Long-distance Relationships Research Articles

Robotic Grasping Detection Algorithm Based on 3D Vision Dual-Stream Encoding Strategy

The automatic generation of stable robotic grasping postures is crucial for the application of computer vision algorithms in real-world settings. This task becomes especially challenging in complex environments, where accurately identifying the geometric shapes and spatial relationships between objects is essential. To enhance the capture of object pose information in 3D visual scenes, we propose a planar robotic grasping detection algorithm named SU-Grasp, which simultaneously focuses on local regions and long-distance relationships. Built upon a U-shaped network, SU-Grasp introduces a novel dual-stream encoding strategy using the Swin Transformer combined with spatial semantic enhancement. Compared to existing baseline methods, our algorithm achieves superior performance across public datasets, simulation tests, and real-world scenarios, highlighting its robust understanding of complex spatial environments.

Examining the validity of an adaptive model of sustaining behaviors in long-distance relationships: Predicting intimacy and marital satisfaction

Long-distance romantic relationships present unique challenges, necessitating couples to employ adaptive strategies to maintain intimacy and marital satisfaction. This study aimed to examine the validity of an adaptive model of sustaining behaviors in long-distance relationships and its capacity to predict marital intimacy and satisfaction. The model included mindfulness, sexual function, religious, and online social support domains. Data were collected online from 366 couples (aged 18 to 57; Mean = 34.82, SD = 7.34; 79.8 % female) engaged in long-distance relationships. A confirmatory factor analysis confirmed the acceptable fit of the four-factor model which was also invariant across gender groups. The results indicated significant associations between sustaining behaviors and marital intimacy and satisfaction, with varying predictive power among specific components. Notably, relationship/sexual mindfulness and marital sanction/religious communication within the religion domain emerged as robust predictors. Conversely, religious practice showed a significant negative association. Additionally, the realization component of the sexual function domain significantly predicted both marital satisfaction and intimacy, while its partnership component significantly predicted only marital intimacy. None of the social support components significantly predicted marital intimacy or satisfaction. These findings are important as they highlight the nuanced interplay between sustaining behaviors and relational well-being in long-distance relationships, emphasizing the need for tailored interventions to enhance relationship quality in this context.

A Transformer-Based Image-Guided Depth-Completion Model with Dual-Attention Fusion Module.

Depth information is crucial for perceiving three-dimensional scenes. However, depth maps captured directly by depth sensors are often incomplete and noisy, our objective in the depth-completion task is to generate dense and accurate depth maps from sparse depth inputs by fusing guidance information from corresponding color images obtained from camera sensors. To address these challenges, we introduce transformer models, which have shown great promise in the field of vision, into the task of image-guided depth completion. By leveraging the self-attention mechanism, we propose a novel network architecture that effectively meets these requirements of high accuracy and resolution in depth data. To be more specific, we design a dual-branch model with a transformer-based encoder that serializes image features into tokens step by step and extracts multi-scale pyramid features suitable for pixel-wise dense prediction tasks. Additionally, we incorporate a dual-attention fusion module to enhance the fusion between the two branches. This module combines convolution-based spatial and channel-attention mechanisms, which are adept at capturing local information, with cross-attention mechanisms that excel at capturing long-distance relationships. Our model achieves state-of-the-art performance on both the NYUv2 depth and SUN-RGBD depth datasets. Additionally, our ablation studies confirm the effectiveness of the designed modules.

Влияние преданности на психологическое благополучие студентов, состоящих в отношениях на расстоянии

