Context-aware Research Articles

Integrated Marketing: A Powerful Strategy for Increasing Brand Awareness

In an increasingly competitive business era, it is important for companies to develop effective marketing strategies to expand coverage and increase brand awareness among consumers. This research aims to explore and analyze the effectiveness of implementing integrated marketing strategies as a means of increasing brand awareness in the current competitive market context. The research method used is a qualitative approach with in-depth interviews with several marketing professionals and analysis of case studies from several companies that have succeeded in implementing integrated marketing strategies effectively. The research results show that implementing an integrated marketing strategy effectively strengthens brand awareness, expands market reach, and strengthens consumer engagement. By focusing on message consistency across various communication channels, this strategy helps companies reach a wider target audience, build strong relationships with consumers, and establish a strong brand image. In addition, integrated marketing strategies are also proven to optimize the use of resources by avoiding the need for expensive content duplication, strengthening efficiency in marketing campaign management. Thus, the results of this research emphasize the importance of an integrated marketing strategy as a solid foundation in winning an increasingly competitive market and ensuring sustainable growth in the long term.

Rural Posting in Public Health Practices: Principles and Strategies

In addressing the global challenge of rural healthcare delivery, this study highlights fundamental principles and strategies to enhance accessibility and outcomes. The study found that understanding rural communities’ socio-cultural, economic, and geographic nuances is the first step for public health specialists to adapt their methods effectively. Community engagement fosters trust and active participation, reducing health inequities. Interdisciplinary teamwork involving doctors, nurses, pharmacists, and community health workers amplifies healthcare delivery’s reach and effectiveness. Telemedicine and technology offer remote areas access to high-quality healthcare, with telemedicine, electronic health records, and mobile apps enabling consultations and information exchange. Customised health education programs empower rural communities by raising awareness and promoting prevention, reducing preventable diseases through improved health literacy. Implementable strategies include mobile clinics for remote access, rural hubs for expert consultations, and incentives to sustain a rural healthcare workforce. Collaborations between governments, businesses, and NGOs ensure comprehensive rural healthcare. Data-driven policy decisions optimise resource allocation and healthcare efficiency. A holistic approach grounded in contextual awareness, community engagement, and technological innovation is vital for rural healthcare improvement. Public health practitioners can enhance rural health outcomes by applying these concepts and strategies.

Improving Kidney Tumor Detection Accuracy Using Hybrid U-Net Segmentation

Kidney cancer stands as a significant factor in cancer-related mortality, highlighting the critical importance of early and precise tumor detection This study introduces a computer-aided approach using the KiTS19 dataset and a hybrid U-Net architecture. Manual tumor segmentation is resource-intensive and prone to errors. Leveraging the hybrid U-Net, known for its proficiency in medical image analysis, we achieve precise tumor identification. Our method involves initial kidney and tumor segmentation in high-resolution CT images, followed by region of interest (ROI) generation and benign/malignant tumor classification. The assessment conducted on the KiTS19 dataset demonstrates encouraging outcomes, with Dice coefficients of 0.974 for kidney segmentation and 0.818 for tumor segmentation, accompanied by a tumor classification accuracy rate of 94.3%.The hybrid U-Net’s advanced feature extraction and spatial context awareness contribute to these outcomes. By streamlining diagnosis, our approach has the potential to significantly improve patient outcomes. The use of the KiTS19 dataset ensures robustness across various clinical cases and imaging modalities. This method represents a valuable advancement in computer-aided kidney tumor detection, promising to enhance patient care.

Capturing the patient’s voice to inform advanced clinical decision support for guideline-based cancer symptom management.

