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Chronic low-back pain (CLBP) is a leading cause of disability, with current treatments offering only modest benefits. Transcranial direct-current stimulation (tDCS) may enhance exercise therapy by modulating cortical excitability and pain-inhibitory pathways. This systematic review and meta-analysis quantified the additive effect of combining tDCS with structured exercise in adults with CLBP. We searched PubMed, Web of Science, CENTRAL, Embase, and CNKI up to 25 September 2025. Randomized controlled trials (RCTs) comparing active tDCS plus identical exercise therapy vs. sham tDCS plus the same exercise in adults with CLBP (≥ 12 weeks) were included. Risk of bias was assessed using Cochrane RoB 2.0. Weighted mean difference (WMD) and standardized mean difference (SMD) were calculated for pain and function, respectively.GRADE was used to assess certainty. Five RCTs ( = 195) were included. For pain intensity (4 studies, = 173), tDCS showed a significant additive effect (WMD = -0.99, 95% CI: -1.68 to -0.31, = 0.006, = 60.1%). For physical function (five studies, = 195), the effect was favorable but non-significant (SMD = -0.65, 95% CI: -1.87 to 0.57, = 0.28, = 90.7%). Meta-regression indicated intervention duration significantly moderated functional outcomes (β = 0.56, < 0.001). GRADE certainty was moderate for pain and low for function. Anodal tDCS combined with exercise provides a modest but significant additional reduction in pain intensity for CLBP. Longer intervention duration may enhance functional outcomes. Clinical significance should be interpreted cautiously. Larger, well-designed trials are needed to confirm these findings and optimize stimulation parameters. https://www.crd.york.ac.uk/PROSPERO/view/CRD420251151315, identifier CRD420251151315.

Background Caring for older adults is a dynamic and complex process, and analyzing personal and environmental factors separately fails to capture its full nature. The person-environment fit theory offers a valuable framework to explore interactions between caregivers and their environments. This review aimed to synthesize evidence on the person-environment fit of formal and informal caregivers for older adults, identify gaps in existing literature, and propose future research directions. Methods The scoping review was conducted following the six-step guidance of the Joanna Briggs Institute 2020. A literature search was conducted in June 2024 across seven databases: Scopus, ProQuest, Web of Science, Embase, PubMed, MEDLINE, and CINAHL. Empirical studies published after 2000 regarding the person-environment fit of caregivers for older adults were included. Results A total of 15 eligible articles were included, with nine focusing on formal caregivers and six focusing on informal caregivers. Research on formal caregivers’ person-environment fit often came from human resource management perspectives and was supported by various theories. In contrast, studies on informal caregivers tended to draw from environmental gerontology theories and lacked a systematic analytical model. Most studies used self-designed questionnaires or interviews, with one employing a standard person-environment fit measurement scale. Caregivers’ person-environment fit was analyzed through “demands-abilities fit” and “needs-supplies fit” dimensions, covering various types like person-physical, person-interpersonal, value, goal, and caregiving-life fit. Findings indicated that a high level of person-environment fit was associated with better physical and mental well-being, stronger caregiving commitment, and improved care quality. Conclusion The holistic and dynamic nature of person-environment fit theory is closely aligned with the caregiving process, enhancing our understanding of the experiences of caregivers. Based on existing evidence, this review proposes a theoretical framework for analyzing informal caregivers’ person-environment fit. Further investigation into the theoretical framework of caregiver person-environment fit, along with the development of standardized assessment instruments, will significantly advance the well-being of both caregivers and care recipients. Trial registration Open Science Framework https://doi.org/10.17605/OSF.IO/YV26C .

In the context of the increasing number of patients with hypertension, exercise intervention is an excellent alternative or adjunctive treatment for hypertension. Traditional Chinese exercises are excellent physical and mental exercise methods. Although some studies have reviewed the effects of Chinese traditional exercise on patients with hypertension, most of the reviews only involved a single category of traditional exercise. Furthermore, few studies have conducted in-depth analysis of the combined intervention methods of traditional Chinese exercise, and there are high heterogeneity. This study evaluates the current clinical evidence of Chinese traditional exercises in the treatment of essential hypertension. A total of 49 randomized controlled trials with 4207 hypertensive patients were selected according to the inclusion criteria by searching all relevant studies from the establishment of six electronic databases until September 10, 2022. Among them, 24 used tai chi and 25 used Qigong, including Yijinjing, Wuqinxi, Liuzijue, Baduanjin, and Guolin Qigong. This study divided four subgroups according to the type of intervention to explore the source of heterogeneity among studies and found that traditional Chinese exercises can assist or even may replace traditional treatments. The results of meta-analysis showed that compared with the use of antihypertensive drugs alone or in health education, the addition of Chinese traditional exercises showed significant effects in regulating the systolic blood pressure and diastolic blood pressure in hypertensive patients. Although the results show that traditional Chinese exercise are effective, the clinical evidence will be affected by the low quality of most randomized controlled trials. More rigorously designed trials are needed in the future to further validate it.

