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Enterococcus faecalis is a major nosocomial uropathogen. We report here the epidemiological investigation of drug resistance and in vitro biofilm formation among E. faecalis strains isolated from patients with urinary tract infection in the Charles Nicolle Tunisian hospital from October 2015 to March 2016. The microbial isolates obtained after urine cultivation on CLED agar were identified by Gram staining, oxidase or catalase reaction, and conventional biochemical tests. The susceptibility to antibiotics was determined by the disk diffusion method according to recommended French standard. Biofilm formation was quantified using the microtiter plate assay with crystal violet staining. The 28 E. faecalis urinary isolates showed systematic susceptibility to rifampicin, vancomycin, teicoplanin, and linezolid; high rates of resistance (75 to 96.4%) to erythromycin, tetracycline, sulfamethoxazole-trimethoprim; medium rates of resistance to norfloxacin and gentamicin (57.1%); and low resistance frequencies (3.6 to 7.1%) to ampicillin, imipenem, and chloramphenicol. Multi-resistance to at least three different classes of antibiotics was detected in 20 isolates (71.4%). The patients infected by E. faecalis were predominantly men with a mean age of 47 years, with an underlying disease. All the 28 E. faecalis isolates effectively formed biofilms to varying degrees. The correlation between biofilm formation, multi-resistance to antibiotics, and underlying diseases is discussed.

Bioenergy is a versatile energy system. A multitude of feedstock, technology pathways, and end products encompass biomass-energy conversion. Amongst the renewable energy options, bioenergy has the unique potential to deliver significant benefits to society and the environment. Considering its prominence in the overall energy mix, the bioenergy sector has not received the desired attention. Its impacts on economic, social, environmental and greener energy factors are numerous. Due to the informal and local nature of most of the feedstock and technology used for biofuel production, it is very challenging to gather, analyze and report accurate and updated information on bioenergy developments. Moreso, it is important to note that there is a lack of reliable and updated data on bioenergy globally and locally. This paper, therefore explored available information on different biomass sources; current statistics on global and national bioenergy demand and utilization, its benefits and challenges; technologies for improving bioenergy production and yield from biomass; current climate challenges, and emerging biotechnology techniques relevant for sustainable biofuel production to meet energy demand and climate challenge.

Diarrhoea can be defined as the occurrence of three or more loose, liquid or watery stools or at least one bloody loose stool in a 24h period. Cholera is worldwide problem, especially in developing countries. It has been very rare in industrialized nations for the hundred years, however, the diseases is common today in other part of the world, including the Indian sub-continent and sub-Sahara Africa. This study is to ascertain the prevalence of Escherichiacoli and Vibriocholera in infants 0 – 5 years with diarrhoea attending some private hospitals in Ekpoma. The study population consisted of children between the ages of 0-5 years, presenting with diarrhoea, whose parents gave consent and the exclusion criteria were children under the ages of 0-5 years, whose parents did not give their consent and those above the age range in study. Fifty (50) stool samples were collected from children under 5 years of age visiting some selected hospital in Ekpoma due to acute diarrhoea. Stool samples were collected using sterile stool containers and transferred to the microbiology laboratory of St Kenny Research Consult, Ekpoma on ice packs for laboratory analysis. Stool samples collected from all of the children enrolled in the study were tested for E. coli and Vibrio cholera during the period of the study. There were altogether 50 stool samples processed for Vibrio choleraeand Escherichia coli. Out of 50 diarrhoeal patients (children) studied, 22 (44%) were male and 28 (56%) were female. Majority of the children with diarrhea were between 13 – 24 months 16 (32%), followed by 25 - 36 months 12 (24%), 0 – 12 months 10 (20%), 37 – 48 months 8 (16%) and 49 – 60 months 4 (8%). Among the 4, children within the age of 0 – 12 months had a prevalence of 1 (5.6%), those within the age range of 13 – 24 months had a prevalence of 2 (20%) while those within the age range of 25 – 36 months had a prevalence of 1 (16.7%). While for Vibrio, out of the fifty (50) stool samples examined, three (3) were positive to Vibrio cholerae. Among the 3, one (1) child from within the age of 13 – 24 months, 25 – 36 months and 37 – 48 months. None was found among children within the age range of 0 – 12 months and 49 – 60 months. This study suggests that E. coliare an important diarrhoea pathogen in subjects in Ekpoma. The implication of this is that our health inspectors in the local government areas of the state must be vigilant and ensure that campaigns are mounted to educate our citizens on ways of improving on the unsanitary environment. The presence of antibiotic resistant strains of the E. coli and V. choleraeisolates in the study population indicates the often unnecessary and uninformed use of these drugs in the treatment of most infantile diarrhoea cases.

The rapid growth of the Internet of Things (IoT) has brought numerous conveniences to our lives, but it has also raised significant concerns regarding privacy and security. As an increasing number of devices become interconnected, the data they generate and share can be vulnerable to unauthorized access, hacking, and misuse. In this paper, we delve into the various dimensions of privacy and security challenges in the IoT landscape and examine potential solutions to mitigate these risks. This research paper provides insight into the evolving landscape of IoT technology and its significant implications for personal privacy and data security.

This study introduces an IoT and deep learning-based framework designed to enhance biomedical waste management through real-time monitoring and efficient waste classification. By utilising IoT sensors in conjunction with advanced machine learning models and autoencoders, the system automates waste tracking, timely collection, and the categorisation of hazardous materials. IoT sensors enable continuous data collection on waste generation, supporting pattern analysis to optimise waste management processes. This approach addresses critical challenges in mismanagement, including the prevention of spills, control of odours, and the handling of hazardous waste types. By leveraging real-time data, healthcare facilities can improve the segregation and disposal of BMW, minimising risks to human health and the environment. The integration of these technologies contributes to a cleaner, more sustainable waste management ecosystem, ensuring regulatory compliance and enhancing operational efficiency. Ultimately, the proposed system demonstrates how IoT and AI-driven solutions can revolutionise waste management in healthcare settings, mitigating environmental impacts and improving overall public health outcomes.

This study presents the development of a Feed-Forward Neural Network (FFNN)-based model for security of Internet of Things (IoT) network protocols. The proposed method applied in the execution of the study involves data collection, preprocessing, feature selection, and model training using the CIC-IoT 2022 dataset, which includes normal and attack traffic from various IoT devices. In the study, Synthetic Minority Oversampling Technique (SMOTE) was used for data balancing, Principal Component Analysis (PCA) technique was used for feature selection and hyperparameter optimization were employed to enhance the performance of the model during training. The system was simulated in the NS-3 environment to replicate real-world IoT network conditions, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrated that the FFNN-based model achieves an average validation accuracy of 89.7%, precision of 88.9%, recall of 86.9%, and an F1-score of 87.9%. The system results showcased robustness in detecting various attacks, including DoS, brute force and RTSP attacks in mixed traffic scenarios, meanwhile this study serves as a strong foundation for leveraging deep learning techniques to enhance IoT network security protocols.