Study concerning the design and functionality of individual emergency shelters.

The divide between the rich and poor in the European housing market is fast rising. Latest research indicates that Europe is dealing with an increasing number of homeless people. Every city in Europe has them-homeless people compelled to live on street corners, frequently hiding themselves with cardboard. Rain, snow, and temperatures below zero pose a threat to their lives on a daily basis. There are many varied kinds of services that have been discovered, but it is difficult to keep track of everyone and guarantee that they have a warm night's sleep in the winter. The current article suggests accommodation as a workaround until they can receive high-intensity support, a way to keep a single person warm and safe during the winter. The focus is on devising a strategy that not only ensures the warmth and safety of individuals during the harsh winter months but also seeks to industrialize the construction of shelters, ensuring affordability below the cost of winter hospitalization for a homeless person. Crucially, the article introduces an additional layer to this initiative by highlighting the dual purpose of these individual shelters. Beyond being a means to provide respite for the homeless during severe weather, these shelters are envisioned as immediate response units in the event of emergencies such as earthquakes in urban areas. The article explores the potential impact of this multi-layered approach on transforming urban landscapes and fostering resilient communities.

Actual uptake pursuance analysis of certification examination among industrial engineers in the Philippines: A TPB-PVT approach.

The demand for Industrial Engineers across the globe is significantly increasing and professional certifications give advantage in the job marketplace. The Philippine Institute of Industrial Engineers (PIIE) - Industrial Engineering Certification Board facilitates the conferment of the Certified Industrial Engineer (CIE) in the Philippines. The goal of this study was to determine the factors affecting the intention IEs in the Philippines to take the CIE examination using an integration of the Perceived Value Theory and the Extended Theory of Planned Behavior. The research data were collected through an online distributed survey questionnaire to 690 graduating students and graduates from private and public universities across the Philippines. Employing a variance-based partial least squares structural equation modeling, the different significant variables and factors were assessed holistically. It was seen that attitude, perceived behavioral control, subjective norms, and understanding of the CIE examination have significant positive effects to intent to take the CIE examination and becoming a CIE. The perceived benefits and positive emotions brought by becoming a CIE significantly affects the attitude and behavior. This study also confirmed that the higher the perceived return on investment, the more the IEs will take the CIE examination. Moreover, demographic characteristics were identified to correlate and are significant among different variables. As a reflection, the findings and the integrated framework can be utilized in future studies related to development, career pathing, lifelong learning, and other related professional education.

Lateness of Acetaminophen and Non-steroidal Anti-inflammatory Drug Administrations in a Retrospective Cohort of Medication Administration Records Among Patients After Cesarean Delivery.

INTRODUCTION: In an earlier study of patients after cesarean delivery, the concurrent versus alternating administration of acetaminophen and non-steroidal anti-inflammatory drugs was associated with a substantial reduction in total postoperative opioid use. This likely pharmacodynamic effect may differ if the times when nurses administer acetaminophen and non-steroidal anti-inflammatory drugs often differ substantively from when they are due. We examined the "lateness" of analgesic dose administration times, the positive difference if administered late, and the negative value if early. METHODS: The retrospective cohort study used all 67,900 medication administration records for scheduled (i.e., not "as needed") acetaminophen, ibuprofen, and ketorolac among all 3,163 cesarean delivery cases at the University of Iowa between January 2021 and December 2023. Barcode scanning at the patient's bedside was used right before each medication administration. RESULTS: There were 95% of doses administered over a 4.8-hour window, from 108 minutes early (97.5% one-sided upper confidence limit 105 minutes early) to 181 minutes late (97.5% one-sided lower limit 179 minutes late). Fewer than half of doses (46%, P <0.0001) were administered ±30 minutes of the due time. The intraclass correlation coefficient was approximately 0.11, showing that there were small systematic differences among patients. There likewise were small to no systematic differences in lateness based on concurrent administrations of acetaminophen and ibuprofen or ketorolac, time of the day that medications were due, weekday, year, or number of medications to be administered among all such patients within 15 minutes. DISCUSSION: Other hospitals should check the lateness of medication administration when that would change their ability to perform or apply the results of analgesic clinical trials (e.g., simultaneous versus alternating administration).

