System Environment Research Articles

A 3D In-vitro model of the human dentine interface shows long-range osteoinduction from the dentine surface

Emerging regenerative cell therapies for alveolar bone loss have begun to explore the use of cell laden hydrogels for minimally invasive surgery to treat small and spatially complex maxilla-oral defects. However, the oral cavity presents a unique and challenging environment for in vivo bone tissue engineering, exhibiting both hard and soft periodontal tissue as well as acting as key biocenosis for many distinct microbial communities that interact with both the external environment and internal body systems, which will impact on cell fate and subsequent treatment efficacy. Herein, we design and bioprint a facile 3D in vitro model of a human dentine interface to probe the effect of the dentine surface on human mesenchymal stem cells (hMSCs) encapsulated in a microporous hydrogel bioink. We demonstrate that the dentine substrate induces osteogenic differentiation of encapsulated hMSCs, and that both dentine and β-tricalcium phosphate substrates stimulate extracellular matrix production and maturation at the gel-media interface, which is distal to the gel-substrate interface. Our findings demonstrate the potential for long-range effects on stem cells by mineralized surfaces during bone tissue engineering and provide a framework for the rapid development of 3D dentine-bone interface models.

Three-Body Binary Formation in Clusters: Analytical Theory

Abstract Binary formation in clusters through triple encounters between three unbound stars, ‘three-body’ binary formation, is one of the main dynamical formation processes of binary systems in dense environments. In this paper, we use an analytical probabilistic approach to study the process for the equal mass case and calculate a probability distribution for the orbital parameters of three-body-formed binaries, as well as their formation rate. For the first time, we give closed-form analytical expressions to the full orbital parameter distribution, accounting for both energy and angular momentum conservation. This calculation relies on the sensitive dependence of the outcomes of three-body scatterings on the initial conditions: here we compute the rate of three-body binaries from ergodic interactions, which allow for an analytical derivation of the distribution of orbital parameters of the binaries thus created. We find that soft binaries are highly favoured in this process and that these binaries have a super-thermal eccentricity distribution, while the few hard three-body binaries have an eccentricity distribution much closer to thermal. The analytical results predict and reproduce simulation results of three-body scattering experiments in the literature well.

SAMI: an M-Health application to telemonitor intelligibility and speech disorder severity in head and neck cancers

Perceptual measures, such as intelligibility and speech disorder severity, are widely used in the clinical assessment of speech disorders in patients treated for oral or oropharyngeal cancer. Despite their widespread usage, these measures are known to be subjective and hard to reproduce. Therefore, an M-Health assessment based on an automatic prediction has been seen as a more robust and reliable alternative. Despite recent progress, these automatic approaches still remain somewhat theoretical, and a need to implement them in real clinical practice rises. Hence, in the present work we introduce SAMI, a clinical mobile application used to predict speech intelligibility and disorder severity as well as to monitor patient progress on these measures over time. The first part of this work illustrates the design and development of the systems supported by SAMI. Here, we show how deep neural speaker embeddings are used to automatically regress speech disorder measurements (intelligibility and severity), as well as the training and validation of the system on a French corpus of head and neck cancer. Furthermore, we also test our model on a secondary corpus recorded in real clinical conditions. The second part details the results obtained from the deployment of our system in a real clinical environment, over the course of several weeks. In this section, the results obtained with SAMI are compared to an a posteriori perceptual evaluation, conducted by a set of experts on the new recorded data. The comparison suggests a high correlation and a low error between the perceptual and automatic evaluations, validating the clinical usage of the proposed application.

Hyper-Flophet: A neural Prophet-based model for traffic flow forecasting in transportation systems

Nowadays, an accurate and reliable traffic forecast is meaningful in making the right decisions for traffic management systems in vehicular environments. Nevertheless, traffic flow prediction is a significant challenge in Vehicular Ad Hoc Networks (VANETs) that has taken much attention. Therefore, in this paper, we propose a hybrid traffic prediction model based on Prophet model and Long Short-Term Memory neural network (LSTM), called Hyper-Flophet, to predict next traffic flow. Hyper-Flophet model adopts the traditional neural prophet model but with major parameter tuning. First, we propose an efficient algorithm for predicting the traffic flow trend then, we develop an interactive LSTM (I-LSTM) model for auto-regression components. After that, we implement a new future regressor component called network mobility and finally, we enhance the event and holiday component by introducing exponential growth term. Through simulations with real VANET data, we show that the proposed hybrid approach can achieve superior forecasting performance over other models.