<p>The phenomenon of long-distance relationships among students that have developed in this era is an opportunity that allowed them to thrive optimally in maintaining their relationships. However, in fact, LDR for students is still a challenge in itself because it requires a greater effort that can affect the achievement of individual psychological well-being. Psychological well-being is a necessary condition for individuals, including students who are in long-distance relationships so that they are able to maintain relationships optimally. Psychological well-being can be achieved by the presence of various factors, one of which is commitment. This study aims to determine the relationship between commitment and psychological well-being in students who are in long-distance relationships. The method used in this research is correlational quantitative. 587 students are undergoing long-distance relationships to become participants in this study with the accidental sample as the sampling technique. This study used two scales The Revised Commitment Inventory and the Ryff Scale of Psychological Well-being. The hypothesis test results with the Pearson correlation were 0,987 with sig = 0,000 (p<0,01), which means that the research hypothesis was accepted. Commitment contributed 97,4% to psychological well-being. This research shows that commitment is a very strong factor related to achieving psychological well-being in students who are in long-distance relationships. Commitment is essential in helping students to maintain relationships with existing different conditions while maintaining their psychological well-being and still carrying out their academic role optimally.</p>

The experiences of individuals in maintaining a long-distance relationship through social media

This qualitative study explores the dynamics of long-distance romantic relationships (LDRRs) in the digital age. Long-distance relationships (LDRs) have emerged as a testament to the enduring strength of human affection in the face of spatial constraints. However, the dynamics of LDRs in the digital era present a diverse array of emotions, obstacles, and coping mechanisms. The purpose of this study is to understand the distinctive experiences of how social media has had a positive influence on the communication and relationship satisfaction between partners who have experienced long distance. The research, conducted through qualitative methods, involved 8 college students, predominantly females, offering insights into their experiences in current LDRs. Through semi-structured interviews with eight participants aged 18 to 24 in LDRRs, themes emerged regarding social media usage, coping strategies for emotional challenges, insecurities and communication, and the influence of family environments. The theme underscores how digital tools serve as essential resources for maintaining emotional intimacy and strengthening the relationship despite the challenges of distance. It also highlights the importance of familial support, privacy management, and individual resilience in sustaining intimate connections across geographical distances. The findings reveal that social media platforms such as WhatsApp and Instagram are integral to communication in LDRRs, with participants emphasising the importance of voice and video calls for maintaining intimacy. Coping with emotional challenges, including insecurities and feelings of disconnection, involves active communication, transparency, reassurance, and planning activities together. Family environments play a significant role in shaping communication dynamics, with participants navigating constraints on privacy and openness based on familial support or constraints. Overall, this study underscores the vital role of social media in sustaining emotional intimacy and connection in LDRRs. Understanding how individuals utilise social media and cope with challenges can inform interventions to support healthy, fulfilling relationships in the digital age.

Organizing Minds and Managing People: J.P. Morgan Bankers on Transatlantic Consolidation of Communication and Capital, 1917–1920

AbstractDuring the Great War, J.P. Morgan bankers Thomas W. Lamont, Henry P. Davison, and Dwight W. Morrow expanded their visions of organizing across distances and supported the development of spaces where like-minded individuals could make coordinated decisions regarding the stability of industrial capitalism. These financial elites focused not only on profits but also on deeper ideas. Their experience organizing across distances, first domestically and then across the Atlantic, demonstrates the importance of these financiers to visions of global economic governance centered on information exchange and communication, intimate long-distance relationships, and deliberation among perceived equals, which are essential elements of merchant banking. Their visions further reflected a hierarchical and racial understanding of a liberal global order. Highly flexible in their strategies, these bankers possessed long-term views of national and global development that engaged overlapping connections among networks, institutions, and the public that privileged the creation of transatlantic spaces for deliberation and socialization among Western economic elites.

On Sitting Down with John Keats

In this essay I focus on the phenomenon of ‘sitting down’ in Keats's letters and poems. Sitting down, in Keats's personal interactions and poetics, has a range of connotations: these range from exercising individual, focused concentration on the task ahead, to enjoying companionable, shared creativity; from maintaining a certain bedside manner, to establishing a long-distance relationship with siblings; from reluctantly resigning oneself to an invalid existence, to summoning the resourceful energy to compose poetry. For Keats, the expression of sitting down points to the required stillness for the imagination to take flight in poetry, but it also features prominently in self-portrayals throughout his correspondence. This essay demonstrates how sitting down, for Keats, is a transformative act with far more dynamic connotations than is usually assumed.

