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The three-amino-loop-extension (TALE) family involves key transcription factors vital for maintaining different aspects of growth including leaf, flower, and fruit development and responses to stressful stimulus. Thus far, a deep understanding of the TALE gene family in wax gourd subjected to low calcium and magnesium stress has been missing. Here, we isolated 24 BhTALE genes from a wax gourd genome database. Comprehensive bioinformatic analysis, including evolutionary tree, gene structures, conserved motifs, and chemical properties, provide structural and functional insights into the BhTALE gene family. Gene ontology (GO) analysis of TALE genes unveils their involvement in growth and stress responses. Promoter analysis indicates that hormones and stresses can influence the expression of BhTALE genes. Tissue-specific expression under low calcium and magnesium stress showed that BhTALE genes were more active in the leaves and roots. Notably, BhTALE7, BhTALE10, and BhTALE14 were expressed differentially in leaves under low calcium and magnesium applications. Similarly, the induced expression pattern of BhTALE4 was recorded in the roots under low calcium and magnesium applications. Our findings underscore the pivotal role of the BhTALE gene family in dealing with low calcium and magnesium stress in the wax gourd.

In this study, we derive multiple incomplete matrix Mittag-Leffler (ML) functions. We systematically investigate several properties of these incomplete matrix ML functions, which include some general properties and distinct representations of integral transforms. We further study the properties of the Riemann–Liouville fractional integrals and derivatives related to the incomplete matrix ML functions. Additionally, some interesting special cases of this work are highlighted. Finally, we establish the solution to the kinetic equations as an application.

The objective of this article is to examine, stabilize, and linearize the incompressible magnetohydrodynamic model equations. The approximate solutions are carried out through the lowest equal order mixed finite element (FE) approach, involving variables such as fluid velocity, hydro pressure, magnetic field, and magnetic pressure. The formulation of the variational form for the approximate solution necessitates the use of a pair of approximating spaces. However, these spaces cannot be arbitrarily chosen; they must adhere to strict stability conditions, notably the Ladyzhenskaya–Babuska–Brezzi (LBB) or inf-sup condition. This study addresses the absence of stabilization and linearization techniques in the incompressible magnetohydrodynamic model equations using the lowest equal order mixed finite element approach. The article introduces a stabilization technique to meet two stability conditions, proving its existence and uniqueness. This novel approach was not previously explored in the literature. The proposed stabilized technique does not necessitate parameters or computing higher-order derivatives, making it computationally efficient. The study offers numerical tests demonstrating optimal convergence and effectiveness of the revised approach in two-dimensional settings.

Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called “TimeTector-Twin-Branch Shared LSTM Autoencoder” which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

In this paper, we explore a study focused on a two-variable extension of matrix Bessel polynomials. We initiate the discussion by introducing the matrix Bessel polynomials involving two variables and derive specific differential formulas and recurrence relations associated with them. Additionally, we present a segment detailing integral formulas for the extended matrix Bessel polynomials. Lastly, we introduce the Laplace–Carson transform for the two-variable matrix Bessel polynomial analogue.

The accumulation of heavy metals in ecosystems can have potential toxic effects on human health. This is one of the most prominent consequences of anthropogenic developments, which threaten biodiversity and the quality of the environment. Industrial effluents and urban wastes contain a large number of heavy metals (Cadmium, Lead, Copper, and Zinc) that are highly toxic to biological systems. The present study investigated the status of heavy metals, including Zinc (Zn), Manganese (Mn), Lead (Pb), Copper (Cu), Cadmium (Cd), and Cobalt (Co), in the bones, feathers, hearts, muscles, and lungs of the cattle egret (Bubulcus ibis). Samples of cattle egrets were collected from the Faisalabad division with the help of the concerned wildlife department. Heavy metals were assessed using atomic absorption spectrophotometry and the protocols supplied by Perkin-Elmers Corp. The results revealed that the trend of heavy metals in bones was Zn > Pb > Cu > Mn > Cd > Co, and the same trend was followed in the case of feathers and hearts. However, the trend of heavy metals in muscles and lungs was in the order Zn > Mn > Cu > Co > Cd. Heavy metals were present in the bones, feathers, hearts, muscles, and lungs of cattle egrets. The contamination levels were ascertained in this study, which indicated that cattle egret is useful for biomonitoring heavy metals. The present study provides baseline data that could be further compared with data from other locations for monitoring heavy metal pollution. These investigations will be helpful for assessing heavy metal accumulation under semi-arid climates.

Cu3SbSe4 is a potential p-type thermoelectric material, distinguished by its earth-abundant, inexpensive, innocuous, and environmentally friendly components. Nonetheless, the thermoelectric performance is poor and remains subpar. Herein, the electrical and thermal transport properties of Cu3SbSe4 were synergistically optimized by S alloying. Firstly, S alloying widened the band gap, effectively alleviating the bipolar effect. Additionally, the substitution of S in the lattice significantly increased the carrier effective mass, leading to a large Seebeck coefficient of ~730 μVK−1. Moreover, S alloying yielded point defect and Umklapp scattering to significantly depress the lattice thermal conductivity, and thus brought about an ultralow κlat ~0.50 Wm−1K−1 at 673 K in the solid solution. Consequently, multiple effects induced by S alloying enhanced the thermoelectric performance of the Cu3SbSe4-Cu3SbS4 solid solution, resulting in a maximum ZT value of ~0.72 at 673 K for the Cu3SbSe2.8S1.2 sample, which was ~44% higher than that of pristine Cu3SbSe4. This work offers direction on improving the comprehensive TE in solid solutions via elemental alloying.

Hybrid beamforming is a viable method for lowering the complexity and expense of massive multiple-input multiple-output systems while achieving high data rates on track with digital beamforming. To this end, the purpose of the research reported in this paper is to assess the effectiveness of the three architectural beamforming techniques (Analog, Digital, and Hybrid beamforming) in massive multiple-input multiple-output systems, especially hybrid beamforming. In hybrid beamforming, the antennas are connected to a single radio frequency chain, unlike digital beamforming, where each antenna has a separate radio frequency chain. The beam formation toward a particular angle depends on the channel state information. Further, massive multiple-input multiple-output is discussed in detail along with the performance parameters like bit error rate, signal-to-noise ratio, achievable sum rate, power consumption in massive multiple-input multiple-output, and energy efficiency. Finally, a comparison has been established between the three beamforming techniques.

