Optimal weighting factor design based on entropy technique in finite control set model predictive torque control for electric drive applications.

In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.

First report on prevalence, molecular characterization and phylogenetic study of Toxoplasma gondii infecting sheep of the Malakand Division of Pakistan.

BACKGROUND: Toxoplasma gondii is an apicomplexan protozoan parasite that infects one-third of the population of the world, including humans, animals, birds, and other vertebrates. The present investigation is the first molecular attempt in the Malakand Division of Pakistan to determine the epidemiology and phylogenetic study of Toxoplasma gondii infecting small ruminants. METHODOLOGY: A total of (N = 450) blood samples of sheep were randomly collected during the study period (December 2020 to November 2021), and DNA detection was done using PCR by amplifying ITS-1 genes. SPSS.20 and MEGA-11 software were used for statistical significance and phylogenetic analysis. RESULTS: The overall prevalence of T. gondii infection among sheep was 14.44 % (65/450). A high infection rate was found in more than five-year-olds at 18.33 % (11/60). Sequencing and BLAST analysis of PCR-positive samples confirmed the presence of T. gondii. Randomly, three isolates were sequenced and submitted to GenBank under accession numbers (PP028089-PP028091), respectively. The BLAST analysis of the obtained sequences based on the ITS-1 gene showed 99 % similarities with reported genotypes found in goats of Malakand, Pakistan (PP028089) and dogs of Brazil (MF766454). The study concludes that T. gondii is notably prevalent among the sheep population in the region, emphasizing the significant role of risk factors in disease transmission across animals and potentially to humans. Further research, zoonotic potential analysis, and targeted control measures are warranted to address and manage this parasitic infection effectively.

Intrinsic Room-Temperature Ferromagnetism in New Halide Perovskite AgCrX3 (X: F, Cl, Br, I) Using Ab Initio and Monte Carlo Simulations.

Herein, we present a detailed comparative study of the structural, elastic, electronic, and magnetic properties of a series of new halide perovskite AgCrX3 (X: F, Cl, Br, I) crystal structures using density functional theory, mean-field theory (MFT), and quantum Monte Carlo (MC) simulations. As demonstrated by the negative formation energy and Born-Huang stability criteria, the suggested perovskite compounds show potential stability in the cubic crystal structure. The materials are ductile because the Pugh's ratio is greater than 1.75, and the Cauchy pressure (C12-C44) is positive. The ground state magnetic moments of the compound were calculated as 3.70, 3.91, 3.92, and 3.91 µB for AgCrF3, AgCrCl3, AgCrBr3, and AgCrI3, respectively. The GGA + SOC computed spin-polarized electronic structures reveal ferromagnetism and confirm the metallic character in all of these compounds under consideration. These characteristics are robust under a ±3% strained lattice constant. Using relativistic pseudopotentials, the total energy is calculated, which yields that the single ion anisotropy is 0.004 meV and the z-axis is the hard-axis in the series of AgCrX3 (X: F, Cl, Br, and I) compounds. Further, to explore room-temperature intrinsic ferromagnetism, we considered ferromagnetic and antiferromagnetic interactions of the magnetic ions in the compounds by considering a supercell with 2 × 2 × 2 dimensions. The transition temperature is estimated by two models, namely, MFT and MC simulations. The calculated Curie temperatures using MC simulations are 518.35, 624.30, 517.94, and 497.28 K, with ±5% error for AgCrF3, AgCrCl3, AgCrBr3, and AgCrI3 compounds, respectively. Our results suggest that halide perovskite AgCrX3 compounds are promising materials for spintronic nanodevices at room temperature and provide new recommendations. For the first time, we report results for novel halide perovskite compounds based on Ag and Cr atoms.

An efficient energy management scheme using rule-based swarm intelligence approach to support pulsed load via solar-powered battery-ultracapacitor hybrid energy system.

This work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Turn-off/turn-on biosensing of tetracycline and ciprofloxacin antibiotics using fluorescent iron oxide quantum dots.

A fluorescence probe based on iron oxide quantum dots (IO-QDs) was synthesized using the hydrothermal method for the determination of tetracycline (TCy) and ciprofloxacin (CPx) in aqueous solution. The IO-QDs were characterized using high-resolution transmission electron microscopy (HR-TEM), powder X-ray diffraction (P-XRD), vibrating sample magnetometry (VSM), and Fourier-transform infrared spectroscopy (FTIR). The as-prepared IO-QDs are fluorescent, stable, and with a fluorescence quantum yield (QY) of 9.8 ± 0.12%. The fluorescence of IO-QDs was observed to be quenched and enhanced in the presence of TCy and CPx, respectively. The fluorescence intensity ratio shows linearity at concentrations from 1-100 µM and 5-100 µM for TCy and CPx, respectively; the detection limit for TCy and CPx was estimated to be 0.71 µM and 1.56 µM, respectively. The proposed method was also successfully utilized in the spiked samples of drinking water and honey with good recoveries. The method offered convenience, rapid detection, high sensitivity, selectivity, and cost-efficient alternative options for the determination of TCy and CPx in real samples.

