Search results for: face sketch image retrieval

Authors: Elham Taghizadeh, Mostafa Abedzadeh, Mostafa Setak

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Logistics network is expected that opened facilities work continuously for a long time horizon without any failure; but in real world problems, facilities may face disruptions. This paper studies a reliable joint inventory location problem to optimize cost of facility locations, customers’ assignment, and inventory management decisions when facilities face failure risks and doesn’t work. In our model we assume when a facility is out of work, its customers may be reassigned to other operational facilities otherwise they must endure high penalty costs associated with losing service. For defining the model closer to real world problems, the model is proposed based on p-median problem and the facilities are considered to have limited capacities. We define a new binary variable (Z_is) for showing that customers are not assigned to any facilities. Our problem involve a bi-objective model; the first one minimizes the sum of facility construction costs and expected inventory holding costs, the second one function that mention for the first one is minimizes maximum expected customer costs under normal and failure scenarios. For solving this model we use NSGAII and MOSS algorithms have been applied to find the pareto- archive solution. Also Response Surface Methodology (RSM) is applied for optimizing the NSGAII Algorithm Parameters. We compare performance of two algorithms with three metrics and the results show NSGAII is more suitable for our model.

Keywords: joint inventory-location problem, facility location, NSGAII, MOSS

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Authors: Saima Batool, Saima Rafique

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Background and Objective: Pandemics like COVID-19 adversely affect children’s behavior and psychological development by disrupting routine life activities. Children of healthcare workers are exposed additionally due to the fear of parental exposure to the virus. The objective of this study was to assess the perception of frontline healthcare workers (HCWs) regarding the psychological impact of the COVID-19 pandemic on their children. We also sought to identify the difference in the psychological impact on children of male and female healthcare workers. Methods: A survey questionnaire was developed comprising 10 questions about the perception of HCWs regarding the psychological impact of COVID-19 on their children. It was distributed both online and face-to-face among 150 healthcare professionals working in training and non-training posts in 4 public and 5 nongovernment hospitals in Pakistan. The mean and standard deviation were calculated for each survey item using Statistical Package for the Social Sciences 26.0. Results: The response rate was 71.3%, and the majority (64.2%) of the healthcare professionals were ≥30 years of age. Ninety-two HCWs (85.98%) either agreed or strongly agreed that parental separation from their kids for long hours during the pandemic had a negative psychological impact on their children. There was a significant difference in the perceived psychological impact of COVID-19 on the children of male and female HCWs, with a mean survey score of 2.29 ± 1.82 and 1.69 ± 0.79, respectively (t = 2.29, p-value = 0.024). Conclusion: Children of healthcare workers experience more stress and anxiety because of long duty hours and working in high-risk settings. Continuous psychological support and counseling services may be adopted formally to prevent unforeseen adverse events or any long-term negative impact on their physical and mental health.

Keywords: healthcare workers, pandemic, COVID-19, anxiety, psychological

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Authors: Ajit Brindhaban

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Background and Purpose: Computed Tomography (CT) scans have become the largest source of radiation in radiological imaging. The purpose of this study was to compare the quality of pediatric Computed Tomography (CT) images reconstructed using Filtered Back Projection (FBP) with images reconstructed using different strengths of Iterative Reconstruction (IR) technique, and to perform a feasibility study to assess the use of IR techniques as a dose reduction tool. Materials and Methods: An anthropomorphic phantom representing a 5-year old child was scanned, in two stages, using a Siemens Somatom CT unit. In stage one, scans of the head, chest and abdomen were performed using standard protocols recommended by the scanner manufacturer. Images were reconstructed using FBP and 5 different strengths of IR. Contrast-to-Noise Ratios (CNR) were calculated from average CT number and its standard deviation measured in regions of interest created in the lungs, bone, and soft tissues regions of the phantom. Paired t-test and the one-way ANOVA were used to compare the CNR from FBP images with IR images, at p = 0.05 level. The lowest strength value of IR that produced the highest CNR was identified. In the second stage, scans of the head was performed with decreased mA(s) values relative to the increase in CNR compared to the standard FBP protocol. CNR values were compared in this stage using Paired t-test at p = 0.05 level. Results: Images reconstructed using IR technique had higher CNR values (p < 0.01.) in all regions compared to the FBP images, at all strengths of IR. The CNR increased with increasing IR strength of up to 3, in the head and chest images. Increases beyond this strength were insignificant. In abdomen images, CNR continued to increase up to strength 5. The results also indicated that, IR techniques improve CNR by a up to factor of 1.5. Based on the CNR values at strength 3 of IR images and CNR values of FBP images, a reduction in mA(s) of about 20% was identified. The images of the head acquired at 20% reduced mA(s) and reconstructed using IR at strength 3, had similar CNR as FBP images at standard mA(s). In the head scans of the phantom used in this study, it was demonstrated that similar CNR can be achieved even when the mA(s) is reduced by about 20% if IR technique with strength of 3 is used for reconstruction. Conclusions: The IR technique produced better image quality at all strengths of IR in comparison to FBP. IR technique can provide approximately 20% dose reduction in pediatric head CT while maintaining the same image quality as FBP technique.

