Mohammad Hussein Alshirah, Anwar Jiries, Amjad Shatnawi

International Journal of Hydrology • 0

<jats:p>Evaluation of the environmental situation inside Zaatarirefugee camp in terms of water, soil and air was done through classic monitoring as well as by the use of new technique (biofilm) to monitor heavy metal pollution in sewage system at Zaatari camp was done. Major ionic composition was determined for surface runoff, groundwater and wastewater whereas six heavy metals Zn, Mn, Cd, Cr, Cu and Pbwere evaluated for all samples. It was found that salinity of surface runoff decreased with rain events that the highest concentration was found at the beginning of the rainy season where the lowest was found at the end of the season.The salinity of wastewater was related to population density within the camp as it was highest in the oldest part of the camp where high population density exist and the lowest was in the new part of the camp with low population density. Heavy metal concentration in groundwater was low indicating that pollution from the refugee camp did not reach the groundwater resources of the area. All biofilm sampling of the same of wastewater samplingsites was done and it was found to be more efficient in wastewater monitoring as it represent longer period of monitoring than traditional method.For heavy metals concentration in the upper soil showed much higher concentration than lower soil indicating that the source of heavy metals are from the activities within the camp. For air concentration of all heavy metals were very low indicating that there is no source of heavy metals pollution in the area as the camp is located in a desert area and relatively far from major cities.</jats:p>

Mohini Gahlot, Pinaki Ghosh

2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) • 2024

Pneumonia continues to pose a considerable worldwide health burden, contributing significantly to morbidity and death across all age categories. The goal of this thorough Analysis study is to provide a thorough analysis of pneumonia, including information on its Pathophysiology, diagnostics, epidemiology, and treatment techniques. We'll investigate epidemiological elements using machine learning and deep learning such as incidence, prevalence, and risk factors to learn more about the disease's using artificial intelligence regional and demographic differences. The intricate Pathophysiology of pneumonia will be covered in detail, along with how host variables, environmental factors, and microbial agents interact. The merits and limits of various diagnostic procedures, such as sophisticated imaging, laboratory techniques, and clinical evaluation, will be analyzed critically. In addition, the discussion will go over current protocols and recommendations for treating pneumonia, stressing the need of supportive care, antibiotic treatment, and preventative measures. In order to provide physicians, researchers, and policymakers a thorough grasp of this common respiratory ailment, the article will discuss recent trends, difficulties, and future prospects in pneumonia research and clinical practice in using machine learning and deep learning.

Upinder Kaur

Journal of Animal Science • 2024

The emergence of precision livestock farming (PLF) offers a transformative lens through which to prioritize animal welfare. At the heart of this transformation lies the rumen, a complex microbial ecosystem within dairy cows responsible for converting feed into nutrients and impacting cow health, productivity, and environmental footprint. Despite its pivotal role, our understanding of the inner workings of the rumen remains limited due to a lack of real-time monitoring tools. My research leverages AI and robotics in PLF to address this gap, focusing on developing a closed-loop system for continuous, in-vivo rumen monitoring. This system employs cyber-physical systems (CPS) embedded with modular sensors and adaptive algorithms. These sensor modules, powered by AI-based data tracking, autonomously monitor rumen temperature, humidity, pressure, and methane concentration, enabling early detection of imbalances and prompt interventions to maintain optimal rumen health. A key innovation is the self-powered in-vivo robot capable of traversing the rumen’s stratified layers. Guided by AI-powered localization, this robot unlocks unparalleled access to new data, aiding in identifying specific regions associated with conditions like subacute ruminal acidosis (SARA) and methane production. This non-invasive approach eliminates the need for traditional cannulation, minimizing stress on the animal and opening new avenues for animal scientists. The impact of this research extends beyond individual cow health. By optimizing rumen function, we can improve feed efficiency, reduce methane emissions, and promote overall sustainability in dairy farming. Additionally, real-time data insights pave the way for precision nutrition, tailored interventions, and improved disease prevention, directly enhancing animal welfare. This novel approach to rumen monitoring represents a significant step towards realizing the full potential of PLF. By integrating AI and robotics, we can usher in a future where animal welfare and environmental sustainability go hand-in-hand, ultimately contributing to a more responsible and ethical dairy industry.

Vinay Kumar Yadav, Manish Dadhich

Advances in Computational Intelligence and Robotics • 2022

<jats:p>The agriculture science system is facing lots of problems from environmental change. Machine learning (ML) and cyber physical systems (CPS) are the best approaches to overcome the problems by building good and effective solutions. Crop yield prediction includes prediction of yield for the crop by analysing the existing data by considering several parameters like weather, soil, water, and temperature. This project addresses and defines the predicting yield of the crop based on the previous year's data using a linear regression algorithm into which you can type your own text. </jats:p>

Shaun Joseph Smyth, Kevin Curran, Nigel McKelvey

Research Anthology on Smart Grid and Microgrid Development • 2022

<jats:p>The introduction of the 21st century has experienced a growing trend in the number of people who choose to live within a city. Rapid urbanisation however, comes a variety of issues which are technical, social, physical and organisational in nature because of the complex gathering of large population numbers in such a spatially limited area. This rapid growth in population presents new challenges for the already stretched city services and infrastructure as they are faced with the problems of finding smarter methods to deal with issues including: traffic congestion, waste management and increased energy usage. This chapter examines the phenomenon of smart cities, their many definitions, their ability to alleviate the discomforts cities suffer due to rapid urbanisation and ultimately offer an improved and more sustainable lives for the city's citizens. This chapter also highlights the benefits of smart grids, their bi-directional real-time communication ability, and their other qualities. </jats:p>