Improved marine predators algorithm for engineering design optimization problems.

The Marine Predator Algorithm (MPA) has unique advantages as an important branch of population-based algorithms. However, it emerges more disadvantages gradually, such as traps to local optima, insufficient diversity, and premature convergence, when dealing with complex problems in practical industrial engineering design applications. In response to these limitations, this paper proposes a novel Improved Marine Predator Algorithm (IMPA). By introducing an adaptive weight adjustment strategy and a dynamic social learning mechanism, this study significantly improves the encounter frequency and efficiency between predators and preys in marine ecosystems. The performance of the IMPA was evaluated through benchmark functions, CEC2021 suite problems, and engineering design problems, including welded beam design, tension/compression spring design, pressure vessel design, and three-bar design. The results indicate that the IMPA has achieved significant success in the optimization process over other methods, exhibiting excellent performance in both solving optimal parameter solutions and optimizing objective function values. The IMPA performs well in terms of accuracy and robustness, which also proves its efficiency in successfully solving complex industrial engineering design problems.

Modelling and assessing new SME digital business status for visualising virtual economics and sustainability economic indicators: Empirical evidence from poultry business.

SMEs are generally recognised as financial contributors to regional economic development. Despite the enormous contributions of digital SMEs to sustainable economic growth and regional development have not yet been reported. This paper assesses and models SME businesses to gain a digital business status and model. These days, visualising economic growth is challenging and powerful to identify business performance and realise sustainable regional development. This research analyses empirical data obtained from a poultry business to form a standard model for implementing a new SME digital business model and status. This standard model is a guideline to measure the other SME businesses and productions. Localisation of the SDGs was analysed using thematic analysis. Mathematical computation is used to visualise virtual economic growth geographically. Hence, our digital business model and status demonstrated business performance, management, and economic growth.

Preventing Prolonged Times to Awakening While Mitigating the Risk of Patient Awareness: Gas Man Computer Simulations of Sevoflurane Consumption From Brief, High Fresh Gas Flow Before the End of Surgery.

Prolonged times to tracheal extubation are associated with adverse patient and economic outcomes. We simulated awakening patients from sevoflurane after long-duration surgery at 2% end-tidal concentration, 1.0 minimum alveolar concentration (MAC) in a 40-year-old. Our end-of-surgery target was 0.5 MAC, the Michigan Awareness Control Study's threshold for intraoperative alerts. Consider an anesthetist who uses a 1 liter/minute gas flow until surgery ends. During surgical closure, the inspired sevoflurane concentration is reduced from 2.05% to 0.62% (i.e., MAC-awake). The estimated time to reach 0.5 MAC is 28 minutes. From a previous study, 28 minutes exceeded ≥95% of surgical closure times for all 244 distinct surgical procedures (N=23,343 cases). Alternatively, the anesthetist uses 8 liters/minute gas flow with the vaporizer at MAC-awake for 1.8 minutes, which reduces the end-tidal concentration to 0.5 MAC. The anesthetist then increases the vaporizer to keep end-tidal 0.5 MAC until the surgery ends. An additional simulation shows that, compared with simulated end-tidal agent feedback control, this approach consumed 0.45 mL extra agent. Simulation results are the same for an 80-year-old patient. The extra 0.45 mL has a global warming potential comparable to driving 26 seconds at 40 kilometers (25 miles) per hour, comparable to route modification to avoid potential roadway hazards.

On the use of deep learning and computational fluid dynamics for the estimation of uniform momentum source components of propellers.

