Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.

The terms “innovation” and “effectuation” are frequently used but not in the same thought. In this order, publications linking innovation to effectuation are presented and discussed through a methodology based on the publish and perish tool. In the last two decades, effectuation has become an active criterion in entrepreneurship research. However, previous studies do not interconnect effectuation to the different innovation approaches. In order to overcome this gap, this paper focuses on studying innovation in an effectual context and linking different innovation approaches to effectual logic. Indeed, effectuation is a way of thinking that serves entrepreneurs in the processes of opportunity identification and new venture creation. Effectuation includes a set of decision-making principles expert entrepreneurs are observed to employ in situations of uncertainty (as defined in Society for Effectual Action). This article outlines the four most-studied innovation approaches from the date of their apparitions until January 2021: frugal innovation, disruptive innovation, lean start-up, and design thinking. In this context, effectuation as the essence of innovation must be clarified as a method that has similarities and differences with frugal and disruptive innovation, lean start-up, and design thinking. To validate the proposed theorical model, a bibliometrics tool, named “Harzing publish” or “perish”, is used. The main finding of this research affirms that the two most linked innovation approaches to effectuation are “lean start-up” and “design thinking”, compared to “frugal innovation” and “disruptive innovation”. In an entrepreneurial innovation context, design thinking and lean start-up are flexible tools that can stimulate and validate the effectual cycle.

Groundwater (GW) studies have been conducted worldwide with regard to several pressures, including climate change, seawater intrusion, and water overexploitation. GW quality is a very important sector for several countries in the world, in particular for Tunisia. The shallow coastal aquifer of Sfax (located in Tunisia) was found to be under the combined conditions of continuous drop in GW and further deterioration of the groundwater quality (GWQ). This study was conducted to identify the processes that control GWQ mainly in relation to mineralization sources in the shallow Sfax coastal aquifer. To perform this task, 37 wells are considered. Data include 10 physico-chemical properties of groundwater analyzed in water samples: pH, EC, calcium (Ca), sodium (Na), magnesium (Mg), potassium (K), chloride (Cl), sulfate (SO4), bicarbonate (HCO3), and nitrate (NO3), i.e., investigation was based on a database of 370 observations. Principal component analysis (PCA) and hydrochemical facies evolution (HFE) were conducted to extract the main factors affecting GW chemistry. The results obtained using the PCA model show that GWQ is mainly controlled by either natural factors (rock–water interactions) or anthropogenic ones (agricultural and domestic activities). Indeed, the GW overexploitation generated not only the GWQ degradation but also the SWI. The inverse distance weighted (IDW) method, integrated in a geographic information system (GIS), is employed to achieve spatial mapping of seawater intrusion locations. Hydrochemical facies evolution (HFE) results corroborate the seawater intrusion and its spatial distribution. Furthermore, the mixing ratio showed that Jebeniana and Chaffar–Mahares localities are characterized by high SWI hazard. This research should be done to better manage GW resources and help to develop a suitable plan for the exploitation and protection of water resources.

The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigate differences in the number of back pain complaints between the pre-pandemic and during the pandemic. A total of 53,234 and 78,559 tweets were analyzed for November 2019 and November 2020, respectively. Because Twitter users do not always complain explicitly when they tweet about the experience of back pain, we have designed an intelligent filter based on natural language processing (NLP) to automatically classify the examined tweets into the back pain complaining class and other tweets. Analysis of filtered tweets indicated an 84% increase in the back pain complaints reported in November 2020 compared to November 2019. These results might indicate significant changes in lifestyle during the COVID-19 pandemic, including restrictions in daily body movements and reduced exposure to routine physical exercise.