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Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Background and aims: The single nucleotide polymorphisms (SNPs) in SLC30A8 gene have been recognized as contributing to type 2 diabetes (T2D) susceptibility and colorectal cancer. This study aims to predict the structural stability, and functional impacts on variations in non-synonymous SNPs (nsSNPs) in the human SLC30A8 gene using various computational techniques. Materials and methods: Several in silico tools, including SIFT, Predict-SNP, SNPs&GO, MAPP, SNAP2, PhD-SNP, PANTHER, PolyPhen-1,PolyPhen-2, I-Mutant 2.0, and MUpro, have been used in our study. Results: After data analysis, out of 336 missenses, the eight nsSNPs, namely R138Q, I141N, W136G, I349N, L303R, E140A, W306C, and L308Q, were discovered by ConSurf to be in highly conserved regions, which could affect the stability of their proteins. Project HOPE determines any significant molecular effects on the structure and function of eight mutated proteins and the three-dimensional (3D) structures of these proteins. The two pharmacologically significant compounds, Luzonoid B and Roseoside demonstrate strong binding affinity to the mutant proteins, and they are more efficient in inhibiting them than the typical SLC30A8 protein using Autodock Vina and Chimera. Increased binding affinity to mutant SLC30A8 proteins has been determined not to influence drug resistance. Ultimately, the Kaplan-Meier plotter study revealed that alterations in SLC30A8 gene expression notably affect the survival rates of patients with various cancer types. Conclusion: Finally, the study found eight highly deleterious missense nsSNPs in the SLC30A8 gene that can be helpful for further proteomic and genomic studies for T2D and colorectal cancer diagnosis. These findings also pave the way for personalized treatments using biomarkers and more effective healthcare strategies.

Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, Personnel Computer Storm Water Management Model (PCSWMM) software was used to develop a numerical model. It was found that the MCDA approach is appropriate to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate. HIGHLIGHTS A comprehensive assessment of nature-based solutions performance was presented.; Multiple methods: numerical model, social survey, and economic analyses were implemented.; First detailed study in a developing country under a tropical climate.;

Abstract A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for human detection, requiring significant analysis time. Consequently, developing a detection system becomes crucial for accurately classifying KUB X-ray images. This article applies a transfer learning (TL) model with a pre-trained VGG16 empowered with explainable artificial intelligence (XAI) to establish a system that takes KUB X-ray images and accurately categorizes them as kidney stones or normal cases. The findings demonstrate that the model achieves a testing accuracy of 97.41% in identifying kidney stones or normal KUB X-rays in the dataset used. VGG16 model delivers highly accurate predictions but lacks fairness and explainability in their decision-making process. This study incorporates the Layer-Wise Relevance Propagation (LRP) technique, an explainable artificial intelligence (XAI) technique, to enhance the transparency and effectiveness of the model to address this concern. The XAI technique, specifically LRP, increases the model's fairness and transparency, facilitating human comprehension of the predictions. Consequently, XAI can play an important role in assisting doctors with the accurate identification of kidney stones, thereby facilitating the execution of effective treatment strategies.

A multiple-input multiple-output Ultra-Wide Band (UWB) antenna with a compact size of 0.8λ x 0.7λ is proposed. The presented structure is constituted with patch geometry on a planar surface. The radiator consists of three counter U-shaped conducting strips united with a patch structure where two are positioned nearby and the third geometry covers the remaining conducting strips. A corrugated design has been created on a ground plane in which zigzag pattern slots are engineered to receive high isolation of more than 20 dB. The configured antenna radiates over a wide frequency spectrum of 3.10 GHz–11.85 GHz. It offers a fractional bandwidth of 195 % with a gain of 2.80 dBi, 3.08 dBi, and 2.72 dBi for targeted resonances. The antenna radiation patterns and MIMO diversity parameters are also presented. The acceptable values of ECC (

Visual Question Answering (VQA) is a promising technology that has the potential to revolutionize the medical field by enabling computers to respond to questions about medical images. VQA holds great potential to transform medical imaging, but several obstacles stand in the way, effective medical VQA model development is hampered by problems like the scarcity of easily accessible medical datasets, complicated medical scenarios, and the complexity of the medical images. To contribute to the advancement of VQA models in the medical field, our research undertakes two key initiatives. Firstly, we introduce a novel dataset for medical VQA, derived from an existing dataset of blood cell images, Secondly, our study proposes a VQA model that is specifically designed to classify images of microscopic blood cells. We use pre-trained transformers like Electra, BERT, and DistilBERT to extract textual features in combination with Vision Transformer (ViT) to extract visual features from images, and then combine textual and visual features and then apply classifiers like Linear SVM and Quadratic SVM. Experimental results show that The Electra & ViT model surpasses other models by achieving high scores across multiple evaluation metrics, including a WUPS score of 90.09%, accuracy of 89.63%, F1-Score of 64.03%, precision of 63.42%, and recall of 65.23%.

Hirschsprung’s disease (HD) and annular pancreas are both uncommon congenital gastrointestinal malformations. Although additional abnormalities are associated with 18% of HD cases, there is limited evidence of concomitant HD and annular pancreas. We present a full-term, two-day-old male with abdominal distended, and failure to pass first stool after 3 days of observation and rectal washout the abdominal X-rays showed a double bubble appearance. Intraoperative finding, the patient had pancreas annulare (PA). Colonic aganglionosis was discovered using full-thickness rectal biopsies (FTRB). At the age of seven months, a transanal endorectal pull-through was performed without complications. Although the diagnosis of these conditions might be difficult due to their clinical similarities, appropriate diagnosis, and surgical management are possible and can dramatically minimize morbidity and death in the newborn.

Abstract A multifunctional beam splitting frequency selective surface (FSS) is modeled, analyzed, and tested in transmission and reflection modes. The proposed FSS comprises a C-shaped split-ring resonator designed and fabricated on an ultrathin, flexible polyimide material. When a linearly polarized incident wave interacts with the unit cell of the proposed FSS, half of the wave is reflected, and the other half is transmitted at two frequency bands from 5.8–6.2 GHz and 18.5–22 GHz. Moreover, the proposed FSS is angularly stable upto 40° and also performs simultaneous beam splitting and quarter-wave operation within one of its two bands of operation i.e., from 16.5–18.2 GHz. Such flexible beam splitting FSSs with polarization transformation operation and having angular stability, size miniaturization and multi-band operation is a specialized component having potential to be used for electromagnetic wave manipulation in antenna systems, radar technology, stealth technology, wireless communication, satellite communication, medical imaging, security and surveillance, aerospace and defense, and automotive radar.

