A Robust UWSN Handover Prediction System Using Ensemble Learning.

The use of underwater wireless sensor networks (UWSNs) for collaborative monitoring and marine data collection tasks is rapidly increasing. One of the major challenges associated with building these networks is handover prediction; this is because the mobility model of the sensor nodes is different from that of ground-based wireless sensor network (WSN) devices. Therefore, handover prediction is the focus of the present work. There have been limited efforts in addressing the handover prediction problem in UWSNs and in the use of ensemble learning in handover prediction for UWSNs. Hence, we propose the simulation of the sensor node mobility using real marine data collected by the Korea Hydrographic and Oceanographic Agency. These data include the water current speed and direction between data. The proposed simulation consists of a large number of sensor nodes and base stations in a UWSN. Next, we collected the handover events from the simulation, which were utilized as a dataset for the handover prediction task. Finally, we utilized four machine learning prediction algorithms (i.e., gradient boosting, decision tree (DT), Gaussian naive Bayes (GNB), and K-nearest neighbor (KNN)) to predict handover events based on historically collected handover events. The obtained prediction accuracy rates were above 95%. The best prediction accuracy rate achieved by the state-of-the-art method was 56% for any UWSN. Moreover, when the proposed models were evaluated on performance metrics, the measured evolution scores emphasized the high quality of the proposed prediction models. While the ensemble learning model outperformed the GNB and KNN models, the performance of ensemble learning and decision tree models was almost identical

Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models.

Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors' knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model

Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory

This paper presents a novel deep learning-based approach for anomaly detection in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested approach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and convolutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is employed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XDViolence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with ConvLSTM can increase precision and reduce false positives, achieving a high accuracy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cuttingedge techniques mentioned in the comparison

Aprepitant, An Antiemetic Agent, Interferes with Metal Ion Homeostasis of Candida Auris and Displays Potent Synergistic Interactions With Azole Drugs

With the rapid increase in the frequency of azole-resistant species, combination therapy appears to be a promising tool to augment the antifungal activity of azole drugs against resistant Candida species. Here, we report the effect of aprepitant, an antiemetic agent, on the antifungal activities of azole drugs against the multidrug-resistant Candida auris. Aprepitant reduced the minimum inhibitory concentration (MIC) of itraconazole in vitro, by up to eight-folds. Additionally, the aprepitant/itraconazole combination interfered significantly with the biofilm-forming ability of C. auris by 95 ± 0.13%, and significantly disrupted mature biofilms by 52 ± 0.83%, relative to the untreated control. In a Caenorhabditis elegans infection model, the aprepitant/itraconazole combination significantly prolonged the survival of infected nematodes by ~90% (five days postinfection) and reduced the fungal burden by ~92% relative to the untreated control. Further, this novel drug combination displayed broad-spectrum synergistic interactions against other medically important Candida species such as C. albicans, C. krusei, C. tropicalis, and C. parapsilosis (ƩFICI ranged from 0.08 to 0.31). Comparative transcriptomic profiling and mechanistic studies indicated aprepitant/itraconazole interferes significantly with metal ion homeostasis and compromises the ROS detoxification ability of C. auris. This study presents aprepitant as a novel, potent, and broadspectrum azole chemosensitizing agent that warrants further investigation

Repurposing Approach IdentifiesPitavastatin as a Potent AzoleChemosensitizing Agent EffectiveAgainst Azole-Resistant CandidaSpecies

