Process Monitoring Research Articles

Formal processes and informal practices: how policy systems and corruption constrain the delivery of housing in Nigeria

ABSTRACT The delivery of housing is a recognised challenge in many Global South countries due to various institutional logjams within formal systems. This paper delves into formal processes and informal practices in Benin City to investigate how policy systems and corruption constrain the delivery of housing. As Benin City increasingly relies on informal mechanisms, bureaucratic bottlenecks impede progress, and corruption has emerged as a major global issue that hinders housing provision. A qualitative method was adopted, using interviews. The findings illustrate how corrupt practices typify housing delivery in Benin City, leading to inadequate and insufficient houses for residents. Among other recommendations, the paper advocates for adequate and effective supervision and monitoring of official processes geared towards housing production. It also suggests the need for increased transparency and accountability in allocating resources for housing projects. These measures would curb corruption and ensure that residents have access to decent and affordable housing.

Towards Precision Biocatalysis ‐ Leveraging Inline NMR for Autonomous Experimentation in Flow Reactors

AbstractReactor automation is a transformative force for chemical processes, but the potential of reaction monitoring for machine‐assisted autonomous biocatalytic reaction optimization is still largely unexplored. To address this gap, we report on automated reactor optimization for biocatalytic flow‐through microreactors. For this purpose, the inline NMR analysis of an enzymatically catalyzed stereoselective reduction of a prochiral diketone was combined with a self‐developed open‐source analysis and control software. The algorithm is continuously fed with spectra from a benchtop NMR instrument acquired from a reaction solution from a microreactor filled with biocatalytically active materials and adjusts the flow rate of the pumps to achieve predetermined target concentrations of the product. We show that through this automated coupling of data analysis and process parameterization, for example, maximum conversion efficiency can be achieved for a given bioreactor. This work illustrates the potential of inline NMR reaction monitoring for biocatalytic processes and provides a starting point for innovation to develop automated processes for precision biocatalysis through integrated data analysis.

Systematic Review on Additive Friction Stir Deposition: Materials, Processes, Monitoring and Modelling

Emerging solid-state additive manufacturing (AM) technologies have recently garnered significant interest because they can prevent the defects that other metal AM processes may have due to sintering or melting. Additive friction stir deposition (AFSD), also known as MELD, is a solid-state AM technology that utilises bar feedstocks as the input material and frictional–deformational heat as the energy source. AFSD offers high deposition rates and is a promising technique for achieving defect-free material properties like wrought aluminium, magnesium, steel, and titanium alloys. While it offers benefits in terms of productivity and material properties, its low technology readiness level prevents widespread adoption. Academics and engineers are conducting research across various subfields to better understand the process parameters, material properties, process monitoring, and modelling of the AFSD technology. Yet, it is also crucial to compile and compare the research findings from past studies on this new technology to gain a comprehensive understanding and pinpoint future research paths. This paper aims to present a comprehensive review of AFSD focusing on process parameters, material properties, monitoring, and modelling. In addition to examining data from existing studies, this paper identifies areas where research is lacking and suggests paths for future research efforts.

The two-stage processing of judgment of confidence: evidence from ERP.

The judgment of confidence (JOC) refers to the confidence in the accuracy of the target item individuals have just retrieved and is a typical retrospective metacognitive monitoring process. In the classical paradigm of JOC, JOC occurs after the recognition or recall task. While initially viewed as a single-stage monitoring process, recent research on JOC suggests its internal mechanisms may be more complex, potentially encompassing both retrieval and monitoring processes. This study aims to delve into these mechanisms concerning neural temporal processes. In this study, event-related potential (ERP) was used to compare N400 and slow-wave ERPs of high and low JOCs at different time windows using a classic JOC paradigm. Behavioral results showed an inverted-U shaped relationship between response time (RT) and JOCs, peaking at magnitude 3 before declining. There were significantly longer RT for low JOCs compared with high JOCs, along with lower recognition scores. The ERP results showed that low JOCs induced larger N400 in the right frontal lobe and right central area, while high JOCs induced larger slow-wave components (500 ~ 700ms) in the right frontal lobe. Based on these findings, the present study suggests that JOC involves two processing stages. N400 reflects the process of cue acquisition, while the slow-wave component reflects the process of cue application. Furthermore, a two-stage model was proposed and validated, enriching the study of metacognition monitoring mechanisms, offering insights into the processing mechanisms of retrospective metacognitive monitoring.

