A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease.

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions.

Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis. This study proposes SBXception: a shallower and broader variant of the Xception network. It uses Xception as the base model for skin cancer classification and increases its performance by reducing the depth and expanding the breadth of the architecture. We used the HAM10000 dataset, which contains 10,015 dermatoscopic images of skin lesions classified into seven categories, for training and testing the proposed model. Using the HAM10000 dataset, we fine-tuned the new model and reached an accuracy of 96.97% on a holdout test set. SBXception also achieved significant performance enhancement with 54.27% fewer training parameters and reduced training time compared to the base model. Our findings show that reducing and expanding the Xception model architecture can greatly improve its performance in skin cancer categorization.

The 2022 outbreak and the pathobiology of the coxsackie virus [hand foot and mouth disease] in India.

Outbreaks of HFMD in children aged <5 years have been reported worldwide and the major causative agents are Coxsackievirus (CV) A16, enterovirus (EV)-A71 and recently CVA6. In India, HFMD is a disease that is not commonly reported. The purpose of the study was to identify the enterovirus type(s) associated with large outbreak of Hand, foot, and mouth disease during COVID-19 pandemic in 2022. Four hundred and twenty five clinical samples from 196-suspected cases were collected from different parts of the country. This finding indicated the emergence of CVA6 in HFMD along with CVA16, soon after the gradual easing of non-pharmaceutical interventions during-pandemic COVID-19 and the relevance of continued surveillance of circulating enterovirus types in the post-COVID pandemic era.

Investigating medical students' satisfaction towards video-based learning versus face-to-face lectures: a Jordanian tertiary teaching hospital experience.

PURPOSE: We aimed to evaluate the disparity between video-based learning and lecture-based learning on Jordanian medical students' satisfaction. METHODS: We conducted this cross-sectional study using a web-based questionnaire adapted from Student Evaluation of Educational Quality survey. Using convenience sampling, medical students studying at the University of Jordan and Jordan University Hospital were recruited. Participants in either clinical or basic-science years that have completed the entire survey were included in the final analysis. RESULTS: We surveyed a total 487 participants among which male to female ratio was 1.19:1. Participants perceived greater benefit in terms of learning, instructor enthusiasm, content organization, breadth of teaching, and quality and number of assignments when using video-based learning (all p<0.01). In contrast, face-to-face learning was associated with significantly higher benefits in terms of group interactions (p<0.01) and capacity for rapport building (p<0.01). There was no significant difference in perceived examination performance between the two learning modalities (p=0.11). CONCLUSION: Video-based learning is the preferred learning modality among Jordanian medical students. Despite its dominance across multiple domains, it should be implemented as an adjunct to traditional classroom teaching for it is vital in the development of good communication skills and building rapport in medical students.

Automated Estimation of Crop Yield Using Artificial Intelligence and Remote Sensing Technologies.

Agriculture is the backbone of any country, and plays a viable role in the total gross domestic product (GDP). Healthy and fruitful crops are of immense importance for a government to fulfill the food requirements of its inhabitants. Because of land diversities, weather conditions, geographical locations, defensive measures against diseases, and natural disasters, monitoring crops with human intervention becomes quite challenging. Conventional crop classification and yield estimation methods are ineffective under unfavorable circumstances. This research exploits modern precision agriculture tools for enhanced remote crop yield estimation, and types classification by proposing a fuzzy hybrid ensembled classification and estimation method using remote sensory data. The architecture enhances the pooled images with fuzzy neighborhood spatial filtering, scaling, flipping, shearing, and zooming. The study identifies the optimal weights of the strongest candidate classifiers for the ensembled classification method adopting the bagging strategy. We augmented the imagery datasets to achieve an unbiased classification between different crop types, including jute, maize, rice, sugarcane, and wheat. Further, we considered flaxseed, lentils, rice, sugarcane, and wheat for yield estimation on publicly available datasets provided by the Food and Agriculture Organization (FAO) of the United Nations and the Word Bank DataBank. The ensemble method outperformed the individual classification methods for crop type classification on an average of 13% and 24% compared to the highest gradient boosting and lowest decision tree methods, respectively. Similarly, we observed that the gradient boosting predictor outperformed the multivariate regressor, random forest, and decision tree regressor, with a comparatively lower mean square error value on yield years 2017 to 2021. Further, the proposed architecture supports embedded devices, where remote devices can adopt a lightweight classification algorithm, such as MobilenetV2. This can significantly reduce the processing time and overhead of a large set of pooled images.