Non-Markovian quantum gate set tomography

Engineering quantum devices requires reliable characterization of the quantum system, including qubits, quantum operations (also known as instruments) and the quantum noise. Recently, quantum gate set tomography (GST) has emerged as a powerful technique for self-consistently describing quantum states, gates, and measurements. However, non-Markovian correlations between the quantum system and environment impact the reliability of GST. To address this, we propose a self-consistent operational framework called instrument set tomography (IST) for non-Markovian GST. Based on the stochastic quantum process, the instrument set describes instruments and system-environment (SE) correlations. We introduce a linear inversion IST (LIST) to describe instruments and SE correlations without physical constraints. The disharmony of linear relationships between instruments is detected. Furthermore, we propose a physically constrained statistical method based on the maximum likelihood estimation for IST (MLE-IST) with adjustable dimensions. MLE-IST shows significant flexibility in adapting to different types of devices, such as noisy intermediate-scale quantum (NISQ) devices, by adjusting the model and constraints. Experimental results demonstrate the effectiveness and necessity of simultaneously describing instruments and SE correlations. Specifically, the LIST and MLE-IST obtains significant improvement on average square error reduction in the imperfect implemented simulations by orders of −23.77 and −6.21, respectively, compared to their comparative methods. Remarkably, real-chip experiments indicate that a polynomial number of parameters with respect to the Markovian order are sufficient to characterize non-Markovian quantum noise in current NISQ devices. Consequently, IST provides an essential and self-consistent framework for characterizing, benchmarking, and developing quantum devices in terms of the instrument set.

Study on the Coordinated Development Model of Ecological Economy and Environmental Protection

This paper establishes a comprehensive evaluation index system of the ecological environment and the economy, and the coupling degrees and coordination degrees from 2015 to 2019 are calculated. The results show that (1) There is a relationship between economic growth and environmental pollution. Discharging industrial pollutants is an important factor affecting the coordinated development of the ecological environment and economic coupling. (2) The comprehensive evaluation index of the ecological environment and economic development systems is increasing yearly, and the quality of the ecological environment and the level of economic development are constantly improving. (3) The value of the coupling degree and coupling coordination degree between ecological environment and economic development is increasing yearly, and the development trend is good. The coupling degree gradually shifts from low-level coupling to high-level coupling stage.

A Wireless Surveillance and Safety Monitoring System for Mine Workers Using ZigBee

n the mining industry, ZigBee technology is used in wireless surveillance and safety monitoring systems specifically designed for mine workers. By continuously monitoring the ambient conditions and vital indicators of miners in real-time, the technology seeks to improve their safety and security. In order to detect dangerous situations in the mine unit, the proposed system consists of a variety of sensors that are integrated together. ZigBee-enabled base stations are used for data collecting and transmission. By means of Zigbee, the system enables smooth communication between the base stations and sensors, guaranteeing prompt alerts and reactions to possible safety hazards. Through the mining industry, where worker safety is of utmost significance, this research advances wireless monitoring systems in dangerous environments. [1]. The bell in an emergency signals a quick reaction from the base station. This research represents a significant advancement in wireless sensor networking, improving mine worker safety. Our goal is to improve the lives of mine workers facing dangerous circumstances by providing a rapid means of rescue through wireless communication.

Research on Soundscape Design Strategies for Public Spaces in Dong Village, Dali

Soundscape reflects the ecological, cultural and social characteristics of regions and ethnic groups and is an important part of the public environment. Because in the past environmental design, the breadth and depth of the design that considers the human settlement environment system as a large framework should be included and involved. Therefore, this article analyzes the existing public space and landscape nodes of the public environment of Dali Dong Village, and uses the knowledge of environmental science, sociology, landscape science and other related disciplines to explore the Dong village public environment as the design practice object. It hopes to provide a useful reference for the research on sound landscape design of traditional ethnic minority settlements in my country.

Unsafe-Net: YOLO v4 and ConvLSTM based computer vision system for real-time detection of unsafe behaviours in workplace

