Variant Of Problem Research Articles

Empowering society and culinary MSMEs for sustainable development goals initiatives in West Java

Purpose: This study aimed to explore the impact of empowering society and culinary MSMEs on Sustainable Development Goals (SDGs) in West Java, focusing on Sindangmukti Village, Karawang Regency. While prior research has often used a top-down approach, neglecting direct societal impact, this study takes a bottom-up perspective, aligning with implicit SDG principles observed in community services. Research Methodology: The study employed online questionnaires with 505 households in West Java to evaluate water consumption, waste generation, and energy usage patterns. Specific investigations in Sindangmukti Village have measured community service effectiveness regarding SDGs. Results: The study found that Only 19.1% of the participants set AC temperatures within the recommended ranges. Additionally, initiatives to promote a sustainable city strategy and establish energy-efficient culinary MSMEs in Sindangmukti Village resulted in a 29% increase in knowledge among 70 participants and generated $36.71 income from bitter melon chip production within two months. Limitations: However, the study focused solely on Sindangmukti Village, neglecting the diverse cultural and problem landscapes of other villages in the Karawang Regency. Contributions: The findings of this study can inform future efforts to enhance energy, water, and waste efficiency in residential buildings in West Java, thereby fostering awareness of energy conservation, environmental sustainability, and economic resilience.

PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models

PPM: Automated Generation of Diverse Programming Problems for Benchmarking Code Generation Models

Carboxymethyl-Cellulose-Based Hydrogels Incorporated with Cellulose Nanocrystals Loaded with Vitamin D for Controlled Drug Delivery

Currently, the development of innovative materials for the treatment of various diseases is highly interesting and effective. Additionally, in recent years, environmental changes, including the search for a sustainable world, have become the main goal behind developing sustainable and suitable materials. In this context, this research produced innovative hydrogels that incorporate cellulose nanocrystals and nanofibres from underutilised fibres from a semiarid region of Brazil; the hydrogels were loaded with vitamin D to evaluate controlled drug release for the treatment of diverse diseases. Spectroscopic (FTIR, Raman, UV–VIS), X-ray diffraction, zeta potential and morphology (SEM, TEM) analyses were used to characterise these hydrogels. In addition, biocompatibility was assessed using a resazurin assay, and the in vitro kinetic accumulative release of vitamin D was measured. The results showed that nanocrystals and nanofibres changed the structure and crystallinity of the hydrogels. In addition, the chemical groups of the hydrogels were red- and blueshifted in the FTIR spectra when the nanocrystals, nanofibres and vitamin D were incorporated. Moreover, the nanocrystals and nanofibres were homogeneously spread into the hydrogel when vitamin D was loaded into the hydrogel matrix. Furthermore, the cytotoxicity was greater than 90%. Additionally, the in vitro accumulative kinetic data of vitamin D release were robust (close to 40 ng·mL−1), with equilibrium being reached in the first 30 min. These results confirm the potential of using these hydrogels as therapeutic biomaterials for diverse diseases and problems in humans, mainly in women, who are the most harmed by vitamin D deficiency.

Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA–WISP approach

Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers’ models, and the industry’s intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decision-making, IVFF–PIPRECIA and IVFF–WISP techniques. IVFF–PIPRECIA determines criteria importance weights, whereas IVFF–WISP identifies optimal alternatives. A case study in the textile industry validates the framework’s practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model’s credibility. The approach effectively handles decision-making uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.

A Scalable Accelerator for Local Score Computation of Structure Learning in Bayesian Networks

A Bayesian network is a powerful tool for representing uncertainty in data, offering transparent and interpretable inference, unlike neural networks’ black-box mechanisms. To fully harness the potential of Bayesian networks, it is essential to learn the graph structure that appropriately represents variable interrelations within data. Score-based structure learning, which involves constructing collections of potentially optimal parent sets for each variable, is computationally intensive, especially when dealing with high-dimensional data in discrete random variables. Our proposed novel acceleration algorithm extracts high levels of parallelism, offering significant advantages even with reduced reusability of computational results. In addition, it employs an elastic data representation tailored for parallel computation, making it FPGA-friendly and optimizing module occupancy while ensuring uniform handling of diverse problem scenarios. Demonstrated on a Xilinx Alveo U50 FPGA, our implementation significantly outperforms optimal CPU algorithms and is several times faster than GPU implementations on an NVIDIA TITAN RTX. Furthermore, the results of performance modeling for the accelerator indicate that, for sufficiently large problem instances, it is weakly scalable, meaning that it effectively utilizes increased computational resources for parallelization. To our knowledge, this is the first study to propose a comprehensive methodology for accelerating score-based structure learning, blending algorithmic and architectural considerations.

