Clustering Effect Research Articles

Synthesis and Antibacterial Activity of Capsaicin Derivatives Containing Phenolic Hydroxyl Groups

AbstractBacterial infections tend to cause a series of cytopathic reactions and have become a major threat to human life. Among them, phenolic compounds are widely used as a natural antibacterial derivative for antifouling and antibacterial applications. In this study, four phenolic hydroxyacrylamide monomers have been successfully prepared using capsaicinoid derivatives as inspiration (named as OHABA, PHABA, AHMA, and AMTHBA, respectively) and the chemical structures were confirmed by FT‐IR, 1H‐NMR, 13C‐NMR. The minimum inhibitory concentration (MIC) assay and plate antibacterial assay were measured to explore the antibacterial properties of the monomer. The results showed that four capsaicin derivatives exhibited antibacterial activity, and the antibacterial properties of the four monomers from strong to weak were PHABA, OHABA, AHMA and AMTHBA. The best antibacterial activity was PHABA (MIC for E. coli: 1.6 mg/mL, bacteriostasis rate: 82.46 %, MIC for S. aureus: 1.6 mg/mL, bacteriostasis rate: 69.58 %). Both the cluster effect of monomers and the spatial distribution of phenolic hydroxyl groups in the structure of derivatives affect the antibacterial properties.

AB042. Combined anti-PD-L1 and anti-VEGFR2 therapy promotes the antitumor immune response in glioblastoma multiforme by reprogramming tumor microenvironment.

Inhibitors of programmed cell death ligand 1 (PD-L1) and vascular endothelial growth factor receptor 2 (VEGFR2) are commonly used in the clinic, but they are beneficial for only a minority of glioblastoma multiforme (GBM) patients. GBM has significant immunosuppressive properties, and there are many immunosuppressive cells and dysfunctional effector T-cell in the tumor microenvironment (TME), which is one of the important reasons for the failure of clinical treatment of GBM. P21-activated kinase 4 (PAK4) is a threonine protein kinase, and as a pivotal immune suppressor in the TME. PAK4 knockdown attenuates vascular abnormalities and promotes T-cell infiltration. Using RNA sequencing (RNA-seq) technology, western blotting, and immunofluorescence, we identified changes in genes expression following VEGFR2 knockdown. The impact of anti-PD-L1 and anti-VEGFR2 on GBM cells apoptosis was assessed using coculture assays, western blotting, and flow cytometry. Additionally, the therapeutic efficacy of anti-PD-L1 and anti-VEGFR2 therapy was evaluated through in vivo experiments, immunohistochemistry, and immunofluorescence. Our studies revealed that VEGFR2 binds and phosphorylates signal transducer and activator of transcription 3 (p-STAT3), thereby regulating the expression of PAK4. Anti-PD-L1 and anti-VEGFR2 therapy can increase the secretion of interferon-gamma (IFN-γ), granzyme B, and perforin by immune cells and promoting the cytotoxic effects of cytotoxic cluster of differentiation 8 (CD8)+ T cells, and overexpression of PAK4 could reverse this effect. We also demonstrated that combination therapy with anti-PD-L1 and anti-VEGFR2 agents prevents tumor growth in an intracranial tumor model. Our results support that anti-VEGFR2 therapy can downregulate PAK4, reprogram the TME by increasing CD8+ T cells infiltration and activation, and enhance the therapeutic effect of anti-PD-L1 therapy on GBM cells.

Effects of Cluster Nursing Strategy on Neurological and Motor Functions in Patients with Moderate to Severe Traumatic Brain Injury.

This study aimed to explore the effects of the cluster nursing strategy applied to traumatic brain injury (TBI) patients. Ninety-eight TBI patients admitted to the hospital were selected as the study subjects. They were randomized into two groups, the control group and the cluster group, with 49 cases in each group. The control group received routine nursing methods, while the cluster group received cluster nursing strategy. The intervention effects were compared between the two groups. After 3 months, the total occurrence of complications in the cluster group was significantly lower than that in the control group. Postintervention, the cluster group had a significantly lower National Institutes of Health Stroke Scale score and significantly higher Fugl-Meyer score and Loewenstein Occupational Therapy Cognitive Assessment score compared with the control group. The serum level of glial fibrillary acidic protein in the control group was significantly higher than that in the cluster group, while the serum level of brain-derived neurotrophic factor was significantly lower. The application of the cluster nursing strategy in the care of patients with TBI could effectively reduce the risk of complications and improve neurological, motor, and cognitive functions.