Since the beginning of the COVID-19 pandemic various studies have reported that healthcare workers (HCWs) are not only at risk of exposure but also experiencing psychological distress. However, less is known about the mental health outcome of HCWs after their clinical recovery from COVID-19 infection. This study addresses a critical gap in our understanding by focusing on the mental health outcomes of HCWs in Pakistan after their clinical recovery from COVID-19 infection and the resumption of their duties. This cross-sectional, study was conducted among 272 HCWs who had recovered COVID-19 infection. The symptoms of depression were assessed by Patient Health Questionnaire-9. Multivariable logistic regression was performed to identify factors associated with depression. The findings revealed a depression prevalence of 33.82% among the HCWs in the study. Factors such as being female, middle-aged, having lower income, and working in COVID-19 wards increased the risk of depression. Moreover, those who perceived less social support from coworkers and supervisors and experienced an increased workload after resuming duties showed higher depression rates. In conclusion, the study emphasizes that clinical recovery from COVID-19 infection does not guarantee functional recovery for HCWs. To support their psychological well-being and resilience, it underscores the importance of enhanced social support in the workplace for who resume their duties after recovering from COVID-19 infection.

Toxoplasma gondii is a zoonotic parasite able of infecting all warm-blooded animals. Toxoplasmosis is one of the major foodborne diseases globally. The consumption of wild boar (Sus scrofa) meat from recreational hunting has been linked to outbreaks of human toxoplasmosis. The island of Sardinia (Italy) contains a large wild boar population, thus providing an opportunity to assess the distribution of Toxoplasma in this species and the associated risks of transmission to humans. A total of 562 wild boars were screened: heart and meat juice samples were tested for T. gondii DNA via nested-PCR and IgG anti-Toxoplasma by commercial ELISA. Anti-Toxoplasma IgG were detected in 24.6% (138/562) of animals, while 37.2% (209/562) of the heart samples were PCR positive. The prevalence of T. gondii antibodies and DNA highlights the potential role of wild boar as an important reservoir for this parasite. The study suggests that wild boar could play a significant role in spreading the parasite to humans. As wild boar numbers are increasing throughout their range, their potential role in transmitting toxoplasmosis should be communicated to stakeholders, and the impact of different population control methods on disease transmission should be thoroughly assessed to mitigate potential threats effectively.

Abstract In this paper, a single-layer reconfigurable reflective metasurface is presented. The proposed metasurface operates at 5.4 GHz and can achieve either absorption or cross-polarization conversion corresponding at two different diode biasing states. The reflective metasurface acts as an absorber for an incident wave when the diodes are forward-biased. Similarly, it changes the polarization state of the reflected wave for a linearly polarized incident wave when the diodes are reverse-biased. The proposed structure maintains the aforementioned performance characteristics for oblique incidence, up to 60° compared to the perpendicular incidence. The proposed metasurface can achieve linear to linear polarization conversion with polarization conversion ratio (PCR) > 95% and absorption, with absorption ratio (AR) > 80% in the same frequency band just by reconfiguring the state of the PIN diodes.

This research study proposes a unique photonic crystal fiber (PCF) consisting of a solid core and a hexa-spiral shape, using tellurite as the background material. The proposed PCF exhibits numerous significant optical guiding properties, such as nonlinearity, dispersion, effective index, effective mode area, etc. Simulated findings from COMSOL Multiphysics v5.5 confirm that the PCF can achieve high nonlinearity and zero-dispersion at 1650 nm. The base material is tellurite, which has a significant nonlinear refractive index (RI) of 5.11 × 10−19 m2 W−1. The resultant wave has a bandwidth of 562.65 nm when pumped at a wavelength of 1650 nm. Additionally, a continuum can be produced by pumping at 1500 and 1550 nm. The designed PCF can generate a broad spectrum of supercontinuum with a pulse duration of 200 fs and 10 kW input power. Here is also performed measurements and inspections on the impact of higher-order Taylor series dispersion coefficients, pulse width, input power, etc. The proposed PCF has many potential applications, including nonlinear optics, high dispersion characteristics, infinitely single-mode fibers, high wattage beam transmission, particle trapping, and sensing. To address these applications, here is used COMSOL Multiphysics to generate data, such as propagation constants, attenuations, effective area, and fields, which were used by Optisystem. To the best of our knowledge, this is the first investigation to verify the performance of our designed PCF in Optisystem, for modeling signal transmission for supercontinuum generation and Raman shift.

The COVID-19 pandemic started in the last week of December, 2019. An emergency was declared throughout the globe as the virus spread over 208 counties within a short amount of time. This pandemic had significant negative impacts on common men living in densely populated countries, including Pakistan. Hence, this research aimed to record people’s perception of the 7th largest settlement in Pakistan, namely Hyderabad. This study mainly focused on socioeconomic and psychological parameters. The close-ended questionnaire was designed concerning the Depression, Anxiety, and Stress Scale (DASS-21). The DASS-21 is an optimal tool for recording the emotional values of depression, anxiety, and stress. A total of 400 questionnaires were filled out; they used a 5-point Likert scale. Significant socioeconomic issues were noted, such as lower household income, economic recession, job cuts, inaccessible Internet services during online teaching or working from home, etc. The primary challenges in the lockdown situation were Internet access (4.06) and anxiety (4.86) in the female population. There were higher levels of depression (N = 200), anxiety (N = 341), and stress (N = 125) in the local inhabitants. Other notable problems were illuminated, such as restrictions on social gatherings, electricity load shedding, and lower incomes. This study concludes that the degree of psychological problems varied according to the typology of gender. Based on study findings, this study recommends the prompt execution of policies considering possible future pandemics to restrain anxiety and depression in the female population. The local government is also advised to revitalize infrastructure to provide uninterrupted power supplies and Internet facilities. The income-generating channels should be open for lower-income households concerning future lockdowns.

Within the sphere of finance, the concept of capital structure has long been a subject of intense debate, serving as a quantitative depiction of the balance between debt, preference shares, and common stock within a company. This structure serves a crucial role in optimizing the utilization of a company's existing resources while simultaneously elevating the revenue streams for stakeholders. This particular study delves into the intricate relationship between corporate performance and capital structure, focusing on 78 publicly listed firms within the Dhaka Stock Exchange (DSE). Bangladesh holds the 29th position globally in terms of purchasing power, lending significant weight to this investigation. To comprehensively analyze this correlation, panel data encompassing the span from 2017 to 2021 was collected for these 78 sample companies operating within the DSE. Several key determinants of capital structure were considered in this analysis, namely the debt-to-equity ratio, short-term leverage ratio, long-term leverage ratio, and total debt ratio. Meanwhile, the performance of these firms was gauged using key metrics such as Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). To ensure a robust analysis, factors such as inflation, liquidity, growth rate, tax rate, and firm size were meticulously controlled for. The findings unveiled a compelling narrative: all forms of debt ratios—be it short-term, long-term, or the total debt ratio—exhibited a substantial negative impact on ROA at a significant level of 1 %. Conversely, specific debt ratios, like the short-term total debt and the total debt-to-total asset ratio, displayed a notable positive correlation with ROE at a 1 % significance level. Intriguingly, the long-term total debt ratio yielded a negative and insignificant effect on ROE. Moreover, within the spectrum of predictors influencing a firm's performance, the liquidity ratio emerged as a non-significant factor—a notable discovery that highlights the nuanced nature of the interplay between capital structure and performance within these companies.

