The effect of inoculation of three plant growth promoting rhizobacteria (PGPR) that is Rhizobium japonicum, Azotobacter chroococcum and Azospirillum brasilense and mixture of them on phytohormones and polyamines of soybean under different irrigation regimes was investigated. Drought stress induced by irrigation withholding until 40, 80 and 120 mm evaporation from evaporation pan. However seed bacterization of soybean was accompanied with 20 kg ha-1 nitrogen. In addition, 20 and 100 kg ha-1 nitrogen were considered as control treatments. The results showed that drought stress significantly decreased cytokinin, gibberellin and auxin accumulation in plant tissues. By contrast, drought stress led to increase in abscisic acid accumulation in soybean plants. Polyamines that are putrescine and spermidine increased due to drought stress and then decreased under severe drought stress. PGPR application had positive effect on growth promoting phytohormones compared to control treatment. However the highest accumulation of cytokinin, gibberellin and auxin was related to 100 kg ha-1 nitrogen treatment. In case of abscisic acid PGPR application decreased its accumulation. A significant decrease as observed on polyamines accumulation when PGPRs were applied on stressed soybean plants.

The current research presents SnSe/Ag-polyaniline (Ag-PANI) heterojunction as S-scheme heterostructures with strong photocatalytic performance to degrade dye and antibiotic molecules under visible light irradiation. In the first step, the PANI composites have been functionalized by Ag NPs. Then, the SnSe/Ag-PANI heterojunction was produced by the co-precipitation method. In addition, the pristine SnSe NPs and SnSe/PANI heterojunction were also synthesized under similar conditions. Surface plasmon resonances (SPR) of Ag nanoparticles (NPs) on the photocatalytic performance of the SnSe/Ag-PANI heterojunction were investigated. The degradation time of more than 95 % of methylene blue (MB) dye was reduced from 60 min for the pristine SnSe NPS to 50 min for the SnSe/Ag-PANI heterojunction. In addition, the SnSe/Ag-PANI presented a higher photocatalytic performance than other samples to remove 99 % of the tetracycline (TC) antibiotic for 105 min. The X-ray diffraction (XRD) patterns of the products indicated that the PANI and Ag-PANI composites changed the growth direction of the SnSe, and a Se peak as an external peak was observed in the XRD patterns of the SnSe/Ag-PANI and SnSe/PANI heterojunctions. Bands and Fermi levels (Ef) alignment of the SnSe, Se, and PANI structures indicated a dual S-scheme heterostructure has been formed by SnSe/Se/PANI and SnSe/Se/Ag-PANI heterojunctions.

ABSTRACT This paper demonstrates the synthesis of molecularly imprinted polymers-solid phase extraction-liquid chromatography method at room temperature using bulk polymerisation of ethion pesticide, by high sensitivity, low costs and high stability. By making MIP for ethion as ethion-MIP, which could be detected with a UV-Vis spectrophotometer at 276 nm, a functional monomer of acrylic acid with cross-linking ethylene glycol dimethyl acrylate. The high-performance liquid chromatography methods developed in this study are accurate, sensitive, and precise to apricot, grapes, strawberry and soil samples. The elution process to the template ethion from the ethion-MIP, caused by minimum volume using solution of 1.0 mL of methanol-acetic acid (9:1) was obtained. Different factors affecting the reaction were studied and optimised. The calibration plot is linear in the concentration range of (0.02 to 1.0 µg mL−1). The relative standard deviations of (±2.0%) and detection limit of the method are 0.02 µg mL−1, and the maximum imprinting factor of ethion-MIP was 6.8 mgg−1. The results obtained the evidence of efficiency and reliability of the method for the ethion pesticide analysis and estimation for the capacitance of molecular imprinted polymer, analytical technical method by molecularly imprinted polymer and high-performance liquid chromatography.

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of “acquiring flexibility rights”, wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method’s effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices.

AbstractThis research introduces strength‐based therapeutic recreation (STR) as an innovative approach to address job stress, work–family conflict, and family–work conflict among employees. STR involves personalized leisure activities tailored to individuals’ signature character strengths in daily life. The study, encompassing 82 married employees (70.73% female, mean age = 36.77 years), reveals significant reductions in job stress and both work–family and family–work conflicts within the experimental group undergoing a 10‐week STR workshop. By empowering employees to engage in strength‐based leisure activities, STR enhances the propensity to reduce job stress, work–family conflict, and family–work conflict. This approach holds practical potential for organizations seeking to improve employee mental health and productivity through tailored interventions.

