Potential of Drought Tolerant Rhizobacteria Amended with Biochar on Growth Promotion in Wheat.

Drought stress is the prime obstacle for worldwide agricultural production and necessitates innovative strategies for enhancing crop resilience. This study explores the efficacy of plant growth-promoting rhizobacteria (PGPR) and biochar (BC) as sustainable amendments for mitigating the effects of drought on wheat growth. Multiple experiments were carried out on isolated strains to assess their drought tolerance potential and multiple plant growth-promoting attributes. Experiments in the laboratory and natural environment were conducted to assess the impact of plant growth-promoting rhizobacteria, biochar, and their synergistic application on various growth parameters of wheat. The results revealed that the drought-tolerant PGPR strains (Bacillus subtilis and Bacillus tequilensis), alongside biochar (rice husk), alleviated the phytotoxic impact of drought by increasing the root length from 17.0% to 70.0% and shoot length from 30.0% to 82.0% as compared to un-inoculated stressed controls. The total chlorophyll and carotenoid contents of the plants were substantially increased to 477% and 423%, respectively, when biochar and PGPR were applied synergistically. Significant enhancements in membrane stability index, relative water content, proline, and sugar level were achieved by combining biochar and bacterial strains, resulting in increases of 19.5%, 37.9%, 219%, and 300%, respectively. The yield of wheat in terms of plant height, spike length, number of spikelets per spike, and number of grains per spike was enhanced from 26.7% to 44.6%, 23.5% to 62.7%, 91.5% to 154%, and 137% to 182%, respectively. It was concluded that the biochar-based application of PGPR induced drought tolerance in wheat under water deficit conditions, ultimately improving the production and yield of wheat.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

Optimal Energy Management System of IoT-Enabled Large Building Considering Electric Vehicle Scheduling, Distributed Resources, and Demand Response Schemes.

In the energy system, various sources are used to fulfill the energy demand of large buildings. The energy management of large-scale buildings is very important. The proposed system comprises solar PVs, energy storage systems, and electric vehicles. Demand response (DR) schemes are considered in various studies, but the analysis of the impact of dynamic DR on operational cost has been ignored. So, in this paper, renewable energy resources and storages are integrated considering the demand response strategies such as real-time pricing (RTP), critical peak pricing (CPP), and time of use (ToU). The proposed system is mapped in a linear model and simulated in MATLAB using linear programming (LP). Different case studies are investigated considering the dynamic demand response schemes. Among different schemes, results based on real-time pricing (58% saving) show more saving as compared to the CPP and ToU. The obtained results reduced the operational cost and greenhouse gas (GHG) emissions, which shows the efficacy of the model.

Application of 0-1 test for chaos on forward converter to study the nonlinear dynamics.

DC-DC converters has significant role in the applied power electronic systems, distributed power systems, computers, home appliances and communication equipment. A converter must remain within the specified range of operation. The main goal of this paper is to discuss the nonlinear behavior of forward converter and highlighted the application of the 0-1 test by applying it on the forward converter. As forward converter may contains electronic components, which cause instability in the system. So, it is necessary to understand its behavior when specifications of components are changed. To study chaotic behavior, 0-1 test will be applied on the forward converter, which is a novel technique outperform in unearthing the subtle chaotic behavior in deterministic dynamical systems. The forward converter goes from period-1, period-2, period-4 and finally become chaotic when the load resistance is varied. This variation in the behavior of the forward converter are analysis through 0-1 test for chaos. Moreover, time series plot, phase portrait and Bifurcation diagram for forward converter is also drawn for the validation of results obtained from 0-1 test. Test algorithm is applied via MATLAB and simulation of forward converter via MultiSim by varying its load resistance.

Scheduling and Sizing of Campus Microgrid Considering Demand Response and Economic Analysis.

Current energy systems face multiple problems related to inflation in energy prices, reduction of fossil fuels, and greenhouse gas emissions which are disturbing the comfort zone of energy consumers and the affordability of power for large commercial customers. These kinds of problems can be alleviated with the help of optimal planning of demand response policies and with distributed generators in the distribution system. The objective of this article is to give a strategic proposition of an energy management system for a campus microgrid (µG) to minimize the operating costs and to increase the self-consuming energy of the green distributed generators (DGs). To this end, a real-time based campus is considered that currently takes provision of its loads from the utility grid only. According to the proposed given scenario, it will contain solar panels and a wind turbine as non-dispatchable DGs while a diesel generator is considered as a dispatchable DG. It also incorporates an energy storage system with optimal sizing of BESS to tackle the multiple disturbances that arise from solar radiation. The resultant problem of linear mathematics was simulated and plotted in MATLAB with mixed-integer linear programming. Simulation results show that the proposed given model of energy management (EMS) minimizes the grid electricity costs by 668.8 CC/day ($) which is 36.6% of savings for the campus microgrid. The economic prognosis for the campus to give an optimum result for the UET Taxila, Campus was also analyzed. The general effect of a medium-sized solar PV installation on carbon emissions and energy consumption costs was also determined. The substantial environmental and economic benefits compared to the present situation have prompted the campus owners to invest in the DGs and to install large-scale energy storage.

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges.

Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.

Optimal Scheduling of Campus Microgrid Considering the Electric Vehicle Integration in Smart Grid.

High energy consumption, rising environmental concerns and depleting fossil fuels demand an increase in clean energy production. The enhanced resiliency, efficiency and reliability offered by microgrids with distributed energy resources (DERs) have shown to be a promising alternative to the conventional grid system. Large-sized commercial customers like institutional complexes have put significant efforts to promote sustainability by establishing renewable energy systems at university campuses. This paper proposes the integration of a photovoltaic (PV) system, energy storage system (ESS) and electric vehicles (EV) at a University campus. An optimal energy management system (EMS) is proposed to optimally dispatch the energy from available energy resources. The problem is mapped in a Linear optimization problem and simulations are carried out in MATLAB. Simulation results showed that the proposed EMS ensures the continuous power supply and decreases the energy consumption cost by nearly 45%. The impact of EV as a storage tool is also observed. EVs acting as a source of energy reduced the energy cost by 45.58% and as a load by 19.33%. The impact on the cost for continuous power supply in case of a power outage is also analyzed.