When distance enhances closeness

Exploring international students’ long-distance relationships with their back-home families is important for understanding how to support their stability and growth. Using qualitative interviews and surveys, this research explored perspectives from both international students and their parents about their remote interactions while oceans apart. Findings indicate three transformative shifts that enhanced the quality of student-parent relationships through their distance: a) incidental to intentionally motivated interactions, b) task-oriented to person-centered attention, and c) authoritarian to communally-oriented dispositions. These shifts fostered a greater sense of trust, support, and intimacy between international students and their back-home parents. Interweaving Construal Level Theory with the findings, this study offers implications for informing international students and their parents on developing effective, long-distance relationships.

Improving Single-Image Super-Resolution with Dilated Attention

Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality.

Graph Neural Networks for Recommendation Leveraging Multimodal Information

Recommender systems act as filtering algorithms to provide users with items that might meet their interests according to the expressed preferences and items' characteristics. As of today, the collaborative filtering paradigm, along with deep learning techniques to learn high-quality users' and items' representations, constitute the de facto standard for personalized recommendation, showing remarkable recommendation accuracy performance. Nevertheless, recommendation remains a highly-challenging task. Among the most debated open issues in the community, this thesis considers two algorithmic and conceptual ones, namely: (i) the inexplicable nature of users' preferences, especially when they come in the form of implicit feedback; (ii) the effective exploitation of the collaborative information in the designing and training of recommendation models. In domains such as fashion, food, and media content recommendation, the shallow item's profile can be enhanced through the multimodal characteristics describing items [Malitesta et al., 2023]. Driven by these assumptions, in the first part of this thesis, we apply multimodal deep learning strategies for multimedia recommendation; the scope is to study and design recommendation algorithms based upon the principles of multimodality to possibly match each item's characteristic to the implicit preference expressed by the user [Deldjoo et al., 2022], thus addressing the (i) issue. Recent collaborative filtering approaches profile users and items through embedding vectors in the latent space. However, such models disregard structural properties naturally encoded into the user-item interaction data. Indeed, recommendation datasets are easily describable under the topology of a bipartite and undirected graph, with users and items being the graph nodes connected at multiple distance hops. In this respect, the application of graph neural networks , recent deep learning techniques specifically tailored to learn from non-euclidean data, can provide a refined representation of users and items to mine near- and long-distance relationships in the user-item graphs [Anelli et al., 2023b]. Indeed, this is one possible solution to exploit the collaborative information, which is effectively propagated within the user-item graph, addressing the (ii) issue. Conclusively, this thesis aims to match the two families of recommendation strategies by leveraging graph neural networks and multimodal information data [Anelli et al., 2022]. In doing so, other numerous micro-aspects within the two macro-areas (introduced above) are examined. Indeed, the thesis is a systematic compendium of careful analyses regarding, among others, reproducibility, novel evaluation dimensions [Anelli et al., 2023a], and tasks/scenarios complementary to recommendation. Awarded by : Politecnico di Bari, Bari, Italy on 30 January 2024. Supervised by : Tommaso Di Noia. Available at : https://hdl.handle.net/11589/264941.

Bridging the Distance: Effective Communication and Honesty Among College Students in Romantic Long-Distance Relationships

This study aimed to explore how college students in long-distance romantic relationships maintain honesty and effective communication. Stress is a prevalent aspect for students in such relationships, and this research aimed to provide a deeper understanding of their experiences. Data from 7 participants were collected through structured interviews, analyzed using thematic analysis, revealing key themes of visits, communication, honesty, and conflicts. Visitation depended on mutual convenience, while communication in long-distance relationships had both advantages and disadvantages. Honesty emerged as a trust-building factor, enhancing communication. Future research could delve into the impact of academic stress on long-distance relationships and include a larger sample for a comprehensive study.