Kurt Yeager

Smart Grid Handbook • 0

<jats:title>Abstract</jats:title> <jats:p>The transformation of distributed electricity service quality to twenty‐first century digital standards is critical to resolving the serious economic and environmental threats facing the world. The smart distribution grid evolution is enabling the global electricity industry to evolve from the traditional huge centralized power plants to a much more customer‐focused diverse electricity generation, asset ownership, and integration of new, clean distributed energy resources. The results will truly electrify the world in the full meaning of the word.</jats:p> <jats:p>Today's commodity electricity business models of simply lighting and powering captive rate payers have not advanced significantly in over 80 years. Smart twenty‐first century distribution grids will enable consumers to significantly reduce the time, effort, and cost to optimize the use of electricity in every context including economy, environment, and sustainability. This reinvention of the distribution grid will not only be a job‐providing super‐project, but also a protocol for innovation. At the heart of this service equality, transformations are literally thousands of smart microgrids encompassing large buildings, office parks, and entire communities.</jats:p> <jats:p>The technical areas of innovation to enable a timely smart distribution grid have been identified and are within reach worldwide. In the wake of Hurricane Sandy, a number of US states are now actively pursuing this initiative, as are many countries worldwide.</jats:p>

Linfei Yin, Dongduan Liu, Mingshan Mo

• 0

<title>Abstract</title> <p>In the research of renewable energy power generation, tubular grid-connected solid oxide fuel cells with the apparent advantage in voltage regulation have been widely applied in power systems. Recently, a model predictive control has been applied to consider the nonlinear constraints of tubular grid-connected solid oxide fuel cells, which cannot be considered by a proportional-integral-derivative controller. While both model predictive control and proportional-integral-derivative controller achieve only 80% fuel efficiency, which should be improved. An adaptive multistep model predictive control (AMMPC) is proposed to improve the fuel efficiency of tubular grid-connected solid oxide fuel cells and simultaneously consider systemic thermodynamics and electrochemistry constraints. The AMMPC contains the advantages of adaptive control and multistep model predictive control. Both adaptive two-step model predictive control and three-step model predictive control are designed for tubular grid-connected solid oxide fuel cells. With the more accurate prediction ability, the AMMPC improves the fuel efficiency of tubular grid-connected solid oxide fuel cells with higher fuel efficiency (86.5%) than model predictive control (80%) and proportional-integral-derivative (80%). Both feasibility and effectiveness of the AMMPC are verified with high fuel efficiency under both simple and complex power demands cases.</p>

NG Kriefall, MR Kanke, GV Aglyamova et al.

• 0

<jats:title>ABSTRACT</jats:title><jats:p>Corals from more thermally variable environments often fare better under thermal stress compared to those from less thermally variable environments, an important finding given that ocean warming threatens corals worldwide. Evidence is mounting that thermal tolerance can be attributed to the coral itself, as well as microbial communities present within the holobiont (coral host and its associated microorganisms). However, few studies have characterized how thermally variable environments structure multiple holobiont members<jats:italic>in situ</jats:italic>. Here, using 2b-RAD sequencing of the coral and metabarcoding of algal (ITS2) and bacterial (16S) communities, we show evidence that reef zones (locales differing in proximity to shore, physical characteristics, and environmental variability) structure algal and bacterial communities at different scales within a highly connected coral population (<jats:italic>Acropora hyacinthus</jats:italic>) in French Polynesia. Fore reef (more stable) algal communities were on average more diverse than the back reef (more variable), suggesting that variability constrains algal diversity. In contrast, microbial communities were structured on smaller scales with site-specific indicator species and enriched functions across reef zones. Our results illuminate how associations with unique microbial communities can depend on spatial scale across highly dispersive coral populations, which may have fitness consequences in thermally divergent regions and rapidly changing oceans.</jats:p>

Paul Aplin, Doreen Boyd

Remote Sensing • 0

<jats:p>Characterizing and monitoring terrestrial, or land, surface features, such as forests, deserts, and cities, are fundamental and continuing goals of Earth Observation (EO). EO imagery and related technologies are essential for increasing our scientific understanding of environmental processes, such as carbon capture and albedo change, and to manage and safeguard environmental resources, such as tropical forests, particularly over large areas or the entire globe. This measurement or observation of some property of the land surface is central to a wide range of scientific investigations and industrial operations, involving individuals and organizations from many different backgrounds and disciplines. However, the process of observing the land provides a unifying theme for these investigations, and in practice there is much consistency in the instruments used for observation and the techniques used to map and model the environmental phenomena of interest. There is therefore great potential benefit in exchanging technological knowledge and experience among the many and diverse members of the terrestrial EO community. [...]</jats:p>

Seven Siren, Rothna Pec, Channareth Srun et al.

International Journal of Electrical and Electronics Research • 2024

Water is one of the natural resources that may be found in the environment. It is the most essential element of life. All living things, including humans, depend on water to survive, as do other species. Regretfully, water contamination has become a significant worldwide issue because of industrialization and irresponsible consumption. Moreover, low-quality water is poisonous to entire ecosystems due to its hazardous chemical and microbial contents. The issue is made much more dangerous by the lack of accessible tools for monitoring water quality other than costly laboratory tests. In this study, we developed an integrated system to measure water quality using the Internet of Things. Among the sensors, the system makes use of are Turbidity, pH (potential of hydrogen), TDS (Total Dissolved Solids), and water temperature. The embedded system is involved in this project as well. It will help with the establishment of wireless data transmission and sensor device detection. We employed LoRa technology alongside a cellular network ensuring effective communication. Firebase was utilized as the backend platform to securely store and manage the sensor data. The information can be tracked via a mobile or online application. Based on the sensor data, the water environment's quality was assessed and problems with the water's quality were anticipated to prevent the spread of contamination.

Xiaoyan Wu, Shu Wang, Xinnan Wang et al.

