Variable Threshold Research Articles

Improving the seed detection accuracy of piezoelectric impact sensors for precision seeders. Part I: A comparative study of signal processing algorithms

One of the crucial issues in seeders and grain drills is measuring the seed flow rate and seeding quality during the seeding operation. An impact sensor based on a piezoelectric sensor was designed and tested for different seeds to detect seeding quality rapidly and accurately in precision seeders. This sensor enables faster and more cost-effective seeding quality determination than grease belt test benches. Seed impact vibrations were detected using a piezoelectric microphone attached to the plate, while seed trajectories were simultaneously recorded with a high-speed camera. The novelty of this work lies in the implementation of a new algorithm called Variable Threshold Peak Detection with Automatic Calculation of Minimum Threshold (VTPD-AM), which enables more efficient signal analysis. The seed detection accuracy of VTPD-AM was comprehensively compared with the literature, specifically the works of Palshikar et al. (2009), Scholkmann et al. (2012), and Ozbek et al. (2014).The performance of the tested algorithms was compared using corn, soybean, and sunflower seeds at simulated forward speeds of 6 and 12 km/h. Based on the experimental results, the algorithm presented in this work (VTPD-AM) and the algorithm developed by Ozbek et al. (2014) achieved the highest seed monitoring accuracy (over 97 %). The advantage of VTPD-AM is that it can be implemented on microcontrollers for real-time applications. In all cases, the percentage of undetected seeds increased with the increase in the seed flow rate.

Integrating Crack Causal Augmentation Framework and Dynamic Binary Threshold for imbalanced crack instance segmentation

Crack analysis is a crucial component of structural health monitoring. A fundamental aspect of this is the identification and quantification of individual crack instances through crack instance segmentation. Due to the visually linear/curvilinear structure of the cracks, data imbalance problems inevitably arise in the instance segmentation task. To address this, a Crack Causal Augmentation Framework (CCAF) which incorporates morphological dilation and erosion is designed to enhance the representation of cracks within the training images. Furthermore, it has been observed that crack data is highly susceptible to variations in threshold during binarization, to address this issue, Dynamic Binary Threshold (DBT) branch has been implemented to enhance the segmentation accuracy by performing a regression task using spatial features in the segmentation network to determine the optimal binary threshold at the image level. Experimental results demonstrate that the implementation of the CCAF in conjunction with the DBT branch results in a synergistic enhancement of performance. We establish the efficacy of our proposed methods through evaluations on four crack datasets with varying sources and distributions, including a dataset with an extremely low density of crack pixels, where other prevalent crack instance segmentation techniques have been inadequate.

A comparative evaluation of Elo ratings- and machine learning-based methods for tennis match result prediction

Elo ratings-based methods, including the recently proposed Weighted Elo method, have been found to perform well when forecasting tennis match results, however, whether they can outperform machine learning (ML) has not been established. In this study, a comparative evaluation of the two types of methods is conducted using the Sports Result Prediction CRISP-DM experimental framework. The first full year of mens ATP tennis data (2006), in a dataset containing matches from 2005 to 2020, was set to be the initial training set and 1 year of data was incrementally added to this set to predict 14 test years, from 2007 to 2020. Features were ranked based on their average rank across five feature selection techniques. It was found that, of the five ML models, Alternating Decision Trees (ADTrees) and Logistic Regression achieved higher accuracies than Elo ratings and similar accuracies to predictions derived from betting odds. Furthermore, ADTrees show potential in this domain, with solid performance achieved with an interpretable decision tree that allows for variation in the average betting odds difference threshold.

Optimal pixel signal intensity threshold for the assessment of endocardial left-ventricular scar by 3D dark blood MRI compared to bipolar voltage electro-anatomical mapping

