Anthropogenic particle abundance and characteristics in seawater and intertidal sediments of the Tonkin Bay Coast (North Vietnam).

Microplastics (MPs, plastic items from 1 µm to 5 mm in size) are present in all environmental compartments. The evaluation of their concentration, fate, and spatial distribution is still a challenge for the scientific community. This concern is just debuting in developing countries, (i.e., Asia, South America, and Africa). This study deals with the MP contamination in the abiotic marine compartments of Northern Vietnam: seawater and intertidal sediments. Four sites located in the intertidal zone or near the coastline in Tonkin Bay, Vietnam were studied. A total of 16 samples (eight for each compartment) were collected in July 2020 (rainy season) and January 2021 (dry season). Anthropogenic particles (total observed fibers and fragments) were found at levels ranging from 3 to 303 particles/m3 in seawater and from 63 to 955 particles/kg dry weight in sediments. Most of these were fibers less than 300-µm long. Higher levels of seawater at the Nam Dinh site were found in the rainy season compared to the dry one. As the river flow was estimated six times higher during the rainy season than during the dry season, these results suggest the river discharge is a potential source of contamination for the coastal zone. The temporal variability was lower for the sediments than for the seawater, suggesting the long-term integration of the anthropogenic particles in this compartment. A small portion of sorted particles were analyzed by µFTIR (8.35%), and this sub-sample was only composed of fragments. Still, fragments were mostly composed of polypropylene (PP, 82%), polyethylene (PE, 9%), and polystyrene (PS, 9%). The fragment size was similar in the two studied compartments, but it was dependent on polymer types since PS fragments (140 ± 17 µm) were smaller than those made of PE (622 ± 123 µm) and PP (869 ± 905 µm). Future works should investigate the smallest fraction of MP (even nanoplastics) as well as find solutions in order to mitigate MP contamination in the marine environment.

Integrating fuzzy-AHP and GIS for solid waste disposal site selection in Kenitra province, NW Morocco.

Selecting an optimal solid waste disposal site is one of the decisive waste management issues because unsuitable sites cause serious environmental and public health problems. In Kenitra province, northwest Morocco, sustainable disposal sites have become a major challenge due to rapid urbanization and population growth. In addition, the existing disposal sites are traditional and inappropriate. The objective of this study is to suggest potential suitable disposal sites using fuzzy logic and analytical hierarchy process (fuzzy-AHP) method integrated with geographic information system (GIS) techniques. For this purpose, thirteen factors affecting the selection process were involved. The results showed that 5% of the studied area is considered extremely suitable and scattered in the central-eastern parts, while 9% is considered almost unsuitable and distributed in the northern and southern parts. Thereafter, these results were validated using the area under the curve (AUC) of the receiver operating characteristics (ROC). The AUC found was 57.1%, which is a moderate prediction's accuracy because the existing sites used in the validation's process were randomly selected. These results can assist relevant authorities and stakeholders for setting new solid waste disposal sites in Kenitra province.

Assessing river water quality for ecological risk in the context of a decaying river in India.

The decay of rivers and river water pollution are common problems worldwide. However, many works have been performed on decaying rivers in India, and the status of the water quality is still unknown in Jalangi River. To this end, the present study intends to examine the water quality of the Jalangi River to assess ecological status in both the spatial and seasonal dimensions. To depict the spatiality of ecological risks, 34 water samples were collected from the source to the sink of the Jalangi River with an interval of 10 km while 119 water samples were collected from a secondary source during 2012-2022 to capture the seasonal dynamics. In this work, the seasonality and spatiality of change in the river's water quality have been explored. This study used the eutrophication index (EI), organic pollution index (OPI), and overall index of pollution (OIP) to assess the ecological risk. The results illustrated that the values of OPI range from 7.17 to 588, and the values of EI exceed the standard of 1, indicating the critical situation of the ecological status of Jalangi River. The value of OIP ranges between 2.67 and 3.91 revealing the slightly polluted condition of the river water. The study signified the ecological status of the river is in a critical situation due to elevated concentrations of biological oxygen demand, chemical oxygen demand, and low concentrations of dissolved oxygen. The present study found that stagnation of water flow in the river, primarily driven by the eastward tilting of the Bengal basin, triggered water pollution and ecological risk. Moreover, anthropogenic interventions in the form of riverbed agriculture and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river. This decay needs substantial attention from the various stakeholders in a participatory manner.

