Comprehensive Representation Research Articles

Sarcasm driven by sentiment: A sentiment-aware hierarchical fusion network for multimodal sarcasm detection

Sarcasm is a form of sentiment expression that highlights the disparity between a person’s true intentions and the content they explicitly present. With the exponential increase in multimodal data on social platforms, the detection of sarcasm across various modes has become a pivotal area of research. Although previous studies have extensively examined multimodal feature extraction, fusion, and the modeling of inter-modal incongruities, they often neglected the subtle sentiment cues inherent in sarcastic multimodal data. Additionally, they did not adequately address the sparse distribution and tenuous connections between sarcastic features both within and cross modalities. To address these gaps, we introduce a hierarchical fusion model that integrates sentiment information for enhanced multimodal sarcasm detection. Specifically, we use attribute-object matching in the image modality, treating it as an auxiliary attribute modality. Sentiment data is then extracted from each modality and combined to achieve a more comprehensive representation within modalities. Moreover, we characterize the relationships of inter-modal incongruities using a crossmodal Transformer. We also implement a sentiment-aware image-text contrastive loss mechanism to synchronize the semantics of images and text better. By intensifying these alignments, our model is better equipped to understand incongruous relationships. Experiments demonstrate that our hierarchical fusion model achieves state-of-the-art performance on the multimodal sarcasm detection task.

Rolling bearing fault diagnosis based on the fusion of sparse filtering and discriminative domain adaptation method under multi-channel data-driven

Currently, the diagnostic performance of many deep learning algorithms may drop dramatically when the distribution of training data is significantly different from that of the test data. Moreover, the fault diagnosis approaches based on single-channel data may suffer problems such as large precision fluctuation, low reliability, and incomplete expression of fault features. To overcome the above deficiencies, a novel multi-channel data-driven fault recognition method based on the fusion of sparse filtering (SF) and discriminative domain adaptation (MSFDDA) is proposed in this article. Firstly, inspired by attention mechanisms and information fusion methods, a spectrum-based weighted multi-channel data fusion strategy is designed to fully utilize the data collected by sensors to obtain a more comprehensive representation of fault features. Then, the joint probability-based discriminative maximum mean discrepancy algorithm is introduced into the SF method to strengthen the capability of extracting the domain invariant features. Finally, two bearing datasets are employed to verify the validity of the MSFDDA method, which proved to be superior to other current domain adaptation methods.

April-GCN: Adjacency Position-velocity Relationship Interaction Learning GCN for Human motion prediction

The Human Motion Prediction (HMP) task attempts to model human kinematics, which requires considering both the physical connections between joints and the continuity of the joints’ trajectories. To handle this complex task, some recent works have employed Graph Convolutional Networks (GCN) in learning dynamic relations among joints. However, HMP task is essentially a time series task, to model the temporal and spatial features of motion, some works extended the common adjacency matrix formulation to create a large-scale mixed adjacency matrix which introduces redundant connection relationships in single-path GCN. Other works proposed dual-path GCNs and multi-path GCNs to make feature representations more informative, while ignoring the significant differences of features. To tackle these issues, we propose a novel adjacency matrix interaction learning framework called Adjacency Position-velocity Relationship Interaction Learning GCN (April-GCN) which alleviates the differences between dual-path features and improves the feature fusion results. The proposed network utilizes a dual-path GCN structure to exploit the adjacency relationship between physical connections and movement amplitude by interactively learning the GCN adjacency matrices of the two paths. The network excavated closely aligned position-velocity correlations to obtain a more comprehensive and richer representation of joint connections. On three datasets, H3.6M, CMU, and 3DPW, it achieves state-of-the-art performance for both short-term and long-term predictions.

Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation

Extensive research has been conducted on analyzing human movements, driven by its diverse practical applications such as human–robot interaction, human learning, and clinical diagnosis. However, the current state-of-the-art still encounters scientific challenges when it comes to modeling human movements. There are two key aspects that need to be addressed. Firstly, new models should consider the stochastic nature of human movement and the physical structure of the human body to accurately predict the patterns in full-body motion descriptors over time. Secondly, while deep learning algorithms have been utilized, they lack explainability in terms of predicting body posture sequences, making it essential to improve their comprehensible representation of human movement. This paper aims to tackle these challenges by presenting three innovative methods for creating explainable representations of human movement. The study formulates human body movement as a state-space model based on the Gesture Operational Model (GOM). Model parameters are estimated through either one-shot training employing Kalman Filters or data-intensive training utilizing artificial neural networks. The trained models are utilized for analyzing the dexterity of expert professionals in full-body movements, enabling the identification of dynamic associations between body joints and gesture recognition. Additionally, these models are employed to generate artificial professional movements.

