Explore the vast range of titles available.

Agriculture in the 21st century is facing multiple challenges, such as those related to soil fertility, climatic fluctuations, environmental degradation, urbanization, and the increase in food demand for the increasing world population. In the meanwhile, the scientific community is facing key challenges in increasing crop production from the existing land base. In this regard, traditional farming has witnessed enhanced per acre crop yields due to irregular and injudicious use of agrochemicals, including pesticides and synthetic fertilizers, but at a substantial environmental cost. Another major concern in modern agriculture is that crop pests are developing pesticide resistance. Therefore, the future of sustainable crop production requires the use of alternative strategies that can enhance crop yields in an environmentally sound manner. The application of rhizobacteria, specifically, plant growth-promoting rhizobacteria (PGPR), as an alternative to chemical pesticides has gained much attention from the scientific community. These rhizobacteria harbor a number of mechanisms through which they promote plant growth, control plant pests, and induce resistance to various abiotic stresses. This review presents a comprehensive overview of the mechanisms of rhizobacteria involved in plant growth promotion, biocontrol of pests, and bioremediation of contaminated soils. It also focuses on the effects of PGPR inoculation on plant growth survival under environmental stress. Furthermore, the pros and cons of rhizobacterial application along with future directions for the sustainable use of rhizobacteria in agriculture are discussed in depth.

Fentanyl is a powerful opioid anesthetic and analgesic, the use of which has caused an increasing public health threat in the United States and elsewhere. Fentanyl was initially approved and used for the treatment of moderate to severe pain, especially cancer pain. However, recent years have seen a growing concern that fentanyl and its analogs are widely synthesized in laboratories and adulterated with illicit supplies of heroin, cocaine, methamphetamine, and counterfeit pills, contributing to the exponential growth in the number of drug-related overdose deaths. This review summarizes the recent epidemic and evolution of illicit fentanyl use, its pharmacological mechanisms and side effects, and the potential clinical management and prevention of fentanyl-related overdoses. Because social, economic, and health problems that are related to the use of fentanyl and its analogs are growing, there is an urgent need to implement large-scale safe and effective harm reduction strategies to prevent fentanyl-related overdoses.

Graphite nano/microplatelets (GNMPs), which are part of the tetral family (group 14 elements), exhibit significant potential to enhance the mechanical and durability attributes of concrete due to their multi-functional filler properties. Mill scale waste (MSW), is a residual byproduct of the steel industry that can be used in concrete as a replacement for sand, thereby avoiding pollution and depletion of natural resources of aggregates. Although (GNMPs) enhance some of the mechanical properties of concrete and (MSW) has also impact on concrete properties, no information is available, particularly on the influence of both (GNMPs) and (MSW) in concrete. Therefore, in this research, an innovative concrete was prepared by using varying amounts of GNMPs (0%, 0.1%, 0.2%, 0.3%, and 0.4% by weight of cement) and replacement of sand with MSW (0%, 25%, 50%, and 75% by weight of sand). These concrete mixes were tested for properties such as workability, density, compressive strength, splitting tensile strength, flexural strength, and durability. Tests including bulk water sorption and acid attack resistance were conducted on the concrete specimens. The microstructural analysis of concrete with GNMPs and MSW was carried out through scanning electron microscopy (SEM). Results indicated that the addition of (GNMPs) and (MSW) increased concrete density and decreased its slump value when compared to the control mix. The compressive, splitting, tensile, and flexural strengths of the mixes were increased at specified proportions of up to 0.3% GNMPs and 50% MSW content. It also resulted in a reduction of water sorptivity, mass, and compressive strength loss against acid attack. The results demonstrated that MSW and GNMPs can be utilized in concrete for sustainable construction practices.

Studies of accretion disc luminosities and quasiperiodic oscillations around black holes may help us understand the gravitational properties of black hole spacetime. This work is devoted to studying the radiation properties of the accretion disk around the black holes in Kalb–Ramond gravity. We investigate the event horizon of the black hole spacetime and calculate the effective gravitational mass of the spacetime. Also, we analyze the circular motion of test particles in the black hole spacetime. The effects of the black hole charge and KR parameters on the particles’ effective mass, energy, and angular momentum at circular orbits and innermost stable circular orbits are studied. The frequency of Keplerian orbits and the radial and vertical oscillations of the particles along stable orbits are calculated and applied to analyze the existence of QPO in relativistic precession, warped disc, and epicyclic resonance models. QPO orbits’ locations with ratios of upper and lower frequencies of twin-peaked QPOs 3:2, 4:3, and 5:4 are analyzed compared to ISCO. We also obtain constrain values for the black hole mass, charge, KR field parameter, and QPO orbits found using Markovian chain Monte Carlo (MCMC) simulations for stellar mass (XTE J1550, GRS 1915+105), intermediate mass (M82-X1), and supermassive black holes (Sgr A*). Finally, we explore the radiative properties of the accretion disk around the charged black hole in KR gravity, such as the total radiation flux, accretion disc temperature, and differential luminosity.