Finite-time robust speed control of synchronous reluctance motor using disturbance rejection sliding mode control with advanced reaching law.

In recent years, there has been a significant focus on synchronous reluctance motors (SynRM) owing to their impressive efficiency and absence of magnetic material. Although the SynRM shows great potential for use in electric vehicles, its widespread adoption is limited by unmodeled dynamics and external disturbances. Moreover, the uncertainty factor significantly restricts SynRM's peak efficiency and superior control performance, leading to an unjustifiable current loop reference command. To address these issues, this work presents various new research contributions which focus on the robust control of SynRM to optimize performance through the novel reaching law-based sliding mode control. Initially, a novel advanced sliding mode control reaching law (ASMCRL) with adaptive gain is proposed, to enhance the acceleration of the system state reaching the sliding surface. After that, an extended state observer (ESO) is designed to estimate and compensate for the overall disturbances of the system. Finally, the ASMCRL and ESO are integrated to design two nonlinear controllers namely, the disturbance-rejection sliding mode controller (DRSMC) and the disturbance-rejection sliding mode speed regulator (DRSMSR) for SynRM. The proposed DRSMSR eliminates the steady-state error and eradicates inherent chattering in DRSMC. Moreover, this yields a system trajectory that converges to a predetermined proximity of the sliding surface, irrespective of any lumped disturbances. The steady-state error of DRSMSR is less as compared to DRSMC. Furthermore, the speed response of this technique is 22.62% faster as compared to the state-of-the-art finite-time adaptive terminal sliding mode control. Additionally, the asymptotic stability of the proposed system is validated using Lyapunov's theorem. Thus the experimental results demonstrate the effectiveness and robustness of the proposed approach.

An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions.

Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

Phenotypic Classification of Eye Colour and Developmental Validation of the Irisplex System on Population Living in Malakand Division, Pakistan.

The core objective of forensic DNA typing is developing DNA profiles from biological evidence for personal identification. The present study was designed to check the validation of the IrisPlex system and the Prevalence of eye colour in the Pakhtoon population residing within the Malakand Division. METHODS: Eye colour digital photographs and buccal swab samples of 893 individuals of different age groups were collected. Multiplexed SNaPshot single base extension chemistry was used, and the genotypic results were analysed. Snapshot data were used for eye colour prediction through the IrisPlex and FROG-kb tool. RESULTS: The results of the present study found brown eye colour to be the most prevalent eye colour in comparison to intermediate and blue coloured. Overall, individuals with brown-coloured eyes possess CT (46.84%) and TT (53.16%) genotypes. Blue eye-coloured individuals are solely of the CC genotype, while individuals of intermediate eye colour carry CT (45.15%) and CC (53.85%) genotypes in rs12913832 SNP in the HERC2 gene. It was also revealed that brown-coloured eyes individuals were dominant among all age groups followed by intermediate and blue. Statistical analysis between particular variables and eye colour showed a significant p-value (<0.05) for rs16891982 SNP in SLC45A2 gene, rs12913832 SNP in HERC2 gene, rs1393350 SNP in SLC45A2, districts and gender. The rest of the SNPs were non-significant with eye colour, respectively. The rs12896399 SNP and SNP rs1800407 were found significant with rs16891982 SNP. The result also demonstrated that the study group differs from the world population based on eye colour. The two eye colour prediction results were compared, and it was discovered that IrisPlex and FROG-Kb had similar higher prediction ratios for Brown and Blue eye colour. CONCLUSIONS: The results of the current study revealed brown eye colour to be the most prevalent amongst members of the local population of Pakhtoon ethnicity in the Malakand Division of northern Pakistan. A set of contemporary human DNA samples with known phenotypes are used in this research to evaluate the custom panel's prediction accuracy. With the aid of this forensic test, DNA typing can be supplemented with details about the appearance of the person from whom the sample was taken in cases involving missing persons, ancient human remains, and trace samples. This study may be helpful for future population genetics and forensics studies.

Impact on Air Quality Index of India Due to Lockdown.