Sulfite determination in foods and alcoholic beverages is a common requirement by food and drug administration organisations in most countries. In this study, the enzyme, sulfite oxidase (SOx), is used to biofunctionalise a platinum-nanoparticle-modified polypyrrole nanowire array (PPyNWA) for the ultrasensitive amperometric detection of sulfite. A dual-step anodisation method was used to prepare the anodic aluminum oxide membrane used as a template for the initial fabrication of the PPyNWA. PtNPs were subsequently deposited on the PPyNWA by potential cycling in a platinum solution. The resulting PPyNWA-PtNP electrode was then biofuntionalised by adsorption of SOx onto the surface. The confirmation of the adsorption of SOx and the presence of PtNPs in the PPyNWA-PtNPs-SOx biosensor was verified by scanning electron microscopy and electron dispersive X-ray spectroscopy. Cyclic voltammetry and amperometric measurements were used to investigate the properties of the nanobiosensor and to optimise its use for sulfite detection. Ultrasensitive detection of sulfite with the PPyNWA-PtNPs-SOx nanobiosensor was accomplished by use of 0.3 M pyrrole, 10 U mL−1 of SOx, adsorption time of 8 h, a polymerisation period of 900 s, and an applied current density of 0.7 mA cm−2. The response time of the nanobiosensor was 2 s, and its excellent analytical performance was substantiated with a sensitivity of 57.33 μA cm−2 mM−1, a limit of detection of 12.35 nM, and a linear response range from 0.12 to 1200 μM. Application of the nanobiosensor to sulfite determination in beer and wine samples was achieved with a recovery efficiency of 97–103%.

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal–organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials’ adaptability. This article investigates the characterisation of the metal–organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

Additive manufacturing (AM) or 3D printing has opened up new opportunities for researchers in the field of electrical machines, as it allows for more flexibility in design and faster prototyping, which can lead to more efficient and cost-effective production. An overview of the primary AM techniques utilized for designing electrical machines is presented in this paper. AM enables the creation of complex and intricate designs that are difficult or impossible to achieve using traditional methods. Topology Optimization (TO) can be used to optimize the design of parts for various purposes such as weight, thermal, material usage and structural performance. This paper primarily concentrates on the most recent studies of the AM and TO of the reluctance machines. The integration of AM with TO can enhance the design and fabrication process of magnetic components in electrical machines by overcoming current manufacturing limitations and enabling the exploration of new design possibilities. The technology of AM and TO both have limitations and challenges which are discussed in this paper. Overall, the paper offers a valuable resource for researchers and practitioners working in the field of AM and TO of electrical machines.

Mushrooms play a crucial role in human life as well as in nature, providing food, medicine, and carrying out vital processes of decomposition, nutrient recycling, and developing mycorrhizal association with plants. The traditional system of knowledge about identification, collection, and usage of mushrooms has been accumulated through the shared experiences of many generations. Unfortunately, there have been continuous threats to the folk knowledge of mushrooms mainly due to habitat degradation, urbanization, and contemporary medication. The current research was, therefore, aimed to document an ethnomycological knowledge possessed by the ethnic communities of Swat, Pakistan. The purposive randomized sampling was carried out using chain referral method. Ethno-mycological information was collected from 62 informants using free listing, preference ranking, and use totaled methods. In total, 34 species of mushrooms belonging to 31 genera and 21 families were reported. About 85% of the reported species belong to Basidiomycetes, and 12.5% to Ascomycetes are used as food and for medicinal purposes. Morchella angusticeps, M. esculenta, Pleurotus sp., Auricularia sp., Flammulina velutipes, Agaricus bisporus, Ganoderma lucidum, and Sanghuangporus sanghuang were among the most cited edible and medicinal mushrooms. The current study revealed that district Swat is rich in wild edible and medicinal mushrooms (WEMs), and the local communities possess rich traditional knowledge about their collection, storage, and utilization. The diversity of WEMs of this region could contribute substantially to the socio-economic uplifting of the local communities through appropriate domestication and commercialization. Anthropogenic factors, coupled with depletion of traditional knowledge, threaten the diversity of WEMs in the region; therefore, in situ and ex situ conservation strategies are highly recommended.

Micro-Doppler signatures obtained from the Doppler radar are generally used for human activity classification. However, if the angle between the direction of motion and radar antenna broadside is greater than 60°, the micro-Doppler signatures generated by the radial motion of human body reduce significantly, thereby degrading the performance of the classification algorithm. For the accurate classification of different human activities irrespective of trajectory, we propose a new algorithm based on dual micro-motion signatures, namely, the micro-Doppler and interferometric micro-motion signatures, using an interferometric radar. First, the motion of different parts of the human body is simulated using motion capture (MOCAP) data, which is further utilized for radar echo signal generation. Second, time-varying Doppler and interferometric spectrograms obtained from time-frequency analysis of a single Doppler receiver and interferometric output data, respectively, are fed as input to the deep convolutional neural network (DCNN) for feature extraction and the training/testing process. The performance of the proposed algorithm is analyzed and compared with a micro-Doppler signatures-based classifier. Results show that a dual micro-motion-based DCNN classifier using an interferometric radar is capable of classifying different human activities with an accuracy level of 98%, where Doppler signatures diminish considerably, providing insufficient information for classification. Verification of the proposed classification algorithm based on dual micro-motion signatures is also performed using a real radar test dataset of different human walking patterns, and a classification accuracy level of approximately 90% is achieved.

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor’s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

The appropriate combination of semiconducting polymer–inorganic nanocomposites can enhance the existing performance of polymers-only-based photovoltaic devices. Hence, polyaniline (PANI)/zinc oxide (ZnO) nanocomposites were prepared by combining ZnO nanoparticles with PANI in four distinct ratios to optimize their photovoltaic performance. Using a simple coating method, PANI, ZnO, and its nanocomposite, with varying weight percent (wt%) concentrations of ZnO nanoparticles, i.e., (1 wt%, 2 wt%, 3 wt%, and 4 wt%), were fabricated and utilized as an active layer to evaluate the potential for the high-power conversion efficiency of various concentrations, respectively. PANI/ZnO nanocomposites are characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), atomic force microscopy (AFM), Fourier transform infrared (FTIR) spectroscopy, ultraviolet-visible (UV-vis) absorption, energy dispersive X-ray (EDX), and I-V measurement techniques. The XRD analysis showed a distinct, narrow peak, which corresponds to the wurtzite ZnO (101) plane. The SEM analysis verified the production of the PANI/ZnO composite by demonstrating that the crystalline ZnO was integrated into the PANI matrix. The elemental composition was determined by energy dispersive X-ray analysis (EDX), which confirmed the existence of PANI and ZnO without any impurities, respectively. Using Fourier transform infrared (FTIR) spectroscopy, various chemical bonds and stretching vibrations were analyzed and assigned to different peaks. The bandgap narrowing with an increasing PANI/ZnO composition led to exceptional optical improvement. The I-V characterization was utilized to investigate the impact of the nanocomposite on the electrical properties of the PANI/ZnO, and various concentrations of ZnO (1 wt%, 2 wt%, 3 wt%, and 4 wt%) in the PANI matrix were analyzed under both light and dark conditions at an STC of 1.5 AM globally. A high PCE of 4.48% was achieved for the PANI/ZnO (3 wt%), which revealed that the conductivity of the PANI/ZnO nanocomposite thin films improved with the increasing nanocomposite concentration.