Investigating medical students’ satisfaction towards video-based learning versus face-to-face lectures: a Jordanian tertiary teaching hospital experience / 한국의학교육

Purpose@#We aimed to evaluate the disparity between video-based learning and lecture-based learning on Jordanian medical students’ satisfaction. @*Methods@#We conducted this cross-sectional study using a web-based questionnaire adapted from Student Evaluation of Educational Quality survey. Using convenience sampling, medical students studying at the University of Jordan and Jordan University Hospital were recruited. Participants in either clinical or basic-science years that have completed the entire survey were included in the final analysis. @*Results@#We surveyed a total 487 participants among which male to female ratio was 1.19:1. Participants perceived greater benefit in terms of learning, instructor enthusiasm, content organization, breadth of teaching, and quality and number of assignments when using video-based learning (all p<0.01). In contrast, face-to-face learning was associated with significantly higher benefits in terms of group interactions (p<0.01) and capacity for rapport building (p<0.01). There was no significant difference in perceived examination performance between the two learning modalities (p=0.11). @*Conclusion@#Video-based learning is the preferred learning modality among Jordanian medical students. Despite its dominance across multiple domains, it should be implemented as an adjunct to traditional classroom teaching for it is vital in the development of good communication skills and building rapport in medical students.

Identifying Lockdown Relaxation Strategies and Policy Implications to Fight against COVID-19: Medical Experts Perspective from Pakistan.

The COVID-19 pandemic, was first identified in Wuhan, China, has had a drastic effect on the world economy and compelled governments to enforce lockdown in territories. However, lockdown is destroying the world economy badly as well as the physical and mental health of people. Therefore, governments must develop Lockdown Relaxation Strategies (LRS) to overcome the negative consequences of lockdown in Pakistan. Identifying LRS is important for public health and economic restoration. Therefore, this work is an initial attempt to develop LRS in a developing country - Pakistan, and prioritize LRS through a novel ISM-MICMAC approach. By taking response from experts, results show that implementation of smart lockdowns in affected areas, meeting minimum criteria of public health standards, limited operations of public transportation are the most important strategies. Results provide a strategic guideline for governments to take necessary measures and allocate resources appropriately.

Estimation of sex based on metrics of the sternum in a contemporary Jordanian population: A computed tomographic study.

ABSTRACT: There is a paucity of osteometric standards for sex estimation from unknown skeletal remains in Jordan and the sexual dimorphism of the sternum has not yet been investigated. The aim of this study was to evaluate the sexual dimorphism in sternal measurements using 3D multidetector computed tomography (MDCT), and to assess their reliability for sex estimation in a Jordanian population. A total of 600 MDCT scans (300 males and 300 females) were used and a total of 8 sternal measurements were studied (manubrium length, sternal body length, combined length of manubrium and body, corpus sterni width at 1st and 3rd sternebrae, sternal index and area). Sexual dimorphism was evaluated by means of discriminant function analyses. Significant sexual dimorphism was found mainly in middle-aged and older adults. Including all subjects, multivariate, and stepwise functions gave an overall accuracy of 83.0% and 84.0%, respectively. Additionally, multivariate and stepwise analyses were conducted separately for each age group. The accuracy of sex estimation in multivariate analysis (all variables) varied from 63.2% in the young, and 83.7% in the middle adults to 84.9% for older adults. In stepwise analysis, the highest accuracy rates were provided by only sternal area in young adults (81.6%), and sternal area combined with sternal body length in middle-aged and older adults (84.2% and 85.3%, respectively). The best sex discriminator using univariate analysis (single variable) was sternal area followed by sternal body length (84.0% and 80.8% respectively). Notably, univariate analyses for most variables gave relatively higher classification accuracies in females but were poor at predicting males in the sample (sex bias ranged between -6.4% and -20%). Our data suggest that dimorphism in the human sternum increases with advancing age and separate discriminant functions are needed for each age group in Jordanians. In addition, multivariate and stepwise analyses using sternum gave higher classification accuracies with comparatively lower sex biases compared to univariate analyses.

Estimation of sex from measurements of foramen magnum region in a contemporary Jordanian population: A computed tomographic study.