This article proposes a novel method based on Deep Learning for the resolution of uniform momentum source terms in the Reynolds-Averaged Navier-Stokes equations. These source terms can represent several industrial devices (propellers, wind turbines, and so forth) in Computational Fluid Dynamics simulations. Current simulation methods require huge computational power, rely on strong assumptions or need additional information about the device that is being simulated. In this first approach to the new method, a Deep Learning system is trained with hundreds of Computational Fluid Dynamics simulations with uniform momemtum sources so that it can compute the one representing a given propeller from a reduced set of flow velocity measurements near it. Results show an overall relative error below the 5% for momentum sources for uniform sources and a moderate error when describing real propellers. This work will allow to simulate more accurately industrial devices with less computational cost.

Protocol: identifying policy, system, and environment change interventions to enhance availability of blood for transfusion in Kenya, a mixed-methods study.

BACKGROUND: Safe blood is essential for the care of patients with life-threatening anemia and hemorrhage. Low blood donation rates, inefficient testing procedures, and other supply chain disruptions in blood administration affect patients in low-resource settings across Sub-Saharan countries, including Kenya. Most efforts to improve access to transfusion have been unidimensional, usually focusing on only point along the blood system continuum, and have excluded community stakeholders from early stages of intervention development. Context-appropriate interventions to improve the availability of safe blood at the point of use in low-resource settings are of paramount importance. Thus, this protocol proposes a multifaceted approach to characterize the Kenyan blood supply chain through quantitative and qualitative analyses as well as an industrial engineering approach. METHODS: This study will use a mixed-methods approach in addition to engineering process mapping, modeling and simulation of blood availability in Kenya. It will be guided by a multidimensional three-by-three-by-three matrix: three socioeconomic settings, three components of the blood system continuum, and three levels of urgency of blood transfusion. Qualitative data collection includes one-on-one interviews and focus group discussions with stakeholders across the continuum to characterize ground-level deficits and potential policy, systems, and environment (PSE) interventions. Prospectively-collected quantitative data will be used to estimate blood collection and transfusion of blood. We will create a process map of the blood system continuum to model the response to PSE changes proposed by stakeholders. Lastly, we will identify those PSE changes that may have the greatest impact on blood transfusion availability, accounting for differences across socioeconomic settings and levels of urgency. DISCUSSION: Identifying and prioritizing community-driven interventions to improve blood supply in low-resource settings are of utmost importance. Varied constraints in blood collection, processing, delivery, and use make each socioeconomic setting unique. Using a multifaceted approach to understand the Kenyan blood supply and model the response to stakeholder-proposed PSE changes may lead to identification of contextually appropriate intervention targets to meet the transfusion needs of the population.

Promises of industry 4.0 under the magnifying glass of interdisciplinarity: revealing operators and managers work and challenging collaborative robot design.

The goal of this article is to propose a cross-perspective around Collaborative Robotics-seen as a remarkable example of technologies 4.0 in an industrial context-by calling on sociology, activity-centred ergonomics, engineering, and robotics expertises. The development of this cross-perspective is thought to be a key issue to improve the design of work organisation for the Industry 4.0. After a socio-historical review of promises of Collaborative Robotics, the interdisciplinary approach developed and applied in a French Small & Medium Enterprise (SME) is presented. In this case study, two work situations are focused on in an interdisciplinary perspective: on the one hand, the one of operators whose professional gestures are intended to be supported by collaborative robots, and on the other the one of managers and executives as responsible for socio-technical changes. Our results reveal the technical and socio-organisational challenges faced by SMEs beyond the introduction of given technologies: analysing the relevance and feasibility of cobotisation projects with regard to the complexity of professional gestures and preserving the quality of work and performance under a continuous pressure to change (organisations, technologies). These findings support discussions of promises of collaborative robotics, and more generally Industry 4.0, regarding effective worker/technology collaboration and the possibility of "healthy" and performant work; they reiterate requirements for work-centred and participatory design, for reconnection in a sensory experience in a more and more digitalized work and open ways for more interdisciplinary approaches.

Narrative review of mathematical and psychological studies of staff scheduling for holidays as applicable to anesthesiologists and nurse anesthetists.

