Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different key points at various locations of the body of the patient. Applying association rules and part affinity fields, the detected key points are later converted into six main body organs. Furthermore, the distance of subsequent key points is measured using coordinates information. Finally, distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions. The accuracy of the proposed system is assessed on various test sequences. The experimental outcomes reveal the worth of the proposed system’ by obtaining a True Positive Rate of 98% with a 2% False Positive Rate.     

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.     

A hyperspectral GA-PLSR model for prediction of pine wilt disease

Pine wilt disease caused by a forest-invasive alien species, the pine wood nematode (Bursaphelenchus xylophilus) is considered as one of the most destructive pest problems. In recent years, spectroscopic technologies have shown great potentials for the assessment of forest damage due to their nondestructive, noninvasive, cost-effective, and rapidly responsive nature. This paper first identified the hyperspectral characteristics of pine wilt disease by measuring and analyzing the changes in spectral reflectance of healthy and infected Pinus massoniana trees. Then 16 spectral features were extracted from the spectral bands covering the green region (510~580 nm), the red region (620~680 nm), the red edge (680~760 nm), the near-infrared region (780~1100 nm), and coded as genes composing the chromosome of a genetic algorithm (GA). Based on the optimal spectral features with suitable fitness from the GA, a partial least squares regression (PLSR) prediction model was built with highest determination coefficient R²_c?=?0.91, R²_v?=?0.82, relative prediction deviation RPD?=?3.3 and lowest root mean square error RMSE_c?=?0.23, RMSE_v?=?0.33 on the calibration and validation datasets. Compared with other PLSR models, our proposed GA-based approach significantly improves the prediction accuracy with few input spectral features.

     

Improving the security of quantum key agreement protocols with single photon in both polarization and spatial-mode degrees of freedom

Recently, Wang and Ma (Quantum Inf Process 16(5):130, 2017) proposed two interesting quantum key agreement protocols with a single photon in both polarization and spatial-mode degrees of freedom. They claimed that the privacy of participants’ secret keys in the multiparty case is protected against dishonest participants. However, in this paper, we prove that two dishonest participants can deduce the secret key of an honest one using a fake sequence of single photons, without being detected. Also, we propose an additional security detection process to avoid the security loophole in their protocol.     

Stabilization of the Reductase Domain in the Catalytically Self‐Sufficient Cytochrome P450BM3 by Consensus‐Guided Mutagenesis

The multidomain, catalytically self‐sufficient cytochrome P450 BM‐3 from Bacillus megaterium (P450_BM3) constitutes a versatile enzyme for the oxyfunctionalization of organic molecules and natural products. However, the limited stability of the diflavin reductase domain limits the utility of this enzyme for synthetic applications. In this work, a consensus‐guided mutagenesis approach was applied to enhance the thermal stability of the reductase domain of P450_BM3. Upon phylogenetic analysis of a set of distantly related P450s (>38 % identity), a total of 14 amino acid substitutions were identified and evaluated in terms of their stabilizing effects relative to the wild‐type reductase domain. Recombination of the six most stabilizing mutations generated two thermostable variants featuring up to tenfold longer half‐lives at 50 °C and increased catalytic performance at elevated temperatures. Further characterization of the engineered P450_BM3 variants indicated that the introduced mutations increased the thermal stability of the FAD‐binding domain and that the optimal temperature (T_opt) of the enzyme had shifted from 25 to 40 °C. This work demonstrates the effectiveness of consensus mutagenesis for enhancing the stability of the reductase component of a multidomain P450. The stabilized P450_BM3 variants developed here could potentially provide more robust scaffolds for the engineering of oxidation biocatalysts.     

Recovery of nano-calcium fluoride and ammonium bisulphate from phosphogypsum waste

ABSTRACT

The production of phosphoric acid generates an enormous amount of phosphogypsum with emission of toxic fluorine acid gas. To remedy these environmental problems, a novel and a simple procedure permits converting phosphogypsum waste by recycled fluorine acid into valuable products. The obtained results confirm the efficiency of this procedure, which synthesises at room temperature the calcium fluoride in the form of nano-crystalline powder and the ammonium bisulphate salt from the exact stoichiometric proportions of phosphogypsum, hydrofluoric acid and ammonia. The total conversion of phosphogypsum is achieved after reaction time equal to one hour and a half. Generally, this novel procedure offers not only a solution for reducing phosphogypsum waste and fluorine gas emission, but also gives rise to valuable products, useful to industry and agriculture.     

Fabrication and mechanical evaluation of hydroxyapatite/oxide nano-composite materials

In the current study, the semiconducting metal oxides such as nano-ZnO and SiO₂ powders were prepared via sol–gel technique and conducted on nano-hydroxyapatite (nHA) which was synthesized by chemical precipitation. The properties of fabricated nano-structured composites containing different ratios of HA, ZnO and SiO₂ were examined using X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, scanning electron microscope (SEM) and transmission electron microscope (TEM) techniques. The effect of the variation of ratios between the three components on mechanical, microstructure and in-vitro properties was assessed to explore the possibility of enhancing these properties. The results proved that the mechanical properties exhibited an increment with increasing the ZnO content at the extent of HA. In-vitro study proved the formation and nucleation of apatite onto the surface of the fabricated composites after one week of immersion. It is concluded that HA composites containing SiO₂ or SiO₂/ZnO content had a suitable mechanical properties and ability to form apatite particles onto the composite surface. Based on bioactivity behavior, Si-HA is more bioactive than pure hydroxyapatite and nano-arrangements will provide an interface for better bone formation. Therefore, these nano-composites will be promising as bone substitutes especially in load bearing sites.     

Application of myrrh extract as an eco-friendly dye and antimicrobial agent on wool and silk fabrics part 2: antimicrobial activity and fastness property

ABSTRACT

We investigate the dyeing and antimicrobial properties of myrrh (Commiphora molmol) extracts on wool and silk fabrics, the use of eco-friendly materials such as sumac (Rhus coriaria), manjakani (Quercus infectoria), alum, and ferrous sulfate as mordants. The mordanting methods were optimized.

The best conditions of dyeing process were discussed in the first part of this study. Dyed fabrics were assessed for color strength (K/S), color fastness, and antimicrobial activity. Non-mordanted dyed wool and silk showed color fastness of grade 4–5. Dyed fabrics showed good antimicrobial activity, which was enhanced by mordanting.