Filtering and regret network for spacecraft component segmentation based on gray images and depth maps

Identifying and segmenting spacecraft components is vital in many on-orbit space missions, such as on-orbit maintenance and component recovery. Integrating depth maps with visual images has been proven effective in improving segmentation accuracy. However, existing methods ignore the noise and fallacy in collected depth maps, which interfere with the network to extract representative features, decreasing the final segmentation accuracy. To this end, this paper proposes a Filtering and Regret Network (FRNet) for spacecraft component segmentation. The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers, avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps. Furthermore, a two-stage feature fusion module is proposed, which involves information interaction and aggregation. This module effectively explores the feature correlation and unifies the multi-modal features into a comprehensive representation. Finally, a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms. Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union (mIOU) of 84.13% and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.

BIM-LADM AMALGAMATION — A REVIEW

Abstract. This paper discussed two International Organization for Standardization (ISO) standards, namely, ISO 19152 – Land Administration Domain Model (LADM) and ISO 16739 – Industry Foundation Classes (IFC), to identify three-dimensional (3D) multi-floor building rights. These days, it is challenging to characterize all multifunctional structures due to the increase in complex buildings. High-rise building land and property information management is recommended through building information modeling (BIM). Countries like the Netherlands, Australia, and Turkey have considered adopting BIM for their land administration. A land administration standard, such as LADM, offers better legal and physical representation in identifying the rights, restrictions, and responsibilities (RRRs) of the spatial units and stakeholders. However, LADM falls short in representing all building elements, such as semantic information and invisible functional spaces. Hence, this paper reviews incorporating additional information from BIM to create a comprehensive three-dimensional building representation including legal information from LADM. Issues and challenges also being highlighted with some recommendations for possible future works.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting.

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

Current methodologies of greenspace exposure and mental health research-a scoping review.

Greenspaces can provide an important resource for human mental health. A growing body of literature investigates the interaction and the influence of diverse greenspace exposures. In order to gain a comprehensive understanding of the complex connection between greenspace and mental health, a variety of perspectives and methodological combinations are needed. The aim of this review is to assess the current methodologies researching greenspace and mental health. A scoping review was conducted. Four electronic databases (Pubmed, Embase, PsycInfo, Web of Science) were searched for relevant studies. A wide range of greenspace and mental health keywords were included to provide a comprehensive representation of the body of research. Relevant information on publication characteristics, types of greenspaces, mental health outcomes, and measurements of greenspace exposure and mental health was extracted and assessed. 338 studies were included. The included studies encompassed a multitude of methods, as well as outcomes for both greenspace and mental health. 28 combinations were found between seven categories each for greenspace and mental health assessment. Some pairings such as geoinformation systems for greenspace assessment and questionnaires investigating mental health were used much more frequently than others, implying possible research gaps. Furthermore, we identified problems and inconsistences in reporting of greenspace types and mental health outcomes. The identified methodological variety is a potential for researching the complex connections between greenspace and mental health. Commonly used combinations can provide important insights. However, future research needs to emphasize other perspectives in order to understand how to create living environments with mental health benefits. For this purpose, interdisciplinary research is necessary.

Multi-scale feature fusion for prediction of IDH1 mutations in glioma histopathological images

Background and objectiveMutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role in the prognosis, diagnosis, and treatment of gliomas. However, current methods for determining its mutation status, such as immunohistochemistry and gene sequencing, are difficult to implement widely in routine clinical diagnosis. Recent studies have shown that using deep learning methods based on pathological images of glioma can predict the mutation status of the IDH1 gene. However, our research focuses on utilizing multi-scale information in pathological images to improve the accuracy of predicting IDH1 gene mutations, thereby providing an accurate and cost-effective prediction method for routine clinical diagnosis. MethodsIn this paper, we propose a multi-scale fusion gene identification network (MultiGeneNet). The network first uses two feature extractors to obtain feature maps at different scale images, and then by employing a bilinear pooling layer based on Hadamard product to realize the fusion of multi-scale features. Through fully exploiting the complementarity among features at different scales, we are able to obtain a more comprehensive and rich representation of multi-scale features. ResultsBased on the Hematoxylin and Eosin stained pathological section dataset of 296 patients, our method achieved an accuracy of 83.575 % and an AUC of 0.886, thus significantly outperforming other single-scale methods. ConclusionsOur method can be deployed in medical aid systems at very low cost, serving as a diagnostic or prognostic tool for glioma patients in medically underserved areas.