Keywords: filtered back projection, image quality, iterative reconstruction, pediatric computed tomography imaging

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Authors: Arūnas Gudinavičius, Vincas Grigas

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Purpose: The current study aims to identify and explore causes and consequences of unauthorized use of books from readers’, publishers’, and authors’ points of view. The case of Lithuania also assessed, especially historical background (banned alphabet, book smuggling, theft as the social norm in Soviet times) of the country. Design/methodology/approach: Aiming for more understanding why readers, authors and publishers are using or not using technology for unauthorized access of books, technology acceptance model approach was used, a total of 30 respondents (publishers, authors and readers) were interviewed in semi-structured face-to-face interviews and thematic analysis of collected qualitative data was conducted. Interviews were coded in English with coding software for further analysis. Findings: Findings indicate that the main cause for the unauthorized use of books is a lack of legal e-book titles and acquisition options. This mainly points at publishers, however, instead of using unauthorized sources as opportunities for author promotion or marketing, they rather concentrate on the causes of unauthorized use of books which they are not in control of, including access to unauthorized sources, habits, and economic causes. Some publishers believe that the lack of legal e-book titles is the consequence of unauthorized use of book rather than its cause. Originality: This research contributed to the body of knowledge by investigating unauthorized use of books from readers’, publishers’, and authors’ points of view which renders to have a better understanding of the causes and consequences of such behavior, as well as differences between these roles. We suggest that these causes lead to the intention to use and actual use of technology which is easier to use and which gives more perceived advantages – technology for unauthorized downloading and reading of books vs legal e-book acquisition options.

Keywords: digital piracy, unauthorized access, publishing industry, book reader, intellectual property rights

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Authors: I. Stathopoulos, V. Syrgiamiotis, E. Karavasilis, A. Ploussi, I. Nikas, C. Hatzigiorgi, K. Platoni, E. P. Efstathopoulos

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Introduction: Brain and central nervous system (CNS) tumors form the second most common group of cancer in children, accounting for 30% of all childhood cancers. MRI is the key imaging technique used for the visualization and management of pediatric brain tumors. Initial characterization of tumors from MRI scans is usually performed via a radiologist’s visual assessment. However, different brain tumor types do not always demonstrate clear differences in visual appearance. Using only conventional MRI to provide a definite diagnosis could potentially lead to inaccurate results, and so histopathological examination of biopsy samples is currently considered to be the gold standard for obtaining definite diagnoses. Machine learning is defined as the study of computational algorithms that can use, complex or not, mathematical relationships and patterns from empirical and scientific data to make reliable decisions. Concerning the above, machine learning techniques could provide effective and accurate ways to automate and speed up the analysis and diagnosis for medical images. Machine learning applications in radiology are or could potentially be useful in practice for medical image segmentation and registration, computer-aided detection and diagnosis systems for CT, MR or radiography images and functional MR (fMRI) images for brain activity analysis and neurological disease diagnosis. Purpose: The objective of this study is to provide an automated tool, which may assist in the imaging evaluation and classification of brain neoplasms in pediatric patients by determining the glioma type, grade and differentiating between different brain tissue types. Moreover, a future purpose is to present an alternative way of quick and accurate diagnosis in order to save time and resources in the daily medical workflow. Materials and Methods: A cohort, of 80 pediatric patients with a diagnosis of posterior fossa tumor, was used: 20 ependymomas, 20 astrocytomas, 20 medulloblastomas and 20 healthy children. The MR sequences used, for every single patient, were the following: axial T1-weighted (T1), axial T2-weighted (T2), FluidAttenuated Inversion Recovery (FLAIR), axial diffusion weighted images (DWI), axial contrast-enhanced T1-weighted (T1ce). From every sequence only a principal slice was used that manually traced by two expert radiologists. Image acquisition was carried out on a GE HDxt 1.5-T scanner. The images were preprocessed following a number of steps including noise reduction, bias-field correction, thresholding, coregistration of all sequences (T1, T2, T1ce, FLAIR, DWI), skull stripping, and histogram matching. A large number of features for investigation were chosen, which included age, tumor shape characteristics, image intensity characteristics and texture features. After selecting the features for achieving the highest accuracy using the least number of variables, four machine learning classification algorithms were used: k-Nearest Neighbour, Support-Vector Machines, C4.5 Decision Tree and Convolutional Neural Network. The machine learning schemes and the image analysis are implemented in the WEKA platform and MatLab platform respectively. Results-Conclusions: The results and the accuracy of images classification for each type of glioma by the four different algorithms are still on process.

Keywords: image classification, machine learning algorithms, pediatric MRI, pediatric oncology

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Authors: Matheus Madureira Matos, Alexandre Rodrigues Farias

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Magnetic Resonance Imaging (MRI) is a vital imaging technique used in both clinical and pre-clinical areas to obtain detailed anatomical and functional information. However, MRI scans can be expensive, time-consuming, and often require the use of anesthetics to keep animals still during the imaging process. Anesthetics are commonly administered to animals undergoing MRI scans to ensure they remain still during the imaging process. However, prolonged or repeated exposure to anesthetics can have adverse effects on animals, including physiological alterations and potential toxicity. Minimizing the duration and frequency of anesthesia is, therefore, crucial for the well-being of research animals. In recent years, various sampling trajectories have been investigated to reduce the number of MRI measurements leading to shorter scanning time and minimizing the duration of animal exposure to the effects of anesthetics. Compressed sensing (CS) and sampling trajectories, such as cartesian, spiral, and radial, have emerged as powerful tools to reduce MRI data while preserving diagnostic quality. This work aims to apply CS and cartesian, spiral, and radial sampling trajectories for the reconstruction of MRI of the abdomen of mice sub-sampled at levels below that defined by the Nyquist theorem. The methodology of this work consists of using a fully sampled reference MRI of a female model C57B1/6 mouse acquired experimentally in a 4.7 Tesla MRI scanner for small animals using Spin Echo pulse sequences. The image is down-sampled by cartesian, radial, and spiral sampling paths and then reconstructed by CS. The quality of the reconstructed images is objectively assessed by three quality assessment techniques RMSE (Root mean square error), PSNR (Peak to Signal Noise Ratio), and SSIM (Structural similarity index measure). The utilization of optimized sampling trajectories and CS technique has demonstrated the potential for a significant reduction of up to 70% of image data acquisition. This result translates into shorter scan times, minimizing the duration and frequency of anesthesia administration and reducing the potential risks associated with it.