Journal of Computational Methods in Sciences and Engineering • 2021

Intelligent underwater pollution cleaning robot is used to release microbial solution which can dissolve into water slowly into polluted river, so that the solution can react fully with pollutants, so as to achieve the purpose of river pollution control. The research of robot wireless monitoring system is based on the comprehensive application of wireless communication technology and intelligent control technology, in order to achieve real-time monitoring and centralized remote control of underwater pollution removal. Through the three-dimensional structure modeling of the intelligent underwater pollution cleaning robot, the overall scheme design and debugging test of the wireless monitoring system, it is proved that the intelligent underwater pollution cleaning robot is feasible in the intelligent and efficient underwater cleaning operation, and it is a research method worthy of reference and promotion.

Miaomiao Zheng, Shanshan Zhang, Yidan Zhang et al.

Complexity • 2021

The Internet of Things is an emerging information industry. Applying the information collection, transmission, and processing technologies in the Internet of Things technology to environmental monitoring, environmental emergency, and other environmental protection supervision fields will greatly improve the speed and accuracy of environmental supervision and facilitate the scientific development of environmental protection. Through the Internet of Things, people can obtain a large amount of reliable real-time information, and it is not easy to be affected by time, place, and environment, while the wireless sensor network has the advantages of easy installation and low cost, so environmental monitoring through the Internet of Things is the future development trend. In this paper, in view of the current situation of water scarcity and serious water pollution in China, combined with the development trend and advantages of the Internet of Things (IoT), and based on the inadequacy of the existing microbial sensor data collection equipment, we propose a design scheme of microbial concentration monitoring system for waters based on IoT. The system is based on Zig Bee wireless sensor network to build a common data acquisition platform and design special hardware to carry out high-precision microbial sensor data acquisition in water and through the PC to complete the real-time measurement data storage, waveform display, and data processing. In this paper, the schematic diagram and PCB board design of the system hardware module NUC120 main control board, CC2530 RF board, Wi-Fi wireless communication module, and high-precision ADC acquisition module are completed and fabricated. Then, the four modules are combined to realize the development of the data aggregation node and data acquisition node of the dedicated Zig Bee wireless network hardware device.

V. Genevskiy, Vivek Chaturvedi, Kristian Thulin et al.

ChemElectroChem • 2025

A wireless potentiometric sensor offers a robust platform for detecting microbial growth, which is crucial for managing infected wounds that can lead to serious complications such as tissue spread, systemic infection, or sepsis, potentially resulting in life‐threatening conditions. Herein, a solid‐state potentiometric working/reference electrode system with a Bluetooth‐enabled system on a chip, supporting continuous wireless monitoring of microbial growth is shown. The sensor monitors open circuit potentials (OCPs) in culture media, which significantly decrease due to bacterial growth after inoculation with Gram‐positive Staphylococcus aureus, Gram‐negative Pseudomonas aeruginosa, and Escherichia coli. Notably, Staphylococcus aureus demonstrates lower electrogenic activity compared with the Gram‐negative bacteria, likely owing to its reduced viability. Following thorough in vitro testing, the sensor is also evaluated ex vivo. Stable connections between the sensor and a smartphone receiver ensure reliable data collection and processing, facilitating remote monitoring. A slight decrease in OCP is observed in rat wounds inoculated with Staphylococcus aureus and significant decrease with Pseudomonas aeruginosa. Incorporation of the wireless sensing module for continuous measurement and data collection can greatly enhance early detection capabilities regarding bacterial infections in wounds. This setup offers a convenient and effective method for point‐of‐care sensing, significantly advancing the management and treatment of wound infections.

SARANYA. S, GOWRI. V

International Journal of Smart Sensor and Adhoc Network. • 2013

<jats:p>Recent technological advances have facilitated the widespread use of wireless sensor networks in many applications such as battle field surveillance, environmental observations, biological detection and industrial diagnostics. In wireless sensor networks, sensor nodes are typically power-constrained with limited lifetime, and so it’s necessary to understand however long the network sustains its networking operations. We can enhance the quality of monitoring in wireless sensor networks by increasing the WSNs lifetime. At the same time WSNs are deployed for monitoring in a range of critical domains such as military, healthcare etc. Accordingly, these WSNs are vulnerable to attacks. Now this proposed work concentrate on maximizing the security of WSNs with the already existing approach (i.e. combination of A* and fuzzy approach) for maximizing the lifetime of WSNs. This paper ensures sensed data security by providing authenticity, integrity, confidentiality. So, this approach provides more effective and efficient way for maximizing the lifetime and security of the WSNs.</jats:p>

Gabriela Marcano, P. Pannuto

Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems • 2021

This demo showcases the power delivery potential of soil-based microbial fuel cells. We build a prototype energy harvesting setup for a soil microbial fuel cell, measure the amount of power that we can harvest, and use that energy to drive an e-ink display. Microbial fuel cells are highly sensitive to environmental conditions, especially soil moisture. In near-optimal, super moist conditions our cell provides approximately 100 &mgr;W of power at around 500 mV, which is ample power over time to power our system several times a day. In sum, we find that the confluence of ever lower-power electronics and new understanding of microbial fuel cell design means that "soil-powered sensors" are now feasible. There remains, however, significant future work to make these systems reliable and maximally performant. This demo is a working copy of the system presented at LP-IoT'21 [6].

Maria Doglioni, Roberto La Rosa, M. Nardello et al.

2024 IEEE SENSORS • 2024

Battery-free Internet of Things devices and sensors are gaining momentum, making energy harvesting an essential component of self-powered systems. Conventional energy harvesting techniques use well-established methods to track the maximum power point during conversion. This approach is not strictly applicable to emerging, more sustainable power sources like Plant Microbial Fuel Cells (PMFCs). This paper addresses the challenge of maximizing power and energy extraction from PMFCs without compromising long-term performance. Maximizing power is complicated by the slow dynamics of the cells and the vulnerability of the biofilm to excessive current extraction. In contrast, maximizing energy is facilitated by cell unloading periods, as constant loading can deteriorate their durability. Electrochemical impedance spectroscopy (EIS) conducted on the cell electrodes provides valuable feedback to determine the most appropriate loading strategy. This approach optimizes PMFC durability and peak power production, while opening up exciting prospects for bio-sensing applications.

