The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, such as primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

The iterative bleaching extends multiplexity (IBEX) Knowledge-Base is a central portal for researchers adopting IBEX and related 2D and 3D immunofluorescence imaging methods. The design of the Knowledge-Base is modeled after efforts in the open-source software community and includes three facets: a development platform (GitHub), static website, and service for data archiving. The Knowledge-Base facilitates the practice of open science throughout the research life cycle by providing validation data for recommended and non-recommended reagents, such as primary and secondary antibodies. In addition to reporting negative data, the Knowledge-Base empowers method adoption and evolution by providing a venue for sharing protocols, videos, datasets, software, and publications. A dedicated discussion forum fosters a sense of community among researchers while addressing questions not covered in published manuscripts. Together, scientists from around the world are advancing scientific discovery at a faster pace, reducing wasted time and effort, and instilling greater confidence in the resulting data.

Background and purpose The prevalence of work-related spinal disorders (WRSDs) is higher among female physical therapists compared to their male counterparts, yet the physical and psychosocial risk factors are not well understood. This study aimed to determine the prevalence and associated risk factors of WRSDs among female physical therapy internship students in Egypt. Materials and Methods The study included 128 female internship students, all free from work-related spinal disorders at the start. Assessments for neck and lower back pain, physical activity levels, psychosocial factors, and spinal angles were conducted using the Nordic Musculoskeletal Questionnaire (NMQ), International Physical Activity Questionnaire (IPAQ), Copenhagen Psychosocial Questionnaire (COPSOQ), and inclinometers. These assessments were performed both before and after 12 months of the internship. Results The prevalence of WRSDs was found to be 73.44%, with 33% reporting neck and upper back pain, 33% low back pain (LBP), and 34% experiencing both. Students with work-related spinal disorders (group A) exhibited significant decreases (p<0.05) in Copenhagen Psychosocial Questionnaire scores and thoracic and lumbar angles compared to those without pain (group B) after 12 months. LBP scores positively correlated with changes in Copenhagen Psychosocial Questionnaire factors, including work pace, recognition, and work-life conflict. Changes in thoracic angles were also positively correlated with work pace and emotional demands. Conclusion The study concludes that the increased prevalence of work-related spinal disorders among female physical therapy internship students is linked to psychosocial elements such as fast work pace, recognition seeking, work-life conflict, and emotional demands, which serve as risk factors for work-related spinal disorders.

The research and development of hardware neuron technologies are accelerating at a very fast pace to provide for increased efficiency in performing artificial intelligence and autonomy functions beyond that possible with emulation on digital computers. Moreover, dedicated hardware for these biologically inspired functions creates capabilities not possible currently—especially regarding the integration of quantum information processing and advanced non-linear dynamical phenomenon necessary for bridging the gap between artificial and biological intelligence. A synthetic artificial neuron network functional in a regime where quantum information processes are co-integrated with spiking computation provides significant improvement in the capabilities of neuromorphic systems in performing artificial intelligence and autonomy tasks. This provides the ability to execute with the qubit coherence states and entanglement as well as in tandem to perform functions such as read-out and basic arithmetic with conventional spike-encoding. Ultimately, this enables the generation and computational processing of information packets with advanced capabilities and an increased level of security in their routing. We now use the dynamical pulse sequences generated by a memristive spiking neuron to drive synthetic neurons with built-in superconductor-ionic memories built in a lateral layout with integrated niobium metal electrodes as well as a gate terminal and an atomic layer deposited ionic barrier. The memories operate at very low voltage and with direct, and hysteretic Josephson tunneling and provide enhanced coherent properties enabling qubit behavior. We operated now specifically in the burst mode to drive the built-in reconfigurable qubit states and direct the resulting quantum trajectory. We analyze the new system with a Hamiltonian that considers an integrated rotational dependence, dependent on the unique co-integrated bursting mode spiking—and where the total above threshold spike-count is adjustable with variation of the level of coupling between the neurons. We then examined the impact of key parameters with a longer-term non-Markovian quantum memory and finally explored a process and algorithm for the generation of information packets with a coupled and entangled set of these artificial neuron-qubits that provides for a quantum process to define the level of regularity or awareness of the information packets. These results, therefore, enable quantum neural networks where qubit/quantum memory states and the associated quantum trajectories are now available for conducting advanced computational algorithms in conjunction with the information processing capabilities of general spiking neurons.

