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Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Silver is a poisonous but precious heavy metal that has widespread application in various biomedical and environmental divisions. Wide-ranging usage of the metal has twisted severe environmental apprehensions. Henceforth there is a cumulative call for the progress of modest, low-cost and, the ecological method for remediation of silver. In the present study, Bacillus cereus was isolated from contaminated soil. Various experimental factors like the amount of AgNO 3 , inoculum size, temperature, time, and pH were improved by using central composite design (CCD) grounded on response surface methodology (RSM). Optimized values for AgNO 3 (1 mM) 10 ml, inoculum size ( Bacillus cereus ) 8.7 ml, temperature 48.5 °C, time 69 h, and pH 9 showed in the form of optimized ramps. The formed nanoparticles stayed characterized by UV–visible spectrophotometer, Scanning Electron Microscopy, Fourier transform infra-red spectrometry, particle size analyzer, and X-ray diffraction. The particle size ranges from 5 to 7.06 nm with spherical form. The antimicrobial effectiveness of synthesized nanoparticles was tested contrary to five multidrug resistant microbial strains, Staphylococcus epidermidis, Staphylococcus aureus , Escherichia coli, Salmonella enterica, Porteus mirabilis by disc diffusion method. The minimum inhibitory concentrations and minimum lethal concentrations were detected by the broth macro dilution method. 2,2-diphenyl-1-picrylhydrazyl-hydrate (DPPH) was used to check the free radical scavenging ability of biogenic silver nanoparticles. Similarly, anti-radical activity was checked by 2,2′-Azino-Bis-3-Ethylbenzothiazoline-6-Sulfonic Acid (ABTS) with varying time intervals. Catalytic potential of biosynthesized silver nanoparticles was also investigated.

This study analyzes spatial and temporal trends in tropospheric nitrogen dioxide (NO2) concentrations across the Lahore Division, Pakistan, drawing on 18 years (2007–2024) of observational data from NASA’s Ozone Monitoring Instrument (OMI) Aura satellite. This study aimed to identify the main drivers of NO2 pollution, including industrial activities, energy generation, transportation systems, and agricultural burning practices. Results indicate a marked rise in annual mean NO2 levels, climbing from an initial 2.69 × 1015 mol/cm2 in 2007 to a record 3.62 × 1015 mol/cm2 by 2016, followed by a gradual reduction to 3.14 × 1015 mol/cm2 by 2024. Vehicular emissions are the primary source, with a fleet of over 7.35 million registered vehicles by 2024, comprising more than two-thirds motorcycles (5.1 million). Industrial activities, notably inefficient brick kilns and textile dyeing facilities, substantially intensify the release of pollutants, whereas thermal power infrastructure, including the 1180 MW Quaid-e-Azam plant, contributes significantly to regional NO2 burdens. Seasonal analysis shows maximum NO2 concentrations during winter months (e.g., 5.19 × 1015 mol/cm2 in December 2017), driven by thermal inversions and increased combustion activities for heating, drop to their lowest in summer with monsoon rains, and remain moderate in spring and autumn, influenced by seasonal dispersion and agricultural activities. Atmospheric modelling through HYSPLIT backwards trajectories further identified cross-border pollutant transport from local and neighboring regions.HighlightsThe spatial and temporal variability of tropospheric NO2 was analyzed using satellite remote sensing techniques over the Lahore Division, Pakistan.Annual mean concentrations reached 3.62 × 1015 mol/cm2 (2016), driven by 7.3 million vehicles, coal-fired industries, and thermal power plants.Post-2016 NO2 reductions (13%) were linked to the Clean Air Policy and electric vehicle incentives, although industrial emissions persisted.HYSPLIT trajectory analysis revealed that pollutant transport from local and transboundary sources (India and Afghanistan) affected Lahore’s NO2 levels.

