Currently, post-mortem computed tomography (PMCT) has become an accessible and contemporary tool for forensic investigations. In the case of burn victims, it provides specific semiologies requiring a prudent understanding to differentiate between the normal post-mortem changes from heat-related changes. The aim of this pictorial essay is to provide to the radiologist the keys to establish complete and focused reports in cases of PMCT of burn victims. Thus, the radiologist must discern all the contextual divergences with the forensic history, and must be able to report all the relevant elements to answer to the forensic pathologist the following questions: Are there tomographic features that could help to identify the victim? Is there evidence of remains of biological fluids in liquid form available for toxicological analysis and DNA sampling? Is there another obvious cause of death than heat-related lesions, especially metallic foreign bodies of ballistic origin? Finally, what are the characteristic burn-related injuries seen on the corpse that should be sought during the autopsy?• CT is highly useful to find features permitting the identification of a severely burned body.• PMCT is a major asset in gunshot injuries to depict ballistic foreign bodies in the burned cadavers.• CT is able to recognise accessible blood for tests versus heat clot (air-crescent sign).• Heat-related fractures are easily differentiated from traumatic fractures.• Epidural collections with a subdural appearance are typical heat-related head lesions. Fire situations  are current and may involve human victims.In such cases, authorities initiate a judicial inquiry and mandate the forensic department to undertake investigation and exclude an arson attack.Although the charred bodies are generally not completely destroyed, it remains a difficult and challenging task for the forensic physician who is expected to determine: the identity of the victim, the presence or absence of essential signs that could indicate whether the deceased was alive or not when the fire broke out, the cause of death, potential poisoning or intoxication , the possibility of a third party intervention and of potential criminal involvement.Primarily, the forensic team starts by collecting relevant information during the removal of corpses, such as the position of the body in the fire debris Fig. , its temSubsequently, the forensic pathologist will carry out specific toxicological screening tests for blood levels of substances such as carbon monoxide and cyanide, to determine whether or not the victim was alive when the fire was initiated.Finally, considerable reliance can be laid upon the various imaging techniques to help the forensic pathologist in the victim’s identification process, and to guide him in the autopsy, fluid analysis and DNA sampling. Indeed, the advanced state of carbonisation often complicates the surgical dissection and some foreign bodies  or bone alterations  could be missed.Thus, via the modernisation and improvement of cross-sectional imaging techniques, post-mortem computed tomography (PMCT) has superseded conventional radiography and can provide an entire-body volumetric exploration to help the forensic team in their investigations , 2.This imaging modality is nowadays accessible, reproducible, reliable and easy to implement.Undoubtedly, contrary to the other post-mortem situations, there is no discussion regarding the use of contrast enhancement in the circumstance of severe burn victims; the severe skin and soft tissue injuries formally prevent the catheterisation of the blood vessels and render the injection of contrast agents impossible.Hence, the protocol is always a non-contrast-enhanced full-body exploration, and technical difficulties only remain in relation to the state of dilapidation of the charred body and its transport and installation on the CT table Fig. .Fig. 2PMThe PMCT of a burned corpse provides specific imaging semiologies due to the presence of multiple heat-related body changes . It requFinally, the aim of this pictorial essay is to provide to the radiologist the keys to establish complete and focused reports in the case of PMCT exploration of burn victims, and to respond to the main inquiries of the forensic pathologist, which include: Are there relevant elements that could help identify the victim? Are there possible sites for fluid analysis or DNA sampling? Is there another obvious cause of death other than heat-related lesions? What are the characteristic burn-related injuries seen on the corpse?The identification of the victim is one of the first duties requested by authorities to the forensic coroner. Sometimes, the victim’s burn injuries are minor and superficial, and identification remains feasible by the relatives. Nonetheless, in the majority of burn casualties, corpses are extensively charred and their identification is quite a challenge for the forensic team.External elements of identification can be collected by macroscopic examination of the body , as well as metallic objects which are highly radio-opaque and thus easily seen on the PMCT. Internal medical devices are useful to be reported in order to correlate them with the medical record of the alleged victim: vascular prothesis, osteosynthesis equipment, dental fillings, surgical clips, pace-maker, intra-uterine device, etc. Fig. .Fig. 3InIn more extreme cases, corpses are in such a damaged state that the victim’s secondary sexual characteristics are no more distinguishable and the gender can no longer be defined. Nevertheless, the deep organs are relatively preserved from the heat by the abdominal wall or the peritoneum. Thus, the uterus or prostate are most of the time present on PMCT, allowing the radiologist to determine the subject’s gender Fig. a.Fig. 4TIn these particular extreme cases, the forensic team may encounter difficulties in collecting bone samples. They usually collect the femoral bone to proceed to the DNA analyses . Yet, whIt is interesting then for the radiologist to point out the preserved structures that remain available for DNA sampling in order to assist the identification process Fig. b-c.One of the crucial tasks during an autopsy is the collection of fluid samples for toxicological screening. This step is particularly important in the case of a charred body; in addition to the usual toxicological screening, the forensic pathologist would seek to ascertain the percentage of serum carboxyhaemoglobin (COHb) in order to determine whether death occurred before or during the fire .The success of this procedure depends on the availability of biological fluids directly related to the burning level of the body. A detailed imaging analysis of the PMCT provided by an expert radiologist can be extremely useful to point out the possible collecting sites.Blood is the preferred medium for this toxicological analysis. The post-mortem circulatory arrest results in an increase in the haematocrit level in the dependent regions of the cadaver . Thus, dUnfortunately, sometimes the intensity of heat-related injuries can be so substantial to the point of destroying the thoracic wall and pulmonary tissue of the mediastinum. Subsequently, the blood coagulates in the heart and great vessels preventing any blood collection for sampling Fig. c. In thiTherefore, the radiological report should always embrace a clear description of the blood status in the heart and great vessels, and precision regarding the presence (or absence) of urine in the bladder Fig. d.The main goal of the forensic investigation is to determine the cause of death: is death subsequent to the fire or was the deceased a victim of homicide, and the fire setting being simply an arson to cover it up? The cause of death may also be traumatic and the fire a consequence, which is the case in some car accidents.Once more, the state of carbonisation complicates the post-mortem autopsy. The charred tissues are difficult to dissect from the bones, and the traumatic fractures could be missed by the macroscopic analysis. In the same way, ballistic foreign bodies as plumb shrapnel or bullets could also be missed during the autopsy.Due to its sensibility to metallic artefacts, PMCT is a major asset in gunshot injuries to depict the ballistic foreign bodies and their locations in the burned cadavers . PMCT isSuspicious skin lesions that could evoke a bladed weapon homicide must also be examined. In such cases, contrary to thermal skin injuries, stab wounds exhibit clean edges with evidence of subcutaneous emphysema surrounding the linear cut lines on PMCT Fig. .Fig. 8A see the section “Bones”).In other circumstances such as car accidents, the victim could first decease from severe traumatic injuries and the fire breaks out thereafter. The PMCT then displays post-traumatic lesions