Keywords: compressed sensing, magnetic resonance, sampling trajectories, small animals

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Authors: Kuo-Cheng Liu

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In the latest research efforts, the structures of the image in the spatial domain have been successfully analyzed and proved to deduce the visual masking for accurately estimating the visibility thresholds of the image. If the structural properties of the video sequence in the temporal domain are taken into account to estimate the temporal masking, the improvement and enhancement of the as-sessing spatio-temporal visibility thresholds are reasonably expected. In this paper, the temporal characteristics of human perception to the change in block structures on the time axis are analyzed. The temporal characteristics of human perception are represented in terms of the significant variation in block structures for the analysis of human visual system (HVS). Herein, the block structure in each frame is computed by combined the pattern masking and the contrast masking simultaneously. The contrast masking always overestimates the visibility thresholds of edge regions and underestimates that of texture regions, while the pattern masking is weak on a uniform background and is strong on the complex background with spatial patterns. Under considering the significant variation of block structures between successive frames, we extend the block structures of images in the spatial domain to that of video sequences in the temporal domain to analyze the relation between the inter-frame variation of structures and the temporal masking. Meanwhile, the subjective viewing test and the fair rating process are designed to evaluate the consistency of the temporal characteristics with the HVS under a specified viewing condition.

Keywords: temporal characteristic, block structure, pattern masking, contrast masking

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Authors: Sujatha Shanmugasundaram

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Background: It has been since one year that COVID-19 has emerged into the world. Since then, healthcare professionals facing a great challenge in to fight against this deadly virus. According to World Health Organization (WHO) 2021, it is estimated that more than 131 million confirmed cases and 2million deaths around the world due to this pandemic. Nurses are the frontline workers who play a major role in safeguarding the lives of the people in acute care settings. Evidence suggests that there are numbers of research have been carried out on nurses' and healthcare provider’s experiences during the pandemic. But, unfortunately, there are no or little evidence available on Muslim nurse’s perspective. Hence, this research will investigate the experiences of Muslim nurses working in an acute care setting during the pandemic. Purpose: The purpose of the study is to explore the experiences of Muslim nurses working in an acute setting during the COVID-19 pandemic. Research Methods: A qualitative research approach will be utilized for the study. Semi-structured interview schedule will be used to collect the data. Face to face interviews will be conducted. All interviews will be conducted in Arabic, and it will be audio recorded. Verbatim will be noted. Muslim nurses working in an acute setting will be included in the study. Convenient sampling technique will be used to recruit the participants. Ethical approval will be obtained from the study sites. Strauss and Corbin's thematic analysis will be used to analyze the data. Conclusion: Considering that nurses are the frontline workers, they have a significant role in dealing with this COVID-19. It is a great challenge for the nurses working in an acute care setting. Thus, this study will bring out significant findings that will impact the nursing practice.

Keywords: acute care, COVID-19, experiences, muslim nurses

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Authors: Dibyendu Das, Santanu Kumar Pal

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Milk protein lactoferrin (Lf) exhibits potent antibacterial activity due to its interaction with Gram-negative bacterial cell membrane component, lipopolysaccharide (LPS). This paper represents fabrication of new Liquid crystals (LCs) based biosensors to explore the interaction between Lf and LPS. LPS self-assembled at aqueous/LCs interface and orients interfacial nematic 4-cyano-4’- pentylbiphenyl (5CB) LCs in a homeotropic fashion (exhibiting dark optical image under polarized optical microscope). Interestingly, on the exposure of Lf on LPS decorated aqueous/LCs interface, an optical image of LCs changed from dark to bright indicating an ordering alteration of interfacial LCs from homeotropic to tilted/planar state. The ordering transition reflects strong binding between Lf and interfacial LPS that, in turn, perturbs the orientation of LCs. With the help of epifluorescence microscopy, we further affirmed the interfacial LPS-Lf binding event by imaging the presence of FITC tagged Lf at the LPS laden aqueous/LCs interface. Finally, we have investigated the conformational behavior of Lf in solution as well as in the presence of LPS using Circular Dichroism (CD) spectroscopy and further reconfirmed with Vibrational Circular Dichroism (VCD) spectroscopy where we found that Lf undergoes alpha-helix to random coil-like structure in the presence of LPS. As a whole the entire results described in this paper establish a robust approach to envisage the interaction between LPS and Lf through the ordering transitions of LCs at aqueous/LCs interface.

Keywords: endotoxin, interface, lactoferrin, lipopolysaccharide

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Authors: Jonathan Glazzard

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National student satisfaction data in England demonstrate that undergraduate students are less satisfied overall with assessment and feedback than other aspects of their higher education courses. Given that research findings suggest that high-quality feedback is a critical factor associated with academic achievement, it is important that feedback enables students to demonstrate improved academic achievement in their subsequent assessments. Given the growing importance of staff-student partnerships in higher education, this research examined students’ perceptions of assessment and feedback in one UK university. Students’ perceptions were elicited through the use of a university-wide survey which was completed by undergraduate students. In addition, three focus groups were used to provide qualitative student perception data across the three university Facilities. The data indicate that whilst students valued detailed feedback on their work, less detailed feedback could be compensated for by the development of pre-assessment literacy skills which are front-loaded into courses. Assessment literacy skills valued by students included the use of clear assessment criteria and assignment briefings which enabled students to fully understand the assessment task. Additionally, students valued assessment literacy pre-assessment tasks which enabled them to understand the standards which they were expected to achieve. Students valued opportunities for self and peer assessment prior to the final assessment and formative assessment feedback which matched the summative assessment feedback. Students also valued dialogic face-to-face feedback after receiving written feedback Above all, students valued feedback which was particular to their work and which gave recognition for the effort they had put into completing specific assessments. The data indicate that there is a need for higher education lecturers to receive systematic training in assessment and feedback which provides a comprehensive grounding in pre-assessment literacy skills.