Concerted Effect of Ion- and Electron-Conductive Additives on the Electrochemical and Thermal Performances of the LiNi0.8Co0.1Mn0.1O2 Cathode Material Synthesized by a Taylor-Flow Reactor for Lithium-Ion Batteries.

To address the issue that a single coating agent cannot simultaneously enhance Li+-ion transport and electronic conductivity of Ni-rich cathode materials with surface modification, in the present study, we first successfully synthesized a LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode material by a Taylor-flow reactor followed by surface coating with Li-BTJ and dispersion of vapor-grown carbon fibers treated with polydopamine (PDA-VGCF) filler in the composite slurry. The Li-BTJ hybrid oligomer coating can suppress side reactions and enhance ionic conductivity, and the PDA-VGCFs filler can increase electronic conductivity. As a result of the synergistic effect of the dual conducting agents, the cells based on the modified NCM811 electrodes deliver superior cycling stability and rate capability, as compared to the bare NCM811 electrode. The CR2032 coin-type cells with the NCM811@Li-BTJ + PDA-VGCF electrode retain a discharge specific capacity of ∼92.2% at 1C after 200 cycles between 2.8 and 4.3 V (vs Li/Li+), while bare NCM811 retains only 84.0%. Moreover, the NCM811@Li-BTJ + PDA-VGCF electrode-based cells reduced the total heat (Qt) by ca. 7.0% at 35 °C over the bare electrode. Remarkably, the Li-BTJ hybrid oligomer coating on the surface of the NCM811 active particles acts as an artificial cathode electrolyte interphase (ACEI) layer, mitigating irreversible surface phase transformation of the layered NCM811 cathode and facilitating Li+ ion transport. Meanwhile, the fiber-shaped PDA-VGCF filler significantly reduced microcrack propagation during cycling and promoted the electronic conductance of the NCM811-based electrode. Generally, enlightened with the current experimental findings, the concerted ion and electron conductive agents significantly enhanced the Ni-rich cathode-based cell performance, which is a promising strategy to apply to other Ni-rich cathode materials for lithium-ion batteries.

Flexoelectric and size-dependent effects on hygro-thermal vibration of variable thickness fluid-infiltrated porous metal foam nanoplates.

The Galerkin-Vlasov approach based on the improved first-order shear deformation theory (i-FSDT) and nonlocal elasticity theory are proposed to investigate the free vibration response of variable-thickness fluid-infiltrated porous metal foam (FPMF) nanoplates with flexoelectricity effect resting on Pasternak elastic foundation in the hygro-thermal environment. The FPMF nanoplate thickness varies according to both the length and width directions. The novelty of the present work is to consider the influence of the nonlocal's spatial variation and flexoelectric coefficients on the free vibration behavior of the nanoplates. Based on Hamilton's principle, the governing equation of FPMF nanoplate is established. The accuracy of the proposed method is checked by comparing the obtained results with those of available work in the literature. The effects of the parameters such as the flexoelectric coefficient, nonlocal coefficient, porosity coefficient, Skempton factor, temperature and moisture, thickness variation, and various boundary conditions on the natural frequency of the nanoplate are examined.

Tunable Nano-Bending Structures of Loosened/Tightened Lassos with Bi-Stable Vibration-Induced Emissions for Multi-Manipulations of White-Light Emissions and Sensor Applications.