similar to a living polytraumatic patient in addition to heat-related lesions Fig. . TraumatThe fire starts by burning off the skin and the soft tissues resulting in skeletal muscle exposure. Exposed muscles then contract and shrink because of the heat, leading to a flexion deformation of the limbs. The upper limbs present a characteristic triple flexion: the body takes the position of a boxer holding his hands in front of him called the pugilistic attitude . Such thBecause of the trivial thickness of the head’s soft tissues and the weak protection on it (absence of clothes), the face and the skull are the predominant areas of heat-related lesions in a burned victim. These injuries are typical, resulting from the effect of prolonged exposure to the heat. Depending on the duration of fire exposure, different patterns are described in forensic medicine .The radiologist must recognise them and distinguish them from the other common post-mortem changes and, more importantly, from other features that could suggest ante-mortem physical trauma or disease.The first injuries occur on the superficial tissues and are macroscopically seen as skin lacerations. They are seen quickly after the beginning of fire exposure and are laboriously detected by PMCT.Then, loss of soft tissues is observed, which is often responsible for large-bone exposure.Under the heat effect, bone lesions begin to appear, visible on PMCT as fine, linear, outer table fracture lines Fig. . The nexIn parallel to these bone lesions, the heat is also responsible for changes inside the cranial cavity.The increasing temperature inside the skull causes dura mater retraction, which exfoliate from the bone and leads to blood exudation from the venous sinuses within the epidural space. Thereafter, under the continuing heat, this blood collection coagulates and forms the characteristic heat epidural haematoma, which is one of the most frequent heat-induced lesions found in burned bodies. The radiologist must distinguish this heat-haematoma from an epidural haematoma caused by blunt trauma. The heat-haematoma is low density and crescent shaped, similar to a subdural haematoma but often crossing the midline and detaching the venous sinus Fig. , whereasIn extreme cases, a complete detachment of the dura mater of the skull arch with retraction of the cerebral hemispheres towards the base of the skull can be observed Fig. .Fig. 14HIn areas where heat acted locally and severely on the skull leading to bone disrupture, the dura mater can still remain intact. On the contrary, sometimes the dura mater is disrupted without being directly burned and heat-induced brain herniation in the epidural spaces develops Fig. a, b. TheThe fire may also damage the teeth, complicating the identification process of the victim. PMCT may show fine tooth fissure (no displacement), tooth fracture (with displacement) Fig.  b and, fWhen the fire starts, the victim—whether conscious or not—will quickly inhale smoke and soot from the fire that will deposit in the deep respiratory tract and the oesophagus. The soot is unfortunately not detected on the PMCT but is visible at the autopsy as black deposits on the mucous membranes.However, other essential signs are viewable on the PMCT and may allow for the deduction that the deceased was breathing, and hence alive, at the beginning of the fire.The first sign of asphyxia is the tongue protrusion through the dental arches and the lips—more easily identified on the sagittal reconstructions Fig.  15]..15].Thereafter, the inhalation of hot gases causes tract mucosal damage inducing pulmonary oedema. This oedema assumes the appearance of a classical pulmonary oedema with diffuse ground glasses opacities reaching the whole pulmonary lobes Fig.. Such leThe intra-abdominal organs are long-time preserved from the fire via the multiple soft-tissue layers protecting them: first the skin and subcutaneous fat, then all the abdominal wall muscles, and finally the peritoneal fascia.The peritoneum’s integrity could be difficult to clarify on the PMCT because of its thinness. However, the visualisation of evisceration of intestinal or other intra-abdominal organs definitely proves its interruption Fig.. This inAs we see, the heat is quickly responsible for skin and soft-tissue thermal lesions. Subsequently, the heat leads to muscle retraction and bone exposure.As for the skull, the first bone heat-related lesions are linear, fine fractures on the exposed bone called thermal cortical fractures.When the limb extremities are completely burned, PMCT shows thermal amputation with transverse, smoothly marginated fractures that are uncovered by soft tissue Fig. a-c. ThisThese thermal fractures and amputations are different from the traumatic ones, which are most of the time covered by soft tissue and presenting clean, angulated margins, sometimes with evident comminution .The heat is also responsible of internal bone structure modifications, especially of the bone marrow. PMCT may show a pattern of mottled lucencies in bone marrow spaces, only where the bone has been directly exposed to the fire Figs. b-19. In PMCT is currently an accessible and contemporary tool for forensic investigations. This pictorial essay highlights the key themes for a practical approach in the scenario of PMCT investigating burned victims and the particularities of PMCT reports, which should contain the available identification elements and technical clues for the forensic team. The semiological analysis must be made according to the fire context and any discrepancies must be brought to the pathologist’s attention in order to guide the autopsy procedure.


PLOS ONE Editors re-assessed the article in collaboration with a member of our Editorial Board and noted several concerns including the following:After publication of this article , concernAcupuncture and related terms are not mentioned in the literature search terms, there are no listed inclusion or exclusion criteria related to acupuncture, and the outcome measures were not clearly defined in terms of reproducible clinical measures.The study included acupuncture and electroacupuncture studies, though this was not clearly discussed or reported in the Title, Methods, or Results.In the "Routine paired meta-analysis" section, both acupuncture and sham acupuncture groups were reported as showing improvement in symptoms compared with placebo. This finding and its implications for the conclusions of the article were not discussed clearly.Several included studies did not meet the reported inclusion criteria requiring that studies use adult participants and assess treatments of >2 weeks in duration.Data extraction errors were identified by comparing the dataset used in the meta-analysis (S1 Table) with details reported in the original research articles. Errors included aspects of the study design such as the experimental groups included in the study, the number of study arms in the trial, number of participants, and treatment duration. There are also several errors in the Reference list.With regard to side effects, 22 out of 40 studies were noted as having reported side effects. It was not made clear whether side effects were assessed as outcome measures for the other 18 studies, i.e. did the authors collect data clarifying that there were no side effects or was this outcome measure not assessed or reported in the original article. Without this clarification the conclusion comparing side effect frequencies is not well supported.The network geometry presented in Fig 5 is not correct and misrepresents some of the study designs, for example showing two-arm studies as three-arm studies.The overall results of the meta-analysis are strongly reliant on the evidence comparing acupuncture versus lactulose treatment. Several of the trials that assessed this comparison were poorly reported, and the meta-analysis dataset pertaining to these trials contained data extraction errors. Furthermore, potential bias in studies assessing lactulose efficacy in acupuncture trials versus lactulose efficacy in other trials was not sufficiently addressed.PLOS ONE Editors retract the article. We apologize that these issues were not adequately addressed during pre-publication peer review.While some of the above issues could be addressed with additional clarifications and corrections to the text, the concerns about study inclusion, the accuracy with which the primary studies’ research designs and data were represented in the meta-analysis, and the reporting quality of included studies directly impact the validity and accuracy of the dataset underlying the meta-analysis. As a consequence, we consider that the overall conclusions of the study are not reliable. In light of these issues, the LZ disagreed with the retraction. YM and XD did not respond.


