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PM2.5, fine particulate matter with a diameter smaller than 2.5 μ m , is associated with a range of health problems. Monitoring PM2.5 levels at the community scale is crucial for understanding personal exposure and implementing preventive measures. While monitoring agencies around the world, such as the U.S. Environmental Protection Agency (EPA), provide accurate data, the spatial coverage is limited due to a sparse monitoring network. Recently, the emergence of low-cost air quality sensor networks has enabled the availability of air quality data with higher spatiotemporal resolution, which is more representative of personal exposure. However, concerns persist regarding the sensitivity, noise, and reliability of data from these low-cost sensors. In this study, we analyzed PM2.5 data from both EPA and Purple Air (PA) sensors in Cook County, Illinois, with two primary goals: (1) understanding the differential impact of meteorological factors on PA and EPA sensor networks and (2) provide a mathematical approach to quantify the individual impact of correlated predictors on both short-term and baseline variations in noisy time series data. We used the Kolmogorov-Zurbenko (KZ) filter to separate the time series into short-term and baseline components, followed by fitting linear models to quantify the impact of meteorological predictors, including temperature, relative humidity (RH), wind speed (WS), and wind direction (WD). Furthermore, we applied the Lindeman, Merenda, and Gold (LMG) method to these linear models to quantify the individual contribution of each predictor in the presence of multicollinearity. Our results show that the PM2.5 data from PA sensors exhibit higher sensitivity to meteorological factors, particularly wind speed, in the short-term and RH in the baseline component. This method provides a structured approach for analyzing noisy sensor data under diverse environmental conditions. [ABSTRACT FROM AUTHOR]

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage. [ABSTRACT FROM AUTHOR]

PM2.5, fine particulate matter with a diameter smaller than 2.5 μm, is associated with a range of health problems. Monitoring PM2.5 levels at the community scale is crucial for understanding personal exposure and implementing preventive measures. While monitoring agencies around the world, such as the U.S. Environmental Protection Agency (EPA), provide accurate data, the spatial coverage is limited due to a sparse monitoring network. Recently, the emergence of low-cost air quality sensor networks has enabled the availability of air quality data with higher spatiotemporal resolution, which is more representative of personal exposure. However, concerns persist regarding the sensitivity, noise, and reliability of data from these low-cost sensors. In this study, we analyzed PM2.5 data from both EPA and Purple Air (PA) sensors in Cook County, Illinois, with two primary goals: (1) understanding the differential impact of meteorological factors on PA and EPA sensor networks and (2) provide a mathematical approach to quantify the individual impact of correlated predictors on both short-term and baseline variations in noisy time series data. We used the Kolmogorov-Zurbenko (KZ) filter to separate the time series into short-term and baseline components, followed by fitting linear models to quantify the impact of meteorological predictors, including temperature, relative humidity (RH), wind speed (WS), and wind direction (WD). Furthermore, we applied the Lindeman, Merenda, and Gold (LMG) method to these linear models to quantify the individual contribution of each predictor in the presence of multicollinearity. Our results show that the PM2.5 data from PA sensors exhibit higher sensitivity to meteorological factors, particularly wind speed, in the short-term and RH in the baseline component. This method provides a structured approach for analyzing noisy sensor data under diverse environmental conditions.

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

Blood is important evidence that can assist an investigator in solving a crime. It connects the suspect to a crime and aid in the reconstruction of the crime scene. Criminals frequently attempt to wipe away blood stained evidence at a crime scene. These efforts may result in the modification or partial or total elimination of blood on the stained parts. Numerous presumptive tests are employed to identify bloodstains on floor surfaces since they are frequently cleansed after a crime using various surfactants. However, the traces of stains remain there and if analysed with appropriate reagents will give conclusive results. In This study Blood-stained floor surfaces were cleaned using a commercially available and widely used floor cleaners and After repeated washings, the Kastle-Meyer (KM), Leucomalachite green (LMG), and Tetra methylbenzidine (TMB) tests were employed to determine the presence of blood on these surfaces, and their sensitivity were assessed, this study will help the forensic investigator to select the appropriate reagents for detection of blood stains on the various washed floor surfaces and effectiveness of reagents for detection of blood stains washed with various floor cleaners. [ABSTRACT FROM AUTHOR]