Superhydrophobic octadecylamine-modified MIL-101(Cr)@polyurethane sponge composite as an oils/organic solvents sorption material

The pollution due to oil spills causes diverse problems such as water pollution, destruction of habitats, and health issues. An easy process to solve this problem is fabricating hydrophobic sorbents. Metal-organic frameworks (MOFs)@polyurethane (PU) sponges in 3D structures are promising candidates to remove pollutants through sorption due to their intrinsic characteristics of MOFs and PU sponges, including porosity, low density, and mechanical strength. In this research, some novel MOFs@PU sponges were fabricated by incorporating MIL-101(Cr) or octadecylamine (ODA)-modified MIL-101(Cr), m-MIL-101(Cr), into PU sponges using PDMS (polydimethylsiloxane) as an adhesive agent. The prepared materials were investigated by various techniques including Fourier transform infrared (FT-IR), x-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive x-ray (EDS) mapping, water contact angle (WCA), and BET (Brunauer–Emmett–Teller) analyses. The WCA increased from 106.36° for the pristine PU to 145.06° and 157.98° for the as-prepared MIL-101(Cr)@PU and m-MIL-101(Cr)@PU, respectively. The uptake efficiency of the as-obtained sorbents for some oils (olive oil, sesame oil, sunflower oil, hydraulic oil, and diesel) and organic solvents (toluene, dichloromethane, ethyl acetate, chloroform, and n-hexane), as well as the ability of the m-MIL-101(Cr)@PU sponge for separation of diesel oil, chloroform, and toluene from water, were studied. The maximum sorption capacity was 79.83 g g−1 and 97.50 g g−1 for sesame oil and chloroform, respectively, using m-MIL-101(Cr)@PU as sorbent, which was higher than the efficiency of the pristine PU adsorbent. Additionally, the m-MIL-101(Cr)@PU sponge composite exhibited proper reusability of up to 7 cycles (∼52.88 g g−1). Overall, the as-obtained m-MIL-101(Cr)@PU sorbent exhibits promising potential to uptake oil and organic solvent contaminants.

From predicting to decision making: Reinforcement learning in biomedicine

AbstractReinforcement learning (RL) is one important branch of artificial intelligence (AI), which intuitively imitates the learning style of human beings. It is commonly derived from solving game playing problems and is extensively used for decision‐making, control and optimization problems. It has been extensively applied for solving complicated problems with the property of Markov decision‐making processes. With data accumulation and comprehensive analysis, researchers are not only satisfied with predicting the results for experimental systems but also hope to design or control them for the sake of obtaining the desired properties or functions. RL is potentially facilitated to solve a large number of complicated biological and chemical problems, because they could be decomposed into multi‐step decision‐making process. In practice, substantial progress has been made in the application of RL to the field of biomedicine. In this paper, we will first give a brief description about RL, including its definition, basic theory and different type of methods. Then we will review some detailed applications in various domains, for example, molecular design, reaction planning, molecular simulation and etc. In the end, we will summarize the essentialities of RL approaches to solve more diverse problems compared with other machine learning methods and also outlook the possible trends to overcome their limitations in the future.This article is categorized under: Data Science > Chemoinformatics Data Science > Computer Algorithms and Programming Data Science > Artificial Intelligence/Machine Learning

CPR++: Object Localization via Single Coarse Point Supervision.

Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance due to the inconsistency of annotated points. Existing POL heavily rely on strict annotation rules, which are difficult to define and apply, to handle the problem. In this study, we propose coarse point refinement (CPR), which to our best knowledge is the first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. Furthermore, We design a sampling region estimation module to dynamically compute a sampling region for each object and use a cascaded structure to achieve end-to-end optimization. We further integrate a variance regularization into the structure to concentrate the predicted scores, yielding CPR++. We observe that CPR++ can obtain scale information and further reduce the semantic variance in a global region, thus guaranteeing high-performance object localization. Extensive experiments on four challenging datasets validate the effectiveness of both CPR and CPR++. We hope our work can inspire more research on designing algorithms rather than annotation rules to address the semantic variance problem in POL.