Keywords: formative assessment, summative assessment, feedback, marking

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Authors: Deepika Verma, Runa Sarkar

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One of the prominent debates in the social entrepreneurship literature has been to establish whether entrepreneurial work for social well-being by for-profit organizations can be classified as social entrepreneurship or not. Of late, the scholarship has reached a consensus. It concludes that there seems little sense in confining social entrepreneurship to just non-profit organizations. Boosted by this research, increasingly a lot of businesses engaged in filling the social infrastructure gaps in developing countries are calling themselves social enterprise. These organizations are diverse in their ownership, size, objectives, operations and business models. The lack of a comprehensive definition of social enterprise leads to three issues. Firstly, researchers may face difficulty in creating a database for social enterprises because the choice of an entity as a social enterprise becomes subjective or based on some pre-defined parameters by the researcher which is not replicable. Secondly, practitioners who use ‘social enterprise’ in their vision/mission statement(s) may find it difficult to adjust their business models accordingly especially during the times when they face the dilemma of choosing social well-being over business viability. Thirdly, social enterprise and social entrepreneurship attract a lot of donor funding and venture capital. In the paucity of a comprehensive definitional guide, the donors or investors may find assigning grants and investments difficult. It becomes necessary to develop an exhaustive and objective definition of social enterprise and examine whether the understanding of the academicians and practitioners about social enterprise match. This paper develops a dictionary of words often associated with social enterprise or (and) social entrepreneurship. It further compares two lexicographic definitions of social enterprise imputed from the abstracts of academic journal papers and trade publications extracted from the EBSCO database using the ‘tm’ package in R software.

Keywords: EBSCO database, lexicographic definition, social enterprise, text mining

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Authors: Abdallah Abu Madi, Dongmei Cao, Alexeis Garcia-Perez, Qile He

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The failure of Enterprise Resource Planning (ERP) implementation in higher education institutions (HEIs) worldwide is much higher in comparison to other sectors, such as banking or manufacturing, to our knowledge limited research has been conducted on this issue. To date, prior literature has identified some key success factors (KSFs) mostly either in the domain of information and system (IS) or in the industrial context. However, evidence of ERP implementation in the higher education sector has had little attention in the extant literature. Hence, this paper identifies and categories KSFs of ERP implementation in HEIs. Semi-structured face-to-face interviews were conducted with technicians and managers from three Jordanian HEIs. From these case studies, three new sector- and context-specific KSFs were identified and categorized according to two dimensions: organizational and technical. The first new KSF is the selection of the ERP system, which is an influential factor in the organizational dimension. Results show that an ERP solution that is suitable to one context may be disastrous in another. The second new KSF, which falls under the technical dimension, is the relationship between vendors and HEIs. This must be fair and impartial to enable successful decision-making and thus the achievement of pre-defined goals. Also within the technical dimension is the third factor: in-house maintenance. Once an appropriate system is selected and a strong relationship is established, the ERP system requires continuous maintenance for effective operation. HEIs should ensure that qualified IT support is in place and in-house to avoid excessive running expenses.

Keywords: Enterprise Resource Planning (ERP)implementation, key success factors, higher education institutions, Jordanian higher education

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Authors: Robson da C. Santos, Carlos Henrique de A. S. P. Coutinho, Lucas Moreira Dias, Gerson Gomes Cunha

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The article refers to the development of an augmented reality 3D display, through the control of servo motors and projection of image with aid of video projector on the model. Augmented Reality is a branch that explores multiple approaches to increase real-world view by viewing additional information along with the real scene. The article presents the broad use of electrical, electronic, mechanical and industrial automation for geospatial visualizations, applications in mathematical models with the visualization of functions and 3D surface graphics and volumetric rendering that are currently seen in 2D layers. Application as a 3D display for representation and visualization of Digital Terrain Model (DTM) and Digital Surface Models (DSM), where it can be applied in the identification of canyons in the marine area of the Campos Basin, Rio de Janeiro, Brazil. The same can execute visualization of regions subject to landslides, as in Serra do Mar - Agra dos Reis and Serranas cities both in the State of Rio de Janeiro. From the foregoing, loss of human life and leakage of oil from pipelines buried in these regions may be anticipated in advance. The physical design consists of a table consisting of a 9 x 16 matrix of servo motors, totalizing 144 servos, a mesh is used on the servo motors for visualization of the models projected by a retro projector. Each model for by an image pre-processing, is sent to a server to be converted and viewed from a software developed in C # Programming Language.

Keywords: visualization, 3D models, servo motors, C# programming language

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Mohit Modi, Rajiv Kumar, Manojraj Saxena, G. Ravi Shankar

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Change detection of coastal ecosystem plays a vital role in monitoring and managing natural resources along the coastal regions. The present study mainly focuses on the decadal change in Kochi islands connecting the urban flatland areas and the coastal regions where sand deposits have taken place. With this, in view, the change detection has been monitored in the Kochi area to apprehend the urban growth and industrialization leading to decrease in the wetland ecosystem. The region lies between 76°11'19.134"E to 76°25'42.193"E and 9°52'35.719"N to 10°5'51.575"N in the south-western coast of India. The IRS LISS-IV satellite image has been processed using a rule-based algorithm to classify the LULC and to interpret the changes between 2005 & 2015. The approach takes two steps, i.e. extracting features as a single GIS vector layer using different parametric values and to dissolve them. The multi-resolution segmentation has been carried out on the scale ranging from 10-30. The different classes like aquaculture, agricultural land, built-up, wetlands etc. were extracted using parameters like NDVI, mean layer values, the texture-based feature with corresponding threshold values using a rule set algorithm. The objects obtained in the segmentation process were visualized to be overlaying the satellite image at a scale of 15. This layer was further segmented using the spectral difference segmentation rule between the objects. These individual class layers were dissolved in the basic segmented layer of the image and were interpreted in vector-based GIS programme to achieve higher accuracy. The result shows a rapid increase in an industrial area of 40% based on industrial area statistics of 2005. There is a decrease in wetlands area which has been converted into built-up. New roads have been constructed which are connecting the islands to urban areas as well as highways. The increase in coastal region has been visualized due to sand depositions. The outcome is well supported by quantitative assessments which will empower rich understanding of land use land cover change for appropriate policy intervention and further monitoring.