AbstractUnsafe behaviour is a leading cause of death or injury in the workplace, including many accidents. Despite regular safety inspections in workplaces, many accidents occur as a result of breaches of occupational health and safety protocols. In these environments, despite efforts to prevent accidents and losses in hazardous environments, human error cannot be completely eliminated. In particular, in computer-based solutions, automated behaviour detection has low accuracy, is very costly, not real-time and requires a lot of time. In this study, we propose Unsafe-Net, a hybrid computer vision approach using deep learning models for real-time classification of unsafe behaviours in workplace. For the Unsafe-Net, a dataset is first specifically created by capturing 39 days of video footage from a factory. Using this dataset, YOLO v4 and ConvLSTM methods are combined for object detection and video understanding to achieve fast and accurate results. In the experimental studies, the classification accuracy of unsafe behaviours using the proposed Unsafe-Net method is 95.81% and the average time for action recognition from videos is 0.14 s. In addition, the Unsafe-Net has increased the real-time detection speed by reducing the average video duration to 1.87 s. In addition, the system is installed in a real-time working environment in the factory and employees are immediately alerted by the system, both audibly and visually, when unsafe behaviour occurs. As a result of the installation of the system in the factory environment, it has been determined that the recurrence rate of unsafe behaviour has been reduced by approximately 75%.

Deep reinforcement learning for artificial upwelling energy management

The potential of artificial upwelling in stimulating seaweed growth, consequently enhancing ocean carbon sequestration has been gaining increasing attention in recent years. This has led to the development of the first solar-powered and air-lifted artificial upwelling system (AUS) in China. However, effective scheduling of the air injection system and energy storage system in dynamic marine environments remains a crucial challenge in the operation of the AUS, as it holds the potential to significantly improve system performance. To tackle this challenge, we propose a novel energy management approach that utilizes the deep reinforcement learning (DRL) algorithm to determine the optimal operational parameters of the AUS at each time interval. Specifically, we formulate the energy optimization problem as a Markov decision process and integrate the quantile network in distributional reinforcement learning with the deep dueling network to solve the problem. Through extensive simulations, we evaluate the performance of our algorithm and demonstrate its superior effectiveness over traditional rule-based approaches and other DRL algorithms in enhancing energy utilization while ensuring the secure and reliable operation of the AUS. Our findings suggest that a DRL-based approach offers a promising way to provide valuable guides for the operation of the AUS and enhance the sustainability of seaweed cultivation and carbon sequestration in the ocean.

The Integration of Virtual Development into the Organizational Complexity of Healthcare

The development of the computer system in a hospital environment improves patients’ quality of life and allows healthcare management to evolve into the greatest virtual and functional techniques. Considering state-of-the-art devices as an aid in various highly complex surgical procedures, this gives society confidence in the services available, with continuous and rehabilitation treatments being the ones that have a greater number of technological devices helping in their favor. The contextualization aims to analyse that the inclusion of digitalization linked to virtual realities with different aspects and robotics helps to reduce hospitalization time. Methodologically, a bibliographic review was used, recommending the topic with articles that reference clinically successful research, and a Capes database was used in the search for the retrospective review. It is considered that the production describes the quality and reliability of virtual techniques and the scope of health improvement, which thus corroborates the benefits of organizational understanding in economic and care terms.

Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

A study on the spatial and temporal evolution patterns of water resource–ecology–social economy composite system resilience in the Huaihe River Basin

ABSTRACT Water resources are important supporting elements in the ecological environment and socio-economic system of sustainable development. The purpose of this study is to evaluate the comprehensive resilience level of the water resources–ecology–socioeconomic system against external disturbances in the Anhui and Shandong sections of the Huaihe River Basin. The comprehensive index system is constructed with the characteristic dimensions of resistance, resilience, and adaptability. Use the game theory combination weighting-VIKOR method to quantify the resilience level, and use the kernel density analysis and Markov chain to explore the spatial and temporal evolution characteristics and transition probability. The results show that (1) the resilience index of the composite system in the Huaihe River Basin shows an increasing trend, and the resilience level of the Anhui section is generally higher than that of the Shandong section. (2) The resilience level gradually evolved into the spatial distribution characteristics of high in the west and low in the east, high in the south and low in the north during the study period. (3) The resilience grade has strong robustness, and the probability of transferring to the higher grade is greater than that of transferring to the lower grade.

Abstract PO5-09-08: Cancer Health Disparities among non-Hispanic African women: A systematic review of the disparities, reasons and probable intervention