In this paper, a biosensing absorber based on phase transition material is presented. Different phases of the Ge2Sb2Te5 (GST) substrate have been studied for the suggested absorber with controllable characteristics. The structure has been examined to determine the infrared absorption characteristics. The detection of varying volumes of hemoglobin and urine biomolecules is studied. The graphene-GST material is utilized for spectrum tuning. The tuning for two distinct phases of GST material, amorphous GST and crystalline GST is examined. The results for aGST and cGST are reported in the form of absorption. Different amounts of hemoglobin and urine biomolecules are used to tune these two GST stages. Based on the wavelength shifts at these various concentrations, the sensitivity is computed. The highest achievable sensitivity for hemoglobin and urine biomolecules is 1500 nm/RIU and 1667 nm/RIU. The developed model is observed for various geometrical parameters and incidence angles, from which it is determined that the suggested structure is insensitive to wide angles between 0° and 60°. For urine biomolecules, the aGST design is more sensitive than the cGST design, but similar results are achieved for hemoglobin biomolecules. Experiments are conducted with Machine Learning-based regression models to minimize the simulation time and resource requirements of biosensor design. The findings of the trials indicate that a regression model can accurately estimate the absorption values for intermediate wavelengths with an R2 score of 0.9999.

Plastic consumption is continuously increasing due to urbanization and growing global demand. The consumption of plastic deteriorates the quality of the urban environment of most cities, especially in developing countries. To overcome this issue to some extent, this study was conducted to prevent the usage of plastic products to provide a healthy environment. The data was collected by using the closed-ended questionnaire survey and by reviewing the literature. The collected data were assessed and analyzed on SPSS (Statistical Package for Social Sciences) software. The survey observed that there is no proper plastic waste disposal system in the study area. People who reside in the area and those who visit the market face an unhygienic environment. The results highlighted that 50% to 60% of residents use plastic items which are dumped at open spaces without proper management after their consumption. This study suggests short-term, mid-term and long-term mitigation measures to reduce the production by using bio-based plastic and consumption by reducing the unnecessary packaging of plastic waste.

Nutrition is a key aspect influencing honey bee health and overwintering. Since honey bee seasonality in southern temperate climates represents a significant research gap, this study conducted long-term monitoring of honey bees in the Mediterranean (Sassari, Italy). Specifically, individual weight, fat body, and size measurements (head, thorax, abdomen, and total body) were recorded monthly so to detect changes in the nutrient storage of worker bees during an annual cycle. Data were analysed according to sampling date, climate (temperature, precipitation, and daylength), and flower diversity and were conducted for nurse and forager bees separately. The nutritional honey bee year-cycle generally followed the nectar flow and showed two critical timepoints: summer and winter dearth. A short cessation of activities in late fall/early winter coupled with an increase in nutrient storage indicated the presence of winter bees. Precipitation was found to play an important role in honey bee nutrition in the study area through its impacts on colony demography and plants in particular illustrating how climate change could pose a threat to European honey bee populations in the future. These results provide valuable insights into the nutritional dynamics of Apis mellifera ligustica in the Mediterranean that could support management decisions to improve overwintering success and prevent unnecessary colony losses.

In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna’s beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing’s low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements.

Advancements in novel combination immunotherapies as well as innovative downstream management courses offer great optimism for the applicability of emerging cancer immunotherapy to prospective treatment of cold tumours. This review comprehensively analyses and discusses notable current research directions in the field and underscores future directions for continued scientific progress alongside relevant clinical applications. Vitiligo is an autoimmune disorder characterised by white depigmented cutaneous macules. Although vitiligo may generally be considered a cosmetic disease, literature has associated broader systemic comorbidities, including a higher risk for atherosclerotic events, dyslipidaemia, and cardiovascular risk. To the authors’ knowledge, this is the first systematic review that assesses the association between vitiligo and cardiovascular disease (CVD)/CVD-associated factors. Utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the authors searched PubMed and Scopus databases to identify studies available as of 30th June 2022, examining CVD and CVD-associated risk factors in patients with vitiligo. Of 2,553 articles, seven studies (four cross-sectional and three case-control), totalling 611 patients diagnosed with vitiligo (56.3% female), met the inclusion criteria for the authors’ review. Six studies suggested a significant association between patients with vitiligo and an increased risk for CVD via increased atherosclerotic events, constraint-induced movement therapy, plaque presence, dyslipidaemia, high-sensitivity C-reactive protein, oxidative stress, as well as decreased levels of vitamin D, calcium, zinc, and antioxidants. However, one study found that patients with vitiligo presented with fewer cardiovascular risk factors and increased levels of high-density lipoprotein. Although few studies indicated an increase in atherosclerotic risk due to elevated low-density lipoprotein-cholesterol and total cholesterol, contradictory high-density lipoprotein and total cholesterol levels in additional studies indicate the need for further investigation. Lastly, the association between vitiligo severity and CVD risk also indicated conflicting results. The authors’ small sample size restrained their ability to compare populations and incorporate racial and ethnic diversity to generalise their conclusions. Additional studies are required to comprehensively understand the association between vitiligo and the risk of CVD.

Predicting fetal brain abnormalities (FBAs) is an urgent global problem, as nearly three of every thousand women are pregnant with neurological abnormalities. Therefore, early detection of FBAs using deep learning (DL) can help to enhance the planning and quality of diagnosis and treatment for pregnant women. Most of the research papers focused on brain abnormalities of newborns and premature infants, but fewer studies concentrated on fetuses. This study proposed a deep learning-CNN-based framework named StackFBAs that utilized the stacking strategy to classify fetus brain abnormalities more accurately using MRI images at an early stage. We considered the Greedy-based Neural architecture search (NAS) method to identify the best CNN architectures to solve this problem utilizing brain MRI images. A total of 94 CNN architectures were generated from the NAS method, and the best 5 CNN models were selected to build the baseline models. Subsequently, the probabilistic scores of these baseline models were combined to construct the final meta-model (KNN) utilizing the stacking strategy. The experimental results demonstrated that StackFBAs outperform pre-trained CNN Models (e.g., VGG16, VGG19, ResNet50, DenseNet121, and ResNet152) with transfer learning (TL) and existing models with the 5-fold cross-validation tests. StackFBAs achieved an overall accuracy of 80%, an F1-score of 78%, 76% sensitivity, and a specificity of 78%. Moreover, we employed the federated learning technique that protects sensitive fetal MRI data, combines results, and finds common patterns from many users, making the model more robust for the privacy and security of user-sensitive data. We believe that our novel framework could be used as a helpful tool for detecting brain abnormalities at an early stage.