Abstract Background Late gadolinium enhancement (LGE) MRI is considered the standard for noninvasive assessment of 3D scar architecture. Intraprocedural integration of 3D scar models improve the outcome of catheter ablation for ventricular tachycardia (VT). A pixel signal intensity (PSI) cut-off of 40 to 60% has been widely used for conventional bright-blood LGE MRI. Novel 3D MRI acquisitions with dark-blood (DB) nulling with higher spatial resolution and superior detection of (subendocardial) scar patterns are increasingly available, but the optimal pixel signal intensity (PSI) value remains unknown. Purpose To compare the accuracy of the 3D DB MRI-derived endocardial scar reconstructions at three different PSI cut-offs with endocardial electro-anatomical contact mapping in patients with scar-related VT. Methods Preprocedural 3D DB LGE MRIs were retrospectively compared to high-density endocardial left-ventricular (LV) bipolar voltage mapping (cut-off 0.5-1.5mV) obtained during endocardial mapping at sinus rhythm using the ADAS3D software. The LV scar models were aligned using the mitral valve, left ventricular apex and aorta cusps. Points with a Euclidean node-to-node distance of ≥10 mm were excluded. Variable upper PSI thresholds were set at 50%, 60% and 70%; the lower threshold at two-thirds of the upper limit. The 10% LV layer from the 3D MRI was used. The pairs of points were checked for agreement for scar or healthy (%). The Pearson correlation was used to analyze the agreement between core scar, border zone and healthy tissue area (in %) at three thresholds. Results Ten patients (90% male, mean age 65±11 year, 8 ischemic/2 nonischemic, LV ejection fraction 41±6%) were analyzed. A mean of 2024 (range 385-6810) pairs were analyzed per patient at three PSI thresholds. A correct correlation for scar or healthy was present in 67%, 69% and 69% for the upper PSI thresholds of 50%, 60% and 70% respectively. The optimal match was obtained for an upper PSI cutoff value of 70%, for healthy (r2=0.57; P=0.01), border zone (r2=0.73; P=0.01), 50% for core (r2=0.09; P=0.39). Using a 60% PSI cutoff the scar core % increased significantly compared to the 70% cutoff (12.8 ±13.8 versus 4.9±7.3, P=0.001). Conclusion The optimal depiction of the boundary between healthy and border zone scar by 3D DB MRI is best depicted at an upper PSI threshold of 70%.

Dimensionality Reduction using Feature Selection Techniques on EDM for Student Academic Performance Prediction

The aim of this study is to perform dimensionality reduction on student datasets using different feature selection strategies in order to better forecast students' academic progress and minimize high-dimensionality concerns. The effectiveness of models built utilizing classification algorithms and pertinent features chosen using different feature selection strategies has been experimentally evaluated in this study. Nine feature selection methods, including Chi-Square, Pearson Correlation, Variance Threshold, Feature Importance, Recursive Feature Elimination (RFE), Lasso, Ridge, Random Forest, and XGBoost, as well as one classification method, logistic regression, were applied to the student dataset to determine the results of the analysis. In this experiment, dimensionality reduction techniques were used to reduce the number of features from 70 to the optimal 21 feature subsets for analysis of student academic performance prediction. Five measures were used to assess the effectiveness of the techniques: r2, precision, recall, f-score, and accuracy. With the logistic regression classifier, the feature subset chosen by the chi-square had the highest accuracy of 72.4%, precision of 71.4%, recall of 72.4%, f-score of 70%, and r-square of -0.221%.

Abstract 17327: Heterogenous Shared Decision-Making Approaches to Sports Eligibility Decisions for Athletes With Cardiovascular Disease

Background: Historically, almost all pathologic cardiovascular (CV) diagnoses led to complete restriction from competitive sports. In 2015, the American College of Cardiology (ACC) and American Heart Association began to embrace a more nuanced, patient-centered approach to sports eligibility using shared decision-making (SDM). While SDM is a conceptually defensible approach to sports eligibility, operationalization is complex, and we know very little about how physicians understand and deploy it. Methods: To assess sports cardiologists’ understandings of SDM and approaches to eligibility decisions, in-depth interactive interviews were conducted with (N=20) self-identified sports cardiologists. Participants were sampled at random from the ACC Sports and Exercise Cardiology Section. Interviews were conducted using video conference or telephone, and transcripts were analyzed for qualitative description of key domains. Results: Analysis revealed heterogeneity regarding core elements of SDM. All 20 clinicians endorsed SDM, but conceptions of what SDM means varied. Some emphasized communication between physicians rather than with patients, and there was variation in describing the clinician’s role primarily as a provider of information or a more active driver of the decision. Furthermore, even though lifestyle considerations are a key component of SDM for sports eligibility, few clinicians (n=6) specifically referenced elicitation of a patient’s preferences as part of the SDM process. Clinicians also stated highly variable thresholds for prohibitive annual risk of sudden cardiac death during sports participation ranging from <1% to >10%, and they expressed heterogenous views of how athletes interpret risk. Some believed athletes can interpret risk in similar ways as other people; others felt they are largely blind to risk. Conclusions: These data collectively demonstrate heterogenous views of and approaches to SDM for sports eligibility decisions that likely result in very different encounters and decision outcomes. There is a need to develop a more robust understanding of how SDM is operationalized in this context and determine an evidence-based approach to SDM for sports and exercise eligibility in the context of CV disease.