This paper presents the simulations of the cooling system of a battery pack (BTPC) consisting of lithium-ion (LIN) plate batteries. The BTPC includes six battery cells (BTCL) in two rows with three BTCLs, which are placed in a channel with one inlet and two outlets. The laminar and steady airflow flows in the channel. Phase-change material (PCM)-filled rectangular cubic enclosures enclose every BTCL. Transiently adjusting the cavity aspect ratio (AR) every 6000 s is how this investigation is conducted. For four values of AR, the values of the PCM volume percentage surrounding each BTCL in the BTPC, and the temperature of each BTCL are calculated. The simulations are performed using the FEM and COMSOL software. The results demonstrate that the maximum changes in temperature of the battery (TOB) pack by changing the AR occur when the TOB pack is reduced. The maximum temperature reduction at this time is 1.88 °C which occurs between AR2 and AR4 at 720 s. The maximum temperature corresponds to AR3 and AR4 and the minimum one is related to AR1 and AR2. From 1260 to 3500 s, the effect of AR on PCM volume fraction is maximal. The value of solid PCM for AR1 and AR2 is higher than that for AR3 and AR4 at different times. Additionally, an increment in the value of the AR enhances the amount of channel pressure drop by 14%.

This paper elaborates on the production of distilled biodiesel of standard EN14214 from waste cooking oil (WCO). Its economic viability is assessed and experimental investigations of a single-cylinder, four-stroke engine using a mixture of distilled biodiesel and diesel of Euro 5 standard are described. The physical and chemical characteristics of biodiesel produced from waste cooking oil were determined. Fuel samples prepared with different percentages of biodiesel and diesel were used to run the engine. We observed the effects of increasing the percentage of biodiesel in the mixture on brake power, brake specific fuel consumption, brake thermal efficiency, and the exhaust emission from the engine. The emission species included O2, CO, CO2, NOx, and SO2. Improved engine performance and reduced emissions from the engine were noticed with blended fuels with 10% and 20% distilled WCO biodiesel mixed with 90% and 80% mineral diesel by volume, respectively. The results of this study indicate that the distilled biodiesel blends with mineral diesel can be used as an alternative fuel to run diesel engines without changing the engine design, thereby providing an alternative energy resource with reduced costs for power generation by using engine fuel. An economic assessment and sensitivity analysis were used to determine the feasibility of distilled WCO biodiesel as an alternative fuel to diesel.

The biological actions of the vitamin D receptor (VDR) have been investigated intensively for over 100 years and has led to the identification of significant insights into the repertoire of its biological actions. These were initially established to be centered on the regulation of calcium transport in the colon and deposition in bone. Beyond these well-known calcemic roles, other roles have emerged in the regulation of cell differentiation processes and have an impact on metabolism. The purpose of the current review is to consider where applying systems biology (SB) approaches may begin to generate a more precise understanding of where the VDR is, and is not, biologically impactful. Two SB approaches have been developed and begun to reveal insight into VDR biological functions. In a top-down SB approach genome-wide scale data are statistically analyzed, and from which a role for the VDR emerges in terms of being a hub in a biological network. Such approaches have confirmed significant roles, for example, in myeloid differentiation and the control of inflammation and innate immunity. In a bottom-up SB approach, current biological understanding is built into a kinetic model which is then applied to existing biological data to explain the function and identify unknown behavior. To date, this has not been applied to the VDR, but has to the related ERα and identified previously unknown mechanisms of control. One arena where applying top-down and bottom-up SB approaches may be informative is in the setting of prostate cancer health disparities.

Gluten proteins are the major storage protein fraction in the mature wheat grain. They are restricted to the starchy endosperm, which defines the viscoelastic properties of wheat dough. The synthesis of these storage proteins is controlled by the endoplasmic reticulum (ER) and is directed into the vacuole via the Golgi apparatus. In the present study, transcriptome analysis was used to explore the potential mechanism within critical stages of grain development of wheat cultivar “Shaannong 33” and its sister line used as the control (CK). Samples were collected at 10 DPA (days after anthesis), 14 DPA, 20 DPA, and 30 DPA for transcriptomic analysis. The comparative transcriptome analysis identified that a total of 18,875 genes were differentially expressed genes (DEGs) between grains of four groups “T10 vs. CK10, T14 vs. CK14, T20 vs. CK20, and T30 vs. CK30”, including 2824 up-regulated and 5423 down-regulated genes in T30 vs. CK30. Further, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment highlighted the maximum number of genes regulating protein processing in the endoplasmic reticulum (ER) during grain enlargement stages (10–20 DPA). In addition, KEGG database analysis reported 1362 and 788 DEGs involved in translation, ribosomal structure, biogenesis, flavonoid biosynthesis pathway and intracellular trafficking, secretion, and vesicular transport through protein processing within ER pathway (ko04141). Notably, consistent with the higher expression of intercellular storage protein trafficking genes at the initial 10 DPA, there was relatively low expression at later stages. Expression levels of nine randomly selected genes were verified by qRT-PCR, which were consistent with the transcriptome data. These data suggested that the initial stages of “cell division” played a significant role in protein quality control within the ER, thus maintaining the protein quality characteristics at grain maturity. Furthermore, our data suggested that the protein synthesis, folding, and trafficking pathways directed by a different number of genes during the grain enlargement stage contributed to the observed high-quality characteristics of gluten protein in Shaannong 33 (Triticum aestivum L.).