In this article, we propose a rectangular hollow core Photonic Crystal Fiber (PCF) sensor for high detection of women’s reproductive hormones (progesterone and estradiol) in the blood sample at THz regime (0.8 THz to 1.7 THz). The numerical sensing performances are evaluated by the full vector finite element method (FVFEM). We have achieved improved relative sensitivity with minimal loss for sensing progesterone concentrations and estradiol concentrations by optimizing structural parameters. The obtained maximum relative sensitivity is 99.87% for 10 n mol/L (progesterone) and 99.88% for 3 n mol/L (Estradiol) at 1.35 THz. Further, we have obtained low effective material loss (EML) and a large effective mode area. The primary takeaway from this research is that it’s critical to monitor estradiol and progesterone levels in order to ensure that a woman’s reproductive system is functioning in a balanced manner and in general health systems. Also, this biosensor can be fabricated by current fabrication technologies. Moreover, the prediction carried out with the help of Locally Weighted Linear Regression, and hyperparameter tuning, we can conclude that for weighting hyperparameter value of 0.02. We have achieved the maximum prediction accuracy with the unity R2 score and this model can be employed for the prediction of relative sensitivity for various parameters.

Abstract Background Cystic echinococcosis (CE) is a widespread zoonosis and a significant economic concern and cause of morbidity in humans. A scarcity of education on the sources of CE infection and containment measures is considered to be a key factor responsible for persistent transmission within communities. Recently, edutainment approaches have captured the attention of health education (HE) professionals due to the benefits of integrating cognitive and emotional learning processes. Methods A study was carried out in Sardinia, Italy, between 2020 and 2022, amid the SARS-Covid-19 pandemic. The project, designed to involve primary school children (via remote or face-to-face learning depending on the evolving Covid-19 containment measures) consisted of four distinct phases: (i) creation of material for school children and teachers focused on cystic echinococcosis; (ii) pre-intervention evaluation of CE knowledge (i.e. True False Don’t Know [TFD] pre-intervention questionnaire based on CE-related knowledge and practices); (iii) edutainment activity (e.g. interactive lessons enhanced by the comic booklet and the “Fight the parasite” cartoon video, hands-on educational activities and drawing activities on CE); and (iv) post-intervention evaluation of CE knowledge (via TFD post-intervention questionnaire [same questionnaire as used for the pre-intervention assessment] on CE-related knowledge and practices) and on-site edutainment tour in primary schools taking part to the project. Results The percentage of correct answers increased from 65% for the questionnaire administered pre-intervention to 87.9% for the same questionnaire administered post-intervention (χ 2 = 648.12, df = 1, P

This research paper proposes a simple hexa-spiral hexagonal photonic crystal fiber (HSH-PCF) design containing circular timbered air holes, exhibiting a highly nonlinear coefficient and negative dispersion with lower confinement loss. The Finite Element Method (FEM) has been applied here to inspect guiding properties. This propounded HSH-PCF covers all optical bands and windows in the design. The simulation analysis proves that the proposed HSH-PCF achieves a high nonlinear coefficient of 113.8(W.km)-1 at the wavelength of 1.55μm, negligible confinement loss, and the maximum negative dispersion amplitude of -864.5ps.(nm.km)-1. At a 40kW peak power with 20fs input pulse through the 1.5cm of fiber length, the PCF represents a wide-band supercontinuum generation band spectrum. So, the HSH-PCF officiates in several applications like high bit-rate data transfer, amplification of optical parameters, sensor designing of optical fibers, maintaining polarization, dispersion-compensating fibers, spectroscopy system, and other various purposes.

The solar spectrum energy absorption is very important for designing any solar absorber. The need for absorbing visible, infrared, and ultraviolet regions is increasing as most of the absorbers absorb visible regions. We propose a metasurface solar absorber based on Ge2Sb2Te5 (GST) substrate which increases the absorption in visible, infrared and ultraviolet regions. GST is a phase-changing material having two different phases amorphous (aGST) and crystalline (cGST). The absorber is also analyzed using machine learning algorithm to predict the absorption values for different wavelengths. The solar absorber is showing an ultra-broadband response covering a 0.2–1.5 µm wavelength. The absorption analysis for ultra-violet, visible, and near-infrared regions for aGST and cGST is presented. The absorption of aGST design is better compared to cGST design. Furthermore, the design is showing polarization insensitiveness. Experiments are performed to check the K-Nearest Neighbors (KNN)-Regressor model’s prediction efficiency for predicting missing/intermediate wavelengths values of absorption. Different values of K and test scenarios; C-30, C-50 are used to evaluate regressor models using adjusted R2 Score as an evaluation metric. It is detected from the experimental results that, high prediction proficiency (more than 0.9 adjusted R2 score) can be accomplished using a lower value of K in KNN-Regressor model. The design results are optimized for geometrical parameters like substrate thickness, metasurface thickness, and ground plane thickness. The proposed metasurface solar absorber is absorbing ultraviolet, visible, and near-infrared regions which will be used in solar thermal energy applications.

This study introduces a distinctive LiNbO3-based five-ring heptagonal-shaped photonic crystal fiber (PCF) geometry that demonstrates exceptionally high birefringence (Br) and nonlinear coefficient (NLC). Fundamental simulation studies are conducted through the COMSOL software utilizing the finite element method (FEM) with a perfectly matched layer (PML) boundary condition in order to comprehensively explore a variety of relevant optical characteristics, including birefringence, effective area, nonlinearity, core power fraction, dispersion, numerical aperture, confinement loss and so on. The simulation outcomes reveal an ultra-high Br and NLC of 0.081 and 5843.32 W-1Km-1 sequentially, at the operative wavelength of 1.55μm. The designed PCF with ultra-high Br and NLC features can be fabricated suitably, which is an advantageous aspect for potential prospective utilization in polarization-maintaining, nonlinear optics, supercontinuum generation, etc.

