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Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

The Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive calibration data sets, which can represent a significant obstacle in cases of limited data availability. Nevertheless, there seems to be no comprehensive study validating such claims and systematically comparing state-of-the-art calibration methods specifically for RF. To close this gap, we investigate a broad spectrum of calibration methods tailored to or at least applicable to RF, ranging from scaling techniques to more advanced algorithms. Our results based on synthetic as well as real-world data unravel the intricacies of RF probability estimates, scrutinize the impacts of hyper-parameters, compare calibration methods in a systematic way. We show that a well-optimized RF performs as well as or better than leading calibration approaches.

The idea to distinguish and quantify two important types of uncertainty, often referred to as aleatoric and epistemic, has received increasing attention in machine learning research in the last couple of years. In this paper, we consider ensemble-based approaches to uncertainty quantification. Distinguishing between different types of uncertainty-aware learning algorithms, we specifically focus on Bayesian methods and approaches based on so-called credal sets, which naturally suggest themselves from an ensemble learning point of view. For both approaches, we address the question of how to quantify aleatoric and epistemic uncertainty. The effectiveness of corresponding measures is evaluated and compared in an empirical study on classification with a reject option.

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learner's aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.

PurposeNosiness is an annoying behavior at the workplace that can lead to negative consequences. It is characterized by being overly curious about other people’s affairs. Specifically, this study aims to identify the factors contributing to nosiness among nurses.Design/methodology/approachWe conducted an exploratory qualitative interview study involving 38 nurses in Iran. The participants were selected by purposive sampling.FindingsWe identified nine themes as the antecedents of nosiness among nurses: defamation motive, the need for certainty, the need for power, recreational motive, empathy, social comparison, the allure of the subject for the individual, having an employee-friendly workplace, and work environment and workload.Originality/valueUnderstanding the antecedents of nosiness can help healthcare organizations curtail this phenomenon and foster a positive work environment, particularly in nursing where empathy, compassion, and attention to detail make them susceptible to nosiness.

Due to their high entrapment efficiency, anodized titanium nanotubes (TiO2-NTs) are considered effective reservoirs for loading/releasing strong antibiotics whose systemic administration is associated with diverse and severe side-effects. In this study, TiO2-NTs were synthesized by anodic oxidation of titanium foils, and the effects of electrolyte percentage and viscosity on their dimensions were evaluated. It was found that as the water content increased from 15 to 30%, the wall thickness, length, and inner diameter of the NTs increase from 5.9 to 15.8 nm, 1.56 to 3.21 µm, and 59 to 84 nm, respectively. Ciprofloxacin, a highly potent antibiotic, was loaded into TiO2-NTs with a high encapsulation efficiency of 93%, followed by coating with different chitosan layers to achieve a sustained release profile. The prepared formulations were characterized by various techniques, such as scanning electron microscopy, differential scanning calorimetry, and contact measurement. In vitro release studies showed that the higher the chitosan layer count, the more sustained the release. Evaluation of antimicrobial activity of the formulation against two endodontic species from Peptostreptococcus and Fusobacterium revealed minimum inhibitory concentrations (MICs) of 1 µg/mL for the former and the latter. To summarize, this study demonstrated that TiO2-NTs are promising reservoirs for drug loading, and that the chitosan coating provides not only a sustained release profile, but also a synergistic antibacterial effect.

Background and Aims. Microalgae are known as a promising source for food, pharmaceutical, and biofuel production while providing environmental advantages. The present study evaluates some newly isolated microalgal strains from north and southwest of Iran as a potential source for high-value products. Methods. Primitive screening was carried out regarding growth parameters. The molecular and morphological identifications of the selected strains were performed using 18S rRNA gene sequencing. After phylogenic and evolutionary studies, the selected microalgal strains were characterized in terms of protein and pigment content, in addition to the fatty acid profile content. Besides, the CO2 fixation rate was determined to assess capability for various environmental applications. Results. All of the selected strains were predominantly belonging to Scenedesmus sp. and Desmodesmus sp. The isolated Scenedesmus sp. VN 009 possessed the highest productivity content and CO2 fixation rate of 0.054 g·L−1d−1 and 0.1 g·L−1d−1, respectively. Moreover, data from GC/MS analysis demonstrated the high robustness of this strain to produce several valuable fatty acids including α-linolenic acid and linoleic acid in 45% and 20% of total fatty acids. Conclusions. The identified strains have a great but different potential for SCP, β-carotene, and ω-3 production, as well as CO2 fixation for environmental purposes. In this study, considering the wide range of microalgal strains in different habitats of Iran, the potential applications of native microalgae for various pharmaceutical, food, and biotechnology purposes were investigated.