For the very first time, on 22-March-2020 the Indian government forced the only known method at that time to prevent the outburst of the COVID-19 pandemic which was restricting the social movements, and this led to imposing lockdown for a few days which was further extended for a few months. As the impact of lockdown, the major causes of air pollution were ceased which resulted in cleaner blue skies and hence improving the air quality standards. This paper presents an analysis of air quality particulate matter (PM)2.5, PM10, Nitrogen Dioxide (NO2), and Air quality index (AQI). The analysis indicates that the PM10 AQI value drops impulsively from (40-45%), compared before the lockdown period, followed by NO2 (27-35%), Sulphur Dioxide (SO2) (2-10%), PM2.5 (35-40%), but the Ozone (O3) rises (12-25%). To regulate air quality, many steps were taken at national and regional levels, but no effective outcome was received yet. Such short-duration lockdowns are against economic growth but led to some curative effects on AQI. So, this paper concludes that even a short period lockdown can result in significant improvement in Air quality.

Data Mining Based Techniques for Covid-19 Predictions.

COVID-19 is a pandemic that has resulted in numerous fatalities and infections in recent years, with a rising tendency in both the number of infections and deaths and the pace of recovery. Accurate forecasting models are important for making accurate forecasts and taking relevant actions. As a result, accurate short-term forecasting of the number of new cases that are contaminated and recovered is essential for making the best use of the resources at hand and stopping or delaying the spread of such illnesses. This paper shows the various techniques for forecasting the covid-19 cases. This paper classifies the various models according to their category and shows the merits and demerits of various fore-casting techniques. The research provides insight into potential issues that may arise during the forecasting of covid-19 instances for predicting the positive, negative, and death cases in this pandemic. In this paper, numerous forecasting techniques and their categories have been studied. The goal of this work is to aggregate the findings of several forecasting techniques to aid in the fight against the pandemic.

Global prevalence of stigmatization and violence against healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis.

PURPOSE: During COVID-19, stigmatization and violence against and between professional healthcare workers worldwide are increasing. Understanding the prevalence of such stigmatization and violence is needed for gaining a complete picture of this issue. Therefore, the purpose of this review was to update estimates of the prevalence of stigmatization and violence against healthcare workers during the pandemic. DESIGN: A systematic review and meta-analysis was conducted. METHODS: This review followed PRISMA guidelines and encompassed these databases: PubMed, Academic Search Complete, CINAHL, Web of Science, MEDLINE Complete, OVID (UpToDate), and Embase (from databases inception to September 15, 2021). We included observational studies and evaluated the quality of the study using the Joanna Briggs Institute methodology. Further, a random effects model was used to synthesis the pooled prevalence of stigmatization and violence in this study. FINDINGS: We identified 14 studies involving 3452 doctors, 5738 nurses, and 2744 allied health workers that reported stigmatization and violence during the pandemic. The pooled prevalence was, for stigmatization, 43% (95% confidence interval [CI]: 21% to 65%) and, for violence, 42% (95% CI: 30% to 54%). CONCLUSIONS: Stigmatization and violence during the COVID-19 pandemic were found to have affected almost half the studied healthcare workers. Healthcare professionals are more prone to be stigmatized by the community and to face workplace violence. CLINICAL RELEVANCE: Health administrators and policymakers should anticipate and promptly address stigmatization and violence against and between healthcare workers, while controlling the spread of COVID-19. Health care systems should give serious attention to the mental health of all health providers.

Quantum-effective exact multiple patterns matching algorithms for biological sequences.

This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο(mN) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves computational speedup. Few quantum methods are available for multiple pattern matching, which executes search oracle for each pattern in successive iterations. Such solutions are likely acceptable because of classical equivalent quantum designs. However, these methods are constrained with the inclusion of multiplicative factor m in their complexities. An optimal quantum design is to execute multiple search oracle in parallel on the quantum processing unit with a single-core that completely removes the multiplicative factor m, however, this method is impractical to design. We have no effective quantum solutions to process multiple patterns at present. Therefore, we propose quantum algorithms using quantum processing unit with C quantum cores working on shared quantum memory. This quantum parallel design would be effective for searching all t exact occurrences of each pattern. To our knowledge, no attempts have been made to design multiple pattern matching algorithms on quantum multicore processor. Thus, some quantum remarkable exact single pattern matching algorithms are enhanced here with their equivalent versions, namely enhanced quantum memory processing based exact algorithm and enhanced quantum-based combined exact algorithm for multiple pattern matching. Our quantum solutions find all t exact occurrences of each pattern inside the biological sequence in O ( ( m / C ) N ) and O ( ( m / C ) t ) time complexities. This article shows the hybrid simulation of quantum algorithms to validate quantum solutions. Our theoretical-experimental results justify the significant improvements that these algorithms outperform over the existing classical solutions and are proven effective in quantum counterparts.