The first tunable nano-bending structures of [1]rotaxane containing a single-fluorophoric N,N'-diphenyl-dihydrodibenzo[a,c]phenazine (DPAC) moiety (i.e., [1]RA) are developed as a loosened lasso structure to feature the bright white-light emission [CIE (0.27, 0.33), Φ = 21.2%] in THF solution, where bi-stable states of bending and twisted structures of DPAC unit in [1]RA produce cyan and orange emissions at 480 and 600 nm, respectively. With acid/base controls, tunable loosened/tightened nano-loops of corresponding [1]rotaxanes (i.e., [1]RA/[1]RB) can be achieved via the shuttling of macrocycles reversibly, and thus to adjust their respective white-light/cyan emissions, where the cyan emission of [1]RB is obtained due to the largest conformational constraint of DPAC moiety in its bending form of [1]RB with a tightened lasso structure. Additionally, the non-interlocked analog M-Boc only shows the orange emission, revealing the twisted form of DPAC fluorophore in M-Boc without any conformational constraint. Moreover, the utilization of solvents (with different viscosities and polarities), temperatures, and water fractions could serve as effective tools to adjust the bi-stable vibration-induced emission (VIE) colors of [1]rotaxanes. Finally, tuning ratiometric emission colors of adaptive conformations of DPAC moieties by altering nano-bending structures in [1]rotaxanes and external stimuli can be further developed as intelligent temperature and viscosity sensor materials.

Mental health impact on patients with COVID-19 in the community under a long period of full lockdown in Vietnam.

OBJECTIVE: We aimed to investigate the impact on the mental health of patients with COVID-19 in a centralized isolation facility in the community who experienced a long period of full lockdown during the fourth wave of the COVID-19 pandemic in Vietnam. METHODS: We performed a retrospective cross-sectional study among 125 patients with COVID-19 in a centralized isolation facility in the community of Ho Chi Minh City from September to November 2021. We collected data on depression, anxiety, and stress symptoms, as indicated by scores on the Depression Anxiety Stress Scale-21, as well as sociodemographic characteristics. RESULTS: The prevalence of depression, anxiety, and stress among patients with COVID-19 was 14.4%, 20.8%, and 20.0%, respectively. Depression scores were significantly and positively correlated with body mass index whereas stress scores were significantly and positively correlated with age. CONCLUSION: Our findings indicated an increased prevalence of depression, anxiety, and stress among patients with COVID-19 who were in a centralized isolation facility during the fourth COVID-19 wave in Vietnam. Overweight and older age were identified as risk factors for adverse mental health in patients with COVID-19. Psychological intervention programs should be implemented in isolation facilities for individuals with COVID-19 infection.

Neutrophil: Lymphocyte and Platelet: Lymphocyte ratios measured before transplantation and their correlation with new-onset diabetes post-transplantation in renal transplant recipients.

PURPOSE: Our goal was to evaluate the neutrophil:lymphocyte (NLR) and platelet:lymphocyte (PLR) ratios measured before transplantation and their correlation with new-onset diabetes after transplantation (NODAT) in renal transplant recipients. PATIENTS AND METHODS: We conducted our study in 324 adult patients consecutively admitted to Military Hospital 103, Ha Noi, Viet Nam, who received kidney allografts from living donors. These patients were followed-up during the first 2 years post-transplantation for NODAT. We examined the association between NLR and PLR measured prior to transplantation in patients with NODAT: NLR and PLR were calculated based on the results of the complete blood count. The criteria for diagnosis of a fully symptomatic NODAT case were based on the guidelines established by the American Diabetes Association and included fasting venous blood glucose and glycosylated hemoglobin A1c (HbA1c) levels, with or without an oral glucose tolerance test. RESULTS: The overall rate of NODAT during the two years after kidney transplantation was 13.6%. We found mean values of age and body mass index (BMI), and median values of NLR, PLR, high sensitivity C-reactive protein (hs-CRP) levels, and the arteriosclerosis ratio in the NODAT group to be significantly higher than those of the non-NODAT group (all p < 0.05). Furthermore, an adjusted multivariate regression analysis showed that age (area under the curve [AUC] = 0.727, p < 0.001), BMI (AUC = 0.846, p < 0.001), serum hs-CRP levels (AUC = 0.884, p < 0.001), NLR (AUC = 0.888; p < 0.001), and PLR (AUC = 0.818; p < 0.001) had predictive value for NODAT. CONCLUSION: NLR and PLR measured before transplantation were good predictors for NODAT in the first 2 years post-renal transplantation.

