Variable Segments Research Articles

Understanding Age-Driven Preferences: A Weighted Sum Analysis of Consumer Behavior in E-commerce

Consumer activity in online commerce plays a crucial role in modern business functions, impacting market trends and guiding approaches for digital merchants. This research utilizes the weighted sum technique to assess consumer actions among different age brackets, emphasizing significant indicators like Average Order Value, Satisfaction with Customer Support, Timeliness of Delivery, and Rate of Product Returns. The results underscore unique preferences and performance patterns across age segments, offering actionable intelligence for e-commerce enterprises aiming to refine their tactics. The examination indicates that individuals aged 45-54 stand out as the leading performers across every metric, showcasing the highest expenditure, most satisfactory customer support ratings, swiftest delivery times, and minimal product return rates. Conversely, the 35-44 age bracket displays the lowest overall performance, marked by comparatively lower spending, moderate satisfaction with customer support, extended delivery durations, and elevated rates of product returns. Intermediate groups include the 18-24 and 55-65 age groups, with the former showing strong overall performance and the latter displaying good customer support satisfaction and reasonable delivery times but lower spending and slightly higher return rates compared to the top performers. Additionally, the 25-34 age group performs moderately well, with decent customer support satisfaction and delivery times but lower spending and higher product return rates. These findings provide invaluable direction for e-commerce enterprises aiming to customize their strategies adeptly for diverse age segments. By comprehending the unique preferences and actions of each demographic, businesses can refine their online shopping offerings, elevate customer contentment, bolster retention figures, and foster comprehensive advancement within the competitive e-commerce.

The aesthetic value of natural vegetation remnants, city parks and vacant lots: The role of ecosystem features and observer characteristics

Physical and mental well-being are linked to exposure to ecosystems perceived as aesthetically pleasing. However, the perceived aesthetic value is shaped by both ecosystem features and observer characteristics. This study explores the relationships between these two influential factors, and perceptions across three Canadian urban greenspace types: natural vegetation remnants (forest stands, marshes and peatlands), city parks, and vacant lots. We administered an online photo-questionnaire, completed by 514 respondents, who assessed the beauty of 45 greenspaces on a five-point Likert scale. Respondents were university students and staff, members of the general public, and specialists in ecosystem services. They also answered questions on socio-demographic factors, pro-environmental attitudes, nature-related travel habits, and ecological knowledge. We calculated five variables from each greenspace photo: vegetation structural complexity, visual complexity, percentage of green pixels, percentage of canopy cover, and presence/absence of artificial structures. On average, natural vegetation remnants and city parks were preferred over vacant lots. Among natural remnants, forests were preferred over semi-open peatlands. Different ecosystem features were associated with perceived beauty across the three greenspace types. For natural remnants and city parks, visual complexity was positively related to appreciation scores, whereas for vacant lots, the presence of artificial structures was associated with lower appreciation scores. Respondents who self-identified as environmental defenders, had greater knowledge of flora, and travelled to observe fauna gave higher appreciation scores to vacant lots compared to other respondents. Younger respondents, and those who engaged in frequent nature-related travelling attributed higher scores to natural remnants. We conclude that aesthetic preferences for natural vegetation remnants, city parks, and vacant lots are influenced by a complex interplay of ecosystem features and observer characteristics. Understanding these intricate relationships can guide the management of vacant lots and promote more inclusive engagement with greenspaces, by considering the varying preferences of diverse segments of the population.

The Impact of Digital Financial Services on Financial Inclusion: A Panel Data Regression Method

This paper delves into the intricate relationship between digital financial services (DFS), traditional payment methods (TPM), economic factors, and demographic variables in a quest to enhance financial inclusion. Panel data regression models were employed to analyse the data encompassing credit card access, borrowing, mobile money accounts, electronic payments, and economic and demographic factors. The findings underscore the pivotal role of digital financial services in promoting financial inclusion, particularly mobile money accounts and electronic payments. These services offer cost-effective and efficient alternatives to traditional banking, facilitating greater economic participation. Furthermore, the study reveals the influence of conventional payment methods, with suggestive continuing relevance on a long run. Economic and demographic factors such as age, income, education, and gender emerge as crucial determinants of financial inclusion, emphasising the need for tailored strategies in diverse population segments. Overall, the results are promising with demonstrable statistically significant evidence in the approach to financial inclusion, leveraging digital innovations while addressing various economic factors and demographic groups' needs. The approach seems to guarantee a more equitable, accessible and efficient global financial system to foster economic growth and societal well-being. Thus, the output in this paper will be economically useful to financial institutions, modern digital technology companies and further researchers who aimed to continuously adapt and improve the approach to financial inclusion.

LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation

Medical image segmentation often faces challenges related to overfitting, primarily due to the limited and complex training samples. This challenge often prompts the use of self-supervised learning and data augmentation. However, self-supervised learning requires well-defined hand-crafted tasks and multiple training stages. On the other hand, basic image augmentation techniques like cropping, rotation, and flipping, effective for natural scene images, have limited efficacy for medical images due to their isotropic nature.While regional dropout regularization data augmentation methods have proven effective in image recognition tasks, their application in image segmentation is not as extensively studied. Additionally, existing augmentation methods often operate on square regions, leading to the loss of crucial contour information. This is particularly problematic for medical image segmentation tasks dealing with regions of interest characterized by intricate shapes. In this work, we introduce LCAMix, a novel data augmentation approach designed for medical image segmentation. LCAMix operates by blending two images and their segmentation masks based on their superpixels, incorporating a local-and-contour-aware strategy. The training process on augmented images adopts two auxiliary pretext tasks: firstly, classifying local superpixels in augmented images using an adaptive focal margin, leveraging segmentation ground truth masks as prior knowledge; secondly, reconstructing the two source images using mixed superpixels as mutual masks, emphasizing spatial sensitivity. Our method stands out as a simple, one-stage, model-agnostic, and plug-and-play data augmentation solution applicable to various segmentation tasks. Notably, it requires no external data or additional models. Extensive experiments validate its superior performance across diverse medical segmentation datasets and tasks. The source codes are available at https://github.com/DanielaPlusPlus/DataAug4Medical.

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR-transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020–2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors.

Identification of the immune repertoire of γδ T-cell receptors in psoriasis.

The γδ Tcells are a subpopulation of Tcells that are abundantly found in the skin and mucous membranes. Their reactivity to self-antigens and ability to secrete various cytokines make them a key component in psoriasis development. Although the correlation between the immune repertoire (IR) of γδ T-cell receptors and the occurrence and severity of psoriasis remains incompletely explored, high-throughput sequencing of γδ Tcells has led to a deeper understanding of IR in psoriasis. This study investigated the differences between γδ Tcells in patients with psoriasis and healthy controls. The γδ Tcells were identified via immunofluorescence staining and a correlation analysis was performed according to the psoriasis area and severity index (PASI) scores. The IR sequencing method was used to detect IR in the γδ T-cell receptors. The findings demonstrated more skin γδ Tcells in patients with psoriasis, which were positively correlated with the PASI score. There were subtle differences in most variable (V), diversity (D) and joining (J) gene segments and VJ/VDJ combination segments between patients with psoriasis and healthy controls. However, a higher diversity of complementarity-determining region 3 (CDR3) was observed in patients with psoriasis. In summary, the IR of skin γδ Tcells was significantly altered in patients with psoriasis, and the diversity in the cell's CDR3 population is a promising biomarker for assessment of psoriasis severity.

Short and long term memory method for predicting the temperature of motor stator based on Harris eagle algorithm optimization

The temperature of a motor significantly affects its control and lifespan. However, due to the influence of motor structure and operating environment, precise temperature measurement of the motor is challenging with temperature sensors. Therefore, machine learning algorithms are often employed to predict the temperature more accurately. To enhance motor control, integrating machine learning algorithm models with the actual motor control terminal is highly beneficial. This paper proposes a Short and Long Term Memory (LSTM) algorithm model based on Harris's hawk optimization to predict the temperature of the motor stator, which is applied in actual motor control. Furthermore, it evaluates the tracking performance of motor control current. Firstly, an experimental platform for temperature measurement is established to acquire the temperature at different positions of the motor as raw data. Subsequently, the raw data is inputted into three algorithms: LSTM, PSO-LSTM, and HHO-LSTM, for comparison. By comparing evaluation metrics, it is demonstrated that HHO-LSTM exhibits excellent predictive performance. Furthermore, utilizing diverse segments of the motor as model input sets enhances the generalization capability and predictive accuracy of the model. Finally, applied in actual motor control, a comparative analysis of q-axis and d-axis currents reveals reduced fluctuation errors, showcasing improved motor control effectiveness.

