Microwave Reflection Based Dielectric Spectroscopy for Moisture Content in Melele Mango Fruit (Mangifera Indica L.)

The Melele mango is one of the special local fruit Malaysia and it has high commercial value. However, the current methods are not efficient in determining optimum period to harvest. The optimum harvest time has close relationship with moisture content in fruit. The reflection based dielectric spectroscopic technique is conducted to measure moisture in Melele mango fruits. Dielectric and reflection measurements were conducted over a frequency range from 200 MHz to 8 GHz on clone Melele mango. Dielectric constant, loss factor and complex reflection coefficient of Melele mango with different moisture content were measured using an Agilent E8362B PNA Network Analyzer in conjunction with an Agilent 85070E High Temperature Probe over a frequency range from 200 MHz to 8 GHz. The measured reflection coefficient is presented in magnitude and phase. Dielectric constant and loss factor decreases when the moisture content in mango fruit decreases. The magnitude of the reflection coefficient descends due to increment of the dielectric constant. The results show that the measured dielectric properties and complex reflection coefficient provides the ability to predict fruit moisture content

Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Microwave reflection based dielectric spectroscopy for moisture content in Melele mango fruit (Mangifera Indica L.)

The Melele mango is one of the special local fruit Malaysia and it has high commercial value. However, the current methods are not efficient in determining optimum period to harvest. The optimum harvest time has close relationship with moisture content in fruit. The reflection based dielectric spectroscopic technique is conducted to measure moisture in Melele mango fruits. Dielectric and reflection measurements were conducted over a frequency range from 200 MHz to 8 GHz on clone Melele mango. Dielectric constant, loss factor and complex reflection coefficient of Melele mango with different moisture content were measured using an Agilent E8362B PNA Network Analyzer in conjunction with an Agilent 85070E High Temperature Probe over a frequency range from 200 MHz to 8 GHz. The measured reflection coefficient is presented in magnitude and phase. Dielectric constant and loss factor decreases when the moisture content in mango fruit decreases. The magnitude of the reflection coefficient descends due to increment of the dielectric constant. The results show that the measured dielectric properties and complex reflection coefficient provides the ability to predict fruit moisture content

The scleractinian fauna of Yemen: diversity and species distribution patterns

Hard coral diversity and species distribution in Yemen are thought to be principally controlled by hydrology and hydrodynamic factors acting in the Arabian region. In particular, a strong seasonal upwelling linked to the SW summer monsoon is typically described as a major forcing function prevailing in the Gulf of Aden and Socotra. The diversity of the hitherto little known scleractinian coral fauna of Yemen was investigated through surveys extending from the Kamaran Island area, in the southern Red Sea, to Socotra in the Arabian Sea, and including Aden, Balhaf, Bir Ali, Burum, and Al Mukallah in the Gulf of Aden. Results showed that the scleractinian fauna of the Gulf of Aden is notably different from that of the Yemen Red Sea and from the Socotra Archipelago. More unexpectedly, striking patterns of species and genera distribution and relative frequency were observed along a relatively short stretch of coastline. It is hypothesized that, at the local scale, the synergistic effects of the seasonal upwelling, of fresh water input from major wadi estuaries, and of westward moving eddies in the Gulf of Aden could play a role as for the observed striking coral species distribution patterns

Design of a 5.2 GHz circularly polarized textile patch antenna for on/off body radio propagation channel evaluation

This paper presents a lightweight and simple structure of slotted circularly polarized (CP) patch antenna using conductive textile. ShieldIt Super is used as a conductive textile and felt is used as a substrate of the antenna. The prototype is designed with a total dimension of 36 mm x 36 mm and spaced by a 3 mm thick felt fabric for Body-centric Wireless Communication (BCWC) operating in the 5.2 GHz Industrial, Scientific and Medical (ISM) Hyper Local Area Network (LAN) band. A parametric study has been carried out in order to investigate the antenna basic characteristics, thereby enhancing the antenna performance. The proposed antenna is benchmarked against the without slot circularly polarized textile patch antenna. The measured results show that the slotted CP patch textile antenna produces better performance compared to the without the slot CP patch textile in terms of bandwidth with an acceptable 3-dB axial ratio bandwidth and gain

Data-driven audiogram classifier using data normalization and multi-stage feature selection

Abstract Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients’ audiograms. Configuring the hearing aid is done by modifying the designed filters’ gains to match the patient’s audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms

Data-driven audiogram classifier using data normalization and multi-stage feature selection

Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients’ audiograms. Configuring the hearing aid is done by modifying the designed filters’ gains to match the patient’s audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms