Knowledge enhanced bottom-up affordance grounding for robotic interaction.

With the rapid advancement of robotics technology, an increasing number of researchers are exploring the use of natural language as a communication channel between humans and robots. In scenarios where language conditioned manipulation grounding, prevailing methods rely heavily on supervised multimodal deep learning. In this paradigm, robots assimilate knowledge from both language instructions and visual input. However, these approaches lack external knowledge for comprehending natural language instructions and are hindered by the substantial demand for a large amount of paired data, where vision and language are usually linked through manual annotation for the creation of realistic datasets. To address the above problems, we propose the knowledge enhanced bottom-up affordance grounding network (KBAG-Net), which enhances natural language understanding through external knowledge, improving accuracy in object grasping affordance segmentation. In addition, we introduce a semi-automatic data generation method aimed at facilitating the quick establishment of the language following manipulation grounding dataset. The experimental results on two standard dataset demonstrate that our method outperforms existing methods with the external knowledge. Specifically, our method outperforms the two-stage method by 12.98% and 1.22% of mIoU on the two dataset, respectively. For broader community engagement, we will make the semi-automatic data construction method publicly available at https://github.com/wmqu/Automated-Dataset-Construction4LGM.

Neurotrauma from fall accidents in Ethiopia.

Background: Ethiopia is a fast-growing economy with rapid urbanization and poor occupational safety measures. Fall injuries are common and frequently result in traumatic brain injury (TBI) or spinal cord injury (SCI). Methods: We prospectively included fall victims who were hospital-treated for neurotrauma or forensically examined in 2017 in Addis Ababa, Ethiopia. We registered sociodemographic factors, fall types, injuries, treatment, and outcome. Results: We included 117 treated and 51 deceased patients (median age 27 vs. 40 years). Most patients were injured at construction sites (39.9%) and only one in three used protective equipment. TBI (64.7%) and SCI (27.5%) were the most common causes of death among the deceased patients, of which most died at the accident site (90.2%). Many patients suffered significant prehospital time delays (median 24 h). Among treated patients, SCI was more frequent than TBI (50.4% vs. 39.3%), and 10.3% of the patients had both SCI and TBI. Most SCIs were complete (49.3%), whereas most TBIs were mild (55.2%). Less than half of TBI patients and less than one in five SCI patients were operated. There were twice as many deaths among TBI patients as SCI patients. Among those discharged alive, at a median of 33 weeks, 50% of TBI patients had a good recovery whereas 35.5% of SCI patients had complete injuries. Conclusion: Falls at construction sites with inadequate safety measures were common causes of SCI and TBI resulting in severe disability and death. These results support further development of prevention strategies and neurotrauma care in Ethiopia.

Using Multimodal Data to Improve Precision of Inpatient Event Timelines.

Textual data often describe events in time but frequently contain little information about their specific timing, whereas complementary structured data streams may have precise timestamps but may omit important contextual information. We investigate the problem in healthcare, where we produce clinician annotations of discharge summaries, with access to either unimodal (text) or multimodal (text and tabular) data, (i) to determine event interval timings and (ii) to train multimodal language models to locate those events in time. We find our annotation procedures, dashboard tools, and annotations result in high-quality timestamps. Specifically, the multimodal approach produces more precise timestamping, with uncertainties of the lower bound, upper bounds, and duration reduced by 42% (95% CI 34-51%), 36% (95% CI 28-44%), and 13% (95% CI 10-17%), respectively. In the classification version of our task, we find that, trained on our annotations, our multimodal BERT model outperforms unimodal BERT model and Llama-2 encoder-decoder models with improvements in F1 scores for upper (10% and 61%, respectively) and lower bounds (8% and 56%, respectively). The code for the annotation tool and the BERT model is available (link).

Exploring wetland change in the Gauteng Province, South Africa.