510 Background: Electronic patient reported outcomes (ePROs) have become routine in cancer care; however, ePROs alone do not provide sufficient information to enable informed symptom management. Informed symptom management requires an awareness of patient context to reduce inappropriate or erroneous advice. In this project we asked: how can information be collected directly from patients to provide clinicians with detailed, contextually-informed, patient-specific, evidence-based recommendations to manage distressing symptoms, especially among patients with complex comorbidities. Methods: The literature states that text messages are the most effective way to engage patients in clinical data collection. We texted patients a link that led them to a Web-based questionnaire that asked them about their common cancer symptoms. Prototypes were tested with 9 cancer patients through structured interviews and think aloud sessions from which themes were identified. Feedback from these interviews was used to develop a patient data collection questionnaire that we iteratively refined through think aloud sessions with 9 cancer patients to achieve universal patient acceptance. We also engaged 28 oncology clinicians in semi-structured interviews to assess the clinical impact of the additional contextual information on the symptom recommendations they received. Results: Formative evaluation identified four themes: 1) make instructions brief, 2) use intuitive navigation buttons with labels, 3) focus on multiple-choice style questions, and 4) extract data from the medical record for patient verification instead of free text entry by patients (e.g., medications). Developers incorporated “more detailed” questions into the patient data collection questionnaire; additional questions were presented if the patient indicated moderate or greater severity of a given symptom. These detailed questions focused on characteristics of the symptoms (e.g. neuropathic or somatic pain), remedies a patient had already initiated for self treatment (e.g., laxatives for constipation), and comorbid conditions that could impact the safety of recommendations (e.g., fall risk). Clinicians indicated that the additional data collected from patients increased the value of the tailored recommendations for enhanced symptom management. Conclusions: Additional data collection from patients using standard text messaging enables clinical decision support algorithms to provide more individually tailored cancer symptom management recommendations. This study is limited in that the design enhancements have been tested with a small number of end-users in a laboratory setting. Acknowledgements: This project was 100% funded using Cancer Moonshot funding through Phase I and II SBIR contracts from the National Cancer Institute, Contract #75N91018C00022 and #75N91020C00019, respectively.

Context-aware machine learning for estimating the remaining useful life of bearings under varying speed operating conditions

Remaining useful life estimation is a crucial and complicated task in predictive maintenance in order to reduce downtime and avoid catastrophic breakdowns in industrial plants. Thanks to the recent advances in our machine learning era, deep learning models can effectively deal with modeling complex phenomena such as the bearing degradation process, specifically under varying operating conditions. However, obtaining large labeled datasets for training the data-dependent deep learning models is challenging and expensive. To overcome this limitation, a phenomenological model has been used in this study as an effective approach to creating synthetic run-to-failure datasets under varying operating conditions. The suggested methodology is able to adjust synthetic run-to-failure datasets to the different periodic speed profiles, including the speed ranges that pass the resonance frequency of the structure. A Context-aware Domain Adversarial Neural Network is proposed to remove the domain shift between the simulated signals and the real ones as well as enable the deep learning model to understand the varying speed operating conditions and the sequential order of the measurements. The simulated signals are used as the source domain and a limited number of the real signals are used as the unlabeled samples for the domain adaptation task. Context awareness is introduced to the network by integrating contextual information into the architecture of the Domain Adversarial Neural Network, leading to an improvement in the model performance and its generalization ability. A dataset captured in a bearing test rig is adopted to verify the proposed method. Results show that context awareness can result in better performance and also more robust predictions against major speed changes in varying speed scenarios compared to the non-context-aware models.

SolarKey: Battery-free Key Generation Using Solar Cells

Solar cells have been widely used for offering energy for Internet of Things (IoT) devices. Recently, solar cells have also been used as sensors for context awareness sensing due to their sensitivity to varying lighting conditions. In this article, we are the first to use solar cells for symmetric key generation. To generate symmetric keys, we take advantage of photovoltage measurements generated from solar cells equipped with a pair of IoT devices. Symmetric keys are essential for pairing IoT devices and further securing wireless communication. Despite the sensitivity to varying lighting conditions, challenges still remain for the use of solar cells for key generation, such as time unsynchronisation and noisy measurements. To solve these challenges, we design a novel key generation framework, SolarKey, which includes the starting point detection and a compressed sensing-based two-tier key reconciliation method. Extensive experiments have been conducted to evaluate the performance of our proposed key generation method in various environments, which shows the proposed method can improve the key matching rate by up to 25%. We also conduct security analysis and the randomness test, which shows that SolarKey is resilient to common attacks such as the eavesdropping attack and the imitating attack and sufficiently random.