Objective: Microalgal pharmaceutical biotechnology is mainly dependent on the biomass yield and also the final concentration of the obtained lipids. β-carotene is one of the most precious nutraceuticals, of both preventive and therapeutics importance in pharmacy and medicine. Dunaliella salina is known as famous β-carotene producer which could accumulate the β-carotene up to 10% of its dry cell weights. The amount of different macro and micronutrients in D. salina culture medium defines its productivity and β-carotene content. Methods: In this study, the effects of sulfur, iron and manganese deprivation, on cell growth and β-carotene biosynthesis in a naturally isolated strain of D. salina was examined. Besides, the fatty acid profile of the naturally isolated strain was also investigated. Results: Sulfur, iron and manganese deprivation caused a noticeable decrease in the cell growth of D. salina. On the other hand, in nutrient depleted media, the maximum β-carotene concentration was significantly improved (14.616 mg L-1 in sulfur starvation, 14.994 mg L-1 in iron starvation and 10.119 mg L-1 in manganese starvation media) compared with initial values (6.753 mg L-1) in basic culture medium. The obtained fatty acids from the studied microalgal strain found to be some important saturated, monounsaturated and polyunsaturated fatty acids. Conclusion: Owing to its significant growth rate, β-carotene contents and fatty acid profile; the naturally isolated microalgal strain could be exploited as a potential producer strain. Besides, the nutrient limitation strategy could be effectively employed to improve the β-carotene production procedure in D. salina.

Meeting the healthy nutrition needs of the increasing population in the arid and semi-arid climates of the different regions of the world such as Iran has become very important for the agriculture ministry and water resources managers. In this study, the system dynamics approach was used in the Vensim software environment to allocate the water of the Zonouz dam reservoir for irrigation purposes in the northwest of Iran. For this purpose, the existing surface water resources in the basin and the amounts of agricultural water and environmental water demands were determined and a water allocation plan was developed. In the first stage of the study, it was found that if the existing water resources and demands will not change, the amount of water stored in the reservoir will provide approximately 91% of irrigation water demands and approximately 99% of environmental water needs. The model created in the study was found to be sensitive to reservoir inputs and irrigation water demands. Within the scope of this study, the impact of two different scenarios that may occur as a result of climate change and irrigation management in the operation of the reservoir was evaluated. The decrease in the amount of water entering the reservoir in the first scenario and the increase in irrigation water needs in the second scenario are assumed within the next 10 years. According to the simulation results of the first scenario, irrigation water demands will not be met sufficiently with the decrease in the amount of water to be stored in the reservoir due to the decrease in the amount of water entering the reservoir in the next 10 years. According to the results of the second scenario, in the next 10 years due to possible climate change or if the cultivated area increases due to some new agricultural policies; The amount of water stored in the reservoir will not meet the irrigation demands and there will be water shortage in the system. In this case, it is necessary to make changes in irrigation water management and use new irrigation systems to save water. Based on the findings of the study, it has been observed that the impact of all types of irrigation water policies can be successfully evaluated within the scope of the system dynamics approach. Meeting the healthy nutrition needs of the increasing population in the arid and semi-arid climates of the different regions of the world such as Iran has become very important for the agriculture ministry and water resources managers. In this study, the system dynamics approach was used in the Vensim software environment to allocate the water of the Zonouz dam reservoir for irrigation purposes in the northwest of Iran. For this purpose, the existing surface water resources in the basin and the amounts of agricultural water and environmental water demands were determined and a water allocation plan was developed. In the first stage of the study, it was found that if the existing water resources and demands will not change, the amount of water stored in the reservoir will provide approximately 91% of irrigation water demands and approximately 99% of environmental water needs. The model created in the study was found to be sensitive to reservoir inputs and irrigation water demands. Within the scope of this study, the impact of two different scenarios that may occur as a result of climate change and irrigation management in the operation of the reservoir was evaluated. The decrease in the amount of water entering the reservoir in the first scenario and the increase in irrigation water needs in the second scenario are assumed within the next 10 years. According to the simulation results of the first scenario, irrigation water demands will not be met sufficiently with the decrease in the amount of water to be stored in the reservoir due to the decrease in the amount of water entering the reservoir in the next 10 years. According to the results of the second scenario, in the next 10 years due to possible climate change or if the cultivated area increases due to some new agricultural policies; The amount of water stored in the reservoir will not meet the irrigation demands and there will be water shortage in the system. In this case, it is necessary to make changes in irrigation water management and use new irrigation systems to save water. Based on the findings of the study, it has been observed that the impact of all types of irrigation water policies can be successfully evaluated within the scope of the system dynamics approach.