Sensitivity of Neoproterozoic snowball-Earth inceptions to continental configuration, orbital geometry, and volcanism

Abstract. The Cryogenian period (720–635 million years ago) in the Neoproterozoic era featured two phases of global or near-global ice cover termed “snowball Earth”. Climate models of all kinds indicate that the inception of these phases must have occurred in the course of a self-amplifying ice–albedo feedback that forced the climate from a partially ice-covered to a snowball state within a few years or decades. The maximum concentration of atmospheric carbon dioxide (CO2) allowing such a drastic shift depends on the choice of model, the boundary conditions prescribed in the model, and the amount of climatic variability. Many previous studies reported values or ranges for this CO2 threshold but typically tested only a very few different boundary conditions or excluded variability due to volcanism. Here we present a comprehensive sensitivity study determining the CO2 thresholds in different scenarios for the Cryogenian continental configuration, orbital geometry, and short-term volcanic cooling effects in a consistent model framework using the climate model of intermediate complexity CLIMBER-3α. The continental configurations comprise two palaeogeographic reconstructions for each of both snowball-Earth onsets as well as two idealised configurations with either uniformly dispersed continents or a single polar supercontinent. Orbital geometries are sampled as multiple different combinations of the parameters obliquity, eccentricity, and argument of perihelion. For volcanic eruptions, we differentiate between single globally homogeneous perturbations, single zonally resolved perturbations, and random sequences of globally homogeneous perturbations with realistic statistics. The CO2 threshold lies between 10 and 250 ppm for all simulations. While the thresholds for the idealised continental configurations differ by a factor of up to 19, the CO2 thresholds for the continental reconstructions differ by only 6 %–44 % relative to the lower thresholds. Changes in orbital geometry account for variations in the CO2 threshold of up to 30 % relative to the lowest threshold. The effects of volcanic perturbations largely depend on the orbital geometry and the corresponding structure of coexisting stable states. A very large peak reduction in net solar radiation of 20 or 30 W m−2 can shift the CO2 threshold by the same order of magnitude as or less than the orbital geometry. Exceptionally large eruptions of up to −40 W m−2 shift the threshold by up to 40 % for one orbital configuration. Eruptions near the Equator tend to, but do not always, cause larger shifts than eruptions at high latitudes. The effects of realistic eruption sequences are mostly determined by their largest events. In the presence of particularly intense small-magnitude volcanism, this effect can go beyond the ranges expected from single eruptions.

Interspecific variation in lower temperature thresholds of an assemblage of wintering bats

Abstract Winter presents environmental and energetic challenges for temperate insectivorous bats as colder temperatures increase metabolic rates while simultaneously reducing resource availability. While bats in northern regions typically hibernate or migrate to circumvent these adverse conditions, there is growing evidence of winter bat activity as weather permits. Bats at lower latitudes may experience shorter, milder winters, increasing opportunities for activity. To better understand the relationship between ambient temperature and winter bat activity, we deployed acoustic detectors in central Louisiana and eastern Texas and examined data at 3 levels of biological organization: overall bat activity, species richness, and species-specific activity. Across 1,576 detector-nights, we recorded 37,435 bat passes. Bats responded positively to warmer temperatures but the temperature threshold for winter activity varied among species, ranging from 7.2 to 15.6 °C. Consequently, observed species richness increased at warmer ambient temperatures. With activity linked to environmental conditions in a species-specific manner, different subsets of the winter bat assemblage may be active from night to night. Additionally, our study adds to a rather limited body of literature of winter bat activity and provides a baseline for future studies as white-nose syndrome and climate change affect North American bat populations.