Several studies have shown variability in basicranial measurements between populations. Therefore, each population should have specific standards to optimize the accuracy of identification. The aim of this study was to evaluate the sexual dimorphism in foramen magnum and occipital condyles measurements using 3D multidetector computed tomography (MDCT), and to assess their utility and reliability for sex estimation in a Jordanian population by means of discriminant function analyses. A total of 500 MDCT scans (288 males and 212 females) were used and a total of 8 basicranial measurements were studied (3 measurements were derived from foramen magnum, and 5 measurements were derived from occipital condyles). Significant sexual dimorphism was found in all basicranial measurements. The most dimorphic variables were length of occipital condyle and maximum bicondylar distance. Including all variables, multivariate and stepwise functions gave an overall accuracy of 77.8% and 78.6%, respectively. However, the multivariate analyses conducted separately for measurements derived from foramen magnum and occipital condyles gave lower overall accuracy of 68.6% and 70.0%, respectively. Basicranial measurements derived from foramen magnum alone predicted males with relatively higher accuracy but were poor at predicting females in the sample (82.6% were males, 49.5% were females, sex bias 33.1%). Adding occipital condyles measurements to the multivariate analysis increased the percentage of correct sexing in females and reduced considerably the sex bias (78.8% male, 76.4% female, sex bias 1.4%). Discriminant function analysis using basicranial measurements derived from both foramen magnum and occipital condyles measurements can be utilized to estimate sex in our population.

Sustainable construction through energy management practices in developing economies: an analysis of barriers in the construction sector.

In the recent era, energy management practices (EMPs) have gained significant importance in developed countries, due to their cost efficiency and the potential to protect the environment. However, developing countries are not particularly serious about adopting and implementing sustainable construction practices, due to the different barriers that hinder them from doing so. Therefore, the complete adoption of effective EMPs is still at the initial stage. The objective of this study is to identify the barriers to EMPs adoption, in the construction sector of Pakistan. Initially, the barriers were identified from the earlier studies and screened by the fuzzy Delphi method (FDM). These screened barriers were then evaluated through the ISM. The results of the ISM show that there happens to be a "lack of communication and collaboration among project stakeholders," "attitudinal, cultural and behavioral resistance to adopt EMPs", "lack of top management support and interest in energy management issues," "no condition for implementing EMPs on construction site," and "lack of financing schemes to implement energy management technology," as the top barriers to EMPs adoption in the construction projects pertaining to Pakistan. Thus in this regard, this study empirically contributes to EMPs literature that is based on the developing countries and supports the policymakers in developing appropriate strategies to reduce energy consumption in construction projects-especially in developing countries.

Effect of electron-phonon interaction and valence band edge shift for carrier-type reversal in layered ZnS/rGO nanocomposites.

The artificial stacking of nanohybrid films helps to enhance their properties and thus intrigues researchers to explore this possibility in emerging technologies. The layer-by-layer approach was used to fabricate samples of zinc sulfide/reduced graphene oxide (ZnS/rGO) by using spin coating technique. The structure and optoelectronic properties has been extensively studied by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), UV-VIS-NIR spectroscopy, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and Hall measurements. Raman spectrum elucidates the phonon contribution of ZnS and breathing mode of κ-point phonons and sp2 bonds of carbon atoms of rGO. The electron-phonon interactions reveal reduction in electron mobility and enhancement in holes contribution with rGO content leading to surface charge transfer doping (SCTD). XPS results explain the valence band edge and conduction band edge to form type-I band alignment to reconfirm carrier-type reversal. A change in the dispersion of refractive indices along with a small rise in the value of absorption coefficient in terahertz (THz) region for ZnS/rGO nanocomposite films has been observed. These results will open up new opportunities to furthering the science of this technologically important class of materials for future electronics.

Performance Evaluation and Validation of QCM (Query Control Mechanism) for QoS-Enabled Layered-Based Clustering for Reactive Flooding in the Internet of Things.

Internet of Things (IoT) facilitates a wide range of applications through sensor-based connected devices that require bandwidth and other network resources. Enhancement of efficient utilization of a heterogeneous IoT network is an open optimization problem that is mostly suffered by network flooding. Redundant, unwanted, and flooded queries are major causes of inefficient utilization of resources. Several query control mechanisms in the literature claimed to cater to the issues related to bandwidth, cost, and Quality of Service (QoS). This research article presented a statistical performance evaluation of different query control mechanisms that addressed minimization of energy consumption, energy cost and network flooding. Specifically, it evaluated the performance measure of Query Control Mechanism (QCM) for QoS-enabled layered-based clustering for reactive flooding in the Internet of Things. By statistical means, this study inferred the significant achievement of the QCM algorithm that outperformed the prevailing algorithms, i.e., Divide-and-Conquer (DnC), Service Level Agreements (SLA), and Hybrid Energy-aware Clustering Protocol for IoT (Hy-IoT) for identification and elimination of redundant flooding queries. The inferential analysis for performance evaluation of algorithms was measured in terms of three scenarios, i.e., energy consumption, delays and throughput with different intervals of traffic, malicious mote and malicious mote with realistic condition. It is evident from the results that the QCM algorithm outperforms the existing algorithms and the statistical probability value "P" < 0.05 indicates the performance of QCM is significant at the 95% confidence interval. Hence, it could be inferred from findings that the performance of the QCM algorithm was substantial as compared to that of other algorithms.