Incremental sheet forming (ISF) is gaining attention as a low cost prototyping and small batch production solution to obtain 3D components. In ISF, the forming force is key to define an adequate setup, avoiding damage and reducing wear, as well as to determine the energy consumption and the final shape of the part. Although there are several analytical, experimental and numerical approaches to estimate the axial forming force for metal sheets, further efforts must be done to extend the study to polymers. This work presents two procedures for predicting axial force in Single Point Incremental Forming (SPIF) of polymer sheets. Particularly, a numerical model based on the Finite Element Model (FEM), which considers a hyperelastic-plastic constitutive equation, and a simple semi-analytical model that extends the known specific energy concept used in machining. A set of experimental tests was used to validate the numerical model, and to determine the specific energy for two polymer sheets of polycarbonate (PC) and polyvinyl chloride (PVC). The approaches provide results in good agreement with additional real examples. Moreover, the numerical model is useful for accurately predicting temperature and thickness. Sheet forming by means of local deformations or incremental sheet forming (ISF) is a prototyping and small batch production solution to obtain 3D components developed in the late 20th . During The main ISF solutions are punctual incremental forming and doubled sided incremental forming assisted by a partial or full die . Single SPIF is usually accomplished by a conventional Computer Numerical Control (CNC) machine equipped with a clamping frame and a punch that follows a G-code program of instructions. The deformation mechanism is related to the forming forces and, therefore, a good knowledge about forming forces allows tackling several key SPIF problems: How to improve dimensional accuracy; how to avoid machine damage and wear; how to extend the technique to different materials, etc.Despite the industrial interest of low cost small batch production of polymer parts at room temperature , the extThe study of the forming force can be an adequate strategy to advance in forming of polymer sheets. Thus, progress can be made from the extension of forming force approaches for metal sheets to polymer sheets or by developing new forming force models. To the best knowledge of the authors, existing analytical and experimental forming force models were specifically developed for metals, and the scarce numerical models for polymers are not comparable to those for metals.Regarding the force approaches for metals, analytical models define relations between process parameters, material mechanical properties and force. For example, Bansal et al.  relates The existing studies about incremental forming of different polymeric materials are limited and force prediction is not their main goal. In this regard, experimental works check new applications and asses how process parameters influence response variables such as force, roughness and incremental depth . On the The present work proposes a semi-analytical model based on fitting forming force measurements when forming truncated cones for different values of the deformed volume. This deformed volume is determined as a function of the contact surface and thickness. The novelty of this model is to extend the concept of specific energy, used in the orthogonal cutting model to SPIF. In this regard, a new concept called specific forming energy is introduced. This specific forming energy relates the forming forces with the geometric parameters characteristic of the process. The solution is simpler than analytical approaches.Moreover, a coupled thermo-mechanical numerical model, with a hyperelastic-plastic constitutive equation suitable for polymer sheets, is presented. Unlike other aforementioned models with viscoplastic and viscoelastic constitutive equations, the proposed approach studies a hyperelastic-plastic material, only defined by six material parameters, which considers mechanical and thermal response. The material model parameters are determined minimizing the differences between experimental and simulated material stress-strain curves at different temperatures.The structure of the paper comprises: z direction, so that is the one analyzed. A simple method that relates the experimental axial forming forces, with a locally deformed volume, is proposed. Note that the main forming force acts along the z direction requires a power P, so that it should be held that:zF is the axial forming force and Δz is the step down.Following a similar reasoning such as that used in orthogonal cutting, the local plastic deformation induced during SPIF along the V should be constant for a specific material and temperature. This constant U, similarly to the machining case in Reference [S is the affected punch-sheet area, and t is the mean thickness beneath the punch.It stands to reason that the power needed to deform a sheet volume eference , can be S and t at a specific position and U for a specific material, it is possible to solve the Equation (2) for obtaining Fz. When knowing The affected area can be calculated from the expression proposed by Bansal et al.  as:(3)S=Δz, the forming tool diameter D and the angle of the sheet α, since γ can be calculated as:This area depends of the incremental depth The mean thickness of the deformed volume can be calculated from the sheet area deformed in a step down  and the Δz, D, and α. In summary, once the material characteristic U is experimentally determined, the axial forming forces can be estimated from the geometric parameters using Equation (2).Based on all of the above, the deformed volume calculated in Equation (2) depends exclusively on the geometric parameters: The numerical model was carried out in a fully coupled-stress dynamic analysis using the ABAQUS commercial software. The analysis considers the inertia effect, the temperature-dependent of the material response, and the transient thermal response. It also includes the integration of the momentum and the heat flow equations coupling the material energy dissipation during plastic flow, rising the local temperature .−6 s that is small enough to avoid instabilities during the entire simulation. Note that, mesh size influences the approach accuracy, nevertheless the authors selected the element size in order to limit the computational time to 10 h (for an Intel Core i8 CPU) maintaining an adequate concordance with the experimental results. The contact between the plastic shell and the forming tool was simulated by surface-to-surface interaction with penalty tangential behavior and hard contact behavior for normal direction.The Finite Element (FE) model used for the numerical simulation of the process can be seen in Ψ between the metal forming tool and the plastic shell was simulated by gap conductance independent of the contact pressure for polycarbonate (PC) and polyvinyl chloride (PVC) with value of 0.183 W/mK [kμ for PC and PVC are taken from Ludena and Bayer [tk and specific heat pC are taken from the manufacturer’s data sheet. The thermal contact conductance 183 W/mK . Other h183 W/mK ,23,24,25nd Bayer . Thermal2-plasticity theory based on isotropic hardening: A simple hardening law that obtains good results with glassy polymers and low material parameters [μ is the shear modulus, k the bulk modulus, and λ is the limiting chain stretch. The variable The material model employed for the two thermoplastics is a non-linear hyperelastic model combined with Jrameters . The hyprameters  that tak2-plasticity component is based on isotropic hardening, that describes the size change of the yield surface σ0 as a function of the equivalent plastic strain ∞Q the maximum change of the size in the yield surface and b defines the rate at which the size of the yield surface changes as plastic strain develops. If the material is rate independent, the yield condition is:S is the deviatoric stress. The yield function is only dependent on the temperature and the equivalent plastic strain  is described in The procedure to determinate the values of the needed model parameters . In order to validate the models, two additional tests with different conditions to that of the aforementioned tests were carried out.z > 15 mm) where the force stands approximately constant. The numerical model was fitted to reproduce the stationary region, and the final numerical result is portrayed in z, the numerical force is the mean of three values obtained at random punch positions, which can be the reason for the oscillation observed in the estimated curve.The measured axial forming force profile and the numerical estimation, for the tuning tests described in U is the specific forming energy and F0 is the minimum forming force. For each of the tested polymers, different values of the specific forming force and the minimum force were obtained.By fitting the experimental data using a linear function, an equation can be obtained that predicts the value of the axial force as a function of the deformed volume and the step down:The high value of the determination coefficient shows a good agreement between experimental measurements and the linear approximation. Only three experimental measurements are enough to obtain the characteristic curve for a specific material. With this curve, any other contact conditions for a material can be computed, predicting the axial forming forces in a simple and accurate way.In order to validate the numerical and semi-analytical models, a test using a different condition to the previous tests was carried out. The numerical model also provides wall thickness reduction respect to the radial distance . NumericMinimum thickness values are correctly predicted for both polymers. For the PVC sheet, the numerical model also estimates the radial distance where the minimum thickness occurs (maximum stretching). SPIF process uniformly pushes the same percentage of material to the strain direction. At both wall ends, the thickness is greater than that at the initial component wall: Initial thickness is reduced through the wall by stretching the material to the bottom of the component.The study of forming force in SPIF is key to improve the process. Poor dimensional accuracy is an important SPIF drawback that can be treated knowing the forming force. There are several analytical, experimental and numerical approaches to determine the forming force but, in general, the existing solutions are focused on metals. For polymers, which also have interesting potential applications, it is difficult to accomplish forming force predictions because of their more complex material behavior.This work shows two axial forming forces approaches: (1) A simple method based on experimental measurements for different deformation volumes and materials, which extends the specific energy concept used in cutting models to incremental forming processes; and (2) a numerical method, that implements a hyperelastic-plastic material.Material property curves and the axial forming force at steady state for a set of process conditions were used to tuning the numerical model. Regarding to the semi-analytical solution, we conducted experiments with different axial depth, tool diameter and forming angle in order to study the deformation volume influence on the forming force. These tests were repeated for two polymers PVC and PC.Once the numerical and the semi-analytical models were ready, we compared their estimations with the real measurements of additional forming tests. The results agree with the experimental measurements, a percentage of relative error below 10% was obtained. Extending the semi-analytical procedure to more materials, and further works on this topic could lead to a fast and robust forming force estimation model. On the other hand, the numerical model not only obtained a good forming force prediction, but also additional variables such as thickness evolution estimation, which together with forming force are two of the main variables in any mechanical forming process.The two approaches could help to improve the knowledge of the SPIF process on polymers. Each solution addresses a research need: Fast computation , or low experimental cost .