Formulating cancer worries: How doctors establish medical expertise and authority to facilitate patients’ care choices

Video recordings of oncology interviews reveal how doctors rely on worry to establish medical expertise, facilitate treatment decision-making, and construct worry parameters to help patients understand whether there is a reasonable need for worry or not. Doctors express worry as frequently as cancer patients during oncology interviews, but they face a dilemma: how to provide care for cancer patients without directly stating they are worried about them? Plausible explanations are offered for why doctors do not state personal worries. Conversation analytic methods were employed to identify how doctors rely on worry to achieve distinct social actions. Four worry formulations are examined: (1) variations of “we worry” (and at times, non-specific and second person “you”), (2) hypothetical worry scenarios, (3) dismissing worry and offering assurance, and (4) doctors claiming they are not worried, bothered, or alarmed. Doctors align with and speak for the professionals and institutions they represent, expressing collective worries and claiming the legitimate right to worry (or not). Doctors also avoid abandoning patients to their own decision-making, yet do not formulate worry to coerce deference or dictate patients' choices. In all cases patients agreed and displayed minimal resistance to doctors’ worry formulations. These findings contribute to ongoing work across institutional settings where participants have been shown to construct objective, legitimate claims meriting worries about diverse problems. Work is underway to examine when and how patients explicitly raise and doctors respond to cancer worries. Clinical implications are raised for how doctors can use worry to legitimize best treatment options, help patients minimize their worries, rely on hypothetical scenarios allowing patients to compare how other patients managed their cancer, and not dismiss the importance of minimizing the need to worry as a resource for offering reassurance.

Religious-Sociocultural Networks and Social Capital Enhancement in Pesantren

Implementing society empowerment models is crucial for addressing micro-level problems where government services often fall short. Despite evidence that social capital significantly contributes to development and poverty reduction, there remains a gap in understanding specific strategies and outcomes of social capital-based empowerment initiatives. This study aims to fill this gap by investigating the unique learning concept of Pesantren Rakyat Sumberpucung (PRS), which emphasizes low- cost, independent, flexible, and mutual assistance principles. Utilizing framework which includes structural opportunity to meet, know-how of social interaction, sense of belonging, and ethos of mutuality, this study employed qualitative research design. Data were gathered through participant observation, semi-structured interviews with village-level stakeholders, in-depth interviews and focus group discussion. The results indicate that the integrative model known as "Pancarukun," which includes initiatives such as Jagong Mathon, Celengan, Ngaji Ngluruk, Lumbung Pesantren Rakyat, and Fatehahan, effectively strengthens socio-cultural-religious networks, fostering harmony and cooperation between PRS and the community. These strategies have successfully promoted a spirit of togetherness and consistent collaboration. In conclusion, PRS’s approach demonstrates the potential of integrative community-based strategies to address diverse problems across sectors such as economy, agriculture, education, and culture. By leveraging social capital, PRS has created a sustainable model of community empowerment that can serve as a blueprint for similar initiatives.

Efficient Method of Road Outlier Recognition Using Deep Learning Coupled with Data Augmentation Approach

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. In response to these challenges, contemporary Road Outlier Recognition Surveillance Systems (RORSS) have been deployed to monitor and mitigate abnormalities on streets, highways, and roads. This research specifically investigated conventional as well as human-centered road anomalies encompassing snatching, accidents, fighting, and car fires. The proposed Real-time Road Outlier Recognition methodology is formulated as a classification task, involving the analysis and processing of real-time CCTV videos. The proposed study investigated different types of Convolutional Neural Network (CNN) pre-trained models with the Data Augmentation (DA) approach to address the frame variance problem in real-time videos. Furthermore, we introduced a benchmark real-world Road Outlier Dataset (ROD) containing roads, streets, and highways videos and images that presented different road anomalies performed by vehicles and humans. Experimentation using different pre-trained CNN models i.e., VGG19, InceptionV3, ResNet50, and DenseNet201 with a data augmentation approach has been performed on the ROD dataset. The experimental results demonstrated that the InceptionV3 model performed best with the augmentation approach as compared to other deep learning models that achieved the best accuracy of 98.80% in recognizing road anomalies

An improved BAT algorithm for collaborative dynamic target tracking and path planning of multiple UAV

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively.

