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To evaluate the sonographic features of secondary involvement of skin and subcutaneous tissues by hematologic malignancies.A review of the ultrasound and pathology databases yielded 10 cases with 13 skin and subcutaneous tissue lesions secondary to hematologic neoplasms, which were confirmed by pathology. We used ultrasound to assess the number, location, size, depth of involvement, echogenicity, and vascularity of the lesions.The study involved five male and five female patients, including four leukemia, two multiple myeloma, and four lymphoma patients. The average age was 45 years (17-66 years). Three patients presented with one lesion, four with two lesions, and three with more than two lesions. All the lesions were located in the trunk and extremities. The lesions ranged from 1.2 to 8.3 cm in size. A total of 10 lesions involved subcutaneous fat tissue. A total of 10 lesions displayed hypoechoic foci within a hyperechoic background, and three appeared hypoechoic, and most of them exhibited abundant vascularity (12 of 13 lesions).Secondary involvement of skin and subcutaneous tissues by hematologic malignancies often present with multiple palpable masses showing the following ultrasound features: (1) subcutaneous fat infiltration, (2) hypoechoic foci with a hyperechoic background, and (3) abundant vascularity.

Flood susceptibility assessment for identifying flood-prone areas plays a significant role in flood hazard mitigation. Machine learning is an optional assessment method because of its high objectivity and computational efficiency, but how to get enough and accurate information of historical flood locations to train the machine learning models has been a key problem. In recent years, news media data from both news websites and social media authentication accounts has emerged as a promising source for natural science studies. However, the application of news media data in urban flood susceptibility assessment is still inadequate. This study proposed an approach of three tasks to use news media data on this topic. Firstly, flood locations were extracted from news media data based on a named entity recognition (NER) model. Then, a frequency or distance-based data quality control method was employed to improve the representativeness of the extracted flooded locations. Finally, flood conditioning factors with information of historical flood locations were input into a Support Vector Machine (SVM) model for flood susceptibility assessment. We took the central city of Dalian, China, as a case study. The results show that there was no significant difference of a T-test between the distributions of most flood conditioning factors at the flood locations from the news media data and the official planning report. In the obtained flood susceptibility map, the high flood susceptibility areas got a recall of 90% compared with the high flood hazard areas in the planning report. Performing data quality control in the frequency-based method can improve the precision of the flood susceptibility map by up to 5%, while the distance-based method is ineffective. This study provides an example and offers the value of applying new data sources and modern deep learning techniques for urban flood management.

Recently, numerous bio-optical algorithms have been proposed to estimate chlorophyll-a (Chla) concentration in global surface waters. The surge of algorithms is mainly due to the heterogeneity of waters, especially in inland systems. To reduce redundant modeling work for inland waters and to enhance the usability of algorithms, it is urgent to evaluate the published algorithms for similar optical water types. However, approaches of the algorithm assessment in previous studies vary in error metrics usage or relative comparison, leading to the current lack of a standard or consensus in the community. Thus, we proposed a round-robin scoring method based on optically fuzzy clustering for algorithm assessment. An improved scoring approach (namely the sort-based method) was employed to aggregate the metrics of candidate algorithms. We generate a new set of optical water types using 1702 in situ samples, showing an improvement on our previous work [Bi et al. (2019)]. Then, an integrated Chla result was produced by blending the estimations of optimal algorithms weighted by the membership values, which showed better performance than that of any single algorithm. The blending framework presents more flexible and expandable than previous studies. We aim to illustrate the importance of assessment which considers key properties of algorithms (i.e., Accuracy, Precision, and Effectiveness) and to advocate more in-depth research on certain water types under the fuzzy clustering-based estimation framework (such as clean inland waters with low Chla). This article is accompanied by the open-source R package FCMm for implementation of the proposed method.

The structural change from a dimer to a 1D chain was achieved. As different hydrogen bonding involving the equatorial coordinated H2O, they show significantly improvements in magnetic dynamics, opening hysteresis up to 11 K for the chain compound.

Spin crossover (SCO) complexes capable of exhibiting distinct spin states under external stimuli have aroused intensive interests in material science. Herein, we report a reverse Hofmann-type metal−organic framework (MOF) [FeII(TPPE){AuI(CN)2}]I·2H2O·7TCE...