THE IMPACT OF SOCIAL MEDIA INFLUENCERS ON CONSUMER BEHAVIOR: A CASE STUDY OF INSTAGRAM INFLUENCERS ON NIKE

In the era of digital marketing, social media influencers have emerged as powerful catalysts shaping consumer behaviour and brand perceptions. This study investigates the impact of Instagram influencers endorsed by Nike on consumer behaviour in India, focusing on brand perceptions, attitudes, purchase decisions, and brand loyalty. Through a mixed-methods approach combining quantitative surveys, qualitative interviews, and content analysis, data was collected from 150 participants representing diverse demographic segments. Key findings reveal that exposure to Instagram influencers positively influences consumer perceptions of Nike, leading to improvements in brand familiarity, image, trust, purchase intent, loyalty, and perceived quality. Participants exposed to influencers demonstrated higher purchase intentions and were more likely to consider and purchase Nike products compared to unexposed participants. Additionally, Nike's influencer marketing strategies significantly contributed to fostering brand loyalty, with influencer engagement, brand authenticity, customer satisfaction, and loyalty programs emerging as key predictors. Content analysis of influencer posts revealed predominant themes such as product features, lifestyle imagery, user testimonials, and promotional offers, highlighting the diverse strategies employed by influencers to engage their audience and promote Nike products. Sentiment analysis of participant comments indicated predominantly positive sentiment towards influencer content, underscoring its effectiveness in eliciting favorable reactions from consumers. This study contributes to the literature by advancing theoretical understanding, providing methodological insights, and offering practical implications for marketers. Recommendations for future research include exploring cross-cultural variations, conducting longitudinal studies, employing experimental designs, and addressing ethical considerations in influencer marketing practices. By embracing these recommendations, researchers can further enrich our understanding of influencer marketing dynamics and its implications for brands and consumers in the digital age. Keywords: Social media influencers, Instagram, Nike, consumer behavior, brand perceptions, purchase decisions, brand loyalty, influencer marketing.

Ultralong CDR3 cattlebody use biased to switched immunoglobulin isotypes in bovine immune tissues

Abstract Alongside typical immunoglobulin (Ig), cattle express unique ultralong immunoglobulins (UL) that contain a CDR H3 comprised of a β-ribbon stalk and disulfide-linked knob that reaches over 70 amino acids in length. UL Ig exhibit potential "reach" to recessed pathogen epitopes and can provide broadly neutralizing antibodies to elusive antigens. This study explores the relative amounts of UL Ig in pre-switch isotypes (IgM and IgD) versus post-switch isotypes (IgG, IgA and IgE). Rearranged immunoglobulin heavy chain transcripts from primary and secondary lymphoid tissues of three steer were assessed with primers specific for the variable segments used in ultralong and canonical rearrangements as well as specific subisotype constant regions. Relative expression suggests biased selection for UL Ig in germinal centers and use in mucosal tissues of the ruminant gastrointestinal tract. Despite the explosion of studies exploring the immunogenetic diversification and binding possibilities of the UL Ig knob "picobody", our understanding of the use of the role of these antibodies in the Bovid immune system has been much slower. With a more complete understanding of tissue-specific expression patterns and isotype-specific immune responses using these exceptional paratopes, we'll be better poised to exploit them for improved bovine and human health.

AGSAM: Agent-Guided Segment Anything Model for Automatic Segmentation in Few-Shot Scenarios.

Precise medical image segmentation of regions of interest (ROIs) is crucial for accurate disease diagnosis and progression assessment. However, acquiring high-quality annotated data at the pixel level poses a significant challenge due to the resource-intensive nature of this process. This scarcity of high-quality annotated data results in few-shot scenarios, which are highly prevalent in clinical applications. To address this obstacle, this paper introduces Agent-Guided SAM (AGSAM), an innovative approach that transforms the Segment Anything Model (SAM) into a fully automated segmentation method by automating prompt generation. Capitalizing on the pre-trained feature extraction and decoding capabilities of SAM-Med2D, AGSAM circumvents the need for manual prompt engineering, ensuring adaptability across diverse segmentation methods. Furthermore, the proposed feature augmentation convolution module (FACM) enhances model accuracy by promoting stable feature representations. Experimental evaluations demonstrate AGSAM's consistent superiority over other methods across various metrics. These findings highlight AGSAM's efficacy in tackling the challenges associated with limited annotated data while achieving high-quality medical image segmentation.

