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Recent Submissions
Economic and Regulatory Aspect of Fungal Bioremediation
(Springer, Cham, 2026) Shom Prakash Kushwaha, Pushpendra Soni, Syed Misbahul Hasan, Kishley Mishra, Alisha Bano, Ahsan Ahmad Khan
Fungal bioremediation is the process of detoxification and degradation of environmental pollutants through the metabolic capability of fungi. It involves heavy metals, hydrocarbons, and persistent organic compounds. Fungi possess diverse enzymatic systems like laccases, peroxidases, and hydrolases that convert contaminants into less toxic forms. It thus serves as an eco-friendly and cost-effective method to reduce pollution. Filamentous fungi are especially suited to wastewater treatment because they can convert organic material directly into proteins, carbohydrates, and valuable byproducts in accordance with circular economy principles. Lifecycle assessments are critical in determining the economic and environmental impacts of fungal-based remediation, mainly when fungi are grown on wastewater for food and feed purposes. Compliance with regulations is important in ensuring the safety and acceptability of products derived from fungi, especially regarding food and agricultural applications. The greatest challenge is still to public perception, and hence education and transparency will be critical to achieve more general acceptance. Market trends indicate massive growth in fungal bioremediation due to tighter environmental regulations, the social responsibility of companies, and increased demand for sustainable technologies. China, the United States, and Europe dominate patents and innovation in this area. Although fungal bioremediation still holds much promise, it still holds hurdles such as scale in operation, significant capital investment, the regulatory barrier, and the variability of environments. Innovations in omics technologies and hybrid remediation models are in process to overcome this limitation. Fungal bioremediation mitigates hazards caused by environment without producing unwanted economically non-viable by-products as it produces value adding byproducts of enzymes, biofertilizers and bio-energy for the mitigation of global environmental challenges.
Contourlet Transform Based Listless Block Cube Tree Coding for Hyperspectral Images
(IEEE, 2026) Shrish Bajpai, Divya Sharma, Naimur Rahman Kidwai
The performance of compression algorithms at low bit rates is a critical benchmark, particularly for hyperspectral imaging where the fidelity of reconstruction is paramount. Although wavelet-based approaches are prevalent in the literature, they frequently present a trilemma of undesirable trade-offs, suffering from insufficient coding efficiency, exorbitant memory requirements, or high computational overhead. The proposed compression algorithm employees advance wavelet transform to leverage both spectral and spatial redundancies found in HS data cubes. Present study explores the utilization of block tree coding algorithm and contourlet transform to compress HS images. The primary goal is to enhance the coding efficiency while minimizing storage and transmission requirements. The proposed compression algorithm is evaluated on four benchmarks against eight state of art other compression algorithms on three performance metrics named coding efficiency, coding memory and coding complexity. In addition, it has low encoding/decoding time than other compression algorithm. From the simulation result, it has been clear that proposed compression algorithm has 2% to 4% increase in coding efficiency compared to the other state of art compression algorithms.
Reinforcement Learning Controlled Variable Speed Limits in Urban Expressway Mixed Traffic
(IEEE, 2026) Alfayez Ahmad, Mohd Sadat, Syed Aqeel Ahmad, Shrish Bajpai, Mehmet Ali Silgu
Increase in Rapid urbanization has intensified traffic congestion, delays, and emissions on urban expressways, revealing the limitations of conventional control strategies. This study aims to develops a Reinforcement Learning (RL) based Variable Speed Limit (VSL) control framework using a Deep QNetwork (DQN) implemented in the Simulation of Urban Mobility (SUMO) environment to enhance traffic efficiency at an expressway merging section. Traffic data used was collected using a video camera recorder and radar speed gun, with vehicle trajectories extracted through the Traffic Data Extractor developed by IIT Bombay. The model implemented was calibrated and validated using field observations from two sitesone representing uninterrupted flow and the other in an onramp merging area. The simulation compared three configurations: a baseline case without control, a conventional rule-based VSL controller, and the proposed DQN-based VSL approach and the findings reveal that the DQN agent achieved a 18.8% reduction in total travel time compared to the baseline, while the rule-based VSL controller worsened performance by 21.7%. The learning-based controller effectively mitigated congestion, reduced shockwave formation, maintained higher average speeds, and improved travel time reliability under dynamic and stochastic traffic conditions demonstrating that a reinforcement learning-driven VSL system can significantly enhance both traffic flow efficiency and user-level reliability, outperforming traditional heuristic control strategies on urban expressways.
Pesticide Contamination in Soil and Water: Type of Pesticide, Route of Expose, and Its Toxic Impacts
(Scrivener Publishing LLC, 2026) Priyanka Yadav, Diksha Singh, Shivani Mishra, Kusum Yadav, Ravi Yadav, Alok Das, Gyanendra Tripathi, Ashish, Alvina Farooqui
One important concept in the sphere of agricultural development is pesticides. Consequently, the use of pesticides results in both acute and chronic toxicities in humans, and their detrimental impacts on the environment along with human health continue to be major concerns in the modern world. Therefore, there needs to be discussion about the methods for applying pesticides, the routes via which they are exposed to them, and the health hazards connected to their use. The health dangers posed by pesticide applications and exposure in developing countries are among the primary concerns. In addition to being physically exposed to pesticides through their work in agriculture and the home, along with other industries, people were also exposed indirectly to them through environmental media, for example, food, trash, and soil. The pesticides were administered to humans orally, respiratorily, and topically. Humans who are indirectly or directly exposed to pesticides may contract the effects of acute toxicity as well as chronic diseases.
Toxicity of Heavy Metals
(Scrivener Publishing LLC, 2026) Priyanka Yadav, Diksha Singh, Shivani Mishra, Kusum Yadav, Ravi Yadav, Alok Das, Gyanendra Tripathi, Ashish
Because of their tendency to persist in the atmosphere, their toxicity, and their capacity to bioaccumulate within our bodies, heavy metals are recognized as environmental contaminants. The pollution of the aquatic and terrestrial ecosystems contaminated with hazardous heavy metals is a major environmental concern, with consequences for public health. Heavy metals are generally found in nature, but some of them are derived from so-called anthropogenic sources. The characterization of heavy metals is based on their toxicity to living organisms and their high atomic mass. Most heavy metals can cause atmospheric and environmental pollution and may also be lethal to humans. The combination of heavy metals with various environmental factors, such as soil, air, and water, as well as human beings, can make them more poisonous. Additionally, other living organisms could come into contact with heavy metals via the food chain.