Anand V. Sastry, Saugat Poudel, K. Rychel et al.

• 2021

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organism’s transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

G. M. Aleid, Anoud Saud Alshammari, A. Ahmad et al.

Processes • 2023

Energy generation using microbial fuel cells (MFC) and removing toxic metal ions is a potentially exciting new field of study as it has recently attracted a lot of interest in the scientific community. However, MFC technology is facing several challenges, including electron production and transportation. Therefore, the present work focuses on enhancing electron generation by extracting sugarcane waste. MFC was successfully operated in a batch mode for 79 days in the presence of 250 mg/L Pb2+ and Hg2+ ions. Sugarcane extract was regularly fed to it without interruption. On day 38, the maximum current density and power density were recorded, which were 86.84 mA/m2 and 3.89 mW/m2, respectively. The electrochemical data show that a sufficient voltage generation and biofilm formation produce gradually. The specific capacitance was found to be 11 × 10−4 F/g on day 79, indicating the steady growth of biofilm. On the other hand, Pb2+ and Hg2+ removal efficiencies were found to be 82% and 74.85%, respectively. Biological investigations such as biofilm analysis and a recent literature survey suggest that conductive-type pili species can be responsible for energy production and metal removal. The current research also explored the oxidation method of sugarcane extract by bacterial communities, as well as the metal removal mechanism. According to the parameter optimization findings, a neutral pH and waste produced extract can be an optimal condition for MFC operation.

E. Sudirjo, P. D. Jager, C. Buisman et al.

Sensors • 2019

A Plant Microbial Fuel Cell (Plant-MFCs) has been studied both in the lab and in a field. So far, field studies were limited to a more conventional Plant-MFC design, which submerges the anode in the soil and places the cathode above the soil surface. However, for a large scale application a tubular Plant-MFC is considered more practical since it needs no topsoil excavation. In this study, 1 m length tubular design Plant-MFC was installed in triplicate in a paddy field located in West Kalimantan, Indonesia. The Plant-MFC reactors were operated for four growing seasons. The rice paddy was grown in a standard cultivation process without any additional treatment due to the reactor instalation. An online data acquisition using LoRa technology was developed to investigate the performance of the tubular Plant-MFC over the final whole rice paddy growing season. Overall, the four crop seasons, the Plant-MFC installation did not show a complete detrimental negative effect on rice paddy growth. Based on continuous data analysis during the fourth crop season, a continuous electricity generation was achieved during a wet period in the crop season. Electricity generation dynamics were observed before, during and after the wet periods that were explained by paddy field management. A maximum daily average density from the triplicate Plant-MFCs reached 9.6 mW/m2 plant growth area. In one crop season, 9.5–15 Wh/m2 electricity can be continuously generated at an average of 0.4 ± 0.1 mW per meter tube. The Plant-MFC also shows a potential to be used as a bio sensor, e.g., rain event indicator, during a dry period between the crop seasons.

N. Chandra, P. Patra, R. Fujita et al.

Communications Earth &amp; Environment • 2024

Methane (CH4) emission reduction to limit warming to 1.5 °C can be tracked by analyzing CH4 concentration and its isotopic composition (δ13C, δD) simultaneously. Based on reconstructions of the temporal trends, latitudinal, and vertical gradient of CH4 and δ13C from 1985 to 2020 using an atmospheric chemistry transport model, we show (1) emission reductions from oil and gas exploitation (ONG) since the 1990s stabilized the atmospheric CH4 growth rate in the late 1990s and early 2000s, and (2) emissions from farmed animals, waste management, and coal mining contributed to the increase in CH4 since 2006. Our findings support neither the increasing ONG emissions reported by the EDGARv6 inventory during 1990–2020 nor the large unconventional emissions increase reported by the GAINSv4 inventory since 2006. Total fossil fuel emissions remained stable from 2000 to 2020, most likely because the decrease in ONG emissions in some regions offset the increase in coal mining emissions in China.

Paloma Medina, Shelbi L. Russell, Russell Corbett-Detig

PLOS ONE • 2023

Bacterial symbionts that manipulate the reproduction of their hosts are important factors in invertebrate ecology and evolution, and are being leveraged for host biological control. Infection prevalence restricts which biological control strategies are possible and is thought to be strongly influenced by the density of symbiont infection within hosts, termed titer. Current methods to estimate infection prevalence and symbiont titers are low-throughput, biased towards sampling infected species, and rarely measure titer. Here we develop a data mining approach to estimate symbiont infection frequencies within host species and titers within host tissues. We applied this approach to screen ~32,000 publicly available sequence samples from the most common symbiont host taxa, discovering 2,083 arthropod and 119 nematode infected samples. From these data, we estimated that Wolbachia infects approximately 44% of all arthropod and 34% of all nematode species, while other reproductive manipulators only infect 1–8% of arthropod and nematode species. Although relative titers within hosts were highly variable within and between arthropod species, a combination of arthropod host species and Wolbachia strain explained approximately 36% of variation in Wolbachia titer across the dataset. To explore potential mechanisms for host control of symbiont titer, we leveraged population genomic data from the model system Drosophila melanogaster. In this host, we found a number of SNPs associated with titer in candidate genes potentially relevant to host interactions with Wolbachia. Our study demonstrates that data mining is a powerful tool to detect bacterial infections and quantify infection intensities, thus opening an array of previously inaccessible data for further analysis in host-symbiont evolution.

