58622 research outputs found
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Photo- and electro-luminescence studies of a new nine-coordinate ternary Eu(III) complex
Full text linkA new 4′-(3,4-dimethoxyphenyl)-2,2′:6′,2″-terpyridine (DmP-TerPy) ligand has been synthesized and utilized as the ancillary ligand in conjunction with the primary antenna ligand 4,4,4-trifluoro-1-phenyl-1,3-butanedione (btfa) to develop a new nine coordinate red emitting ternary Eu(III) complex, [Eu(btfa) 3(DmP-TerPy] (Eu1). Eu1 was characterised by analytical and spectroscopic techniques and its photophysical properties were analysed. The experimental results were complemented by computational methods, namely, density functional theory (DFT), time-dependent density functional theory (TD-DFT), and the Lanthanide Luminescence Software Package (LUMPAC), to establish the photoluminescence (PL) properties of the sensitised Eu(III) complex and the energy transfer (EnT) processes involved. Finally, to test the application of Eu1 as a narrow band red emitter, the material was used as the emitting layer (EML) in a multi-layered host-guest device to fabricate highly monochromatic red organic light-emitting diodes (R-OLED).</p
Does preferred technique influence how kinematics change during a run to exhaustion?—A cluster based approach
Full text linkFatigue-related changes in running technique may depend on a runner’s preferred style. Understanding these changes can inform targeted training to enhance performance. In previous work, we identified two technique-based clusters of runners: the “neutral pelvis” and the “tilted pelvis” clusters. This follow-up study examined whether fatigue induces cluster-specific technique adaptations. Sixty runners (neutral pelvis, n = 32; tilted pelvis, n = 28) completed a treadmill run to exhaustion at 5% above their individual lactate threshold speed. Stride frequency, duty factor, trunk and lower limb kinematics were compared between clusters at the start, middle, and end of the run using a 2-way repeated measures analysis of variance (ANOVA). All runners reached exhaustion in ∼20 minutes, covering ∼5 km. Runners from the tilted pelvis cluster consistently showed greater trunk-to-pelvis extension, more pelvic anterior tilt and greater hip flexion, and a smaller duty factor compared with the neutral pelvis cluster throughout the run. Fatigue-related adaptations were similar across clusters: reduced stride frequency, increased duty factor, greater trunk flexion during stance, increased plantar flexion, and higher coordination variability (trunk-to-pelvis–hip, hip–knee, knee–ankle) during swing. Although fatigue affected both groups similarly, the underlying technique differences suggest these adaptations may have distinct mechanical or performance consequences. Understanding such cluster-specific responses can help coaches tailor training and fatigue management strategies to individual running styles
A data-driven heuristic for the dynamic vehicle routing problem with multiple soft time windows
Full text linkIn the Dynamic Vehicle Routing Problem with Multiple Soft Time Windows (Dynamic VRPMSTW), customer requests arrive in real time and must be scheduled within flexible service intervals. This problem is complicated by operational constraints, such as vehicle capacities, travel durations, and heterogeneous fleets, which make it difficult for classical optimization methods to adapt quickly to changing conditions. Following recent trends in contextual optimization, we propose a Data-Driven Dynamic Heuristic that integrates Artificial Neural Networks for predicting travel times and demands into a Dynamic Hybrid Adaptive Large Neighborhood Search (DD-Dynamic HALNS). Using cluster assignment and genetic crossover operators, the method generates high-quality initial solutions and continuously re-optimizes them as new requests emerge, ensuring adaptability and service reliability. The effectiveness of the proposed method is evaluated on real-world logistics data and benchmark instances. Results from real-world delivery operations demonstrate an average distance reduction of 11.6% compared with the current solution, with further improvements up to 15.5% when a 10-minute time window flexibility is introduced. These findings highlight the practical benefits of integrating predictive analytics with heuristic optimization, leading to improved cost efficiency, reduced operational constraints, and enhanced service reliability.</p
SPAN:Learning Similarity between Scene Graphs and Images with Transformers
Full text linkLearning similarity between scene graphs and images aims to estimate a similarity score given a scene graph and an image. There is currently no research dedicated to this task, although it is critical for scene graph generation and downstream applications. Scene graph generation is conventionally evaluated by Recall@K and mean Recall@K, which measure the ratio of predicted triplets that appear in the human-labeled triplet set. However, such triplet-oriented metrics fail to demonstrate the overall semantic difference between a scene graph and an image and are sensitive to annotation bias and noise. Using generated scene graphs in the downstream applications is therefore limited. To address this issue, for the first time, we propose a Scene graPh-imAge coNtrastive learning framework, SPAN, that can measure the similarity between scene graphs and images. Our novel framework consists of a graph Transformer and an image Transformer to align scene graphs and their corresponding images in the shared latent space. We introduce a novel graph serialization technique that transforms a scene graph into a sequence with structural encodings. Based on our framework, we propose R-Precision measuring image retrieval accuracy as a new evaluation metric for scene graph generation. We establish new benchmarks on the Visual Genome and Open Images datasets. Extensive experiments are conducted to verify the effectiveness of SPAN, which shows great potential as a scene graph encoder.</p
Determining the best discriminatory physical functioning outcome measurement instrument for psoriatic arthritis trials:A meta-epidemiological study
