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Cyanobacterial harmful algal blooms (cyanoHAB) have become more frequent and prominent in Atlantic Canada freshwater bodies over the last several years, especially in Nova Scotia (NS). Inspired by the trophic index of Vollenweider, a new index was developed with modification and adaptation for freshwater systems. Our model TRINDEX shows the effectiveness of estimation for the variation of cyanobacterial dominance in phytoplankton communities. TRINDEX can assist in determining the threshold for cyanobacterial bloom onset. Combinations of nutrients and pigments under TRINDEX were tested by a binary discrimination test to find the optimal range of threshold for cyanoHAB formation in freshwater lakes.

Forecasting thunderstorms, along with their intensity and phenomenon, is still one of the most challenging tasks in modern weather forecasting. One of the methods for this prediction is based on the indices of convective instability in the atmosphere. For the first time, we analysed the values and trends of 23 stability indices on days when hail occurred. From 2005 to 2020, the most frequently observed hailstones had a diameter between 13 and 20 mm, which accounted for 35.8% of all hail days, which was 826. Huge hailstones with a greater than 50 mm diameter were observed on only two days. Eight of the 23 stability indices show a monotonically decreasing (Showalter Index, Lifted Index, Lifted Index using the virtual temperature, and Humidity Index) or increasing trend (K Index, Convective Available Potential Energy for the most unstable air parcel and for mixing layer, and Convective Available Potential Energy in the layer between air temperatures −10 and −30 °C). These trends indicate that the environment is becoming increasingly favourable for the formation of thunderstorms. However, this potential does not appear to be fully realised, as the frequency of severe and large hail (with diameters of 21 mm or more) has not increased during the period studied.

Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.

Recently a novel pain recognition indicator derived from electroencephalogram(EEG) signals, pain threshold index(PTI) has been developed. The aim of this study was to determine whether PTI can be used for prediction of postoperative acute pain while surgical pleth index(SPI) applied as control. Eighty patients undergoing laparoscopic urological surgery under general anesthesia were enrolled. Data of SPI, PTI and a sedative index-wavelet index(WLI) were recorded within last 10 min at the end of surgery. The postoperative pain scores (NRS, numerical rating scale) were obtained. The Bland–Altman analysis was used for evaluation of consistency between PTI and SPI, whereas receiver-operating characteristic (ROC) curves was used for the mean values of PTI, SPI, and WLI to distinguish between mild (NRS 0–3) and moderate-severe (NRS 4–10) pain, and calculate their “best-fit” cut-off values. Data from 76 patients were included for final analysis. There was a good agreement between SPI and PTI values at the end of surgery. The ROC analysis showed a cut-off PTI value of 53 to discriminate between mild and moderate-to-severe pain, while SPI is 44 for this discrimination. Further analysis indicated that PTI had a best predictive accuracy reflected by highest area under curve (AUC)(0.772, 95% CI: 0.661–0.860)with sensitivity(62.50%) and specificity(90.91%) and a best positive predictive value(83.3%,95% CI: 68.4–98.2%). PTI obtained at the end of surgery, which have better predictive accuracy for postoperative pain than SPI, could differentiate the patients with moderate-to-severe pain from those with mild pain after they awaken from anesthesia.Clinical trial registration Chinese Clinical Trials Registry: ChiCTR1900024789.

Deep mining, characterized by high stress, elevated geothermal gradients, and significant moisture content, significantly increases the risk of Coal Spontaneous Combustion (CSC), posing a major threat to mine safety. This study delves into the impact of these factors on the self-ignition properties of coal, leveraging data from four distinct mines in Heilongjiang Province, China: Shuangyashan Dongrong No. 2 Mine, Hegang Junde Coal Mine, Qitaihe Longhu Coal Mine, and Jixi Ronghua No. 1 Mine. We have honed the theoretical framework to account for variations in gas content during CSC. Our investigation, conducted through programmed temperature rise experiments, scrutinized the generation and temperature-dependent evolution of gases, emphasizing individual indicators such as CO, O2, and CxHy, in addition to composite indicators like the ratio of change in CO to change in O2 concentration (:) and the ratio of C2H4 to C2H6. These insights have catalyzed the development of a CSC state energy level transition model and a precise method for phase-based quantification of combustion progression. Our findings furnish a scientific foundation for the formulation of early warning and prevention strategies in deep mining settings.