Keywords: land use land cover, multiresolution segmentation, NDVI, object based classification

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Authors: Abdullah Mohammadi

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Years of prolong war in Afghanistan has led to one of the largest refugee and migrant populations in the contemporary world. During this continuous unrest which began in 1970s (by military coup, Marxist revolution and the subsequent invasion of USSR), over one-third of the population migrated to neighboring countries, especially Pakistan and Iran. After the Soviet Army withdrawal in 1989, a new wave of conflicts emerged between rival Afghan groups and this led to new refugees. Taliban period, also, created its own refugees. During all these years, I.R. of Iran has been one of the main destinations of Afghan refugees and migrants. At first, due to the political situation after Islamic Revolution, Iran government didn’t restrict the entry of Afghan refugees. Those who came first in Iran received ID cards and had access to education and healthcare services. But in 1990s, due to economic and social concerns, Iran’s policy towards Afghan refugees and migrants changed. The government has tried to identify and register Afghans in Iran and limit their access to some services and jobs. Unfortunately, there are few studies on Afghan refugees and migrants’ situation in Iran and we have a dim and vague picture of them. Of the few studies done on this group, none of them focus on the illegal Afghan migrants’ situation in Iran. Here, we tried to study the social and economic aspects of illegal Afghan migrants’ living in Iran. In doing so, we interviewed 24 illegal Afghan migrants in Iran. The method applied for analyzing the data is thematic analysis. For the interviews, we chose family heads (17 men and 7 women). According to the findings, illegal Afghan migrants’ socio-economic situation in Iran is very undesirable. Its main cause is the marginalization of this group which is resulted from government policies towards Afghan migrants. Most of the illegal Afghan migrants work in unskilled and inferior jobs and live in rent houses on the margins of cities and villages. None of them could buy a house or vehicle due to law. Based on their income, they form one of the lowest, unprivileged groups in the society. Socially, they face many problems in their everyday life: social insecurity, harassment and violence, misuse of their situation by police and people, lack of education opportunity, etc. In general, we may conclude that illegal Afghan migrant have little adaptation with Iran’s society. They face severe limitations compared to legal migrants and refugees and have no opportunity for upward social mobility. However, they have managed some strategies to face these difficulties including: seeking financial and emotional helps from family and friendship networks, sending one of the family members to third country (mostly to European countries), establishing self-administered schools for children (schools which are illegal and run by Afghan educated youth).

Keywords: illegal Afghan migrants, marginalization, social insecurity, upward social mobility

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Authors: Moses Emetere, Marvel Akinyemi, S. E. Sanni

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At the moment, AERONET or AMMA database shows a large volume of data loss. With only about 47% data set available to the scientist, it is evident that accurate nowcast or forecast cannot be guaranteed. The calibration constants of most radiosonde or weather stations are not compatible with the atmospheric conditions of the West African climate. A dispersion model was developed to incorporate salient mathematical representations like a Unified number. The Unified number was derived to describe the turbulence of the aerosols transport in the frictional layer of the lower atmosphere. Fourteen years data set from Multi-angle Imaging SpectroRadiometer (MISR) was tested using the dispersion model. A yearly estimation of the atmospheric constants over Ouagadougou using the model was obtained with about 87.5% accuracy. It further revealed that the average atmospheric constant for Ouagadougou-Niger is a_1 = 0.626, a_2 = 0.7999 and the tuning constants is n_1 = 0.09835 and n_2 = 0.266. Also, the yearly atmospheric constants affirmed the lower atmosphere of Ouagadougou is very dynamic. Hence, it is recommended that radiosonde and weather station manufacturers should constantly review the atmospheric constant over a geographical location to enable about eighty percent data retrieval.

Keywords: aerosols retention, aerosols loading, statistics, analytical technique

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Authors: Mariyan Tomov

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Bulgarian working class immigrants are increasingly concerned with UK’s recent immigration policies in the context of Brexit. The new ID system would exclude many people currently working in Britain and would break the usual immigrant travel patterns. Post-Brexit Britain would aim to repeal seasonal immigrants. Measures for keeping long-term and life-long immigrants have been implemented and migrants that aim to remain in Britain and establish a household there would be more privileged than temporary or seasonal workers. The results of such regulating mechanisms come at the expense of migrants’ longings for a ‘normal’ existence, especially for those coming from Central and Eastern Europe. Based on in-depth interviews with Bulgarian working class immigrants, the study found out that their major concerns following the decision of the UK to leave the EU are related with the freedom to travel, reside and work in the UK. Furthermore, many of the interviewed women are concerned that they could lose some of the EU's fundamental rights, such as maternity and protection of pregnant women from unlawful dismissal. The soar of commodity prices and university fees and the limited access to public services, healthcare and social benefits in the UK, are also subject to discussion in the paper. The most serious problem, according to the interview, is that the attitude towards Bulgarians and other immigrants in the UK is deteriorating. Both traditional and social media in the UK often portray the migrants negatively by claiming that they take British job positions while simultaneously abuse the welfare system. As a result, the Bulgarian migrants often face social exclusion, which might have negative influence on their health and welfare. In this sense, some of the interviewed stress on the fact that the most important changes after Brexit must take place in British society itself. The aim of the proposed study is to provide a better understanding of the Bulgarian migrants’ economic, health and sociocultural experience in the context of Brexit. Methodologically, the proposed paper leans on: 1. Analysing ethnographic materials dedicated to the pre- and post-migratory experiences of Bulgarian working class migrants, using SPSS. 2. Semi-structured interviews are conducted with more than 50 Bulgarian working class migrants [N > 50] in the UK, between 18 and 65 years. The communication with the interviewees was possible via Viber/Skype or face-to-face interaction. 3. The analysis is guided by theoretical frameworks. The paper has been developed within the framework of the research projects of the National Scientific Fund of Bulgaria: DCOST 01/25-20.02.2017 supporting COST Action CA16111 ‘International Ethnic and Immigrant Minorities Survey Data Network’.

