Enhanced Power Extraction with Sediment Microbial Fuel Cells by Anode Alternation

Sediment microbial fuel cells (SMFCs) are energy harvesting devices where the anode is buried inside marine sediment, while the cathode stays in an aerobic environment on the surface of the water. To apply this SCMFC as a power source, it is crucial to have an efficient power management system, leading to development of an effective energy harvesting technique suitable for such biological devices. In this work, we demonstrate an effective method to improve power extraction with SMFCs based on anodes alternation. We have altered the setup of a traditional SMFC to include two anodes working with the same cathode. This setup is compared with a traditional setup (control) and a setup that undergoes intermittent energy harvesting, establishing the improvement of energy collection using the anodes alternation technique. Control SMFC produced an average power density of 6.3 mW/m2 and SMFC operating intermittently produced 8.1 mW/m2. On the other hand, SMFC operating using the anodes alternation technique produced an average power density of 23.5 mW/m2. These results indicate the utility of the proposed anodes alternation method over both the control and intermittent energy harvesting techniques. The Anode Alternation can also be viewed as an advancement of the intermittent energy harvesting method

Improved barnacle mating optimizer-based least square support vector machine to predict COVID-19 confirmed cases with total vaccination

Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable

Internet of Things (IoT) based ECG System for Rural Health Care

Nearly 30% of the people in the rural areas of Bangladesh are below the poverty level. Moreover, due to the unavailability of modernized healthcare-related technology, nursing and diagnosis facilities are limited for rural people. Therefore, rural people are deprived of proper healthcare. In this perspective, modern technology can be facilitated to mitigate their health problems. ECG sensing tools are interfaced with the human chest, and requisite cardiovascular data is collected through an IoT device. These data are stored in the cloud incorporates with the MQTT and HTTP servers. An innovative IoT-based method for ECG monitoring systems on cardiovascular or heart patients has been suggested in this study. The ECG signal parameters P, Q, R, S, T are collected, pre-processed, and predicted to monitor the cardiovascular conditions for further health management. The machine learning algorithm is used to determine the significance of ECG signal parameters and error rate. The logistic regression model fitted the better agreements between the train and test data. The prediction has been performed to determine the variation of PQRST quality and its suitability in the ECG Monitoring System. Considering the values of quality parameters, satisfactory results are obtained. The proposed IoT-based ECG system reduces the health care cost and complexity of cardiovascular diseases in the future

A hybrid method for analyzing the situation based on cumulative fully vaccinated and confirmed cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters

Gooseneck barnacle optimization algorithm: A novel nature inspired optimization theory and application

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimisation stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimisation outcome

An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approach

SARS-CoV-2 is a multi-organ disease characterized by a wide range of symptoms, which also causes severe acute respiratory syndrome. When it initially began, it rapidly spread from its origin to adjacent nations, infecting millions of people around the globe. In order to take appropriate preventative and precautionary actions, it is necessary to anticipate positive Covid19 instances in order to better comprehend future risk. Therefore, it is vital to build mathematical models that are resilient and have as few prediction mistakes as feasible. This research recommends an optimization based Least Square Support Vector Machines (LSSVM) for forecasting Covid19 confirmed cases along with the daily total vaccination frequency. In this work, a novel hybrid Barnacle Mating Optimizer (BMO) via Gauss Distribution is combined with Least Squares Support Vector Machines algorithm for time series forecasting. The data source consists of the daily occurrences of cases and frequency of total vaccination since 24 February,2021 to 27 July,2022 in Malaysia. LSSVM will thereafter conduct the prediction job with the optimized hyper-parameter values using BMO via gauss distribution. This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters

Design and implementation of intelligent dustbin with garbage gas detection for hygienic environment based on IoT

