in the sensitivity of the different threshold concepts to acute heat exposure was found and a large individual variation was noticed. Test design and procedures should be taken into account when interpreting exercise test outcomes.

Determination of a cost-effectiveness threshold for cancer interventions in Iran.

The estimation of a cost- Effectiveness (CE) threshold from the perspective of those who have experienced a life-threatening disease can provide empirical evidence for health policy makers to make the best allocation decisions on limited resources. The aim of the current study was to empirically determine the CE threshold for cancer interventions from the perspective of cancer patients in Iran. A composite time trade-off (cTTO) task for deriving quality adjusted life-year (QALY) and a double-bounded dichotomous choice (DBDC) approach followed by open-ended question for examining patients' willingness-to-pay were performed. A nationally representative sample of 580 cancer patients was recruited from the largest governmental cancer centers in Iran between June 2021 and January 2022, and data were gathered using face-to-face interviews. The CE threshold was calculated using the nonparametric Turnbull model and parametric interval-censored Weibull regression model. Furthermore, the factors that affect the CE threshold were determined using the parametric model. The estimated CE threshold using the nonparametric Turnbull model and parametric interval-censored Weibull regression model was IRR 440,410,000 (USD 10,485.95) and IRR 595,280,000 (USD 14,173.33) per QALY, respectively. Gender, age, education, income, type of cancer, and current treatment status were significantly associated with the estimated CE threshold. The value of parametric model-based threshold in this study was 1.98 times the Iranian GDP per capita, which was lower than the CE threshold value recommended by the WHO (i.e., 3 times the GDP per capita) for low-and middle-income countries.

Predicting the Porosity in Selective Laser Melting Parts Using Hybrid Regression Convolutional Neural Network

Assessing the porosity in Selective Laser Melting (SLM) parts is a challenging issue, and the drawback of using the existing gray value analysis method to assess the porosity is the difficulty and subjectivity in selecting a uniform grayscale threshold to convert a single slice to binary image to highlight the porosity. This paper proposes a new approach based on the use of a Regression Convolutional Neural Network (RCNN) algorithm to predict the percent of porosity in CT scans of finished SLM parts, without the need for subjective difficult thresholding determination to convert a single slice to a binary image. In order to test the algorithm, as the training of the RCNN would require a large amount of experimental data, this paper proposed a new efficient approach of creating artificial porosity images mimicking the real CT scan slices of the finished SLM part with a similarity index of 0.9976. Applying RCNN improved porosity prediction accuracy from 68.60% for image binarization method to 75.50% using the RCNN. The algorithm was then further developed by optimizing its parameters using Bees Algorithm (BA), which is known to mimic the behavior of honeybees, and the hybrid Bees Regression Convolutional Neural Network (BA-RCNN) produced better prediction accuracy with a value of 85.33%.