Tunable diode laser absorption spectroscopy monitoring of the water vapor condensation process in the high-speed flow

Abstract In this work, non-invasive high-precision quantitative measurements of the water vapor condensation process have been carried out using tunable diode laser absorption spectroscopy(TDLAS) at the sub-millisecond level. The high-speed condensation of water vapor is achieved by a self-designed rarefaction wave reflection system. Combined with different sizes of experimental cavities, the condensation process is realised at various time scales of sub-milliseconds (0.2-0.66 ms). The processes of temperature and water vapor content during the high-speed condensation are measured using the water vapor absorption spectra near 7168.437 cm-1 and 7185.597 cm^-1. The experimental results show that the hypervelocity expansion flow field generated by the experimental system demonstrates good uniformity and reaches a cooling rate of 10^5 K/s, which has the same order as that of the supersonic nozzle. The condensation process is similar on different sub-millisecond timescales and the normalised temperature change curves are approximately the same. Moreover, the higher the water vapor content, the shorter the condensation time.

Study protocol of the ALMA-CKD trial; an electronic triggering decision-support system to improve the detection, recognition, and management of patients with chronic kidney disease in primary care.

Chronic kidney disease (CKD) is a global health problem affected by under-recognition and under-treatment in primary care settings. Electronic clinical decision support (CDS) triggering systems have the potential to improve detection and management of people with CKD by assisting clinicians in adhering to guideline recommendations. We aimed to test whether an electronic CDS triggering system would improve the detection, recognition, and management of patients with CKD in primary care. This is a pragmatic cluster-randomized controlled trial where 66 primary healthcare centers from the Stockholm Region, Sweden were randomized 1:1 to receive either a new expanded CDS-triggering system offering kidney-specific advice or to continue with their current CDS-triggering system. The expanded CDS system reminds and provides practical facilitators of the processes of CKD screening, recognition with a diagnosis, management and referral to specialist care. The trial duration is 24 months and it is embedded into the Stockholm CREAtinine measurements (SCREAM) project, a repository of healthcare data from the region, which minimizes disturbances with healthcare praxis due to the trial and makes it fully pragmatic. The primary outcomes are the number of eligible patients screened for creatinine and albuminuria once annually and the re-testing of these labs within 6 months in patients with abnormal eGFR or albuminuria. Secondary outcomes are the proportions of issued clinical diagnoses among those fulfilling criteria, proportions of patients with significant albuminuria receiving prescribed nephroprotective medications, proportions of accepted referrals to nephrologist care among those fulfilling criteria and proportion of referrals for ultrasound of the kidneys. Prior pragmatic trials of CDS-systems in CKD has shown an improvement in quality indicators primarily in patients already diagnosed with CKD. This study expands this evidence by focusing on the process of screening, identification, monitoring and diagnostic work-up. This pragmatic trial will assess the value of CDS for improved adherence to CKD guidelines in primary care. gov registration: NCT06386172, submitted 2024-04-23.

Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage

This paper is devoted to the real-time monitoring of close transportation devices, namely, belt conveyors. It presents a novel measurement system based on the linear strain gauges placed on the tail pulley surface. These gauges enable the monitoring and continuous collection and processing of data related to the process. An initial assessment of the machine learning application to the load identification was made. Among the tested algorithms that utilized machine learning, some exhibited a classification accuracy as high as 100% when identifying the load placed on the moving belt. Similarly, identification of the preset damage was possible using machine learning algorithms, demonstrating the feasibility of the system for fault diagnosis and predictive maintenance.