Trading on the stock market is a very complicated activity, and the main reasons for this are: high volatility, a lot of market noise, and changes in the behaviour of the investors, which are taking place at a very fast pace, and all these factors together are making the trading signals less reliable when the single technical indicators are used one by one. To solve these problems, the authors of this paper come up with a super resilient multi-indicator trading system that combines Simple Moving Average Crossover (SMAC) for trend detection with Average Traded Volume (ATV), Money Flow Index (MFI), and Put–Call Ratio (PCR) as supporting indicators. SMAC is the main method that provides buy and sell signals; at the same time, ATV approves market participation, MFI states the strength of both momentum and money flow, and PCR reflects the sentiment of the market, which is derived from the derivatives market. The new system is tested on large-cap stocks through considerable backtesting and using various time frames, which include short-term, medium-term, and long-term trading horizons. The evaluation is conducted with the help of important metrics like cumulative returns, trade accuracy, Sharpe ratio, and Sortino ratio. The experimental results show that the strategy of combining SMAC with ATV, MFI, and PCR has significantly surpassed the traditional SMAC-only methods, as it has reduced false signals, overtrading and risk-adjusted returns have been improved. One major point that the research has made is that by merging trend, volume, momentum, and sentiment data into a single trading model, a more reliable and understandable automated trading system is created, which is fit for the real-world market conditions.

The problem of school maladjustment is a classic issue in educational psychology, as it can lead to poor academic performance, frequent conflicts, and a deterioration in the student's psychological well-being.Due to rapid social change, information overload, rising divorce rates, and the fast pace of life for both parents and adolescents, cases of adolescent maladjustment in schools have become more frequent. However, given the importance of adolescence, a period during which personality and the foundations of social behavior are formed, it is imperative to promptly identify and address this problem.School maladjustment is a "systemic development that influences a child's development at the cognitive, regulatory, and communicative levels." Successful school adaptation has a significant impact on the emotional and overall psychological well-being of children.Maladjustment is largely interdisciplinary in nature – it is studied in pedagogy, personality psychology, social and educational psychology, and psychophysiology. Its prevention facilitates the identification and resolution of problems, which contributes to a favorable educational environment, improved academic performance, and the harmonious development of adolescents.Many schools focus exclusively on students' academic performance, neglecting other aspects of their personal development, including adaptation to school.The purpose of this article is to examine the problem of adolescent maladjustment in a comprehensive school. The following methods were used in the study: M. Gavlinova's method for assessing social adaptation and autonomic lability, R.V. Ovcharova's test for determining self-esteem in adolescents, and statistical data processing methods.

The digital infrastructure has become more complex than ever because of the fast pace of data exchange and the constant development of advanced cybercrime in the digital realm, the management of the network traffic securely and efficiently has become more challenging than ever before. Conventional rule based and statistical techniques can be expected not to keep abreast with changing network characteristics, resulting in false or slow anomaly detection. To overcome this drawback, the current study employs machine learning methods to advance the quality and effectiveness of the network anomaly detecting process as an analytical basis of smart network traffic management. To analyze the effects of various feature selection techniques on the performance of machine learning classifiers, the study uses the NSLKDD dataset that consists of labelled samples of both normal and attack traffic. Three feature selection algorithms Chi-Square (CHI), Correlation based Selection (CORR), and Feature Importance (FI) were implemented separately and the outcome feature subsets were used to train three classifiers; K-Nearest Neighbors (KNN), Decision Tree (DT) and Random Forest (RF). Accuracy, F1-score and the training time were the performance indicators used to test the models. The analysis of the experimental results showed that the Feature Importance (FI) of feature subset performed the best in terms of detection performance whereas the crux of the analysis was the classifier of the experimental results, the Random Forest (RF) and Decision Tree (DT) as compared to the KNN model. The optimal combination of the RF classifier and FI feature selection obtained a 1.0 F1-score, which proves that this method has a high ability to differentiate between normal and anomalous network traffic. The findings attest to the importance of feature selection in optimizing machine learning based anomaly detection systems. The study makes a practical contribution to the implementation of artificial intelligence methods in the management of network traffic, which will be essential in making more flexible, precise, and intelligent network monitoring systems.