Going beyond vision has been essential for many areas, from providing immersive experience in virtual reality to providing robots with a human-like sensing capability. This need is particularly important when touch interactions occur or play a dominant role in the scenarios, such as perceiving the roughness of a material through scratching or localizing the contacts in a game of Jenga. Touch interaction is a multimodal exploration procedure (i.e., grasp, tap, hold) with multisensory feedback (i.e., forces, sounds, vibrations) produced during the action. The multimodal exploration and multisensory feedback around touch interactions constitute an interactive loop, where each action influences subsequent perception, and the perception inversely guides the next actions. The intricate interplay between action and perception in this loop forms the basis of our rich touch experiences, allowing us to gather detailed information about object properties, manipulate items with precision, and respond meaningfully to our environment. The interactive and multisensory nature of touch enables a rich array of feedback types triggered by various exploratory actions. Through careful design, these feedback mechanisms can even extend beyond natural human touch capabilities. The feedback produced during both human and artificial (robotic) touch interactions presents unique challenges and opportunities in terms of analysis, synthesis, perception, and applications. These challenges range from accurately capturing and interpreting complex tactile stimuli to synthesizing realistic touch sensations in virtual environments. In this thesis, I contribute to two main directions focusing on a multimodal and multisensory experience in touch-based interactions, to develop methods (1) to simulate and reconstruct touch-produced multisensory signals and (2) to analyze and decipher the multisensory information generated from active multimodal touch explorations. For (1), I present a new preference-driven haptic texture modeling framework, aimed at addressing the limitations of state-of-the-art data-driven frameworks, such as expensive recording devices, expertise in data collection, and inadaptability in models. The proposed framework combines the power of generative models for automatic texture generation and humans' capability of discerning texture details for interactive texture search via evolutionary strategies. It composes an iterative process for continuous tuning and refining of the modelled texture guided by human preference. Furthermore, I explore another important sensory modality produced in touch interactions - auditory feedback, particularly texture sounds. I propose a data-driven texture sound modeling and rendering approach for unconstrained tool-surface interactions, taking advantage of the hierarchical tree structure in wavelet transformations to decompose the recorded texture sound, and then reconstruct the new sounds with controllable uncertainties for different frequency components. The new generated virtual texture sounds realistically match user's motion in real time. For (2), inspired by the cross-talk across cortex regions for different sensory processing in the human brain, I present a feature extraction method using the crossmodal congruence between auditory and vibrotactile feedback. I propose a crossmodal inter-band spectral mapping that relates the frequency bands between the modalities to achieve a robust texture signature extraction. The proposed feature is evaluated using a large-scale texture classification task, indicating significant improvement in classification accuracy with a small amount of training data. Furthermore, with a focus on the grand state-aware robotic manipulation problem, I design a new touch sensing method using objects' acoustic responses under excitation, so-called active acoustic sensing. Important sensing capabilities, including object shape and material, grasping point, internal structure, and external contact with environments, are validated by both simulated and physical experiments. Lastly, I integrate this active acoustic sensing method into the robotic learning from demonstration pipeline and demonstrate its superior performance on contact-rich manipulation tasks.

Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the integration of visual and haptic perception is not compact and is easily biased. We propose to address these limitations by developing an active acoustic sensing method for robot manipulation. Active acoustic sensing relies on the resonant properties of the object, which are related to its material, shape, internal structure, and contact interactions with the gripper and environment. The sensor consists of a vibration actuator paired with a piezo-electric microphone. The actuator generates a waveform, and the microphone tracks the waveform's propagation and distortion as it travels through the object. This paper presents the sensing principles, hardware design, simulation development, and evaluation of physical and simulated sensory data under different conditions as a proof-of-concept. This work aims to provide fundamentals on a useful tool for downstream robot manipulation tasks using active acoustic sensing, such as object recognition, grasping point estimation, object pose estimation, and external contact formation detection.

Authoring realistic haptic textures typically requires low-level parameter tuning and repeated trial-and-error, limiting speed, transparency, and creative reach. We present a language-driven authoring system that turns natural-language prompts into multimodal textures: two coordinated haptic channels - sliding vibrations via force/speed-conditioned autoregressive (AR) models and tapping transients - and a text-prompted visual preview from a diffusion model. A shared, language-aligned latent links modalities so a single prompt yields semantically consistent haptic and visual signals; designers can write goals (e.g., \"gritty but cushioned surface,\" \"smooth and hard metal surface\") and immediately see and feel the result through a 3D haptic device. To verify that the learned latent encodes perceptually meaningful structure, we conduct an anchor-referenced, attribute-wise evaluation for roughness, slipperiness, and hardness. Participant ratings are projected to the interpretable line between two real-material references, revealing consistent trends - asperity effects in roughness, compliance in hardness, and surface-film influence in slipperiness. A human-subject study further indicates coherent cross-modal experience and low effort for prompt-based iteration. The results show that language can serve as a practical control modality for texture authoring: prompts reliably steer material semantics across haptic and visual channels, enabling a prompt-first, designer-oriented workflow that replaces manual parameter tuning with interpretable, text-guided refinement.