A review of graphene biopolymer composite in piezoelectric sensor applications

Abstract The amazing electrical, optical, mechanical and thermal properties combined with high specific surface area of graphene making it as an appealing integrant for stimuli responsive high performance smart materials. Typical graphene-based smart materials encompass mechanically exfoliated perfect graphene, chemical vapor deposited first-class graphene, chemically moded graphene including graphene oxide and reduced graphene oxide and their macroscopic assemblies or composites. The ability of these graphene-based materials ending up interacting with biopolymers to come up with quite fascinating electrical, mechanical, optical, thermal and sensing characteristics has have attracted a considerable number of attentions. The biggest advantage of using biopolymer-based materials is non-corrosiveness, ease in coloration, good tensile strength, and biodegradability but are abided by drawback of the poor mechanical strength, lack of response, and unstable environmental stability. However, graphene incorporated biopolymers provided beneficent attributes for example ability to detect various forms of stimuli such as gaseous molecules include biomolecules, pH value, mechanical flexibility, electrical and thermal conductivity to enable ongoing promising advancement of the piezoelectric sensor applications. This review explores the piezoelectric development based on several graphene fabricated biopolymer composite and it is use in healthcare monitoring, structural health monitoring, industrial process monitoring, consumer electronics applications. Furthermore, we enlighten the challenges and future perspectives of graphene biopolymer piezoelectric sensors.

In Situ Potentiometric Monitoring of Nitrate Removal from Aqueous Solution by Activated Carbon and Ion Exchange Resin

A nitrate selective electrode was used for real-time in situ potentiometric monitoring of a batch nitrate removal process using activated carbon and ion exchange resin. A plasticized polymeric membrane consisting of polyvinyl chloride, 2-nitrophenyl octyl ether and tridodecyl methyl ammonium chloride was incorporated into an ion-selective electrode body. First, the dynamic potential response of the electrode to nitrate was investigated. Two commercial activated carbons with different physical properties were then tested. Nitrate removal with these carbons was monitored potentiometrically using several nitrate concentrations. The extreme turbidity of the solutions was not a drawback during potentiometric monitoring of the process, which is a clear advantage over other methods such as optical monitoring. The potential versus time recordings were converted into nitrate concentration versus time plots, which were evaluated with different adsorption kinetic models. A pseudo-second order kinetic model for nitrate adsorption on both activated carbons was found to fit the experimental data very well. The values of the kinetic parameters were very different between the two activated carbons. The proposed methodology was also satisfactorily applied to the study of nitrate removal by an ion exchange resin. In this case, the experimental results clearly follow a pseudo-first order kinetic model. Potential applications of the proposed methodology for monitoring nitrate removal in real water samples are discussed.

Exploring Real-Time Monitoring of Adsorption Processes at Solid–Liquid Interfaces through Chemical Force Microscopy

Exploring Real-Time Monitoring of Adsorption Processes at Solid–Liquid Interfaces through Chemical Force Microscopy

Monitoring of zero-inflated COM-Poisson processes

This paper investigates control charts for count data with a significant number of zeros. The proposed models are generalized to handle data exhibiting different types of dispersion, i.e. equidispersion, overdispersion, and underdispersion. To this effect, the zero-inflated Conway–Maxwell Poisson (ZICMP) distribution is employed. A Shewhart and an exponentially weighted moving average (EWMA) control chart are developed, referred to as ZICMP-Shewhart and ZICMP-EWMA charts, respectively. The ZICMP distribution incorporates various distributions, including the Conway–Maxwell–Poisson (COM-Poisson) and zero-inflated Poisson (ZIP), Geometric, and Bernoulli distributions, as special cases. Consequently, the flexibility and versatility of the ZICMP distribution enhance the applicability of the proposed control charts, thereby providing practitioners with an adaptable tool suitable for various scenarios.The control charts are examined for their ability to detect upward shifts in the process mean level, and their statistical performance is evaluated in terms of the average run-length (ARL). Through a simulation study, we demonstrate that the ZICMP-Shewhart chart is more effective for monitoring over-dispersed data, while the ZICMP-EWMA chart is more suitable for under-dispersed data. Finally, we provide two real-life examples to illustrate the applicability of the proposed charts.

Intervention development and optimisation of a multi-component digital intervention for the monitoring and management of hypertensive pregnancy: the My Pregnancy Care Intervention.