A novel single material-based anti-resonant fiber is designed, investigated, and explored in this article with supports of up to 64 OAM modes over the 0.6 μm to 1.0 μm as operating wavelength. The other properties of the designed fiber including confinement loss (CL), OAM purity, effective refractive index differences (ERIDs), and dispersion variations provide the exceptional output. This fiber, the CL approximately varies between the 5.7031 × 10−5 dB/m to 9.2537 × 10−4 dB/m, the OAM purity is 97% to 99%, the ERIDs are higher than 10-4 for all the modes, and the least dispersion variations is −8.838 ps/km-nm for the EH1,1 mode. All the outstanding properties of the presented fiber are calculated using the FEM and the PML within the COMSOL Multiphysics simulator. Therefore, to the ideal of our knowledge, the proposed anti-resonant fiber is mostly applicable in the high-quality long-distance fiber communications system.

Introduction: Solid organ transplant recipients (SOTR) are at an increased risk for developing keratinocyte carcinomas (KC). Four ultraviolet (UV) modifying factors have been identified that impact the incidence of KC: Fitzpatrick Skin Type (FST), race, sun exposure, and sun-protective factors. Objectives: We conducted a systematic review to summarize the association between UV modifying factors and the incidence of KC in SOTR. Methods: We systematically searched PubMed, Scopus, and Web of Science databases, and after screening for inclusion and exclusion criteria, we included 13 studies with 6,910 solid organ transplant recipients in our analysis. Results: Our review found that lower FST (I-II), white and Latinx populations, lack of regulated sunscreen application, and occupational and residential sun exposure are individual risk factors among solid organ transplant recipients for KC incidence. Although previous studies showed an increased SCC:BCC ratio, some studies found a contradictory increased BCC:SCC ratio. Limitations include few research studies that analyze these UV modifying factors and a lack of incorporating both varying immunosuppressant factors and transplantation follow-up times. Conclusions: These findings support the need for dermatological advice in increased risk patient demographic populations, lower FST and white and Latinx populations, and subsequently moderating sun exposure and protective factors.

IntroductionEffective communication in pediatric palliative cancer care is an important aspect of practice to enhance patient- and family-centered care, and to optimize decision-making. However, little is known about communication preferences practices from the perspectives of children, caregivers and their health care professionals (HCPs) in the Middle Eastern region. Furthermore, involving children in research is crucial but limited. This study aimed to describe the communication and information-sharing preferences and practices of children with advanced cancer, their caregivers, and health care professionals in Jordan.MethodsA qualitative cross-sectional study was conducted using semi-structured face to face interviews with three groups of stakeholders (children, caregivers and HCPs). Purposive sampling recruited a diverse sample from inpatient and outpatient settings at a tertiary cancer center in Jordan. Procedures were in line with the Consolidated criteria for reporting qualitative research (COREQ) reporting guidelines. Verbatim transcripts were thematically analyzed.FindingsFifty-two stakeholders participated: 43 Jordanian and 9 refugees (25 children, 15 caregivers and 12 HCPs). Four major themes emerged: 1) Hiding information between the three stakeholders which includes parents who hide the information from their sick children and ask the HCPs to do so to protect the child from distressful emotions and children who hide their suffering from their parents to protect them from being sad; 2) Communication and sharing of clinical versus non-clinical information; 3) Preferred communication styles such as use of a companionate approach by acknowledging patients and caregivers’ suffering, building a trustful relationship, proactive information sharing, considering child age and medical status, parents as facilitators in communication, and patients’ and caregivers’ health literacy; 4) Communication and information sharing with refugees where they had dialect issues, which hindered effective communication. Some refugees had unrealistically high expectations regarding their child’s care and prognosis, which posed challenges to communication with staff.DiscussionThe novel findings of this study should inform better child-centered practices and better engage them in their care decisions. This study has demonstrated children’s ability to engage in primary research and to express preferences, and parents’ ability to share views on this sensitive topic.

IntroductionPalliative care access is limited in the Middle-East, with few specialist centers and forcibly displaced migrants facing additional struggles to access care. Little is known about the specificities of providing palliative care to children and young people (CYP) with cancer. They are rarely asked directly their concerns and needs, which limits the provision of quality patient-centered care. Our study aims to identify the concerns and needs of CYP with advanced cancer and their families, in Jordan and Turkey.MethodA qualitative cross-national study in Jordan and Turkey with framework analysis was conducted two pediatric cancer centers in Jordan and Turkey. In each country, 25 CYP, 15 caregivers and 12 healthcare professionals participated (N=104). Most caregivers (70%) and healthcare professionals (75%) were women.ResultsWe identified five areas of concern: (1) Physical pain and other symptoms (e.g. mobility, fatigue); (2) Psychological concerns and needs (e.g. anger, psychological changes); (3) Spirituality, uncertainty over the future and use of “Tawakkul” (e.g. use of religion as a coping mechanism); (4) Negative impact on social life (e.g. social isolation, loss of support); (5) Burden on caregiver and their families (e.g. financial issues, siblings left behind). Psychological concerns were a priority for both CYPs and caregivers (particularly for refugee and displaced families) but often overlooked during routine care. CYP were able to share their own concerns and care priorities.ConclusionsAdvanced cancer care must ensure assessment and management of concerns across the concerns identified. Developing child- and family-centered outcomes would ensure monitoring the quality of care. Spirituality had a more important role compared to similar investigation in other regions.

In this work, a modified square photonic crystal fiber (MS-PCF) architecture is exposed with ultra-high negative dispersion for communication window. The validation of the model is successfully performed by applying an efficient full-vector finite element method (FV-FEM) with anisotropic perfectly matched layers (PMLs) for precise simulation of PCFs. Additionally, waveguide dispersion engineering is accomplished by tuning the structural parameters of the MS-PCF. From the numerical investigation, negative dispersion behavior permits remarkable suppression rather than positive dispersion. It offers the high negative dispersion of −2357.54 ps/nm/km and Kerr nonlinearity (γ) of 74.68 W−1 km−1 simultaneously at the operating wavelength, λ = 1550 nm. Nevertheless, the designed fiber ensures the mono mode operation in the entire band of interest from λ = 1340 nm to λ = 1580 nm. MS-PCF with these outstanding optical performances has distinguished potency to be practiced as a dispersion compensating fiber in optical communication systems.