Investigating response behavior through TF-IDF and Word2vec text analysis: A case study of PISA 2012 problem-solving process data

The process data in computer-based problem-solving evaluation is rich in valuable implicit information. However, its diverse and irregular structure poses challenges for effective feature extraction, leading to varying degrees of information loss in existing methods. Process-response behavior exhibits similarities to textual data in terms of the key units and contextual relationships. Despite the scarcity of relevant research, exploring text analysis methods for feature recognition in process data is significant. This study investigated the efficacy of Term Frequency-Inverse Document Frequency (TF-IDF) and Word to Vector (Word2vec) in extracting response behavior features and compared the predictive, analytical, and clustering effects of classical machine learning methods (supervised and unsupervised) on response behavior. An analysis of the PISA 2012 computer-based problem-solving dataset revealed that TF-IDF effectively extracted key response behaviors, whereas Word2vec captured effective features from sequenced response behaviors. In addition, in supervised machine learning using both methods, the random forest model based on TF-IDF performed the best, followed by the SVM model based on Word2vec. Word2vec-based models outperformed TF-IDF-based ones in the F1-score, accuracy, and recall (except for precision) across the logistic regression, k-nearest neighbor, and support vector machine algorithms. In unsupervised machine learning, the k-means algorithm effectively clustered different response behavior patterns extracted by these methods. The findings underscore the theoretical and methodological transferability of these text analysis methods in educational and psychological assessment contexts. This study offers valuable insights for research and practice in similar domains by yielding rich feature representations, supplementing fine-grained assessment evidence, fostering personalized learning, and introducing novel insights for educational assessment.

Dynamic magnetic and magnetocaloric behaviors of a Kagome-like cluster

By utilizing the Monte Carlo simulation, how different physical parameters affect the dynamic magnetic properties and magnetocaloric effects of a Kagome-like cluster with mixed spins (3/2, 2) are explored. The findings show that changing the values of the exchange coupling |J|, |Js|, and |J2| had no impact on the saturation order parameters of the system. Under specific parameter conditions, the magnetization susceptibility curve displays double peaks, and compensation phenomena are noted. The impacts of hbias and hosc on the blocking temperature of the system are opposite. In addition, the effects of various parameters on the magnetization, magnetic entropy change, and relative cooling power of the ferromagnetic system under a static magnetic field are given. The strong exchange coupling is found to negatively affect the magnetic refrigeration efficiency of the material, while a strong magnetic field enhances the system's relative cooling power.

Fast correntropy-based multi-view clustering with prototype graph factorization

As a consequence of the ability to incorporate information from different perspectives, multi-view clustering has gained significant attention. Nevertheless, 1) its high computational cost, particularly when processing large-scale and high-dimensional multi-view data, restricts its applications in practice; and 2) complex noise in real-world data also challenges the robustness of existing algorithms. To tackle the above challenges, we develop a fast correntropy-based multi-view clustering algorithm with prototype graph factorization (FCMCPF). FCMCPF first adopts prototype graphs to effectively mitigate the complexity associated with graph construction, thereby reducing it from a quadratic complexity to a linear one. Then, it decomposes these prototype graphs under the correntropy criterion to robustly find the cluster indicator matrix without any post-processing. To solve the non-convex and non-linear model, we devise a fast half-quadratic-based strategy to first convert it into a convex formulation and then swiftly complete the optimization via the matrix properties of orthogonality and trace. The extensive experiments conducted on noisy and real-world datasets illustrate that FCMCPF is highly efficient and robust compared to other advanced algorithms, with comparable or even superior clustering effectiveness.

Cutaneous Melanoma Mortality-to-Incidence Ratio and Its Association With Socioeconomic and Healthcare Factors in Canada: A National Ecological Study.