Full text linkOBJECTIVES: To empirically compare the discriminant capacities of three outcome measurement instruments for assessment of physical functioning for psoriatic arthritis (PsA): HAQ-DI, SF36-PF and SF36-PCS.METHODS: We applied a network meta-analysis technique in a sample of randomized trials (RCTs) for PsA. For randomized comparison, we calculated net effect size estimates for each outcome measurement instrument using standardized mean differences (SMDs); positive values indicated a beneficial effect of the intervention compared to the control groups. We analyzed the differences between outcome measurement instruments at the trial level by applying a multiple-treatment meta-analysis to compare the SMDs within and across randomized comparisons for each outcome measurement instrument.RESULTS: From 42 articles (31 RCTs), 57, 18, and 18 randomized comparisons enabled a direct comparison between HAQ-DI and SF36-PCS (difference in SMDs: 0.057, 95 % confidence interval, CI: 0.003 to 0.110), SF36-PF and SF36-PCS (difference in SMDs: 0.101, 95 % CI: 0.018 to 0.184); and HAQ-DI and SF36-PF (difference in SMDs:0.059, 95 % CI:0.142 to 0.024), respectively. The network meta-analysis technique confirmed that both HAQ-DI and SF36-PF were more responsive to change than SF36-PCS, with differences between SMDs of 0.057 (95 % CI: 0.003 to 0.110) and 0.109 (95 % CI: 0.032 to 0.185), respectively. No difference in discriminatory capacity between HAQ-DI and SF36-PF was noted.CONCLUSIONS: HAQ-DI and SF-36-PF were equally responsive to change and superior to SF36-PCS in PsA RCTs. We illustrated a new method for quantitative comparison of the performance of different outcome measurement instruments for a particular domain.</p
Teacher agency in practiced language policy in higher education in East and Southeast Asia (2010–2025):A systematic review
Full text linkThis paper presents the first systematic review connecting Teacher Agency (TA) and Practiced Language Policy (PLP) in English-medium instruction (EMI) within higher education (HE) across East and Southeast Asia. Following the PRISMA protocol, the review included twenty empirical studies published from 2010 to early 2025. Patterns of TA development and the pathways supporting them were identified through visualized co-occurrence. Findings show that agency is developmental and shaped by institutional arrangements, professional learning, and identity commitment. Classroom outcomes play a critical role in informing TA when implementing translanguaging, reassessing program and institutional contexts. A 3 × 3 Integrated Model was proposed viewing PLP as Context, TA as Action, and practiced policy as Outcomes across macro, meso, and micro levels. This model provides actionable insights for optimizing resource allocation at critical decision points, granting contextual autonomy at the program level, and integrating professional development with the cyclical process of policy interpretation, reframing, and enactment.</p
Pioneering Net Zero Carbon Construction Policy in Bath & North East Somerset:Evaluating the effectiveness of novel planning policies over time
Full text linkIn January 2023, Bath and North East Somerset Council (B&NES) introduced the UK’s first planning policies requiring all new buildings to have net zero operational emissions and limiting embodied emissions for larger developments. Coinciding with this, a collaboration was formed between University of Bath academics, planning and climate officers at B&NES, and other local organisations, which studied the impacts of, and reception to, these pioneering policies in the first six months following their introduction [1]. This 2023 pilot study was published in a previous report [2], and the current publication builds on the findings of the first. This project evaluates the success of the policies two years on, establishing long-term trends, opportunities for refinement, and the national policy implications of this unique policy case study. This report provides the outcomes of this follow-on project, aiming to:• Understand the effectiveness and experiences with the policy, with a comparison to the initial pilot study. • Investigate how projects perform compared to their initial planning applications.• Gather in-depth insights from a variety of applicants and stakeholders.Incoming planning applications received over a 12-month period were analysed, and a questionnaire was sent to applicants to understand their views on the policy two years after its adoption. In total, 59 planning applications were reviewed, with 56 out of 59 submissions for minor residential buildings, meaning the homes need to be net-zero operationally. Only five responses to the questionnaire were received, representing a significantly lower response rate compared to the 2023 pilot study. Additionally, four interviews were conducted with participants from the 2023 study to capture, in more detail, the experiences of applicants working with the B&NES policy.The rate of compliance and key design parameters, such as thermal performance and air tightness, were compared for the 2023 and 2025 studies to see if there have been any noticeable changes in submissions and the reasons for non-compliance. This study has highlighted the need to follow projects through to completion after they receive planning to track the performance of real-world as-built thermal performance, air tightness and resulting energy use of new residential buildings in the region. Additionally, we make recommendations for clearer policy communication to applicants, refinement of the energy policy and an increase in ambition for the embodied carbon target values. <br/
SPAN:Learning Similarity between Scene Graphs and Images with Transformers