Hypertensive disorders of pregnancy affect around 10% of pregnancies and remain a major cause of maternal and foetal morbidity and mortality. Trials have shown that self-monitoring blood pressure during pregnancy is safe, but self-monitoring alone does not improve blood pressure control or pregnancy outcomes. This study aimed to develop and optimise a multicomponent intervention to support blood pressure monitoring, hypertension management and urine testing within current care pathways. Relevant literature, input from patient and public contributors (PPI) and stakeholder groups, and the researcher's previous experience were used to develop an initial intervention. Think-aloud interviews and focus groups with women from diverse backgrounds with lived experience of hypertension in pregnancy and healthcare professionals provided feedback on the intervention prototype (n = 29). The MRC Framework for Developing Complex Interventions guided the processes to optimise the intervention's acceptability and maximise engagement. A detailed tabulation of participants' views and logic models was produced using the COM-B model of Behaviour Change. The prototype intervention was acceptable and viable to both pregnant women with experience of hypertensive pregnancy and healthcare professionals. Emerging themes centred on how the intervention could be optimised within current National Health Service care pathways and the lives of pregnant women to support behaviour change. Key target behaviours to support the intervention included increasing understanding of blood pressure management, engagement with the intervention, monitoring blood pressure and urine and taking appropriate actions based on those readings. This informed the development of recommendations involving clear action timelines for women and evidence-based guidance to support decision-making by healthcare professionals. The findings were used to produce the multi-component My Pregnancy Care intervention, consisting of a smartphone application and an information leaflet to support blood pressure self-monitoring and proteinuria self-testing, self-management of antihypertensive medication and smartphone application use. This research provided comprehensive insight into the needs of pregnant women with hypertension and their healthcare teams regarding self-monitoring and management of blood pressure. This supported the development of a tailored multi-component digital intervention that addresses barriers to blood pressure self-management by being user-friendly, persuasive and acceptable. It is hoped that the intervention will support the monitoring and management process, collaboration between healthcare professionals and women, clinical action and improved clinical outcomes.

Multimodal sensor-to-machined surface image diffusion for defect detection in industrial processes

Generative models, particularly diffusion-based approaches, have gained significant attention recently due to their ability to create realistic outputs. Despite their potential, the application of these models in manufacturing remains largely unexplored. This work presents a framework that addresses this gap by generating machined surface images guided by multiple sensor inputs in manufacturing. The proposed model integrates information from multiple sensors with varying sampling rates using multimodal embedding and employs a latent diffusion model to translate the fused sensor embedding into an image embedding, which is then converted into a machined surface image. The effectiveness of the framework is validated using real-world time-series data, including force, torque, acceleration, and sound, collected from various industrial processes, such as a carbon-fiber-reinforced plastic drilling process. The results demonstrate the model’s ability to predict defects from the generated machined surface images. The proposed approach can potentially revolutionize prognostics and health management (PHM) in smart manufacturing by enabling sensor-guided visual inspection, defect detection, process monitoring, and predictive maintenance.

The effects of embedded quizzes on self‐regulated processes and learning performance during a multimedia lesson

AbstractBackgroundPrevious research has demonstrated that quizzing can improve self‐regulation processes and learning performances. However, it remains unclear whether quizzes in multimedia material bring similar benefits, and whether interindividual differences such as working memory capacity (WMC) modulate quizzing effects.AimsThis study aimed to examine the effects of embedded quizzes (multiple quizzes within the learning sequence) with feedback during a multimedia learning sequence on self‐regulated processing (monitoring and control) and learning performances on repeated (material already tested) and new (untested) questions, and possible WMC moderation on performances.Sample and MethodsParticipants were 59 students who learned a multimedia neuroscience course module in either a no‐quiz or embedded quizzes condition. With regard to self‐regulated processing, control was assessed with eye‐tracking measures (fixation durations and transitions between texts and illustrations), and monitoring by comparing self‐reported judgements of learning with actual performances (calibration). At the end, students' WMC was measured, and learning performances were assessed with repeated and new questions.ResultsQuizzing improved learning performances on repeated and new questions, and enhanced monitoring (better calibration). Students with low WMC benefited more from quizzing for new questions than those with high WMC. However, contrary to our hypothesis, the embedded quizzes group allocated less time to the learning than the no‐quiz group.ConclusionsEmbedded quizzes are relevant tools to help students recalibrate their monitoring process and foster more effective self‐regulated learning. Results confirm that the use of quizzes can be recommended to improve the learning of multimedia material, especially among students with low WMC.