This paper argues that rather than being a process that can be objectively planned or predicted, regional engagement on the part of universities is a learning activity featuring characteristics of a subjective deliberation process. This subjective deliberation process is simultaneously influenced by factors operating at the intra-organizational and regional level, as well as the field in which the university is located. A model that takes this multilevel environment into account is applied to a single case study region: Kaiserslautern (Germany). A Q-methodological approach is employed to reveal and analyze the aforementioned subjective perspectives regarding drivers of universities’ regional engagement. Two key viewpoints emerge from this analysis: one perspective reflects a highly institutionalized reading of regional engagement, and can be traced to the strong policy push to create universities as drivers of regional development in the Palatinate. The second perspective recognizes greater diversity among the various engagement activities pursued by universities beyond this narrow institutional engagement, driven through individual interaction with regional partners. These insights question the widespread pipeline-dominated perspective on universities’ regional engagement and argue for a more systemic understanding of the role of higher education institutions within their region. While it is commonly accepted that universities are key repositories of new knowledge and highly qualified graduates, the roles they are expected to play have changed significantly over the past 30 years. It was in the 1980s when claims regarding universities’ regional engagement first arose in regional policy. In contrast to the first two pillars – research and teaching – regional engagement is a multifaceted and hard-to-demarcate phenomenon. Various perspectives exist on the mode of interaction with the surroundings (the how) as well as the types of activities pursued (the what). These perspectives reveal that universities’ interactions with their respective region have a dual nature. This duality refers, on the one hand, to contributions via linear, direct knowledge transfer activities (pipeline-dominated perspective), while, on the other, it entails contributions that emerge from formal and informal participation in regional networks , the standardization of the attendant processes, as well as the adaptation of regulative frameworks in order to reward these kinds of activities. However, this pipeline-dominated approach covers only a small part of universities’ regional engagement activities and provides an incomplete representation of the wider processes of knowledge exchange and co-production, networking and collaboration between university members and regional stakeholders  lead the author to question the pipeline-dominant perspective and, consequently, the assumed potential to strategically ‘plan’ universities’ regional engagement activities. First, universities are complex organizations of loosely coupled entities Weick,  with higThe contribution of the present paper is to reflect on the interplay between these three factors, namely (1) the multilevel nature of the model of action displayed by individual university members, (2) the high autonomy of the individual within academia and (3) the low degree of institutionalization of regional engagement . The paper addresses this by asking the research question: Which organizational, regional and organizational-field-level factors most strongly influence the individual deliberation process related to regional engagement? To answer this question, the paper develops a conceptual model that aims to grasp the factors from the multilevel environment which influence this subjective deliberative process. This is applied to a single case study region, Kaiserslautern (Germany), where a Q-methodological approach involving 18 key stakeholders is applied. Q-methodology offers the opportunity to highlight subjective perspectives on commonly occurring themes. These individual perspectives are clustered through factor analysis to reveal shared viewpoints on the topic. Actors from university management and faculty as well as regional actors from politics, business and regional development are involved to capture various perspectives on the occurrence of and drivers behind universities’ regional engagement. By identifying a number of key factors influential in these deliberations other than those typically highlighted in the literature, this paper argues for a more systematic consideration of these subjective deliberation processes to improve understanding of university engagement.The literature review depicts the current scientific discourse on factors influencing the occurrence of universities’ regional engagement activities and derives a multilevel model that leads the further operationalization and analysis.Higher education institutions (HEIs) have been subject to various reforms during the last decades. These reforms aim to modernize the higher education (HE) sector in order to increase its efficiency and effectiveness, and to orient its service more towards the expectations of the customers  the founding history as well as reasons for founding the university, or ‘historical imprinting’ Scott, ; (2) theThere are two components to the geographical environment of the host region: territorial and relational. The territorial element is defined by the spatial domain designed by the administrative or geographical barriers of the territory within which the HEI is located. The relational component implies regional innovation system (RIS) network structures, partnerships and social capital which define the regional boundaries. The factors within the host region which may exert influence on regional engagement activities comprise: (1) the regional absorptive capacity and willingness to cooperate; (2) economic policy and regional policy and their programmes and strategies; (3) expectations towards regional engagement which are published in the regional and national media; and (4) regional networks and governance structures.The organizational field conceptualizes the interactions between diverse, interdependent organizations that participate in a common meaning system , Enschede (the Netherlands) and Linz (Austria). The current paper presents results from the Kaiserslautern case study.The first step was identifying the key stakeholders in this domain from within the university, from the region and also from the wider system-level domain. Apart from scientists, also representatives from the management as well as intermediaries  from the Technical University of Kaiserslautern and the University of Applied Sciences Kaiserslautern were asked to conduct the Q-sorting exercise. In addition, also cooperation partners from politics , regional development agencies and interest groups representing the local and regional economy were addressed. They were asked to give their subjective perceptions as to the relative importance of various factors in influencing university–region cooperation. Each participant underwent a semi-structured in-depth interview in addition to participating in the Q-methodological ranking procedure.Q-methodology was developed by William Stephenson as a means of gaining access to subjective viewpoints  inhabitants. Forecasts indicate a total population loss of around 3% by 2020 , but the oil crises in 1973 as well as the land-intensive settlement structures of the military inhibited the further development of the industry. In the 1970s several companies went into crisis; in 1981, the Kammgarnspinnerei became insolvent, while Pfaff as well as Opel dismissed employees. The primary and secondary sectors declined in importance , while the tertiary sector became a job engine . Industrial foci are: the automotive supply industry, chemical industry, information technology and media, logistics, engineering, and utility vehicles.The city of Kaiserslautern and the surrounding region face several challenges, despite the facts that they are the centre of the regional labour market and their gross domestic product (GDP) per inhabitant is above the German average. For years Kaiserslautern has had the highest per capita debt of all German cities and therewith a lack of investment capital. Furthermore the city and region are characterized by youth unemployment rates and low-skilled worker unemployment rates which are above the German average. Although various job opportunities have been created via active firm settlement policies as well as spin-off foundations of the HEIs, the skills requested do not match with those of the low-skilled workers who had previously been employed in industry.It was in the late 1960s and early 1970s when the Technical University Kaiserslautern (TUK) as well as the University of Applied Sciences Kaiserslautern (UASK) were founded. The political intent was to realize a structural policy via HE policy. Both HEIs were founded with a clear expectation of spurring the economic development of the region and engaging with the regional environment. The initial focus was on natural sciences and technology, followed by the integration of social and economic sciences. Due to the foundation and attraction of cutting-edge research institutes  as well as international companies  and the efforts of the UASK, the city was nominated ‘city of science’ in 2012.This section presents the results of the Q-sort, followed by an analysis and discussion of the findings. The Q-sort methodology discloses two shared viewpoints in the case study Kaiserslautern. These two viewpoints differ in the importance they assign to organizational, regional and organizational-field-level influencing factors, according to the relative ranks they assign them. They are subsequently referred to as viewpoint 1 (factor 1) and viewpoint 2 (factor 2). The individual perspectives of stakeholders from regional development agencies (R), policy-makers (P), regional economy (W), as well as the HEIs  split between the two viewpoints.Viewpoint 1 emphasizes the influential role of drivers from the universities’ organizational level . It is cViewpoint 2, on the other hand, emphasizes the influence of factors from the organizational-field-level as well as intra-organizational factors. (1) The change of meaning of universities’ regional engagement on the national and international level ranks first in terms of perceived importance, followed by (2) the self-perception of the university, and (3) the role of individual personalities. Nearly no influence is attributed to the role model effect, experiences of other HEIs with regional engagement activities, or HE legislation. Expectations towards regional engagement activities of universities which are published in the regional and national media are seen as exerting no influence at all.Surprisingly, the regional environment and drivers therein, such as regional networks and governance structures, economic policy and regional policy, as well as the regional absorptive capacity seem to be of low to no importance with regards their influence on the occurrence of HEI–region collaborations.The Q-factor analysis provides an objective account of commonalities between subjective viewpoint, in this case of what matters in influencing engagement in the Kaiserslautern region. To understand these results in the context of the Kaiserslautern region, it is necessary to take into account other empirical data gathered within the case study.One of the emerging perspectives relates to a highly