A DNA barcode survey of insect biodiversity in Pakistan.

Although Pakistan has rich biodiversity, many groups are poorly known, particularly insects. To address this gap, we employed DNA barcoding to survey its insect diversity. Specimens obtained through diverse collecting methods at 1,858 sites across Pakistan from 2010-2019 were examined for sequence variation in the 658 bp barcode region of the cytochrome c oxidase 1 (COI) gene. Sequences from nearly 49,000 specimens were assigned to 6,590 Barcode Index Numbers (BINs), a proxy for species, and most (88%) also possessed a representative image on the Barcode of Life Data System (BOLD). By coupling morphological inspections with barcode matches on BOLD, every BIN was assigned to an order (19) and most (99.8%) were placed to a family (362). However, just 40% of the BINs were assigned to a genus (1,375) and 21% to a species (1,364). Five orders (Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera) accounted for 92% of the specimens and BINs. More than half of the BINs (59%) are so far only known from Pakistan, but others have also been reported from Bangladesh (13%), India (12%), and China (8%). Representing the first DNA barcode survey of the insect fauna in any South Asian country, this study provides the foundation for a complete inventory of the insect fauna in Pakistan while also contributing to the global DNA barcode reference library.

An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers.

Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques.

Effect of temperature and humidity on coronavirus infection in Pakistan.

Ongoing Coronavirus epidemic (COVID-19) identified first in Wuhan, China posed huge impact on public health and economy around the globe. Both cough and sneeze based droplets or aerosols encapsulated COVID-19 particles are responsible for airborne transmission of this virus and caused an unexpected escalation and high mortality worldwide. Current study intends to investigate the correlation of COVID-19 epidemic with meteorological parameters, particularly temperature and humidity. A data set of Epidemiological data of COVID-19 for highly infected provinces of Pakistan was collected from the official website of (https://www.covid.gov.pk/) and weather data was collected from (https://www.timeanddate.com/) during the time period of 1st March to 30th September 2020. The GrapPad prism 5 Software was used to calculate the mean and standard error of mean (SEM). In the current study the incident of daily covid cases is recorded higher in the month of June while the less number of case were reported in the month of May as compared to the other months (April, May, June, July, September and August) in the four province of Pakistan. We also find out that the incident of Covid19 were high at higher temperature (like the average temperature in the month of June 37 °C) while less cases were reported in May the average temperature was 29.5 °C. Furthermore the incident of covid cases were less reported at low humidity while more intendant with high humidity. Pearson's (r) determine the strength of the relationship between the variables. Pearson's correlation coefficient test employed for data analysis revealed that temperature average (TA) and average humidity is not a significant correlated with COVID-19 pandemic. The results obtained from the current analysis for selected parameters indirect correlation of COVID-19 transmission with temperature variation, and humidity. In the present study association of parameters is not correlated with COVID-19 pandemic, suggested need of more strict actions and control measures for highly populated cities. These findings will be helpful for health regulatory authorities and policy makers to take specific measures to combat COVID-19 epidemic in Pakistan.

Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour.

For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required. The gene sample value can be missing due to hardware failure, software failure, or manual mistakes. The missing data in gene expression research dramatically affects the analysis of the collected data. Consequently, this has become a critical problem that requires an efficient imputation algorithm to resolve the issue. This paper proposed a technique considering the local similarity structure that predicts the missing data using clustering and top K nearest neighbor approaches for imputing the missing value. A similarity-based spectral clustering approach is used that is combined with the K-means. The spectral clustering parameters, cluster size, and weighting factors are optimized, and after that, missing values are predicted. For imputing each cluster's missing value, the top K nearest neighbor approach utilizes the concept of weighted distance. The evaluation is carried out on numerous datasets from a variety of biological areas, with experimentally inserted missing values varying from 5 to 25%. Experimental results prove that the proposed imputation technique makes accurate predictions as compared to other imputation procedures. In this paper, for performing the imputation experiments, microarray gene expression datasets consisting of information of different cancers and tumors are considered. The main contribution of this research states that local similarity-based techniques can be used for imputation even when the dataset has varying dimensionality and characteristics.

Impact of parameter control on the performance of APSO and PSO algorithms for the CSTHTS problem: An improvement in algorithmic structure and results.