Internet of Things (IoT) devices are well-connected; they generate and consume data which involves transmission of data back and forth among various devices. Ensuring security of the data is a critical challenge as far as IoT is concerned. Since IoT devices are inherently low-power and do not require a lot of compute power, a Network Intrusion Detection System is typically employed to detect and remove malicious packets from entering the network. In the same context, we propose feature clusters in terms of Flow, Message Queuing Telemetry Transport (MQTT) and Transmission Control Protocol (TCP) by using features in UNSW-NB15 data-set. We eliminate problems like over-fitting, curse of dimensionality and imbalance in the data-set. We apply supervised Machine Learning (ML) algorithms, i.e., Random Forest (RF), Support Vector Machine and Artificial Neural Networks on the clusters. Using RF, we, respectively, achieve 98.67% and 97.37% of accuracy in binary and multi-class classification. In clusters based techniques, we achieved 96.96%, 91.4% and 97.54% of classification accuracy by using RF on Flow & MQTT features, TCP features and top features from both clusters. Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.

Continuous cropping (CC) is a common practice in agriculture, and usually causes serious economic losses due to soil degeneration, decreased crop yield and quality, and increased disease incidence, especially in medicinal plants. Continuous cropping obstacles (CCOs) are mainly due to changes in soil microbial communities, nutrient availability, and allelopathic effects. Recently, progressive studies have illustrated the molecular mechanisms of CCOs, and valid strategies to overcome them. Transcriptomic and metabolomics analyses revealed that identified DEGs (differently expressed genes) and metabolites involved in the response to CCOs are involved in various biological processes, including photosynthesis, carbon metabolism, secondary metabolite biosynthesis, and bioactive compounds. Soil improvement is an effective strategy to overcome this problem. Soil amendments can improve the microbial community by increasing the abundance of beneficial microorganisms, soil fertility, and nutrient availability. In this review, we sum up the recent status of the research on CCOs in medicinal plants, the combination of transcriptomic and metabolomics studies, and related control strategies, including uses of soil amendments, crop rotation, and intercropping. Finally, we propose future research trends for understanding CCOs, and strategies to overcome these obstacles and promote sustainable agriculture practices in medicinal plants.

Wireless sensor network (WSN)-based Internet of Things (IoT) applications suffer from issues including limited battery capacity, frequent disconnections due to multi-hop communication and a shorter transmission range. Clustering and routing are treated separately in different solutions and, therefore, efficient solutions in terms of energy consumption and network lifetime could not be provided. This work focuses data collection from IoT-nodes distributed in an area and connected through WSN. We address two interlinked issues, clustering and routing, for large-scale IoT-based WSN and propose an improved clustering and routing protocol to jointly solve both of these issues. Improved clustering and routing provide area-based clustering derived from the transmission range of network nodes. During process of clustering, cluster-heads are selected in such a way that provide fail-over-proof routing. An efficient routing path is achieved by finding the minimal hop-count with the availability of alternate routing paths. The results are compared with state-of-the-art benchmark protocols. Theoretical and simulation results demonstrate reliable network topology, improved network lifetime, efficient node density management and improved overall network capacity.

The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

The data quality of low-cost sensors has received considerable attention and has also led to PM 2.5 warnings. However, the calibration of low-cost sensor measurements in an environment with high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM 2.5 sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.

Background Melatonin is considered a potential plant growth regulator to enhance the growth of plants and increase tolerance to various abiotic stresses. Nevertheless, melatonin’s role in mediating stress response in different plant species and growth cycles still needs to be explored. This study was conducted to understand the impact of different melatonin concentrations (0, 50, 100, and 150 μM) applied as a soil drench to maize seedling under drought stress conditions. A decreased irrigation approach based on watering was exposed to maize seedling after drought stress was applied at 40–45% of field capacity. Results The results showed that drought stress negatively affected the growth behavior of maize seedlings, such as reduced biomass accumulation, decreased photosynthetic pigments, and enhanced the malondialdehyde and reactive oxygen species (ROS). However, melatonin application enhanced plant growth; alleviated ROS-induced oxidative damages by increasing the photosynthetic pigments, antioxidant enzyme activities, relative water content, and osmo-protectants of maize seedlings. Conclusions Melatonin treatment also enhanced the stomatal traits, such as stomatal length, width, area, and the number of pores under drought stress conditions. Our data suggested that 100 μM melatonin application as soil drenching could provide a valuable foundation for improving plant tolerance to drought stress conditions.