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Drought is one of the most complex phenomena in the world; so, proper management is very important in monitoring and reducing its damage. For this purpose, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Reconnaissance Drought Index (RDI) indices were used to analyze the intensity and frequency of drought in the coastal wet, mountain, semi-mountain, semi-desert, desert, and coastal desert climates of Iran in four seasons, separately: autumn, winter, spring, and summer. Forty-three synoptic stations with a common statistical period of 50 years (1969–2019) were selected. The results showed that the trend of drought in winter and summer is increasing in all studied climates. The comparison of the results in the trend analysis of the drought showed the same trend, but the SPEI index compared to the other indicators showed a quicker response to changes in drier climates. The highest correlation (0.80–0.99) between SPI-RDI and SPEI-RDI indices in coastal desert, mountain, and semi-mountain climates and the lowest correlation (0.34) between SPI-SPEI and SPEI-RDI indices in semi-desert, desert, and coastal desert climates were obtained. SPI-RDI variations showed similar values in colder climates. The SPEI is based on precipitation and temperature data, and it has the advantage of combining multi-scalar character with the capacity to include the effects of temperature variability in the drought assessment. Thus, SPEI is recommended as a suitable index for studying and identifying the effect of climate change on drought conditions.

The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

Infection diagnosis and antibiotic susceptibility testing (AST) are time-consuming and often laborious clinical practices. This paper presents a microwave-microfluidic biosensor for rapid, contactless and non-invasive device for testing the concentration and growth of Escherichia Coli ( E. Coli ) in medium solutions of different pH to increase the efficacy of clinical microbiology practices. The thin layer interface between the microfluidic channel and the microwave resonator significantly enhanced the detection sensitivity. The microfluidic chip, fabricated using standard soft lithography, was injected with bacterial samples and incorporated with a microwave microstrip ring resonator sensor with an operation frequency of 2.5 GHz and initial quality factor of 83 for detecting the concentration and growth of bacteria. The resonator had a coupling gap area on of 1.5 × 1.5 mm 2 as of its sensitive region. The presence of different concentrations of bacteria in different pH solutions were detected via screening the changes in resonant amplitude and frequency responses of the microwave system. The sensor device demonstrated near immediate response to changes in the concentration of bacteria and maximum sensitivity of 3.4 MHz compared to a logarithm value of bacteria concentration. The minimum prepared optical transparency of bacteria was tested at an OD 600 value of 0.003. The sensor’s resonant frequency and amplitude parameters were utilized to monitor bacteria growth during a 500-minute time frame, which demonstrated a stable response with respect to detecting the bacterial proliferation. A highly linear response was demonstrated for detecting bacteria concentration at various pH values. The growth of bacteria analyzed over the resonator showed an exponential growth curve with respect to time and concurred with the lag-log-stationary-death model of cell growth. This biosensor is one step forward to automate the complex AST workflow of clinical microbiology laboratories for rapid and automated detection of bacteria as well as screening the bacteria proliferation in response to antibiotics.

The current study systematically reviewed, summarized and meta-analyzed the clinical features of the vaccines in clinical trials to provide a better estimate of their efficacy, side effects and immunogenicity. All relevant publications were systematically searched and collected from major databases up to 12 March 2021. A total of 25 RCTs (123 datasets), 58,889 cases that received the COVID-19 vaccine and 46,638 controls who received placebo were included in the meta-analysis. In total, mRNA-based and adenovirus-vectored COVID-19 vaccines had 94.6% (95% CI 0.936–0.954) and 80.2% (95% CI 0.56–0.93) efficacy in phase II/III RCTs, respectively. Efficacy of the adenovirus-vectored vaccine after the first (97.6%; 95% CI 0.939–0.997) and second (98.2%; 95% CI 0.980–0.984) doses was the highest against receptor-binding domain (RBD) antigen after 3 weeks of injections. The mRNA-based vaccines had the highest level of side effects reported except for diarrhea and arthralgia. Aluminum-adjuvanted vaccines had the lowest systemic and local side effects between vaccines’ adjuvant or without adjuvant, except for injection site redness. The adenovirus-vectored and mRNA-based vaccines for COVID-19 showed the highest efficacy after first and second doses, respectively. The mRNA-based vaccines had higher side effects. Remarkably few experienced extreme adverse effects and all stimulated robust immune responses.