Novel insights into the NOx emissions characteristics in PEMS tests of a heavy-duty vehicle under different payloads

Real Drive Emission (RDE) test with Portable Emission Measurement System (PEMS) is a widely adopted way to assess vehicle emission compliance. However, the current NOx emissions calculation method stipulated in the China VI emission standard easily ignores the NOx emissions during cold start and low-power operation. To study the effect of cold start and low-power operation on the calculation of NOx emissions in the PEMS test, in this study, a China VI Heavy-Duty Vehicle (HDV) for urban use was used to conduct PEMS tests under various vehicle payload conditions. The data analysis results show that the increase in vehicle payload is beneficial to reducing the specific NOx emissions and passing the NOx emission compliance test because the increased payload improves the NOx conversion efficiency of the SCR system. Cold start duration has no obvious relationship with vehicle payload, accounting for only about 4∼6% in each test, but contributing more than 30% of NOx emissions. Due to the effect of the power threshold and the 90th cumulative percentile, the cold start data has little influence on the result of the NOx emissions assessment and the maximum variation of the NOx emissions result in this study is an 8% rise. For the HDV for urban use, the variation of the power threshold resulting from vehicle payload is small, no more than 2% in this study. The presence of the power threshold makes almost only the low-power operation in the second half of urban driving have an impact on the NOx emissions calculation, which may make more than 50% of NOx emissions in the PEMS test be neglected. The impact of the low-power operation on NOx emissions calculation result will be significantly enhanced if all windows are considered in the Moving Average Window (MAW) method. In the meantime, the degree of variation is closely related to the NOx emissions level during the first half of urban driving. The maximum deterioration of NOx emission assessment result can be more than 90% in this study.

State switching in the wake of a transverse circular cylinder in the transonic regime

The flow past a circular cylinder in a cross-flow configuration was investigated using a wind tunnel experiment across a range of Mach numbers M∞ from 0.30 to 0.85 and corresponding Reynolds numbers, based on diameter, ranging from 2×105 to 5×105. The boundary layer at the cylinder surface is either free or fixed turbulent using artificial tripping at azimuth 12.5°, 25°, and 50°. Measurements combine temporal recordings of the wall pressure and schlieren high-speed visualizations of the flow past and downstream of the cylinder. First, by attaching the tripping strips to the cylinder at the different azimuthal positions, the effect of the boundary layer state on the cylinder wake is found to be strong at subsonic speeds but much smaller in the transonic regime. Multiple flow states are observed depending on the value of M∞. Of particular interest was the narrow region between M∞=0.80 and =0.85, which yields three different states, namely the vortex shedding state, the parallel shear layer state, and the crossed shear layer state. By precisely managing back and forth travels in Mach number with the wind tunnel, it is found that the transitions between these states have a hysteresis behavior. This sub-critical behavior could explain the surprisingly variable threshold values between these different states reported in the literature.

Automatic Tracking Method for 3D Human Motion Pose Using Contrastive Learning

Automatic tracking of three-dimensional (3D) human motion pose has the potential to provide corresponding technical support in various fields. However, existing methods for tracking human motion pose suffer from significant errors, long tracking times and suboptimal tracking results. To address these issues, an automatic tracking method for 3D human motion pose using contrastive learning is proposed. By using the feature parameters of 3D human motion poses, threshold variation parameters of 3D human motion poses are computed. The golden section is introduced to transform the threshold variation parameters and extract the features of 3D human motion poses by comparing the feature parameters with the threshold of parameter variation. Under the supervision of contrastive learning, a constraint loss is added to the local–global deep supervision module of contrastive learning to extract local parameters of 3D human motion poses, combined with their local features. After normalizing the 3D human motion pose images, frame differences of the background image are calculated. By constructing an automatic tracking model for 3D human motion poses, automatic tracking of 3D human motion poses is achieved. Experimental results demonstrate that the highest tracking lag is 9%, there is no deviation in node tracking, the pixel contrast is maintained above 90% and only 6 sub-blocks have detail loss. This indicates that the proposed method effectively tracks 3D human motion poses, tracks all the nodes, achieves high accuracy in automatic tracking and produces good tracking results.