Stimuli-responsive materials that can be reversibly switched by light are of immense interest. Among them, photo-responsive spin crossover (SCO) complexes have great promises to combine the photoactive inputs with multifaceted outputs into switchable materials and devices. However, the reversible control the spin-state change by photochromic guests is still challenging. Herein, we report an unprecedented guest-driven light-induced spin change (GD-LISC) in a Hofmann-type metal-organic framework (MOF), [Fe(bpn){Ag(CN) 2 } 2 ]·azobenzene. ( 1 , bpn = 1,4-bis(4-pyridyl)naphthalene). The reversible trans-cis photoisomerization of azobenzene guest upon UV/vis irradiation in the solid state results in the remarkable magnetic changes in a wide temperature range of 10-180 K. This finding not only establishes a new switching mechanism for SCO complexes, but also paves the way toward the development of new generation of photo-responsive magnetic materials.

The great founding spirit of the Communist Party of China (CPC) inspires the Party to unite and lead the Chinese people to create miracles in the struggle over more than a century. The great founding spirit of the CPC is a valuable resource for conducting a series of ideological and political education courses in China. Integrating it into college ideological and political courses can help the students strengthen their ideals and beliefs, enhance their sense of responsibility, consolidate their sense of responsibility, and enhance their emotional and value identity of patriotism, loving the party, and loving the people. This paper aims to explore the value and practical path of integrating the great founding spirit of the CPC into ideological and political education, and it provides a reference for the study of integrating ideological and political education really into college campuses, teaching, and student’s mind.

The particulate backscattering coefficient (bbp) plays an important role in the underwater light field. However, it is difficult to accurately estimate bbp(λ) in turbid inland water with complex optical properties. To accurately estimate the backscattering coefficients in inland water, a simple classification method based on the shape of remote sensing reflectance was first proposed to distinguish two water types (i.e., water type 1 and water type 2) with different backscattering characteristics. Then, trigonometric functions were developed to simulate the backscattering coefficients at all bands in water type 1 and the backscattering coefficients in the visible band of water type 2, whereas a linear function was built to estimate the backscattering coefficients in the near-infrared band of water type 2. The proposed algorithm was compared with four state-of-the-art methods and validated by an independently measured dataset of three lakes in the middle and lower reaches of the Yangtze River in 2020. The results showed that the proposed algorithm performed well in inland waters, with all mean absolute percentage errors < 40% and root-mean-square errors < 0.25 m−1. Finally, the algorithm was applied to Ocean and Land Color Instrument images from 2016 to 2020 in Lake Taihu and Lake Hongze. It was found that the backscattering coefficients in Lake Taihu and Lake Hongze showed opposite seasonal variation trends, and the bbp(676) in Lake Hongze began to decrease since 2017, whereas no obvious interannual variation was observed in Taihu Lake in recent five years.

Through a mixed-ligand strategy, the structural change from a discrete dinuclear Dy

Phytoplankton carbon, an important biogeochemical and ecological parameter, plays a critical role in the carbon cycle and in global warming reduction. Estimation of phytoplankton carbon in inland waters on a large scale using remote sensing is useful for understanding, evaluating, and monitoring the carbon dynamics, and, in particular, for determining the spatial–temporal variation of primary production in inland waters. In a correlation analysis of the phytoplankton carbon concentration and water components, the result revealed no significant correlation between the chlorophyll-a concentration and phytoplankton carbon concentration in inland waters. However, the absorption peak height of particles at 675 nm, which is defined as the absorption at 675 nm subtracted by that at 660 nm, was found to be closely correlated with the phytoplankton carbon concentration. Thus, the absorption peak height of particles at 675 nm could be used as an indicator of the phytoplankton carbon concentration. A semianalytical method based on the remote-sensing reflectance in Sentinel-3 Ocean and Land Color Instrument (OLCI) bands 8, 9, and 17 was developed to derive the absorption peak of particles at a wavelength of 675 nm. Finally, an algorithm for estimating the phytoplankton carbon concentration in inland waters using OLCI bands 8, 9, and 17 was constructed. From 2013 to 2018, eight field campaigns were conducted in inland lakes in different seasons, and the optical properties, optically active water components, and phytoplankton carbon concentrations were obtained. An assessment of its accuracy using an independent data set demonstrated that the algorithm performance is acceptable (mean absolute percentage error, 48.6%, and root mean square error, 0.36 mg/L). As a demonstration, the algorithm was successfully applied to map the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake, China, using OLCI images acquired on December 5, 2017, and August 5, 2018 and December 8, 2017, and August 7, 2017, and the spatial variation of the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake was thoroughly examined. A semianalytical remote-sensing method, instead of a traditional time-consuming approach, was developed in this article, which can be used to estimate phytoplankton carbon concentration quickly, and provides an effective way to map the phytoplankton carbon concentration in inland water on a large scale.