Abstract Health equity places health inequities in historical and social context and calls for action to address injustice and enhance health. As an ethical and human rights value that drives us to end health inequities, health equality is defined as everyone having a fair and just opportunity to be as healthy as feasible. According to this perspective, structural discrimination and marginalization cause health disparities that can be avoided. If these issues are not resolved, social and economic injustices, bias, and unfavorable outcomes that affect all of us will continue to exist. The idea of cancer health equality recognizes that there is still considerable work to be done to change the social structures and historical momentum that have contributed to different cancer outcomes, and that this work can only succeed if it is done. In USA, non-Hispanic black women experience a complicated and multifaceted disparity in breast cancer incidence and outcome. It's possible that social, economic, and behavioral variables contribute to discrepancies. Black women are less likely to breastfeed after childbirth, statistically more likely to have diabetes, heart disease, and obesity, all of which are risk factors for breast cancer. Compared to white women, they are more likely to lack adequate health insurance or access to medical facilities, which could have an impact on screening, follow-up treatment, and therapy completion. It has become evident via more study that biology also plays a part. Black women are more likely to be diagnosed at younger ages and at higher rates with more aggressive subtypes of breast cancer, such as triple-negative breast cancer (TNBC) and inflammatory breast cancer. The National Institute on Minority Health and Health Disparities (NIMHD) established a research framework to characterize the multiple domains of influence (biological, behavioral, physical environment, sociocultural environment, and health care system) that may act across various levels of influence to impact health. A literature search was conducted using the National Library of Medicine's PubMed search engine. Triple negative breast cancer (TNBC) is a common malignancy in African American women. The incidence of TNBC is higher in women of African ancestry and is associated with overall poor prognosis. This disparity is due to the presence/absence of oncogenes/tumor suppressor genes, mutations and altered signaling pathways that might predispose premenopausal African American women to TNBC. Multiple socioeconomic factors influence the access to standard care, novel treatments and inclusion in clinical trials and contribute to overall prognoses. This review focuses on the epidemiology, molecular alterations, and treatment-related disparities in African Americans with breast cancer. This poster will elaborate the racial disparity in terms of breast cancer among the African-American women in USA, the reasons and it's effects on health equity. Citation Format: Tasnuva Khan Efa. Cancer Health Disparities among non-Hispanic African women: A systematic review of the disparities, reasons and probable intervention [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-08.

Cyber‐physical‐based welding systems: Components and implementation strategies

AbstractThe conditions for a feasible Cyber‐Physical System (CPS) in a welding environment are explored for the manufacturing technology components while also focusing on machine learning tools. Increasing manufacturing efficiency means making digitalisation feasible for all technologies, including welding, given today's challenges. Early versions of manufacturing management, such as Computer Integrated Manufacturing, are already leading the way, and one of the latest milestones in these developments is CPS. It can be shown that the digital migration of specific sub‐domains (e.g. visual inspection of the weld seam during quality assurance) is significantly more challenging and unimaginable without artificial intelligence applications. However, it is also true that the full integration needed to achieve autonomous manufacturing has yet to be fully achieved, although there is a strong demand in the industry for these CPS to work. In some areas, the digital switchover has already been prepared. However, the interconnection of these subsystems requires modern information systems or, in the case of existing ones, their upgrading to the appropriate level. This research area is set to be addressed comprehensively by initiating several projects. In the initial phase, the aim is to develop an architecture that integrates the various Information Technology applications. In this work, the digital manufacturing environment under CPS is studied, the relevant components are explored, the conditions for the transition from traditional to CPS‐based manufacturing are examined and examples of planned further specific studies on the components are listed.

On information value and decision analyses

This paper addresses value of information, structural health information, and decision analyses in built environment engineering. Decision analyses are used for the joint optimization of information collection and the identification of physical measures for the management and enhancement of the expected utility of built environment systems. To these ends, this paper contains (1) principal descriptions of decision scenarios and the model basis in conjunction with the objective functions, embedded in (a) a summary of the historical background and (b) the origins of the methods of decision and value of information analyses, (2) an overview of methods for decision analyses highlighting solution strategies to handle comprehensiveness and computational challenges, (3) principal application areas and case studies, and (4) insights into recent advances. The main insights and an outreach to potential future research perspectives are summarised at the end of this paper.

Different smartphone tasks and traffic complexity affect pedestrian awareness of co-existing road objects and cerebral oxygenation during shared space walking