This study investigated land use and land cover changes in the Gauteng Province of South Africa with emphasis on wetland ecosystems. Using Landsat images and various image manipulation software such as ArcGIS and ENVI, this study conducted a predictive analysis of the potential state of wetland ecosystems in the Gauteng Province of South Africa by year 2040, using illegal dumping trends in the last 20 years. Based on this methodological framework, it is found that continued illegal dumping trends would lead to an almost total disappearance of wetlands in the study area by 2040. It is argued that despite the pro-environmental initiatives introduced in the early 2000s to conserve wetlands in South Africa, wetland deterioration has increased rapidly due to the associated methodological flaws, which further supports the findings of this study. Ultimately, it is recommended that a collaborative approach to wetland conservation, and a robust methodological shift are required to mitigate the threats faced by wetlands in the Gauteng Province of South Africa, and the model can be deployed across developing countries.

Design, construction and application of algae-bacteria synergistic system for treating wastewater.

The wastewater treatment technology of algae-bacteria synergistic system (ABSS) is a promising technology which has the advantages of low energy consumption, good treatment effect and recyclable high-value products. In this treatment technology, the construction of an ABSS is a very important factor. At the same time, the emergence of some new technologies (such as microbial fuel cells and bio-carriers, etc.) has further enriched constructing the novel ABSS, which could improve the efficiency of wastewater treatment and the biomass harvesting rate. Thus, this review focuses on the construction of a novel ABSS in wastewater treatment in order to provide useful suggestions for the technology of wastewater treatment.

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

Validating the AI-assisted second language (L2) learning attitude scale for Chinese college students and its correlation with L2 proficiency.

The positive impact of Artificial Intelligence (AI) on second language (L2) learning is well-documented. An individual's attitude toward AI significantly influences its adoption. Despite this, no specific scale has been designed to measure this attitude, particularly in the Chinese context. To address this gap, our study aims to construct the AI-Assisted L2 Learning Attitude Scale for Chinese College Students (AL2AS-CCS) and evaluate its reliability, validity, and relationship with L2 proficiency. Our research comprises two phases, each involving separate samples. In Phase One (Sample 1: n = 379), we conducted exploratory factor analysis (EFA) to determine the factor structure of the AL2AS-CCS. The resulting two-factor structure consists of 12 items, categorized into cognitive and behavioral components. In Phase Two (Sample 2: n = 429), we performed confirmatory factor analysis (CFA) to validate the factor structure and assess model fit. CFA in Sample 2 confirmed the factor structure and demonstrated a good model fit. Additionally, the AL2AS-CCS exhibited high criterion validity, internal consistency, and cross-gender invariance. Our findings suggest that the AL2AS-CCS is a valid measurement tool for assessing Chinese college students' attitude toward AI-assisted L2 learning. Moreover, Chinese college students were discovered to maintain a moderately positive attitude toward AI-assisted L2 learning. Additionally, a positive correlation was identified between this attitude and their L2 proficiency.

The interplay of facilitation and competition drives the emergence of multistability in dryland plant communities.

Within communities, species are wrapped in a set of feedbacks with each other and with their environment. When such feedbacks are strong enough they can generate alternative stable states. So far, research on alternative stable states has mostly focused on systems with a small number of species and a limited diversity of interaction types. Here, we analyze a spatial model of plant community dynamics in stressed ecosystems such as drylands, where each species is characterized by a strategy, and the different species interact through facilitation and competition for space and resources, such as water. We identify three different types of multistability emerging from the interplay of competition and facilitation. Under low-stress levels, plant communities organize in small groups of coexisting species, maintained by space, competition and facilitation ("cliques"). Under higher stress levels, positive feedback from facilitation lead to the dominance of a single facilitating species ("mutual exclusion states"). At the highest stress levels, the single facilitating species left in the system coexists with the desert state. By linking community ecology and alternative stable states theory using a spatial plant community model for stressed ecosystems, our study contributes to highlight the importance of positive feedback loops for the stability of ecological communities.

Burden of carpal tunnel syndrome and its associated factors among construction industry workers in Gondar town, Ethiopia.

