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Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

There has been an avalanche of global natural disasters in recent times. In recent years approximately 210 million people were affected, an estimated economic cost of US$153bn was incurred and 68,000 deaths were recorded. This was a work up call that made it imperative for humanitarian actors to impetuously adopt information and communication technologies (ICTs) to timeously assist affected populations in disaster prevention, mitigation response and recovery However, the use of ICTs in the humanitarian field is still at its infancy in most third world countries. The purpose of this study was, therefore, to evaluate the utilization of ICTs in humanitarian relief operations associated with Cyclone Idai in Zimbabwe.

Using a pragmatic approach, the study gathered data using semistructured questionnaires that were triangulated with interviews of humanitarian staff that were involved in Cyclone Idai relief efforts.

An observed suboptimal utilization of ICTs was further disadvantaged by the inequitable distribution of communication infrastructure. However, despite the suboptimal usage, there was a significant positive influence of ICT adoption on effectiveness, efficiency and flexibility in humanitarian relief operations.

Optimal use of ICTs has the potential to revolutionize humanitarian supply chain management. A smooth transition to new technologies is recommended in which personnel are given professional development opportunities on a regular basis.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Severe bacterial infection is a major cause of neonatal morbidity and mortality worldwide. Geographical-based demographic laboratory and clinical data are required to get a conclusion about the bacterial infection and their antibiotic susceptibility for the empiric antibiotic treatment in infants who presented with suspected infection. This study was aimed to find the most prevalent bacterial infection and antibiotic sensitivity among infants in the post-neonatal period presented at a tertiary care centre in South India.

A cross-sectional study was designed among infants (29 days to 1 year old) presented with suspected infection in the paediatric department. Infants with positive culture report were analysed for the bacteriological and antibiotic profile from the medical records. Antibiotic sensitivity was determined for the isolated bacteria according to standard procedure and data statically analysed.

Total of 218 samples (138 male and 80 female) were analysed. Most of the samples (171/218, 78.4%) were throat swab (p = 0.0247). Only one sample was cerebrospinal fluid from case of meningitis. Sample from upper RTI was major (162/218, 74.3%) with male dominance followed by stool samples from cases of diarrhoea (22/218, 10.0%). Staphylococcus aureus was the major organism identified in 46/171 (26.9 %) throat swabs. The most sensitive antibiotic against bacteria isolated from throat swab and CSF was gentamicin and cloxacillin. Netilmicin and piperacillin plus tazobactam were the sensitive antibiotics against bacteria isolated from stool, ear secretion and urine samples.

Upper RTI was the prevalent bacterial infection followed by diarrhoea in infants in the post-neonatal period. Klebsiella pneumoniae was the common organism identified in the overall report followed by E. coli and S. aureus. Community-based awareness should be provided to follow good hygiene regularly in child care. Furthermore, avoid delay in seeking treatment and provide the medicine prescribed at the right time and in the right dose to limit the morbidity and bacterial resistance.

The first objective of this study is to review the mechanism of conducting extra-curricular activities (ECAs) in the open and distance learning (ODL) setting. To achieve this objective, the procedure of ECAs at the Virtual University of Pakistan has been studied. The second objective of this study is to find the impact of ECAs on student engagement.

This is a cross-sectional quantitative study. The questionnaire has been used to collect the data. The purposive sampling technique has been used, while this study's sample size is 970. An independent sample t-test has been used to find the difference between the groups.

This study shows a significant difference between the engagement levels of students who have been part of any ECA at university compared to the students who never participated in any ECA.

The results have been derived from the data gathered from one university only that might hinder the generalizability of the findings. The same study can be conducted in other ODL institutions to authenticate the findings.

This study will help in realizing the policymakers of ODL about the importance of ECCAs. This study has also discussed an existing system of conducting ECCAs in an ODL setting that can be generalized and implemented across all the ODL universities to enhance student engagement.

This study has highlighted the importance of ECAs in ODL institutions that have been neglected since forever. This study is novel because it has highlighted the importance of social interaction of students in ODL and its relation with student engagement that has not been highlighted by any study so far.