Environmental problems associated with chemical catalysts to fulfil an ever-increasing energy demand have led to the search for an alternative environment friendly heterogeneous catalyst. If a catalyst being used in the biodiesel production is not environment friendly, then the environment is being contaminated in another way while trying to avoid pollution caused by burning of fossil fuels. The present study reports the use of nano-magnetic catalyst Fe/SnO supported on feldspar for the transesterification of various non-edible feedstocks oil, including Pongamia pinnata (karanja), Carthamus oxyacantha (wild safflower), Citrullus colocynthis (bitter apple), Sinapis arvensis ( wild mustard ) and Ricinus communis (castor). The optimized transesterification parameter was oil to methanol ratio (1:5, 1:10, 1:15, 1:20 and 1:25), catalyst amount (0.5, 1, 1.5, 2, 2.5%), temperature (40, 50, 60, 70 and 80 °C), and reaction times (30, 60, 90, 120 and 150 min). The biodiesel yield was found to be more than 97% for all the tested feedstocks with a maximum biodiesel yield of 98.1 ± 0.6% obtained for bitter apple seed oil under optimum conditions (oil to methanol ratio of 1:10, catalyst amount of 1% at 50 °C for 120 min). The catalysts used for transesterification were magnetically extracted after completion of the reaction. Different physico-chemical parameters like pour point, density, cloud point, iodine value, acid value, saponification and cetane number were determined and the quality of all the biodiesel samples were found to be in the standard range (ASTM D6751 and EN 1404). Different techniques like XRD, FTIR, SEM and EDX were used to characterize the prepared nano-magnetic (Fe/SnO/Feldspar) catalyst.

The extraction of cellulose nanofibers (CNFs) from lignocellulosic biomass provides a sustainable alternative to synthetic materials due to their biodegradability, mechanical strength, and environmental compatibility. However, conventional extraction methods are often affected by high chemical consumption, energy intensity, and limited scalability. This study presents a comparative and optimized approach for the sustainable extraction of CNFs using two distinct methods, including chemo-mechanical treatment and Soxhlet extraction, applied to sugarcane bagasse and eucalyptus bark. Unlike previous studies, this work systematically compares both methods under controlled conditions to evaluate their efficiency, fiber integrity, and environmental impact. The extracted CNFs were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), dynamic light scattering (DLS), and zeta potential analysis. The FTIR spectra confirmed the presence of C-O-C fundamental vibrational stretching of cellulose and effective removal of non-cellulosic components such as lignin and hemicellulose. XRD results displayed the moderate crystalline nature of the extracted cellulose, with variation in intensity attributed to extraction technique and biomass type. Zeta potential analysis showed that CNFs extracted from eucalyptus bark via Soxhlet extraction exhibited superior colloidal stability (-32.5 mV), while those from sugarcane bagasse through chemo-mechanical treatment showed lower stability (-15.3 mV). These findings offer new insights into the method-material interaction and highlight the Soxhlet extraction route as more effective in producing stable, high-purity nanofibers. The protocols can be vital in reducing production costs and chemical utilization, enhancing material performance, and enabling large-scale application in packaging and biomedical industries.

Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Testing gravity theories is an important and interesting issue in relativistic astrophysics using astrophysical observations. In recent years, Event horizon telescope collaboration provided valuable data from shadow of supermassive black holes located at the center of the galaxies Milky Way and Meissner 87, which may help to get information about the spacetime around the black holes. In this work, first, we consider rotating black hole solution for charged black hole with the NUT charge in the presence of clouds of strings, and study the event horizon properties of spacetime around rotating twisting and charged black holes in the cloud of strings and quintessential fields. The null geodesics of the spacetime is derived and the shadow case, radius of the shadow of the black and its distortion, due to the effects of the black hole spin, also analyzed. It is obtained that gravitational effects of black hole charge and quintessential field compensate each other and the rotating black hole’s shadow can be circular. We obtain constraints on charge and spin parameters of supermassive black holes Sgr A ∗ and M87 ∗ using observational data from their shadow (size and observational angle) for various values of the NUT charge, cloud of strings and quintessential field parameters. Moreover, we also investigate gravitational capture cross-section for electromagnetic waves and emission rate of the black hole radiation energy through Hawking evaporation.

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.