Keywords: Bulgarian migrants in UK, economic experiences, sociocultural experiences, Brexit

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Authors: Shaira L. Kee, Michael Aaron G. Sy, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar AlDahoul

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Skin diseases are considered the fourth most common disease, with melanoma and non-melanoma skin cancer as the most common type of cancer in Caucasians. The alarming increase in Skin Cancer cases shows an urgent need for further research to improve diagnostic methods, as early diagnosis can significantly improve the 5-year survival rate. Machine Learning algorithms for image pattern analysis in diagnosing skin lesions can dramatically increase the accuracy rate of detection and decrease possible human errors. Several studies have shown the diagnostic performance of computer algorithms outperformed dermatologists. However, existing methods still need improvements to reduce diagnostic errors and generate efficient and accurate results. Our paper proposes an ensemble method to classify dermoscopic images into benign and malignant skin lesions. The experiments were conducted using the International Skin Imaging Collaboration (ISIC) image samples. The dataset contains 3,297 dermoscopic images with benign and malignant categories. The results show improvement in performance with an accuracy of 88% and an F1 score of 87%, outperforming other existing models such as support vector machine (SVM), Residual network (ResNet50), EfficientNetB0, EfficientNetB4, and VGG16.

Keywords: deep learning - VGG16 - efficientNet - CNN – ensemble – dermoscopic images - melanoma

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Authors: Francisca Alves, Diana Figueiredo, Paula Vagos, Luiza Lima, Maria do Céu Salvador, Daniel Rijo

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In recent years, Acceptance and Commitment Therapy (ACT) has been shown to be effective in the treatment of numerous anxiety disorders, including social anxiety disorder (SAD). However, limited evidence exists on its therapeutic gains for adolescents with SAD. The current work presents a weekly 10-session manualized online ACT approach to adolescent SAD, being the first study to do so in a clinical sample of adolescents. The intervention ACT@TeenSAD addresses the six proposed processes of psychological inflexibility (i.e., experiential avoidance, cognitive fusion, lack of values clarity, unworkable action, dominance of the conceptualized past and future, attachment to the conceptualized self) in social situations relevant to adolescents (e.g., doing a presentation). It is organized into four modules. The first module explores the role of psychological (in)flexibility in SAD (session 1 and 2), addressing psychoeducation (i.e., functioning of the mind) according to ACT, the development of an individualized model, and creative hopelessness. The second module focuses on the foundation of psychological flexibility (session 3, 4, and 5), specifically on the development and practice of strategies to promote clarification of values, contact with the present moment, the observing self, defusion, and acceptance. The third module encompasses psychological flexibility in action (sessions 6, 7, 8, and 9), encouraging committed action based on values in social situations relevant to the adolescents. The fourth modules’ focus is the revision of gains and relapse prevention (session 10). This intervention further includes two booster sessions after therapy has ended (3 and 6-month follow-up) that aim to review the continued practice of learned abilities and to plan for their future application to potentially anxious social events. As part of an ongoing clinical trial, the intervention will be assessed on its feasibility with adolescents diagnosed with SAD and on its therapeutic efficacy based on a longitudinal design including pretreatment, posttreatment, 3 and 6-month follow-up. If promising, findings may support the online delivery of ACT interventions for SAD, contributing to increased treatment availability to adolescents. This availability of an effective therapeutic approach will be helpful not only in relation to adolescents who face obstacles (e.g., distance) when attending to face-to-face sessions but also particularly to adolescents with SAD, who are usually more reluctant to look for specialized treatment in public or private health facilities.

Keywords: acceptance and commitment therapy, social anxiety disorder, adolescence, manualized online approach

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Hanan Yousef, Nisreen Naji Al-Khawaldeh

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The present study investigates the use of the expression of refusal by native speakers of Jordanian Arabic (NSsJA) in different social situations (i.e. invitations, suggestions, and offers). It also investigates the influence of gender on the refusal realization patterns within the Jordanian culture to provide a better insight into the relation between situations, strategies and gender in the Jordanian culture. To that end, a group of 70 participants, including 35 male and 35 female students from different departments at the Hashemite University (HU) participated in this study using mixed methods (i.e. Discourse Completion Test (DCT), interviews and naturally occurring data). Data were analyzed in light of a developed coding scheme. The results showed that NSsJA preferred indirect strategies which mitigate the interaction such as "excuse, reason and, explanation" strategy more than other strategies which aggravate the interaction such as "face-threatening" strategy. Moreover, the analysis of this study has revealed a considerable impact of gender on the use of linguistic forms expressing refusal among NSsJA. Significant differences in the results of the Chi-square test relating the effect of participants' gender indicate that both males and females were conscious of the gender of their interlocutors. The findings provide worthwhile insights into the relation amongst types of communicative acts and the rapport between people in social interaction. They assert that refusal should not be labeled as face threatening act since it does not always pose a threat in some cases especially where refusal is expressed among friends, relatives and family members. They highlight some distinctive culture-specific features of the communicative acts of refusal.