The presented study evaluated a set of beads primarily originating from the Hallstatt period (800–400 BC) and uncovered in the region of Bohemia. Utilizing an SEM/EDS method, the chemical composition of the glass samples was determined and their homogeneity measured. Owing to the presence of opaque glass, Raman spectroscopy was applied, enabling the definition of the phases causing the opacity of the glass, as well as its coloring. This article discusses opacifying agents, including the possible ways in which they entered the artefacts. In addition, the techniques used to produce the glass beads are described, for both the single-colored beads, as well as the so-called eye beads that are present in a significant amount in the set. The majority of the beads examined were found to be made of the LMG glass type (low-magnesium soda-lime glass). An unexpected result was the identification of glass with a high content of K2O not corresponding to the mixed alkali type (LMHK), which is frequently discussed in the literature. The glass type in question most likely does not come from the traditional area of glass production: the eastern Mediterranean territory. [ABSTRACT FROM AUTHOR]

In this work, we present for the first time three-dimensional (3D) gel dosimeters for 3D UV dose distribution measurements. These new gel dosimeters are aqueous compositions containing 2,3,5-triphenyltetrazolium chloride or 4,4′ -benzylidenebis( N,N -dimethylaniline) (leucomalachite green) and poly(ethylene oxide)- block -poly(propylene oxide)- block -poly(ethylene oxide) (Pluronic F-127), which form a physical gel matrix. The unique feature of these gels is their high transparency and colourlessness, comparable to crystal clear water. This differentiates them from 3D gel dosimeter compositions that are made of gelatine and used for radiotherapy dosimetry purposes only. If UV irradiated, the gels become coloured (red or green); the colour depends on the radiation sensitive compound in the composition. Both gels were examined versus UVA (range: 315–400 nm; a peak at 369 nm), UVB (range: 280–360 nm; a peak at 306 nm) and UVC (253.7 nm) irradiation. The typical parameters of such dosimeters were derived after obtaining their calibrations: linear dose range, dynamic dose range, dose threshold and dose sensitivity, as well as the UV subranges to which the gels were the most sensitive. The 2,3,5-triphenyltetrazolium chloride gels were the most sensitive to UVA radiation, whereas the leucomalachite green gels were sensitive to UVB. The highest dose sensitivity of the gels was obtained for 3 mmol/dm 3 2,3,5-triphenyltetrazolium chloride and 7 mmol/dm 3 leucomalachite green, which amounted to 3.60 ± 0.12 and 42.85 ± 1.53 cm 2 /J, respectively (the linear dose range was equal to 0.15–0.9 and 0–0.3 J/cm 2 ; respectively, and the dynamic dose range was equal to 0–2 and 0–5 J/cm 2 , respectively). The 2,3,5-triphenyltetrazolium chloride gels were more sensitive to UVA but less sensitive to UVB and UVC than the leucomalachite green gels. Additionally, the gel dosimeters were examined with respect to their temporal stability, dose response for fractionated and non-fractionated doses as well as the repeatability and spatial stability of the dose distribution. The spatial stability results obtained suggested that 2,3,5-triphenyltetrazolium chloride gels keep the dose distribution integral for over 10 days after irradiation, contrary to the leucomalachite green gels that lose it soon after irradiation. However, this does not disqualify the leucomalachite green gels from use as 3D radiation dosimeters, on the condition that they are measured in 3D soon after irradiation. The proposed gel dosimeters can be used as UV dose sensors, 1D UV dosimeters or 3D high resolution dosimeters. [ABSTRACT FROM AUTHOR]