Studies on regional ozone formation sensitivities and transport with higher spatiotemporal resolutions in a stereoscopic dimension: GEMS and vertical observations

Currently, ozone pollution is one of the key environmental concerns, and to provide more effective strategies for ozone control, this paper proposes the use of the Geostationary Environmental Monitoring Spectrometer (GEMS) in conjunction with MAX-DOAS for observations and ozone sensitivity analysis. Compared to traditional polar-orbiting satellites, GEMS, launched by South Korea in 2019, offers superior spatiotemporal resolution. However, there is a lack of ground validation for GEMS data related to ozone and its precursors. In this study, we conducted validation in the Jing-Jin-Ji (Beijing-Tianjin-Hebei) region using two typical MAX-DOAS sites and the China National Environmental Monitoring Center (CNEMC) sites, confirming the accuracy of GEMS data. Analysis of the seasonal horizontal distribution results from GEMS and vertical distribution results from MAX-DOAS in 2022 reveals that the concentrations of NO2, HCHO, O3, and ozone sensitivity not only display seasonality and regional variations but also exhibit distinct characteristics at different altitudes and times, influenced by local climatic conditions, human activities, and topography. When formulating ozone control policies, it becomes imperative to consider factors such as time, regional characteristics, and seasonality. Comparisons between daily results from TROPOMI and GEMS indicate that polar-orbiting satellites may overlook high-pollution periods within a single day, and their measurements may not provide a comprehensive representation of an entire day's data. Consequently, relying solely on polar-orbiting satellite data for shaping ozone prevention and control policies may inadvertently encourage ozone formation during specific time periods, rendering these policies ineffective. Therefore, leveraging GEMS is essential for the development of comprehensive, around-the-clock ozone control policies. Finally, we used monitoring data from Beijing on October 6, 2022, to illustrate how the combination of GEMS and MAX-DOAS enables three-dimensional monitoring of pollutant transport processes on an hourly scale.

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

The Impact of Perceived Quality and Brand Loyalty on Venture’s Equity: The Mediating Role of Customer Trust

The primary focus of this study is to provide new insights into the correlation between brand loyalty, perceived quality, and entrepreneurial venture’s value, while taking into account the role of customer trust as a mediator. It is found that perceived quality plays a vital role in augmenting venture’s value, with customer trust acting as an intermediary between venture’s value and the aforementioned factors. Noteworthy theories such as the Trust-Commitment theory, Expectation-Confirmation theory, and the Edelman Trust report emphasize the significance of customer trust in building venture’s value. As brands continue to wield greater influence in consumer decision-making processes, venture’s value assets become a metric for measuring consumer trust. The research methodology employed is based on survey-based random polling method. The statistical validity of these assumptions is established through econometric tools, specifically analysis of variance, structural equation modeling (SEM), and fitness testing. However, it is important to note that the limited sample size is a crucial constraint in this study, and it is recommended to expand the comprehensive representation of venture’s value to encompass a wider range of geographical areas in order to enhance its external validity.

Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach.

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification. However, the reliance on large labeled datasets poses challenges in the medical domain due to privacy issues and data silos. This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor. By fine-tuning the higher layers of ResNet using breast cancer datasets from diverse medical centers, we enable the model to learn specialized features relevant to different domains while leveraging the comprehensive image representations acquired from large-scale datasets like ImageNet. To overcome domain shift challenges caused by variations in data distributions across medical centers, we introduce domain adversarial training. The model learns to minimize the domain discrepancy while maximizing classification accuracy, facilitating the acquisition of domain-invariant features. We conducted extensive experiments on diverse breast cancer datasets obtained from multiple medical centers. Comparative analysis was performed to evaluate the proposed approach against traditional standalone training and federated learning without domain adaptation. When compared with traditional models, our proposed model showed a classification accuracy of 98.8% and a computational time of 12.22s. The results showcase promising enhancements in classification accuracy and model generalization, underscoring the potential of our method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment.