In this research, silver/graphene oxide (Ag/GO) nanohybrid was first synthesized and used in production of polysulfone (PSF) ultrafiltration (UF) membranes via phase inversion method for concentrating phycocyanin (PC) and treating methylene blue (MB) dye effluent. Designing the experiment (DOE) using Box-Behnken method by Design Expert software helped to calculate the optimal values of the variables under study. The studied variables included PSF polymer concentration, polyvinyl pyrrolidone (PVP) pore-former concentration and Ag/GO nanohybrid content, which were investigated for their effects on pure water flux (PWF) and MB pigment rejection. According to the results of the DOE, the membrane containing 19.485 wt% PSF, 0.043 wt% PVP and 0.987 wt% Ag/GO was selected as the optimal membrane. Due to the high price of PC as drug, and the importance of removing MB pigment from the effluent of dyeing and textile industries, the membranes were first optimized with MB pigment and then the optimal membrane was used for concentrating PC. The results showed that PWF reaches from 40.05 L.m − 2 .h − 1 (LMH) for the neat membrane to 156.73 LMH for the optimized membrane, which shows about 4 times improvement. Compared to the neat membrane, flux recovery ratio (FRR) of the optimized membrane increased by about 20% and its total fouling (R t ) decreased by about 10%. Also, the results showed that the optimized membrane can remove 81.6% of MB, as well as to reject 93.8% of PC.

Microglial activation can release free radicals and various pro-inflammatory cytokines, which implicates the progress of a neurodegenerative disease. Therefore suppression of microglial activation can be an appropriate strategy for combating neurodegenerative diseases. Betanin is a red food dye that acts as free radical scavenger and can be a promising candidate for this purpose. In this study, purification of betanin from red beetroots was carried out by normal phase colum chromatography, yielding 500 mg of betanin from 100 g of red beetroot. The purified betanin was evaluated by TLC, UV-visible, HPLC, ESI-MASS, FT-IR spectroscopy. Investigation on the inhibitory effect of betanin on activated microglia was performed using primary microglial culture. The results showed that betanin significantly inhibited lipopolysaccharide induced microglial function including the production of nitric oxide free radicals, reactive oxygen species, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and interleukin-1 beta (IL-1β). Moreover, betanin modulated mitochondrial membrane potential, lysosomal membrane permeabilization and adenosine triphosphate. We further investigated the interaction of betanin with TNF-α, IL-6 and Nitric oxide synthase (iNOS or NOS2) using in silico molecular docking analysis. The docking results demonstrated that betanin have significant negative binding energy against active sites of TNF-α, IL-6 and iNOS.

The reproducibility of ultrasensitive biosensors is vital for clinical research, scalable manufacturing, commercialization, and reliable clinical decision-making, as batch-to-batch variations introduce significant uncertainty. However, most biosensors lack robust quality control (QC) measures. This study introduces an innovative QC strategy to produce highly reproducible molecularly imprinted polymer (MIP) biosensors by leveraging real-time data from the electrofabrication process. Prussian Blue nanoparticles (PB NPs) embedded within the MIP structure enable precise monitoring of surface properties, conductivity, MIP film thickness, and template extraction efficiency. The QC strategy utilizes variations in the current intensity of PB NPs during fabrication to implement real-time, non-destructive QC protocols at critical fabrication stages, minimizing measurement variability and ensuring consistency. This approach was validated by fabricating MIP biosensors for detecting agmatine metabolite and glial fibrillary acidic protein (GFAP) in phosphate-buffered saline (PBS). The QC strategy reduced relative standard deviation (RSD) by 79% for agmatine (RSD = 2.05% QC, RSD = 9.68% control) and 87% for GFAP (RSD = 1.44% QC, RSD = 11.67% control). Moreover, quality-controlled biosensors achieved success rates of 45% for agmatine and 36% for GFAP detection, significantly outperforming bare screen-printed electrodes. This work marks a significant advancement in biosensor development by integrating robust QC protocols directly into the fabrication process. By embedding PB NPs and monitoring electrochemical signals in real-time, this strategy delivers an unprecedented level of reproducibility, scalability, and reliability for MIP biosensors, addressing critical challenges in point-of-care diagnostics and commercial applications.