Current study solves heat transfer and fluid flow problem in Newtonian and non-Newtonian nano-fluids through a permeable surface with a magnetic field effects which is done in the presence of injection and suction for the first time. In order to solve the governing partial differential equations numerically, we used the Runge-Kutta Fehlberg (RKF45) technique in which the similarity transformation method is applied. This approach converts the governing partial differential equations into ordinary differential equations. In this particular investigation nano-particles of copper, copper oxide, titanium dioxide, and aluminium oxide are studied by considering CMC/water as a base fluid with the effect of magnetic field on the classical Blasius and Sakiadis flows of nano-fluids. Validation is carried out using the previously obtained numerical findings. We looked at the power-law index (n), the volume fraction (φ) of nano-particles and the permeability parameter (fw) which affects the flow of nano-fluid and the transfer of heat. Non-Newtonian nano-fluid demonstrates superior performance in terms of heat transfer when compared to Newtonian nano-fluid in both the injection and the impermeable surfaces. Altering the nano-particles’ composition, on the other hand, has a far greater impact on the heat transfer process that occurs during suction. Graphics show the impacts of governing physical parameters on Blasius and Sakiadis flow velocity, temperature, skin friction coefficient, and reduced Nusselt number. Physical and engineering interest are explored in detail.

To determine the combining effects of nitrogen application rate and planting density on rice yield and grain quality formation under salinity conditions, a field experiment was conducted in the coastal saline area using Oryza sativa L. cv. Nangeng 9108 from 2019 to 2020. The experiment was designed with six nitrogen rates (0, 210, 255, 300, 345, and 390 kg ha−1; denoted as N0-N390, respectively) and two transplanting densities (334,000 and 278,000 hills ha−1; denoted as D1 and D2, respectively). The results indicated that, with the increase of nitrogen input rate, the panicles number and spikelets per panicle increased first, subsequently decreased, and peaked under 300 kg ha−1 N, whereas the filled-kernel rate and grain weight displayed a decreasing trend. The panicle number and grain weight were higher under D1 treatment compared to those under D2 treatment, while the spikelet number per panicle and the filled-kernel rate displayed an opposite trend. The grain yield displayed highest under N300D1 treatment among all treatments, accompanied by the highest agronomic N use efficiency, and the actual yield reached 8060.4 kg ha−1 and 7869.8 kg ha−1 in 2019 and 2020, respectively. Increased nitrogen application rate significantly improved the grain processing quality and nutritional quality, while reducing the appearance quality and cooking/eating quality. Higher transplant density was conductive to grain nutritional quality, but notably reduced the processing quality, appearance quality and cooking/eating quality. Overall, a combination of 300 kg ha−1 nitrogen rate and 334,000 hills ha−1 planting density was recommended for relatively higher rice yield and better grain quality in the saline area.

For the various climatic zones of India, machine learning (ML) models are created in the current work to forecast monthly-average diffuse solar radiation (DSR). The long-term solar radiation data are taken from Indian Meteorological Department (IMD), Pune, provided for 21 cities that span all of India’s climatic zones. The diffusion coefficient and diffuse fraction are the two groups of ML models with dual input parameters (sunshine ratio and clearness index) that are built and compared (each category has seven models). To create ML models, two well-known ML techniques, random forest (RF) and k-nearest neighbours (KNN), are used. The proposed ML models are compared with well-known models that are found in the literature. The ML models are ranked according to their overall and within predictive power using the Global Performance Indicator (GPI). It is discovered that KNN models generally outperform RF models. The results reveal that in diffusion coefficient models perform well than diffuse fraction models. Moreover, functional form 2 is the best followed by form 6. The ML models created here can be effectively used to accurately forecast DSR in various climates.

Nanoparticles are useful in improving the efficiency of convective heat transfer. The current study addresses this gap by making use of an analogy between Al2O3 and γ-Al2O3 nanoparticles in various base fluids across a stretched sheet conjunction with f. Base fluids include ethylene glycol and water. We address, for the first time, the stagnation-point flow of a boundary layer of γ-Al2O3 nanofluid over a stretched sheet with slip boundary condition. Al2O3 nanofluids employ Brinkman viscosity and Maxwell’s thermal conductivity models with thermal radiations, whereas γ-Al2O3 nanofluids use viscosity and thermal conductivity models generated from experimental data. For the boundary layer, the motion equation was solved numerically using the fourth-order Runge–Kutta method and the shooting approach. Plots of the velocity profile, temperature profile, skin friction coefficient and reduced Nusselt number are shown. Simultaneous exposure of the identical nanoparticles to water and ethylene glycol, it is projected, would result in markedly different behaviors with respect to the temperature profile. Therefore, this kind of research instills confidence in us to conduct an analysis of the various nanoparticle decompositions and profile structures with regard to various base fluids.

The most well-known, effective medicines for acne therapy are clindamycin phosphate and tretinoin. For the first time, we have developed and validated a reversed-phase HPLC stability-indicating technique for the detection of clindamycin phosphate (CLP), tretinoin (TRN), and two preservatives, methylparaben (MP) and imidazolidinyl urea (IU), simultaneously in this work. Most of the chromatographic conditions in the present study were optimized to achieve better separation. The best separation results were obtained using gradient elution on a C-18 (250 × 4.6 mm), 5 µm column, with a mobile phase consisting of solution A (1 mL/L ortho-phosphoric acid in water) and solution B (methanol), at a flow rate of 1.0 mL/min, with UV detection at wavelengths of 200 nm and 353 nm. Standard parameters such as system suitability, precision, accuracy, specificity, robustness, linearity, range, detection limit, quantification limit, and reagent stability were used to validate the developed technique. According to the standards of the International Council for Harmonization, all of the experimental parameters were found to be within allowable bounds (ICH). The simultaneous concentrations of clindamycin phosphate, tretinoin, methylparaben, and imidazolidinyl urea in pharmaceutical formulations were successfully determined using the suggested approach. The proposed RP-HPLC method detected no interfering peaks in the chromatogram. We may conclude from the data that the new RP-HPLC method can be utilized in pharmaceutical laboratories to simultaneously assess clindamycin phosphate, tretinoin, and two preservatives, methylparaben and imidazolidinyl urea, for both qualitative and quantitative analyses.

Multi-target tracking (MTT) generally needs either a Doppler radar network with spatially separated receivers or a single radar equipped with costly phased array antennas. However, Doppler radar networks have high computational complexity, attributed to the multiple receivers in the network. Moreover, array signal processing techniques for phased array radar also increase the computational burden on the processing unit. To resolve this issue, this paper investigates the problem of the detection and tracking of multiple targets in a three-dimensional (3D) Cartesian space based on range and 3D velocity measurements extracted from dual-orthogonal baseline interferometric radar. The contribution of this paper is twofold. First, a nonlinear 3D velocity measurement function, defining the relationship between the state of the target and 3D velocity measurements, is derived. Based on this measurement function, the design of the proposed algorithm includes the global nearest neighbor (GNN) technique for data association, an interacting multiple model estimator with a square-root cubature Kalman filter (IMM-SCKF) for state estimation, and a rule-based M/N logic for track management. Second, Monte Carlo simulation results for different multi-target scenarios are presented to demonstrate the performance of the algorithm in terms of track accuracy, computational complexity, and IMM mean model probabilities.