The mortality-to-incidence ratio (MIR) can be used to approximate healthcare inequities and is helpful to understand/compare cancer survival between geographic regions/jurisdictions. We investigated cutaneous melanoma (CM) outcomes through MIR analysis in Canadian jurisdictions and census divisions (CDs) between 1992 and 2016. Data were obtained from the national databases from 1992 to 2016 for all Canadian jurisdictions, except Quebec. Age-standardized overall and median MIRs were calculated per province per year, while crude MIRs were calculated for CDs. Generalized linear regression models were conducted to study the effect of province and year on MIR, while a mixed effect regression model was used to determine how healthcare and socioeconomic factors affect MIR, while accounting for possible clustering effects (eg, year and province). We identified 106,015 CM cases and 20,570 CM deaths between 1992 and 2016. National MIR from 1992 to 2016 demonstrated a significant linear decrease (P value < .0001). The national median MIR was 15.4 (ie, 0.154 × 100), whereby Manitoba (19.9), Ontario (19.5), Saskatchewan (18.5), British Columbia (16.1), and Newfoundland and Labrador (15.9) demonstrated higher MIRs than the Canadian average. CDs with the highest MIRs were commonly identified in the southern regions of provinces. No healthcare or socioeconomic factors were found to be significantly associated with higher MIR at the provincial level. MIRs have decreased at the national and provincial levels in recent decades, which is reassuring. Higher MIRs were noted in select rural CDs and in the Canadian territories, reinforcing the importance of proper dermatological care in all parts of the country.

Prevalence of anemia of varying severity, geographic variations, and association with metabolic factors among women of reproductive age in China: a nationwide, population-based study.

To investigate the epidemiological characteristics of anemia of varying severity among women of reproductive age, we conducted a nationwide, cross-sectional study between January 1, 2019 and December 31, 2019, including 4 184 547 nonpregnant women aged 18-49 years from all 31 provinces in the mainland of China. Anemia was defined as having hemoglobin concentration < 120.0 g/L and categorized as mild, moderate, and severe. Multivariate logistic models with cluster effect were used to explore the association of anemia and metabolic risk factors. The standardized prevalence of anemia and moderate and worse anemia among women of reproductive age in China was 15.8% (95% CI 15.1%-16.6%) and 6.6% (6.3%-7.0%), respectively. The prevalence of anemia and the proportion of moderate and worse anemia significantly increased with age. We also observed great geographic variations in the prevalence of anemia, with a high likelihood in south, central, and northwest China. Moderate and/or severe anemia was positively associated with overweight and obesity, diabetes, and impaired kidney function. In conclusion, anemia remains a significant challenge for women of reproductive age in China. Geographic variations and metabolic risk factors should be considered in the comprehensive and targeting strategy for anemia reduction.

The evolution of cooperation and global synchronization in the evolutionary Kuramoto dilemma combined with the prisoner's dilemma

In this work, we have studied the evolution of the cooperation and the global synchronization on the Kuramoto model upon random networks with two games, in which one is the evolutionary Kuramoto dilemma and the other is the weak prisoner's dilemma. Agents can gain their payoffs from these two games synchronously. In the evolutionary Kuramoto game, cooperators prefer the formation of local synchronization but pay less cooperative cost. Cooperators in weak prisoner's dilemma can form cooperator clusters because of the network reciprocity. Combining these two games shows a mutual promotion effect upon the order of the global synchronization but a competition effect on the cooperation level. The global synchronization begins to rise only when most agents choose cooperation. It is manifested by the fact that the optimal coupling strength for the highest cooperation level is smaller than that for the highest synchronization level. The high cooperation cost makes defection the most advantageous strategy when the coupling strength reaches rather high, which diminishes the effect of cooperator clusters, and ultimately results in the absence of both cooperators and global synchronization. When agents have more neighbors in the random network, it is observed that cooperation is consistently hindered unless the coupling strength is relatively small. Additionally, there is always an optimal average degree that allows for global synchronization.

