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.

Carbon based materials are really an integral component of our lives and widespread research regarding their properties was conducted along this process. The addition of dopants to carbon materials, either during the production process or later on, has been actively investigated by researchers all over the world who are looking into how doping can enhance the performance of materials and how to overcome the current difficulties. This study explores synthesis methods for nitrogen-doped carbon materials, focusing on advancements in adsorption of different pollutants like CO₂ from air and organic, inorganic and ions pollutants from water, energy conversion, and storage, offering novel solutions to environmental and energy challenges. It addresses current issues with nitrogen-doped carbon materials, aiming to contribute to sustainable solutions in environmental and energy sciences. Alongside precursor types and synthesis methods, a significant relationship exists between nitrogen content percentage and adsorption capacity in nitrogen-doped activated carbon. Nitrogen content ranges from 0.64% to 11.23%, correlating with adsorption capacities from 0.05 mmol/g to 7.9 mmol/g. Moreover, an electrochemical correlation is observed between nitrogen atom increase and specific capacity in nitrogen-doped activated carbon electrodes. Higher nitrogen percentage corresponds to increased specific capacity and capacity retention. This comprehensive analysis sheds light on the potential of nitrogen-doped carbon materials and highlights their significance in addressing critical environmental and energy challenges.

Important modifications are occurring in temperate forests due to climate change; in polar latitudes their distribution area is increasing, while in tropical latitudes it is decreasing due to temperature increase and droughts. One of the biotic regulators of temperate forests are the debarking insects that cause the mortality of certain trees. These insects have increased in number, favored by climate change, and the consequences on forests have not been long in coming. In recent times in the northern hemisphere, the massive mortality of conifers due to the negative synergy between climate change and debarking insects has been evident. In Mexico, we have also experienced infestations by bark stripping insects never seen before; therefore, we are trying to understand the interactions between climate change, forest health and bark stripping insects, to detect the areas with greater susceptibility to attack by these insects and propose management measures to reduce the effects.

Due to rising global environmental challenges, air/water pollution treatment technologies, especially membrane techniques, have been focused on. In this context, air or purification membranes have been considered effective for environmental remediation. In the field of polymeric membranes, high-performance polymer/graphene nanocomposite membranes have gained increasing research attention. The polymer/graphene nanomaterials exposed several potential benefits when processed as membranes. This review explains the utilization of polymer and graphene-derived nanocomposites towards membrane formation and water or gas separation or decontamination properties. Here, different membrane designs have been developed depending upon the polymer types (poly(vinyl alcohol), poly(vinyl chloride), poly(dimethyl siloxane), polysulfone, poly(methyl methacrylate), etc.) and graphene functionalities. Including graphene in polymers influences membrane microstructure, physical features, molecular permeability or selectivity, and separations. Polysulfone/graphene oxide nanocomposite membranes have been found to be most efficient with an enhanced rejection rate of 90%–95%, a high water flux >180 L/m²/h, and a desirable water contact angle for water purification purposes. For gas separation membranes, efficient membranes have been reported as polysulfone/graphene oxide and poly(dimethyl siloxane)/graphene oxide nanocomposites. In these membranes, N₂, CO₂, and other gases permeability has been found to be higher than even >99.9%. Similarly, higher selectivity values for gases like CO₂/CH₄ have been observed. Thus, high-performance graphene-based nanocomposite membranes possess high potential to overcome the challenges related to water or gas molecular separations.

During and after any disaster, a situation report (SITREP) is prepared, based on the Daily Incident Updates (DIU), as an initial decision support information base. It is observed that the decision support system and best practices are not optimized through the available formal reporting on disaster incidents. The rapidly evolving situation, misunderstood terms, inaccurate data and delivery delays of DIU are challenges to the daily SITREP. Multiple stakeholders stipulated with different tasks should be properly understood for the SITREP to initiate relevant response tasks. To fill this research gap, this paper identifies the weaknesses of the current practice and discusses the upgrading of the incident-reporting process using a freely available software tool, enabling further visualization, and producing a comprehensive timely output to share among the stakeholders. In this case, “Power-BI” (a data visualization software) is used as a 360-degree view of useful metrics—in a single place, with real-time updates while being available on all devices for operational decision-making. When a dataset is transformed into several analytical reports and dashboards, it can be easily shared with the target users and action groups. This article analyzed two sources of data, namely the Disaster Management Center (DMC) and the National Disaster Relief Service Center (NDRSC) of Sri Lanka. Senior managers of disaster emergencies were interviewed and explored social media to develop a scheme of best practices for disaster reporting, starting from just before the occurrence, and following the unfolding sequence of the disasters. Using a variety of remotely acquired imageries, rapid mapping, grading, and delineating impacts of natural disasters, were made available to concerned users.

Entomopathogens are microorganisms that pathogenic to insect pest. Several species of naturally occurring viz; fungi, bacteria, viruses and nematodes, infect a variety of insect pests and play an important role in agricultural crops controlling insect pest management.  This kind of biopesticide has many advantages and alternative to chemical insecticides, highly specific, safe, and environmentally sustainable. Pest problems are an almost inevitable part of agriculture. They occur largely because agricultural systems are simplified and modifications of natural ecosystems. Viruses, bacteria are host specific and fungi generally have broader host range and can infect both underground and aboveground pests, soil-dwelling nature nematodes are more suitable for managing soil pests. Growing crops in monoculture provides concentrated food resource that allows pest populations to achieve higher densities in natural environments. Some of the most important problems occur when pests develop resistance to chemical pesticides. These cause highly significant damage to crops, there are also threats from emerging new strains of pests. Crops cultivation can make the physico-chemical environment more favourable for pest activity. Agricultural pests are reducing the yield and quality of produce by feeding on crops, transmitting diseases. Agricultural production significantly loss crop yields, suggest that improvements in pest management are significant forward for improving yields. Crop growers are under immense pressure to reduce the use of chemical pesticides without sacrificing yields, but at the same time manage of pests is becoming difficult due to pesticide resistance and the decreasing availability of products. Alternative methods are needed urgently. These need to be used as part of Integrated Pest Management safety and environmental impact.