Towards a precision medicine Solution for optimal pediatric Laparoscopy: An exploratory data analysis for features Selections

Over the past years, healthcare methodologies were commonly applied to all patients affected by the same pathology as an overall treatment. However, recent scientific works were mainly aimed at personalized care, according to the patient’s clinical conditions and morphological characteristics. Pediatric surgery was also involved in this process, although laparoscopy has been set aside so far. In this regard, the physical characteristic that correlates intra-abdominal pressure and intra-abdominal volume (i.e., abdominal com-pliance) was always studied as a unique curve. This work aims to propose an alternative approach to treat laparoscopy surgery and abdominal compli-ance for pediatric individuals, which is based on personalized medicine. We perform an exploratory data analysis considering a real dataset, in order to study the data model (i.e., descriptive univariate and multivariate analysis) and the predictors (i.e., variance thresholding, Shapiro-Wilk test, Pearson’s correlation coefficient) that will allow physicians to build clusters of pediatric individuals. Data analysis proves that there are strong correlations among features referring to thorax circumference and xifo-bisialic lenght with the sum of thorax and pelvis volumes. This leads to identify the optimal number of clusters through the Elbow method.

Modified Decoupled Sense Amplifier with Improved Sensing Speed for Low-Voltage Differential SRAM

A modified decoupled sense amplifier (MDSA) and modified decoupled sense amplifier with NMOS foot-switch is proposed for improved sensing in differential SRAM for low-voltage operation at the 22-nm technology node. The MDSA and MDSANF both offer notable improvements to read delay over conventional voltage and current sense amplifiers. At an operating voltage of 0.8 V, the MDSA exhibited a reduced delay of 28.6%, 41.79%, 37.74%, and 30.94% compared to modified clamped sense amplifier (MCSA), double tail sense amplifier (DTSA), modified hybrid sense amplifier (MHSA), and conventional latch-type sense amplifier (LSA), respectively. Similarly, the MDSANF demonstrated a delay reduction of 26.13%, 39.78%, 35.58%, and 28.55% over MCSA, DTSA, MHSA, and LSA, respectively. To validate the performance, the MDSA and MDSANF are evaluated using the variation in delay and power consumption across various supply voltages, process corners, input differential bit line voltage (ΔV BL ), bit line capacitance (C BL ), and the sizing of decoupling transistors. Monte Carlo simulations were conducted to analyse the impact of voltage threshold variations on transistor mismatch which leads to an increased occurrence of read failures and a decline in SRAM yield. The performance analysis of various voltage and current sense amplifiers is presented along with MDSA and MDSANF. Area consideration for selection of sensing scheme is important and as such layout of MDSA and MDSANF was performed conforming to the design rules and estimated area for MDSA is 0.297 μm 2 whereas MDSANF occupies 0.5192 μm 2 .

Trend of the polyp and adenoma detection rate by sex and age in asymptomatic average-risk and high-risk individuals undergoing screening colonoscopy, 2012–2019

Adenoma detection rate (ADR) is an imperative quality measure for colorectal cancer (CRC) screening. This retrospective observational study aimed to determine the trend of polyp detection rate (PDR) and ADR in asymptomatic average- and high-risk participants in different age groups who underwent screening colonoscopy over the seven years from April 2012 to March 2019 in a tertiary gastroenterologyreferral center of Iran. Of 1676 participants, 51.8 % were men (mean age 52.3 years). The overall PDR and ADR were 22.7 %, and 13.5 %, respectively. Both Polyps and adenomas were more common in age groups 51–59 and ≥60 years in high-risk patients than in the corresponding groups of average-risk patients (p < 0.05). Also, both PDR and ADR were more frequent in men than in women among all studied age groups, but it was statistically significant only for the youngest age group (16.8 % versus 10.5 %, p < 0.05) for PDR and the oldest age group (19.7 % versus 13 %, p < 0.05) for ADR, respectively. The trend of total ADR was upward over 7 years in both average-risk (6.7 % to 13.3 %) and high-risk (9.8 % to 27 %) groups and across all age groups in both sexes. Multivariable logistic regression revealed that high-risk individuals had an elevated risk of adenoma compared with average-risk patients (OR: 1.6, p = 0.006). Substantial variation in thresholds of polyp and adenoma detection by age, sex, and risk categories emphasizes the need for a risk-adapted approach to CRC screening and prevention programs.