Methyl jasmonate (MeJA) is a potent player that fine-tunes growth and developmental activities under salinity stress. In this study, we investigated the influence of MeJA on two rice cultivars (NJ9108 and XD22) subjected to different salinity stresses. Following stress treatment, reduction in the water use efficiency, relative water contents, and membrane stability index in both cultivars were observed, whereas MeJA treatment partially alleviated the negative effects. MeJA treatment significantly increased the maximum photochemical efficiency (Fv/Fm) and electron transfer to photosystem II (Fv/Fo). Under salinity stress, MeJA treatment significantly triggered the H2O2 and APX accumulation, while POD and SOD remained unchanged in both cultivars. Salt stress increased Na+ concentration in the roots and leaves but decreased K+ concentration and the K+/Na+ ratio in both cultivars. However, MeJA-treated plants had the maximum K+ accumulation in both leaves and roots under saline conditions. The differential expression pattern of OsHKT and OsHAK genes implied that ion homeostasis is crucial to growth under salt stress. These findings suggest that the application of MeJA can be an alternative source of reducing salinity without compromising growth and yield.

Water scarcity has emerged as an intense global threat to humanity and needs prompt attention from the scientific community. Solar-driven interfacial evaporation and seawater desalination are promising strategies to resolve the primitive water shortage issue using renewable resources. However, the fragile solar thermal devices, complex fabricating techniques, and high cost greatly hinder extensive solar energy utilization in remote locations. Herein, we report the facile fabrication of a cost-effective solar-driven interfacial evaporator and seawater desalination system composed of carbon cloth (CC)-wrapped polyurethane foam (CC@PU). The developed solar evaporator had outstanding photo-thermal conversion efficiency (90%) with a high evaporation rate (1.71 kg m−2 h−1). The interfacial layer of black CC induced multiple incident rays on the surface allowing the excellent solar absorption (92%) and intensifying heat localization (67.37 °C) under 1 kW m−2 with spatially defined hydrophilicity to facilitate the easy vapor escape and validate the efficacious evaporation structure using extensive solar energy exploitation for practical application. More importantly, the long-term evaporation experiments with minimum discrepancy under seawater conditions endowed excellent mass change (15.24 kg m−2 in consecutive 8 h under 1 kW m−2 solar irradiations) and promoted its operational sustainability for multi-media rejection and self-dissolving potential (3.5 g NaCl rejected from CC@PU surface in 210 min). Hence, the low-cost and facile fabrication of CC@PU-based interfacial evaporation structure showcases the potential for enhanced solar-driven interfacial heat accumulation for freshwater production with simultaneous salt rejection.

The high-affinity potassium transporter (HKT) genes are key ions transporters, regulating the plant response to salt stress via sodium (Na+) and potassium (K+) homeostasis. The main goal of this research was to find and understand the HKT genes in rice and their potential biological activities in response to brassinosteroids (BRs), jasmonic acid (JA), seawater, and NaCl stress. The in silico analyses of seven OsHKT genes involved their evolutionary tree, gene structures, conserved motifs, and chemical properties, highlighting the key aspects of OsHKT genes. The Gene Ontology (GO) analysis of HKT genes revealed their roles in growth and stress responses. Promoter analysis showed that the majority of the HKT genes participate in abiotic stress responses. Tissue-specific expression analysis showed higher transcriptional activity of OsHKT genes in roots and leaves. Under NaCl, BR, and JA application, OsHKT1 was expressed differentially in roots and shoots. Similarly, the induced expression pattern of OsHKT1 was recorded in the seawater resistant (SWR) cultivar. Additionally, the Na+ to K+ ratio under different concentrations of NaCl stress has been evaluated. Our data highlighted the important role of the OsHKT gene family in regulating the JA and BR mediated rice salinity tolerance and could be useful for rice future breeding programs.

The main challenge of the health risk assessment of the aerosol transport and deposition to the lower airways is the high computational cost. A standard large-scale airway model needs a week to a month of computational time in a high-performance computing system. Therefore, developing an innovative tool that accurately predicts transport behaviour and reduces computational time is essential. This study aims to develop a novel and innovative machine learning (ML) model to predict particle deposition to the lower airways. The first-ever study uses ML techniques to explore the pulmonary aerosol TD in a digital 17-generation airway model. The ML model uses the computational data for a 17-generation airway model and four standard ML regression models are used to save the computational cost. Random forest (RF), k-nearest neighbour (k-NN), multi-layer perceptron (MLP) and Gaussian process regression (GPR) techniques are used to develop the ML models. The MLP regression model displays more accurate estimates than other ML models. Finally, a prediction model is developed, and the results are significantly closer to the measured values. The prediction model predicts the deposition efficiency (DE) for different particle sizes and flow rates. A comprehensive lobe-specific DE is also predicted for various flow rates. This first-ever aerosol transport prediction model can accurately predict the DE in different regions of the airways in a couple of minutes. This innovative approach and accurate prediction will improve the literature and knowledge of the field.

Chickpeas are rich source of protein and predominantly grown in boron (B)-deficient sandy-loam soils in Pakistan. Boron-tolerant bacteria (BTB) could tolerate higher B levels in soil and increase B availability to the plants. Field trials were conducted under irrigated (district Layyah) and rainfed (district Chakwal) conditions to evaluate the interactive effects of pre-optimized B application methods and BTB (Bacillus sp. MN54) on the nodule’s population, grain quality, productivity, and grain-B concentration in desi chickpea during 2019–2020 and 2020–2021. Boron was applied as soil application (1 kg B ha−1), foliar application (0.025% B), osmopriming (0.001% B), and seed coating (1.5 g B kg−1 seed) with or without BTB inoculation. Untreated seeds receiving no B through any of the methods were regarded as control. The individual and interactive effects (up to three-way interaction of location × BTB inoculation × B application methods) of year, location, B application methods and BTB inoculation significantly altered the growth and yield-related traits of desi chickpea. The four-way interaction of year × location × BTB inoculation × B application methods was non-significant for all recorded growth and yield-related traits. Regarding individual effects, the higher values of growth and yield-related traits were noted for 2020–2021, rainfed location, BTB inoculation and B application through seed priming. Similarly, in two-way interactions 2020–2021 with rainfed location and BTB inoculation, rainfed location with BTB inoculation and osmopriming and osmopriming with BTB inoculation recorded higher values of the growth and yield-related traits. Osmopriming combined with BTB inoculation significantly improved dry matter accumulation and leaf area index in both locations. Boron application through all the methods significantly improved grain quality, yield grain B concentration. The highest grain and biological yields, and nodules’ population were recorded with osmopriming followed by soil application of B combined with BTB inoculation. The highest plant B concentration (75.05%) was recorded with foliar application of B followed by osmopriming (68.73%) combined with BTB inoculation. Moreover, the highest economic returns (USD 2068.5 ha−1) and benefit–cost ratio (3.7%) were recorded with osmopriming + BTB inoculation in 2020–2021 under rainfed conditions. Overall, B application through osmopriming and soil application combined with BTB inoculation could be used to increase productivity and profitability of desi chickpea, whereas foliar application is a better method to enhance grain and plant B concentration.