This study aims to explore the impact of forced migration on the eating habits of migrants. It also seeks to determine whether people who have experienced forced migration have transmitted the foods they consider their own to future generations. For this purpose, in-depth interviews were conducted with 13 families who migrated to Türkiye. After World War II, approximately 500,000 Bosnians, Albanians, and Pomaks in Yugoslavia and Bulgaria were forced or encouraged to leave their lands. Türkiye accepted these migrants at that time. Long before these migrations, Crimean Tatars had begun migrating to the Ottoman Empire when the Russian army entered Crimea in 1771, and this migration continued throughout the 19th century. The participants in the study are members of families who experienced these migrations. According to the interviews, although Türkiye welcomed the migrants after the forced migration, the newcomers experienced difficulties in adaptation. They went through a process of acculturation as they became familiar with Turkish cuisine and culture, integrating many Turkish dishes into their own culinary traditions. However, the best way for them to ease their longing for home was to continue cooking and eating their traditional dishes, at least on special occasions. They sought to pass on their culinary knowledge and signature recipes to future generations, and the younger generations regarded this food culture as a part of their family’s historical heritage and identity. Despite the fast pace of life in a changing world, they have continued to cook and consume these traditional foods.

Urban communities in densely populated areas often face spiritual challenges due to the fast pace of life and limited access to religious education. This condition results in low literacy in Islamic jurisprudence (fiqh) and results in worship that is less than compliant with Islamic law. This community service program aims to improve Islamic jurisprudence (fiqh) and establish a balance between worldly and spiritual life for urban communities in Kertosari and Karangrejo Villages, Banyuwangi. The methods used are participatory outreach and education through lectures, Q&A sessions, role-plays, and the use of information technology such as videos and infographics to simplify complex Islamic jurisprudence concepts. Mentoring involves studying the Taqrib book as a practical guide and the Bajuri book for deeper understanding, culminating in hands-on practice of ablution and prayer. The program results show a significant increase in community understanding of the requirements and pillars of worship. In addition to the spiritual impact of increased consistency in worship, this activity also strengthens social cohesion and solidarity among residents through collective activities such as completing the Quran and regular recitation. Overall, the contextual non-formal education approach has proven effective in improving the spiritual and social quality of society amidst the dynamics of urban life.

The tariff dispute between India and the U.S. in 2024 2025 is their most substantial trade relation disturbance since before the 21st century. The U.S. decision to slap the highest tariffs of 50% on significant Indian exports is the main reason behind this confrontation. Also, growing protectionism in the U.S., a tough domestic political situation, and worries about India selling subsidized competitive products have all factored in the dispute. India, which is very dependent on the U.S. market for value added goods, suffered economic shocks right from the get go like cancellation of exports, fluctuations of the exchange rate, rising of inflation, and sectors dominated by MSMEs experiencing textiles, engineering goods, chemicals, and processed foods being hard hit. The conflict had a longer duration because the opposing tariffs India declared also deepened its current account deficit and slowed down GDP growth rate forecasts. In addition to the negative effects on the macroeconomy, the dispute caused the two sides to distrust each other more at the strategic level, thus complicating the partnership which would otherwise be enhanced by the agreements in defense, technology, and energy. The crisis made India rethink its foreign economic strategy by beginning export diversification at a faster pace, picking up domestic manufacturing through PLI schemes, and adjusting her trade diplomacy again. This research traces the escalation timeline, the sectors affected, the macroeconomic effects, and India’s strategic conundrum and presents a lot of valuable information on how emerging economies handle tariff wars amid growing global protectionism.