İklim Değişikliği İle Mücadelede Kurumsal Karbon Emisyon Farkındalığını Arttırma: Zonguldak Bülent Ecevit Üniversitesi Devrek Meslek Yüksekokulu Örneği

Dünyadaki hızlı nüfus artışı ve sanayileşmenin hızla ilerlemesi doğal kaynakların tüketilmesine, fosil yakıtların kullanılmasına ve çevre kirliliğine neden olmaktadır. Bu durum, atmosfere salınan sera gazı miktarının artmasını ve küresel ısınmayı beraberinde getirmektedir. Bu etkilerin azaltılması için salınan karbon miktarının düşürülmesi gerekmektedir. Bu kapsamda ülkesel, bölgesel, kurumsal ve bireysel farkındalıklar oluşturulmalı ve salınım miktarının düşürülmesi hedeflenmelidir. Bu çalışmada, Zonguldak Bülent Ecevit Üniversitesi Devrek Meslek Yüksekokulunun karbon ayak izi İngiltere Çevre Gıda ve Köy İşleri Departmanı (DEFRA) kriterlerine göre hesaplanmıştır. Devrek Kampüsü’nün yakıt tüketimi (kömür ve doğalgaz), elektrik tüketimi, atıklar, personel araçları, öğrenci ve personel servislerinin cinsi, araçlarda kullanılan yatık tipi ve yakıt miktarları hesaplamalara dahil edilmiştir. Sonuç olarak, CO2 salınım miktarı 2022 yılı için 427,92 bin tCO2e olarak hesaplanmış ve en yüksek emisyon salınımının ısınma amaçlı kullanılan yakıtlardan (yaklaşık 284 bin tCO2e) ve personelin kullandıkları araçlardan (yaklaşık 77 bin tCO2e) kaynaklandığı, en düşük emisyon salınımının ise atıklardan kaynaklandığı tespit edilmiştir.

Sensor-fused simulation-based decision support system for earthmoving projects

Discrete Event Simulation (DES) is an effective method to study cyclic and uncertain projects at the operation level. However, qualified DES results highly depend on the input data quality. This paper describes a simulation-enabled context-aware Decision Support System (DSS) based on sensor fusion to model earthwork operations realistically. The DSS backbone is a DES continually updated using real-time data. The information on earthmoving operations is exchanged between reality and DES using GPS and equipment-mounted cameras. The equipment spatiotemporal data and shovel-mounted cameras enhance the DES quality (i.e., locational and contextual awareness) by providing timely information about task durations, load types, and bucket teeth status. Sensor data is fused and fed into the DES model to replicate the earthmoving project. The proposed method is successfully implemented and validated in a large-scale earthmoving case study. The developed DES is proven promising as a reliable DSS for enhancing resource allocation and time-cost reduction.

A Note on the Validity of Metaphor Universality

Metaphor universality has been a topic of long debate among interested researchers. A byproduct of claiming the universality of metaphors is admitting a common human cognition. This cognition is based on different factors external to the cognition itself. In the present study, metaphors from Arabic tweets were analyzed and compared to metaphors in English found in published research. Based on this comparison, it was concluded that metaphors are universal and culturally specific at two different levels. On one level, the shared human anatomy poses universality, and on the other level, factors working against such universality affect the cultural specificity of metaphors. Kövecses (2010b) proposes a list of factors including awareness of context, differential memory, differential concerns and interests, differential cognitive preference and styles, and creativity. Considering languages other than Arabic and English in the comparison can be a promising future research.