The pain threshold index (PTI) is a novel measure of nociception based on integrated electroencephalogram parameters during general anesthesia. The wavelet index (WLI) reflects the depth of sedation. This study aims to evaluate the ability of the PTI and WLI to predict hemodynamic reactivity after tracheal intubation and skin incision in pediatric patients. Pediatric patients (n=134) undergoing elective general surgery or urinary surgery were analyzed. Measurements at predefined time-points during tracheal intubation and skin incision included the PTI, WLI, heart rate (HR), and mean blood pressure (MBP). Receiver-operating characteristic (ROC) curves were computed to evaluate the predictive performance of the PTI and WLI in measuring hemodynamic reactivity (an increase of more than 20% in either MBP or HR) during general anesthesia. Of the 134 patients evaluated, positive reactivity of HR and MBP was observed in 95 (70.9%) and 61 (45.5%) patients induced by intubation, respectively, and 19 (14.2%) and 24 (17.9%) patients induced by skin incision, respectively. Using either HR or MBP reactivity induced by intubation as a dichotomous variable, the areas under the curves (AUCs) [95% CI] of PTI and WLI were 0.81[0.73-0.87] and 0.58[0.49-0.67] with the best cutoff values of 62 and 49. The AUCs [95% CI] of PTI and WLI were 0.82[0.75-0.88] and 0.61[0.52-0.69] after skin incision. The best cutoff values of PTI and WLI were 60 and 46, respectively. The PTI can predict hemodynamic reactivity with the best cutoff values of 62 and 60 after tracheal intubation and skin incision in pediatric patients during general anesthesia. The WLI failed in predicting hemodynamic changes.

Background: Taste perception is an integral part of a person's life. This perception gets altered due to many factors, one of which is diabetes mellitus. There is limited data on the taste alteration for sweet in Type 1 diabetics. Objective: To evaluate the sweet taste perception in subjects with type 1 diabetes by the mouth threshold index test. Methods: A cross-sectional study with 200 subjects inclusive of both sexes. The subjects were grouped into 2:100 control, composed of non-diabetics, and 100 tests, with Type 1 diabetic patients were recruited from Endocrinology Out patient department at Osmania General Hospital, Hyderabad to take part in this study. Sensitivity test in determining threshold index for sensory perception was analyzed. The tests were conducted on 5 sections containing different concentrations of glucose. Statistical analysis: The two groups were statistically analyzed using Chi square test with P value < 0.05 was considered statistically significant. Results: Among the study population, majority of participants had 0.25M (51 (51%) in non-diabetic and 28 (28%) in diabetic), 0.50M (26 (26%) in non-diabetic and 37 (37%) in diabetic) and 1M (11 (11%) in non-diabetic and 23 (23%) in diabetic) as concentration at which sweet taste was perceived. Type diabetics showed less sensitive to sweet stimuli compared to controls. Conclusion: Type 1 diabetes patients showed greater threshold index for sweet taste perception, this finding could further result in increased sweet intake leading increased blood sugar levels in these patients.

We are immersed in the so-called digital energy network, continuously introducing new technological advances for a better way of life. As a consequence, numerous emerging words are relevant to this point: Internet of Things (IoT), big data, smart cities, smart grid, industry 4.0, etc. To achieve this formidable goal, systems should work more efficiently, a fact that inevitably leads to power quality (PQ) assurance. Apart from its economic losses, a bad PQ implies serious risks for machines and, consequently, for people. Many researchers are endeavouring to develop new analysis techniques, instruments, measurement methods, and new indices and norms that match and fulfil the requirements regarding the current operation of the electrical network. This book, and its associated Special Issue, offer a compilation of some of the recent advances in this field. The chapters range from computing to technological implementation, going through event detection strategies and new indices and measurement methods that contribute significantly to the advance of PQ analysis and regulation. Experiments have been developed within the frameworks of research units and projects and deal with real data from industry practice and public buildings. Human beings have an unavoidable commitment to sustainability, which implies adapting PQ monitoring techniques to our dynamic world, defining a digital and smart concept of quality for electricity.

As urbanization has accelerated, the form of river and lake systems has changed greatly in cities, which has caused variations in the functioning of the interconnected river system network (IRSN). To quantitatively evaluate the influence of the urbanization process on IRSN, the form index system and function index system were established in this study. The form index system involved eight indexes, and the function index system included 18 indexes. We also used statistical methods to analyze the correlations between the form indexes and the function indexes of the IRSN. Multiple linear regression equations were established between the variations in the IRSN form indexes and the function indexes. Finally, we determined the threshold values of the IRSN form indexes that can meet Zhengzhou City's future development demands for water function. The threshold value for the drainage network density is between 0.32 and 0.33; the threshold value for the node connection rate is between 1.54 and 2; the threshold value for the degree of connectivity between river systems ranges from 0.55 to 0.77; and the threshold value for circuitry of the river ranges from 0.40 to 0.60. Validation shows that these values are reasonable.