Towards understanding climate change: Impact of land use indices and drainage on land surface temperature for valley drainage and non-drainage areas.

The continuous increase of urbanization and industrialization brought various climatic changes, leading to global warming. The unavailability of meteorological data makes remotely sensed data important for understanding climate change. Therefore, the land surface temperature (LST) is critical in understanding global climate changes and related hydrological processes. The main objective of this work is to explore the dominant drivers of land use and hydrologic indices for LST in drainage and non-drainage areas. Specifically, the relationship between LST changes, land use, and hydrologic indices in Northeast Qena, Egypt, was investigated. The Landsat 5 and 8 imagery, Geographic Information System (GIS), and R-package were applied to identify the change detection during 2000-2021. The normalized difference between vegetation index (NDVI), bare soil index (BSI), normalized difference built-up, built-up index (BUI), modified normalized difference water index (MNDWI), and soil-adjusted vegetation index (SAVI) were employed. The non-drainage or mountain areas were found to be more susceptible to high LST values. The comprehensive analysis and assessment of the spatiotemporal changes of LST indicated that land use and hydrologic indices were driving factors for LST changes. Considerably, LST retrieved from the Landsat imaginary showed significant variation between the maximum LST during 2000 (44.82°C) and 2021 (50.74°C). However, NDBI has got less spread during the past (2000) with 10-13%. A high negative correlation was observed between the LST and NDVI, while the SAVI and LST positively correlated. The results of this study provide relevant information for environmental planning to local management authorities.

Climate change and future challenges to the sustainable management of the Iraqi marshlands.

The application of restoration plans for the Iraqi marshlands is encountering significant challenges due to water scarcity and the impacts of climate change. This paper assesses the impact of water scarcity on the possibility of continuing the application of restoration and sustainable management plans for the main marshlands in Iraq. This assessment was conducted based on the available data and expected situation of available water resources under climate change conditions until the year 2035. Additionally, a satellite image-based index model was prepared and applied for the period 2009-2020 to obtain the spatiotemporal distribution of the restored marshlands. The results show that the shortage in water resources and insufficient inundation rates prevented the adequate application of the restoration plans. Also, applying the scenarios of distributing the deficit equally over all water demand sectors (S1) and according to the percentage of demand for each sector (S2) shows that the expected deficit in available water for the three marshes by the years 2025 and 2035 will be approximately 25% and 32% for S1 and 9% for S2. Consequently, the considered marshes are expected to lose approximately 20 to 33% of their eligible restoration areas. Accordingly, looking for suitable alternatives to support the water resources of these marshes became a very urgent matter and/or recourse to reduce the areas targeted by inundation and being satisfied with the areas that can be sustainable and maintain the current status of the rest of the regions as an emerging ecosystem characterized by lands that are inundated every few years. Accordingly, steps must be urged to develop plans and programs to maintain the sustainability of these emerging ecosystems within the frameworks of climate change and the conditions of scarcity of water resources and water and air pollution to ensure that they are not lost in the future.

Classification of precipitation types in Poland using machine learning and threshold temperature methods.

The phase in which precipitation falls-rainfall, snowfall, or sleet-has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966-2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station.

Improving PM2.5 prediction in New Delhi using a hybrid extreme learning machine coupled with snake optimization algorithm.