Bat algorithm based on kinetic adaptation and elite communication for engineering problems

AbstractThe Bat algorithm, a metaheuristic optimization technique inspired by the foraging behaviour of bats, has been employed to tackle optimization problems. Known for its ease of implementation, parameter tunability, and strong global search capabilities, this algorithm finds application across diverse optimization problem domains. However, in the face of increasingly complex optimization challenges, the Bat algorithm encounters certain limitations, such as slow convergence and sensitivity to initial solutions. In order to tackle these challenges, the present study incorporates a range of optimization components into the Bat algorithm, thereby proposing a variant called PKEBA. A projection screening strategy is implemented to mitigate its sensitivity to initial solutions, thereby enhancing the quality of the initial solution set. A kinetic adaptation strategy reforms exploration patterns, while an elite communication strategy enhances group interaction, to avoid algorithm from local optima. Subsequently, the effectiveness of the proposed PKEBA is rigorously evaluated. Testing encompasses 30 benchmark functions from IEEE CEC2014, featuring ablation experiments and comparative assessments against classical algorithms and their variants. Moreover, real‐world engineering problems are employed as further validation. The results conclusively demonstrate that PKEBA exhibits superior convergence and precision compared to existing algorithms.

Upaya Meningkatkan Motivasi Belajar Peserta Didik Pada Mata Pelajaran Matematika Melalui Pembelajaran Berdiferensiasi Berdasarkan Konten Di Kelas Xi TFLM-3 SMK Negeri 5 Surabaya

In learning activities, motivation is an encouragement or process to direct students in achieving learning goals. The research was motivated by the very diverse problem of students' learning motivation in mathematics subjects in class XI TFLM-3 SMK Negeri 5 Surabaya. This can be seen from the results of the researcher's observations, where when they are about to start learning activities, many students still think that mathematics is less interesting and boring so that students are always less ready to start learning mathematics and the learning that was usually carried out by previous mathematics teachers was just learning. just lectures and assignments. This research aims to determine the increase in students' learning motivation in mathematics subjects through differentiated learning based on content. This research uses a type of Classroom Action Research (PTK) which consists of two cycles using data collection techniques in the form of questionnaires and observation. This questionnaire is used to determine learning satisfaction that motivates students after carrying out learning strategy treatment. The results of research using differentiated learning based on content show an increase in students' learning motivation from pre-cycle activities 63,8% to 73,1% in cycle I, then continued with cycle II. It shows that the presentation average of students' learning motivation in mathematics subjects is 83,2% From the results of this research it can be concluded that using differentiated learning based on content is successful in increasing students' learning motivation in mathematics subjects in class XI TFLM-3 SMK Negeri 5 Surabaya.

Improving compressed matrix multiplication using control variate method

The seminal work by Pagh [1] proposed a matrix multiplication algorithm for real-valued squared matrices called Compressed Matrix Multiplication (CMM) having a sparse matrix output product. The algorithm is based on a popular sketching technique called Count-Sketch [2] and Fast Fourier Transform (FFT). For input square matrices A and B of order n and the product matrix AB with Frobenius norm ||AB||F, the algorithm offers an unbiased estimate for each entry, i.e., (AB)i,j of the product matrix AB with a variance bounded by ||AB||F2/b, where b is the compressed bucket size. Thus, the variance will eventually become high for a small bucket size. In this work, we address the high variance problem of CMM with the help of a simple and practical technique based on classical variance reduction methods in statistics. Our techniques rely on the Control Variate (CV) method. We suggest rigorous theoretical analysis for variance reduction and complement it via supporting empirical evidence.