Abstract Taenia multiceps is a taeniid cestode that inhabits the small intestines of both wild and domestic carnivores. The larval stage, Coenurus cerebralis, is typically found in the central nervous system (CNS) of a wide range of livestock and, to a lesser extent, in the extra-cerebral tissues of sheep and goats. This review covers all aspects of the life cycle of T. multiceps and its epidemiology, molecular characterization, pathogenesis, diagnosis, therapy, control and zoonotic potential. Coenurosis caused by the larval stage of T. multiceps has a worldwide distribution and is often fatal in intermediate hosts, which can result in substantial economic losses in livestock farming. Molecular characterization using the mitochondrial genes cytochrome c oxidase subunit 1 and nicotinamide adenine dinucleotide dehydrogenase subunit 1 of different T. multiceps populations has revealed significant genetic variation and the presence of three major haplotypes. The disease mostly affects young sheep and is referred to as either acute or chronic coenurosis. Acute coenurosis occurs as a result of oncospheres migrating through the CNS, while chronic coenurosis occurs as a consequence of the coenurus maturing, which causes displacement and pressure atrophy of brain tissue. Non-cerebral coenurosis has been most commonly reported in goats. The best diagnostic method for cerebral coenurosis involves the interpretation of clinical signs with accurate localization of the cyst using diagnostic imaging techniques. A vaccine based on recombinant oncosphere antigens has proved to be an effective tool against T. multiceps infection in sheep. Additionally, use of anthelmintics during the parasite’s migration stages reduces the development of cysts in the sheep brain. Surgery is considered the most effective method for the treatment of cerebral coenurosis in small ruminants, but is often not carried out because of the limited finances of many sheep and goat breeders. However, coenurosis can also be controlled effectively through preventative measures, such as anthelmintic treatment of dogs and the proper disposal of intermediate host carcasses. The parasite is also zoonotic, and cases of coenurosis have been reported in humans with coenuri located in the brain, spinal cord and eyes. Graphical abstract

Abstract Background With more than 3.6 million Syrian refugees Turkey hosts the world's largest number of Syrians. Considering the morbidity, mortality, and healthcare spending, cancer is one of the leading health and economic burden for patients and healthcare systems. However, very limited information available in the scientific literature to understand the burden and characteristics of cancer in countries hosting Syrian refugees. The aim of the present study is to evaluate the demographic and clinical characteristics, treatment outcome of Syrian cancer patients living in Konya, Turkey. Methods We retrospectively reviewed medical records of Syrian cancer patients at three major institutions from 2005 to 2020. The information regarding demographic and clinical characteristics of patients were identified. The number of days between the first symptom and diagnosis was considered as the “diagnostic interval”. Patients who failed to attend clinics within four weeks of appointment were assumed abandoned treatment. Survival curves were estimated using the Kaplan–Meier method. Results We identified 230 adult and 38 children refugee diagnosed with cancer during the study period. With regards to adult patients, there were 114 (49.6%) male and 116 (50.4%) female. The median age at diagnosis was 52.4, 47.3 years for male, female respectively. The five most common cancer by site among all were; breast (24.8%), colorectal (10.9%), lung (7.4%), central nervous system (CNS) (7.0%), and stomach (5.2%). 93 (40.4%) had metastatic disease at diagnosis. The overall survival probability was 37.5% at five years for the adult population. Data were extracted for 20 boys and 18 girls with childhood cancer. Their median age at diagnosis was 5.8 and 6.0 years respectively. The three most common childhood cancer were; leukemias (21.1%), lymphomas (21.1%), and CNS (13.2%). Excluding leukemia, 13 (43.3%) of childhood cancer cases had the advanced disease at diagnosis. Three year survival probality was 69.5%. The median diagnostic interval for adult and childhood cancer was 96.5 (IQR = 53–165) and 23 (IQR = 13.5–59) days respectively. Twenty-one adults and four children had treatment abandonment. Conclusion This study contributes to understanding the burden of cancer among Syrian refugees living in Konya, growing health issue for refugees. Larger and prospective studies will help to measure the real burden and compare the difference in cancer risk factors, care, and outcomes among the refugee and host populations.

We have investigated graphene-based three various refractive index sensors (split ring resonator (SRR), split ring resonator with thin wire (SRRTW), and thin wire (TW) refractive index sensors) for the encoding and sensing-based applications. The sensors are designed to detect the presence of hemoglobin biomolecules with high sensitivity. The results are analyzed in the form of transmittance, and electric field and detailed sensitivity analysis is also carried out for the proposed graphene-based refractive index sensors for four various concentrations of hemoglobin biomolecules. We have also investigated the sensor’s performance in terms of quality factor, Q, and figure of merit (FOM). The encoding of ‘0’ and ‘1’ is attained by varying the graphene chemical potential fulfilling the one-digit coding. An array of these sensors can then be used for encoding-based applications. The detailed analysis of reported sensors is also carried out by checking the effect of varying physical parameters such as substrate thickness, split ring gap, and thin wire width on tunability. These sensors can be applied in biomedical or encoding-based applications. Experiments are performed using XGBoost regressor to determine, whether simulation time and resources can be reduced by using regression analysis to predict the transmittance values of intermediate frequency or not. Experimental results prove that regression analysis using XGBoost Regressor can reduce the simulation time and resources by at least 70 percent.

Mobile edge computing (MEC) is receiving more attention than centralized cloud computing due to the massive increase in transmission and compute requirements in 5G vehicle networks. It offers a significant amount of processing and storage resources to the edge of networks, offloading applications from vehicle terminals that are computation-intensive and delay-sensitive. For devices with limited resources, it uses edge resources to provide computationally heavy operations while conserving energy. This paper proposes a novel approach for computing offloading in MEC. To effectively optimize the MEC resources, this paper proposes a novel algorithm. First, the joint optimization and service cache decision subproblems were determined from continuous and discrete variables. Then, the near-optimal solution is determined from the subproblems through convex optimization and Karush–Kuhn–Tucker method. Simulation results show that the proposed algorithm has better computational offloading and resource allocation performance as compared to existing algorithms.

Abstract This paper presents a multi-functional bi-anisotropic metasurface having ultra-wide out of band transmission characteristics. The proposed metasurface is comprised of 90° rotated T-shaped configuration yielding greater than or equal to 50% out-of-band transmission from above L- to X-band. Moreover, this metasurface achieves a maximum of 99% out-of-band transmission at lower frequency bands (i.e., L-band). The simultaneous absorptive and controlled reflection functionalities are achieved at 15.028 to 15.164 GHz along with polarization-insensitive and angular stable properties. The proposed metasurface yields state-of-the-art features compared to already published papers and has broader scope for Fabry Perot cavity, Radar cross-section (RCS) reduction, electromagnetic compatibility and interference (EMC/I) shielding, selective multi-frequency bolometers, ultrathin wave trapping filters, sensors and beam-splitters in the microwave domain.

This article introduced a double guided ring-based spiral photonic crystal fiber (S-PCF) supporting both the orbital angular momentum (OAM) and linear polarization (LP) mode simultaneously with the efficient transmission. The outcomes exhibition that the designed S-PCF have good optical features such as less confinement loss (4.05342 × 10−12 dB/m for the EH9,1 mode), better mode quality and ISO (up to 98.87% and 126 dB, respectively), supported a decent amount of modes which is numerically 64 OAM and 6 LP modes, maintaining the large refractive index differences (>10-4) of all the eigenmodes, and a relatively flat dispersion variation (0.0743 ps/km-nm for the EH1,1 mode). Also, we obtained the crosstalk less than −12 dB and the power fraction is approximately 93%-99% of the S-PCF. All the optimizing properties are obtained using the full-vector finite element method (FEM) and also the perfectly matched layer (PML) as a boundary condition. Thus, the above mentioned optimizing propagation characteristic based S-PCF has effective applications like reliable long-distance communication into the space division multiplexing (SDM) process of the large-capacity fiber communication systems.