Full text linkLearning similarity between scene graphs and images aims to estimate a similarity score given a scene graph and an image. There is currently no research dedicated to this task, although it is critical for scene graph generation and downstream applications. Scene graph generation is conventionally evaluated by Recall@K and mean Recall@K, which measure the ratio of predicted triplets that appear in the human-labeled triplet set. However, such triplet-oriented metrics fail to demonstrate the overall semantic difference between a scene graph and an image and are sensitive to annotation bias and noise. Using generated scene graphs in the downstream applications is therefore limited. To address this issue, for the first time, we propose a Scene graPh-imAge coNtrastive learning framework, SPAN, that can measure the similarity between scene graphs and images. Our novel framework consists of a graph Transformer and an image Transformer to align scene graphs and their corresponding images in the shared latent space. We introduce a novel graph serialization technique that transforms a scene graph into a sequence with structural encodings. Based on our framework, we propose R-Precision measuring image retrieval accuracy as a new evaluation metric for scene graph generation. We establish new benchmarks on the Visual Genome and Open Images datasets. Extensive experiments are conducted to verify the effectiveness of SPAN, which shows great potential as a scene graph encoder.</p
Dataset for "Feasibility and acceptability of 7-day smartphone-based, activity-triggered Ecological Momentary Assessment among low-income older adults"
No full textSmartphone-based Ecological Momentary Assessment (EMA) is increasingly used to collect real-time data on physical activity behaviour. The current study aimed to assess the feasibility and acceptability of activity-triggered EMA in low-income older adults. For 7 days, 39 older adults (76.4 ± 8.5 years; 76% earning below £25,000/year) received EMA surveys, delivered via the movisensXS application (version 1.5.23, movisens GmbH, Karlsruhe, Germany) for Android operating systems, when they surpassed a predefined activity/inactivity threshold, or when two hours elapsed between prompts. Participants wore a Move 4 activity sensor (movisens GmbH, Karlsruhe, Germany) to measure their steps. A post-study questionnaire assessed perceptions of acceptability. The dataset includes all quantitative data needed to replicate analyses in the article "Feasibility and acceptability of 7-day smartphone-based, activity-triggered Ecological Momentary Assessment among low-income older adults." The "Descriptives" sheet contains a unique participant identifier, demographic information, and responses to the post-study questionnaire. The "EMA" sheet contains a unique participant identifier (Participant_ID), age (Age_years), biological sex (Biological_sex), time of day (Time_of_day), day of week (Weekday), and EMA compliance (EMA_compliance; whether participants completed the EMA prompt or missed the EMA prompt) variables needed to perform the multilevel logistic regression models. It also contains the data necessary to limit the sample to participants with valid activity sensor wear and run Model 2, including the length of time in minutes that participants were not wearing the activity sensor in the 15-minute window before (Nonwear_before) and after (Nonwear_after) the EMA survey, and concurrent physical activity (Concurrent_PA; the number of steps in the ± 15-minute window around the EMA prompt). Day of study (day number from 1 to 7), trigger type (whether participants received an activity-triggered, inactivity-triggered, or timeout EMA prompt), trigger time (absolute time of the auditory signal and/or vibration alerting participants that it was time to complete an EMA survey), EMA outcome (whether the EMA prompt was completed, not answered, or answered but incomplete), form start time (absolute time when the EMA survey was answered), form completion time (absolute time when the EMA survey was completed), observation number (variable that assigns the observation number to each row by participant ID), and observation counter (variable that assigns the number of total observations to each row of data for a given participant) variables are also provided to enable researchers to replicate all of the summary statistics presented in the article. A complete description of the variables, including the text of questionnaires (where relevant), is provided in the "Overview" sheet
Procurement Scheduling for Assemble-to-Order Systems
Full text linkDue to the increasing complexity and diversity of customer demand, assemble-to-order (ATO) systems should account for more realistic factors in their procurement decisions. The classical literature offers analytical elegance but oversimplifies reality. This study bridges this gap by developing a multiperiod procurement problem for multi-product ATO systems under scenario-based demand uncertainty, considering realistic factors like varying product prices, bill of materials (BOM) structures, multiple suppliers with different lead times and costs, and contract options provided by suppliers with different discount prices. A multi-stage stochastic programming model is formulated to maximize the profit ofthe ATO system by optimally making decisions on (i) pre-stocked inventory, (ii) supplier selection, (iii) contract signing with options, and (iv) assembly planning. To solve the model efficiently, we propose an exact Benders decomposition algorithm with tailored subproblem (SP) relaxation, valid inequalities, and Pareto-optimal cuts. Experiments based on real data validate the exact algorithm’s effectiveness. For large-scale instances, the proposed algorithm improves the objective value by 13.7% over Gurobi’s best-found solution within a one-hour time limit. To further accelerate the algorithm, we introduce an efficient scenario reduction method based on forward-looking distance matrices. The proposedreduction method is proven to be more effective than traditional approaches, improving solution quality by up to 15.4% while accelerating computation by a factor of 3.5. This study also provides managerial insights for contract design, pre-stocking strategies, and supplier selection. For example, decisionmakers should negotiate with suppliers for more contract options in volatile markets, prioritize slow suppliers while maintaining a pool of fast suppliers as backups, and increase pre-stocked inventory for those with high and very high commonality
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