ABSTRACT Generative AI systems like ChatGPT are changing the digital interaction landscape at an incredibly fast pace. This research examines the primary factors affecting user experience and its influence on perceived scalability of performance. Using mixed-method approach, qualitative interviews revealed common themes that were used to construct a widespread quantitative model, which was tested on 1127 users. The findings of the Partial Least Squares Structural Equation Modeling showed that usefulness, ease of use, interactivity, autonomy, personalisation and novelty-seeking could boost user experience, whereas credibility could not. User experience has a positive impact on the perceived performance scalability. The research extends uses and gratification theory and social interaction theory in the context of generative AI. The practical implications on AI developers are the enhancement of adaptive personalisation, interactive and user-support systems to enhance long-term engagement and perceived system performance.

Purpose To describe life-space mobility (LSM) and its relationship with physical capacity (PC) (walking endurance, walking speed, leg strength, balance), and outdoor walking in older adults with difficulty walking outdoors. Methods A secondary analysis of baseline data from 173 adults aged ≥ 65 years with mobility limitations in the Getting Older Adults Outdoors (GO-OUT) study was conducted. LSM was measured using the Life-space Assessment (LSA), and PC was assessed using the 6-minute walk test (6MWT), 10-meter walk test (10mWT) at a comfortable and fast pace, 30-second sit-to-stand (30sSTS), and mini-Balance Evaluation Systems Test (mini-BESTest). The relationship between scores on PC measures and outdoor walking time, assessed by the CHAMPS-OUTDOORS questionnaire, and LSA score, was examined using Spearman correlations and backward stepwise regression modelling, adjusted for age and sex. Results Mean LSA score was 64.6 ± 20.2. Scores on each study measure had a fair and positive correlation (p < 0.01) with LSA score: 6MWT (ρ = 0.42), 10mWT (ρ = 0.44, 0.45), 30sSTS (ρ = 0.43), mini-BESTest (ρ = 0.43), and CHAMPS-OUTDOORS (ρ = 0.28). The final regression model, including 10mWT, 30sSTS, mini-BESTest, explained 31% of LSA score variance. Conclusion PC and outdoor walking time relate to LSM in community-dwelling older adults with difficulty walking outdoors, though outdoor walking time has a weaker correlation with LSM.

Abstract The Industrial Revolution triggered rural abandonment in Europe and had a profound impact on land configuration and ecosystem dynamics, mainly the growth of forests at the expense of open agricultural habitats. However, rural abandonment has been asynchronous in space and time, depending on regional socio‐economic dynamics. In Mediterranean islands, abandonment occurred at a very fast pace, and it started much later when the tourist industry substituted for the Industrial Revolution. Here, we analysed historical (1956) and recent (2019) aerial images in the Tramuntana range (Mallorca Island, western Mediterranean) to quantify changes in major land covers (woodlands, shrublands and agricultural areas) with insights into the mechanisms of change gained through semi‐structured interviews documenting the traditional ecological knowledge (TEK) of elderly residents regarding past land‐use practices. The results confirm a transition from agricultural landscapes, which lost 40% of their area, to forests, which doubled their surface. This transition was similar in all study regions, although wildfires in the drier region delayed the expansion of forests. The forest transition was faster in patches that received greater legal protection. We also found a significant transformation in landscape structure due to reduced complexity from the reconnection of forest patches. The results of TEK showed a decrease and extinction of many traditional agricultural practices, mainly extensive livestock grazing and charcoal production from oak trees. TEK should be preserved due to its rapidly fading presence in the Mediterranean islands. Forest transition and the end of cultural fire practices have likely increased the number and magnitude of wildfires in the last decades. The study concludes that the forest transition on this Mediterranean island is a clear consequence of intertwined socio‐economic changes and the abandonment of traditional practices, with multifaceted ecological impacts. It highlights the value of integrating quantitative analysis based on historical data and local knowledge for understanding and managing landscape changes, emphasising the need for balanced approaches to address issues like wildfires and hydrological changes in these evolving socio‐ecological systems. Read the free Plain Language Summary for this article on the Journal blog.