Cascaded Short Term Hydro-Thermal Scheduling problem (CSTHTS) is a single objective, non-linear multi-modal or convex (depending upon the cost function of thermal generation) type of Short Term Hydro-Thermal Scheduling (STHTS), having complex hydel constraints. It has been solved by many metaheuristic optimization algorithms, as found in the literature. Recently, the authors have published the best-achieved results of the CSTHTS problem having quadratic fuel cost function of thermal generation using an improved variant of the Accelerated PSO (APSO) algorithm, as compared to the other previously implemented algorithms. This article discusses and presents further improvement in the results obtained by both improved variants of APSO and PSO algorithms, implemented on the CSTHTS problem.

Plant growth promoting and antifungal asset of indigenous rhizobacteria secluded from saffron (Crocus sativus L.) rhizosphere.

Saffron (Crocus sativus L.) is an important plant in medicine. The Kashmir Valley (J&K, India) is one of the world's largest and finest saffron producing regions. However, over the past decade, there has been a strong declining trend in saffron production in this area. Plant Growth Promoting Rhizobacteria (PGPR) are free living soil bacteria that have ability to colonize the surfaces of the roots and ability to boost plant growth and development either directly or indirectly. Using the efficient PGPR as a bio-inoculant is another sustainable agricultural practice to improve soil health, grain yield quality, and biodiversity conservation. In the present study, a total of 13 bacterial strains were isolated from rhizospheric soil of saffron during the flowering stage of the tubers and were evaluated for various plant growth promoting characteristics under in vitro conditions such as the solubilization of phosphate, production of indole acetic acid, siderophore, hydrocyanic acid, and ammonia production and antagonism by dual culture test against Sclerotium rolfsii and Fusarium oxysporum. All the isolates were further tested for the production of hydrolytic enzymes such as protease, lipase, amylase, cellulase, and chitinase. The maximum proportions of bacterial isolates were gram-negative bacilli. About 77% of the bacterial isolates showed IAA production, 46% exhibited phosphate solubilization, 46% siderophore, 61% HCN, 100% ammonia production, 69% isolates showed protease activity, 62% lipase, 46% amylase, 85% cellulase, and 39% showed chitinase activity. Three isolates viz., AIS-3, AIS-8 and AIS-10 were found to have the most plant growth properties and effectively control the growth of Sclerotium rolfsii and Fusarium oxysporum. The bacterial isolates were identified as Brevibacterium frigoritolerans (AIS-3), Alcaligenes faecalis subsp. Phenolicus (AIS-8) and Bacillus aryabhattai (AIS-10) respectively by 16S rRNA sequence analysis. Therefore, these isolated rhizobacterial strains could be a promising source of plant growth stimulants to increase cormlets growth and increase saffron production.

Plant Part-Derived Carbon Dots for Biosensing.

Carbon dots (CDs) are a new cluster of carbon atoms with particle size less than 10 nm. CDs also exhibit interesting fluorescence (FL) properties. CDs are attractive because of their fascinating characteristics including low toxicity, good water solubility, and tremendous biocompatibility. Recently, CDs have been investigated as biosensors for numerous target analytes. Meanwhile, the utilization of cheap and renewable natural resources not only fulfills the pressing requirement for the large-scale synthesis of CDs but also encourages the establishment of sustainable applications. The preparation of CDs using natural resources, i.e., plants, offers several advantages as it is inexpensive, eco-friendly, and highly available in the surroundings. Plant parts are readily available natural resources as the starting materials to produce CDs with different characteristics and attractive applications. Several review articles are now available covering the synthesis, properties, and applications of CDs. However, there is no specific and focused review literature discussing plant part-derived CDs for biosensing applications. To handle this gap, we provide a review of the progress of CDs derived from various plant parts with their synthesis methods, optical properties, and biosensing applications in the last five years. We highlight the synthesis methods and then give an overview of their optical properties and applications as biosensors for various biomolecules and molecules in biological samples. Finally, we discuss some future perspectives for plant part-derived CDs for better material development and applications.

In vitro effects of cobalt nanoparticles on aspartate aminotransferase and alanine aminotransferase activities of wistar rats.

Cobalt nanoparticles (Co-NPs) have been extensively used in clinical practices and medical diagnosis. In this study, the potential toxicity effects of Co-NPs with special emphasis over the biochemical enzyme activities, such as aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) in serum, liver, and kidney of Wistar rats were investigated. This toxicity measurement of nanomaterials can support the toxicological data. The biochemical enzymatic variations are powerful tools for the assessment of toxicity. ASAT and ALAT enzymes have been widely used to predict tissue-specific toxicities associated with xenobiotic. The biochemical changes induced by Co-NPs have significance in their toxicological studies because the alterations in biochemical parameters before clinical symptoms indicate either their toxicant safety or detrimental effect. Herein, Co-NPs with particle size <50 nm significantly activated ASAT and ALAT enzymes in the serum, liver, and kidney of rats at concentration-dependent order.