We performed a narrative review of articles applicable to anesthesiologists' and nurse anesthetists' choices of who works each statutory holiday for operating room and non-operating room anesthesia. We include search protocols and detailed supplementary annotated comments. Studies showed that holiday staff scheduling is emotional. Working on holidays often is more stressful and undesirable than comparable workdays. Intrinsic motivation may overall, among practitioners, be greater by preferentially scheduling practitioners who choose to work on holidays, for compensation, before mandating that practitioners who would prefer to be off must work on holidays. Granting each practitioner (who so desires) at least one major holiday off can depend on identifying and scheduling other clinicians who want to work holidays for monetary compensation or extra compensatory time off. Scheduling holidays by random priority (i.e., a lottery choosing who gets to pick their holiday[s] first, second, etc.) is inefficient, resulting in fewer practitioners having their preferences satisfied, especially for small departments or divisions (e.g., cardiac anesthesia). No article that we reviewed implemented a random priority mechanism for staff scheduling. The selection of practitioners to take turns in choosing their holidays is perceived to have less fairness than a selection process that collects each participants' preferences. Although holidays often are scheduled separately from regular workdays and weekends, doing so will not increase efficiency or fairness. Holidays can, in practice, be scheduled simultaneously with non-holidays. Models can explicitly include fairness as an objective. For example, fairness can be based on the difference between the maximum and minimum number of holidays for which practitioners of the same division are scheduled. Holidays can be given greater weights than other shifts when estimating fairness. Staff scheduling for holidays, when done simultaneously with regular workdays, nights, and weekends, can also use personalized weights, specifying practitioners' preferences to be satisfied if possible.

Objective and subjective evaluation of a passive low-back exoskeleton during simulated logistics tasks.

Musculoskeletal disorders remain the most common work-related health problem in the European Union. The most common work-related musculoskeletal disorder reported by workers is backache, especially in the logistics sector. Thus, this article aims to evaluate the effects of a commercial passive low-back exoskeleton during simulated logistics tasks. Thirty participants were recruited for this study. Typical logistics tasks were simulated in a laboratory environment. Cross-over research design was utilized to assess the effects of the exoskeleton on heart rate, trunk inclination, trunk acceleration, throughput, and perceived exertion. Also, usability and acceptance were obtained using a custom questionnaire. We found mostly non-significant differences. Effects on throughput varied widely between workplaces. Usability ratings were poor and acceptance moderate. The study suggests that a holistic evaluation and implementation approach for industrial exoskeletons is necessary. Further, prior to exoskeleton implementation, workplace adaptation might be required.

Association Between the Community Prevalence of COVID-19 and Daily Unscheduled Absences of Anesthesiologists, Nurse Anesthetists, and Residents in an Academic Anesthesia Department.

Introduction An "unscheduled absence" refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department's modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID­19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P [Formula: see text] 0.0002. For example, there was a 1% unscheduled absence rate among anesthesiologists working Mondays and Fridays early in the pandemic when the prevalence of COVID-19 among asymptomatic patients was 1.3%. At a 1% unscheduled absence rate, 67 would be the minimum scheduled to maintain a <5.0% risk for being unable to run all 65 anesthetizing locations. In contrast, there was a 3% unscheduled absence rate among nurse anesthetists working Mondays and Fridays during the Omicron variant surge when the prevalence was 4.5%. At a 3% unscheduled absence rate, 70 would be the minimum scheduled to maintain the same risk of not being able to run 65 rooms. Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.

Patients in Iowa Counties Lacking Hospitals With Labor and Delivery Services Disproportionately Receive Care at Level III Maternal Care Hospitals When Undergoing Cesarean Delivery: A Retrospective Longitudinal Study.