Anti-stigma interventions in low-income and middle-income countries: a systematic review

Stigma exacerbates power imbalances and societal disparities, significantly impacting diverse identities and health conditions, particularly for low and middle-income countries (LMICs). Though crucial for dismantling harmful stereotypes, and enhancing healthcare utilisation, existing research on anti-stigma interventions is limited with its condition-focused approach. We aimed to thoroughly evaluate peer-reviewed and non-peer-reviewed literature for a comprehensive review of anti-stigma interventions for diverse identities and all health conditions in LMICs. This review systematically explored peer-reviewed and non-peer-reviewed literature, in ten electronic databases up to January 30, 2024, covering all anti-stigma interventions across various stigmatised identities and health conditions in LMICs. Quality assessment for this systematic review was conducted as per Cochrane Collaboration's suggested inclusions. The review was registered with PROSPERO (Registration: 2017 CRD42017064283). Systematic synthesis of the 192 included studies highlights regional imbalances, while providing valuable insights on robustness and reliability of anti-stigma research. Most studies used quasi-experimental design, and most centred on HIV/AIDS or mental health related stigma, with very little work on other issues. Certain high-population LMICs had no/little representation. The interventions targeted diverse segments of populations and consequently yielded a multitude of stigma-related outcomes. However, despite the heterogeneity of studies, most reported positive outcomes underscoring the effectiveness of existing interventions to reduce stigma. This study is supported by the UK Medical Research Council Indigo Partnership (MR/R023697/1) award.

The moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance: evidence from Egypt

Purpose The purpose of this paper is to investigate whether audit committee characteristics, in particular audit committee size, audit committee activity and audit committee gender diversity, are associated with financial performance in Egyptian banks. The second purpose of this paper is to explore the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance. Design/methodology/approach A multiple regression analysis is used to estimate the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance of a sample of Egyptian banks during the period between 2018 and 2022. Findings The results indicate that audit committee size has a negative and insignificant effect impact on return on assets (ROA) and return on equity (ROE), respectively. The results also indicate that the audit committee gender diversity has a significant positive impact on ROA and ROE, respectively. Regarding audit committee activity, the number of board meetings has a negative and insignificant effect on ROA and ROE, respectively. Regarding gender diversity as a moderating variable, in general there is a positive effect of gender diversity on the relationship between audit committee characteristics and financial performance. Research limitations/implications The study was limited to 20 banks in one country, but it sets the tone for future empirical research on this subject matter. The study also relied on one moderating variable, which is board gender diversity. This study provides an avenue for future research in the area of corporate governance and financial performance in other emerging countries, especially other African countries. Practical implications This study provides useful insights for managers and policymakers to better understand which audit committee characteristics can best encourage a company to improve financial performance. Furthermore, regulators should ensure that banks strictly adhere to corporate governance principles to build a strong banking industry capable of achieving economic development. Social implications Banks will benefit equally from valuable qualities across demographic groupings in society by having females on the audit committee and appropriate audit committee meetings. Additionally, if audit committee members are correctly selected, banks with more females in audit committee and suitable audit committee meetings can successfully contribute to strengthening financial performance and social welfare of diverse segments of society. A culture of good banking governance must emerge to improve bank financial stability and, as a result, greater stability and economic growth. Originality/value To the best of the authors’ knowledge, the study is, perhaps, the first to examine the moderating role of board gender diversity on the relationship between audit committee characteristics and financial performance in Egyptian banks. This study adds to the literature by investigating such an issue in a developing economy that operates in a different context than those in developed countries.

The effect of harmonization on the variability of PET radiomic features extracted using various segmentation methods