Keywords: gender, Jordanian Arabic, politeness, refusals, speech act

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Authors: Pradeep Kumar Singh Chauhan, Gayatri Devi, Zamir Ahmad, Komal Chauhan, Abha Mittal

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The application of geophysical methods to study the subsurface profile for site investigation is becoming popular globally. These methods are non-destructive and provide the image of subsurface at shallow depths. Seismic refraction method is one of the most common and efficient method being used for civil engineering site investigations particularly for knowing the seismic velocity of the subsurface layers. Resistivity imaging technique is a geo-electrical method used to image the subsurface, water bearing zone, bedrock and layer thickness. Integrated approach combining seismic refraction and 2-D resistivity imaging will provide a better and reliable picture of the subsurface. These are economical and less time-consuming field survey which provide high resolution image of the subsurface. Geophysical surveys carried out in this study include seismic refraction and 2D resistivity imaging method for delineation of sub-surface strata in different parts of Srinagar, Garhwal Himalaya, India. The aim of this survey was to map the shallow subsurface in terms of geological and geophysical properties mainly P-wave velocity, resistivity, layer thickness, and lithology of the area. Both sides of the river, Alaknanda which flows through the centre of the city, have been covered by taking two profiles on each side using both methods. Seismic and electrical surveys were carried out at the same locations to complement the results of each other. The seismic refraction survey was carried out using ABEM TeraLoc 24 channel Seismograph and 2D resistivity imaging was performed using ABEM Terrameter LS equipment. The results show three distinct layers on both sides of the river up to the depth of 20 m. The subsurface is divided into three distinct layers namely, alluvium extending up to, 3 m depth, conglomerate zone lying between the depth of 3 m to 15 m, and compacted pebbles and cobbles beyond 15 m. P-wave velocity in top layer is found in the range of 400 – 600 m/s, in second layer it varies from 700 – 1100 m/s and in the third layer it is 1500 – 3300 m/s. The resistivity results also show similar pattern and were in good agreement with seismic refraction results. The results obtained in this study were validated with an available exposed river scar at one site. The study established the efficacy of geophysical methods for subsurface investigations.

Keywords: 2D resistivity imaging, P-wave velocity, seismic refraction survey, subsurface

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Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: Reni Francis

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Learning has transcended the classroom boundaries to create a learner centric, interactive, and integrative teaching learning environment. This study analysed the impact of iLearn on the teaching, learning, and evaluation among 100 teacher trainees. The objectives were to cater to the different learning styles of the teacher trainees, to incorporate innovative teaching learning activities, to assist in peer tutoring, to implement different evaluation processes. i: Identifying the learning styles among the teacher trainees through VARK Learning style checklist was followed by planning the teaching-learning process to meet the learning styles of the teacher trainees. L: Leveraging innovations in teaching- learning by planning and creating modules incorporating innovative teaching learning and hence the concept based year plan was prepared. E: Engage learning through constructivism using different teaching methodology to engage the teacher trainees in the learning process through Workshop, Round Robin, Gallery walk, Co-Operative learning, Think-Pair-Share, EDMODO, Course Networking, Concept Map, Brainstorming Sessions, Video Clippings. A: Assessing the learning through an Open Book assignment, Closed book assignment, and Multiple Choice Questions and Seminar presentation. R: Remediation through peer tutoring through Mentor-mentee approach in the tutorial groups, Group work, Library Hours. N: Norming new standards. This was done in the form of extended remediation and tutorials to understand the need of the teacher trainee and support them for further achievements in learning through Face to face interaction, Supervised Study Circle, Mobile (Device) learning. The findings of the study revealed the positive impact of iLearn towards student achievement and enhanced social skills.

Keywords: academic achievement, innovative strategy, learning styles, social skills

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Authors: Mimi My Tse

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The prevalence of chronic non-cancer pain is high among community-dwelling older adults. Pain affects physical and psychosocial abilities. Older adults tend to be less mobile and have a high tendency to fall risk. In addition, older adults with pain are depressed, anxious, and not too willing to join social activities. This will make them feel very lonely and social isolation. Instead of giving pain management education and programs to older adults/clients, both older adults and their caregivers, it is sad to find that the majority of existing services are given to older adults only. Given the importance of family members in increasing compliance with health-promoting programs, we proposed to offer pain management programs to both older adults with his/her caregiver as a “dyad.” We used the Health Promotion Model and implemented a dyadic pain management program (DPM). The DPM is an 8-week group-based program. The DPM comprises 4 weeks of center-based, face-to-face activities and 4 weeks of digital-based activities delivered via a WhatsApp group. There were 30 dyads (15 in the experimental group with DPM and 15 in the control group with pain education pamphlets). Upon the completion of DPM, pain intensity and pain interference were significantly lower in the intervention group as compared to the control group. At the same time, physical function showed significant improvement and lower depression scores in the intervention group. In conclusion, the study highlights the potential benefits of involving caregivers in the management of chronic pain for older adults. This approach should be widely promoted in managing chronic pain situations for community-dwelling older adults and their caregivers.