A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

Sequential Anaerobic/Aerobic Digestion for Enhanced Carbon/Nitrogen Removal and Cake Odor Reduction.

Anaerobic digestion (AD) has been proven to be an effective process for the treatment of wastewater sludge. However, it produces high levels of ammonia in the digester effluent, which may jeopardize meeting stringent nutrient discharge limits. In this study, the effect of a sequential anaerobic/aerobic (AN/AERO) digestion and a single-stage conventional AN digestion (as control) was investigated on mixed (primary + secondary) sludge generated by the Annacis Island wastewater treatment plant (WWTP) (BC, Canada). An overall sludge retention time (SRT) of 22.5 days under three different scenarios was chosen based on the current operational SRT of the digesters at the Annacis Island WWTP. The steady state results have shown that sequential AN/AERO digestion configurations achieved up to 11% higher volatile solids (VS) removal and 72% lower ammonia generation over single-stage conventional AN digestion. Furthermore, sequential AN/AERO system also showed enhanced dewaterability, improved fecal coliform destruction and reduced digested cake odors over control digesters.

On fuzzy semantic similarity measure for DNA coding.

A coding measure scheme numerically translates the DNA sequence to a time domain signal for protein coding regions identification. A number of coding measure schemes based on numerology, geometry, fixed mapping, statistical characteristics and chemical attributes of nucleotides have been proposed in recent decades. Such coding measure schemes lack the biologically meaningful aspects of nucleotide data and hence do not significantly discriminate coding regions from non-coding regions. This paper presents a novel fuzzy semantic similarity measure (FSSM) coding scheme centering on FSSM codons׳ clustering and genetic code context of nucleotides. Certain natural characteristics of nucleotides i.e. appearance as a unique combination of triplets, preserving special structure and occurrence, and ability to own and share density distributions in codons have been exploited in FSSM. The nucleotides׳ fuzzy behaviors, semantic similarities and defuzzification based on the center of gravity of nucleotides revealed a strong correlation between nucleotides in codons. The proposed FSSM coding scheme attains a significant enhancement in coding regions identification i.e. 36-133% as compared to other existing coding measure schemes tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

Physical and electrical properties of graphene grown under different hydrogen flow in low pressure chemical vapor deposition.

Hydrogen flow during low pressure chemical vapor deposition had significant effect not only on the physical properties but also on the electrical properties of graphene. Nucleation and grain growth of graphene increased at higher hydrogen flows. And, more oxygen-related functional groups like amorphous and oxidized carbon that probably contributed to defects or contamination of graphene remained on the graphene surface at low H2 flow conditions. It is believed that at low hydrogen flow, those remained oxygen or other oxidizing impurities make the graphene films p-doped and result in decreasing the carrier mobility.

In vitro studies on the antimicrobial peptide human beta-defensin 9 (HBD9): signalling pathways and pathogen-related response (an American Ophthalmological Society thesis).

PURPOSE: Human ß-defensins (HBDs) are an important part of the innate immune host defense at the ocular surface. Unlike other defensins, expression of HBD9 at the ocular surface is reduced during microbial infection, but activation of toll-like receptor 2 (TLR2) in corneal epithelial cells has been shown to up-regulate HBD9. Our purpose was to test the hypothesis that TLR2 has a key role in the signalling pathway(s) involved in the overexpression or underexpression of HBD9, and accordingly, different pathogens would induce a different expression pattern of HBD9. METHODS: The in vitro RNAi silencing method and response to dexamethasone were used to determine key molecules involved in signalling pathways of HBD9 in immortalized human corneal epithelial cells. The techniques included cell culture with exposure to specific transcription factor inhibitors and bacteria, RNA extraction and cDNA synthesis, quantitative real-time polymerase chain reaction, and immunohistology. RESULTS: This study demonstrates that TLR2 induces HBD9 mRNA and protein expression in a time- and dose-dependent manner. Transforming growth factor-ß-activated kinase 1 (TAK1) plays a central role in HBD9 induction by TLR2, and transcription factors c-JUN and activating transcription factor 2 are also involved. Dexamethasone reduces TLR2-mediated up-regulation of HBD9 mRNA and protein levels in mitogen-activated protein kinase phosphatase 1 (MKP1)-dependent and c-JUN-independent manner. HBD9 expression differs with gram-negative and gram-positive bacteria. CONCLUSIONS: TLR2-mediated MKPs and nuclear factor-κB signalling pathways are involved in HBD9 expression. TAK-1 is a key molecule. These molecules can be potentially targeted to modulate HBD9 expression. Differential expression of HBD9 with different bacteria could be related to differences in pathogen-associated molecular patterns of these organisms.