Introduction Many obstetrical patients from rural areas in the United States lack hospitals that provide labor and delivery care. Our objective was to examine the effects of such patients on caseloads of cesarean deliveries at Iowa hospitals with level III maternal care, as defined by the Iowa Department of Public Health (e.g., with obstetric anesthesiologists). Methods This retrospective longitudinal study included every discharge with cesarean delivery in the state of Iowa from October 2015 through June 2021. There were N=60,534 such deliveries from 76 hospitals, of which three were level III, and the rest were level I or II. Poisson regression models with robust variance estimation and controlling for geography, maternal risk factors, and insurance, were used to evaluate the binary outcome of whether patients received care at the university level III hospital in Eastern Iowa, or not. Similar models were also developed for care at the two private level III hospitals in Central Iowa, or not. Differences in the mean probabilities of receiving care at the level III hospitals were then estimated using logistic regression, with results reported in units of changes in cases per week at the hospitals. Results Statewide, the university level III hospital performed 7.4% of the cesarean deliveries, and the two private level III hospitals performed 23.4%. Patients from counties in which no cesarean deliveries were performed during the quarter of the year when they underwent a cesarean delivery disproportionately received care at level III hospitals versus levels I and II hospitals. Lower 99% confidence limits for incremental risk ratios were 1.46 and 4.20, respectively. Cesarean deliveries among patients residing in counties where no hospital had a labor and delivery ward were distributed unequally between the counties of the hospitals with level III maternal care. There were approximately 1.09 (standard error 0.10) extra cesarean deliveries per week at the university hospital versus 5.81 (standard error 0.11) at the private hospitals. The 1.09 vs 5.81 difference was caused, in part, by the effects of insurance and other hospitals with similar services. Conclusions Patients residing in counties without labor and delivery care disproportionately go to level III hospitals. These results can help anesthesiologists, obstetricians, and analysts at hospitals with large tertiary (level III) programs interpret their annual increases in total obstetric anesthesia activity.

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines.

Induction Motors (IMs) have multiple advantages, as they are robust and easy to maintain. A small fault in a single rotor bar could cause the break of this bar, subsequently breaking a large adjacent bar number because of the oscillations created by the unbalance of the rotor. For this reason, early detection of any small failure is recommended to remove some posterior damage and the maintenance expenses. This work proposes a new and original method that uses the cyclostationnarity of the electrical signal instead of the vibratory signal to detect the incipient failure. The experimental data has been manipulated. The results prove that this new designed strategy can identify and classify the level of cracking that occurs in a single rotor bar, even under various loads.

Feasibility of Anesthesiologists Giving Nurse Anesthetists 30-Minute Lunch Breaks and 15-Minute Morning Breaks at a University's Facilities.

Background Managers of an anesthesia department sought an estimation of how often each anesthesiologist can give lunch breaks and morning breaks to nurse anesthetists to plan staff scheduling. When an anesthesiologist supervising the nurse anesthetists can give a break, it would be preferred because fewer extra nurse anesthetists would be scheduled to facilitate breaks. Methodology Our methodological development used retrospective cohort data from the three surgical suites of a single anesthesia department. Surgical times were estimated using three years of data from October 2016 through September 2019, with 95,146 cases. Comparison was made with the next year from October 2019 through September 2020, with 30,987 cases. The 5% lower prediction bounds for surgical time were estimated based on two-parameter, log-normal distributions. The times when two and three sequential rooms had overlapping lower prediction limits were calculated. Sequential rooms were used because that was how anesthesiologists' assignments were made at the studied department, when feasible given constraints. Percentages of cases were reported with 15 minutes available starting sometime between 9:00 and 10:30 and 30 minutes starting sometime between 11:15 and 12:45, times characteristic for the studied department. At the studied university's facilities, the nurse anesthetists were independent practitioners (e.g., an anesthesiologist supervising two nurse anesthetists each with a long case could give a break to one of the two rooms). Results The percentage of days for which an anesthesiologist could give a lunch break (11:15-12:45) was close to the percentage of cases when an anesthesiologist could give the same-length break anytime throughout the workday. In other words, the length of the break was important, not the time of the day of the break. The absolute percentages also depended on how many rooms the anesthesiologist supervised, the duration of cases, and facility. For example, among anesthesiologists at the adult surgical suite supervising three nurse anesthetists, a lunch break could be given by the anesthesiologist on at most one-third of the days without affecting workflow. Conclusions Our results show that the feasibility of an anesthesiologist clinically supervising one, two, or three rooms to give lunch breaks to the nurse anesthetists in the rooms depends principally on how many rooms are supervised, the duration of the break, and the facility's percentage of cases with surgical times longer than that duration. The specific numerical results will differ among departments. Our methodology would be useful to other departments where anesthesiologists are clinically supervising independent practitioners, sometimes during cases long enough for a break, and there is anesthesiologist backup help. Such departments can use our methodology to plan their staff scheduling for additional nurse anesthetists to give the remaining breaks.

