Impact of Anemia on Clinical Outcomes in Atrial Fibrillation Patients on Oral Anticoagulants: A Prognostic Meta-Analysis.

Background: Anemia is frequently observed as a comorbidity in atrial fibrillation (AF), especially in elderly patients and in those on anticoagulation. This has been associated with poor clinical outcomes. We aim to investigate the impact of anemia on clinical outcomes in patients with AF on oral anticoagulation.

Antegrade approach versus retrograde approach percutaneous coronary intervention for chronic total occlusion: An updated meta-analysis.

Background: Retrograde approach has notably improved success rates of chronic total occlusion (CTO) percutaneous coronary intervention (PCI). However, longer procedural time, increase use of fluoroscopy and contrast dye have been reported in retrograde techniques in CTO PCI. We aimed to study in-hospital and long-term outcomes of retrograde approach versus antegrade approach in CTO PCI.

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 NaCO) and pretreatment durations (1, 3 and 5 h) were used for straw pretreatment.

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.

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.

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 -heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima.

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.

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.

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.

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.