The limited number of antifungals and the rising frequency of azole-resistant Candida species are growing challenges to human medicine. Drug repurposing signifies an appealing approach to enhance the activity of current antifungal drugs. Here, we evaluated the ability of Pharmakon 1600 drug library to sensitize an azole-resistant Candida albicans to the effect of fluconazole. The primary screen revealed 44 non-antifungal hits were able to act synergistically with fluconazole against the test strain. Of note, 21 compounds, showed aptness for systemic administration and limited toxic effects, were considered as potential fluconazole adjuvants and thus were termed as “repositionable hits”. A follow-up analysis revealed pitavastatin displaying the most potent fluconazole chemosensitizing activity against the test strain (ΣFICI 0.05) and thus was further evaluated against 18 isolates of C. albicans (n = 9), C. glabrata (n = 4), and C. auris (n = 5). Pitavastatin displayed broad-spectrum synergistic interactions with both fluconazole and voriconazole against ~89% of the tested strains (ΣFICI 0.05–0.5). Additionally, the pitavastatin-fluconazole combination significantly reduced the biofilm-forming abilities of the tested Candida species by up to 73%, and successfully reduced the fungal burdens in a Caenorhabditis elegans infection model by up to 96%. This study presents pitavastatin as a potent azole chemosensitizing agent that warrant further investigation

Ospemifene Displays Broad-Spectrum Synergistic Interactions with Itraconazole Through Potent Interference with Fungal Efflux Activities

Azole antifungals are vital therapeutic options for treating invasive mycotic infections. However, the emergence of azole-resistant isolates combined with limited therapeutic options presents a growing challenge in medical mycology. To address this issue, we utilized microdilution checkerboard assays to evaluate nine stilbene compounds for their ability to interact synergistically with azole drugs, particularly against azole-resistant fungal isolates. Ospemifene displayed the most potent azole chemosensitizing activity, and its combination with itraconazole displayed broad-spectrum synergistic interactions against Candida albicans, Candida auris, Cryptococcus neoformans, and Aspergillus fumigatus (ΣFICI = 0.05–0.50). Additionally, in a Caenorhabditis elegans infection model, the ospemifene-itraconazole combination significantly reduced fungal CFU burdens in infected nematodes by ~75–96%. Nile Red efflux assays and RT-qPCR analysis suggest ospemifene interferes directly with fungal efflux systems, thus permitting entry of azole drugs into fungal cells. This study identifies ospemifene as a novel antifungal adjuvant that augments the antifungal activity of itraconazole against a broad range of fungal pathogens

Effect of grain size reduction of AA2124 aluminum alloy powder compacted by spark plasma sintering

AbstractNanocrystalline (Average grain size ∼200nm) bulk AA2124 alloy was produced through high energy ball milling of microcrystalline powder followed by spark plasma sintering (SPS) at 480°C with a holding time of 10min. The effect of initial particle and grain size on the microstructural evolution as well as on the relative density and mechanical properties of the specimens consolidated through SPS and hot pressing (HP) at the same temperature for 60min was investigated for ball milled nano-powders (NP), as well as as-received micro-powders (MP). Results showed that the NP specimens consolidated with SPS had the highest microhardness values compared to the other specimens despite not achieving full densification. On the other hand, a general increase in density, hardness, and compressive strength was observed for all SPS consolidates compared to HP. The presence of aluminum oxide and its influence on the consolidation process as well as the resulting mechanical properties of the bulk specimens is also discussed

Linked Data Supported Content Analysis for Sociology

Philology and hermeneutics as the analysis and interpretation of natural language text in written historical sources are the predecessors of modern content analysis and date back already to antiquity. In empirical social sciences, especially in sociology, content analysis provides valuable insights to social structures and cultural norms of the present and past. With the ever growing amount of text on the web to analyze, also numerous computer-assisted text analysis techniques and tools were developed in sociological research. However, existing methods often go without sufficient standardization. As a consequence, sociological text analysis is lacking transparency, reproducibility and data re-usability. The goal of this paper is to show, how Linked Data principles and Entity Linking techniques can be used to structure, publish and analyze natural language text for sociological research to tackle these shortcomings. This is achieved on the use case of constitutional text documents of the Netherlands from 1884 to 2016 which represent an important contribution to the European cultural heritage. Finally, the generated data is made available and re-usable as Linked Data not only for sociologists, but also for all other researchers in the digital humanities domain interested in the development of constitutions in the Netherlands