Automating the Generation of Construction Checklists

Construction inspection is a critical component of INDOT’s quality assurance (QA) program. Upon receiving an inspection notice/assignment, INDOT inspectors review the plans and specifications to identify the construction quality requirements and conduct their inspections accordingly. This manual approach to gathering inspection requirements from textual documents is time-consuming, subjective, and error-prone. This project addresses this critical issue by developing an inspection requirements database along with a set of tools to automatically gather the inspection requirements and provide field crews with customized construction checklists during the inspection with the specifics of what to check, when to check, and how to check, as well as the risks and the actions to take when noncompliance is encountered. This newly developed toolset eliminates the manual effort required to acquire construction requirements, which will enhance the efficiency of the construction inspection process at INDOT. It also enables the incorporation of field-collected data to automate future compliance checking and facilitate construction documentation

Risk-Based Construction Inspection

Construction inspection is a critical component in the quality assurance (QA) program to ensure the quality and long-term performance of pavements. Over the years, INDOT has been developing and modifying its standard specification to set requirements for construction inspection and material testing. With the retirement of experienced employees, INDOT is challenged with the lack of knowledge to effectively inspect the critical elements of construction results/deliverables such as pavement, soil embankment, and bridge (decks). There is a critical need for INDOT to allocate limited resources to the riskiest areas and equip construction inspectors with necessary knowledge to conduct inspection, ensure the quality of construction results, and minimize risks to INDOT. This study developed a risk-based inspection guide that has addressed the aforementioned problems of shortage in staffing and loss and lack of knowledge by providing answers in aspects of what, when, how, and how often to inspect. A comprehensive list of testing and inspection activities were extracted from INDOT’s material testing manual, INDOT’s standard specification, and the QA implementation at the Ohio River Bridge (ORB) project. This list was narrowed down to a core set of items based on survey responses and interviews with INDOT domain experts. Testing and inspection activities in the core set were aligned with the construction process. The risk associated with each inspection activity was assessed by considering both the probability of failure and consequence severity of failure in four dimensions: cost, time, quality, and safety. A composite risk index was developed as a single measure for the overall risk. All inspection activities were prioritized based on the composite index. For implementation, a linking mechanism was developed to link inspection activity, pay item, and check items (extracted from specification). This linking mechanism aligns with the business process of construction inspection at INDOT: starting with a pay item, field inspectors retrieve the associated check items and their inspection priority (based on risk), inspection frequency, and inspection criteria. A digital, ontology- and risk-based inspection system was proposed and its conceptual model was delivered to INDOT for its incorporation in the field application of construction documentation, a component of the e-Construction initiatives at INDOT. It will be tested on Project R-30397 through a pilot study

Emotional brain network decoded by biological spiking neural network

IntroductionEmotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown.MethodsTo find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions.ResultsThe analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the α frequency band could effectively represent negative emotions, while the α frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation.DiscussionThe introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery

Oligomeric Proanthocyanidins Confer Cold Tolerance in Rice through Maintaining Energy Homeostasis

Oligomeric proanthocyanidins (OPCs) are abundant polyphenols found in foods and botanicals that benefit human health, but our understanding of the functions of OPCs in rice plants is limited, particularly under cold stress. Two rice genotypes, named Zhongzao39 (ZZ39) and its recombinant inbred line RIL82, were subjected to cold stress. More damage was caused to RIL82 by cold stress than to ZZ39 plants. Transcriptome analysis suggested that OPCs were involved in regulating cold tolerance in the two genotypes. A greater increase in OPCs content was detected in ZZ39 than in RIL82 plants under cold stress compared to their respective controls. Exogenous OPCs alleviated cold damage of rice plants by increasing antioxidant capacity. ATPase activity was higher and poly (ADP-ribose) polymerase (PARP) activity was lower under cold stress in ZZ39 than in RIL82 plants. Importantly, improvements in cold tolerance were observed in plants treated with the OPCs and 3-aminobenzamide (PARP inhibitor, 3ab) combination compared to the seedling plants treated with H2O, OPCs, or 3ab alone. Therefore, OPCs increased ATPase activity and inhibited PARP activity to provide sufficient energy for rice seedling plants to develop antioxidant capacity against cold stress