Artificial Neural Network for Classification of Dengue Fever Using Backpropagation Algorithm

Fever is an increase in body temperature to higher than usual. Normal human body temperature is at 37oC, if the body temperature is more than this figure, it indicates a fever caused by infectious or non-infectious factors. The main symptom of Dengue hemorrhagic fever is high fever with a temperature between 30oC - 40oC which appears suddenly, the fever lasts for 7 days and occurs continuously, body temperature can be normal or low, then will rise slowly every day and can reach 40oC . These two diseases are still a public health problem in urban areas, including in the cities of Binjai and Medan. The problem that has occurred so far is that people in general cannot differentiate the symptoms of Dengue Fever from Malaria, so the treatment given only provides ordinary fever medicine, so that within three days there is no change and the high body temperature makes the patient know that someone has dengue fever. Therefore, the solution provided in this research is to find out the physical characteristics experienced by the sufferer before further diagnosis is carried out. If someone has a fever above 38oC, the body has red spots, irregular breathing, immediately go to the doctor because these symptoms indicate symptoms of dengue hemorrhagic fever or malaria fever. Artificial neural networks are an information processing system designed to imitate the workings of the human brain by carrying out a learning process through changing the weights of synapses. The human brain consists of millions of interconnected neurons known as biological neurons. Each neuron consists of a cell that has a number of dendrites (input) and an axon (output). Axons connect to other neurons through connecting pathways that produce chemical reactions when responding to incoming input. The input required includes the number of input variables, input variable values, weights, learning rate, threshold, maximum epoh and target (output) with the error value classification used is Mean Absolute Error (MAE), there are 2 types of disease with fever symptoms used. The types of disease are dengue hemorrhagic fever and malaria and the system will be designed using the Visual Basic 2010 programming language. From the results of the research that has been carried out, classification results are obtained with a value of 0.893619481 or rounded to equal 1 and classified as dengue hemorrhagic fever.

Performance degradation mechanism of γ-ray irradiated sol-gel silica film

γ-ray radiation effect on sol-gel silica films was investigated at different absorbed doses ranging from 10 kGy to 1000 kGy. The defect and microstructure evolutions of silica films were analyzed to understand the performance degradation behavior and mechanism of the γ-ray irradiated silica films. The cracks appear on the silica film surface and further expand at larger γ-ray absorbed dose, while surface roughness slightly decreases. During γ-ray irradiation, the thermal effect and the increased rate of diffusion-controlled process lead to the condensation of Si-OH groups and the recombination of ODCs. However, the ionization of atoms leads to generation and accumulation of E′ centers and NBOHCs. γ-ray irradiation leads to the reductions of bond lengths and bond angles of Si-O-Si groups, thus causing the densification of films. The refractive index and mechanical property firstly enhance and then reduce with the increase of absorbed dose associated with the change of silica film density. After γ-ray irradiation, the decreased contact angle and increased surface free energy indicate the reduction of anti-pollution ability of silica films. The dose-dependent surface quality, defects and mechanical properties determine the variation in laser-induced damage threshold (LIDT) of silica film. The results provide a reference to control the structural damage and performance degradation of silica films, which is very important for the research of safe operation of high-power laser facilities for inertial confinement fusion (ICF).

Individual differences in the expansiveness of mental disorder concepts: development and validation of concept breadth scales