Deepfake detection based on cross-domain local characteristic analysis with multi-domain transformer

Deepfake videos present a significant challenge in the current media landscape. While current deepfake detection methods demonstrate satisfactory performance, there is still room for improvement in their ability to generalize and detect unseen scenarios, particularly those involving imperceptible cues. This paper introduces a novel multi-modal deepfake detection model named SpectraVisionFusion Transformer (SVFT), which incorporates spatial and frequency domain statistical artifacts to improve generalization performance. The SVFT framework uses two different backbone encoder models to take advantage of both spatial and frequency domain cues in video sequences, along with a decoder and classifier, for common cross-attention and classification, respectively. The spatial domain branch uses a convolutional transformer-based encoder to analyze facial visual features. In contrast, the frequency domain branch employs a language transformer encoder. Additionally, we introduce a weighted feature embedding fusion mechanism that integrates spectral-based statistical feature embeddings and visual cues to achieve a more comprehensive and balanced spatial-frequency feature representation. By coordinately analyzing these modalities, our model exhibits improved detection and generalization capabilities in unseen scenarios. Our proposed SVFT model achieved 92.57% and 80.63% accuracy in extensive cross-manipulation and dataset evaluation, respectively, while surpassing the performance of traditional and single-domain-based approaches.

Ideological Revelations in Narratives: A Perusal of The Mark on the Wall

This text delves into the realm of narratology, examining the structuralist perspective in understanding narratives and their inherent connection to cultural and societal ideologies. Gerard Genette emerges as a key figure for his comprehensive representation of narratology, emphasizing the importance of analyzing the system (langue) rather than individual texts (parole). The structuralist approach posits that stories derive from a general system, reflecting the unconscious molding of authors by society over time. Vladimir Propp's work on fairy tales and Lennard J. Davis's analysis of novel structures further contribute to the exploration of narrative elements. The study highlights the significance of narratology in uncovering the ideological motives behind texts, emphasizing its utility in understanding cultural concepts. By examining Virginia Woolf's "The Mark on the Wall," the analysis reveals how the absence of a clear story mirrors the chaotic ideologies of the time, while narrative and narration expose conflicting perspectives. The abstract concludes by underscoring the essential role of narratology in deciphering the complexities of narratives, serving as a crucial tool for diagnosing the working ideologies of texts and enhancing our understanding of the world.

Efficient prediction of individual head-related transfer functions based on 3D meshes

Individual head-related transfer functions (HRTFs) are critical for binaural spatial audio rendering. In contrast to anthropometric parameters and pinnae images, 3D meshes allow for a more direct and comprehensive representation of the anthropometric structure, which provides highly effective inputs for modeling individualized HRTFs. This paper presents a neural network-based method for predicting individualized HRTFs in full space based on 3D meshes. Unlike many previous methods that estimate HRTF spectra at sampling grids or frequencies separately, the proposed model predicts the HRTF spectra of each vertical plane by considering the spectral correlation and continuity across adjacent sampling grids and frequencies. Evaluation results indicate that the proposed method enhances the prominence of peaks and notches in the obtained HRTF spectra and improves the speed and accuracy of HRTF individualization. The log spectral distortion of the proposed method is lower than that of state-of-the-art methods using anthropometric parameters and pinnae images. Further evaluation confirms that the proposed method requires significantly fewer points in 3D meshes when compared to numerical simulation methods. The evaluation based on localization models demonstrates that the HRTFs predicted by the proposed method are perceptually similar to the measured HRTFs.

A method for rapid estimation of residual stresses in metal samples produced by additive manufacturing

The mechanical methods for measuring residual stresses typically rely on so-called destructive techniques where some stress components can be determined based on part deflection after material removal (cutting, etching, drilling, etc.). While these methods don't provide a comprehensive representation of residual stresses within the entire part, they can be readily applied in most manufacturing labs. In this study, we propose an efficient method for determining residual stress within additively manufactured cylindrical samples of stainless steel. The method is based on the assumption of a relation between the axial component of residual stress (normal to cross-section) and the cylinder radius. The general form of this relation is proposed based on data from numerical simulations using linear, parabolic or piecewise approximations. The parameters for the proposed relation are defined using equilibrium equations for total force and moment. The proposed method relies on an experiment with a mechanical cut along the cylinder. Consequently, the deflection of the cylinder halves after the cut allows for obtaining the equivalent bending moment.