Pneumocystis jirovecii is an important etiological agent of pneumonia that is underdiagnosed due to the inability to culture the organism. The 2019 PERCH study identified Pneumocystis as the top fungal cause of pneumonia in HIV-negative children using a PCR cutoff of 104 copies of Pneumocystis per mL of sample in nasopharyngeal/oropharyngeal (NP/OP) specimens. Given that Pneumocystis consists of an environmental ascus form and a trophic from (the latter is the form that attaches to the lung epithelium), it is possible that life-form-specific molecular assays may be useful for diagnosis. However, to accomplish this goal, these assays require genotypic information, as the current fungal genomic data are largely from the US and Europe. To genotype Pneumocystis across the globe, we developed an NGS-based genotyping assay focused on genes expressed in asci as well as trophs using PERCH throat swabs from Africa, Bangladesh, and Thailand, as well as North American samples. The NGS panel reliably detected 21 fungal targets in these samples and revealed unique genotypes in genes expressed in trophs, including Meu10, an ascospore assembly gene; two in mitochondrial gene ATP8, and the intergenic region between COX1 and ATP8. This assay can be used for enhanced Pneumocystis epidemiology to study outbreaks but also permits more accurate RT-CPR- or CRISPR-based assays to be performed to improve the non-bronchoscopic diagnosis of this under-reported fungal pathogen.

Public construction infrastructure projects have been recognized as one of the fundamental tools in enhancing community socio-economic conditions for community development. The purpose of this study is to empirically investigate the impacts of socio-economic factors on community development of rural regions. Through a questionnaire survey administrated in Pakistan, public construction practitioners’ views were sought and evaluated. Empirical support for the argument originated from the data collected from 213 respondents in Pakistan’s public construction industry. The questionnaire’s data were evaluated employing the partial least square structural equation modeling (PLS-SEM). The study’s findings showed that economic and social factors influence the community development of rural regions. The result obtained from PLS-SEM proposed a more viable method to realize community development and objectives. The study’s results have offered precious lessons for local authorities, policymakers, and project stakeholders to strengthen the drive for achieving community development goals. Strategies supporting community development in rural regions are indispensable for local community development. This study provides empirical confirmation for the understanding and combining the community development concept; it illuminates absent knowledge about community development, especially in rural regions. This is one of the few studies investigating the influencing dimensions of community development in rural regions. To the authors’ best knowledge, this is the first research article providing empirical evidence of the influencing dimensions of community development in rural regions in a developing country.

A quasi-least-squares (QLS) mixed finite element method (MFE) based on the L2-inner product is utilized to solve an incompressible magnetohydrodynamic (MHD) model. These models are associated with the three unknown terms, i.e., fluid velocity, fluid pressure, and magnetic field. For the MHD-based models, common theories and algorithms for approximation of the solutions are not always applicable because of the choice of the functional spaces during the utilization of the weak formulation. It is well known that the spaces used for the approximation of the different unknowns, e.g., the spaces for the unknowns, cannot be chosen independently for the variational formulation, and may have to satisfy strict stability conditions such as the inf-sup, or Ladyzhenskaya–Babuska–Brezzi (LBB) condition. The dependency of the selection of the spaces for the unknowns are critical and always not applicable for some pair of unknowns. Because of this, the numerical or theoretical solutions must have to face some stability issue. The proposed scheme (L2-inner product) is introduced to circumvent this deficiency of the conditions (inf-sup or LBB) and obtained a well-posed solution theoretically. The model equations are nonlinear and highly coupled with the combination of Navier–Stokes and Maxwell relations. First, these nonlinear models are made linear around a specific state wherein the modified system represents an algebraic equation in a first-order symmetric form. Secondly, a direct iteration technique is applied to solve the nonlinearities and obtain a theoretical convergent rate for a general initial guess. Theoretical results show that only a single parameter with a single initial guess is sufficient to establish the well-posedness of the solution.

The presence of phyto-hormones in plants at relatively low concentrations plays an indispensable role in regulating crop growth and yield. Salt stress is one of the major abiotic stresses limiting cotton production. It has been reported that exogenous phyto-hormones are involved in various plant defense systems against salt stress. Recently, different studies revealed the pivotal performance of hormones in regulating cotton growth and yield. However, a comprehensive understanding of these exogenous hormones, which regulate cotton growth and yield under salt stress, is lacking. In this review, we focused on new advances in elucidating the roles of exogenous hormones (gibberellin (GA) and salicylic acid (SA)) and their signaling and transduction pathways and the cross-talk between GA and SA in regulating crop growth and development under salt stress. In this review, we not only focused on the role of phyto-hormones but also identified the roles of GA and SA responsive genes to salt stress. Our aim is to provide a comprehensive review of the performance of GA and SA and their responsive genes under salt stress, assisting in the further elucidation of the mechanism that plant hormones use to regulate growth and yield under salt stress.

The TUBBY gene family is a group of transcription factors found in animals and plants with many functions. TLP genes have a significant role in response to different abiotic stresses. However, there is limited knowledge regarding the TUBBY gene family in T. aestivum. Here we identified 40 TaTLP genes in wheat to reveal their potential function. This study found that TUBBY (TaTLP) genes are highly conserved in wheat. The GO analysis of TaTLP genes revealed their role in growth and stress responses. Promoter analysis revealed that most TaTLPs participate in hormone and abiotic stress responses. The heatmap analysis also showed that TaTLP genes showed expression under various hormonal and abiotic stress conditions. Several genes were upregulated under different hormonal and temperature stresses. The qRT-PCR analysis confirmed our hypotheses. The results clearly indicate that various TaTLP genes showed high expression under temperature stress conditions. Furthermore, the results showed that TaTLP genes are expressed in multiple tissues with different expression patterns. For the first time in wheat, we present a comprehensive TaTLP analysis. These findings provide valuable clues for future research about the role of TLPs in the abiotic stress process in plants. Overall, the research outcomes can serve as a model for improving wheat quality through genetic engineering.