Distraction, primarily owing to the widespread use of cell phones and mobile devices while walking, is a contributing factor to pedestrian injuries. Pedestrians distracted by smartphones exhibit decreased situational awareness, influenced by the nature of concurrent tasks. However, little is known about the neural correlations between smartphone use and pedestrian responses to safety–critical events in a walking environment. This study recruited 30 young adults to investigate pedestrians’ ability to selectively respond to road objects while using smartphones (texting and reading) while walking in an immersive virtual environment system. The level of traffic complexity for the road conditions was manipulated by varying the number of safety–critical road objects coexisting in the environment at any given time and the number of required responses to the number of objects. During each 7-min task condition, cerebral oxygenation was measured using functional near-infrared spectroscopy. Furthermore, smartphone task performance, response times to road objects, and perceived workload were also measured. The participants exhibited reduced texting speed and reading performance when responding sequentially to coexisting road objects. Compared with other road conditions, those requiring two responses to four objects resulted in an increased workload and longer object response times. Participants perceived a higher workload in conditions involving tasks with smartphones than those without smartphones. More importantly, increased concentrations of oxygenated hemoglobin in the prefrontal cortex were observed when participants were concurrently performing smartphone and pedestrian tasks. Compared to smartphone reading, smartphone texting induced greater cerebral oxygenation in the bilateral frontopolar cortex. These results suggest that continuously texting while walking may demand additional cognitive resources for executive function. The findings of this study represent an initial step towards elucidating the cognitive mechanisms underlying attentional impairments experienced during smartphone distraction and working towards a viable solution to mitigate pedestrian risk.

Patterns of red blood cell utilization: Harnessing electronic health records data from the Information Standard for Blood and Transplant (ISBT) 128 system within the Biologics Effectiveness and Safety (BEST) initiative.

Current hemovigilance methods generally rely on survey data or administrative claims data utilizing billing and revenue codes, each of which has limitations. We used electronic health records (EHR) linked to blood bank data to comprehensively characterize red blood cell (RBC) utilization patterns and trends in three healthcare systems participating in the U.S. Food and Drug Administration Center for Biologics Evaluation and Research Biologics Effectiveness and Safety (BEST) initiative. We used Information Standard for Blood and Transplant (ISBT) 128 codes linked to EHR from three healthcare systems data sources to identify and quantify RBC-transfused individuals, RBC transfusion episodes, transfused RBC units, and processing methods per year during 2012-2018. There were 577,822 RBC units transfused among 112,705 patients comprising 345,373 transfusion episodes between 2012 and 2018. Utilization in terms of RBC units and patients increased slightly in one and decreased slightly in the other two healthcare facilities. About 90% of RBC-transfused patients had 1 (~46%) or 2-5 (~42%)transfusion episodes in 2018. Among the small proportion of patients with ≥12 transfusion episodes per year, approximately 60% of episodes included only one RBC unit. All facilities used leukocyte-reduced RBCs during the study period whereas irradiated RBC utilization patterns differed across facilities. ISBT 128 codes and EHRs were used to observe patterns of RBC transfusion and modification methods at the unit level and patient level in three healthcare systems participating in the BEST initiative. This study shows that the ISBT 128 coding system in an EHR environment provides a feasible source for hemovigilance activities.

Indoor Navigation System

Abstract: This research article explores the importance of indoor use in modern environments and focuses on their role in facilitating navigation in indoor spaces such as malls, malls, airports, and hospitals. These systems, which integrate various technologies such as smartphones, offer simple and easy solutions to users. Additionally, this article examines the commercial application of indoor navigation systems in retail environments in terms of their ability to direct customers to specific products and promotions. Indoor Navigation System Integrating Scalable Vector Graphics (SVG) and the Quick Response (QR) specification brings a new approach to indoor navigation-related problems. This technology uses SVG to create elaborate and detailed interior design, while QR codes play an important role in determining the exact location.

Developing hybrid systems to address oxygen uncoupling in multi-component Rieske oxygenases

Rieske non-heme iron oxygenases (ROs) are redox enzymes essential for microbial biodegradation and natural product synthesis. These enzymes utilize molecular oxygen for oxygenation reactions, making them very useful biocatalysts due to their broad reaction scope and high selectivities. The mechanism of oxygen activation in ROs involves electron transfers between redox centers of associated protein components, forming an electron transfer chain (ETC). Although the ETC is essential for electron replenishment, it carries the risk of reactive oxygen species (ROS) formation due to electron loss during oxygen activation. Our previous study linked ROS formation to O2 uncoupling in the flavin-dependent reductase of the three-component cumene dioxygenase (CDO). In the present study, we extend this finding by investigating the effects of ROS formation on the multi-component CDO system in a cell-free environment. In particular, we focus on the effects of hydrogen peroxide (H2O2) formation in the presence of a NADH cofactor regeneration system on the catalytic efficiency of CDO in vitro. Based on this, we propose the implementation of hybrid systems with alternative (non-native) redox partners for CDO, which are highly advantageous in terms of reduced H2O2 formation and increased product formation. The hybrid system consisting of the RO-reductase from phthalate dioxygenase (PDR) and CDO proved to be the most promising for the oxyfunctionalization of indene, showing a 4-fold increase in product formation (20 mM) over 24 h (TTN of 1515) at a 3-fold increase in production rate.