Cutaneous reactions have been commonly associated with the Moderna messenger RNA (mRNA) COVID-19 vaccine. Among the reported cutaneous side effects, there have not been any associations reported yet regarding keratoacanthoma development after COVID-19 mRNA vaccination. We report a novel case of an 86-year-old man who experienced an eruption of multiple keratoacanthomas 2 weeks after inoculation with the Moderna mRNA-1273 vaccine that resolved following treatment with intralesional 5-fluorouracil.

Abstract In this paper, a flexible bianisotropic metasurface possessing omega-type coupling is presented. The designed metasurface behaves differently when excited from either forward (port 1) or back (port 2) sides. It provides an absorption of 99.46% at 15.1 Gigahertz (GHz), when illuminated from port 1, whereas, on simultaneous illumination from port 2, it behaves like a partially reflective surface (PRS). Furthermore, the presented metasurface not only acts as an in-band absorptive surface (port 1) and partially reflective surface (port 2), but it also provides 97% out-of-band transmission at 7.8 GHz. The response of the presented metasurface remains the same for both transverse Electric (TE) and transverse magnetic (TM) polarized wave or any arbitrary linearly polarized wave. Additionally, the response of the metasurface is angularly stable for any oblique incidence up to 45º. The proposed ultrathin flexible metasurface with absorption, partial reflection and out-of-band transmission properties can be used in the Fabry Perrot cavity antenna for gain enhancement with radar cross-section (RCS) reduction both for passband and stop-band filtering, and conformal antenna applications.

Mental health is a major concern for all classes of people, but especially physicians in the present world. A challenging task is to identify the significant risk factors that are responsible for depression among physicians. To address this issue, the study aimed to build a machine learning-based predictive model that will be capable of predicting depression levels and finding associated risk factors. A raw dataset was collected to conduct this study and preprocessed as necessary. Then, the dataset was divided into 10 sub-datasets to determine the best possible set of attributes to predict depression. Seven different classification algorithms, KNN, DT, LGBM, GB, RF, ETC, and StackDPP, were applied to all the sub-datasets. StackDPP is a stacking-based ensemble classifier, which is proposed in this study. It was found that StackDPP outperformed on all the datasets. The findings indicate that the StackDPP with the sub-dataset with all the attributes gained the highest accuracy (0.962581), and the top 20 attributes were enough to gain 0.96129 accuracy by StackDPP, which was close to the performance of the dataset with all the attributes. In addition, risk factors were analyzed in this study to reveal the most significant risk factors that are responsible for depression among physicians. The findings of the study indicate that the proposed model is highly capable of predicting the level of depression, along with finding the most significant risk factors. The study will enable mental health professionals and psychiatrists to decide on treatment and therapy for physicians by analyzing the depression level and finding the most significant risk factors.

Purpose – With the growing use of technology in second language learning (L2), many techniques of incorporating digital video in L2 learning and platforms of task implementation appear in the field, however, with little, if any, research on how tasks can be designed and developed in these contexts. Based on Chapelle (2001, 2014) task design criteria, the current paper evaluates specifically the “interactivity” of task design interface and how it may contribute towards either dispersing or directing the learners' attention (Robinson, 2011) during the process of task completion in video-based L2 listening. Design/methodology/approach – Using a qualitative approach – mainly focus groups and interviews – the current study evaluated a number of tasks that were used for computer-based L2 listening when digital video is the mode of presentation. The participants, i.e. English as a foreign language (EFL) teachers and learners, were presented with a number of task designs to try and evaluate. Findings – The findings revealed that some task designs are perceived to be less interactive and can disperse the learner's attentional resources during the process of task completion. They also shed light on the importance of improving EFL teachers' current practices of task design in computer-based L2 listening. Originality/value – This paper has contributed to our growing understanding of interactivity in relation to video-based learning and its task designs.

Metformin is the most widely used drug to treat type 2 diabetes. Monitoring spatial patterns of metformin use could provide new insights into treatment of type 2 diabetes and the distribution among populations. This study applied a wastewater-based epidemiological (WBE) approach to estimate metformin use in different populations across Australia and compared these estimates with traditional approaches of surveys and prescription data. Twenty-four-hour influent samples were collected from 75 wastewater treatment plants (WWTPs) across Australia in 2016 and analysed for metformin. Metformin was detected in all samples ranging in concentration from 8.2 to 191 µg/L (median 58 µg/L). Concentrations were converted to population-weighted average consumption at the national level, resulting in an average consumption of 28.6 g/day/1000 people across Australia, which was within 7% of estimates from national prescription statistics. In addition, results for five out of seven states had an estimated prevalence of type 2 diabetes within 20% compared to the traditional epidemiology surveys. Spatial patterns were also observed between urban and rural settings, with higher consumption rates of metformin found in Major Cities (22.5 ± 10.9 g/d/1000 people) and Inner Regional cities (25.4 ± 13.4 g/d/1000 people) than in Outer Regional (17.0 ± 8.1 g/d/1000 people) and Remote areas (15.1 ± 7.4 g/d/1000 people). Consumption estimates were also correlated against socioeconomic factors of the specific catchment areas. Greater metformin use was correlated with populations of lower education and income levels, while positive correlations were found between metformin consumption and consumption of allopurinol, caffeine and venlafaxine. Our study provides more evidence on the distribution of metformin use across Australia, which can be used to develop public health strategies to reduce the overall burden of type 2 diabetes in specific regions.

There is a growing body of research in the literature that investigates the relationship between emotions and decision-making in socio-economic contexts. Previous research has used Serious Games (SGs) based on game theory paradigms with socio-economic contexts to explore this relationship in controlled settings, but it is unclear whether such SGs can be applied in the wild, which would improve their usability in natural settings. The emergence and widespread availability of mobile computing technologies have enabled the creation of diverse SG implementations. We developed a mobile SG that combines four socio-economic interactions (trust game, ultimatum game, dictator game, prisoner’s dilemma game) into one narrative and conducted an initial experiment with eight participants. This study discusses the challenges of deploying SGs in the wild and proposes solutions to overcome them. The results from the preliminary user experience experiment indicate that the SG is a suitable tool for emotion elicitation in the wild and that the results are similar to those observed in previous studies under controlled environments. We argue that these findings suggest that mobile SGs have potential for studying the relationship between emotions and decision-making in socio-economic contexts in the wild, improving the reach of these methods beyond laboratory settings.