Assessment of Rural Industry Integration Development, Spatiotemporal Evolution Characteristics, and Regional Disparities in Ethnic Regions: A Case Study of Inner Mongolia Autonomous Region Counties

Ethnic regions in China primarily focus on the development of agricultural and animal husbandry economies, which are relatively underdeveloped. Rural industry integration development (RIID) is considered the foundation and guarantee for ethnic regions to achieve high-quality modernization of agriculture. The purpose of this article is to measure the level of rural industrial integration in ethnic minority areas, analyze the spatial evolution and regional differences, and explore the actual situation of RIID in these regions. The aim is to provide a decision-making basis for local governments to effectively promote the development of rural industrial integration. Based on the improvement of the evaluation index system for rural industrial integration development, this paper takes the counties of the Inner Mongolia Autonomous Region as the research area. Utilizing panel data from the statistical yearbooks of 68 banners and counties in Inner Mongolia from 2011 to 2020, the panel entropy weight TOPSIS method is employed to assess the average level of rural industrial integration in the research area. The ArcGIS natural breakpoint method is employed to classify the level of RIID in county areas. Exploratory Spatial Data Analysis (ESDA) and GeoDa are utilized to analyze the spatial distribution characteristics of RIID. Finally, the Theil index is employed to analyze the regional differences in the level of RIID. The results show the following: (1) The overall level of RIID in ethnic regions is relatively low, with the contributions of the four dimensions in the evaluation index system as follows: integration path &gt; integration foundation &gt; integration sustainability &gt; integration effect. The level of RIID in the study area is as follows: western region &gt; eastern region &gt; central region. (2) Spatially, there are positive correlations and significant spatial clustering in the level of RIID, with the spatial clustering effect of RIID weakening. (3) There are regional differences in the level of RIID, which are expanding. The inter-regional differences are decreasing, while the intra-regional differences are increasing. (4) The construction of agricultural processing facilities, financial investment, financial support, and talent policies are important influencing factors for the current stage of RIID in ethnic regions. Therefore, in the low-level development stage of RIID in ethnic regions, it is necessary to fully utilize the advantages of resource endowment, increase investment in rural infrastructure, and strengthen the guidance of talent flow into rural revitalization construction.

Glucose metabolism and radiodensity of abdominal adipose tissue: A 5-year longitudinal study in a large PET cohort.

18F-Fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) allows noninvasive assessment of glucose metabolism and radiodensity in visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). We aimed to address the effects of ageing and metabolic factors on abdominal adipose tissue. We retrospectively analyzed data from 435 healthy men (mean 42.8 years) who underwent a health check-up programme twice, at baseline and the 5-year follow-up. The mean standardized uptake value (SUV) was measured using SAT and VAT and divided by the liver SUV. The mean Hounsfield units (HU) of the SAT and VAT were measured from the CT scans. The effects of clinical variable clusters on SUVR were investigated using Bayesian hierarchical modelling; metabolic cluster (BMI, waist-to-hip ratio, fat percentage, muscle percentage*-1, HOMA-IR), blood pressure (systolic, diastolic), glucose (fasting plasma glucose level, HbA1c)and C-reactive protein. All the clinical variables changed during the 5-year follow-up period. The SUVR and HU of the VAT increased during follow-up; however, those of the SAT did not change. SUVR and HU were positively correlated with both VAT and SAT. SAT and VAT SUVR were negatively associated with metabolic clusters. Ageing led to increased glucose metabolism and radiodensity in VAT, but not in SAT. VAT may reflect the ageing process more directly than SAT. Glucose metabolism was higher and radiodensity was lower in VAT than in SAT, probably owing to differences in gene expression and lipid density. Both glucose metabolism and radiodensity of VAT and SAT reflect metabolic status.

Industry Class Clustering of Software Expertise Competency at SMKN 2 Kraksaan Using Constrained K-Means Clustering Algorithm