Keywords: pain, older adults, dyadic approach, education

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Authors: Inigo Beckett

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In forensic investigations, it is often the case that the most potentially useful recorded evidence derives from coincidental imagery, recorded immediately before or during an incident, and that during the incident (e.g. a ‘failure’ or fire event), the evidence is changed or destroyed. To an image analysis expert involved in photogrammetric analysis for Civil or Criminal Proceedings, traditional computer vision methods involving calibrated cameras is often not appropriate because image metadata cannot be relied upon. This paper presents an approach for resolving this problem, considering in particular and by way of a case study, the angle of a simple ladder shown in a photograph. The UK Health and Safety Executive (HSE) guidance document published in 2014 (INDG455) advises that a leaning ladder should be erected at 75 degrees to the horizontal axis. Personal injury cases can arise in the construction industry because a ladder is too steep or too shallow. Ad-hoc photographs of such ladders in their incident position provide a basis for analysis of their angle. This paper presents a direct approach for ascertaining the position of the camera and the angle of the ladder simultaneously from the photograph(s) by way of a workflow that encompasses a novel application of projective geometry and matrix analysis. Mathematical analysis shows that for a given pixel ratio of directly measured collinear points (i.e. features that lie on the same line segment) from the 2D digital photograph with respect to a given viewing point, we can constrain the 3D camera position to a surface of a sphere in the scene. Depending on what we know about the ladder, we can enforce another independent constraint on the possible camera positions which enables us to constrain the possible positions even further. Experiments were conducted using synthetic and real-world data. The synthetic data modeled a vertical plane with a ladder on a horizontally flat plane resting against a vertical wall. The real-world data was captured using an Apple iPhone 13 Pro and 3D laser scan survey data whereby a ladder was placed in a known location and angle to the vertical axis. For each case, we calculated camera positions and the ladder angles using this method and cross-compared them against their respective ‘true’ values.

Keywords: image analysis, projective geometry, homography, photogrammetry, ladders, Forensics, Mathematical modeling, planar geometry, matrix analysis, collinear, cameras, photographs

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Lauren Schuldt Wilson

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Felicia Hemans’ memorializing of the unwritten lives of women and the consequent allowance for marginalized voices to remember and be remembered has been considered by many critics in terms of ekphrasis and elegy, terms which privilege the question of whether Hemans’ poeticizing can represent lost voices of history or only her poetic expression. Amy Gates, Brian Elliott, and others point out Hemans’ acknowledgement of the self-projection necessary for imaginatively filling the absences of unrecorded histories. Yet, few have examined the complex temporal positioning Hemans inscribes in these moments of self-projection and imaginative historicizing. In poems like ‘The Image in Lava,’ Hemans maps not only a lost past, but also a lost potential future onto the image of a dead infant in its mother’s arms, the discovery and consideration of which moves the imagined viewer to recover and incorporate the ‘hope’ encapsulated in the figure of the infant into a reevaluation of national time embodied by the ‘relics / Left by the pomps of old.’ By examining Hemans’ acknowledgement and response to Percy Bysshe Shelley’s ‘Ozymandias,’ this essay explores how Hemans’ depictions of imaginative historicizing open new horizons of possibility and reevaluate temporal value structures by imagining previously undiscovered or unexplored potentialities of the past. Where Shelley’s poem mocks the futility of national power and time, this essay outlines Hemans’ suggestion of alternative threads of identity and temporal meaning-making which, regardless of historical veracity, exist outside of and against the structures Shelley challenges. Counter to previous readings of Hemans’ poem as celebration of either recovered or poetically constructed maternal love, this essay argues that Hemans offers a meditation on sites of reproduction—both of personal reproductive futurity and of national reproduction of power. This meditation culminates in Hemans’ gesturing towards a method of historicism by which the imagined viewer reinvigorates the sterile, ‘shattered visage’ of national time by forming temporal identity through the imagining of trans-historical hope inscribed on the infant body of the universal, individual subject rather than the broken monument of the king.

Keywords: futurity, national temporalities, reproduction, revisionary histories

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Authors: Sabrina S. W. Chan, Emily Schwartzman, Nicholas O. Rule

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Past literature showed mixed findings on how smiling affects a person’s chance of getting hired. On one hand, smiling suggests enthusiasm, cooperativeness, and enthusiasm, which can elicit positive impressions. On the other hand, smiling can suggest weaker professionalism or a filler to hide nervousness, which can lower a candidate’s perceived competence. Emotion expressions can also be perceived differently depending on the person’s gender and can activate certain gender stereotypes. Women especially face a double bind with respect to hiring decisions and smiling. Because women are socially expected to smile more, those who do not smile will be considered stereotype incongruent. This becomes a noisy signal to employers and may lower their chance of being hired. However, women’s smiling as a formality may also be an obstacle. They are more likely to put on fake smiles; but if they do, they are also likely to be perceived as inauthentic and over-expressive. This paper sought to investigate how smiling affects hiring decisions, and whether this relationship is moderated by gender. In Study 1, participants were shown a series of smiling and emotionally neutral face images, incorporated into fabricated LinkedIn profiles. Participants were asked to rate how hireable they thought that candidate was. Results showed that participants rated smiling candidates as more hireable than nonsmiling candidates, and that there was no difference in gender. Moreover, individuals who did not study business were more biased in their perceptions than those who did. Since results showed a trending favoritism over female targets, in suspect of desirability bias, a second study was conducted to collect implicit measures behind the decision-making process. In Study 2, a mouse-tracking design was adopted to explore whether participants’ implicit attitudes were different from their explicit responses on hiring. Participants asked to respond whether they would offer an interview to a candidate. Findings from Study 1 was replicated in that smiling candidates received more offers than neutral-faced candidates. Results also showed that female candidates received significantly more offers than male candidates but was associated with higher attractiveness ratings. There were no significant findings in reaction time or change of decisions. However, stronger hesitation was detected for responses made towards neutral targets when participants perceived the given position as masculine, implying a conscious attempt of making situational judgments (e.g., considering candidate’s personality and job fit) to override automatic processing (evaluations based on attractiveness). Future studies would look at how these findings differ for positions which are stereotypically masculine (e.g., surgeons) and stereotypically feminine (e.g., kindergarten teachers). Current findings have strong implications for developing bias-free hiring policies in workplace, especially for organizations who maintain online/hybrid working arrangements in the post-pandemic era. This also bridges the literature gap between face perception and gender discrimination, highlighting how engendered facial cues can affect individual’s career development and organization’s success in diversity and inclusion.

Keywords: engendered facial cues, face perception, gender stereotypes, hiring decisions, smiling, workplace discrimination

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