PurposeThis study aimed to examine the robustness of positron emission tomography (PET) radiomic features extracted via different segmentation methods before and after ComBat harmonization in patients with non-small cell lung cancer (NSCLC).MethodsWe included 120 patients (positive recurrence = 46 and negative recurrence = 74) referred for PET scanning as a routine part of their care. All patients had a biopsy-proven NSCLC. Nine segmentation methods were applied to each image, including manual delineation, K-means (KM), watershed, fuzzy-C-mean, region-growing, local active contour (LAC), and iterative thresholding (IT) with 40, 45, and 50% thresholds. Diverse image discretizations, both without a filter and with different wavelet decompositions, were applied to PET images. Overall, 6741 radiomic features were extracted from each image (749 radiomic features from each segmented area). Non-parametric empirical Bayes (NPEB) ComBat harmonization was used to harmonize the features. Linear Support Vector Classifier (LinearSVC) with L1 regularization For feature selection and Support Vector Machine classifier (SVM) with fivefold nested cross-validation was performed using StratifiedKFold with ‘n_splits’ set to 5 to predict recurrence in NSCLC patients and assess the impact of ComBat harmonization on the outcome.ResultsFrom 749 extracted radiomic features, 206 (27%) and 389 (51%) features showed excellent reliability (ICC ≥ 0.90) against segmentation method variation before and after NPEB ComBat harmonization, respectively. Among all, 39 features demonstrated poor reliability, which declined to 10 after ComBat harmonization. The 64 fixed bin widths (without any filter) and wavelets (LLL)-based radiomic features set achieved the best performance in terms of robustness against diverse segmentation techniques before and after ComBat harmonization. The first-order and GLRLM and also first-order and NGTDM feature families showed the largest number of robust features before and after ComBat harmonization, respectively. In terms of predicting recurrence in NSCLC, our findings indicate that using ComBat harmonization can significantly enhance machine learning outcomes, particularly improving the accuracy of watershed segmentation, which initially had fewer reliable features than manual contouring. Following the application of ComBat harmonization, the majority of cases saw substantial increase in sensitivity and specificity.ConclusionRadiomic features are vulnerable to different segmentation methods. ComBat harmonization might be considered a solution to overcome the poor reliability of radiomic features.

Consumer Perception Variations Brand Loyalty in Diverse Cultural Contexts

In today's fiercely competitive markets, fostering brand loyalty is paramount for companies, reflecting their ability to cultivate enduring customer relationships. However, achieving this loyalty involves navigating a complex landscape where consumer sentiments intertwine with cultural dynamics. This study delves into this intricate relationship, illuminating how diverse societal norms, values, and traditions shape consumer behavior across regions and demographics. By examining existing literature and empirical research, this paper aims to untangle the cultural elements influencing brand perceptions and loyalty. Through meticulous analysis, we aim to uncover the cultural drivers steering consumer decision-making, providing valuable insights for businesses aiming to foster brand allegiance in a globalized, culturally diverse environment. Understanding cultural intricacies is not just academic but a practical necessity for businesses seeking growth and competitive advantage. By recognizing nuances within different cultural contexts, companies can tailor branding strategies to resonate with diverse consumer segments authentically. Moreover, comprehending cultural underpinnings helps navigate potential pitfalls, such as inadvertently alienating demographics. Thus, this research serves as a guide for businesses navigating global markets, offering actionable insights to cultivate brand loyalty transcending cultural boundaries.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3[Formula: see text] (MSGAM-AUNet3[Formula: see text]), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.

Emergence of epizootic hemorrhagic disease in red deer (Cervus elaphus), Spain, 2022

Epizootic hemorrhagic disease (EHD) virus serotype 8 (EHDV-8) emerged in Spain in autumn 2022. In this study, we aimed to (1) characterize the clinical and lesional presentation of EHDV infection in European red deer (Cervus elaphus), and (2) study the spatial spread of the virus in wild ruminants in Spain after its introduction, in 2022/2023. We confirmed EHDV infection in two clinically compatible sick red deer by PCR and detection of anti-EHDV specific antibodies. EHDV infection occurred in red deer with hyperacute to acute clinical signs and lesions associated to vascular changes leading to death of the animals. Partial sequences of variable segment 2 (VP2) and segment 5 (NS1) genes of the detected viruses had >99% nucleotide identity with EHDV-8 sequences from Tunisia and Italy. In a cross-sectional serological study of EHDV in 592 wild ruminants, mainly red deer (n=578), in southwestern Spain, we detected anti-EHDV antibodies in 37 of 592 samples (6.3%; 95% confidence interval: 4.3–8.2), all from red deer and from the localities where clinical cases of EHD were confirmed in red deer. We conclude that EHDV-8 infection causes severe EHD in European red deer. The serosurvey revealed a limited spread of EHDV-8 in Spanish wild ruminant populations in the first year of virus detection in Spain.

Determinants of Green Energy Use: Household Level Comparative Analysis of Southern Khyber Pakhtunkhwa

In recent times, the utilization of renewable energy sources has gained significant traction and garnered widespread attention across diverse social segments. The prominent driver behind this surge in popularity is its cost-effectiveness and long-term durability. Notably, even individuals with limited financial means are increasingly drawn toward various forms of renewable energy. This research endeavor aims to draw comparisons between users and non-users of green energy and their corresponding household behaviors. Upon conducting a rigorous data analysis, the study will present a comprehensive and well-founded conclusion, along with offering pertinent recommendations. This study is slated to take place in the rural regions of Southern Khyber Pakhtunkhwa, Pakistan. The study employed random sampling to select a sample from the population and focused on examining the relationship between the utilization of green energy, its characteristics, conventional green energy use, and the quality of solar energy. The primary objective of the study was to explore the choices made by individual households concerning green energy consumption and the factors that influence this choice. The analysis was carried out using Ordinary Least Squares (OLS) regression. This research gap presents a significant opportunity for advancing the promotion and adoption of renewable energy sources.