Private educational institutions located within residential neighbourhoods are devices that often lead to urban transformation at a fast pace. Unfortunately, the majority of residents do not control these changes. Expansion around institutions like Minhaj University, University of Management and Technology (UMT) and other in Lahore has changed its land use pattern adversely. The surrounding areas has become congestedd and has started acting as a magnet for informal trade, which is gradually leading to decline in its identity as a residential zone. This research traces the changes and asks the question of how placemaking can be used to recover spatial coherence and inclusivity in the academic district after the gentrification process. The research used satellite imagery (2005, 2025), mental mapping, field observations, and stakeholder interviews to gauge the size of the area covered by the park, which it had increased seven times. Moreover, the research reported a simultaneity of commercial pressures. The research found that there are continuous conflicts between pedestrians and vehicles, while students have insufficient public spaces. Furthermore, vendor activities remain unregulated, and conversation of residential units into hostels has become widespread. The study offers a detailed account of gentrification caused by education in Pakistan and the strategies of design to democratize the redevelopment of similar high, pressure university areas.

This design research study investigates how pace-sensitive and inclusive mathematics instruction can support students who learn at slower, moderate, and faster paces in Indonesian junior high classrooms. Using the PRIME framework—Profiling, Reconstruction, Implementation, Measurement, and Engagement—the study was implemented across two iterative cycles in two comparable inclusive classes (N = 80, Grade 8). Each cycle employed contextual mathematical tasks integrating local and global themes, such as fishing tides, travel-based time zones, temperature changes, skyscraper scaling, and communal food sharing, all grounded in Realistic Mathematics Education (RME). Students’ learning-pace profiles were established through triangulated diagnostics including cognitive screening instruments, self-assessment, teacher interviews, and Level 0 contextual tasks. Within each cycle, descriptive analyses indicated clear improvements in students’ representational clarity, strategic reasoning, and movement through the RME phases, particularly among students who initially struggled with abstraction. Cycle 2 demonstrated more substantial gains than Cycle 1, with students exhibiting increased independence, reduced need for teacher prompting, and stronger transitions from model-of to model-for reasoning. Qualitative thematic analysis further showed enhanced engagement, contextual understanding, and confidence, while teachers reported more manageable routines for differentiation. The findings suggest that learning pace is malleable and responsive to purposeful instructional design. The PRIME framework offers a practical bridge between RME, differentiated instruction, Universal Design for Learning (UDL), and Multi-Tiered Systems of Support (MTSS), providing a feasible model for promoting equitable mathematics learning in resource-variable inclusive classrooms.

Multimedia design education is currently changing at a fast pace due to the rapid development of digital technologies, which require a more personalized and flexible learning environment. The old model of teaching does not usually recognize any differences in learning between individuals in terms of speed, manner and understanding. The promising alternative offered by adaptive learning systems (ALS) is the idea that the instructional material will be customized and designed to meet the needs of a particular learner through the use of artificial intelligence, data analytics, and real-time feedback. This paper examines the construction and deployment of an adaptive learning system that is created specifically to serve as an education of multimedia design. The system architecture incorporates the following important technologies: Learning Management Systems (LMS), adaptive learning engines, and AI-driven analytics, which can evaluate the performance of the learners and dynamically modify the content. This study determines concepts of adaptive learning based on the depth of literature review, which identifies the basic building blocks and overall theoretical basis of the concept, and how such building blocks apply to multimedia education. The system architecture suggested is in line with the multimedia design curriculum in that it makes interactive learning, visual learning, and project-based learning easy. One case study on the performance of a system is carried out among a group of undergraduate design students, where the system is tested based on engagement, usability, and learning outcomes. The results have shown that adaptive learning is very important to student motivation, understanding the concepts and abilities to solve problems creatively as compared to the conventional classroom methods.

