Influences of straw alkaline pretreatment on biogas production and digestate characteristics: artificial neural network and multivariate statistical techniques.

Anaerobic digestion is one of the best options for producing valuable end products (biogas and biofertilizer). The aim of this study was to investigate the influences of thermoalkaline pretreatment of wheat straw on biogas production and digestate characteristics from codigestion with waste-activated sludge. Different alkaline conditions (NaOH, KOH and Na2CO3) and pretreatment durations (1, 3 and 5 h) were used for straw pretreatment. Batch anaerobic codigestion of sludge and pretreated straw was conducted under different pretreatment conditions. A feedforward neural network (FFNN) model, logistic model and statistical analysis were applied to the experimental data to predict biogas and investigate the significance and relationships among the variables. NaOH pretreatment for 5 h showed the best treatment conditions: biogas yield was 6.59 times higher than that without treatment. Moreover, the proportions of total solids, total volatile solids, chemical oxygen demand and microbial count removed reached 63.52%, 74.60%, 78.15% and 82.22%, respectively. The methane content was 67.50%, indicating that the biogas had a high quality. The thermoalkaline pretreatment significantly affected biogas production and digestate characteristics, allowing it to be used as a biofertilizer. Experimental data were successfully modelled for predicting biogas production using the applied models. The R2 values reached 0.985 and 0.999 for the logistic and FFNN models, respectively.

Streptococcus oralis-Induced Meningoencephalitis and Ventriculitis in a Geriatric Female Patient.

A 71-year-old female with a history of hypertension, diabetes, and dyslipidemia developed altered mental status, fever, headache, and vomiting. Subsequent evaluation revealed meningoencephalitis and ventriculitis due to Streptococcus oralis, which was found to be ceftriaxone-sensitive. The patient's condition improved with ceftriaxone treatment, leading to complete recovery. This case underscores the significance of including Streptococcus oralis in the differential diagnosis of meningitis or encephalitis.

The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis.

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications.

Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things.

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19.

The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods.

A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors.

Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human-computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%.

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models.

Condition monitoring (CM) is a useful application in industry 4.0, where the machine's health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system's design to decide timely on the required sensors.

Action Recognition Using Single-Pixel Time-of-Flight Detection.

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject's privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47 % accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

Mercury accumulation in Lethrinus nebulosus from the marine waters of the Qatar EEZ.

Total mercury (THg) and methylmercury (MeHg) were recorded in the commercial demersal fish Lethrinus nebulosus, caught from six locations in Qatar EEZ (Exclusive Economic Zone). Concentrations of THg decreased in the order: liver˃muscle˃gonad. THg concentrations in fish tissue ranged from 0.016ppm in gonad to 0.855ppm (mgkg-1w/w) in liver tissues, while concentrations in muscle tissue ranged from 0.24 to 0.49ppm (mgkg-1w/w) among sampling sites. MeHg concentrations were used to validate food web transfer rate calculations. Intake rates were calculated to assess the potential health impact of the fish consumption. There is no major threat to human health from the presence of Hg in L. nebulosus, based upon reasonable consumption patterns, limited to no more than three meals of L. nebulosus per week.