BackgroundWhat people consider to be a mental disorder is likely to influence how they perceive others who are experiencing problems and whether they seek help for their own problems. However, no measure is available to assess individual differences in the expansiveness or breadth of concepts of mental disorder. Four studies aimed to develop and validate two such measures. The Concept Breadth-Vertical (CB-V) scale assesses variability in the severity threshold at which unusual behavior or experience is judged to reflect disorder, whereas the Concept Breadth-Horizontal (CB-H) scale assesses variability in the range of phenomena judged to be disorders.MethodsIn a pilot study (N = 201) for the CB-V, participants read vignettes of varying severity for each of the 10 mental disorders, and rated whether the subject had a disorder. Study 1 (N = 502) used exploratory factor analyses to examine 10 CB-V items from the pilot study and 20 vignette-based items for constructing the CB-H. Study 2 (N = 298) employed confirmatory factor analysis to validate the scales’ structure and examined their convergent validity with a measure of harm concept breadth and their discriminant validity with measures of mental health literacy. Study 3 (N = 298) explored associations of the scales with other mental health variables, including stigma and help-seeking attitudes.ResultsStudy 1 supported the unifactorial structure of each item set, refined each set into a scale, and demonstrated acceptable reliabilities. Study 2 provided support for the scales’ convergent and discriminant validities. Study 3 showed that the scales were associated negatively with stigma, and positively with help-seeking attitudes and self-reported mental health problems. Studies 2 and 3 further indicated that younger and more politically liberal participants hold broader concepts of mental disorder.ConclusionsThe new concept breadth scales are psychometrically sound measures of a promising new concept in the study of beliefs and attitudes about mental health. Potential future research directions are discussed.

A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals

Distributed manufacturing can reduce the production cost through the cooperation among factories, and it has been an important trend in the industrial field. For the enterprises with daily delivered production tasks, the random job arrivals are regular. Thus, the Distributed Job-shop Scheduling Problem (DJSP) with random job arrivals is studied, and it is a typical case from the equipment manufacturing industry. The DJSP involves two coupled decision-making processes, job assigning and job sequencing, and the distributed and uncertain production environment requires the scheduling method to be more responsive and adaptive. Thus, a Deep Reinforcement Learning (DRL) based multi-agent method is explored, and it is composed of the assigning agent and the sequencing agent. Two Markov Decision Processes (MDPs) are formulated for the two agents respectively. In the MDP for the assigning agent, fourteen factory-and-job related features are extracted as the state features, seven composite assigning rules are designed as the candidate actions, and the reward depends on the total processing time of different factories. In the MDP of the sequencing agent, five machine-and-job related features are set as the state features, six sequencing rules make up the action space, and the change of the factory makespan is the reward. Besides, to enhance the learning ability of the agents, a Deep Q-Network (DQN) framework with variable threshold probability in the training stage is designed, which can balance the exploitation and exploration in the model training. The proposed multi-agent method’s effectiveness is proved by the independent utility test and the comparison test that are based on 1350 production instances, and its practical value in the actual production is implied by the case study from an automotive engine manufacturing company.

Switching mode allocation in planning paths for vehicular network communication

Because of the increased mobility of vehicle users, it might be difficult to keep communication services in vehicle networks effective and dependable. Huge hurdles have been presented to vehicular networks as a result of the meteoric rise in the amount of data, which comes with the needs of high dependability and low latency. The deployment of access point servers at geographic locations that are closer to the vehicles in order to provide real-time service to applications that are based on the vehicles is one possible option. However, there is a limited amount of cache store space, and there is also a lack of a tractable access mode allocation method. As a result of these factors, it is very difficult to strike a compromise between the network transmission performance and fronthaul savings. Because the signal-to-interference-ratio (SIR) can be enhanced with switching mode in vehicular infrastructure, it may be possible to achieve higher levels of dependability. To serve all of the vehicles, the conventional allocation in vehicular network may not be sufficient on its own for two reasons: (1) the number of vehicles exceeds the number of paths, and (2) a vehicle may be located outside of the coverage path. Therefore, the implementation of switching mode allocation in vehicular communication is very necessary in order to increase the number of vehicles that can be supplied. In this paper, allocation using V2I, V2V, and V2X modes have been analyzed to provide dependable coverage for vehicles. These methods are used for communicating with other vehicles. In this paper, the numerical analysis has been performed such that SIR is optimized. In switching mode allocation, it has been shown that establishing a variable SIR threshold is helpful in achieving a path coverage that can be relied upon. It has been shown beyond a reasonable doubt that the coverage probability is likewise directly dependent on SIR thresholds. The theoretical analysis is verified, and it is confirmed that the suggested method is capable of achieving significant performance improvement in terms of coverage probability and data rate.

Patterns of last-deglacial diatom-inferred summer temperature variability and ecological thresholds in the alpine lakes from southeastern margin of the Tibetan Plateau

Patterns of last-deglacial diatom-inferred summer temperature variability and ecological thresholds in the alpine lakes from southeastern margin of the Tibetan Plateau