Background: Carpal tunnel syndrome is characterized by symptoms such as pain, numbness, or tingling on the anterior surface of the index, middle, or radial half of the ring finger, which is frequently associated with weakness of hand grip, and nocturnal pain and/or numbness resulting from compression of the median nerve at the carpal tunnel between the carpal bones and the transverse ligament. The construction industry involves many activities aside from the building process, such as landscaping, painting, electrical supply, plastering, paving, and telecommunications. Performing such tasks involves repetition of wrist flexion and extension, forceful grip with the hand, and/or vibrations of the hand and arm. This study aimed to assess the prevalence of carpal tunnel syndrome and its associated risk factors among construction workers. Method: An institutional-based cross-sectional study design was conducted among six construction sectors in Gondar from April to July 2021. An interviewer-administered questionnaire was prepared from the literature with a Katz hand diagram, and a physical examination and a special test (carpal compression test, Phalen's, and Tinel's test) were carried out for those participants who reported pain during the interview. Binary logistic regression was conducted with SPSS 25 to identify the associated risk factors for carpal tunnel syndrome. The strength of the association was detected by the adjusted odds ratio. Result: A total of 333 study participants aged 18-70 years were included in this study. Among the participants, 11.7% (AOR: 95%CI: 8.1-15.3) had carpal tunnel syndrome. Age, cigarette smoking, work experience, and working with finger-pressing tools were risk factors significantly associated with carpal tunnel syndrome among construction workers with a p-value of <0.05. Conclusion: The magnitude of carpal tunnel syndrome was 11.7% among construction workers. Being of older age, having more experience, cigarette smoking, and working with finger-pressing tools were risk factors significantly associated with carpal tunnel syndrome among construction workers. Employers should implement work safety education programs that raise awareness about the risks of cigarette smoking and encourage employers and supervisors to seek early medical intervention and treatment for carpal tunnel syndrome before it becomes a chronic problem.

The impact of safety attitude on hazard identification and risk analysis: evidence from event-related potentials.

A questionnaire survey and an event-related potential (ERP) experiment were used to reveal the impact of safety attitudes on risk perception. The results revealed that during hazard identification, the N130 amplitude of subjects with negative safety attitude was significantly higher, which implied that subjects with negative safety attitude were more likely to feel confused. During risk analysis, subjects with positive safety attitude were more inclined to overestimate the probability and damage degree of risks; subjects with positive safety attitudes displayed higher P150 and late positive potential amplitudes, which indicated that subjects with positive safety attitudes devoted more attention to risks in the early stage of risk analysis and had a more intense affective response in the later period. The risk judgment ability of subjects with positive safety attitude was affected by time pressure, and they exhibited higher risk judgment accuracy only under no time pressure.

Innovative BIM technology application in the construction management of highway.

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Establishment of a promising vitiligo mouse model for pathogenesis and treatment studies.

AIMS: Vitiligo is a chronic dermatological condition characterized by the progressive loss of melanocytes, for which traditional therapy has shown limited efficacy. This study aimed to establish a vitiligo model with easy operability, high repeatability, and stable depigmentation to provide a foundation for studying the pathogenesis and developing novel therapies for vitiligo. METHODS: (1) Establishing vitiligo model: Firstly, deliver B16F10 cells to the back skin of C57BL/6 J via intradermal injection (day 0), and the CD4 depletion antibody was injected intraperitoneally on day 4 and 10. Secondly, the melanoma was surgically removed on day 12. Thirdly, CD8 antibody was administered intraperitoneally every fourth day till day 30. (2) Identification of vitiligo model: H&E staining, immunohistochemistry, and immunofluorescence were used to detect the melanocytes. The melanin was detected by transmission electron microscopy (TEM), Lillie ferrous sulfate staining and L-DOPA staining. RESULTS: (1) The back skin and hair began to appear white on day 30. Melanin loss reached peak on day 60; (2) Hematoxylin and eosin (H&E) staining, immunohistochemistry and immunofluorescence results showed melanocytes were reduced. L-DOPA staining, Lillie ferrous sulfate staining and TEM results showed that melanin decreased in the epidermis. CONCLUSION: We successfully establishment a vitiligo mouse model which can be more capable to simulate the pathogenesis of human vitiligo and provide an important basis for the study of pathogenesis and therapy of vitiligo.