Eutrophication is a severe environmental problem in the most inland lakes. Taihu Lake, the third largest freshwater lake in China, is one of the most eutrophic lakes. Increased phosphorus concentra...

The proportion of cyanobacterial biomass (PCB) can indicate the dominance of cyanobacteria in water, and it provides important information regarding the phytoplankton composition. Variations in the PCB can clarify the cyanobacteria accumulation process. Therefore, using remote sensing to obtain the spatial–temporal distribution of the PCB in inland lakes is very important for understanding the cyanobacterial bloom process. A total of 357 water samples was collected from three Chinese eutrophic inland lakes: Taihu Lake, Dianchi Lake, and Chaohu Lake, over eight field campaigns from 2013 to 2018. A normalized index of the PCB (NIPCB) was proposed using the absorption of chlorophyll-a (Chl-a) at 665 nm ( $a_{\mathrm {chl}}$ (665)) and the absorption of phycocyanin (PC) at 620 nm ( $a_{\mathrm {pc}}$ (620)) to indicate the PCB. A classification method using the PC index (PCI) was developed to identify the water types using the different algae composition features, and the relationship between the NIPCB and PCB in two water types was extensively examined. Finally, a semianalytical algorithm was developed to estimate the PCB based on the NIPCB index and the OLCI bands. The result of the estimated accuracy assessment using the independent validating data set demonstrated that the algorithm performance is satisfactory with mean absolute percentage error (MAPE) of 31.01% and root mean square error (RMSE) of 0.20. The MUMM method was then applied to conduct an OLCI image atmospheric correction with a satisfactory performance with the MAPE calculated between the measured remote sensing reflectance and the retrieved remote sensing from a satellite image of less than 25%. Consequently, the developed algorithm was successfully applied to three OLCI images of Dianchi Lake on April 12, 2017, Taihu Lake on May 18, 2017, and Chaohu Lake on September 8, 2018. The spatial distribution of the PCB in these three inland lakes is reasonable, and it demonstrated that the developed algorithm could be used to effectively monitor PCB at a large scale and provide important assistance in predicting the cyanobacterial blooms in eutrophic inland lakes by using remote sensing.

The first spin-crossover (SCO) complex with an organic-inorganic hybrid perovskite structure (PPN)[Fe{Au(CN)2}3] (1) is reported, which displays three-step SCO behaviour. The light-induced excited spin-state trapping measurement gives T0 = 134 K for a three-dimensional FeL3-type (L = bis-monodentate ligand) SCO complex. Moreover, spin-state dependent fluorescence is observed in 1.

Detection of ponding levels timely and accurately during urban floods is the basis of effective disaster prevention and mitigation. New data sources such as social media and road surveillance video record the process of urban floods in the form of images, and the development of computer vision technology bring new opportunities for extracting ponding information from these image data. This study proposes a computer vision-based method to identify the spatial-temporal distribution of ponding levels in the scene of the road surveillance video. Firstly, a dataset of sedan images compiled from three sources was collected to train an object detection algorithm, You Only Look Once vision 3 (YoloV3). Then the trained YoloV3 model was adopted to identify the ponding levels whenever and wherever sedans were detected from the videos. Secondly, the outlier detection was employed to detect and delete the outliers of ponding levels in each time step. Finally, Inverse Distance Weighted was leveraged to estimate the ponding distribution in the scene. This method was employed for two urban flood events at a street crossing, Dongguan Street, in Dalian, China. The evaluation index mAP of the trained YoloV3 model reached 78%, which stated the model’s validity. The ponding level estimated by our method was validated well with the submerged depth of a static reference, and ponding process had a strong correlation with the rainfall time series. The results can be used to analyze the process of flood rising and receding, which contributes to arrange drainage facilities and improve urban flood management.