Fine particulate matter (PM2.5) is a significant air pollutant that drives the most chronic health problems and premature mortality in big metropolitans such as Delhi. In such a context, accurate prediction of PM2.5 concentration is critical for raising public awareness, allowing sensitive populations to plan ahead, and providing governments with information for public health alerts. This study applies a novel hybridization of extreme learning machine (ELM) with a snake optimization algorithm called the ELM-SO model to forecast PM2.5 concentrations. The model has been developed on air quality inputs and meteorological parameters. Furthermore, the ELM-SO hybrid model is compared with individual machine learning models, such as Support Vector Regression (SVR), Random Forest (RF), Extreme Learning Machines (ELM), Gradient Boosting Regressor (GBR), XGBoost, and a deep learning model known as Long Short-Term Memory networks (LSTM), in forecasting PM2.5 concentrations. The study results suggested that ELM-SO exhibited the highest level of predictive performance among the five models, with a testing value of squared correlation coefficient (R2) of 0.928, and root mean square error of 30.325 µg/m3. The study's findings suggest that the ELM-SO technique is a valuable tool for accurately forecasting PM2.5 concentrations and could help advance the field of air quality forecasting. By developing state-of-the-art air pollution prediction models that incorporate ELM-SO, it may be possible to understand better and anticipate the effects of air pollution on human health and the environment.

Eye Lesions in Patients After One Year of Kidney Transplantation.

Purpose: Determine the incidence of some eye lesions in kidney transplant patients after one year at Military Hospital 103 and comment on related factors. Patients and Methods: A cross-sectional study description of 111 kidney transplant patients (222 eyes) at Military Hospital 103. We assessed several eye lesions, including dry eyes, corneal conjunctival calcification, cataracts, and retinopathy. Results: The rate of retinopathy was 84.7%, dry eye was 59.5%, cataract was 29.7%, and corneal conjunctival calcification was 24.8%, atrophy optic nerve was 9.9%, epiretinal membrane was 1.8%. Post-transplant influence factors associated with cataracts include the dose of prednisolone (OR= 1.6, p < 0.05) and post-transplant diabetes (OR=1.4, p < 0.05). The influence factor related to the atrophy of the optic nerve is systemic infection after transplantation (OR=2.4, p < 0.05). Conclusion: Retinopathy accounted for the highest rate, followed by dry eye disease; cataracts ranked third; and finally, calcified corneal conjunctiva. Factors that affect cataracts are diabetes mellitus and prednisolone dose. Factors affecting optic nerve atrophy are infections after kidney transplantation.

Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area.

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope.

Diastereoselective Pd-catalyzed Decarboxylative (4+2) Cycloaddition Reactions of 4-Vinylbenzoxazinanones and 2-Nitro-1,3-enynes.

A formal palladium-catalyzed decarboxylative (4+2) cycloaddition reaction between 4-vinylbenzoxazinanones and 2-nitro-1,3-enynes has been developed to produce highly valuable, densely functionalized tetrahydroquinolines in moderate to excellent yields with high diastereoselectivity under mild reaction conditions. The optimised protocol tolerates a range of substituted 2-nitro-1,3-enynes, which represent an under-utilized class of dipolarophile for transition-metal catalyzed cycloadditions. The employed reaction methodology facilitates efficient cycloaddition with both N-H- and N-Ts-4-vinylbenzoxazinanone dipole precursors. The stereochemistry of the major and minor diastereomeric (4+2) cycloadducts was determined by single crystal X-ray analyses. A mechanistic rationale for the high intrinsic diastereoselectivity and preliminary enantioselective experiments are also presented. The tetrahydroquinoline cycloadduct products feature numerous pendant functionalities, including a vinyl handle, an internal alkyne motif and a nitro functionality (which functions as a latent C-3 nitrogen substituent) for further synthetic manipulations.

Rapidly Determine the Maximum Power Point in the Parallel Configuration of the Photovoltaic System.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (Isc) and Open-Circuit Voltage (Voc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

Individual Locating of Soccer Players from a Single Moving View.

Positional data in team sports is key in evaluating the players' individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder-decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.

Determination of tacrolimus in human whole blood in kidney transplant recipients using a rapid and specific LC-MS/MS method.