Exploiting social context awareness for intelligent data forwarding in social Internet of Things

In the social internet of things, community structure exists objectively and affects the transmission of network messages. If the social context such as community is fully utilized, the efficiency of data forwarding will be effectively improved. A community-based routing algorithm (MSAR) is proposed by studying the multiple social relationships. First, we propose four measures of social relationships. They are social closeness degree, in-community activeness, cross-community activeness and community interaction. Then, the design of routing algorithm considers two stages. One is in-community forwarding and the other is cross-community forwarding. The measurement of node forwarding capability depends on closeness degree and in-community activeness in the in-community forwarding stage. In the cross-community stage, the measurement of node forwarding capability depends on closeness degree, cross-community activeness and community interaction. The relay node with higher cross-community forwarding utility will be selected. This prevents messages from being limited to the local community. Therefore, messages can always travel in the direction of the destination node’s community. Finally, a lot of simulation experiments and analyses are carried out. The analysis results show that the proposed algorithm has good performance in the following two aspects, the average latency and the message delivery rate respectively.

Tired or Inspired: A Conceptual Model for Using Regenerative Artificial Intelligence to Create Context, User, and Time-Aware Individualized Shopping Guidance

This paper conceptualizes a consumer-centric, regenerative artificial intelligence (“ReGenAI”) model for the Fast-Moving Consumer Goods (“FMCG”) retailing channel. The system uses its awareness of context, time, and users to (re)generate customer touchpoints and other marketing communications. Its output provides deep insights into regular and altered FMCG customer journeys, such as shopping behaviors under stressors like lifestyle choices or cataclysmic socio-economic and weather events. The recursive model advances from current, generative AI systems. It uses “tired or inspired” as a simplified bifurcated grocery shopper taxonomy to operationalize customers’ purchasing and consumption behaviors into actionable data for demand planning and retail operations.

HG-CAD: Hierarchical Graph Learning for Material Prediction and Recommendation in Computer-Aided Design

Abstract To support intelligent computer-aided design (CAD), we introduce a machine learning architecture, namely HG-CAD, that recommends assembly body material through joint learning of body- and assembly-level features using a hierarchical graph representation. Specifically, we formulate the material prediction and recommendation process as a node-level classification task over a novel hierarchical graph representation of CAD models, with a low-level graph capturing the body geometry, a high-level graph representing the assembly topology, and a batch-level mask randomization enabling contextual awareness. This enables our network to aggregate geometric and topological features from both the body and assembly levels, leading to competitive performance. Qualitative and quantitative evaluation of the proposed architecture on the Fusion 360 Gallery Assembly Dataset demonstrates the feasibility of our approach, outperforming selected computer vision and human baselines while showing promise in application scenarios. The proposed HG-CAD architecture that unifies the processing, encoding, and joint learning of multi-modal CAD features indicates the potential to serve as a recommendation system for design automation and a baseline for future work.

A Lightweight Network for Contextual and Morphological Awareness for Hepatic Vein Segmentation.

Accurate segmentation of the hepatic vein is crucial for enhancing the precision of liver disease diagnosis and treatment. Since the hepatic venous system is a small target and sparsely distributed, with various and diverse morphology, data labeling is difficult. Therefore, automatic hepatic vein segmentation is extremely challenging. We propose a lightweight contextual and morphological awareness network and design a novel morphology aware module based on attention mechanism and a 3D reconstruction module. The morphology aware module can obtain the slice similarity awareness mapping, which can enhance the continuous area of the hepatic veins in two adjacent slices through attention weighting. The 3D reconstruction module connects the 2D encoder and the 3D decoder to obtain the learning ability of 3D context with a very small amount of parameters. The experimental results on the public dataset show that when using the proposed network structure, it only needs less than 1/14 of the parameters of U-Net to outperform other comparison methods. Meanwhile, compared with other SOTA methods, using the proposed method demonstrates an enhancement in the dice coefficient, achieving an improvement of 2.86% and 1.45% on the two datasets, respectively. A small number of parameters can reduce hardware requirements and potentially have stronger generalization, which is an advantage in clinical deployment.

PyMAF-X: Towards Well-Aligned Full-Body Model Regression From Monocular Images.