When geoscience meets generative AI and large language models: Foundations, trends, and future challenges

AbstractGenerative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum of applications ranging from biology, medicine, education, legislation, computer science, and finance. As one strives for enhanced safety, efficiency, and sustainability, generative AI indeed emerges as a key differentiator and promises a paradigm shift in the field. This article explores the potential applications of generative AI and large language models in geoscience. The recent developments in the field of machine learning and deep learning have enabled the generative model's utility for tackling diverse prediction problems, simulation, and multi‐criteria decision‐making challenges related to geoscience and Earth system dynamics. This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. These tools have helped the geoscience community in several applications, including (but not limited to) data generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, and land surface changing. Some challenges still remain, such as ensuring physical interpretation, nefarious use cases, and trustworthiness. Beyond that, GAI models show promises to the geoscience community, especially with the support to climate change, urban science, atmospheric science, marine science, and planetary science through their extraordinary ability to data‐driven modelling and uncertainty quantification.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison.

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

An improved hybrid genetic algorithm using the affine combination-based reproduction

Genetic algorithms (GAs) are a type of search procedure that emulates the process of natural selection and genetics to solve complex optimization problems. The effectiveness of GAs in finding global solutions has led to their widespread applications in solving diverse optimization problems. However, their search performance and convergence not only depend highly on the operators used but are also sensitive to the choice of control parameters. This article presents an improved real-coded GA, called hybrid genetic algorithm (HGA), that employs affine combination-based reproduction and non-uniform mutation. The reproduction is a formula-based operator that helps to improve the convergence and introduce some degree of genetic diversity in the HGA. The non-uniform mutation helps to further maintain diversity within the population and prevent premature convergence to suboptimal solutions. The performance of the HGA can be affected by the parameters of its operators. The optimal settings of these parameters will be obtained to find the best performance through a set of simulation on 36 benchmark functions with diverse properties. Through the evaluation on a set of benchmark functions, it was found that the HGA outperforms the MATLAB ga and particleswarm (PSO) functions in terms of the offline performance.

Urban morphology as a key parameter for mitigating urban heat? – A literature review

Abstract More frequent and intense heat waves, especially in urban centers, represent a growing challenge for urban designers and building planners. In the last five years, extensive research has been undertaken on the relation between urban form, including density, and urban heat phenomena. Dense urban configurations are often considered central drivers of hot microclimates. However, less dense cities easily cause other ecological (e.g. land consumption), functional (public mobility), and socioeconomic (social diversity) problems. Consequently, the current panoply of recommended heat mitigation and sustainability measures constitutes an unclear basis for strategic planning decisions. Thus, this study examines the literature on urban morphology in relation to urban heat events. Around 800 scientific articles and studies are categorized regarding the applied methodology, the studied geographic location, the observed urban form parameters, and the examined thermal parameter. Most identified literature uses traditional field measurement, remote sensing, numerical simulation, or a combination. Air temperature and land surface temperature are the most observed thermal parameters, while the growing number of studies that focus on human outdoor thermal comfort is highly relevant for effective heat mitigation and adaptation. This study suggests that from a scientific point of view, urban morphology measures do not principally carry a paramount role in heat mitigation compared to other aspects, such as vegetation or materialization. Current planning approaches for climate-resilient cities are highly case-specific, where no generally applicable rules or effective recipes regarding urban built form are available.

How patients experience discussing couple relationship problems with GPs: an interview study.

Couple relationship satisfaction is related to good physical health, good mental health, and longevity. Many patients have discussed or wish to discuss their couple relationship with their GP and look for personalised care and support when discussing topics they perceive as sensitive. To explore patient experiences of discussing couple relationship problems in GP consultations. Qualitative study employing semi-structured interviews with patients from general practice in Norway. Individual interviews with 18 patients who had discussed their couple relationship with their GP. Participants were recruited through both social media and traditional media, and all interviews were digitally recorded. The purposive sample comprised 13 women and five men, representing diverse age groups, backgrounds, and relationship problems. All participants identified as heterosexual. We analysed interview data thematically using systematic text condensation. Three main themes emerged: 1) GPs in a facilitating role, not on an assembly line; 2) navigating the 'elephant in the room'; and 3) GPs as biomedically competent life witnesses. GP continuity was vital in fostering the trust required to discuss sensitive topics, such as relationship issues. Participants valued a biopsychosocial approach that incorporated knowledge of close relationships into medical consultations. They appreciated both GP support and constructive challenges that prompted them to take responsibility for relationship improvements. Patients value their GPs' holistic, supportive, and direct approach in addressing couple relationship problems, although they perceive that GPs do not always have sufficient time. They welcome relevant challenges that can drive positive change.