OBJECTIVE: To develop and validate an LC-M/SMS method for the determination of tacrolimus in human whole blood. METHOD: The LC-MS/MS method for the determination of tacrolimus in whole blood was developed and validated according to the guidelines. Concentrations of TAC in 100 kidney transplant patients measured by LC-MS/MS were compared with CMIA using correlation analysis and Bland-Altman plots. RESULTS: The method had a total chromatographic run time of 5 min. The calibration curves were linear over the range of 0.5-100.0 ng/mL with a lower limit of quantification of 1 ng/mL. The intra- and interday accuracy was within the range of 93.3%-109.2% and 96.0%-108.4%, respectively, with precision ranging from 0.8 to 9.4%. The mean extraction recoveries of TAC ranged from 102.6 to 107.8%. The mean concentrations of TAC in whole blood of kidney transplant patients measured by the two assays were different at 1, 3 months and all time points (p < 0.001), but no significant difference was observed at 6 months (p = 0.094). The correlation of data was good with the correlation coefficients (r2 ) of 0.7581, 0.8811, 0.8777, and 0.8077, respectively. Passing-Bablok regression analysis demonstrated good correlations with r2 values higher than 0.88 between TAC levels measured by LC-MS/MS and CMIA. Using Bland-Altman plots yielded average biases of 1.29, 0.79, 0.11, and 0.65 ng/mL at 1, 3, and 6 months and all time points. CONCLUSION: The LC-MS/MS method was validated for the accurate determination of TAC in human whole blood. The comparison of tacrolimus concentrations measured by the LC-MS/MS with CMIA showed a good correlation and agreement of two methods, suggesting LC-MS/MS should be used routinely to monitor TAC concentrations in kidney transplant patients.

Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: case of upper Ziz, southeastern Morocco.

The selection of appropriate areas for reforestation remains a complex task because of influence by several factors, which requires the use of new techniques. Based on the accurate outcomes obtained through machine learning in prior investigations, the current study evaluates the capacities of six machine learning techniques (MLT) for delineating optimal areas for reforestation purposes specifically targeting Quercus ilex, an important local species to protect soil and water in upper Ziz, southeast Morocco. In the initial phase, the remaining stands of Q. ilex were identified, and at each site, measurements were taken for a set of 12 geo-environmental parameters including slope, aspect, elevation, geology, distance to stream, rainfall, slope length, plan curvature, profile curvature, erodibility, soil erosion, and land use/land cover. Subsequently, six machine learning algorithms were applied to model optimal areas for reforestation. In terms of models' performance, the results were compared, and the best were obtained by Bagging (area under the curve (AUC) = 0.98) and Naive Bayes (AUC = 0.97). Extremely favorable areas represent 8% and 17% of the study area according to Bagging and NB respectively, located to the west where geological unit of Bathonian-Bajocian with low erodibility index (K) and where rainfall varies between 250 and 300 mm/year. This work provides a roadmap for decision-makers to increase the chances of successful reforestation at lower cost and in less time.

Relationship Between Metacognitive Awareness of Undergraduate Students and Students' Academic Performance at Vietnam Military Medical University.

Introduction: Metacognition plays an essential role in competency-based medical education. Metacognitive skills consist of knowledge and regulation metacognition. This study was conducted to investigate the metacognition of undergraduate students and its correlation with students' academic performance. Methods: The metacognitive skills inventory comprised 52 binary-scale items administered to 202 Vietnam Military Medical University medical students. The entire semester and clinical results were used to measure their academic performance. Results: Medical students' total metacognitive awareness score was high (median 0.8). The median metacognitive knowledge score was significantly lower than the metacognitive regulation score (0.7 vs 0.8, respectively). The participants with a total metacognition score ≥0.8 had significantly higher academic results (full semester exam results of 7.4 and clinical exam of 7.5). The group of participants in the military, having sports habits and usually searching academic documents in English, had a higher proportion of total metacognitive awareness score ≥0.8 than the group without these above characteristics (with the percentages of 53.3%, 59%, and 64.3%, respectively; p < 0.05). The number of books read by participants with a total metacognitive awareness score ≥ 0.8 was significantly higher than those with a total metacognitive awareness score <0.8 (3.5 compared to 2.4 books). Conclusion: Metacognitive awareness of Vietnam Military Medical University medical students was likely to be high. A high score of metacognitive awareness could predict high academic performance. Being a military student, playing sports, reading books, and searching English documents were predictors of better metacognitive awareness.