DDViT: Double-Level Fusion Domain Adapter Vision Transformer (Student Abstract)

With the help of Vision transformers (ViTs), medical image segmentation was able to achieve outstanding performance. In particular, they overcome the limitation of convolutional neural networks (CNNs) which rely on local receptive fields. ViTs use self-attention mechanisms to consider relationships between all image pixels or patches simultaneously. However, they require large datasets for training and did not perform well on capturing low-level features. To that end, we propose DDViT, a novel ViT model that unites a CNN to alleviate data-hunger for medical image segmentation with two multi-scale feature representations. Significantly, our approach incorporates a ViT with a plug-in domain adapter (DA) with Double-Level Fusion (DLF) technique, complemented by a mutual knowledge distillation paradigm, facilitating the seamless exchange of knowledge between a universal network and specialized domain-specific network branches. The DLF framework plays a pivotal role in our encoder-decoder architecture, combining the innovation of the TransFuse module with a robust CNN-based encoder. Extensive experimentation across diverse medical image segmentation datasets underscores the remarkable efficacy of DDViT when compared to alternative approaches based on CNNs and Transformer-based models.

BLADE: Box-Level Supervised Amodal Segmentation through Directed Expansion

Perceiving the complete shape of occluded objects is essential for human and machine intelligence. While the amodal segmentation task is to predict the complete mask of partially occluded objects, it is time-consuming and labor-intensive to annotate the pixel-level ground truth amodal masks. Box-level supervised amodal segmentation addresses this challenge by relying solely on ground truth bounding boxes and instance classes as supervision, thereby alleviating the need for exhaustive pixel-level annotations. Nevertheless, current box-level methodologies encounter limitations in generating low-resolution masks and imprecise boundaries, failing to meet the demands of practical real-world applications. We present a novel solution to tackle this problem by introducing a directed expansion approach from visible masks to corresponding amodal masks. Our approach involves a hybrid end-to-end network based on the overlapping region - the area where different instances intersect. Diverse segmentation strategies are applied for overlapping regions and non-overlapping regions according to distinct characteristics. To guide the expansion of visible masks, we introduce an elaborately-designed connectivity loss for overlapping regions, which leverages correlations with visible masks and facilitates accurate amodal segmentation. Experiments are conducted on several challenging datasets and the results show that our proposed method can outperform existing state-of-the-art methods with large margins.

The Role of Digital Marketing in Consumer Behaviour

In the dynamic landscape of contemporary commerce, the symbiotic relationship between digital marketing and consumer behavior stands as a pivotal nexus, shaping the very fabric of market dynamics. This research embarks on an odyssey to dissect and comprehend the intricate interplay between digital marketing strategies and the multifaceted tapestry of consumer behavior. As we traverse the digital frontier, this study employs a kaleidoscopic lens, synthesizing an amalgamation of theoretical frameworks, empirical analyses, and innovative methodologies. A panorama of digital marketing channels, including social media, search engine optimization, and targeted advertising, serves as the backdrop against which the evolving dance of consumer preferences unfolds. Our investigation transcends traditional paradigms, delving into the nuances of personalized digital experiences, algorithmic decision-making, and the burgeoning realm of influencer marketing. By unraveling the intricate threads of these digital interventions, we aim to decipher the triggers and catalysts that prompt consumers to engage, react, and transact in the online marketplace. The empirical core of this research comprises a comprehensive analysis of real-time data, surveys, and case studies, providing a granular understanding of the impact of digital marketing on diverse consumer segments. The findings promise to unravel not only the overt influences but also the subtle undercurrents that guide consumer choices in the digital realm. In conclusion, our exploration of the role of digital marketing in consumer behavior transcends the conventional boundaries of academic inquiry. By fostering a nuanced understanding of the interconnectedness between digital stimuli and consumer responses, this research contributes to the evolving narrative of marketing in the digital age. As businesses navigate the uncharted waters of the digital landscape, this study serves as a compass, guiding them toward a deeper comprehension of consumer behavior and illuminating pathways to forge meaningful connections in the digital marketplace.