The leucomalachite green (LMG) test is one of catalytic tests for the detection of latent bloodstains and generally used in forensic field because of convenience and cost/time-effectiveness. However, contamination of latent bloodstains at crime scenes can interfere with the LMG reaction, resulting in false-negative or false-positive decisions. Herein, we examined if ascorbic acid and vitamin C ( l -ascorbic acid or ascorbate)-containing beverages affect the LMG reaction. Ascorbic acid showed the inhibitory activities on the LMG reaction in a dose-dependent manner. Similarly, vitamin C-containing beverages also inhibited the LMG reaction and the inhibitory effects were proportional to the concentrations of vitamin C in beverages. It was also identified that as incubation time after adding LMG reagent to the mixtures of blood and ascorbic acid or beverages was increased, the inhibitory effects of ascorbic acid vitamin C-containing beverages on LMG test were disappeared. These results suggest that the LMG reaction is delayed but not stopped by ascorbic acid and vitamin C-containing beverages. Neither incubation at room temperature around 20–25 °C nor the addition of acetic acid affects the inhibitory activity of ascorbic acid on LMG reaction. We also showed that ascorbic acid does not affect DNA stability, allowing us to obtain full short tandem repeat (STR) profiles through amplification of DNA using commercial STR kits. In conclusion, ascorbic acid and vitamin C-containing beverages delayed the LMG reaction, suggesting that it should be considered that negative results of LMG test could be false negative due to contamination of bloodstains with inhibitory factors on LMG test. [ABSTRACT FROM AUTHOR]

Poverty affects many people worldwide and varies in space and time, although its determinants are geographical factors. This paper presents a case study from Hubei Province, Central China, investigating the spatial and temporal changes in poverty determinants at the county from 2013-2019 and village levels from 2013 to 2017. We investigated the variation in the spatial autocorrelation of poverty incidence at the two levels using global and local Moran's I. We then explored the spatial and temporal variations of poverty determinants using the Lineman, Merenda, and Gold method. We found that the overall spatial autocorrelation gradually mitigated, whereas the local spatial pattern remained unchanged at both levels. Deeply poor areas were concentrated in the western part of Hubei Province and the southwestern part of Yunyang County. The effects of geographical conditions on poverty decreased across the study period, with the R2 value decreasing from 85% to 73% at the county level and from 57% to 38% at the village level. Furthermore, the contribution of natural environmental factors to poverty slightly decreased at both scale levels, whereas the socioeconomic factors had a significantly increased effect on county-level poverty over time. By contrast, the factors that have a major effect on village-level poverty remained stable. The results might indicate that the implementation of various targeted poverty alleviation measures since 2013 have mitigated the restrictions of local geographical factors on poverty alleviation. [ABSTRACT FROM AUTHOR]

The present project was undertaken with the key objective to find out relationship of HMW and LMW glutenin sub-units and immunological test with bread making quality. The antibody response assessed through animal modelling by using rabbits as test animals showed significant variation towards high molecular weight glutenin (HMG) and low molecular weight glutenin (LMG) of different spring wheat varieties. A significant and positive relationship of bread sensory attributes with antibody response against HMG was observed. The mastication (r=0.64), texture (r=0.69), taste (r=0.72), aroma (r=0.68), grain (r=0.71) and total score (r=0.83) showed highly significant association with antibody response against HMG. The loaf volume correlated linearly with that of HMW antibody response (r=0.51). The LMG antibody response was negatively correlated with flour characteristics and sensorial attributes with non significant level. [ABSTRACT FROM PUBLISHER]

Identifying poverty determinants in a region is crucial for taking effective poverty reduction measures. This paper utilizes two variable importance analysis methods to identify the relative importance of different geographic factors to explain the spatial distribution of poverty: the Lindeman, Merenda, and Gold (LMG) method used in multiple linear regression (MLR) and variable importance used in random forest (RF) machine learning. A case study was conducted in Yunyang, a poverty-stricken county in China, to evaluate the performances of the two methods for identifying village-level poverty determinants. The results indicated that: (1) MLR and RF had similar explanation accuracy; (2) LMG and RF were consistent in the three main determinants of poverty; (3) LMG better identified the importance of variables that were highly related to poverty but correlated with other variables, while RF better identified the non-linear relationships between poverty and explanatory variables; (4) accessibility metrics are the most important variables influencing poverty in Yunyang and have a linear relationship with poverty. [ABSTRACT FROM AUTHOR]