Chemical oxygen demand concentration (C

The Yangtze River Delta (YRD) is one of the regions with the most intensive human activities. The eutrophication of lakes in this area is becoming increasingly serious with consequent negative impacts on the water supply of the surrounding cities. But the spatial-temporal characteristics and driving factors of the trophic state of the lake in this region are still not clearly addressed. In this study, a semi-analytical algorithm for estimating the trophic index (TSI) using particle absorption at 645 nm based on MODIS images is proposed to monitor and evaluate the trophic state of 41 large lakes (larger than 10 km2) in the YRD from 2002 to 2020. The performance of the proposed algorithm is evaluated using an independent dataset. Results showed that the root-mean-square error (RMSE) of the algorithm is less than 6 and the mean absolute percentage error (MAPE) does not exceed 8%, indicating that it can be applied for remotely deriving the TSI in the YRD. The spatial-temporal patterns revealed that there were significantly more lakes with moderate eutrophication in the Lower Yangtze River (LYR) than in the Lower Huaihe River (LHR). The overall average value of the TSI reaches a maximum in summer and a minimum in winter. The TSI value in the YRD over the period 2002–2020 showed a downward trend, especially after 2013. Individually, 33 lakes showed a downward trend and 8 lakes showed an upward trend. Furthermore, marked seasonal and interannual temporal variations can be clearly observed in the LYR and LHR and the sum of the variance contributions of seasonal and interannual components is more than 50%. Multiple linear regression analysis showed that human activities can explain 65% of the variation in the lake TSI in the YRD.

Tracking the spatiotemporal dynamics of particulate phosphorus concentration (C

Understanding the spatiotemporal dynamics of total dissolved phosphorus concentration (C

Eight novel containing sulfur heterocyclic curcumins were synthesized and characterized by 1H-NMR, FTIR and MS spectroscopy. Their antimicrobial activities against Staphylococcus aureus, Bacillus subtilis, Escherichia coli and Aspergillus niger were also tested for MIC by using serial tube dilution method. The results showed that the antimicrobial activities of synthesized curcumin derivatives were better than curcumin. Especially, the compound 4-(1,3-dithiolan-2-ylidene)-1,7-di(thiophen-2-yl) hepta-1,6-diene-3,5- dione (2g) exhibited excellent the antimicrobial activities among these curcumin derivatives.

Particle size distribution (PSD) is an important characterization parameter of the particle size structure of suspended particles, which is vulnerable to human activities such as sand dredging. In this study, sand dredging numbers were evaluated by the accumulated radiance algorithm using the night-time light composite data of the visible infrared imaging radiometer suite, and the PSD slope was derived by band ratio algorithm of Landsat-8/operational land imager (OLI) imagery. Then, the influence of sand mining on PSD slope was analyzed. We draw several crucial findings. First, the ratio of ${\boldsymbol{R}_{{{\bf rs}}({{{\bf Red}}})}}$ to ${\boldsymbol{R}_{{{\bf rs}}({{{\bf Green}}})}}$ based on OLI imagery was capable of retrieving PSD slope with good robustness ( R 2 = 0.71, p n = 41) and verified acceptance (mean absolute percentage error = 4.29%, root mean square error = 0.19, n = 20), and the PSD slope in HZL was lower in summer than in the other seasons from 2014 to 2017. Second, The night active vessels extracted from the night-time images show that sand dredging activities began at the outlet of Chengzi Lake (CZL) in April 2012, spread to the whole lake between 2014 and 2016, and then stopped in the northeast region in March 2017 due to the strict control of the local government. Third, the PSD slope is strongly influenced by sand mining, which is positively correlated with the number of sand dredgers ( R 2 = 0.92), i.e., the PSD slope increases 0.21 per 100 working sand dredgers in CZL.