From self-commitment to career crafting: The mediating role of career adaptability and the moderating role of job autonomy.

The objective of this study is to explore the influence of self-commitment on career crafting, examining the mediating role of career adaptability and the moderating effect of job autonomy within the context of career construction theory. This research aims to deepens our understanding of the key mechanisms that underpin successful career development, providing valuable insights for both individuals and organizations to enhance career success and adaptability. Utilizing a two-wave survey methodology, we collected data from 363 full-time employees across various industries in the United States. Hierarchical regression analysis and the PROCESS Macro were employed to test the proposed hypotheses. Our findings reveal that self-commitment significantly enhances career crafting through career adaptability. Additionally, job autonomy was found to influence both the direct relationship between self-commitment and career adaptability, and the indirect relationship between self-commitment and career crafting. This study highlights the pathways linking self-commitment to career crafting, underscoring the importance of career adaptability as a developmental tool facilitated by job autonomy. We recommend that individuals commit to their personal growth to enhance their career adaptability and actively shape their careers. Meanwhile, organizations should focus on promoting job autonomy to foster career development, benefiting both employees and the broader organizational ecosystem.

Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods.

Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.

Harnessing the potential of microalgae for the production of monoclonal antibodies and other recombinant proteins.

Monoclonal antibodies (mAbs) have become indispensable tools in various fields, from research to therapeutics, diagnostics, and industries. However, their production, primarily in mammalian cell culture systems, is cost-intensive and resource-demanding. Microalgae, diverse photosynthetic microorganisms, are gaining attention as a favorable option for manufacturing mAbs and various other recombinant proteins. This review explores the potential of microalgae as a robust expression system for biomanufacturing high-value proteins. It also highlights the diversity of microalgae species suitable for recombinant protein. Nuclear and chloroplast genomes of some microalgae have been engineered to express mAbs and other valuable proteins. Codon optimization, vector construction, and other genetic engineering techniques have significantly improved recombinant protein expression in microalgae. These accomplishments demonstrate the potential of microalgae for biopharmaceutical manufacturing. Microalgal biotechnology holds promise for revolutionizing the production of mAbs and other therapeutic proteins, offering a sustainable and cost-effective solution to address critical healthcare needs.

Linking the assessment of ecological engineering construction with zoning management in the typical agro-pastoral area of China: A perspective from quantity, quality and function.

Combatting land damage has become a global priority, and China has adopted a series of ecological engineering measures, especially in the agro-pastoral area with fragile ecological environment. The effectiveness of ecological engineering construction (EEC), from a comprehensive recognition encompassing its quality, quantity, and function, has remained largely unknown. To this end, Zhangbei County, a typical agro-pastoral ecotone of northern China, was chosen as our focal area. After summarizing the timelines, aims and results of the EEC during various periods in Zhangbei, the linear spectral mixture analysis was employed to process Landsat 5 TM images in 2000 and 2010, as well as Landsat 8 OLI images in 2020. Then, a comprehensive evaluation framework of EEC was established from the perspective of "quantity-quality-function", and the ecological effectiveness of EEC was evaluated from 2000 to 2020 in Zhangbei. Results revealed that EEC played a critical role in enhancing quantity, quality and function, in spite of that, there were still numerous regions showing varying degrees of degradation in terms of these aspects. Then, by extending the three-dimensional cube as the theoretical basis for the zoning management of EEC, we merged four zones according to the space matching relationship among quantity, quality and function of EEC, namely, Ecological conservation area, Ecological improvement area, Ecological restoration area and Ecological remodeling zone. More targeted ecological measures were required for specific matching relationship among quantity, quality and function of EEC. This study is expected to present an empirical case for assessing the ecological effectiveness of EEC in areas or countries with similar restoration demand and support regional management.

Assessing life cycle sustainability: A comprehensive review of concrete produced from construction waste fine fractions.