Comprehensive Study of Antibiotic Resistance in Enterococcus spp.: Comparison of Influents and Effluents of Wastewater Treatment Plants

Background/Objectives: The spread of antibiotic resistance, particularly through Enterococcus spp., in wastewater treatment plants (WWTPs) poses significant public health risks. Given that research on antibiotic-resistant enterococci and their antibiotic-resistance genes in aquatic environments is limited, we evaluated the role of Enterococcus spp. in WWTPs by comparing the antibiotic resistance rates, gene prevalence, biofilm formation, and residual antibiotics in the influent and effluent using culture-based methods. Methods: In 2022, influent and effluent samples were collected from 11 WWTPs in South Korea. Overall, 804 Enterococcus strains were isolated, and their resistance to 16 antibiotics was assessed using the microdilution method. Results: High resistance to tetracycline, ciprofloxacin, kanamycin, and erythromycin was observed. However, no significant differences in the overall resistance rates and biofilm formation were observed between the influent and effluent. Rates of resistance to ampicillin, ciprofloxacin, and gentamicin, as well as the prevalence of the tetM and qnrS genes, increased in the effluent, whereas resistance rates to chloramphenicol, florfenicol, erythromycin, and tylosin tartrate, along with the prevalence of the optrA gene, decreased. E. faecium, E. hirae, and E. faecalis were the dominant species, with E. faecalis exhibiting the highest resistance. Conclusions: Our results suggest that WWTPs do not effectively reduce the rates of resistant Enterococcus spp., indicating the need for continuous monitoring and improvement of the treatment process to mitigate the environmental release of antibiotic-resistant bacteria.

Biodegradable and Implantable Triboelectric Nanogenerator Improved by β-Lactoglobulin Fibrils-Assisted Flexible PVA Porous Film.

Triboelectric nanogenerators (TENGs) are highly promising as implantable, degradable energy sources and self-powered sensors. However, the degradable triboelectric materials are often limited in terms of contact electrification and mechanical properties. Here, a bio-macromolecule-assisted toughening strategy for PVA aerogel-based triboelectric materials is proposed. By introducing β-lactoglobulin fibrils (BF) into the PVA aerogel network, the material's mechanical properties while preserving its swelling resistance is significantly enhanced. Compared to pure PVA porous film, the BF-PVA porous film exhibits an eightfold increase in fracture strength (from 1.92 to 15.48J) and a fourfold increase in flexibility (from 10.956 to 39.36MPa). Additionally, the electrical output of BF-PVA in triboelectric performance tests increased nearly fivefold (from 45 to 203V). Leveraging these enhanced properties, a biodegradable TENG (bi-TENG) for implantable muscle activity sensing is developed, achieving real-time monitoring of neuromuscular processes. This innovation holds significant potential for advancing implantable medical devices and promoting new applications in bio-integrated electronics.

Quality Management in Spanish Community Pharmacy: Implementation of ISO 9001.

Spanish community pharmacy has been incorporating some methods and approaches in the evaluation and improvement of quality, with the ISO 9001 standard being the most used reference. The experience of a community pharmacy in Spain is presented. The pharmacy implemented a management system based on the ISO 9001 standard in 2020, using the Deming cycle with the aim of enhancing the quality of pharmaceutical care. The results obtained in the processes over the following 2 years are also presented. The ISO 9001 standard was reviewed to ensure compliance. The Quality Management System control documentation was updated and included the following documents: quality manual, process map, operational processes and procedures, quality indicators and incident records. The pharmacy has planned and implemented a process of monitoring, measurement, analysis of results and continuous improvement for operational, strategic and support processes during the time period of the study. The main operational processes were associated with a set of indicators which permitted to know the results obtained, analyse deviations and implement improvements. The planning and development of the project encompassed approximately 1 year and involved several key stages: (1) diagnosis and assessment of quality situation, (2) analysis of processes, (3) preparation of documentation, (4) implementation of the Quality Management System, (5) internal audit, (6) monitoring of activities and continuous improvement, (7) follow-up audit by the certifying body, (8) obtaining the ISO 9001:2015 quality certificate. The planned schedule was adhered and the documents were successfully drafted. In total, eight operational processes, nine support processes and six strategic processes were reviewed and included in the Quality Management System. Twenty indicators were established to monitor and analyse the performance of these processes. During audits, deviations in the system or nonconformances were identified and analysed. In the first year of system implementation, five nonconformances were identified, decreasing to two in the subsequent two periods. In 2020, three improvement actions were implemented with the objective of: (1) implementing a medication reconciliation service, (2) developing a medication dose adjustment service for polymedicated patients, and (3) implementing a service to measure and improve inhalation technique in patients using inhalers. The implementation of a methodology based on the ISO 9001 standard has facilitated: (1) Integrating process management as a regular working methodology, (2) A focus on the continuous improvement of services, (3) Achieving recognition of the staff efforts and involvement.