We present PyMAF-X, a regression-based approach to recovering a parametric full-body model from a single image. This task is very challenging since minor parametric deviation may lead to noticeable misalignment between the estimated mesh and the input image. Moreover, when integrating part-specific estimations into the full-body model, existing solutions tend to either degrade the alignment or produce unnatural wrist poses. To address these issues, we propose a Pyramidal Mesh Alignment Feedback (PyMAF) loop in our regression network for well-aligned human mesh recovery and extend it as PyMAF-X for the recovery of expressive full-body models. The core idea of PyMAF is to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status. Specifically, given the currently predicted parameters, mesh-aligned evidence will be extracted from finer-resolution features accordingly and fed back for parameter rectification. To enhance the alignment perception, an auxiliary dense supervision is employed to provide mesh-image correspondence guidance while spatial alignment attention is introduced to enable the awareness of the global contexts for our network. When extending PyMAF for full-body mesh recovery, an adaptive integration strategy is proposed in PyMAF-X to produce natural wrist poses while maintaining the well-aligned performance of the part-specific estimations. The efficacy of our approach is validated on several benchmark datasets for body, hand, face, and full-body mesh recovery, where PyMAF and PyMAF-X effectively improve the mesh-image alignment and achieve new state-of-the-art results. The project page with code and video results can be found at https://www.liuyebin.com/pymaf-x.

大学英语课程中的语篇分析与教学策略研究

Abstract: This article delves into the theme of discourse analysis and teaching strategies in college English courses, with the objective of exploring ways to enhance teaching strategies in this context through discourse analysis. To begin, the article introduces fundamental concepts of discourse analysis, encompassing its types, methodologies, essential components, and its relevance within college English courses. Subsequently, it investigates the specific applications of discourse analysis in oral practice, vocabulary instruction, listening comprehension, and translation education. Drawing on practical teaching experience, the article proposes four discourse analysis-based teaching strategies for college English, namely theme-driven, contextual awareness, communicative function-oriented, and contextually simulated strategies.

A Case Study of Edge Computing Implementations: Multi-access Edge Computing, Fog Computing and Cloudlet

With the explosive growth of intelligent and mobile devices, the current centralized cloud computing paradigm is encountering difficult challenges. Since the primary requirements have shifted towards implementing real-time response and supporting context awareness and mobility, there is an urgent need to bring resources and functions of centralized clouds to the edge of networks, which has led to the emergence of the edge computing paradigm. Edge computing increases the responsibilities of network edges by hosting computation and services, therefore enhancing performances and improving quality of experience (QoE). Fog computing, multi-access edge computing (MEC), and cloudlet are three typical and promising implementations of edge computing. Fog computing aims to build a system that enables cloud-to-thing service connectivity and works in concert with clouds, MEC is seen as a key technology of the fifth generation (5G) system, and Cloudlet is a micro-data center deployed in close proximity. In terms of deployment scenarios, Fog computing focuses on the Internet of Things (IoT), MEC mainly provides mobile RAN application solutions for 5G systems, and cloudlet offloads computing power at the network edge. In this paper, we present a comprehensive case study on these three edge computing implementations, including their architectures, differences, and their respective application scenario in IoT, 5G wireless systems, and smart edge. We discuss the requirements, benefits, and mechanisms of typical co-deployment cases for each paradigm and identify challenges and future directions in edge computing.

MicroCam

The primary focus of this research is the discreet and subtle everyday contact interactions between mobile phones and their surrounding surfaces. Such interactions are anticipated to facilitate mobile context awareness, encompassing aspects such as dispensing medication updates, intelligently switching modes (e.g., silent mode), or initiating commands (e.g., deactivating an alarm). We introduce MicroCam, a contact-based sensing system that employs smartphone IMU data to detect the routine state of phone placement and utilizes a built-in microscope camera to capture intricate surface details. In particular, a natural dataset is collected to acquire authentic surface textures in situ for training and testing. Moreover, we optimize the deep neural network component of the algorithm, based on continual learning, to accurately discriminate between object categories (e.g., tables) and material constituents (e.g., wood). Experimental results highlight the superior accuracy, robustness and generalization of the proposed method. Lastly, we conducted a comprehensive discussion centered on our prototype, encompassing topics such as system performance and potential applications and scenarios.