Rapid population expansion necessitated increased resource use in everyday living. As a result, the pace of trash gen-eration has increased dramatically, affecting the environment's hygiene system and other health concerns. Waste overflows in public spaces, and improved management is necessary. The purpose of this study is to develop a model of an intelligent trashcan for usage in smart cities. Additionally, to identify dangerous gases emitted by dustbins for subsequent management operations, as well as to monitor the amount of trash in the waste bin and warn the municipality through SMS. This system includes two ultrasonic sonar sensors for measuring trash level, a GSM module for sending SMS, three gas sensors for detecting harmful garbage gas, an infrared sensor for counting garbage droplets, and an Arduino Uno for managing all activities. The system notifies you whether the bin is full or empty and can also be controlled by voice command. Additionally, released gas may be monitored to determine the severity of the impairment and to notify the appropriate authorities. Most significantly, it will identify a failed trash drop in the bin and alert the user through alarm for truly considering the reduction of spilled garbage surrounding bins while using the system

Anodic microbial community analysis of microbial fuel cells based on enriched inoculum from freshwater sediment.

Abstract: The characterization of anodic microbial communities is of great importance in the study of microbial fuel cells (MFCs). These kinds of devices mainly require a high abundance of anode respiring bacteria (ARB) in the anode chamber for optimal performance. This study evaluated the effect of different enrichments of environmental freshwater sediment samples used as inocula on microbial community structures in MFCs. Two enrichment media were compared: ferric citrate (FeC) enrichment, with the purpose of increasing the ARB percentage, and general enrichment (Gen). The microbial community dynamics were evaluated by polymerase chain reaction followed by denaturing gradient gel electrophoresis (PCR-DGGE) and real time polymerase chain reaction (qPCR). The enrichment effect was visible on the microbial community composition both during precultures and in anode MFCs. Both enrichment approaches affected microbial communities. Shannon diversity as well as β-Proteobacteria and γ-Proteobacteria percentages decreased during the enrichment steps, especially for FeC (p < 0.01). Our data suggest that FeC enrichment excessively reduced the diversity of the anode community, rather than promoting the proliferation of ARB, causing a condition that did not produce advantages in terms of system performance. Graphical abstract: [Figure not available: see fulltext.]

A novel hybrid evolutionary mating algorithm for Covid19 confirmed cases prediction based on vaccination

Microorganisms may cause illness when they enter the body, multiply, and spread to other parts. The rapid spread of COVID-19 to neighboring countries is examined in this research. Anticipating a positive COVID-19 occurrence helps in determining risks and creating countermeasures. As a result, developing robust mathematical models with small error margins for predictions is crucial. Based on these findings, a combined method of evaluating confirmed cases of COVID-19 with universal immunization is recommended. First, the best hyperparameter values of the RBF kernel-based LSSVM (least square support vector machine) were determined using the most recent Evolutionary Mating Algorithm (EMA). After that, LSSVM will complete the task of prediction. This hybrid method has been utilized for time series forecasting in Malaysia since the country's immunization program against COVID-19 got underway. We evaluate our results next to those of well-known methodologies in nature-inspired metaheuristics

Isolation and characterization of Staphylococcus aureus from raw cow milk in Bangladesh

The study was intended for identification and characterization of Staphylococcus aureus isolated from raw cow milk. A total of 47 milk samples were collected from Sheshmore, Shutiakhali and Bangladesh Agricultural University Dairy Farm, Mymensingh. Using bacteriological, biochemical and PCR-based identification schemes, 12 (25.53%) isolates were confirmed as S. aureus. All the isolates showed ?-hemolysis on 5% sheep blood agar. S. aureus specific nuc gene (target size 279-bp) was amplified in the cases of all isolates. The isolates were found as resistant to Penicillin (100%), Erythromycin (75%) and Amoxicillin (100%). On the other hand, the isolates were sensitive to Ciprofloxacin (83.33%), Oxacillin (100%), Cloxacillin (100%) and Neomycin (100%). The isolated S. aureus showed increased resistance to broad spectrum antibiotic (e.g., Ciprofloxacin). As many people have a tendency to drink raw milk and raw milk products, there is high risk of S. aureus infection in human