Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing.

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.

Health system description and assessment: a scoping review of templates for systematic analyses.

BACKGROUND: Understanding and comparing health systems is key for cross-country learning and health system strengthening. Templates help to develop standardised and coherent descriptions and assessments of health systems, which then allow meaningful analyses and comparisons. Our scoping review aims to provide an overview of existing templates, their content and the way data is presented. MAIN BODY: Based on the WHO building blocks framework, we defined templates as having (1) an overall framework, (2) a list of indicators or topics, and (3) instructions for authors, while covering (4) the design of the health system, (5) an assessment of health system performance, and (6) should cover the entire health system. We conducted a scoping review of grey literature published between 2000 and 2023 to identify templates. The content of the identified templates was screened, analyzed and compared. We found 12 documents that met our inclusion criteria. The building block `health financing´ is covered in all 12 templates; and many templates cover ´service delivery´ and ´health workforce'. Health system performance is frequently assessed with regard to 'access and coverage', 'quality and safety', and 'financial protection'. Most templates do not cover 'responsiveness' and 'efficiency'. Seven templates combine quantitative and qualitative data, three are mostly quantitative, and two are primarily qualitative. Templates cover data and information that is mostly relevant for specific groups of countries, e.g. a particular geographical region, or for high or for low and middle-income countries (LMICs). Templates for LMICs rely more on survey-based indicators than administrative data. CONCLUSIONS: This is the first scoping review of templates for standardized descriptions of health systems and assessments of their performance. The implications are that (1) templates can help analyze health systems across countries while accounting for context; (2) template-guided analyses of health systems could underpin national health policies, strategies, and plans; (3) organizations developing templates could learn from approaches of other templates; and (4) more research is needed on how to improve templates to better achieve their goals. Our findings provide an overview and help identify the most important aspects and topics to look at when comparing and analyzing health systems, and how data are commonly presented. The templates were created by organizations with different agendas and target audiences, and with different end products in mind. Comprehensive health systems analyses and comparisons require production of quantitative indicators and complementing them with qualitative information to build a holistic picture. CLINICAL TRIAL REGISTRATION:   Not applicable.

Design of Path-Planning System for Interventional Thermal Ablation of Liver Tumors Based on CT Images.

OBJECTIVE: Aiming at the shortcomings of artificial surgical path planning for the thermal ablation of liver tumors, such as the time-consuming and labor-consuming process, and relying heavily on doctors' puncture experience, an automatic path-planning system for thermal ablation of liver tumors based on CT images is designed and implemented. METHODS: The system mainly includes three modules: image segmentation and three-dimensional reconstruction, automatic surgical path planning, and image information management. Through organ segmentation and three- dimensional reconstruction based on CT images, the personalized abdominal spatial anatomical structure of patients is obtained, which is convenient for surgical path planning. The weighted summation method based on clinical constraints and the concept of Pareto optimality are used to solve the multi-objective optimization problem, screen the optimal needle entry path, and realize the automatic planning of the thermal ablation path. The image information database was established to store the information related to the surgical path. RESULTS: In the discussion with clinicians, more than 78% of the paths generated by the planning system were considered to be effective, and the efficiency of system path planning is higher than doctors' planning efficiency. CONCLUSION: After improvement, the system can be used for the planning of the thermal ablation path of a liver tumor and has certain clinical application value.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Innovating Household Food Waste Management: A User-Centric Approach with AHP-TRIZ Integration.

Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users' actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users' genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments.

A generic approach to developing human factors-quality assessment tools exemplified by the warehouse error prevention tool.

This study proposes a generic approach for creating human factors-based assessment tools to enhance operational system quality by reducing errors. The approach was driven by experiences and lessons learned in creating the warehouse error prevention (WEP) tool and other system engineering tools. The generic approach consists of 1) identifying tool objectives, 2) identifying system failure modes, 3) specifying design-related quality risk factors for each failure mode, 4) designing the tool, 5) conducting user evaluations, and 6) validating the tool. The WEP tool exemplifies this approach and identifies human factors related to design flaws associated with quality risk factors in warehouse operations. The WEP tool can be used at the initial stage of design or later for process improvement and training. While this process can be adapted for various contexts, further study is necessary to support the teams in creating tools to identify design-related human factors contributing to quality issues.

This paper describes a generic approach to creating human factors­based quality assessment tools. The approach is illustrated with the Warehouse Error Prevention (WEP) tool, which is designed to help users identify HF-related quality risk factors in warehouse system designs (available for free: Setayesh et al. 2022b).