institutionalized reading of regional engagement, and can be traced to the strong policy push to create universities as drivers of regional development in the Palatinate. The second perspective is a much more diverse view, and reflects the fact that universities engage in many kinds of engagement activities beyond this narrow institutional engagement, driven through individual interaction with regional partners. More information is provided on these two perspectives below.Viewpoint 1 is shaped by the influence of high personal motivation, the need for third-party funds, as well as organizational support and incentives. The underlying understanding of regional engagement is an economic one, insofar as knowledge transfer to regional businesses and industry, research and development (R&D) cooperation and spin-off creations receive foremost attention.numerus clausus system. While at the location in Trier the focus was on social sciences and humanities, the location in Kaiserslautern was clearly aimed at providing ‘development aid’ to the region, which was also mentioned as a reason to focus on natural and technological sciences. The TUK started its operation in 1969 with the faculties of mathematics, physics and technology as well as environmental and spatial planning, biology and chemistry, plus engineering a year later.Although the importance of the founding history is not consciously outlined as influential (array 0), viewpoint 1 is historically imprinted. The Minister for Culture and Education of Rhineland-Palatinate argued in the 1970s for the establishment of a two-campus university in Trier and Kaiserslautern, with Kaiserslautern having a more technical profile. The education-policy driven motivations were (1) the continuing shortage of teachers in the federal state, (2) the increasing number of students with an university entrance qualification, and (3) the lack of study places due to the Even at a time when there was no encouragement for engagement, engagement still happened. The understanding of regional engagement at the TUK was for decades strongly shaped by the personal engagement by two professors in physics and mathematics. Although it was rather uncommon and referred to as ‘unscientific’ at that time to approach partners in the regional economy and offer solutions to their everyday challenges, these professors perceived it as their normative obligation as well as an opportunity to raise additional funding. Especially the mathematics professor was well known for approaching regional industries and companies, offering them solutions for their everyday problems, and establishing cooperation. Via student projects, informal consulting and contract research solutions for the problems of regional businesses and industry partners have been elaborated, which later on developed into more formal R&D cooperation. It was also this professor who established the first Fraunhofer Institute on the campus of the university and initiated the ‘Science Alliance’ in 2007, a network between university researchers, university management, the UASK and partners from the regional economy.Based on the increasing number of regional engagement activities, the university management established a TTO to institutionalize and foster university–industry cooperation in other areas as well as to support spin-off foundations. Furthermore, incentives such as a reduction of teaching obligations for engagement in industry cooperation are established at both HEIs. These types of regional engagement activities became well known for the TUK as well as the UASK, although they are limited to the natural science and technology faculties. These economically focused activities benefit from new organizational units, reduction of teaching obligations, recognition of scientific achievements, and the mission and self-perception of the HEIs.Viewpoint 2 in the case of Kaiserslautern describes regional engagement activities in the broader context of an overall normative and cognitive change towards a more comprehensive understanding of universities’ regional engagement in the German HE system. Although the HE system is the responsibility of the federal states, the German government exerts influence via flagship reports  or funding programmes . Furthermore, most federal ministries foster the implementation of boards of trustees (‘Hochschulkuratorium’) within the HEIs. They represent new organizational units which, in Rhineland-Palatine, assemble 13 external representatives from the regional and federal level .The main purpose of this board of trustees is to consult with the university senate on how to better anchor the HEI in the region and regional knowledge demands, as well as  need for further education opportunities. Although this top-down policy push is aimed towards strengthening the regional mission of the respective HEI, the focus of the board of trustees depends very much on the elected representatives. Interview partners who support viewpoint 2 emphasized that apart from the commercialization-based activities of TUK and UASK there are several engagement activities which are undertaken by faculty from social and economic sciences. These activities entail consultation for public authorities , participation in regional initiatives and platforms as well as student projects to support the city government in topics such as integration of the long-term unemployed and other forms of segregation. Most of these activities are accompanied by small project budgets if not performed on a completely voluntary basis, which is also reflected in the low ranking of the driver ‘need for third-party funds (U)’.The respective interview partners point out that these kind of regional engagement activities are often seen in a field of tension between provincialism and the need to compete for international excellence. University institutes engaged in these activities do not publish as much or raise as much third-party funding as others do, which brings them into the situation of having to justify these activities. Due to these aspects it is noted that these kinds of regional engagement activities are conducted mainly by senior personnel with mostly permanent working contracts, who feel a normative obligation due to the role/position they occupy. Though these types of engagement activities are tolerated, they are not perceived as being actively supported or rewarded within the organization .Based on these two viewpoints and the associated reflections, one can deduce a couple of ‘stylized facts’. First, individual personalities play an outstanding role in pursuing regional engagement based on their convictions, values, norms and motivation. Second, networks are important but not dominant in the subjective deliberation process towards regional engagement. Third, technology transfer is anchored in university strategies and institutional support . In contrast, this is not the case for non-technological knowledge exchange, which is more reliant on individuals. Fourth, the region’s absorptive capacity does not decisively shape engagement behaviours and collaborative structures.The investigations confirm that the decision to engage regionally is a deliberation process which is simultaneously influenced by several drivers operating on multiple levels. This paper corroborates the essential role of individual personalities  for initiating regional engagement activities and serving as role models. They undertake commercialization- or non-commercialization-focused regional engagement activities due to their normative conviction of their role , a feeling of responsibility , or also the need to fulfil certain evaluation criteria . Interestingly, within the present case study the role of regional networks and governance structures ranks somehow in a neutral position. This is surprising as there are strong networks between the university and various regional stakeholders. One tentative explanation is that they are taken for granted and thus not consciously given the attention they might deserve. Especially new organizational structures such as boards of trustees at HEIs, which was initiated by the ministry, but also platforms such as the ‘Science Alliance’ foster and strengthen the relational ‘thickness’ in the region. The latter was bottom-up initiated by one professor of the TUK, who became an agenda-setter in shaping regional networks. What becomes evident is that those activities which refer to the linear transfer and commercialization of academic knowledge  enjoy a high degree of support and institutionalization by the university management as well as ministry, while non-commercial regional engagement activities rely very much on the ambitions of individual personalities inside and outside the HEI.In contrast to the literature , the paper shows how the multilevel environment, within which the university is located, influences the universities’ regional engagement. It becomes apparent that the occurrence of regional engagement activities  cannot be explained by individual indicators  but has to be viewed more systemically.The identified two viewpoints illustrate that university engagement is a balancing act between individual autonomy and expectations expressed by the university environment. Two shared viewpoints were identified among stakeholders, each of which is shaped by different intra-organizational, regional and organizational-field factors. One perspective relates to a highly institutionalized reading of regional engagement, and can be traced to the strong policy push to create universities as drivers of regional development in the Palatinate. The second perspective is a much more diverse view, and reflects the fact that universities are engaged in many kinds of engagement activities beyond this narrow institutional engagement, driven through individual interaction with regional partners.The case study and analysis highlighted four stylized facts evident in this multilevel perspective. The first and arguably most influential is the role of individual personalities (‘frontrunners’). These frontrunners are well known in the region and important contact persons for regional stakeholders. Furthermore they function as role models for their university’ colleagues. Second, networks are important but not dominant in the subjective deliberation process towards regional engagement. Third, technology transfer is anchored in university strategies and institutional support . Non-technological knowledge exchange, on the other hand, is reliant on individuals.The study also sheds a light on the persistence of a number of multilevel tensions. On the one hand, universities and their members have a rather high autonomy in defining their understanding of regionalism. On the other hand, as an open system they are prone to regional expectations and general developments of the (inter-)national HE system, which affect the subjective deliberation process and have to be considered in the process of creating a strategy. A strategy which is based on the pipeline-dominated approach covers only a small part of universities’ regional engagement activities and gives an incomplete representation of the wider processes of knowledge exchange and co-production as well as networking and collaboration between university members and regional stakeholders. Recognizing and incorporating the subjective deliberative processes, which are influenced by the multilevel environment the university is located in, may lead to a more systematic and balanced perception of the role an HEI can play within its region.


