CofiFab: Coarse-to-fine fabrication of large 3D objects

This paper presents CofiFab, a coarse-to-fine 3D fabrication solution, which combines 3D printing and 2D laser cutting for cost-effective fabrication of large objects at lower cost and higher speed. Our key approach is to first build coarse internal base structures within the given 3D object using laser-cutting, and then attach thin 3D-printed parts, as an external shell, onto the base to recover the fine surface details. CofiFab achieves this with three novel algorithmic components. First, we formulate an optimization model to compute fabricatable polyhedrons of maximized volume, as the geometry of the internal base. Second, we devise a new interlocking scheme to tightly connect laser-cut parts into a strong internal base, by iteratively building a network of nonorthogonal interlocking joints and locking parts around polyhedral corners. Lastly, we also optimize the partitioning of the external object shell into 3D-printable parts, while saving support material and avoiding overhangs. These components also consider aesthetics, stability and balancing in addition to cost saving. As a result, CofiFab can efficiently produce large objects by assembly. To evaluate its effectiveness, we fabricate objects of varying shapes and sizes, where CofiFab significantly improves compared to previous methods

Chinese EFL Students’ Learning Needs for Speaking Performance: A Case Study

Despite the importance of speaking skills, most Chinese graduates leave universities unable to communicate effectively in English. Therefore, this study aimed to investigate the differences between EFL students' perceived and actual speaking performance and to understand their learning needs better. The study involved 45 EFL learners and utilized a mixed-methods research design. Data were collected via a questionnaire, a speaking test, and semi-structured interviews. The findings revealed that EFL students overestimated their speaking performance, implying they were unaware of their limitations. Hence EFL instructors need to provide better guidance to enhance their speaking skills

Joint Inference on Truth/Rumor and Their Sources in Social Networks

In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and identify their \emph{sources}, i.e., the users who initially spread those claims. While most prior arts have been dedicated to the two tasks respectively, this paper aims to offer the joint inference on truth/rumor and their sources. Our insight is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., \emph{credibility-reliability training} for truth/rumor inference and \emph{division-querying} for source detection, in an iterative manner. To elaborate, the former module performs a simultaneous estimation of claim credibility and user reliability by virtue of an Expectation Maximization algorithm, which takes the source reliability outputted from the latter module as the initial input. Meanwhile, the latter module divides the network into two different subnetworks labeled via the claim credibility, and in each subnetwork launches source detection by applying querying of theoretical budget guarantee to the users selected via the estimated reliability from the former module. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35\% accuracy of credibility gain and 29\% source detection rate gain compared with the separate counterparts

Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models

The demand for psychological counseling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counseling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based system leveraging Large Language Models (LLMs) for question-answering in online psychological consultation. Our framework combines pre-trained LLMs with real-world professional Q&A from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, with human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article concludes by discussing the potential of large language models to enhance mental health support through AI technologies in online psychological consultation

Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance

Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries. If so, how should we address the cognitive acceptance and ambient control of elderly assistive robots through design? In this paper, we proposed an explorative design of an ambient SuperLimb (Supernumerary Robotic Limb) system that involves a pneumatically-driven robotic cane for at-home motion assistance, an inflatable vest for compliant human-robot interaction, and a depth sensor for ambient intention detection. The proposed system aims at providing active assistance during the sit-to-stand transition for at-home usage by the elderly at the bedside, in the chair, and on the toilet. We proposed a modified biomechanical model with a linear cane robot for closed-loop control implementation. We validated the design feasibility of the proposed ambient SuperLimb system including the biomechanical model, our result showed the advantages in reducing lower limb efforts and elderly fall risks, yet the detection accuracy using depth sensing and adjustments on the model still require further research in the future. Nevertheless, we summarized empirical guidelines to support the ambient design of elderly-assistive SuperLimb systems for lower limb functional augmentation.Comment: 8 pages, 9 figures, accepted for IEEE RoboSoft 202

Microbial Community Responses to Vanadium Distributions in Mining Geological Environments and Bioremediation Assessment

Vanadium mining activities can cause contamination of the surrounding geological environment. Vanadium may exist in multiple matrices due to its migration and transformation, forming interactive relationships; however, the connection between vanadium distributions in multiple matrices and microbial community responses remains largely unknown. Vanadium is a redox-sensitive metal that can be microbiologically reduced and immobilized. To date, bioremediation of vanadium-contaminated environments by indigenous microorganisms has rarely been evaluated. This paper reports a systematic investigation into vanadium distributions and microbial communities in soils, water, and sediment from Panzhihua, China. Large vanadium contents of 1130.1 ± 9.8 mg/kg and 0.13 ± 0.02 mg/L were found in surface soil and groundwater. Vanadium in surface water tended to precipitate. Microbial communities isolated from similar environments were alike due to similarity in matrix chemistry whereas communities were distinct when compared to different matrices, with lower richness and diversity in groundwater. Proteobacteria was distributed widely and dominated microbial communities within groundwater. Redundancy analysis shows that vanadium and nutrients significantly affected metal-tolerant bacteria. Long-term cultivation (240 days) suggests the possibility of vanadium bioremediation by indigenous microorganisms, within acid-soluble fraction. This active fraction can potentially release mobile vanadium with shifted redox conditions. Vanadium (V) was bio-reduced to less toxic, mobile vanadium (IV) primarily by enriched Bacillus and Thauera. This study reveals the biogeochemical fate of vanadium in regional geological environments and suggests a bioremediation pathway via native vanadium-reducing microbes

Tunable Charge Transport and Spin Dynamics in Two-Dimensional Conjugated Metal-Organic Frameworks

Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) have attracted increasing interest in electronics due to their (semi)conducting properties. Charge-neutral 2D c-MOFs also possess persistent organic radicals that can be viewed as spin-concentrated arrays, affording new opportunities for spintronics. However, the strong π-interaction between neighboring layers of layer-stacked 2D c-MOFs annihilates active spin centers and significantly accelerates spin relaxation, severely limiting their potential as spin qubits. Herein, we report the precise tuning of the charge transport and spin dynamics in 2D c-MOFs via the control of interlayer stacking. The introduction of bulky side groups on the conjugated ligands enables a significant dislocation of the 2D c-MOFs layers from serrated stacking to staggered stacking, thereby spatially weakening the interlayer interactions. As a consequence, the electrical conductivity of 2D c-MOFs decreases by 6 orders of magnitude, while the spin density achieves more than a 30-fold increase and the spin-lattice relaxation time (T1) is increased up to ∼60 μs, hence being superior to the reference 2D c-MOFs with compact stackings whose spin relaxation is too fast to be detected. Spin dynamics results also reveal that spinless polaron pairs or bipolarons play critical roles in the charge transport of these 2D c-MOFs. Our strategy provides a bottom-up approach for enlarging spin dynamics in 2D c-MOFs, opening up pathways for developing MOF-based spintronics