Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

Low temperature is one of the most significant environmental factors that threaten the survival of subtropical and tropical plant species. By conducting a study, which was arranged in a completely randomized design with three replicates, the relative freezing tolerance (FT) of four Iranian pomegranate cultivars, including ‘Alak Torsh’, ‘Tabestaneh Torsh’, ‘Poost Sefid’, and ‘Poost Syah’, as well as its correlation with some biochemical indices, were investigated. From each cultivar, pieces of one-year-old shoot samples were treated with controlled freezing temperatures (−11, −14, and −17 ℃) to determine lethal temperatures (LT₅₀) based on survival percentage, electrolyte leakage, phenolic leakage, and tetrazolium staining test (TST) methods. Results showed that FT was higher in the second year with a lower minimum temperature and a higher concentration of cryoprotectants. The stronger correlation of electrolyte leakage with survival percentage (r = 0.93***) compared to the other three indices explained that this index could be the most reliable injury index in determining the pomegranate FT to investigate freezing effects. Of all four cultivars, ‘Poost Syah’ was the hardest by presenting a higher FT than ~ −14 ℃ in mid-winter. Accordingly, this pomegranate cultivar seems to be promising to grow in regions with a higher risk of freezing and to be involved in breeding programs to develop novel commercial cultivars.

Potassium is an essential macronutrient for living creatures on earth and in plants, it plays a very significant role in determining the overall health of the plants. Although potassium is present in the soil, it is present in a form that is inaccessible to the plants, and hence synthetic harmful non-eco-friendly potassium fertilizers are used. To overcome this problem, the use of eco-friendly potassium-solubilizing bacteria comes into play. The goal of the present study was to assess the potassium-solubilizing bacteria that inhabit the farm rhizosphere, which demonstrate the presence of enzymes associated with plant growth promotion and antagonistic properties. A total of thirty-four isolates were isolated from the rhizosphere. All these isolates were subjected to a potassium solubilization test on Aleksandrov agar medium, out of which fourteen were found to possess potassium solubilizing ability. On the basis of the 16S rRNA gene sequencing, the most potential potassium-solubilizing bacterium was identified as Proteus mirabilis PSCR17. The plant growth promoting abilities and production of biocontrol enzymes of this isolate were evaluated, and the results indicated, in addition to potassium solubilization, the isolate was positive for indole acetic acid production, hydrogen cyanide production, amylase, catalase, cellulase, chitinase, and protease. The use of potassium fertilizers is harmful to the environment and ecosystem; hence, this study concludes that P. mirabilis PSCR17 can be used as a substitute for chemical potassium fertilizers to improve the growth and biocontrol traits of the plants in a sustainable manner after further research.

The present work conducts a comprehensive thermodynamic analysis of a 150 MW_e Integrated Gasification Combined Cycle (IGCC) using Indian coal as the fuel source. The plant layout is modelled and simulated using the “Cycle-Tempo” software. In this study, an innovative approach is employed where the gasifier's bed material is heated by circulating hot water through pipes submerged within the bed. The analysis reveals that increasing the external heat supplied to the gasifier enhances the hydrogen (H₂) content in the syngas, improving both its heating value and cold gas efficiency. Additionally, this increase in external heat favourably impacts the Steam-Methane reforming reaction, boosting the H₂/CH₄ ratio. The thermodynamic results show that the plant achieves an energy efficiency of 44.17% and an exergy efficiency of 40.43%. The study also identifies the condenser as the primary source of energy loss, while the combustor experiences the greatest exergy loss.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Given the heavy workload faced by teachers, automatic speaking scoring systems provide essential support. This study aims to consolidate technological configurations of automatic scoring systems for spontaneous L2 English, drawing from literature published between 2014 and 2024. The focus will be on the architecture of the automatic speech recognition model and the scoring model, as well as on features used to evaluate phonological competence, linguistic proficiency, and task completion. By synthesizing these elements, the study seeks to identify potential research areas, as well as provide a foundation for future research and practical applications in software engineering.