Data-driven distributionally robust optimization approach for the coordinated dispatching of the power system considering the correlation of wind power

With the increasing penetration of large-scale wind power into the power grid, it is crucial to develop a precise model to accurately depict the stochasticity and correlation among wind farm outputs, which is highly important for ensuring the safe and efficient utilization of wind energy in grid dispatching. In this article, a data-driven distributionally robust optimization (DDRO) dispatching approach that accounts for spatial correlations among outputs from multiple wind farms is proposed. The proposed approach is applied to a source-network-load-storage grid system to ascertain unit start-stop schedules and resource allocation effectively. First, a truncated spatial correlation model is proposed, enabling a comprehensive representation of spatial correlations and output constraints between distinct wind farms. Second, the ISODATA clustering algorithm is employed to generate typical scenarios, reduce model complexity, and expedite the computation process. Third, a unit commitment model considering the demand response is constructed and solved using the DDRO approach. Finally, the proposed model is applied to the IEEE 30-bus system to test its robustness and cost-effectiveness compared to the traditional robust optimization model. Additionally, it is applied to the IEEE 118-bus system to demonstrate its scalability and stability.

The Pillars of Safety: Unveiling the Impact of Medication Usage on Public and Patient Wellbeing

Background: The comprehensive management of various health conditions within the community is heavily reliant on the crucial role of medications. Objective: The primary objective of this research is to investigate medication usage patterns, adherence, and associated factors among a diverse participant pool. The study aims to assess the prevalence of prescription medication use, consumption patterns, adherence rates, and the methods employed by participants for managing their medicines. Furthermore, the study explores participants' experiences with side effects and evaluates their satisfaction with prescribed treatments. Methods: A prospective cross-sectional design was employed for data collection, utilizing a self-administered Medication Usage Survey distributed through Google Forms. Participants were recruited through various channels, and data were collected anonymously. Results: A total of 103 participants contributed to the study, with a diverse demographic composition. The majority identified as female (60.19%), and participants spanned various age groups, reflecting a comprehensive representation. Geographically, the study included participants from multiple locations, with Bengaluru being the predominant location (80.58%). Participants reported diverse health conditions, with 69 individuals (66.99%) on prescription medications. Consumption patterns revealed that 57.3% took medications daily, while adherence varied, with 36.9% reporting missed doses. Side effects were reported by a small percentage (12.66%) of participants, and various methods were employed for managing medicines. Overall, treatment satisfaction varied among participants. Conclusion: This research provides valuable insights into medication usage patterns and associated factors among a diverse participant pool.

Healthcare Expatriate Adjustment in Qatar: Analyzing Challenges and Opportunities.

The healthcare sector in the Middle East, particularly in Qatar, relies heavily on expatriate professionals. The successful adaptation of these expatriates to the local environment is crucialas it significantly affects their job satisfaction and the quality of care they deliver to patients. This study aimed to identify and analyze the key factors influencing the adjustment of healthcare expatriates in Qatar to their professional roles and the cultural context of the region. This study involved the use of a cross-sectional survey approach targeting healthcare professionals in Qatar, with a targeted sample size of 385 participantsselected to ensure a comprehensive representation of the expatriate workforce. The survey was disseminated through expatriate-specific WhatsApp groups from October 1, 2022, to December 31, 2022. It was structured to examine a variety of factors influencing expatriate adjustment, including age, gender, proficiency in the Arabic language, social support networks, job type, and length of stay in Qatar. The primary focus of this study is a thematic analysis of the respondents' free-text comments. These comments offer valuable insights into their experiences, both positive and negative, in adapting to life and work in Qatar. This study achieved a response rate of 51.69% (199 out of 385), with participants representing 10 nationalities, being predominantly aged between 35 and 44 years, and having spent four to seven years in Qatar. The majority of respondents were nurses and doctors, accounting for 83.9% of the sample. The following five main themes emerged from the analysis of 218 free-text comments: personal and family concerns, work-related matters, government and system-related challenges, social and cultural challenges, and Arabic language challenges. Although participants valued supportive employer practices in family relocation and professional environments, they encountered language barriers, bureaucratic complexities, and cultural adaptation challenges. The results of this study shed light on the multifaceted nature of expatriate adjustment in the healthcare sector of Qatar, highlighting the supportive elements and the obstacles encountered. They emphasize the need for comprehensive support mechanisms, including language training, streamlined bureaucratic processes, and cultural orientation programs, to facilitate successful and fulfilling expatriate experiences in the Middle Eastern healthcare context.