The effect of chronic pain on memory: A systematic review and meta-analysis exploring the impact of nociceptive, neuropathic and nociplastic pain

Chronic pain is becoming increasingly prevalent in modern society. Much research to date has focused on the physical symptoms of pain associated with various conditions, yet living with chronic pain is also known to impact an individual's cognition. Within cognition, memory is particularly vulnerable to outside factors, yet our understanding of the impact of chronic pain on memory is inconclusive. This systematic review and meta-analysis examined the association between chronic pain type and memory performance. Chronic pain samples were classified as nociceptive, neuropathic or nociplastic and were compared to healthy controls. Studies were sourced from Embase, Web of Science, MEDLINE, PubMed, PsycINFO, Scopus and CINAHL databases between December 2023 and July 2024. A total of 15 good – strong studies with 1865 participants were included (106 who experienced chronic nociceptive pain, 315 who experienced chronic neuropathic pain, 589 who experienced chronic nociplastic pain and 855 healthy controls). Results indicated that individuals with nociceptive and nociplastic pain had impaired short-term and long-term memory performance compared to healthy controls. The same was not true for individuals with neuropathic pain. These findings demonstrate that the type of pain one experiences impacts memory performance. This has profound implications both clinically and with regard to research and offers a new lens for how we can consider chronic pain when trying to understand the impact on cognition

Application of a multi-objective approach integrating solar-wind co-generation with response surface method to optimize zero-energy buildings

Achieving zero energy in residential buildings is particularly challenging in hot climates due to high cooling loads and reliance on conventional energy. This study introduces a co-generation system integrating a hybrid solar-wind setup with a Modified Steam Rankine Cycle and a Reverse Osmosis desalination unit to supply electricity, thermal energy, cooling, and potable water to a 360 m2 apartment complex in Ahvaz, Iran. BEopt software was used to extract energy consumption data, while energy and exergy assessments were performed using the Engineering Equation Solver and Response Surface Method. The system achieves an Exergetic Round Trip Efficiency of 34.02 % and an Energy Efficiency of 33.05 %. It meets annual energy needs with surplus energy fed back to the grid, generating 1,672 kWh of power, 8,760 kWh of heating, and 1,279 kWh of cooling while reducing 1,334.47 tons of carbon dioxide emissions. Pitch angle control in the 5 MW wind turbine enhances electricity generation by 4 %, leading to annual outputs of 6,541,564 kWh of electricity, 14,717,841 kWh of heating, 2,023,099 kWh of cooling, and 380,858 m3 of freshwater. The system stores excess thermal regulation for heating and air conditioning energy, contributing to cost-effectiveness and ecological sustainability. This study highlights the system's ability to achieve net-zero energy, with significant reductions in carbon emissions and the provision of substantial freshwater, demonstrating its potential in extreme climates. Future studies can explore the dynamic optimization of hybrid systems, focusing on real-time energy distribution between cooling, desalination, and energy storage

The Development and Content of Movement Quality Assessments in Athletic Populations: A Systematic Review and Multilevel Meta-Analysis

Abstract Background Despite their prominence in the sport and human movement sciences, to date, there is no systematic insight about the development and content of movement quality assessments in athletic populations. This is an important gap to address, as it could yield both practical and scientific implications related to the continued screening of movement quality in athletic contexts. Hence, this study aimed to systematically review the (i) developmental approach, (ii) movements included, (iii) scoring system utilised, and (iv) the reliability of movement competency assessments used in athletic populations. Methods Electronic databases (SPORTDiscus, MEDLINE, CINAHL, Web of Science, Scopus) were searched for relevant articles up to 12 May 2023. Studies were included if they reported data about the developmental approach, movements included, scoring system utilised and reliability of assessment in an athletic population. A modified Downs and Black checklist was used to measure study quality. Results From a total of 131 identified studies: (i) 26 (20%) described the developmental approach of an assessment; (ii) 113 (86%) included descriptions of the movements included; (iii) 106 (81%) included a description of scoring system and criteria; and (iv) 77 (59%) studies included reliability statistics. There were 36 assessments identified within these studies, comprising 59 movements in total. Each assessment scored movement quality through a Likert or binary classification system. Conclusion First, the results demonstrate that choosing an appropriate movement quality assessment in an athletic population may be a complex process for practitioners as the development approach, movements included and scoring criteria vary substantially between assessments. Second, academics could use these results to help design new assessments for novel applications that meet rigour and reliability requirements. Third, these results have the potential to foster guidelines of use for the reliable assessment of movement quality in athletic populations

From Data to Cultural Response: A Machine Learning–Driven Digital Twin Model for Smart Heritage Precincts in Urban Context

In the context of Smart Cities, Smart Heritage has emerged as a forward-oriented strategy aimed at enhancing the construction, management, accessibility, and sustainability of culturally significant environments. Yet, within Smart Heritage discourse, the distinction between basic digital representations and truly responsive, sensor-informed systems remains underdeveloped. This study addresses this gap by proposing a machine learning–enhanced digital twin simulation framework that enables both real-time and anticipatory heritage interventions. Using Chinatown Melbourne as an urban heritage case study, five open-access urban datasets, pedestrian counting, on-street parking, microclimate conditions, dwelling functionality, and Microlab sensor data (CO₂, sound level, and accelerometer), were evaluated, with three integrated into a pilot simulation model. A key contribution is the inclusion of a conceptual ‘Heritage Layer’ that overlays cultural significance and symbolic meaning across all stages of system logic and design response. The model also incorporates a dedicated machine learning layer, trained on full-year 2024 sensor data, to forecast environmental and behavioural triggers such as crowd build-up. This predictive capability enables the system to shift from reactive monitoring to proactive design interventions aligned with cultural rhythms. A December 2024 simulation validated the frequency and relevance of trigger-based activations. Rather than relying on platform-specific code, the framework is designed for adaptability across construction informatics environments and heritage precincts globally. Findings demonstrate how Smart Heritage systems can bridge environmental sensing, cultural identity, and post-construction evaluation, offering a scalable methodology for digitally responsive, culturally attuned urban heritage management

Validation of the Cognition Scale of the Hong Kong Comprehensive Assessment Scales for Toddlers

Background/Objectives: This study aimed to examine the psychometric properties of the Cognition Scale of the Hong Kong Comprehensive Assessment Scales for Toddlers (HKCAS-T) including its measurement properties, concurrent validity, and reliability. Methods: Participants included 282 children aged 18 to 41 months. These children were assessed on the HKCAS-T and the Cognitive Scale in the Cognitive Battery of the Merrill-Palmer-Revised Scales of Development (M-P-R). For test–retest reliability, 41 children were reassessed four weeks after the initial assessment. Results: Rasch analysis supported the unidimensionality of the HKCAS-T Cognition Scale. The scale differentiated among children of different ages, with older children achieving higher scores. The HKCAS-T Cognition Scale scores also correlated positively with the Cognitive Scale scores in the Cognitive Battery of the M-P-R. Internal consistency and test–retest reliability were both 0.98. Conclusions: The Cognition Scale of the HKCAS-T demonstrated strong psychometric properties and shows promise as an assessment tool for toddlers