THE IN-CONTROL PERFORMANCE OF THE ROBUST MULTIVARIATE SYNTHETIC CONTROL CHARTS FOR MONITORING MEAN SHIFT

Multivariate synthetic control chart enables monitoring of multiple process variable simultaneously and hence, negate the inflation of false alarm rates as can be seen in an individual statistical chart. A robust version of the synthetic chart is necessary to mitigate the issue faces by the traditional multivariate synthetic chart, which is unable to produce reliable parameter estimates when Phase I data are contaminated. Therefore, this study proposed three new robust multivariate synthetic control charts (CMRCD, CWS and CWP) which were constructed via minimum regularized covariance determinant (MRCD) and winsorized modified one-step M-estimator (WMOM) for efficient process monitoring. The effectiveness of the proposed robust charts was evaluated in terms of false alarm rates by comparing their in-control performances to the traditional multivariate synthetic chart, Cmean, which is based on the sample mean. Via extensive simulation studies, the findings indicate that the proposed robust control charts outperform the traditional chart regardless of the dimensions or the level of contaminations in the dataset. The real data study further validates that the CMRCD, CWS and CWP perform better than the Cmean. Specifically, the three robust control charts show significant observations, illustrating better capability in monitoring river water quality when compared to the Cmean, i.e., the traditional multivariate synthetic chart.

An artificial intelligence-enabled consumables tracking system for medical laboratories

Abstract The medical laboratory plays a crucial role within a hospital setting and is responsible for the examination and analysis of patient specimens to accurately diagnose various ailments. The burden on medical laboratory personnel has significantly increased, particularly in the context of the ongoing global COVID-19 pandemic. Worldwide, the implementation of comprehensive and extended COVID-19 screening programs has placed a significant strain on healthcare professionals. This burden has led to exhaustion among medical employees, limiting their ability to effectively track laboratory resources, such as medical equipment and consumables. Therefore, this study proposed an artificial intelligence (AI)-based solution that contributes to a more efficient and less labor-intensive workflow for medical workers in laboratory settings. With the ultimate goal to reduce the burden on healthcare providers by streamlining the process of monitoring and managing these resources, the objective of this study is to design and develop an AI-based system for consumables tracking in medical laboratories. In this work, the effectiveness of two object detection models, namely, YOLOv5x6 and YOLOv8l, for the administration of consumables in medical laboratories was evaluated and analyzed. A total of 570 photographs were used to create the dataset, capturing the objects in a variety of settings. The findings indicate that both detection models demonstrate a notable capability to achieve a high mean average precision. This underscores the effectiveness of computer vision in the context of consumable goods detection scenarios and provides a reference for the application of real-time detection models in tracking systems within medical laboratories.

Real-time in-situ ultrasound monitoring of soft hydrogel 3D printing with subwavelength resolution

3D bioprinting has excellent potential in tissue engineering, regenerative medicine, and drug delivery systems due to the ability to fabricate intricate structures that are challenging to make with conventional manufacturing methods. However, the complexity of parametric combinations and lack of product quality control have restricted soft hydrogel bioprinting from practical applications. Here we show an in-situ ultrasound monitoring system that reveals the alginate-gelatin hydrogel’s additive manufacturing process. We use this technique to understand the parameters that influenced transient printing behaviors and material properties in approximately real-time. This unique monitoring process can facilitate the detection of minor errors/flaws during the printing. By analyzing the ultrasonic reflected signals in both time and frequency domains, transient printing information can be obtained from 3D printed soft hydrogels during the processes with a depth subwavelength resolution approaching 0.78λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document}. This in-situ technique monitors the printing behaviors regarding the constructed film, interlayer bonding, transient effective elastic constant, layer-wise surface roughness (elastic or plastic), nozzle indentation/scratching, and gravitational spreading. The simulation-verified experimental methods monitored fully infilled printing and gridded pattern printing conditions. Furthermore, the proposed ultrasound system also experimentally monitored the post-crosslinking process of alginate-gelatin hydrogel in CaCl2 solution. The results can optimize crosslinking time by balancing the hydrogel’s stiffness enhancement and geometrical distortion.