Addressing the gap between school education and industry needs is a recurring concern, as many graduates struggle to enter the workforce due to lacking practical skills. Industry Classes aim to bridge this gap by preparing students with relevant skills and knowledge aligned with real-world industry demands. This study focuses on the application of Constrained K-Means Clustering to categorize students in the software engineering competency classes at SMKN 2 Kraksaan. This algorithm modifies traditional K-Means by utilizing Linear Programming Algorithm (LPA), ensuring each cluster meets predefined subject requirements. The research involves analyzing academic proficiency test data (TKDA) from 96 X-grade students, evaluating their abilities in analogy, series, figural, mathematical, and recall skills. Using a 3-cluster approach, each with 32 to 60 student capacity constraints, the study aims to optimize student distribution for effective learning outcomes. Evaluation through silhouette method yielded a score of 0.3199, indicating satisfactory separation between clusters with overlap to address. Cluster analysis revealed Cluster 2 as the most proficient, showcasing strengths in recall and series attributes critical for software engineering. These findings suggest that Constrained K-Means Clustering is effective in classifying students, highlighting Cluster 2 as optimal for software engineering competencies at SMKN 2 Kraksaan. Future research should focus on enhancing data quality, expanding sample size, and refining algorithms for improved clustering accuracy and effectiveness.

Contentious public finance: how entanglement shapes the costs of resisting elitist budgetary preferences

Entangled political economy views societal phenomena as featuring substantial interaction between economic and political entities, but questions have been raised about the conceptual properties of entanglement. The political economist Randall Holcombe has raised questions concerning the economic influences affecting uneven patterns of entanglement between entities. Drawing upon his own transaction costs-based framework of political stratification, Holcombe suggests that political elites incur relatively low transaction costs associated with bargaining over policies, whereas non-elites incur relatively high costs. This suggests that elites actively participate in policy design and implementation and can outmaneuver the non-elite public to externalize the costs of political decisions, yielding noticeable clustering effects within entangled network structures. This article seeks to build upon Holcombe’s insights, as well as the transaction cost politics of Charlotte Twight, illustrating how groups engaging in political processes attempt to manipulate transaction costs to secure favorable outcomes. Transaction cost manipulation by elites to secure advantages is commonly studied, but less so is how non-elitists succeed in adjusting the transaction costs of political exchanges to help prevent fiscal exploitation by elitists. The public finance case of Colorado’s Taxpayer’s Bill of Rights is used to illustrate how dynamic entanglements between elites and non-elites delivered institutional change better aligning with non-elite fiscal preferences.

Re-evaluating Methods for Assessing Differences in Response in Ileal vs. Colonic Crohn's Disease: A Post-hoc Analysis of the FITZROY Trial.

The ileum is the most commonly affected segment of the gastrointestinal tract in Crohn's disease (CD). We aimed to determine whether disease location affects response to filgotinib, a Janus kinase (JAK) inhibitor, in patients with moderate-to-severely active Crohn's disease (CD) and applying appropriate methods to account for differences in measuring disease activity in the ileum compared to the colon. This post-hoc analysis of data from the FITZROY phase 2 trial (NCT02048618) compared changes in the Crohn's Disease Activity Index (CDAI) and Simple Endoscopic Score for Crohn's Disease (SES-CD) amongst patients with ileal-dominant and isolated colonic CD treated with 10 weeks of filgotinib 200mg daily or placebo. A mixed effects model for repeated measures was used to test whether ileal disease responded differently than colonic disease, by evaluating for effect modification using the interaction term of treatment assignment-by-disease location. Numerically greater proportions of patients with isolated colonic disease compared to ileal-dominant CD achieved clinical remission (CDAI <150, 75.9% vs. 41.6%) and endoscopic response (SES-CD reduction by 50%, 52.5% vs. 15.5%) at Week 10. However, after adjusting for baseline disease activity by disease location and within-patient clustering effects, there was no significant difference in treatment response by disease location (mean difference in ΔCDAI between ileal-dominant vs. isolated colonic disease +9.24 [95% CI: -87.19, +105.67], p=0.85; mean difference in ΔSES-CD -1.93 [95% CI: -7.03, +3.44], p=0.48). Filgotinib demonstrated similar efficacy in ileal-dominant and isolated colonic CD when controlling for baseline disease activity and clustering effects.

Integrating Polyoxometalates and Silver Clusters into Atomically Precise Molecular Heterojunction.

