This study examined the potential of stress-tolerant phosphate-solubilizing bacteria (PSB) to enhance drought and salinity tolerance of Vicia faba (Faba bean) and Pisum sativum (Pea). The assessment focused on soil parameters, morphological, physiological, and biochemical responses of the host plants, to clarify the mechanisms underlying PSB-mediated stress reduction under extreme conditions. After screening, DNA sequence analysis of 16 S rRNA genes of five isolates revealed that they belong to Pseudomonas spp and Bacillus spp. The five PSB isolates that exhibit plant growth-promoting properties were used as a consortium in three different treatments BC1 (3 isolates), BC2 (3 isolates), and BC3 (5 isolates). The results show that, under drought and salt stress conditions, grain number per plant increased in BC3 inoculated seeds, followed by BC1 for both tested crops, compared to untreated plants. Moreover, BC3 is the best performing, showing the highest seed protein content (83.22
Groundwater is an essential reserve for comprehensive water resources management. Territories such as the Manabí Hydrographic Demarcation (MHD) in Ecuador have considerable water scarcity during dry periods due to low rainfall, which generates significant losses for the agriculture sector. This work aims to map groundwater potential (GWP) in the MHD using a methodological approach combining remote sensing, geographic information systems (GIS), and analytic hierarchy process (AHP) modeling to support sustainable water management strategies. For the identification and reduction of variables, the multi-objective optimization (MOO) approach was used as a heuristic reference. By applying the Pareto principle, the variables with the greatest influence on the model were selected for the development of the final adjusted map of the groundwater potential index (GWPIF). The spatial effectiveness (SE) and predictability results based on the receiver operating characteristic (ROC) and area under the curve (AUC) revealed that the GWPIF (AUC = 72
This study investigates how negative publicity, country-of-origin (COO) and psychological contracts affect consumer response to local versus foreign brands in relational and transactional contexts. Three experimental studies were undertaken to assess these relationships. The findings show that corporate ability (CA)-related negative publicity is significantly related to foreign brand evaluations, whereas corporate social responsibility (CSR)-related publicity disproportionately affects local brands. Psychological contract has a significant mediation effect on the relationship between negative publicity and brand evaluations. The study provides insights into the interplay between corporate communication, branding and consumer behaviors. The study findings can be tailored for optimizing corporate communication strategies and brand management.
This study explores the reuse of post-consumer polyethylene terephthalate (PET) bottles through the development of recycled PET (RPET)/perlite composites exhibiting enhanced mechanical stiffness and thermal insulation properties. Untreated and alkaline-treated perlite were incorporated into the RPET matrix at filler loadings of 0–20 wt
Detecting and classifying suspicious or malicious domain names and URLs is fundamental task in cybersecurity. To leverage such indicators of compromise, cybersecurity vendors and practitioners often maintain and update blacklists of known malicious domains and URLs. However, blacklists frequently fail to identify emerging and obfuscated threats. Over the past few decades, there has been significant interest in developing machine learning models that automatically detect malicious domains and URLs, addressing the limitations of blacklists maintenance and updates. In this paper, we introduce DomURLs_BERT, a pre-trained BERT-based encoder adapted for detecting and classifying suspicious/malicious domains and URLs. DomURLs_BERT is pre-trained using the Masked Language Modeling (MLM) objective on a large multilingual corpus of URLs, domain names, and Domain Generation Algorithms (DGA) dataset. In order to assess the performance of DomURLs_BERT, we have conducted experiments on several binary and multi-class classification tasks involving domain names and URLs, covering phishing, malware, DGA, and DNS tunneling. The evaluations results show that the proposed encoder outperforms state-of-the-art character-based deep learning models and cybersecurity-focused BERT models across multiple tasks and datasets. The pre-training dataset, the pre-trained DomURLs_BERT encoder, and the experiments source code are publicly available.