R. Preen, Jiseon You, L. Bull et al.

Soft Computing • 2016

Microbial fuel cells (MFCs) perform wastewater treatment and electricity production through the conversion of organic matter using microorganisms. For practical applications, it has been suggested that greater efficiency can be achieved by arranging multiple MFC units into physical stacks in a cascade with feedstock flowing sequentially between units. In this article, we investigate the use of cooperative coevolution to physically explore and optimise (potentially) heterogeneous MFC designs in a cascade, i.e. without simulation. Conductive structures are 3D-printed and inserted into the anodic chamber of each MFC unit, augmenting a carbon fibre veil anode and affecting the hydrodynamics, including the feedstock volume and hydraulic retention time, as well as providing unique habitats for microbial colonisation. We show that it is possible to use design mining to identify new conductive inserts that increase both the cascade power output and power density.

Josephine Giard, Jennifer Pratscher, Leena Kerr

Access Microbiology • 2022

<jats:p>There is an urgent need for new antimicrobials due to constantly advancing antimicrobial resistance. Here, we worked with environmental samples from diverse habitats including different savannah and forest soils, volcanic caves, and termite mounds and assessed their microbial communities for the potential of biosynthesis of secondary metabolites. We analysed and compared microbial composition by applying the QIIME2 pipeline to 16S rRNA gene data. We focused on the abundance of Actinobacteria and Streptomyces as they are important producers of antimicrobials. Out of the samples analysed, the highest abundance of Actinobacteria was found in termite mound and volcanic cave samples. Moreover, the termite mound samples also had the highest abundance of Streptomyces. When comparing microbial composition, soil samples and termite mound samples each formed their own clusters, but volcanic cave samples appeared more dispersed. We assessed the antimicrobial potential of a subset of samples by analysing metagenomic data to predict biosynthetic gene clusters (BGCs) using antiSMASH5.2.0, which resulted in over 800 hits per sample. This number was narrowed down by evaluating identified BGCs based on antimicrobial potential, completeness, size, presence/absence of regulatory and transport-related genes, and dissimilarity with known BGCs. This resulted in an average of 20 BGCs per sample. These BGCs will be subjected to further sequence-based analyses before attempting heterologous expression. Following successful expression, antimicrobial potential will be assessed by screening for growth inhibition of multidrug resistant E.coli strains and the ESKAPE pathogens.</jats:p>

Chi Liu, Yaoming Cui, Xiangzhen Li et al.

FEMS Microbiology Ecology • 2021

<jats:title>ABSTRACT</jats:title> <jats:p>A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).</jats:p>

A. Shaheen, A. Elsayed, R. El-Sehiemy et al.

Engineering Optimization • 2022

This article proposes an enhanced quasi-reflection jellyfish optimization (QRJFO) algorithm for solving the optimal power flow (OPF) problem. The multi-dimension objective functions are the fuel costs, transmission losses and pollutant emissions. Despite the simple structure of the jellyfish optimization algorithm, it requires significant exploitation and exploration control characteristics to support its capability. In the proposed QRJFO, a cluster is chosen randomly for every jellyfish from the population to reflect the social group that shares information in it. It varies from one to the next. The exploration phase is supported by introducing quasi-opposition-based learning. The performance of the proposed QRJFO algorithm is evaluated on the IEEE 57-bus, practical West Delta Region system and large-scale IEEE 118 bus. The simulation results demonstrate the quality of the solution and resilience of QRJFO. It is very significant for operating power systems from economic, technical and environmental perspectives.

Mara Stadler, Roberto Olayo-Alarcon, Jacob Bien et al.

• 0

<jats:title>Abstract</jats:title><jats:p>Microbial interactions are of fundamental importance for the functioning and the maintenance of microbial communities. Deciphering these interactions from (time-series) observational data or controlled lab experiments remains a formidable challenge due to their context-dependent nature, such as, e.g., (a)biotic factors, host characteristics, and overall community composition. Complementary to the classical ecological view, recent research advocates an empirical “community-function landscape” framework where an outcome of interest, e.g., a community function, is learned via statistical regression models that include pairwise or higher-order<jats:italic>statistical</jats:italic>species interaction effects. Here, we adopt the latter viewpoint and present penalized quadratic interaction models that can accommodate all common microbial data types, including microbial presence-absence data, relative (or compositional) abundance data from microbiome surveys, and quantitative (absolute abundance) microbiome data. We propose novel interaction models for compositional data and bring modern statistical techniques such as hierarchical interaction constraints and stability-based model selection to the microbial realm. To illustrate our framework’s versatility, we consider prediction tasks across a wide range of microbial datasets and ecosystems, including butyrate production in model communities in designed experiments and environmental covariate prediction from marine microbiome data. We show improved predictive performance of these interaction models and assess their limits in the presence of extreme data sparsity. On a large-scale gut microbiome cohort data, we identify interaction models that can accurately predict the abundance of antimicrobial resistance genes, enabling novel biological hypotheses about microbial community composition and antimicrobial resistance.</jats:p><jats:sec><jats:title>Author Summary</jats:title><jats:p>Microbes live in complex communities where interactions between species shape their function and stability. Understanding these interactions is crucial for predicting how microbial communities respond to environmental changes, medical treatments, or shifts in their host organisms. However, identifying these relationships is challenging because they depend on many factors, including the surrounding environment and community composition. In this study, we introduce a new statistical modeling approach to uncover microbial interactions from different types of data, including presence-absence patterns, relative abundance from microbiome surveys, and absolute abundance measurements. Our method builds on modern statistical techniques to improve accuracy and reliability, even when data are sparse or noisy. We demonstrate the power of our approach by applying it to diverse microbial datasets, from marine ecosystems to gut microbiomes. In one case, we successfully predicted antimicrobial resistance gene abundance based on microbial interactions, opening new avenues for understanding how resistance spreads in microbial communities. By advancing statistical tools for microbiome research, our work provides a new way to explore the hidden relationships between microbes, with potential applications in medicine, environmental science, and biotechnology.</jats:p></jats:sec>

Zhang Cheng, Weibo Xia, Sean McKelvey et al.