Urban-urban split households in China: Gender, migration, and stay-behind women

The study of split households in China and beyond has traditionally centered on rural-urban migration, overlooking the prevalence of split householding among urban-urban migrants. Despite receiving scant scholarly attention, urban-urban migrant dynamics, particularly concerning relational power negotiation among women in long-distance relationships, warrant exploration. This paper delves into the experiences of urban-urban migrants in China, spotlighting how stay-behind women negotiate relational power within split households. Drawing from 30 in-depth, semi-structured interviews conducted with various types of migrant households and focusing on 12 split households, this study sheds light on the relationships of contemporary urban, educated Chinese couples with patriarchal values entrenched in the Confucian family system. The findings reveal that split households expose the tension between entrenched Confucian patriarchy and women’s aspirations for autonomy. While serving as a respite from traditional gender roles, split households inadvertently offer women newfound independence. Although not overtly challenging conventional gender norms, stay-behind women in urban-urban split households leverage agency and empowerment within the existing gender framework. This research underscores the evolving gender dynamics in China, poised between entrenched patriarchy and emerging spaces for alternative gender practices, offering insights into the nuanced experiences of women in split households.

Communication Patterns of Overseas Students with Parents

Communication is an activity that is a human need. Overseas students of course still maintain communication even though there are differences in the intensity of communication so that there are obstacles experienced in communicating. The purpose of this study is to understand the communication patterns applied by overseas students with their parents by using Family Relationship Schematic Theory. This study uses a qualitative and technical approach to data analysis by Miles and Huberman. By using a purposive sampling technique, the research subjects were only limited to overseas students of the Communication Studies Program, State University of Jakarta, and Class of 2020 from outside Java. The results of this study are that there are several matches between informants such as communication patterns applied in the family, freedom of expression, intensity of communication, consistency of mutual agreement, and compliance with the media. Secondary communication patterns are communication patterns used by overseas students in long-distance relationships with parents.

The Role of Avoidant Attachment and Anxious Attachment in Romantic Jealousy Among Young Adults in Long-Distance Relationships.

This study explores the role of adult attachment in romantic jealousy among young adults engaged in long-distance relationships. With 102 participants aged 20-25 in long-distance relationships, the research employed a qualitative method using questionnaires. The analysis results indicate that avoidant attachment is not significantly associated with romantic jealousy (significance value 0.232 < 0.05), whereas anxious attachment plays a significant role (significance value 0.000 < 0.05). Consequently, the findings suggest that avoidant attachment does not contribute to romantic jealousy, while anxious attachment is a significant factor in romantic jealousy among young adults in long-distance relationships.

Portrait of a Sakinah Mawaddah Warahmah Family in the Community of Lenggerong Village, Bantarbolang District, Pemalang Regency

Purpose – Creating a sakinah mawaddah warahmah family is a goal for every married couple. These efforts are certainly faced with various challenges and obstacles. This is experienced by some people in Lenggerong Village, Bantarbolang District, Pemalang Regency. This research aims to see how the portrait of a sakinah mawaddah warahmah family in the community of Lenggerong Village, Bantarbolang District, Pemalang Regency.
 Methods – This research uses qualitative research with a descriptive approach technique. The subjects and data sources of this research came from Mrs Sitih, Mrs Istiana and Mrs Eka Jayanti. The data collection techniques were interviews and observations.
 Findings – The results showed that the portrait of a Sakinah, Mawaddah, warahmah family is a calm family condition, mutual understanding, no turmoil, serene, happy and harmonious. The husband fulfils the obligation of physical and mental nafkah and provides love and affection to the wife. Meanwhile, the wife's obligations towards the husband or the husband's rights over the wife are, among others: Obeying the husband, taking care of yourself when the husband is not around, maintaining self-respect and protecting the husband's property.
 Research implications/limitations – Based on the results of the study, the researcher recommends that the authorities conduct socialization on the formation of a sakinah family, especially for married couples who are in a long-distance relationship in Lenggerong Village.
 Originality/value – The existence of this research is expected to be a praxis reference for the community in an effort to form a sakinah mawaddah warahmah family. The empirical experiences of the informants in this study are expected to inspire married couples in other communities to form a sakinah family.

Pressurizing or encouraging: Health behaviors among long-distance couples.