Consistent state estimation is a vital requirement in numerous real life applications from localization to multi-source information fusion. The Kalman filter and its variants have been successfully used for solving state estimation problems. Kalman filtering-based estimators are dependent upon system model assumptions. A deviation from defined assumptions may lead to divergence or failure of the system. In this work, we propose a Kalman filtering-based robust state estimation model using statistical estimation theory. Its primary intention is for multiple source information fusion, although it is applicable to most non-linear systems. First, we propose a robust state prediction model to maintain state constancy over time. Secondly, we derive an error covariance estimation model to accept deviations in the system error assumptions. Afterward, an optimal state is attained in an iterative process using system observations. A modified robust MM estimation model is executed within every iteration to minimize the impact of outlying observation and approximation errors by reducing their weights. For systems having a large number of observations, a subsampling process is introduced to intensify the optimized solution redundancy. Performance is evaluated for numerical simulation and real multi sensor data. Results show high precision and robustness of proposed scheme in state estimation.  Reliable state estimation from noisy measurements is one of the essential requirements in numerous real time scientific and engineering problems. Therefore, accuracy is the primary constraint in applications such as target tracking, automotive land vehicle and flight control systems, non-linear process control and optimization, real time surveillance and life safety applications etc. ,2. HowevThe presence of outliers severely degrades the reliability of the state estimation process of non-linear dynamic systems. In most of state-of-the-art estimation techniques , observation non-linearities are not treated discretely and assumed to be corrected along with modeled noise during estimation process. However, outlier observation’s impact on the estimates is more severe and cannot be treated the same as model errors. In addition, the accuracy of estimated states also directly depends upon the error models of system dynamics which are established based on predefined assumptions. The accuracy of the estimation process is highly impaired in the case of divergence of the defined assumptions. In the case where system dynamics diverge from assumptions, most likely the estimates will diverge or at least will not be as useful. Hence, for reliable state estimations, the process must likely be able to handle the divergence from model assumptions. There are several types of error sources which effects the estimation accuracy, specifically in filtering-based techniques. Outliers, as pointed out by Gandhi in , can be From the defined outlier types, observation outliers originate from the measuring systems themselves such as random walk, Gaussian distributed errors, measurement error drifts, etc. or can be caused by a poor system calibration process. Observation outliers’ results in wrong measurements that can lead to divergence of the system from the true state or to wrong convergence points. In contrast, innovation outliers are caused by poor system state dynamic assumptions, i.e., a stable system assumption for an unstable system, wrong state forward and process noise models. Innovation outliers affect the accuracy of system state prediction and the process noise transfer model. Structural outliers are caused by wrong system models as a whole, for example a system structure where states are wrongly modeled against true system dynamics and/or true observations. As a result, observation and innovation outliers can be handled through robust state estimation processes, however, it is not possible to remove or recover from structural outliers.Multiple estimation models, including filter-based and direct estimation procedures, have been proposed in the literature to minimize the effect of outlying observations on final estimates. In direct estimation approaches, the present state depends only on the current measurement and not affected by prior measurements or states. These approaches are specifically useful in situations where estimation/measurement errors do not keep on in future predictions such as GNSS-based localization systems, data analysis frameworks in statistics and econometrics, etc. Least squares estimation (LSE) and its variants , the Gau4 battery. However, the proposed framework is only beneficial in applications where the system model precisely (or closely) defines the system characteristics having very little rate of change. In [The Kalman filter (KF) is one of state-of-the-art estimation algorithms that has been extensively studied for all kinds of state estimation problems for noisy measurements in discrete-time dynamic systems. The primary objective of the KF is to track the dynamic changing states in the presence of noisy and incomplete measurements. Classical KF statistically provides an optimal state of a linear system by incorporating various noise characteristics, in addition of a forward model and an observation model. However, KF is considered as the optimal filter only for linear systems having Gaussian distributed noise. On the other hand, most real world problems are non-linear, and the performance of the KF severally degrades when the assumptions related to system linearity and Gaussian distribution are violated. In order to enhance the performance and to overcome limitations of KF against system nonlinearities, Extended Kalman Filter (EKF)  was intrange. In , authors(1)A filtering-based optimization scheme founded on IEKF process.(2)Robust state approximation and error covariance estimation model.(3)Robust dynamic state update model built on a robust statistical estimation process .In this paper we have developed a framework for handling system and measuring model nonlinearities either because of modeled or unmodeled errors. In our prior work , we studThe proposed estimation model is named Robust-Reweighted IEKF. The rest of the paper is organized as follows: in The extended Kalman Filter (EKF) is one of the most widespread tools for state approximation or data assimilation for non-linear dynamic systems. In EKF, the state estimation problem is carried out sequentially, where first the model states are approximated using prior system states and are then updated from the new available observations.Every time a set of measurements Within the above equation, (1)First step involves state transition matrix and system states approximations between two time instances from m × m continuous time state transition matrix based on partial derivatives of In the above expression, A is a m × m identity matrix, the solution of state transition equation is expressed as:As the initial expression (2)In a similar manner, the forward model error covariance is projected to the next time instance using the following expression:With an initial state (1)The observation update operator involves the calculation of the observation residual vector (2)Computation of the Kalman gain matrix using the following expression:(3)Update the estimated states using the current observations:(4)Update the forward model error covariance matrix:Every time a new observation is available, EKF provides a new state estimate. For a real time solution, corrections are added to the system states for each observation. Moreover, partial derivatives of system dynamic functions are recomputed along with each observation resulting in a more accurate updated state transition matrix.EKF is seriously capable of tracking non-linear system dynamics with higher accuracy. However, the performance of EKF is degraded because of truncation, system modeling, linearization and cumulative rounding errors, in addition to unknown non-Gaussian noise. The impact of these errors can be reduced to some extent by taking a few careful measures such as increasing the observation frequency, a careful filter calibration process and offline data tests to design an appropriate model for the system dynamics. Despite all efforts, the performance of EKF is badly influenced by an increasing amount of nonlinearities and the wrong state transition model that results in divergence from true system dynamics.An alternative solution to overcome the EKF limitations is the IEKF estimation model. In IEKF, linearization is achieved in multiple iterations based on an initial guess In the above expression, The complete IEKF process is summarized in Algorithm 1.Algorithm 1. IEKF estimation model(1)Step 1 : Initialization of IEKF is similar to EKF as the initial state vector (2)Step 2 (measurement update iterations): Measurement iterations are started by initializing Step 2 is iteratively executed once a stopping criterion is achieved, i.e., the difference between two successive approximations is less than a predefined threshold (3)Step 3 : Once the stopping criteria is achieved, the state vector and covariance matrix is finalizedIEKF can only attain the optimal states in the presence of an error-free state prediction model, and having a limited number of observation outliers. Although, IEKF can attain an optimal state in the presence of observation outliers, it has minimal effect on state innovation outliers (errors). In order to manage the non-linearity within the system model and outliers within observations, in this section, a robust reweighted iterative extended Kalman filter (RRIEKF) is proposed. Before derivation of the proposed filtering algorithm, some details related to problem formulation and robust estimation are discussed.n number of total data points and m number of contaminated observations where An optimal estimator is focused on predicting state variables maintaining the efficiency and robustness. By robustness it means that the estimator must preserve a small variance within observations though observations are not following the prior  system model assumptions related to innovation and measurement errors. Theoretically, a robust estimator defines an estimation model that fits most non-linear systems. In the robust estimation process, the influence of the outlying observations is minimized by reducing their weights throughout an estimation process. Additionally, robust estimation models can also be used to detect and remove observation outliers within highly non-linear and dynamically changing system models. Let us consider an estimation function BDP provides a measure of contaminated observations for estimator failure. On the contrary, the relative efficiency of an estimation function can be defined as the ratio of the real variance to the possible minimum variance . RelativThe influence function is another robustness parameter that describes the effect of very small contamination bias at some random point. Mathematically, the asymptotic influence function for an estimation algorithm Robustness