• 0

<jats:title>Abstract</jats:title><jats:p>Modeling microbial communities can provide predictive insights into microbial ecology, but current modeling approaches suffer from inherent limitations. In this study, a novel modeling approach was proposed to address those limitations based on the intrinsic connection between the growth kinetics of guilds and the dynamics of individual microbial populations. To implement the modeling approach, 466 samples from four full-scale activated sludge systems were retrieved from the literature. The raw samples were processed using a data transformation method that not only increased the dataset size by three times but also enabled quantification of population dynamics. Most of the 42 family-level core populations showed overall dynamics close to zero within the sampling period, explaining their resilience to environmental perturbation. Bayesian networks built with environmental factors, perturbation, historical abundance, population dynamics, and mechanistically derived microbial kinetic parameters classified the core populations into heterotrophic and autotrophic guilds. Topological data analysis was applied to identify keystone populations and their time-dependent interactions with other populations. The data-driven inferences were validated directly using the Microbial Database for Activated Sludge (MiDAS) and indirectly by predicting population abundance and community structure using artificial neural networks. The Bray-Curtis similarity between predicted and observed communities was significantly higher with microbial kinetic parameters than without parameters (0.70 vs. 0.66), demonstrating the accuracy of the modeling approach. Implemented based on engineered systems, this modeling approach can be generalized to natural systems to gain predictive understandings of microbial ecology.</jats:p>

Jaron C. Thompson, Victor M. Zavala, Ophelia S. Venturelli

• 0

<jats:title>Abstract</jats:title><jats:p>Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behaviors of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it outperforms existing machine learning methods in the prediction of experimentally measured species abundance and metabolite concentrations. Further, we present an experimental design algorithm to select a set of experimental conditions that simultaneously maximize the expected gain in information and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions.</jats:p><jats:sec><jats:label>1</jats:label><jats:title>Author summary</jats:title><jats:p>The functions performed by microbiomes hold tremendous promise to address grand challenges facing society ranging from improving human health to promoting plant growth. To design their properties, flexible computational models that can predict the temporally changing behaviors of microbiomes in response to key environmental parameters are needed. When considering bottom-up design of microbiomes, the number of possible communities grows exponentially with the number of organisms and environmental factors, which makes it challenging to navigate the microbiome function landscape. To overcome these challenges, we present a physically constrained machine learning model for microbiomes and a Bayesian experimental design framework to efficiently navigate the space of possible communities and environmental factors.</jats:p></jats:sec>

R. Barbato, Robert M. Jones, Michael A. Musty et al.

PLOS ONE • 2021

Electrogenic bacteria produce power in soil based terrestrial microbial fuel cells (tMFCs) by growing on electrodes and transferring electrons released from the breakdown of substrates. The direction and magnitude of voltage production is hypothesized to be dependent on the available substrates. A sensor technology was developed for compounds indicative of anthropological activity by exposing tMFCs to gasoline, petroleum, 2,4-dinitrotoluene, fertilizer, and urea. A machine learning classifier was trained to identify compounds based on the voltage patterns. After 5 to 10 days, the mean voltage stabilized (+/- 0.5 mV). After the entire incubation, voltage ranged from -59.1 mV to 631.8 mV, with the tMFCs containing urea and gasoline producing the highest (624 mV) and lowest (-9 mV) average voltage, respectively. The machine learning algorithm effectively discerned between gasoline, urea, and fertilizer with greater than 94% accuracy, demonstrating that this technology could be successfully operated as an environmental sensor for change detection.

Peng Wang, Shangkun Liu, Yong He et al.

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<jats:p>Soil microbial necromass carbon (MNC) is an important component of soil organic carbon (SOC) in croplands. Microbial communities contribute over 50% of the SOC in croplands through continuous turnover and the formation of necromass, which is characterized by its large scale and strong persistence. Agricultural production systems are widely influenced by human activities. There is still a lack of understanding regarding key issues such as the dynamics of MNC and SOC under management practices and their global distribution potential. In this study, we combined meta-analysis with machine learning methods, revealed the impact patterns of cropland management on soil MNC components and SOC. The results showed that the MNC storage reached the highest value of 5.93 Mg C ha&amp;#8315;&amp;#185; under the practice of mineral fertilizer combined with manure. The fungal necromass carbon storage in cropland soils is much higher than that of bacterial necromass carbon, which dominates the changes in microbially-derived organic carbon storage. Assessment results of global potential distribution patterns of MNC and SOC storage under management practices based on machine learning indicate that conservation tillage has the highest global carbon storage potential, reaching up to 2.58 Mg C ha&amp;#8315;&amp;#185; yr&amp;#8315;&amp;#185; and 3.55 Mg C ha&amp;#8315;&amp;#185; yr&amp;#8315;&amp;#185;. This study emphasizes the impact and importance of soil microorganisms in croplands as a driving force on SOC storage, accurately quantifies their response to management practices, and comprehensively evaluates the application potential of different management practices on a global scale, enhancing our understanding of the relationship patterns between MNC and SOC in agricultural systems.</jats:p>

Chenxi Ji

SNAME 26th Offshore Symposium • 2021

<jats:p>The prediction of marine fuel consumption and ship exhaust gas emissions are indispensable to evaluating ship sustainable performance under current shipping fuel standards. Big data with evolved machine learning techniques have been proved to be an effective way to contain uncertainties for ship activities. This work collects the latest global LNG carrier fleet with 435 data points and attempts to predict the marine fuel consumptions and ship-resulted global warming potential (GWP) gas emissions, including CO2, CH4, N2O, and black carbon aerosols. Gaussian process regression and ensemble machine learning approaches, to achieve this goal, are employed to infer the relationship between predictors (i.e., dimensional parameters, machinery parameters, and tonnage) and response variables (fuel consumptions and GWP exhaust gas emissions), providing exceptional insight into ship sustainable solutions. To improve the prediction accuracy, the hyperparameter optimization analysis via random search and Bayesian optimization is adopted to find the optimal machine learning model. The appealing results are in line with the validation data, illustrating high effectiveness and robustness of the proposed machine learning models. The procedure established in this study presents a novel approach for accelerating the research and development of sustainable shipping fuels under normal ship activities.</jats:p>