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Affective disorders can greatly influence the everyday lives of neurotypical and autistic individuals. As platforms that promote engagement, computer-based serious games (CSGs) have been previously proposed as therapies to treat affective disorders for both populations. However, these CSGs were assessed on a wide variety of experimental conditions, and there is a lack of comparative studies on their effectiveness. In this study, we identified and analyzed 37 interventions of CSGs for affective disorders in autistic and neurotypical individuals from 507 initial search results from four databases (Embase, Scopus, Web Of Science and IEEE Xplore), using concepts such as ‘serious-games’, ‘affective-disorders’, ‘autism’ and ‘neurotypical’. A total of 21 different CSGs were identified that were evaluated with 30 different outcome measures in the reviewed interventions. A positive impact was reported in 22 of them; specifically, all instances of depression interventions reported a positive impact of CSG therapies. Our comparative analysis indicates that CSG applications could be effective in treating affective disorders in autistic and neurotypical individuals. Additionally, our analysis identifies CSG design characteristics that might be useful in applications involving depression, anxiety and phobias. Based on these characteristics, we provide a set of recommendations for CSG interventions for affective disorder therapies.

An ultra wideband (UWB) radar cross-section (RCS) reduction metasurface has received attention in recent years. However, the majority of the research has concentrated on the physics and design of planar surfaces, which do not meet the standards of modern aerodynamics and aesthetics. In this paper, we offer a sophisticated strategy for designing a metasurface that can conform to the shape of any object, even those of moderate curvature, and can also achieve UWB RCS reduction by combining absorption, polarization conversion, and diffusion mechanisms. Firstly, an absorbing-polarization converter is designed, composed of a square patch with a truncated diagonal strip and ring. A thin Rogers RT/Duroid 5880 dielectric substrate layer is used in the structure, which is also appropriate for conformal conditions. The substrate layer and the ground plane are separated by an air gap to enhance the polarization conversion bandwidth (PCBW). For normal incident electromagnetic (EM) waves, the PCBW ranges from 10.8 to 31.3 GHz with polarization conversion ratio (PCR) values greater than 0.9 dB. Up to a 45∘ oblique incidence angle over the aforementioned band, the PCR efficiency is well maintained. Then, the optimized coding metasurface is formed by the Pancharatnam–Berry (PB) phase, consisting of meta-atoms “0” and “1” of the same size but different orientations, to realize the concept of cross-polarization diffusion. A theoretical investigation has been performed to analyze the RCS reduction performance of planar as well as conformal cylindrical surfaces. The results show that more than 10 dB of RCS reduction is experienced over UWB (10.8–31.3 GHz) for planar metasurfaces under linearly and circularly polarized incidence waves. Furthermore, the RCS reduction for cylindrical surfaces can be achieved in a similar frequency band above 10 dB up to an angle of 90∘. It can be deduced that our proposed flexible metasurface can be used as an absorber or a polarization converter and provide broadband RCS reduction, which is essential for multi-function and conformal stealth applications.

Abstract In this study, a novel design of microstructure ring‐based triangular mold photonic crystal fibre has been numerically investigated and introduced as an alcohol sensor in Terahertz (THz) band region to get a very high relative‐sensitivity and negligible confinement‐loss profile. Perfectly Matched Layer contains circular air holes with a semi‐poly crystalline symmetric core structure in the triangular cladding region. The entire evaluation process is done by analyzing 2,888,310 mesh elements with the finite element method. Also, the proposed model is justified for an operating frequency of the THz band gap between 1.4 THz and 3.5 THz along with a base material of TOPAS. Last of all, the relative sensitivity profiles of the proposed model are 79.39%, 79.99%, and 80.27% respectively to the water, ethanol, benzene at an optimum frequency of 1.5 THz. In fact, the most optimum performance indicators are 1.18 × 10−12 dB/m, 1.8 × 10−5 µm2, 80%, 0.1 ps/THz/cm and 0.1‐1.5 respectively to the confinement‐loss (Lc), effective area (Aeff), power fraction (η′), dispersion (β2) and V‐Parameter (Veff) at the following optimum frequency for the proposed structure. By evaluating these performance profiles, the proposed model can prove itself a better choice than any other prior model in the field of sensing.

OBJECTIVES: This study aimed to estimate the risk of lymphoma and its major subtypes in relation to occupational exposure to specific organic dusts. METHODS: We explored the association in 1853 cases and 1997 controls who participated in the EpiLymph case–control study, conducted in six European countries in 1998–2004. Based on expert assessment of lifetime occupational exposures, we calculated the risk of the major lymphoma subtypes associated with exposure to six specific organic dusts, namely, flour, hardwood, softwood, natural textile, synthetic textile, and leather, and two generic (any types) groups: wood and textile dusts. Risk was predicted with unconditional regression modeling, adjusted by age, gender, study center, and education. RESULTS: We observed a 2.1-fold increase in risk of follicular lymphoma associated with ever exposure to leather dust [95% confidence interval (CI) 1.01–4.20]. After excluding subjects who ever worked in a farm or had ever been exposed to solvents, risk of B-cell lymphoma was elevated in relation to ever exposure to leather dust [odd ratio (OR) 2.2, 95% CI 1.00–4.78], but it was not supported by increasing trends with the exposure metrics. Risk of Hodgkin lymphoma was elevated (OR 2.0, 95% CI 0.95–4.30) for exposure to textile dust, with consistent upward trends by cumulative exposure and three independent exposure metrics combined (P=0.023, and P=0.0068, respectively). CONCLUSIONS: Future, larger studies might provide further insights into the nature of the association we observed between exposure to textile dust and risk of Hodgkin lymphoma.

Data, information, and knowledge processing systems, in the domain of healthcare, are currently plagued by heterogeneity at various levels. Current solutions have focused on developing a standard-based, manual intervention mechanism, which requires a large number of human resources and necessitates the realignment of existing systems. State-of-the-art methodologies in the field of natural language processing and machine learning can help to partially automate this process, reducing the resource requirements and providing a relatively good multi-class-based classification algorithm. We present a novel methodology for bridging the gap between various healthcare data management solutions by leveraging the strength of transformer-based machine learning models, to create mappings between the data elements. Additionally, the annotated data, collected against five medical schemas and labeled by four annotators is made available for helping future researchers. Our results indicate, that for biased, dependent multi-class text classification, transformer-based models provide better results than linguistic and other classical models. In particular, the Robustly Optimized BERT Pretraining Approach (RoBERTa) provides the best schema matching performance by achieving a Cohen’s kappa score of 0.47 and Matthews Correlation Coefficient (MCC) score of 0.48, with human-annotated data.