Metal-oxide-semiconductors (MOS) gas sensors are widely used for detecting and measuring the concentration of various gases in different applications. Changing the electrical resistance when the MOS surface reacts with a specific gas is the basis of the operation of the gas sensor of MOS. They offer versatility in detecting various gases and fabricating them suitable for supervising energy efficiency, monitoring health and safety, and controlling hazardous emissions. However, traditional MOS sensors suffer from poor selectivity and usually require high operating temperatures. To overcome these limitations, researchers have explored strategies such as doping, bimetallic/co-doping, and composite structures with conductive polymers and 2D materials such as polyaniline (PANI), polymethyl methacrylate (PMMA), polyvinyl alcohol (PVA), reduced graphene oxide (rGO), graphitic carbon nitride (g-C3N4), and graphene. Among the 2D materials, g-C3N4 stands out due to its distinct characteristics, including chemical stability, porosity structure, abundance, lack of toxicity, and numerous surface defects. The exfoliated structure and surface defects of g-C3N4 provide active sites for adsorbing atmospheric oxygen and facilitating reactions with specific gas molecules. This review introduces MOS gas sensors, covering their fabrication methods and electrical measurements. It then attentions on the properties of g-C3N4, synthesis methods, and its potential for composition with the MOS. The review highlights the enhanced gas sensing performance achieved by MOS/g-C3N4 nanocomposites to detect different gases.

In this study, the residue of toxic 2-mercaptobenzothiazole, a semi-volatile heteroaromatic, was extracted from wastewater using a new spectrofluorometric method. The method is based on measuring 2-Mercaptobenzothiazole the quenching effect on the fluorescence intensity of ZnS quantum dot–gelatin nanocomposite in (pH 4, λex 310 and λem 345 nm, in time 60 s). Different factors affecting the reaction were studied and optimized. The calibration plot is linear in the concentration range of (0.05–10.0 µgL−1). The relative standard deviations and the detection limit of the method were ±1.0% and 0.05 μgL−1, respectively. Observed, outcomes confirmed the suitability recovery and a meager detection limit for analyzing toxic 2-Mercaptobenzothiazole in water samples. The preliminary results from this study demonstrate that this new method can be used to analyze 2-mercaptobenzothiazole in wastewater.

Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning algorithms may be caused by unimportant features that increase the time and memory required to build a classifier. Feature selection (FS) is one of the efficient approaches to reducing the unimportant features. This paper, therefore, presents a new FS, named BDE-BSS-DR, that utilizes Binary Differential Evolution (BDE), Binary Stochastic Search (BSS) algorithm, and Dimensionality Reduction (DR) mechanism. The BSS algorithm increases the search capability of the BDE by escaping from local optimal points and exploring the search space. The DR mechanism then reduces the dimensions of the search space gradually. As a result of using DR, the local optima of the search space and the problem of wrong removal of important features before starting the search process are reduced. The algorithm's efficiency is evaluated on 20 different medical datasets. The obtained outcomes indicate that the BDE-BSS-DR outperforms the BDE and BDE-BSS algorithms significantly. Furthermore, the effectiveness of the proposed algorithms in selecting the most important features of the heart disease data, several cancer diseases, and COVID-19 are also compared with several other state-of-the-art methods. Our results show that the BDE-BSS-DR with SVM classifier has a significant advantage over other methods with an average classification accuracy of 95.05% in heart disease and 99.40% in COVID-19 disease. In addition, the comparisons made with KNN and SVM classification prove the efficiency of the DR and BSS in generating a subset of optimal and informative features.

In this paper, a new feature selection algorithm, called SFE (Simple, Fast, and Efficient), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: non-selection and selection. It comprises two phases: exploration and exploitation. In the exploration phase, the non-selection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features, and changes the status of the features from selected mode to non-selected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results, and changes the status of the features from non-selected mode to selected mode. The proposed SFE is successful in feature selection from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this paper proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for feature selection are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed feature selection algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.