The Ag cluster-POM assemblies have been shown to possess interesting and potentially useful properties. However, there is no precedent example of atomically precise Ag cluster-POM assemblies showing heterojunction effects in photocatalysis. Herein, the synthesis and total structure determination of the periodically distributed molecular heterojunction [Ag12(SCy)6(CH3CN)12(PW12O40)]n (Ag12-PW12) are reported. The assembly of Ag/W clusters into 3D network can endow the resulting binary structure with an aesthetic topology and unique physicochemical properties. More remarkably, the incorporation of Ag12 cluster with PW12 can efficiently facilitate the separation of photogenerated electrons and holes, thus significantly promoting the catalytic efficiency in selective oxidation of sulfides. The Ag12-PW12 heterojunction can be recovered and reused five times with no drastic change in the catalytic performance. This research is expected to assist in the rational design of cluster-based heterojunction catalysts. The increase of catalytic activity of the Ag12-PW12 assembly in comparison with the unassembled Ag12 and PW12 clusters is attributed to the synergistic effect of Ag12 and PW12 clusters, offering the splendid opportunity for deciphering structure-reactivity relationship of heterostructure-coupled photosystem.

Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning

Integrated learning has attracted much attention from industry and academia. In the new era, colleges and universities need to discuss information management in light of actual conditions, integrate different data in each information system into the same database, so as to form a data warehouse based on the integrated database which can truly reflect the historical changes of data and provides support for managers' decision-making. This paper analyzes the clustering effect of standard differential evolution algorithm, improved differential evolution algorithm and K-means algorithm. The algorithm is tested using Iris and Wine database marts, the results show that the K-means algorithm is a relatively poor algorithm and its accuracy is significantly lower than the other two. Based on big data, multi-factor interactive variance analysis technology is used to analyze different data indicators and influencing factors. Therefore, colleges and universities can use the database to better understand the problems and advantages in management, thus to improve management efficiency and teaching level.

Anchor-based fast spectral ensemble clustering

Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky problems. First, spectral clustering can identify clusters of arbitrary shapes, but the high time and space complexity limit its application in generating base clusterings. Most existing algorithms utilize k-means to generate base clusterings, and the clustering effect on nonlinearly separable datasets needs further improvement. Second, ensemble clustering algorithms should generate multiple base clusterings. Even if low-complexity algorithms are applied, the running time is also long, which seriously affects the application of ensemble clustering algorithms on large-scale datasets. To tackle these problems, we propose a fast K-nearest neighbors approximation method, construct an anchor graph to approximate the similarity matrix, and use singular value decomposition (SVD) instead of eigenvalue decomposition (EVD) to reduce the time and space complexity of conventional spectral clustering. At the same time, we obtain multiple base clusterings by running spectral embedding once. Finally, we convert these base clusterings into a bipartite graph and use transfer cut to get the final clustering results. The proposed algorithms significantly reduce the running time of ensemble clustering. Experimental results on large-scale datasets fully prove the efficiency and superiority of our proposed algorithm.

Effect of salt substitute and antihypertensive medications among high cardiovascular risk patients: A sub-study of Salt Substitute and Stroke Study (SSaSS).

The relationship between the differential protective effect of salt substitute between hypertensive and normotensive individuals and the use of cardiovascular medications remains unclear. This study involved 4211 individuals with a history of stroke or hypertension who participated in the Salt Substitute and Stroke Study (SSaSS) from 120 villages in Shanxi Province. The aim of this study was to investigate the differences in major adverse cardiovascular events and blood pressure changes between the salt substitute and the regular salt group in the subgroups of participants taking different antihypertensive medications. Mixed models were employed and adjusted for the cluster effect (village) and potential confounding variables. During the average follow-up period of 4.66 years, a significantly protective effect of salt substitute on reducing the risk of cardiovascular events was observed in the participants who taking antihypertensive medications (rate ratio: 0.81, 95% CI: 0.68 to 0.95. p=0.011), whereas no significant effect in participants not taking antihypertensive medications (rate ratio: 0.91, 95% CI: 0.62 to 1.32, p=0.612). Significant effects to lower systolic blood pressure of the salt substitutes were observed in the participants who took different antihypertensive medications. This study emphasized that the use of salt substitutes might enhance the efficacy of anti-hypertensive medications in lowering blood pressure and reducing the risk of adverse cardiovascular events.