P. Bhatt, A. Verma, S. Gangola et al.

Microbial Cell Factories • 2021

The large-scale application of organic pollutants (OPs) has contaminated the air, soil, and water. Persistent OPs enter the food supply chain and create several hazardous effects on living systems. Thus, there is a need to manage the environmental levels of these toxicants. Microbial glycoconjugates pave the way for the enhanced degradation of these toxic pollutants from the environment. Microbial glycoconjugates increase the bioavailability of these OPs by reducing surface tension and creating a solvent interface. To date, very little emphasis has been given to the scope of glycoconjugates in the biodegradation of OPs. Glycoconjugates create a bridge between microbes and OPs, which helps to accelerate degradation through microbial metabolism. This review provides an in-depth overview of glycoconjugates, their role in biofilm formation, and their applications in the bioremediation of OP-contaminated environments.

A. Mobinikhaledi, Najmieh Ahadi, M. Haseli

Organic Preparations and Procedures International • 2022

Multi-component reactions (MCRs) are useful and powerful tools for the reduction of environmental pollution and the elimination of problematic intermediate steps in the synthesis of organic compounds. They have the advantages of short reaction times, little forma-tion of by-products, high yield, and atom economy. MCR reactions have attracted much attention for the synthesis of heterocyclic compounds, including benzopyrans. 1 – 4 The latter have long been known for their important biological properties, 5 including anti-HIV, 6 general anti-microbial, 7 and anti-fungal 8 properties. Several research investigations have been reported for the synthesis of benzopyrans in the presence of catalysts, and of these we may particularly note Fe 3 O 4 @ MCM-41 @ Zr-piperazine magnetite nanocatalyst, 9 sodium polyas-partate-functionalized silica-coated magnetic nanoparticles (MNPs-SPAsp), 10 [ c -Fe 2 O 3 @ HAp-Si (CH 2 ) 3 BF 4 @ DMIM] MNPs, 11 ZIF @ ZnTiO 3 nanocomposite, 12 and SiO 2 / H 3 PW 12 O 40 . 13 The design and use of green catalysts for the synthesis of benzopyrans con-tinues to be a current topic of wide interest in synthetic organic chemistry. Spinel are 14 15 16 17 19

Kay Yeoman, Beatrix Fahnert, David Lea-Smith et al.

Microbial Biotechnology • 2020

<p>This chapter examines the important role microbes play in the future of sustainable agriculture. It looks into microbes as biological control agents and the use of bacteria as genetic tools and microbial inoculants. The challenge of microbial biotechnology and agriculture involves combating climate change, plant pathogens, and sustainable living. The chapter then cites the limitation of biological control agents as a result of their ability to control a narrow range of pests, slow action, and short field life. It also presents microbes that are used as tools in genetic modification, referencing the <italic>Agrobacterium</italic>-mediated system for the genetic modification of plants.</p>

Kay Yeoman, Beatrix Fahnert, David Lea-Smith et al.

Microbial Biotechnology • 2020

<p>This chapter discusses microbial growth, and looks particularly at how microorganisms grow in liquid culture. The usefulness of microbes in biotechnology applications originates from their ability to convert low-cost substrates into higher-value products. The key to optimizing production is understanding how microbes interact with and grow in the substrates provided, and how microbial metabolism is linked to product formation. Moreover, yield and productivity are important parameters that can be determined from culture experiments. The chapter then looks at how to model bacterial growth in bioreactors before considering the differences between continuous cultures and fed-batch cultures. It then highlights the importance of productivity and maintenance in fermentation.</p>

Amir Zamani, Brij Maini, Pedro Pereira Almao

Canadian Unconventional Resources Conference • 2011

<jats:title>Abstract</jats:title><jats:p>This paper presents results of an experimental study that systematically examined the propagation of nanodispersed catalyst suspension in sand packs at Athabasca reservoir conditions. The concentration and size distribution of the particles at the injection and production end were measured. The pressure drops in different segments along length of the sand pack were monitored continuously. The retention behavior of particles at the end of each experiment was examined by measuring the catalyst concentration in the bed as a function of the distance from injection end of the sand pack and also by analysis of extracted samples using scanning electron microscopy.</jats:p><jats:p>This research is a part of a large multidisciplinary effort aimed at developing a nanoparticles based process for in situ upgrading of heavy oil by catalytic hydrogenation during thermal recovery processes. An essential element of such in situ upgrading is the placement of nanodispersed catalyst particles deep into the formation where it can accelerate the high temperature upgrading reactions. Therefore, an understanding of the propagation behavior of nanoparticles in reservoir sand is essential for developing such technology. The results of this work would also be useful for modeling any other process involving transport of nanoparticles through porous media.</jats:p><jats:p>The results show that it is possible to propagate the nanodispersed catalyst suspension through sand beds without causing permeability damage but a small fraction of the injected particles are retained in the sand. It was found that much higher retention occurs in the entrance region of the bed and such retention was higher in the Athabasca sand beds than in clean silica sand with the same flow and suspension properties. A modified deep bed filtration model was developed to history match the macroscopic propagation behavior of suspended particles in sand beds.</jats:p><jats:p>To best of our knowledge, this is the first experimental study on transport of nanoparticles dispersed in viscous oil through sand beds. It provides valuable information on propagation and retention behavior of nanoparticles. Considering the rapidly rising use of nanoparticles in industry, such transport will be encountered in many industrial applications and environmental problems.</jats:p>

Piu Das, Bapan Bairy, Sanjukta Ghosh et al.