Herbicides are the extensively used class of pesticides, which beside the active ingredient, in their formulation accompanying substances such as emulsifiers, surfactants and others is needed. The potential toxicity of these synthetic chemicals could pose serious risks to the human health, nontarget organism and environment. In this work we developed biodegradable lignin nanoparticles (LNPs) as environmentally friendly and controlled release carriers of 2,4-dichlorophenoxyacetic acid 2,4-D and 2-methyl-4-chlorophenoxyacetic acid MCPA. LNPs were synthesized via solvent-free nanoprecipitation, achieving high entrapment efficiencies of 90.7% (2,4-D) and 97.4% (MCPA), confirmed by ultraviolet–visible spectroscopy. In vitro release studies revealed sustained herbicide release in buffer solutions (pH 5.5–7.5), with 68–74% release over 72 h, compared to rapid release from commercial formulations. Bioactivity assays of on Descurainia sophia showed that LNP-encapsulated formulation of herbicides reduced weed dry weight by 62.31% and density by 56.09% compared to untreated controls, statistically matching the weed control efficacy of commercial formulations. Field trials further validated these results. LNP-encapsulated 2,4-D + MCPA reduced Amaranthus blitoides dry weight by 91.10% and density by 65.09%, while this new formulation decreased Chenopodium album dry weight and density by 96.01% and by 66.75%, respectively. Notably, lignin’s inherent biodegradability and non-toxic nature provide a sustainable alternative to conventional synthetic adjuvants, significantly reducing the risks of environmental contamination. Our study highlights the potential of lignin-based nanoencapsulation to preserve weed control efficacy while promoting environmentally friendly and safer herbicide formulations.

This study examines the effectiveness of various quantile regression (QR) and machine learning (ML) methodologies developed for analyzing the relationship between meteorological parameters and daily reference evapotranspiration (ET ref ) across diverse climates in Iran spanning from 1987 to 2022. The analyzed models include D-vine copula-based quantile regression (DVQR), multivariate linear quantile regression (MLQR), Bayesian model averaging quantile regression (BMAQR), as well as machine learning algorithms such as extreme learning machine (ELM), random forest (RF), M5 model Tree (M5Tree), least squares support vector regression algorithm (LSSVR), and extreme gradient boosting (XGBoost). Additionally, empirical equations (EEs) such as Baier and Robertson (BARO), Jensen and Haise (JEHA), and Penman (PENM) models were considered. While the EEs demonstrated acceptable performance, the QR and ML models exhibited superior accuracy. Among these, the MLQR model displayed the highest accuracy compared to DVQR and BMAQR models. Moreover, LSSVR, XGBoost, and M5Tree models outperformed ELM and RF models. Notably, LSSVR, XGBoost, and MLQR models exhibited comparable performance (R2 and NSE > 0.92, MBE and RMSE < 0.5, and SI > 0.05) to M5Tree and BMAQR models across all climates. Importantly, these models significantly outperformed EEs, DVQR, ELM, and RF models in all climates. In conclusion, high-dimensional QR and ML models are recommended as promising alternatives for accurately estimating daily ET ref in diverse global climate conditions.

This paper investigates a novel model-free optimal controller for nonlinear autonomous underwater vehicles (AUVs). It is considered that the AUV considered as the case study is subject to multi-asymmetric constrained inputs. To achieve the optimal controller, a performance index function with exponential discounted value term and input hyperbolic function is developed. Since it is assumed that the AUV dynamics are completely unknown, a model-free integral reinforcement learning (RL) strategy is established. The suggested approach uses the sampled data pairs of input and states. To implement the model-free Integral RL optimal controller, a neural network structure is suggested to estimate the performance index function and control policy. Finally, a numerical simulation and comparative results are given to verify the effectiveness of the proposal.