Traditional remote sensing observation can only derive chlorophyll-a concentration in surface water (Chlasurf), which is insufficient for water quality monitoring or biogeochemical applications. In this paper, a column-integrated biomass (CIB) estimation algorithm was established in Lake Dianchi, China, and the CIB was estimated by Ocean and Land Color Instrument (OLCI) data from 2016 to 2018. First, the results show that the method is applicable to CIB estimation in nonbloom conditions [mean absolute percentage error (MAPE) = 10.55%, mean ratio (MR) = 0.996]. On the one hand, Chlasurf could be estimated using a universal model of Chla (UMOC) estimation for case-II waters; on the other hand, CIB could be obtained from Chlasurf according to the linear relationship between the Chlasurf and CIB. Second, both the spatial and temporal variabilities of Chlasurf are larger than those of CIB, which indicates that the biomass of the whole lake is relatively stable from day to day. During algal bloom periods, the CIB at nonbloom areas tends to decrease, suggesting that algae from nonbloom regions have migrated to bloom regions. Third, wind speed (WS) is the most critical meteorological factor affecting the water surface biomass, compared with other parameters. The average percentage of surface-to-total biomass in nonbloom conditions is about 7% when the WS is less than 2 m/s.

The reactions of chiral ligand (R)/(S)-1,1′-binaphthyl-2,2′-diyl phosphate (R-HL/S-HL) and ErCl3·6H2O afford two pairs of di- and tetranuclear enantiomers [Er2(R/S-L)4(EtOH)6]Cl2·6.5EtOH (R-1, S-1)...

Particulate organic matter (POM) plays an important role in biological pumping as a source of energy and nutrients in aquatic systems, as well as being the mechanism for algal bloom formation. However, research on its sources and composition, particularly the research on the contribution of algae in the bloom season, is still insufficient. In this study, the sources and composition of the POM in the surface water of Lake Taihu during the algal bloom season were quantitatively analysed. Dual stable carbon (δ13C) and nitrogen (δ15N) isotopes revealed that the algae were not the only sources of POM during algal blooms, and their contributions were inconsistent in the different lake regions. On average, algae made up 57.2% of the POM in Lake Taihu. Due to wind farms and nutrient runoff, algal POM was mainly concentrated in Meiliang Bay, Gonghu Bay and Central Taihu. The proportions of algal POM in the western estuary area and East Taihu were the smallest with a minimum of 10.8%. The proportion of terrestrial POM in the surface water and vertical sections was 18.3% and 40.0%, respectively. Furthermore, the highest value was 76.8%, which accounted for a large proportion in the algal bloom season. The terrestrial POM that came from rivers was mainly concentrated in the western part of Lake Taihu, while the resuspension of sediments also increased the proportion of terrestrial sources in the surface water. Affected by the distribution of the vegetation in Lake Taihu, the proportion of macrophyte POM in East Taihu was higher than that of algal sources. Although the contribution of endogenous POM might further increase due to the dual effects of climate change and eutrophication, our isotopic evidence suggested that terrestrial POM cannot be ignored, even in the algal bloom season.

Terrestrial humic-like substances (Peak C), the main constituent of chromophoric dissolved organic matter (CDOM), which is one of the optically active constituents present in water, is a good indicator of the ecological statuses of global aquatic ecosystems. In this study, the optical relationship between the absorption spectrum and the fluorescence spectrum of CDOM were investigated in a total of 168 samples collected from Lakes Taihu and Chaohu, and four components of the fluorescence spectrum were identified. There are significant positive correlations between the component arising from Peak C and the specific ultraviolet absorbance at 254 nm (SUVA), and the absorption coefficient of CDOM at 443 nm (a(443)), whereas the correlation between this component and the ratio of two absorption coefficients of CDOM (M) is negative. In addition, total nitrogen (TN), total phosphorus (TP), chlorophyll a and dissolved organic carbon (DOC) were shown to increase significantly with the increase in Peak C, highlighting the importance of the Peak C affecting the water quality in inland lakes. An optical algorithm was developed, which can be used to estimate the fluorescence intensity of Peak C based on R r s (B6) and R r s (B11) of the Ocean and Land Color Instrument (OLCI); the validation dataset showed that the mean absolute percentage error (MAPE) is 28% and the root mean square error (RMSE) is 0.13 QSU. This algorithm was successfully applied to OLCI images and used to analyze the distribution of Peak C in Lake Taihu. The resulting map indicated that the fluorescence intensity of Peak C was significantly higher near the lake entrance than in East Taihu, and the terrestrial input in the estuary area in the west is marked larger than other regions in Taihu Lake.