Couples in long-distance relationships face unique challenges that affect their health outcomes and relationship dynamics in ways that are different from couples in close proximal relationships (PR). The results of previous literature analyzing health outcomes for long-distance relationship (LDR) couples have been mixed, and factors such as couple satisfaction and gender of the individuals contribute to the variance. This study examined the good health practices of couples in LDRs, the ways in which partners influence each other's health, and the health outcomes of these couples as compared to couples in PRs. Multilevel multivariate analysis showed that couples in LDRs had better health practices than those in PRs, and men overall had poorer health practices than women. There was no main effect seen for LDRs when we examined strategies used for influencing health. There was a main effect for gender, though, and we found that women tend to use more collaborative and pressurizing strategies for influencing their partner's health. On health indices, we found that LDR couples tended to have lower levels of fatigue and sleep disturbance as compared to PR couples. In the adjusted model with covariates, which included age, income, couple satisfaction, and married or unmarried couples, we found that lower couple satisfaction and lower income significantly predicted poorer health on all health indices. These results are discussed in light of unequal gender roles, social control, and positive aspects of LDRs on health in an attempt to understand LDRs better and destigmatize the narrative of these relationships as 'less than' PRs. Implications for health and mental health practitioners are also discussed.

INTERPERSONAL COMMUNICATION ON LONG DISTANCE RELATIONSHIPS ANALYSIS OF ISLAMIC EDUCATION PSYCHOLOGY IN STUDENTS WHO CARRY OUT REAL WORK LECTURE (KKN) ACTIVITIES

Student life is often colored by the dynamics of interpersonal relationships, especially for those in long distance relationships (LDR). This study aims to analyze the way interpersonal communication affects the quality of LDR relationships, focusing on the psychological dimensions of Islamic education. The research methods are qualitative, paradigms and interpretive approaches to phenomenology. Data collection techniques, namely in-depth interviews. The results of this study show that interpersonal communication plays a key role in forming and maintaining LDR relationships of students who are undergoing KKN. Psychological factors such as self-confidence, empathy, and commitment to the values of Islamic Education also influence the dynamics of long-distance relationships. The implications of this research can be used as a basis for the development of educational and counseling programs that support students in managing their LDR relationships more effectively. Some interpersonal communication strategies are effective in strengthening LDR relationships, such as the use of information technology, wise time management, and the inculcation of spiritual values in daily communication. The practical implications of these findings can be the basis for the development of practical guidance for students undergoing LDR, especially in the context of community service activities in the Islamic Education environment.

Context-aware and local-aware fusion with transformer for medical image segmentation

Objective. Convolutional neural networks (CNNs) have made significant progress in medical image segmentation tasks. However, for complex segmentation tasks, CNNs lack the ability to establish long-distance relationships, resulting in poor segmentation performance. The characteristics of intra-class diversity and inter-class similarity in images increase the difficulty of segmentation. Additionally, some focus areas exhibit a scattered distribution, making segmentation even more challenging. Approach. Therefore, this work proposed a new Transformer model, FTransConv, to address the issues of inter-class similarity, intra-class diversity, and scattered distribution in medical image segmentation tasks. To achieve this, three Transformer-CNN modules were designed to extract global and local information, and a full-scale squeeze-excitation module was proposed in the decoder using the idea of full-scale connections. Main results. Without any pre-training, this work verified the effectiveness of FTransConv on three public COVID-19 CT datasets and MoNuSeg. Experiments have shown that FTransConv, which has only 26.98M parameters, outperformed other state-of-the-art models, such as Swin-Unet, TransAttUnet, UCTransNet, LeViT-UNet, TransUNet, UTNet, and SAUNet++. This model achieved the best segmentation performance with a DSC of 83.22% in COVID-19 datasets and 79.47% in MoNuSeg. Significance. This work demonstrated that our method provides a promising solution for regions with high inter-class similarity, intra-class diversity and scatter distribution in image segmentation.

Remote Management as the New Long-Distance Relationship

Remote Management as the New Long-Distance Relationship

TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

Background and objectiveRecent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. MethodsTo address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. ResultsExperimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. ConclusionsIn this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.