properties, i.e., boundedness and continuity, are satisfied by the influence function. The former property ensures that the presence of the small segment of bias or outlier should have a small effect on the estimate. The latter property guarantees a little change in the guess in the presence of a small change in data. A filter satisfying both properties has robustness in terms of qualitative robustness.n former estimates is maintained and an initial prediction is optimized using current and prior estimates. The second step involves the pre-whitening of observations through robust statistics in order to uncorrelate the predicted and measured observations. The pre-whitening process involves the batch mode prediction model. Thirdly, a robust process error covariance matrix is predicted prior to starting the iterative optimization process. Finally, robust regression statistics are involved in an iterative manner to minimize the error residual between estimated and measured observations.The proposed estimator is developed on four key foundations of filtering-based estimation approaches, first the final estimate is highly dependent upon the initial guess. A reliable and bounded initial guess can quickly lead to a convergence point. For a reliable initial guess, a window of n prior estimates along time. Prior states are processed in batch mode regression simultaneously for maintaining data redundancy. Data redundancy improves the capabilities of the estimator for dynamic state estimation and helps to suppress outliers. Let us consider a non-linear dynamic system with some prior information related to system dynamics and prior distribution For a reliable state prediction, a window is maintained over The probability density function (PDF) of prior information is expressed as;In the above expression, Our objective is to compute an aposteriori estimate that results in maximum density function. The required objective can be attained by grouping the dynamic system model, stated forward process model and prior information in batch-processing regression form as follows:The above expression defines the error covariance matrix for the initial estimate.It is well known that the performance of the Kalman filter is highly dependent upon the exact knowledge of system dynamics and statistical noise properties such as process noise Considering a random variable Considering n, new error covariance is computed using the algorithm as described in the following.Using the definition that Having initial estimate Using Equation (4), Equation (22) is modified as:According to the least squares estimation (LSE) principle:Replacing Equation (24) in Equation (23):According to the definition that Representing n observations, the optimal state estimation algorithm is described in the following sections.The primary objective of robust filtering is to minimize the residual function. The required objective is achieved through projection statistics within the robust filtering process in an iterative manner. According to the standard definition that using projection statistics in estimation process, the estimator can track possible future changes in observations only if the assumptions about the system dynamics are actually occurring. The projection statistics of the A subsampling process is involved just in case enough observations are available against the minimum requirement. The primary objective of the subsampling optimization is to increase the redundancy of error-free solutions. In the subsampling process, all candidate solutions are optimized iteratively. However, if it is necessary, all observations are considered as a single subsample. Using the least square principle:In Equation (30), Using a standardized residual vector, observation weights are calculated according to the following expression:Weights are applied in Equation (30) to update the estimate.Once the system states are updated using weighted least squares (WLS), the updated states are used in the IEKF observation model using Equation (9) in an iterative manner to determine the optimal point. If more than one solution are available, Equation (9) is applied to all solutions from the first step. The residual vector is computed between recent and last estimates and is used to compute a scale factor. Using Equation (31), weights are computed using Tukey’s biweight function as:In the above expression, 4.685 is selected as the threshold as it provides a 50% break down point (BDP). In the above expression, In the case of a single solution, the optimized state in an iterative process is taken as the final state. On the contrary, for multiple solutions, an optimized state having a minimal scale factor is considered as final state:Here In this section, some key aspects of the proposed estimation algorithm are discussed. Later on, the performance of the proposed algorithm is analyzed. Primarily, all filtering-based approaches consists of two key steps, state prediction, and state approximation. State prediction steps involve an initial guess of future changes in system dynamics. This initial guess is made by considering the last approximation as a true system state and using a system dynamics model. Later on, the initial guess is corrected using system observations. However, if either the initial guess or correction model deviate from the true dynamics, this error will pass on in future estimates and may result in a complete system failure. In our proposed estimation procedure, inconsistency in the initial guess is handled through batch mode processing. The key objective of using a batch mode model is to attain reliability and consistency at the state prediction level through increasing redundancy. Another important aspect to enhance the estimation accuracy is the precise knowledge of the process error covariance matrix. Here, we provided an online scheme for handling the process error covariance over time adaptively. The second phase of the algorithm involves the correction of the initial guess using observations. To reduce the influence of noisy/outlying observations, a weighting model is adopted for calculating and assigning weights to measurements. This process is iteratively performed to achieve an optimal estimate. Within every iteration, new weights are calculated and the prior estimate is updated. A subsampling process is used in numerous statistical estimation techniques to provide a generic understanding of the data in robust and multivariate regression theory ,30. The Most of the research focused towards robust filtering  is for model-based systems where the system dynamics are stable or are changing very slowly. Within these algorithms, the primary objective is to handle observation nonlinearities ,20,23,25Performance of the proposed algorithm is assessed using extensive numerical simulations and real sensor measurements for multirate sensor data integration. Numerical simulations provide a way of performance assessment in a controlled environment under defined conditions such as number of outliers, scenario complexity, noise level, etc. On the contrary, physical measurements show the performance in real stable and dynamic environments. For performance assessment, conventional EKF, UKF and IEKF results are compared against the proposed estimator. For numerical analysis, datasets are simulated using non-linear state models under Gaussian distributed noise conditions. For true scenario analysis, inertial, and UWB sensor measurements are integrated over time for system localization. However, for creation of complex scenarios, additional noise and outliers are introduced within the sensor observations. In this subsection, two scenarios are simulated. The first scenario involves an accelerative and turning model maneuver with additive noise. The simulated trajectory and tracking results for UKF, EKF, IEKF and RRIEKF are shown in The tracking performance over turn provides more realistic analysis of an estimation algorithm. The Estimation errors are shown in A careful analysis of For analysis of performance over complexity, a non-linear multivariate system is modeled with added Gaussian distributed noise. Estimation errors are shown in the Estimation errors provide a generic overview of an estimation algorithm, however, step by step analysis provides a more realistic understanding. A deep analysis of The performance of the UKF is a little better among the three filters, UKF, EKF and IEKF. However the performance of the proposed estimation process is far better than UKF. Another measure of the performance is the residual confidence level. In The primary objective of this analysis is to prove the effectiveness of the proposed estimation algorithm for integration of noisy multirate sensors. Observations from IMU and UWB sensors are integrated over time for localization of moving bodies. The frequencies of the IMU and UWB outputs are 100 Hz and 25 Hz, respectively. IMU measurements are used in the state prediction model while UWB measurements are used for correction of the predicted states. Hence, compared to the observation frequency, the state prediction phase is very important because several times only noisy IMU measurements are used for localization. Therefore, it is an essential aspect to control errors at the state prediction level and this is handled through an online process error covariance estimation model. The results are compared in terms of estimated position errors and estimated speed. Position estimation errors from different filters are shown in the In In The estimation performance of the proposed algorithm is significantly influenced by the presence of observations noise and measurement outlier in comparison to results in A novel algorithm is presented in this paper for estimation of non-linear and dynamic system states. Non-linear system state approximation and tracking over time is a contiguous problem, especially in navigation and localization systems, and has been addressed by several researchers. The proposed estimation algorithm is derived from the concept of robust regression theory and the Kalman filter. A weighting scheme is used to reduce the cumulative effect of outlying and noisy observations through down-weighting in an iterative process. For performance assessment, extensive numerical simulations and real tests are conducted and results are compared with the state-of-the-art estimation algorithms, EKF, UKF and IEKF. Numerical simulations demonstrate the higher approximation performance of the proposed estimator over conventional estimators, especially in tracking of dynamic states of complex model-based non-linear systems. On the other hand, the proposed algorithm is proved to be a promising process for integration of multirate sensor measurements in the presence of noise and outlying observations. In the near future, we are planning to use the proposed algorithm for localization through integration of global satellite navigation system (GNSS), IMU and visual sensor measurements and comparative analysis with proposed state of the art estimation techniques.