Vegetation is a key factor affecting soil erosion, which depends not only on the horizontal structure of vegetation, but also on its vertical structure. The leaf area index (LAI) of plants, as an important structural parameter of the terrestrial ecosystem, can reflect horizontal vegetation coverage, vertical structure of vegetation, litter thickness and underground biomass. All these are the principal aspects of vegetation affecting soil erosion. The multi-angle remote sensing data can provide the stereoscopic and directional information of the surface target, which is helpful to extract the vertical structure parameters of vegetation effectively. Therefore, in order to improve the estimation accuracy of soil erosion, a two-step method was developed in this study to estimate the forest LAI under different vertical structures using the PROSAIL model and multi-angle CHRIS/PROBA data based on the field data. A total of 70 quadrats (10 m × 10 m sized) were deployed in Mount Zijin with undisturbed natural vegetation, considering the vegetation types and accessibility in the middle of August 2017. The pixels were first classified into two categories depending on complexity of vertical structure; then, a specific algorithm was applied to the different classes. For Class 1, the single angle data of 55° had the highest accuracy, indicating that, for forests with a simple vertical structure, the single angle data was sufficient for LAI estimation. For Class 2, the multi-angle data combination of 0°, 55° and −55° had the highest accuracy, indicating that, for forests with complex vertical structure, the multi-angle data can improve the accuracy of LAI estimation. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) were applied to evaluate the accuracy of the LAI estimation algorithm. The results indicated that the two-step method can significantly improve the LAI estimation accuracy of forests with complex vertical structure, with a decrease in MAPE and RMSE of 19.52% and 23.08%, respectively, compared to the traditional empirical algorithm. The developed LAI estimation algorithm can be widely applied to forests with different vertical structures. Lastly, the two-step method was applied to the CHRIS/PROBA image acquired on September 7th, 2015 for mapping the spatial distribution of forest LAI in the western part of Mount Zijin. The result showed that the northern part and the southern part had higher LAI than the middle part of Mount Zijin.

High-frequency and reliable data on cyanobacteria blooming over a long time period is crucial to identify the outbreak mechanism of blooms and to forecast future trends. However, in cloudy and rainy areas, it is difficult to retrieve useful satellite images, especially in the rainy season. To address this problem, we used data from the HJ-1/CCD (Chinese environment and disaster monitoring and forecasting satellite/charge coupled device), GF-1/WFV (Chinese high-resolution satellite/wide field of view), and Landsat-8/OLI (Operational Land Imager) satellites to generate a time series of the bloom area from 2009 to 2016 in Dianchi Lake, China. We then correlated the responses of bloom dynamics to meteorological factors. Several findings can be drawn: (1) a higher bloom frequency and a larger bloom area occurred in 2011, 2013, and 2016, compared to the other years; (2) the frequency of blooms peaked in April, August, and November each year and expanded from north to south starting in July; (3) air temperature in spring and sunshine hours in summer greatly correlated to the yearly bloom area; (4) wind speed and sunshine hours strongly affected the short-term expansion of blooms and thereafter influenced the monthly bloom scale; and (5) rainfall had a strong short-term influence on the occurrence of blooms. Cyanobacteria blooms often occurred when wind speeds were less than 2.35 ± 0.78 m/s in the dry season and 2.01 ± 0.75 m/s in the rainy season, when there were 48 to 72 h of sunshine in the dry season and 35 to 57 h of sunshine in the rainy season, and when there was more than 10 mm of daily precipitation.