In this paper, we report on the synthesis—via the wet chemical precipitation route method—and thin film characteristics of inorganic semiconductor, cuprous oxide (Cu2O) nanoparticles, for their potential application in enhancing the humidity-sensing properties of semiconducting polymer poly(9,9-dioctylfluorene) (F8). For morphological analysis of the synthesized Cu2O nanoparticles, transmission electron microscope (TEM) and scanning electron microscope (SEM) micrographs are studied to investigate the texture, distribution, shape, and sizes of Cu2O crystallites. The TEM image of the Cu2O nanoparticles exhibits somewhat non-uniform distribution with almost uniform shape and size having an average particle size of ≈24 ± 2 nm. Fourier transformed infrared (FTIR) and X-ray diffraction (XRD) spectra are studied to validate the formation of Cu2O nanoparticles. Additionally, atomic force microscopy (AFM) is performed to analyze the surface morphology of polymer-inorganic (F8-Cu2O) nanocomposites thin film to see the grain sizes, mosaics, and average surface roughness. In order to study the enhancement in sensing properties of F8, a hybrid organic–inorganic (F8-Cu2O) surface-type humidity sensor Ag/F8-Cu2O/Ag is fabricated by employing F8 polymer as an active matrix layer and Cu2O nanoparticles as a dopant. The Ag/F8-Cu2O/Ag device is prepared by spin coating a 10:1 wt% solution of F8-Cu2O nanocomposite on pre-patterned silver (Ag) electrodes on glass. The inter-electrode gap (≈5 μm) between Ag is developed by photolithography. To study humidity sensing, the Ag/F8-Cu2O/Ag device is characterized by measuring its capacitance (C) as a function of relative humidity (%RH) at two different frequencies (120 Hz and 1 kHz). The device exhibits a broad humidity sensing range (27–86%RH) with shorter response time and recovery time, i.e., 9 s and 8 s, respectively. The present results show significant enhancement in the humidity-sensing properties as compared to our previously reported results of Ag/F8/Ag sensor wherein the humidity sensing range was 45–78%RH with 15 s and 7 s response and recovery times, respectively. The improvement in the humidity-sensing properties is attributed to the potential use of Cu2O nanoparticles, which change the hydrophobicity, surface to volume ratio of Cu2O nanoparticles, as well as modification in electron polarizability and polarity of the F8 matrix layer.

Barley-based cropping systems (BCS) alter barley production by influencing weed infestation rates and soil nutrient dynamics. This two-year field study evaluated the interactive effects of five BCS and five weed control strategies (WCS) on soil properties and the growth and yield of barley. Barley was planted in five different cropping systems, i.e., fallow-barley (FB), maize-barley (MaB), cotton-barley (CB), mungbean-barley (MuB) and sorghum-barley (SB). Similarly, five different WCS, weed-free (control, WF), weedy-check (control, WC), false seedbeds (FS), chemical control (CC) and use of allelopathic water extracts (AWE), were included in the study. The SB system had the highest soil bulk density (1.48 and 1.47 g cm−3 during the period 2017–2018 and 2018–2019, respectively) and lowest total soil porosity (41.40 and 41.07% during the period 2017–2018 and 2018–2019, respectively). However, WCS remained non-significant for bulk density and total soil porosity during both years of the study. Barley with WF had a higher leaf area index (5.28 and 4.75) and specific leaf area (65.5 and 64.9 cm−2 g−1) compared with barley grown under WC. The MuB system under WC had the highest values of extractable NH4-N (5.42 and 5.58 mg kg−1), NO3-N (5.79 and 5.93 mg kg−1), P (19.9 and 19.5 mg kg−1), and K (195.6 and 194.3 mg kg−1) with statistically similar NO3-N in the MaB system under WC and extractable K in the MuB system under FS. Grain yield ranged between 2.8–3.2 and 2.9–3.3 t ha−1 during the period 2017–2018 and 2018–2019, respectively, among different WCS. Similarly, grain yield ranged between 2.9–3.2 and 3.0–3.2 t ha−1 during the period 2017–2018 and 2018–2019, respectively, within different BCS. Among WCS, the highest grain yield (3.29 and 3.32 t ha−1) along with yield-related traits of barley were in WF as compared to WC. Overall, MuB system recorded better yield and yield-related traits, whereas the lowest values of these traits were recorded for FB systems. In conclusion, the MuB system with WF improved soil characteristics and barley yield over other cropping systems. The AWE significantly suppressed weeds and was equally effective as the chemical control. Therefore, MuB and AWE could be used to improve barley productivity and suppress weeds infestation.

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees.

Magnetorheological (MR) materials are classified as smart materials that can alter their rheological features once exposed to peripheral magnetic fields. MR materials have been a standard and one of the primary smart materials for the last few decades due to their outstanding vibration control performance in adaptive sandwich structures and systems. This paper reviews the vibration suppression investigations of flexible constructions using MR elastomers (MREs). In relations of field-dependent controllability, physical features such as stiffness and the damping of different geometrical structures integrated with the core layer of MREs are explored. The veracity of the knowledge is discussed in this article, whereby sandwich structures with different MR treatment configurations are analyzed for free and forced vibration, MRE sandwich structures are analyzed for stability under different working conditions, and the optimal positions of fully and partially treated MRE sandwich structures for improved vibration control are identified. MR materials′ field-dependent stiffness and damping characteristics are also discussed in this article. A few of the most noteworthy research articles over the last several years have been summarized.

Salt stress reduces the yield and quality of rice. It is of great significance to screen out salt-tolerant varieties for the development and utilization of saline land. The study was carried out on 114 rice varieties; first, seven varieties were selected and treated with different salt concentrations (0, 50, 85, 120, 155, 190, 225 mM), and seven traits, including germination energy, germination capacity, shoot length, root length, root number, plant fresh weight, and seedling vigor index, were measured. The salt concentration at which the sodium chloride injury index was 50% of the control was considered the optimal salt concentration. Second, 114 rice germplasms were carried out under an optimal salt concentration (120 mM). Then, principal component analysis, fuzzy function analysis, stepwise regression analysis, correlation analysis, and systematic cluster analysis were carried out on each parameter. There was a significant correlation between each parameter and the D-value, and the correlation coefficient between the seedling vigor index and D-value was the highest. D-value = − 0.272 + 1.335 × STI − SVI + 0.549 × STI − RN − 0.617 × STI-RL + 0.073 × STI − GE, R2 = 0.986. Using this equation, the sodium chloride tolerance of rice in the germination experiment could be quickly identified. This study showed that the seedling vigor index was a reliable parameter to identify the salinity tolerance of rice varieties. Five groups were obtained by classification at a Euclidean distance of 5. There were 8 highly salt-tolerant cultivars, 23 salt-tolerant cultivars, 42 cultivars with moderate salt tolerance, 33 salt-sensitive cultivars, and 8 highly salt-sensitive cultivars. In this study, we found that Riguang was the most salt-tolerant rice variety, and Xiangxuejing15 was the most salt-sensitive variety.