As a dynamic system in which different factors affect human performance via dynamic interactions, mental workload needs a dynamic measure to monitor its factors and evidence in a complicated system, an approach that is lacking in the literature. The present study introduces a system dynamics-based model for designing feedback mechanisms related to the mental workload through literature review and content analysis of the previous studies. A human-based archetype of mental workload was detected from the data collection process. The archetype is presented at various stages, including dynamic theory, behavior over time, leverage points and model verification. The real validation of the dynamic model was confirmed in an urban train simulator. The dynamic model can be used to analyze the long-term behavior of the mental workload. Decision-makers can benefit from the developed archetypes in evaluating the dynamic impact of their decisions on accident prevention in the complicated systems. The reason for errors, crashes, accidents and disasters made by human can be due to unbalance mental workload resulting in overload and underload situations exposing operators to approach or exceed the redlines of their performance  AND . We screened 339 articles out of 979 articles based on the inclusion criteria, including original article, full-text article, publication in English, quantitative data analysis, transparency in presenting research methods and results, and other quality rating appraisal . Direct content analysis followed by theme analysis helped to extract contributing factors, evidence, and conceptual static and dynamic models . The VeThe third stage was the definition of the stock-flow diagrams to the math equations ruling on the variables. The formulation in the Vensim software was done through simple math equations such as integration. The look-up function in the Vensim software allowed us to estimate the internal relations of some variables in relation to each other over time. The fourth stage was the simulation and testing process. We extracted the simulation outputs as the graphs displaying the relationships between the variables over time. There are different methods of validation, including the structure test, boundary test, dimension consistency, parameter verification, extreme conditions, and structurally oriented behavior test to validate the stock-flow diagrams  volunteered to participate in an experimental study. The experimental route was a part of Tehran metro line 1 . The scenarios were designed according to the real train operation and the rule books. The experimental tasks were divided into two categories: getting ready to drive and driving between stations. The getting ready to drive consisted of changing the train's direction; completing the train dispatching procedure, including train activation, train safety activation, and check-up; observing and obeying signaling indications and train warning systems. The driving between stations consisted of driving the train along the track; obeying speed limits and other train protection orders; monitoring the surrounding environment; observing and obeying signaling indications and train warning systems; stopping the train at a specific area in the stations; opening and closing train doors; entering and leaving stations within the speed limit. A high-fidelity simulator was used for the experimental driving tasks in this study. Train drivers evaluated their workload status by using the integrated workload scale (IWS) during the particular driving tasks. The mental workload can be assessed by specific subjective scales. The IWS is a self-rating tool to assess mental workload in real-world settings, and it is sensitive to several environmental and task-related factors in railway industry , the wet bulb globe temperature (in °C) and the noise level (in dBA) were measured to ensure that these parameters were consistent between the real and simulated environment. The average judgment of drivers from their mental workload in different situations was entered manually in the reference mode of the Vensim software. In the fifth stage, the model behavior over time was utilized to design the leverage points. The leverage points could have advantages for decision makers to improve the complicated systems . The flAccording to previous reports and content analysis, we extracted the main variables (or measures) probably useful for the assessment of the mental workload in a complex system. Accordingly, the result was 90 measures (variables) in this regard. These variables were further condensed according to some of the common themes that were later classified under 9 categories. These variables were grouped into 23 themes (sub-factors) and three factors (or evidence), namely 1) task demand and job characteristics 2), external and environmental stress, and 3) individual capabilities and characteristics. Previous research on the mental workload underlined three main gaps, which may prevent previous findings widely used in practical fields in the complex systems. First, the studies are limited to one or two variables. Second, the results are too narrow with no approaches for generalizing the results. Third, all results are static, where just one variable has been assigned to mental workload during the study. In contrast, mental workload is usually a time-based issue, and the type of demand assigned to operators is a dynamic pattern.To address these gaps, the human-based archetypes were developed by system dynamics to account for the various factors of mental workload. Using this dynamic model, the dynamic relations among mental workload and related contributing factors were depicted to optimize mental workload balance resulting in improved performance. The human-based archetype of mental workload is presented as follows.The basic principles of the physiological dimensions of mental workload follow the assumption that physiological responses depend on physiologically active mechanisms in high-demand jobs .The alterations in resource capacity and operator performance can be seen in the physiological feedbacks . MoreovMental activation, and B2 Mental burnout). According to the theory of limited resources, the human mind has limited resources for thoughtful processing .Individual characteristic is a key auxiliary variable that influences the internal variables, including psycho-physiological response, workload modification, resource consumption, resource recovery, and productivity ratio and ultimately the recognized feedback loops. (The formulated model and its equations are presented in Supplementary Table 3.)Mental activation loop will be activated and the resources will be available. Simultaneously, the new resources are replaced by resource recovery process .Mental burnout loop. This archetype focuses more on the human aspect of complex systems. Behavior over time of this archetype shows that the mental overload leads to overcoming the resources consumption rate to resources recovery rate. As a result, mental fatigue appears in the short-term and mental burnout in the long-term. Therefore, moderation of mental demand and external stress should be proportional to individual characteristics. This moderation, on the one hand, should be made in such a way as to provide an opportunity to recover resources over time. On the other hand, the performance pressure should be appropriate as the other input of the mental workload.The leverage point of resource supply and mental workload archetype relies on the balancing B2 The verification methods used in this study were the structure assessment, the extreme conditions, the sensitivity analysis, and the real validation. The structure assessment tests verify that the model is compatible with real-world knowledge . In thiThe results of behavior over time for the human-based archetype of mental workload showed the behavior of the dynamic model is logical at different levels of the task demand. Sensitivity analysis  identify changes in mental workload and its outcomes, such as task performance and mental fatigue during operations. 2) extract corrective and preventive measures based on a dynamic approach to optimize workload during complex operations. The current study in line with the previous studies has shown that both overload and underload could have adverse effects on humans and their performance .In this study, factors affecting the workload were reviewed over a ten-year period. Although data saturation was achieved in the extraction of themes and factors, the researchers encountered a lack of studies to confirm some of the relationships between variables. To overcome this shortcoming, content validity and expert judgment were used . An updIn the present study, the conceptual framework of mental workload and its contributing factors was developed using literature review. The system dynamic approach indicated the interactions between the factors affecting the mental workload in the form of behavioral patterns (archetypes). The behavior over time of mental workload archetype was simulated with a focus on the human-based variables. The result showed that the dynamic models could be used to analyze the long-term behavior of the mental workload, taking into account the various contributing factors and the associated uncertainty. In addition, this study can be considered as a starting point for future research through a combination of mental workload and system dynamics.This paper was submitted as part of a thesis for the Ph.D. degree in Occupational Hygiene, School of Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. This work was supported by Shahid Beheshti University of Medical Sciences (Grant number: 10692). None to report.