Case duration prediction and estimating time remaining in ongoing cases.

In this issue of the British Journal of Anaesthesia, Jiao and colleagues applied a neural network model for surgical case durations to predict the operating room times remaining for ongoing anaesthetics. We review estimation of case durations before each case starts, showing why their scientific focus is useful. We also describe managerial epidemiology studies of historical data by the scheduled procedure or distinct combinations of scheduled procedures included in each surgical case. Most cases have few or no historical data for the scheduled procedures. Generalizability of observational results such as theirs, and automatic computer assisted clinical and managerial decision-making, are both facilitated by using structured vocabularies when analysing surgical procedures.

Managing capacity for urgent surgery: staffing, staff scheduling in-house or on-call from home, and work assignments.

Parmar and colleagues developed and validated a graphical method for choosing the number of operating theatres to set aside for urgent surgical cases. We address appropriate usage of their new method for calculating anaesthesia staffing, including comparison with previously published techniques. Parmar and colleagues' method is based on all staff scheduled in-house, rather than some on-call from home. We review that this is not nearly as large a limitation as it may seem because of behavioural factors of staff assignment.

Training the Next Industrial Engineers and Managers about Industry 4.0: A Case Study about Challenges and Opportunities in the COVID-19 Era.

Training the next generation of industrial engineers and managers is a constant challenge for academia, given the fast changes of industrial technology. The current and predicted development trends in applied technologies affecting industry worldwide as formulated in the Industry 4.0 initiative have clearly emphasized the needs for constantly adapting curricula. The sensible socioeconomic changes generated by the COVID-19 pandemic have induced significant challenges to society in general and industry. Higher education, specifically when dealing with Industry 4.0, must take these new challenges rapidly into account. Modernization of the industrial engineering curriculum combined with its migration to a blended teaching landscape must be updated in real-time with real-world cases. The COVID-19 crisis provides, paradoxically, an opportunity for dealing with the challenges of training industrial engineers to confront a virtual dematerialized work model which has accelerated during and will remain for the foreseeable future after the pandemic. The paper describes the methodology used for adapting, enhancing, and evaluating the learning and teaching experience under the urgent and unexpected challenges to move from face-to-face university courses distant and online teaching. The methodology we describe is built on a process that started before the onset of the pandemic, hence in the paper we start by describing the pre-COVID-19 status in comparison to published initiatives followed by the real time modifications we introduced in the faculty to adapt to the post-COVID-19 teaching/learning era. The focus presented is on Industry 4.0. subjects at the leading edge of the technology changes affecting the industrial engineering and technology management field. The manuscript addresses the flow from system design subjects to implementation areas of the curriculum, including practical examples and the rapid decisions and changes made to encompass the effects of the COVID-19 pandemic on content and teaching methods including feedback received from participants.

Monitoring photovoltaic soiling: assessment, challenges, and perspectives of current and potential strategies.

Soiling is the process whereby dirt, dust, and organic/inorganic contaminants deposit on the surface of a photovoltaic (PV) module. It causes significant economic losses and can have a substantial impact on the expansion of photovoltaic technologies for energy generation. The first step to address soiling adequately is monitoring, as soiling mitigation has to be tailored to the specific conditions of each PV system and no universally valid strategy exists. The main focus of this study is to assess the current state of the art in soiling monitoring, in order to help the community better understand the needs and the challenges in this area. The potentials and the limitations of each monitoring method are discussed thoroughly in the paper, with the support of original experimental data. An estimation of the future soiling monitoring market trends is also presented, with a forecasted need for tens of thousands of new soiling monitors every year.

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management.

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.