Particulate composition provides important information for understanding the changes in underwater light fields and primary productivity. In this study, a semianalytical algorithm, based on Rayleigh-corrected reflectance at 678 nm and 748 nm on Moderate Resolution Imaging Spectroradiometer (MODIS) images was used to estimate the ratio of chlorophyll a to total suspended solids (Chla/TSS), which characterizes the particulate composition of the Great Lakes. The long-term spatial and temporal characteristics of Chla/TSS in the Great Lakes from 2000 to 2020 were obtained. The results demonstrated that Lake Superior had the highest average Chla/TSS values (5.79±0.76 µg/mg), while Lake Erie had the lowest average Chla/TSS values (2.93±0.76 µg/mg). The Mann-Kendall test showed that the Chla/TSS of the Great Lakes all showed an increasing trend, notably in Lake Michigan, with 88.23% pixels showing significant increasing trend. Climatic and hydrological factors dominated the intra-annual variation of Chla/TSS, with contribution rates ranging from 71.47% to 92.54%. Through the annual Chla/TSS change pattern analysis, it was found that the contribution of wind speed to the annual variation in Chla/TSS was slight. Changes in temperature played a major role in the interannual variability of Chla/TSS in Lake Superior and Ontario; runoff and settlement were the major contributors in Lake Huron and Michigan, while cropland dominated the Chla/TSS interannual variability in Lake Erie. Furthermore, the significantly low values of Chla/TSS in spring had the potential to predict the occurrence of blooms in western Lake Erie, and the spatial distribution of Chla/TSS could help predict the location of blooms in the next few days.

Two pentafluorinated β-diketone ligands, 4,4,5,5,5-pentafluoro-1-(4-methoxyphenyl)pentane-1,3-dione (PFMP) and 4,4,5,5,5-pentafluoro-1-(4-dimethyl amino-phenyl)pentane-1,3-dione (PFAP), had been employed to synthesize six novel europium (III) complexes with ancillary ligands 2,2-dipyridine, 1,10-phenanthroline and 4,7-diphenyl-1,10-phenanthroline. The synthesized europium (III) complexes were characterized by FTIR, 1H NMR, UV–Vis, luminescence spectroscopy, elemental analysis and thermogravimetric analysis. The photoluminescence spectra of these complexes showed the typical europium (III) red emissions in solid state and chloroform solution, assigned to 5D0 → 7Fj (j = 0–4) transitions. The higher values of intensity parameter Ω2 indicated that the europium ion was in a highly polarizable ligand field in these complexes. Europium (III) complexes with the β-diketone PFMP exhibited much better photoluminescence properties than complexes with the β-diketone PFAP. Especially, the europium (III) complex of the β-diketone PFMP with the auxiliary ligand 2,2-dipyridine displayed the longest lifetime value, the highest quantum yield and good CIE color coordinates matching the pure red color (x = 0.67, y = 0.33) in these complexes. In addition, the proposed energy transfer mechanisms and the thermal stability of these complexes were also investigated and analyzed.

Herein, we reported two didysprosium single-molecule magnets constructed with {Dy(bbpen)(MeOH)} subunits and a π-conjugated tpb or non-conjugated tpcb bridging ligand. The former exhibits extremely weak luminescence that makes it difficult to simulate its emission spectra. However, the later shows obviously enhanced and well-resolved luminescence, which helps us to gain knowledge about the magneto-optical correlation and the relevant magnetic energy levels.

Serious cyanobacterial blooms (CBs) caused by lake eutrophication have become a global ecological and environmental problem and have adversely affected the production, life, and health of human beings. Lake Chaohu and Lake Taihu are two large closed shallow eutrophication lakes in the Yangtze River Delta in China with frequent CBs. In this study, the floating algae index (FAI) algorithm was applied to detect a long-time CBs sequence using Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2019. The common characteristics and differences of the CBs patterns were further explored in both lakes over the last 20 years. The results showed that the severity of CBs in Lakes Chaohu and Taihu presented a similar trend of decreasing and then increasing during the period of 2000-2004 and 2005-2007, respectively. Although the severity of CBs in the two lakes was alleviated after 2008, CBs in Lake Taihu has gradually increased since 2011 and severe CBs broke out again in 2017 and 2019. Meanwhile, the CBs in Lake Chaohu have varied significantly in different years, and severe CBs were observed in 2012, 2014-2015, and 2018-2019, while in other years, CBs remained relatively low level. The high-frequency regions of CBs were mainly concentrated in the western part in Lake Chaohu and in Zhushan Bay and Meilian Bay in Lake Taihu in the initial years of 2000. However, since 2005, the CBs in Lake Chaohu gradually expanded to the central and eastern parts, and to the northwestern and western shore in Lake Taihu. Furthermore, the relationship between the monthly mean area of CBs (CBs