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.

Malaria is a mosquito-borne infectious disease that affects humans and poses a severe public health problem. Nigeria has the highest number of global cases. Geospatial technology has been widely used to study the risks and factors associated with malaria hazards. The present study is conducted in Ibadan, Oyo State, Nigeria. The objective of this study is to map out areas that are at high risk of the prevalence of malaria by considering a good number of factors as criteria that determine the spread of malaria within Ibadan using open-source and Landsat remote sensing data and further analysis in GIS-based multi-criteria evaluation (MCE). This study considered factors like climate, environmental, socio-economic, and proximity to health centers as criteria for mapping malaria risk. The MCE used a weighted overlay of the factors to produce an element at-risk map, a malaria hazard map, and a vulnerability map. These maps were overlaid to produce the final malaria risk map, which showed that 72% of Ibadan has a risk of malaria prevalence. Identification and delineation of risk areas in Ibadan would help policymakers and decision-makers mitigate the hazards and improve the health status of the state.

Industry 4.0 is revolutionizing businesses’ operations and relationships with the communities to which they cater. The widespread use of computing and network programs compels firms to digitize their operations and offer novel goods, solutions, and business for practice. Universities appear to be slow to adapt to the changes in the education sector. This study suggests using consolidated digital transformation sources to evaluate the level of ability that universities have achieved in the implementation of digital procedures and to compare it to that of other business sectors across all cities and provinces in Vietnam. The text outlines specific factors that universities should consider when implementing the model. Although the objective with the expectation of education from digital transformation is high, compare it with other industries. And the scores achieved in structural agility and create of benefit for the transformative goals are 3.4, but the score of benefit of technologies is 3.0 lower than. Additionally, the organizational component’s scores were primarily focused on leadership and culture, digital strategy, market digitalization, dynamic and digital capabilities, and strengthened logistics within each industry during the digital transformation. Our findings indicate that universities lag behind other industries, perhaps as a consequence of inadequate leadership and cultural shifts. This is exacerbated by a lack of innovation and inadequate financial assistance.

Since the external environment on a global level is very unstable, recovering from various unexpected shocks becomes a challenging question for all countries. Thus, for each country it is necessary to understand its weaknesses and threats. Further, the preparation for any level of uncertainty in various fields must be imperative. Even for the most unpredictable shocks such as pandemic, cyberthreat, or even war. The aim of the article is to evaluate the state resilience of the Baltic States by creating the national resilience index. A state’s resilience is based on four pillars: economic, social, good governance, and defence. The methodology is based the SAW method, data has been collected from NATO and Eurostat databases. As the result of the study, resilience index has been estimated for each year from 2015 to 2022. Results revealed vulnerability and problematic areas of each country.

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.

Banana (Musa spp.) productivity is limited by sodic soils, which impairs root growth and nutrient uptake. Analyzing root traits under stress conditions can aid in identifying tolerant genotypes. This study investigates the root morphological traits of banana cultivars under sodic soil stress conditions using Rhizovision software. The pot culture experiment was laid out in a Completely Randomized Design (CRD) under open field conditions, with treatments comprising the following varieties: Poovan (AAB), Udhayam (ABB), Karpooravalli (ABB), CO 3 (ABB), Kaveri Saba (ABB), Kaveri Kalki (ABB), Kaveri Haritha (ABB), Monthan (ABB), Nendran (AAB), and Rasthali (AAB), each replicated thrice. Parameters such as the number of roots, root tips, diameter, surface area, perimeter, and volume were assessed to evaluate the performance of different cultivars. The findings reveal that Karpooravali and Udhayam cultivars exhibited superior performance in terms of root morphology compared to other cultivars under sodic soil stress. These cultivars displayed increased root proliferation, elongation, and surface area, indicating their resilience to sodic soil stress. The utilization of Rhizovision software facilitated precise measurement and analysis of root traits, providing valuable insights into the adaptation mechanisms of banana cultivars to adverse soil conditions.