Vision-Language-Action (VLA) models have shown remarkable generalization by mapping web-scale knowledge to robotic control, yet they remain blind to physical contact. Consequently, they struggle with contact-rich manipulation tasks that require reasoning about force, texture, and slip. While some approaches incorporate low-dimensional tactile signals, they fail to capture the high-resolution dynamics essential for such interactions. To address this limitation, we introduce DreamTacVLA, a framework that grounds VLA models in contact physics by learning to feel the future. Our model adopts a hierarchical perception scheme in which high-resolution tactile images serve as micro-vision inputs coupled with wrist-camera local vision and third-person macro vision. To reconcile these multi-scale sensory streams, we first train a unified policy with a Hierarchical Spatial Alignment (HSA) loss that aligns tactile tokens with their spatial counterparts in the wrist and third-person views. To further deepen the model's understanding of fine-grained contact dynamics, we finetune the system with a tactile world model that predicts future tactile signals. To mitigate tactile data scarcity and the wear-prone nature of tactile sensors, we construct a hybrid large-scale dataset sourced from both high-fidelity digital twin and real-world experiments. By anticipating upcoming tactile states, DreamTacVLA acquires a rich model of contact physics and conditions its actions on both real observations and imagined consequences. Across contact-rich manipulation tasks, it outperforms state-of-the-art VLA baselines, achieving up to 95% success, highlighting the importance of understanding physical contact for robust, touch-aware robotic agents.

�?Recent advances in promising CCUS technologies are assessed. �?Research status and trends in CCUS are visually analyzed. �?Carbon capture remains a hotspot of CCUS research. �?State-of-the-art capture technologies is summarized. �?Perspective research of carbon capture is proposed Carbon capture, utilization and storage (CCUS) technologies play an essential role in achieving Net Zero Emissions targets. Considering the lack of timely reviews on the recent advancements in promising CCUS technologies, it is crucial to provide a prompt review of the CCUS advances to understand the current research gaps pertained to its industrial application. To that end, this review first summarized the developmental history of CCUS technologies and the current large-scale demonstrations. Then, based on a visually bibliometric analysis, the carbon capture remains a hotspot in the CCUS development. Noting that the materials applied in the carbon capture process determines its performance. As a result, the state-of-the-art carbon capture materials and emerging capture technologies were comprehensively summarized and discussed. Gaps between state-of-art carbon capture process and its ideal counterpart are analyzed, and insights into the research needs such as material design, process optimization, environmental impact, and technical and economic assessments are provided.

Identifying the driving forces that cause changes in forest ecosystem services related to water conservation is essential for the design of interventions that could enhance positive impacts as well as minimizing negative impacts. In this study, we propose an assessment concept framework model for indirect-direct-ecosystem service （IN-DI-ESS） driving forces within this context and method for index construction that considers the selection of a robust and parsimonious variable set. Factor analysis was integrated into two-stage data envelopment analysis （TS-DEA） to determine the driving forces and their effects on water conservation services in forest ecosystems at the provincial scale in China. The results showed the following. 1） Ten indicators with factor scores more than 0.8 were selected as the minimum data set. Four indicators comprising population density, per capita gross domestic product, irrigation efficiency, and per capita food consumption were the indirect driving factors, and six indicators comprising precipitation, farmland into forestry or pasture, forest cover, habitat area, water footprint, and wood extraction were the direct driving forces. 2） Spearman＇s rank correlation test was performed to compare the overall effectiveness in two periods： stage 1 and stage 2. The calculated coefficients were 0.245, 0.136, and 0.579, respectively, whereas the tabulated value was 0.562. This indicates that the driving forces obviously differed in terms of their contribution to the overall effectiveness and they caused changes in water conservation services in different stages. In terms of the variations in different driving force effects in the years 2000 and 2010, the overall, stage 1, and stage 2 variances were 0.020, 0.065, and 0.079 in 2000, respectively, and 0.018, 0.063, and 0.071 in 2010. This also indicates that heterogeneous driving force effects were obvious in the process during the same period. Identifying the driving forces that affect service changes and evaluating their efficiency have significant policy implications for the management of forest ecosystem services. Advanced effectiveness measures for weak regions could be improved in an appropriate manner. In this study, we showed that factor analysis coupled with TS-DEA based on the IN-D1-ESS framework can increase the parsimony of driving force indicators, as well as interpreting the interactions among indirect and direct driving forces with forest ecosystem water conservation services, and reducing the uncertainty related to the internal consistency during data selection.

In response to ecosystem degradation from rapid economic development, China began investing heavily in protecting and restoring natural capital starting in 2000. We report on China's first national ecosystem assessment (2000–2010), designed to quantify and help manage change in ecosystem services, including food production, carbon sequestration, soil retention, sandstorm prevention, water retention, flood mitigation, and provision of habitat for biodiversity. Overall, ecosystem services improved from 2000 to 2010, apart from habitat provision. China's national conservation policies contributed significantly to the increases in those ecosystem services.