Equity-Oriented Learning Design: An Entangled Future

There is growing interest in the literature towards a more distributed, collaborative view of learning design that focuses on relationships and connection. In this paper, we propose a vision of learning design that is entangled and crosses boundaries, framed by an equity-oriented mindset that blurs and resists boundaries, and merges learning design with facilitation, external with internal communities, and academia with activism. Based on interviews with learning design practitioners across the world, and framed by theories of posthumanism and postdigital education, we share eight orientations that form a future and equity-oriented learning design pattern: awareness of context, matter matters, co-creating with humans and non-humans, relationality and connection, modelling vulnerability, the entanglement of the personal and political, and creating transformative spaces. We then use one of our responses to the advent of ChatGPT to show how these orientations have helped us in times of uncertainty and disruption, an agential cut that highlights the value of acknowledging the material-discursive relationships emerging in and through our work. As such, rather than focusing on conventional, static definitions and models, we are interested in knowledge-making processes that come into existence when we practise learning design and engage with each other and the world around us, and who we become in our relationships with others, both human and non-human, and the patterns that we form in this entanglement.

Mobile Learning in Higher Education: A Systematic Literature Review

Mobile learning (M-Learning) has become a popular and effective method of education that leverages the ubiquity of mobile devices. M-Learning has digitally transformed the process of teaching and learning. It has tremendous potential to empower all sections of society through education and training. This study presents a systematic literature review of M-Learning. The articles were retrieved from Scopus and Web of Science databases. After applying inclusion and exclusion criteria, a final selection of 161 articles published between 2016 and 2022 was included in the review. To analyze the articles, the researchers employed the TCCM (Theory, Context, Characteristics, Methods) framework, which facilitated addressing the research questions. This review identified various theories, such as behaviorism, constructivism, cognitivism, situated learning, problem-based learning, context awareness learning, socio-cultural theory, collaborative learning, conversational learning, lifelong learning, informal learning, activity theory, connectivism, navigation, and location-based learning, that are used to support and guide the implementation of M-Learning. In terms of context, developing countries contributed to 70.8% of the studies, while developed countries contributed to 29.1%. Further, a majority of the studies, 93%, involved students followed by faculty members and only two studies involved staff from higher education management. A total of 19 unique characteristic factors have been identified, such as personal, intention, attitude, usage, utility, ease of use, learnability, social, technological, pedagogical, anxiety, enjoyment, accessibility, knowledge, experience, trust, price, and habit. A quantitative research design was used in 90% of the studies, followed by mixed methods research design in 7% of the studies, and qualitative research design in only 3% of the studies. Further, this article synthesizes previous research findings and highlights gaps for future research. Overall, this review contributes to the understanding and advancement of M-Learning as a valuable educational platform.

A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer

Citrus fruits are globally significant crops, valued for their unique taste, nutritional value, and versatility in culinary applications. However, these crops are vulnerable to diseases that can substantially reduce their yield and quality, leading to significant financial losses. It is crucial to efficiently identify and manage diseases affecting citrus fruits to ensure food security and support sustainable agriculture. Traditionally, identifying symptoms associated with these diseases requires expert scientific and observational skills. With the advent of deep learning methods, it has become possible to automatically identify disease patterns from images of affected plants. In this research, a novel dual branch deep-learning network was proposed for the classification of citrus plant diseases. The Group Shuffle Depthwise Feature Pyramid (GSDFP), which constitutes the first branch, utilizes convolution blocks to extract local features. The second branch leverages the Swin transformer to integrate global contextual awareness into the extracted features and facilitates the learning process through long-term dependencies. Subsequently, the features from the two branches are fused and passed through a shuffle attention module, effectively capturing contextual relationships between them. The effectiveness of the proposed approach was validated on two benchmark datasets, namely the Citrus Plant Dataset and the Citrus Disease Image Gallery Dataset. The proposed network obtained a classification accuracy of 98.19%.