Abstract In today’s fast growing digitalized world, modern business has emerged with digital innovation and advance management tools, with the development of Artificial Intelligence (AI) in the such an era Business are becoming more Advance, Business Intelligent and much aware with AI tools and Machine Learning parameters. However, with Globalization the business are not limited to a certain geographical indexes but have expanded to a vast scope of potential. With the vast amount of data floating, data management and handling such an information carrying data with lightning speed was a challenge in traditional methods of conducting business and making decisions. However, with the avalanche of AI and Business Intelligence tools business can now make automated decisions and predict any future needs and demands based upon the historical data and logical analysis. AI’s potential is not limited to certain area and prospects, as with the growth of Neural Network AI can not only brings innovation in business process management (BPM) but can also be used to act as mediator or an arbitrator to resolve conflicts in case of business dispute resolution. However there is still a challenge that can be seen as these two fields which is AI in Business Process Management (BPM) and AI in Alternative Dispute Resolution (ADR) still remains individual entities and in business scenario these two never crosses path. Since businesses are now growing with much faster pace and several joint businesses emerges with the needs of Mergers and Acquisition’s to fulfil big business tasks. There is always a chance of dispute that can emerge amongst the entities and resolving it with the traditional legal way is very time consuming and costly. Therefore there is a need of an intelligent adoptable system which is driven by emerging business complexities and challenges of legal compliances faced by modern organizations. This review of literature primarily focuses on integration of AI driven Business Intelligence in Business Process Optimization (BPO) and Resolving conflict with Alternative Dispute Resolution (ADR) targeting operational optimization and decision automation. The study summarizes the literature on AI in BPM and AI in ADR together and tries to find out research gap in previous studies along with-it the study tries to find out possibilities of integration of both BPM and ADR with AI based business intelligent systems. This study explores how AI-Technologies like Natural Language Processing (NLP), Machine Learning (ML) and predictive analysis are being used to improve workflow efficiency, decision making and resolving conflict in Business Management Optimization (BPO). Moreover the study explores limitations and ethical constraints that need to be taken into considerations while creating such system to eliminate biasness from AI “Black Box” systems. Drawn based upon the earlier studies by Davenport (2018), Panda et al.,(2021) and Katash &amp; Robinovich-Einy (2017) the study outlines the process by which AI improves performance, consistency and transparency in both legal and business management fields. In addition case studies like LawConnect (Beck, 2023) and LLMediator (Westermann el at., 2023) shows practical use of AI assisted tools for legal consulting and assisting mediation procedures. The study concludes that; “while AI has the ability to improve business processes and assist mediation procedures there is a need to integrate both legal and management aspects together for analysing, predicting and resolving business difficulties” which is necessary for creating an ethical structure termed as Machine-Human Hybrid (MHH). Such structures will provide openness, transparency and decisions with human sentiments for effective control on business procedures, reducing time and cost. Therefore in order to create a sustainable and data driven decision systems with human cognition, AI in business and conflict resolution has to move from “Limited Machine Procedures” to an adoptive ecosystem. Such system follows responsible technology adoption, with management strategies which are free from biasness and are ethical, transparent and legally innovative.

Supermarket industry in Pakistan is growing at a fast pace, technology-driven retail experiences regarding customer reactions in emerging-market contexts is not verified. This research examines effects of technological improvement on behavioral outcomes. Based on technology acceptance model and stimulus organism response viewpoint, study uses a cross-sectional survey of 226 customers in Pakistan supermarkets. PLS-SEM was used to evaluate data. Findings showed significant mediating role of experiential factors mediated by satisfaction on behavioral outcomes. Direct influences indicate that channel integration leads to willingness to pay and technological advancement and engagement/interactivity lead to loyalty. Work contributes to existing body of knowledge in digital retail by providing a practical suggestion on how supermarkets can enhance advocacy and value perception by designing experience with interactive and integrated experience.