This paper presents an overview of the scholarly works employing the life cycle assessment (LCA) approach to evaluate the environmental impact of construction and demolition waste (CDW) fine fractions derived from concrete elements throughout their life cycle. Unlike conventional studies, this work addresses the challenge of reducing the carbon footprint associated with CDW-based building materials, emphasizing environmental impact mitigation. The study highlights that approximately 30% of CDW is landfilled, 50% is recycled, and 20% is used as fill material, underscoring the potential for increasing recycling rates through improved processing techniques and management practices. In the reviewed studies, most research has been conducted in Europe, Asia, the USA, and China. The primary and secondary data sources for the life cycle inventory (LCI) vary depending on the study region and locality. By exploring innovative practices and critical stages in CDW fine fractions utilization for concrete components, the study aims to contribute to greener construction practices and sustainable resource management. The distinctive aspect of this research lies in its comprehensive review of CDW-based aggregates, binders, and alternative cementitious materials, highlighting the significance of sustainable energy resources and transportation strategies in enhancing the sustainability of CDW-derived concrete. Key findings highlight the necessity of sustainable energy for pretreatment and optimized transportation strategies, including route planning and vehicle selection, to produce greener CDW fine fraction-based building materials. Additionally, the study suggests key steps and parameters required for defining the system boundary and preparing the inventory for conducting an LCA of building materials based on CDW fine fractions. Through a detailed analysis of environmental burdens at each production stage, this study seeks to promote the adoption of greener concrete solutions worldwide. The use of CDW in concrete production promotes environmental sustainability and greener concrete regardless of the region.

Learning from disability studies to introduce the role of the individual to naturalistic accounts of disease.

Disability studies have been successfully focusing on individuals' lived experiences, the personalization of goals, and the constitution of the individual in defining disease and restructuring public understandings of disability. Although they had a strong influence in the policy making and medical modeling of disease, their framework has not been translated to traditional naturalistic accounts of disease. I will argue that, using new developments in evolutionary biology (Extended Evolutionary Synthesis [EES] about questions of proper function) and behavioral ecology (Niche conformance and construction about the questions of reference classes in biostatistics accounts), the main elements of the framework of disability studies can be used to represent life histories at the conceptual level of the two main "non-normative" accounts of disease. I chose these accounts since they are related to medicine in a more descriptive way. The success of the practical aspects of disability studies this way will be communicated without causing injustice to the individual since they will represent the individuality of the patient in two main naturalistic accounts of disease: the biostatistical account and the evolutionary functional account. Although most accounts criticizing the concept of disease as value-laden do not supply a positive element, disability studies can supply a good point for descriptive extension of the concept through inclusion of epistemic agency.

Construction of PARI public health education programs for Chinese undergraduates: a Delphi study.

Objectives: The objective of this study is to develop a consensus among experts on a comprehensive and scientifically sound physical activity-related injuries (PARI) public health education program specifically tailored for undergraduates. Methods: This study designed three rounds of expert consultation by using a Delphi method. A panel of 30 experts from the fields of public health education, sports medicine, anesthesia pain, emergency medicine, and emergency nursing participated in the study. Results: This study successfully established a consensus among experts on the goals, content, teaching methods, and time allocation for the PARI Public Health Education Program for undergraduates. The program encompasses 10 objectives divided into 2 main categories: professional knowledge and skill goals. In terms of content, it includes 5 primary indicators, 22 secondary indicators, and 56 detailed tertiary indicators. Six teaching methods were identified as suitable. Additionally, a typical 60-min educational session was segmented into eight parts, with a proposed time arrangement for each, ensuring comprehensive coverage of all topics. Conclusion: The consensus achieved in this study on the PARI Public Health Education Program for undergraduates lays a crucial foundation for the advancement of health literacy and proactive health management within this demographic. We presented a comprehensive framework for PARI public health education, integrating diverse learning methods and content areas. This systematic approach not only enriched the resources available for undergraduate health education, especially of PARI but also had the potential to significantly impact the implementation and effectiveness of health promotion strategies.