Biochar imparted constructed wetlands (CWs) for enhanced biodegradation of organic and inorganic pollutants along with its limitation.

The remediation of polluted soil and water stands as a paramount task in safeguarding environmental sustainability and ensuring a dependable water source. Biochar, celebrated for its capacity to enhance soil quality, stimulate plant growth, and adsorb a wide spectrum of contaminants, including organic and inorganic pollutants, within constructed wetlands, emerges as a promising solution. This review article is dedicated to examining the effects of biochar amendments on the efficiency of wastewater purification within constructed wetlands. This comprehensive review entails an extensive investigation of biochar's feedstock selection, production processes, characterization methods, and its application within constructed wetlands. It also encompasses an exploration of the design criteria necessary for the integration of biochar into constructed wetland systems. Moreover, a comprehensive analysis of recent research findings pertains to the role of biochar-based wetlands in the removal of both organic and inorganic pollutants. The principal objectives of this review are to provide novel and thorough perspectives on the conceptualization and implementation of biochar-based constructed wetlands for the treatment of organic and inorganic pollutants. Additionally, it seeks to identify potential directions for future research and application while addressing prevailing gaps in knowledge and limitations. Furthermore, the study delves into the potential limitations and risks associated with employing biochar in environmental remediation. Nevertheless, it is crucial to highlight that there is a significant paucity of data regarding the influence of biochar on the efficiency of wastewater treatment in constructed wetlands, with particular regard to its impact on the removal of both organic and inorganic pollutants.

Portable and integrated microfluidic flow control system using off-the-shelf components towards organs-on-chip applications.

Organ-on-a-chip (OoC) devices require the precise control of various media. This is mostly done using several fluid control components, which are much larger than the typical OoC device and connected through fluidic tubing, i.e., the fluidic system is not integrated, which inhibits the system's portability. Here, we explore the limits of fluidic system integration using off-the-shelf fluidic control components. A flow control configuration is proposed that uses a vacuum to generate a fluctuation-free flow and minimizes the number of components used in the system. 3D printing is used to fabricate a custom-designed platform box for mounting the chosen smallest footprint components. It provides flexibility in arranging the various components to create experiment-specific systems. A demonstrator system is realized for lung-on-a-chip experiments. The 3D-printed platform box is 290 mm long, 240 mm wide and 37 mm tall. After integrating all the components, it weighs 4.8 kg. The system comprises of a switch valve, flow and pressure controllers, and a vacuum pump to control the diverse media flows. The system generates liquid flow rates ranging from 1.5 [Formula: see text]Lmin[Formula: see text] to 68 [Formula: see text]Lmin[Formula: see text] in the cell chambers, and a cyclic vacuum of 280 mbar below atmospheric pressure with 0.5 Hz frequency in the side channels to induce mechanical strain on the cells-substrate. The components are modular for easy exchange. The battery operated platform box can be mounted on either upright or inverted microscopes and fits in a standard incubator. Overall, it is shown that a compact integrated and portable fluidic system for OoC experiments can be constructed using off-the-shelf components. For further down-scaling, the fluidic control components, like the pump, switch valves, and flow controllers, require significant miniaturization while having a wide flow rate range with high resolution.

Application of a new nano-TiO2 composite antibacterial agent in nursing management of operating room: Based on real-time information push assistant system.

With the maturity of the hospital real-time information management system at this stage, the main nursing informatics of the hospital has also been fully developed. Based on the analysis of the application status of medical information technology and Internet technology, the real-time information management system is applied to mobile medical and its development status. This paper realizes the auxiliary system of real-time information push. Several 5 nm thick two-dimensional TiO2 ultra-thin nanosheets were prepared by low-cost liquid phase method, and the application of new nano-TiO2 composite antibacterial agent in the nursing management of the operating room was systematically studied. In the nursing management of the operating room, the patients in the control group were dissatisfied with 18%, basically satisfied with 40%, and very satisfied with 42%, while the patients in the experimental group were dissatisfied with 6%, basically satisfied with 42%, and very satisfied with 52%. However, in the control group, the dissatisfaction rate of nurses' nursing care accounted for 14%, accounting for 34% of the basic satisfaction rate, 52% of the very satisfied rate. In the experimental group, the dissatisfaction rate was 4%, the basic satisfaction rate was 28%, and the very satisfied degree was 68%. The real-time information management system provides a wide range of medical potential space, and can help hospitals realize intelligent medical management, so as to have a great understanding of the digitalization of medical information, scientific medical operation process, human communication and services, and can provide more comprehensive nursing services for patients.

Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment.

Estimates of ocean [Formula: see text] uptake from global ocean biogeochemistry models and [Formula: see text]-based data products differ substantially, especially in high latitudes and in the trend of the [Formula: see text] uptake since 2000. Here, we assess the effect of data sparsity on two [Formula: see text]-based estimates by subsampling output from a global ocean biogeochemistry model. The estimates of the ocean [Formula: see text] uptake are improved from a sampling scheme that mimics present-day sampling to an ideal sampling scheme with 1000 evenly distributed sites. In particular, insufficient sampling has given rise to strong biases in the trend of the ocean carbon sink in the [Formula: see text] products. The overestimation of the [Formula: see text] flux trend by 20-35% globally and 50-130% in the Southern Ocean with the present-day sampling is reduced to less than [Formula: see text] with the ideal sampling scheme. A substantial overestimation of the decadal variability of the Southern Ocean carbon sink occurs in one product and appears related to a skewed data distribution in [Formula: see text] space. With the ideal sampling, the bias in the mean [Formula: see text] flux is reduced from 9-12% to 2-9% globally and from 14-26% to 5-17% in the Southern Ocean. On top of that, discrepancies of about [Formula: see text] (15%) persist due to uncertainties in the gas-exchange calculation. This article is part of a discussion meeting issue 'Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities'.

An integrated target field framework for point-of-care halbach array low-field MRI system design.

OBJECTIVE: Low-cost low-field point-of-care MRI systems are used in many different applications. System design has correspondingly different requirements in terms of imaging field-of-view, spatial resolution and magnetic field strength. In this work an iterative framework has been created to design a cylindrical Halbach-based magnet along with integrated gradient and RF coils that most efficiently fulfil a set of user-specified imaging requirements. METHODS: For efficient integration, target field methods are used for each of the main hardware components. These have not been used previously in magnet design, and a new mathematical model was derived accordingly. These methods result in a framework which can design an entire low-field MRI system within minutes using standard computing hardware. RESULTS: Two distinct point-of-care systems are designed using the described framework, one for neuroimaging and the other for extremity imaging. Input parameters are taken from literature and the resulting systems are discussed in detail. DISCUSSION: The framework allows the designer to optimize the different hardware components with respect to the desired imaging parameters taking into account the interdependencies between these components and thus give insight into the influence of the design choices.

Ultrasound Tomography.

Ultrasound tomography (USCT) is a promising imaging modality, mainly aiming at early diagnosis of breast cancer. It provides three-dimensional, reproducible images of higher quality than conventional ultrasound methods and additionally offers quantitative information on tissue properties. This chapter provides an introduction to the background and history of USCT, followed by an overview of image reconstruction algorithms and system design. It concludes with a discussion of current and future applications as well as limitations and their potential solutions.

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI.

Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair-the latter raises safety concerns-thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes' lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.

Design Methodology for a Magnetic Levitation System Based on a New Multi-Objective Optimization Algorithm.

Multi-objective (MO) optimization is a developing technique for increasing closed-loop performance and robustness. However, its applications to control engineering mostly concern first or second order approximation models. This article proposes a novel MO algorithm, suitable for the design and control of mechanical systems, which does not require any order reduction techniques. The controller parameters are determined directly from a special type of rapid analysis of simulated transient responses. The case study presented in this article consists of a magnetic levitation system. Certain difficulties such as the nonlinearity identification of the magnetic force and duo magnetic field sensor scheme were addressed. To point out the advantages of using the developed approach, the simulations as well as the experiments performed with the help of the created algorithm were compared to those made with common MO algorithms.

Development and Evaluation of a Full-Waveform Resistance Training Monitoring System Based on a Linear Position Transducer.

Recent advances in training monitoring are centered on the statistical indicators of the concentric phase of the movement. However, those studies lack consideration of the integrity of the movement. Moreover, training performance evaluation needs valid data on the movement. Thus, this study presents a full-waveform resistance training monitoring system (FRTMS) as a whole-movement-process monitoring solution to acquire and analyze the full-waveform data of resistance training. The FRTMS includes a portable data acquisition device and a data processing and visualization software platform. The data acquisition device monitors the barbell's movement data. The software platform guides users through the acquisition of training parameters and provides feedback on the training result variables. To validate the FRTMS, we compared the simultaneous measurements of 30-90% 1RM of Smith squat lifts performed by 21 subjects with the FRTMS to similar measurements obtained with a previously validated three-dimensional motion capture system. Results showed that the FRTMS produced practically identical velocity outcomes, with a high Pearson's correlation coefficient, intraclass correlation coefficient, and coefficient of multiple correlations and a low root mean square error. We also studied the applications of the FRTMS in practical training by comparing the training results of a six-week experimental intervention with velocity-based training (VBT) and percentage-based training (PBT). The current findings suggest that the proposed monitoring system can provide reliable data for refining future training monitoring and analysis.

Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability.

In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.

How Terminology Affects Users' Responses to System Failures.

OBJECTIVE: The objective of our research is to advance the understanding of behavioral responses to a system's error. By examining trust as a dynamic variable and drawing from attribution theory, we explain the underlying mechanism and suggest how terminology can be used to mitigate the so-called algorithm aversion. In this way, we show that the use of different terms may shape consumers' perceptions and provide guidance on how these differences can be mitigated. BACKGROUND: Previous research has interchangeably used various terms to refer to a system and results regarding trust in systems have been ambiguous. METHODS: Across three studies, we examine the effect of different system terminology on consumer behavior following a system failure. RESULTS: Our results show that terminology crucially affects user behavior. Describing a system as "AI" (i.e., self-learning and perceived as more complex) instead of as "algorithmic" (i.e., a less complex rule-based system) leads to more favorable behavioral responses by users when a system error occurs. CONCLUSION: We suggest that in cases when a system's characteristics do not allow for it to be called "AI," users should be provided with an explanation of why the system's error occurred, and task complexity should be pointed out. We highlight the importance of terminology, as this can unintentionally impact the robustness and replicability of research findings. APPLICATION: This research offers insights for industries utilizing AI and algorithmic systems, highlighting how strategic terminology use can shape user trust and response to errors, thereby enhancing system acceptance.

The Usability of Face Coverings Used to Prevent the Spread of COVID-19.

OBJECTIVE: To describe the perceived usability and usability problems associated with face coverings used to prevent the spread of COVID-19. BACKGROUND: Since public health experts have now identified the appropriate use of facemasks as one of the critical elements in an effective COVID mitigation strategy, understanding how people use and care for them has become important. METHOD: Data were collected via a survey that was shared on social media to which 2148 people responded. Participants were asked to identify the category class of the face covering they most often wear, rate its usability, answer demographic information, and questions about their mask use and hygiene, and identify issues they may suffer in relation to face cover use. RESULTS: Overall, users appear to perceive their face coverings favorably from a usability and satisfaction standpoint, even though almost two-thirds of users indicated that they experienced discomfort and problems with glasses fogging with the most popular mask types. When considering demographic information, users' political party affiliation appears related to how they perceive the usability of their face covering. CONCLUSION: Designers should work to improve the fit and comfort properties of protective masks; evidence suggests the System Usability Scale may be a useful tool in those efforts. APPLICATION: Understanding mask design and behavioral issues related to their use can help in the development of masks and will maximize their acceptance and effectiveness in the field.

Entropy as a Measure of Consistency in Software Architecture.

In building software architectures, the relations between elements in different diagrams are often overlooked. The first stage of building IT systems is the use of ontology terminology, not software terminology, in the requirements engineering process. Then, when constructing software architecture, IT architects more or less consciously however introduce elements that represent the same classifier on different diagrams with similar names. These connections are called consistency rules and are usually not attached in any way in a modeling tool, and only a significant number of them in the models increase the quality of the software architecture. It is mathematically proved that the application of consistency rules increases the information content of software architecture. Authors show that increasing readability and ordering of software architecture by means of consistency rules have their mathematical rationale. In this article, we found proof of decreasing Shannon entropy while applying consistency rules in the construction of software architecture of IT systems. Therefore, it has been shown that marking selected elements in different diagrams with these same names is, therefore, an implicit way to increase the information content of software architecture while simultaneously improving its orderliness and readability. Moreover, this increase in the quality of the software architecture can be measured by entropy, which allows for checking whether the number of consistency rules is sufficient to compare different architectures, even of different sizes, thanks to entropy normalization, and checking during the development of the software architecture, what is the improvement in its orderliness and readability.

A novel solution for controlling hardware components of accelerators and beamlines.

A novel approach to the remote-control system for the compact multi-crystal energy-dispersive spectrometer for X-ray emission spectroscopy (XES) applications has been developed. This new approach is based on asynchronous communication between software components and on reactive design principles. In this paper, the challenges faced, their solutions, as well as the implementation and future development prospects are identified. The main motivation of this work was the development of a new holistic communication protocol that can be implemented to control various hardware components allowing both independent operation and easy integration into different SCADA systems.