The current study demonstrates the culture characteristics of adipose tissue and bone marrow-derived mesenchymal stem cells (MSC). The study evaluates the effect of ambient temperature, physiological status of the donor and the tissue source on sheep (Ovis aries) mesenchymal stem cells. The tissue samples were harvested from full term pregnant female sheep (n = 9) and male sheep (n = 10). Adipose tissue was harvested from n = 9 sheep and bone marrow from n = 10 sheep. The samples (adipose tissue, n = 2; bone marrow, n = 3) transported at cold ambient temperature (n = 14) at higher (>20 °C) ambient temperature successfully yielded MSCs. Bone marrow mononuclear cell (MNC) fraction was higher than the adipose tissue-derived stromal vascular fraction (SVF), but the percent adherent cells (PAC) was higher in the later cell fraction. Adipose tissue-derived MSCs from the full term female sheep had a significantly (p < 0.05) higher proliferation potential as compared to those of the male sheep-derived MSCs. Female sheep MSCs also had rapid differentiation potential. The cryopreserved MSCs had morphological features comparable to that of the fresh cells. In conclusion, the tissue type and physiological status of donor animal may affect MSCs’ characteristics and should be taken into consideration while applying in clinical settings.

In this paper, we consider decision trees that use both conventional queries based on one attribute each and queries based on hypotheses of values of all attributes. Such decision trees are similar to those studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithm based on entropy for the construction of the above decision trees and discuss the results of computer experiments on various data sets and randomly generated Boolean functions.

The present work reports the assessment of the effectiveness of a foliar-spray of salicylic acid (SA) on growth attributes, biochemical characteristics, antioxidant activities and osmolytes accumulation in wheat grown under control (100% field capacity) and water stressed (60% field capacity) conditions. The total available water (TAW), calculated for a rooting depth of 1.65 m was 8.45 inches and readily available water (RAW), considering a depletion factor of 0.55, was 4.65 inches. The water contents corresponding to 100 and 60% field capacity were 5.70 and 1.66 inches, respectively. For this purpose, seeds of two wheat cultivars (Fsd-2008 and S-24) were grown in pots subjected to water stress. Water stress at 60% field capacity markedly reduced the growth attributes, photosynthetic pigments, total soluble proteins (TSP) and total phenolic contents (TPC) compared with control. However, cv. Fsd-2008 was recorded as strongly drought-tolerant and performed better compared to cv. S-24, which was moderately drought tolerant. However, water stress enhanced the contents of malondialdehyde (MDA), hydrogen peroxide (H2O2) and membrane electrolyte leakage (EL) and modulated the activities of antioxidant enzymes (superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), as well as accumulation of ascorbic acid (AsA), proline (Pro) and glycine betaine (GB) contents. Foliar-spray with salicylic acid (SA; 0, 3 mM and 6 mM) effectively mitigated the adverse effects of water stress on both cultivars. SA application at 6 mM enhanced the shoot and root length, as well as their fresh and dry weights, and improved photosynthetic pigments. SA foliage application further enhanced the activities of antioxidant enzymes (SOD, POD, and CAT) and nonenzymatic antioxidants such as ascorbic acid and phenolics contents. However, foliar-spray of SA reduced MDA, H2O2 and membrane permeability in both cultivars under stress conditions. The results of the present study suggest that foliar-spray of salicylic acid was effective in increasing the tolerance of wheat plants under drought stress in terms of growth attributes, antioxidant defense mechanisms, accumulation of osmolytes, and by reducing membrane lipid peroxidation.

Unconventional myosins, linked to deafness, are also proposed to play a role in retinal cell physiology. However, their direct role in photoreceptor function remains unclear. We demonstrate that systemic loss of the unconventional myosin MYO1C in mice, specifically causes rhodopsin mislocalization, leading to impaired visual function. Electroretinogram analysis of Myo1c knockout (Myo1c-KO) mice showed a progressive loss of photoreceptor function. Immunohistochemistry and binding assays demonstrated MYO1C localization to photoreceptor inner and outer segments (OS) and identified a direct interaction of rhodopsin with MYO1C. In Myo1c-KO retinas, rhodopsin mislocalized to rod inner segments (IS) and cell bodies, while cone opsins in OS showed punctate staining. In aged mice, the histological and ultrastructural examination of the phenotype of Myo1c-KO retinas showed progressively shorter photoreceptor OS. These results demonstrate that MYO1C is important for rhodopsin localization to the photoreceptor OS, and for normal visual function.

Long-term survival after lung transplantation is lower than that associated with other transplanted organs. Infectious complications, most importantly invasive fungal infections, have detrimental effects and are a major cause of morbidity and mortality in this population. Candida infections predominate in the early post-transplant period, whereas invasive mold infections, usually those related to Aspergillus, are most common later on. This review summarizes the epidemiology and risk factors for invasive fungal diseases in lung transplant recipients, as well as the current evidence on preventive measures. These measures include universal prophylaxis, targeted prophylaxis, and preemptive treatment. Although there is consensus that a preventive strategy should be implemented, current data show no superiority of one preventive measure over another. Data are also lacking regarding the optimal antifungal regimen and the duration of treatment. As all current recommendations are based on observational, single-center, single-arm studies, it is necessary that this longstanding debate is settled with a multicenter randomized controlled trial.

The steady-state electrical conduction current for single and multilayer polyimide (PI) nanocomposite films was observed at the low and high electric field for different temperatures. Experimental data were fitted to conduction models to investigate the dominant conduction mechanism in these films. In most films, space charge limited current (SCLC) and Poole–Frenkel current displayed dominant conduction. At a high electric field, the ohmic conduction was replaced by current–voltage dependency. Higher conduction current was observed for nanocomposite films at a lower temperature, but it declined at a higher temperature. PI nanocomposite multilayer films showed a huge reduction in the conduction current at higher electric fields and temperatures. The conclusions derived in this study would provide the empirical basis and early breakdown phenomenon explanation when performing dielectric strength and partial discharge measurements of PI-based nanocomposite insulation systems of electric motors.