Essential oils of Cordia species, compounds and applications: a systematic review

The purpose of this systematic review was to identify the available literature on the essential oil from species of genus Cordia. This study followed the Preferred Reporting Items for Systematic Reviews. The search was conducted on four databases: LILACS, PubMed, Science Direct, and Scopus until June 5th, 2020, with no time or language restrictions. Sixty out of the 1,333 initially gathered studies fit the inclusion criteria after the selection process. Nine species of Cordiawere reported in the selected studies, out of which 79% of the evaluated studies reported essential oil from Cordia curassavica. The essential oil extraction methods identified were hydrodistillation and steam distillation. As for biological application, antimicrobial, anti-inflammatory, larvicidal and antioxidant activities were the most reported. The main compounds reported for essential oil were β-caryophyllene, α-humulene, α-pinene, bicyclogermacrene, and sabinene. The information reported in this systematic review can contribute scientifically to the recognition of the importance of the genus Cordia.

Classification of three pathological voices based on specific features groups using support vector machine

<span>Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.</span>

A Comparative Analysis Using Silhouette Extraction Methods for Dynamic Objects in Monocular Vision

Moving or dynamic object analysis continues to be an increasingly active research field in computer vision with many types of research investigating different methods for motion tracking, object recognition, pose estimation, or motion evaluation (e.g. in sports sciences). Many techniques are available to measure the forces and motion of the people, such as force plates to measure ground reaction forces for a jump or running sports. In training and commercial solution, the detailed motion of athlete's available motion capture devices based on optical markers on the athlete's body and multiple calibrated fixed cameras around the sides of the capture volume can be used. In some situations, it is not practical to attach any kind of marker or transducer to the athletes or the existing machinery are being used, while it is required by a pure vision-based approach to use the natural appearance of the person or object. When a sporting event is taking place, there are opportunities for computer vision to help the referee and other personnel involved in the sports to keep track of incidents occurring, which may provide full coverage and analysis in details of the event for sports viewers. The research aims at using computer vision methods, specially designed for monocular recording, for measuring sports activities, such as high jump, wide jump, or running. Just for indicating the complexity of the project: a single camera needs to understand the height at a particular distance using silhouette extraction. Moving object analysis benefits from silhouette extraction and this has been applied to many domains including sports activities. This paper comparatively discusses two significant techniques to extract silhouettes of a moving object (a jumping person) in monocular video data in different scenarios. The results show that the performance of silhouette extraction varies in dependency on the quality of used video data.

Hypericum spp.—An Overview of the Extraction Methods and Analysis of Compounds

The Hypericum genus contains one of the few genera of flowering plants that contains a species with authorization for marketing as a traditional medicine, H. perforatum. Due to the fact that this is a large genus, comprising numerous species, a large amount of interest has been shown over the years in the study of its various pharmacological activities. The chemical composition of these species is quite similar, containing compounds belonging to the class of phloroglucinol derivatives, naphthodianthrones, phenols, flavonoids and essential oils. Taking all of this into consideration, the present study aims to offer an overview of the species of the genus from the point of view of their extraction techniques and analysis methods. An extensive study on the scientific literature was performed, and it revealed a wide range of solvents and extraction methods, among which ethanol and methanol, together with maceration and ultrasonication, are the most frequent. Regarding analysis methods, separation and spectral techniques are the most employed. Therefore, the present study provides necessary data for future studies on the species of the genus, offering a complete overview and a possible basis for their development.

Optimization of extraction techniques for processing and utilization of Cyperus Dichrostachus A. Rich Plant as fiber

The Textile industry is an important contributor to the GDP of countries worldwide. Both natural and synthetic fibers are the main raw materials for this sector. Environmental concerns, depletion of non-renewable resources, the high price of oil and limited oil reserves with consumer demand is driving research into cheap, biodegradable, sustainable, renewable and abundantly available green materials. Natural fibers are of the good substitute sources for swapping synthetic fibers and reinforcing polymer matrices because of their contributions in maintaining of ecology, nature of disposal, low energy requirement for processing and sustainability. The current research emphases on evaluating and determining the best extraction methods to process and treat cyperus Dichrostachus A.Rich plant in order to make the fiber suitable for variety of applications. Cyperus Dichrostachus A.Rich plant was treated with two conditions (cold and warm conditions) using statistically planned tests. Process conditions were optimised using central composite design methodology with the experimental design. Under optimised conditions, the strength and fiber yield of CDA fibers were significantly compared. The strength and fiber yield of the fiber was at maximized with optimized conditions and use for valorisation applications.

Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults

Wearable technologies allow the measurement of unhindered activities of daily living (ADL) among patients who had a stroke in their natural settings. However, methods to extract meaningful information from large multi-day datasets are limited. This study investigated new visualization-driven time-series extraction methods for distinguishing activities from stroke and healthy adults. Fourteen stroke and fourteen healthy adults wore a wearable sensor at the L5/S1 position for three consecutive days and collected accelerometer data passively in the participant’s naturalistic environment. Data from visualization facilitated selecting information-rich time series, which resulted in classification accuracy of 97.3% using recurrent neural networks (RNNs). Individuals with stroke showed a negative correlation between their body mass index (BMI) and higher-acceleration fraction produced during ADL. We also found individuals with stroke made lower activity amplitudes than healthy counterparts in all three activity bands (low, medium, and high). Our findings show that visualization-driven time series can accurately classify movements among stroke and healthy groups using a deep recurrent neural network. This novel visualization-based time-series extraction from naturalistic data provides a physical basis for analyzing passive ADL monitoring data from real-world environments. This time-series extraction method using unit sphere projections of acceleration can be used by a slew of analysis algorithms to remotely track progress among stroke survivors in their rehabilitation program and their ADL abilities.

BSIRNet: A Road Extraction Network with Bidirectional Spatial Information Reasoning

Automatic extraction of road information from remote sensing images is widely used in many fields, such as urban planning and automatic navigation. However, due to interference from noise and occlusion, the existing road extraction methods can easily lead to road discontinuity. To solve this problem, a road extraction network with bidirectional spatial information reasoning (BSIRNet) is proposed, in which neighbourhood feature fusion is used to capture spatial context dependencies and expand the receptive field, and an information processing unit with a recurrent neural network structure is used to capture channel dependencies. BSIRNet enhances the connectivity of road information through spatial information reasoning. Using the public Massachusetts road dataset and Wuhan University road dataset, the superiority of the proposed method is verified by comparing its results with those of other models.

A Decision Support System for Sustainable Agriculture: The Case Study of Coconut Oil Extraction Process

The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign.

Comparison of different feature extraction methods for applicable automated ICD coding

Background Automated ICD coding on medical texts via machine learning has been a hot topic. Related studies from medical field heavily relies on conventional bag-of-words (BoW) as the feature extraction method, and do not commonly use more complicated methods, such as word2vec (W2V) and large pretrained models like BERT. This study aimed at uncovering the most effective feature extraction methods for coding models by comparing BoW, W2V and BERT variants. Methods We experimented with a Chinese dataset from Fuwai Hospital, which contains 6947 records and 1532 unique ICD codes, and a public Spanish dataset, which contains 1000 records and 2557 unique ICD codes. We designed coding tasks with different code frequency thresholds (denoted as $$f_s$$ f s ), with a lower threshold indicating a more complex task. Using traditional classifiers, we compared BoW, W2V and BERT variants on accomplishing these coding tasks. Results When $$f_s$$ f s was equal to or greater than 140 for Fuwai dataset, and 60 for the Spanish dataset, the BERT variants with the whole network fine-tuned was the best method, leading to a Micro-F1 of 93.9% for Fuwai data when $$f_s=200$$ f s = 200 , and a Micro-F1 of 85.41% for the Spanish dataset when $$f_s=180$$ f s = 180 . When $$f_s$$ f s fell below 140 for Fuwai dataset, and 60 for the Spanish dataset, BoW turned out to be the best, leading to a Micro-F1 of 83% for Fuwai dataset when $$f_s=20$$ f s = 20 , and a Micro-F1 of 39.1% for the Spanish dataset when $$f_s=20$$ f s = 20 . Our experiments also showed that both the BERT variants and BoW possessed good interpretability, which is important for medical applications of coding models. Conclusions This study shed light on building promising machine learning models for automated ICD coding by revealing the most effective feature extraction methods. Concretely, our results indicated that fine-tuning the whole network of the BERT variants was the optimal method for tasks covering only frequent codes, especially codes that represented unspecified diseases, while BoW was the best for tasks involving both frequent and infrequent codes. The frequency threshold where the best-performing method varied differed between different datasets due to factors like language and codeset.