Advances in Natural Sciences: Nanoscience and Nanotechnology • 2023

<jats:title>Abstract</jats:title> <jats:p>The green synthetic approaches are the alternative methods for the preparation of various types of nanoparticles to keep sustainable evolution. A novel green synthesis of gold- reduced graphene oxide nanocomposites was conducted through simple heating method using <jats:italic>Alstonia scholaris (A. scholaris)</jats:italic> bark extract. There are several techniques that confirm the formation of the nanocomposites for synthesis of gold nanoparticles on reduced graphene oxide (RGO), such as X-ray diffraction (XRD), UV–visible spectroscopy (UV–vis) and Fourier transformed infrared spectroscopy (FT-IR). The size distributions of the gold nanoparticles (Au NPs) grown on RGO surface was measured using two different methods: particle distribution study and transmission electron microscopy (TEM) image. These two methods provided similar size distribution which is around 5–8 nm. Subsequently, the catalytic performance was evaluated by 4-nitro aniline (4-NA). The photocatalytic activities were investigated using different organic hazardous dyes, such as methylene blue (MB), methyl orange (MO) and the change of photocatalytic behaviour was shown by varying the catalyst amount and pH. The chemical oxygen demand (COD) analyses for complete removal of organic dye were carried out using the two nanocomposite samples. To perceive the effect on different bacterial strains, antibacterial and antiprotozoal studies have been carried out with this nanocomposite.</jats:p>

Lu Lu, Zhida Li, Xi Chen et al.

SSRN Electronic Journal • 2020

The photoelectrochemical (PEC) CO2 reduction to syngas is an attractive strategy for solar to fuel conversion, however, the high overpotential, inadequate selectivity, and high cost call for alternative solutions. Here we demonstrate a hybrid microbial photoelectrochemical (MPEC) system which contains a microbial anode capable of oxidizing free waste organics in wastewater and reducing the oxidation potential by 1.1 V, compared to abiotic water oxidation on PEC anode. Moreover, the MPEC employs a power management circuit (PMC) to enable several low-energy producing reactions operated in the same solution medium to conquer high-overpotential reactions. The nanowire silicon photocathode integrated with a selective single-atom Nickel catalyst (Si NW/Ni SA) achieved up to ~80% Faradaic efficiency for CO generation with a highly tunable CO:H2 generation ratio (0.1 to 6.8). When the bioanode couple with the Si NW/Ni SA, up to 1.1 mA cm−2 spontaneous photocurrent density can be accomplished for syngas generation.

Hocheol Gwac, Yongwoo Jang, J. Moon et al.

Advanced Materials Technologies • 2024

Wearable ion‐selective potentiometric sensors have received considerable interest in enabling taste sensing in robots and for monitoring abnormal conditions, such as poor water quality, spoiled food (freshness), and microbial contamination. Despite advances in wearable ion‐selective sensors, the production of a stretchable and miniaturized ion‐selective sensor to detect various ions remains a challenge for practical applications. Herein, a stretchable multi‐ply potentiometric sensor is reported based on ion‐selective coiled yarn (ISCY) with Carbon nanotube. Three types of ISCYs show high sensitivity and selectivity toward a specific target ion, such as K+, Na+, and H+. The sensitivity and selectivity are maintained even at 27% strain and under mechanical deformation, such as being bent by 180° or tied into a knot. Furthermore, an attempt is made to miniaturize the sensor into a single fiber by plying three types of ISCYs and a reference electrode together. This multi‐ion potentiometric sensor is successfully woven into fabrics, such as clothes or gloves, and exhibits a functional sensing performance in various water‐based solutions (sea, river, tap, and distilled waters) and fruit juices as practical applications. These results suggest that this potentiometric sensor has a high potential application as a taste sensor and a monitoring sensor in an electronic tongue.

V. Sanderford, B. Barna, R. Barrington et al.

Journal of Nanomedicine &amp; Nanotechnology • 2020

Background The pathological consequences of interaction between environmental carbon pollutants and microbial antigens have not been fully explored. We developed a murine model of multi-wall carbon nanotube (MWCNT)-elicited granulomatous disease which bears a striking resemblance to sarcoidosis, a human granulomatous disease. Because of reports describing lymphocyte reactivity to mycobacterial antigens in sarcoidosis patients, we hypothesized that addition of mycobacterial antigen (ESAT-6) to MWCNT might elicit activation in T cells. Methods Macrophage-specific peroxisome-proliferator-activated receptor gamma (PPARγ) knock out (KO) mice were studied along with wild-type mice because our previous report indicated PPARγ deficiency in sarcoidosis alveolar macrophages. MWCNT+ESAT-6 were instilled into mice. Controls received vehicle (surfactant-PBS) or ESAT-6 and were evaluated 60 days post-instillation. As noted in our recent publication, lung tissues from PPARγ KO mice instilled with MWCNT+ESAT-6 yielded more intensive pathophysiology, with elevated fibrosis Results Inspection of mediastinal lymph nodes (MLN) revealed no granulomas but deposition of MWCNT. MLN cell counts were higher in PPARγ KO than in wild-type instilled with MWCNT+ESAT-6. Moreover, the CD4:CD8 T cell ratio, a major clinical metric for human disease, was increased in PPARγ KO mice. Bronchoalveolar lavage (BAL) cells from PPARγ KO mice instilled with MWCNT+ESAT-6 displayed increased Th17 cell markers (RORγt, IL-17A, CCR6) which associate with elevated fibrosis. Conclusion These findings suggest that PPARγ deficiency in macrophages may promote ESAT-6-associated T cell activation in the lung, and that the MWCNT+ESAT-6 model may offer new insights into pathways of lymphocyte-mediated sarcoidosis histopathology.