Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

This work presents the design and fabrication of a metamaterial-based stimulated dual band antenna on FR4 material (dielectric constant 4.3) to operate in Industrial, Scientific and Medical (ISM) and Radio-frequency Identification (RFID) applications. The antenna model had an overall dimension of 70 × 31 × 1.6 mm³ with etched T-slots and L-slots for dual band resonance. The main objective of this work was to enhance the gain performance characteristic at the selected dual band frequencies of 0.915 GHz and 2.45 GHz. Initially, it achieved a narrow bandwidth of 0.018 GHz with a gain of 1.53 dBi at a lower frequency, and 0.13 GHz of bandwidth featuring 4.49 dBi of gain at a higher frequency. The antenna provided an impedance bandwidth of 2% (0.905–0.923 GHz) and 5% (2.382–2.516 GHz) at two resonating frequencies. The antenna was integrated with a designed novel AMC structure to enhance the gain in CST Microwave Studio software with the finite integration method. The characteristic features of the AMC unit cell were observed at 0.915 GHz and 2.45 GHz frequencies and after antenna integration, the final prototype achieved a gain of 2.87 dBi at 0.915 GHz and 6.8 dBi at 2.45 GHz frequencies.

A conformal tri-band antenna tailored for flexible devices and body-centric wireless communications operating at the key frequency bands is proposed. The antenna is printed on a thin Rogers RT 5880 substrate, merely 0.254 mm thick, with an overall geometrical dimension of 15 × 20 × 0.254 mm³. This inventive design features a truncated corner monopole accompanied by branched stubs fed by a coplanar waveguide. The stubs, varying in length, serve as quarter-wavelength monopoles, facilitating multi-band functionality at 2.45, 3.5, and 5.8 GHz. Given the antenna’s intended applications in flexible devices and body-centric networks, the conformability of the proposed design is investigated. Furthermore, an in-depth analysis of the Specific Absorption Rate (SAR) is conducted using a four-layered human tissue model. Notably, the SAR values for the proposed geometry at 2.45, 3.5, and 5.8 GHz stand at 1.48, 1.26, and 1.1 W/kg for 1 g of tissue, and 1.52, 1.41, and 0.62 W/kg for 10 g of tissue, respectively. Remarkably, these values comfortably adhere to both FCC and European Union standards, as they remain substantially beneath the threshold values of 1.6 W/kg and 2 W/kg for 1 g and 10 g tissues, respectively. The radiation characteristics and performance of the antenna in flat and different bending configurations validate the suitability of the antenna for flexible devices and body-centric wireless communications.

Urban air and soil quality has been deteriorating during the past few years due to urbanization, industrialization and increased number of vehicles. The goal of the current study was to assess the Air Pollution Tolerance Index (APTI) and heavy metal absorption (Pb, Cd, Zn, and Ni) potential by ten selected trees planted along the roadside in the metropolitan city of Lahore, Pakistan. APTI was estimated on the basis of biochemical parameters (chlorophyll content, ascorbic acid, pH and relative water contents) of plant extract, while heavy metals (HMs) accumulation potential was measured by a digestion method. The highest APTI was estimated in P. longifolia (78.9), followed by A. scholarils (75.9) and M. indica (71.9). Overall, these three species have significant closeness among the higher pollution-tolerance results. The poor APTI result was determined in F. religiosa (19.5) and E. citriodora (14.9). The highest Pb contents were observed in P. longifolia and M. indica i.e., 135 and 132 mg/kg, respectively. Similarly, the highest Zn contents were found in P. longifolia and S. cumini with 130 and 132 mg/kg, respectively. The Ni concentration was observed highest in P. longifolia (34 mg/kg), but in the remaining species, it is almost the same trend of Ni accumulation. Combining these trees can be useful for fostering green-belt growth along roadsides to reduce air and soil pollution and achieve environmental sustainability. But unfortunately, these species are not planted well across the roadside as they have very little biodiversity index, as compared to other species. These species should be planted in urban areas to enhance biodiversity in the urban ecosystem and make them sustainable cities and communities.

For economical and sustainable benefits, conventional retaining walls are being replaced by geosynthetic reinforced soil (GRS). However, for safety and quality assurance purposes, prior tests of pullout capacities of these materials need to be performed. Conventionally, these tests are conducted in a laboratory with heavy instruments. These tests are time-consuming, require hard labor, are prone to error, and are expensive as a special pullout machine is required to perform the tests and acquire the data by using a lot of sensors and data loggers. This paper proposes a data-driven machine learning architecture (MLA) to predict the pullout capacity of GRS in a diverse environment. The results from MLA are compared with actual laboratory pullout capacity tests. Various input variables are considered for training and testing the neural network. These input parameters include the soil physical conditions based on water content and external loading applied. The soil used is a locally available weathered granite soil. The input data included normal stress, soil saturation, displacement, and soil unit weight whereas the output data contains information about the pullout strength. The data used was obtained from an actual pullout capacity test performed in the laboratory. The laboratory test is performed according to American Society for Testing and Materials (ASTM) standard D 6706-01 with little modification. This research shows that by using machine learning, the same pullout resistance of a geosynthetic reinforced soil can be achieved as in laboratory testing, thus saving a lot of time, effort, and money. Feedforward backpropagation neural networks with a different number of neurons, algorithms, and hidden layers have been examined. The comparison of the Bayesian regularization learning algorithm with two hidden layers and 12 neurons each showed the minimum mean square error (MSE) of 3.02 × 10⁻⁵ for both training and testing. The maximum coefficient of regression (R) for the testing set is 0.999 and the training set is 0.999 for the prediction interval of 99%.