Promoting healthy sleep in 0-2-year-old infants: a study protocol for the development and mixed method evaluation of a sleep health program tailored to Dutch youth healthcare regions

BackgroundSleep problems are common among infants and can have a serious impact on the health and wellbeing of both child and parents. To sustainably promote infant sleep on a population level, it is necessary to develop evidence-based programs that can be implemented on a large scale. The Youth Health Care setting, with its focus on prevention, child health promotion and services widely available for parents, can be a suitable setting to do so. Currently however, sleep health promotion in this setting seems to be suboptimal. To promote healthy infant sleep on a population level, programs need to be accessible and comprehensible for all parents, including parents with limited (health) literacy. Therefore, this study aims to develop, implement and evaluate a program called ‘Sleep on number 1’, that is tailored to Dutch Youth Health Care, to sustainably promote healthy sleep in 0-2-year-old infants.MethodsThe program was developed based on co-creation with parents and Youth Health Care professionals, evidence-based behaviour change theories and sleep health promotion methods. Program effectiveness is investigated with a quasi-experimental study design comparing the program group with the care as usual control group. Participants consist of parents of 0-2-year-old children. Primary outcome is infant sleep quality at the age of 10 weeks and 6, 9, 14 and 24 months, measured with a sleep diary. The primary data analysis focuses on night awakenings at 9 months. Secondary outcomes focus on parental behaviour regarding infant sleep, related behavioural determinants and parental satisfaction with Youth Health Care sleep advice. Program effectiveness is analysed using a linear mixed-model in case of data clustering, and an independent samples T-test or linear regression in case no substantial clustering effects are found. A mixed methods process evaluation is performed with parents and Youth Health Care professionals, assessing program reach, adoption, implementation, maintenance and working mechanisms.DiscussionThe ‘Sleep on number 1’ program is an evidence-based sleep health program for 0-2-year-old children, tailored to Dutch Youth Health Care. If effective, this program has the potential to improve infant sleep on a population level.Trial registrationISRCTN, ISRCTN27246394, registered on 10/03/2023. https://www.isrctn.com/ISRCTN27246394.

STTraj2Vec: A spatio-temporal trajectory representation learning approach

Computing trajectory similarity plays a critical role in various spatio-temporal applications that involve trajectory analysis. In recent years, trajectory representation learning has been extensively studied and applied for trajectory similarity calculation. However the majority of existing algorithms for trajectory representation generally have two problems. The first problem is the emphasis of spatial similarity over temporal similarity, and even to discard the temporal dimension of spatio-temporal trajectories. As a result, the outputs of these approaches cannot fully represent the similarity of spatio-temporal trajectories. The second problem is the introduction of additional information, such as the topology of the road network, which increases the uncertainty of capturing the spatio-temporal correlation of trajectories and prevents their application in scenarios where it is difficult to obtain such information. This poses a significant challenge when dealing with complex and time-varying traffic networks. This paper proposes a novel method, named STTraj2Vec (Spatio-temporal Trajectory 2 Vector), which relies only on spatio-temporal trajectories to capture their similarity without spatio-temporal separation. This takes into account the whole spatio-temporal trajectory information. In this method, an extended clustering algorithm is introduced, which maps the trajectory into a point-region quadtree, and constructs a time-varying virtual network structure based on the point-region quadtree. In this method, an extended clustering algorithm is introduced, which maps each trajectory into a point-region quadtree, and then completes density clustering through the adjacency relation of leaf nodes to construct a time-varying virtual network structure. This virtual network structure not only considers the spatial proximity, but also the time, so as to reflect the spatio-temporal characteristics of the trajectory more accurately. Then, a novel spatially and temporally integrated random walk algorithm is designed, which carries out spatiotemporal random walk on the virtual network structure, to capture the spatiotemporal characteristics of the trajectory, and thus obtains the representation of all nodes in the virtual network. Furthermore, each trajectory is converted into a sequence of vectors on the virtual road network according to the latitude, longitude, and time of the trajectory points. Finally, based on these node representations and trajectories, a transformer model with ranking loss is employed to capture the distinct contributions of the various locations and times to the similarity computation and encode each sequence of vectors into target vectors. Experiments on two public datasets show that STTraj2Vec is superior to the state-of-the-art methods in terms of effectiveness for top-k trajectory similarity search and trajectory clustering, while exhibiting low parameter sensitivity and high model robustness.