HighlightsWhat are the main findings?Shorter stride length during fast walking, mild cognitive impairment, depressive symptoms, and female sex were identified as significant predictors of the transition from non-frail to prefrail status.Additionally, increased stride time variability at fast pace and poorer working memory performance were independently associated with the progression from prefrailty to frailty.What are the implications of the main findings?Spatiotemporal gait variability and executive dysfunction represent sensitive multidomain markers for the early detection of frailty in community-dwelling older adults.Integrating gait and cognitive assessments into routine geriatric evaluations may enhance early identification and prevention efforts, supporting a multidimensional approach to aging care.Background/Objectives: Frailty is an age-related clinical syndrome characterized by diminished physiological reserves and increased vulnerability to adverse outcomes. Growing evidence suggests that frailty involves shared brain networks that regulate both gait and cognitive functions. This study aimed to examine the relationship between frailty status, spatiotemporal gait parameters, and cognitive functions in community-dwelling older adults. Methods: A cross-sectional study was conducted with 99 adults aged ≥70 years, classified as non-frail, prefrail, or frail according to the Fried phenotype. Gait parameters were measured under usual and fast walking conditions using the OptoGait® photoelectric system. Cognitive status was assessed with the Montreal Cognitive Assessment (MoCA) and a comprehensive neuropsychological battery. Multivariate logistic regression analyses were performed to identify factors associated with transitions between frailty stages. Results: The prevalence of frailty was 9.1%, with 51.5% prefrail and 39.4% non-frail. The transition from non-frail to prefrail was associated with shorter stride length at fast pace (OR = 0.92, 95% CI: 0.88–0.96), mild cognitive impairment (OR = 3.71, 95% CI: 1.08–12.69), depressive symptoms (OR = 1.82, 95% CI: 1.26–2.62), and female sex (OR = 4.94, 95% CI: 1.20–16.77). The transition from prefrail to frail was linked to increased stride time variability at fast pace (OR = 2.94, 95% CI: 1.34–6.44) and poorer working memory (OR = 0.40, 95% CI: 0.16–0.97). Conclusions: Shorter stride length, mild cognitive impairment, and depressive symptoms emerged as key markers of the transition from non-frailty to prefrailty, whereas increased stride time variability and poorer working memory distinguished prefrail from frail individuals. These findings highlight gait- and executive-function-related markers as sensitive early indicators of vulnerability. Incorporating quantitative gait assessment and brief cognitive screening into routine geriatric evaluations may substantially enhance early detection and support targeted preventive strategies for healthy aging.

Fall prevention recommendations include mobility or balance testing to identify older adults with high fall risk who require further intervention. However, there is no consensus on the best tests or optimal cut-off values. The Initial Test for Fall RIsk Assessment in The Elderly (INITIATE) study was designed to determine the optimal screening test(s) for predicting falls among community-dwelling older adults. Here we describe the study protocol, sample characteristics, and baseline differences between participants with and without a history of falling. We undertook a 1-year prospective cohort study of community-dwelling older adults (≥ 65 years) able to walk 10m without assistance at baseline and living in Ontario, Canada. Participants underwent a 2-hour baseline visit where 7 validated balance and mobility tests (Timed up and go (TUG) usual pace, TUG fast pace, TUG with a cognitive dual task, Brief Balance Evaluation Systems Test (BESTest), 5 times sit-to-stand (5TSTS), single leg stance, gait speed) were administered. Falls were tracked for 12 months using monthly diaries and follow-up calls for context. Participants received quarterly calls to monitor general wellbeing, healthcare utilization, and changes to mobility. Descriptive statistics were calculated and differences by 12-month fall history were tested using t-tests, chi square tests, and Wilcox Rank Sum tests as appropriate. From 3211 contacted older adults, 514 (19%) consented. The mean age was 76.4 years (SD 6.7), 64% were female, 68% had a postsecondary degree/diploma, and 231(45%) reported a fall in the last 12 months. Means(SD) for the performance-based tests were as follows: TUG = 11.8s(4.0), TUG fast pace = 9.2s(3.4), TUG cog = 14.2s(5.9), Brief BESTest = 15.9 score(5.3), 5TSTS = 12.5s(4.3), single leg stance = 14.1s(16.3), gait speed = 1.14m/s(0.28). Comparisons between baseline fallers and non-fallers showed no differences in age, sex, income, or education(p > 0.05) but did show differences in all 7 tests(p < 0.05). Participants are representative of community-dwelling older adults with fall risk. Balance and mobility test differences between fallers and non-fallers support the need for prospective comparisons of their predictive validity. Follow-up results, expected in late 2025, will help inform future updates to fall risk assessment and prevention guidelines.