Large deformation of rigid-viscoplastic cantilevers subjected to impulsive loading

Bibliography: pages 64-66.The problem of a ductile metal cantilever structure (not necessarily initially straight) subjected to dynamic loads leading to deformations of the order of the dimensions of the structure is considered. The material is treated as rigid-viscoplastic; in this idealisation elastic effects are ignored, and the dependence of the yield stress on the rate of strain is taken into account. The problem is first analysed as one of impulsive loading, using the concepts of the mode approximation technique. A new algorithm for the determination of mode shapes is presented for small displacement assumptions and then extended to incorporate geometric effects. An algorithm is given for the time integration of the motion in which the geometry of the st ructure is updated. Applications of the method are described for impulsive loading, and extended to a type of pipe-whip problem where the loading is a combination of an impulse and a pulse which acts in the direction of the tangent at the tip of the cantilever structure at each instant. Illustrative examples are presented which show that the algorithms can be used to give very good predictions of the displaced shape of the structures under consideration

Neural networks in control engineering

The purpose of this thesis is to investigate the viability of integrating neural networks into control structures. These networks are an attempt to create artificial intelligent systems with the ability to learn and remember. They mathematically model the biological structure of the brain and consist of a large number of simple interconnected processing units emulating brain cells. Due to the highly parallel and consequently computationally expensive nature of these networks, intensive research in this field has only become feasible due to the availability of powerful personal computers in recent years. Consequently, attempts at exploiting the attractive learning and nonlinear optimization characteristics of neural networks have been made in most fields of science and engineering, including process control. The control structures suggested in the literature for the inclusion of neural networks in control applications can be divided into four major classes. The first class includes approaches in which the network forms part of an adaptive mechanism which modulates the structure or parameters of the controller. In the second class the network forms part of the control loop and replaces the conventional control block, thus leading to a pure neural network control law. The third class consists of topologies in which neural networks are used to produce models of the system which are then utilized in the control structure, whilst the fourth category includes suggestions which are specific to the problem or system structure and not suitable for a generic neural network-based-approach to control problems. Although several of these approaches show promising results, only model based structures are evaluated in this thesis. This is due to the fact that many of the topologies in other classes require system estimation to produce the desired network output during training, whereas the training data for network models is obtained directly by sampling the system input(s) and output(s). Furthermore, many suggested structures lack the mathematical motivation to consider them for a general structure, whilst the neural network model topologies form natural extensions of their linear model based origins. Since it is impractical and often impossible to collect sufficient training data prior to implementing the neural network based control structure, the network models have to be suited to on-line training during operation. This limits the choice of network topologies for models to those that can be trained on a sample by sample basis (pattern learning) and furthermore are capable of learning even when the variation in training data is relatively slow as is the case for most controlled dynamic systems. A study of feedforward topologies (one of the main classes of networks) shows that the multilayer perceptron network with its backpropagation training is well suited to model nonlinear mappings but fails to learn and generalize when subjected to slow varying training data. This is due to the global input interpretation of this structure, in which any input affects all hidden nodes such that no effective partitioning of the input space can be achieved. This problem is overcome in a less flexible feedforward structure, known as regular Gaussian network. In this network, the response of each hidden node is limited to a -sphere around its center and these centers are fixed in a uniform distribution over the entire input space. Each input to such a network is therefore interpreted locally and only effects nodes with their centers in close proximity. A deficiency common to all feedforward networks, when considered as models for dynamic systems, is their inability to conserve previous outputs and states for future predictions. Since this absence of dynamic capability requires the user to identify the order of the system prior to training and is therefore not entirely self-learning, more advanced network topologies are investigated. The most versatile of these structures, known as a fully recurrent network, re-uses the previous state of each of its nodes for subsequent outputs. However, despite its superior modelling capability, the tests performed using the Williams and Zipser training algorithm show that such structures often fail to converge and require excessive computing power and time, when increased in size. Despite its rigid structure and lack of dynamic capability, the regular Gaussian network produces the most reliable and robust models and was therefore selected for the evaluations in this study. To overcome the network initialization problem, found when using a pure neural network model, a combination structure· _in which the network operates in parallel with a mathematical model is suggested. This approach allows the controller to be implemented without any prior network training and initially relies purely on the mathematical model, much like conventional approaches. The network portion is then trained during on-line operation in order to improve the model. Once trained, the enhanced model can be used to improve the system response, since model exactness plays an important role in the control action achievable with model based structures. The applicability of control structures based on neural network models is evaluated by comparing the performance of two network approaches to that of a linear structure, using a simulation of a nonlinear tank system. The first network controller is developed from the internal model control (IMC) structure, which includes a forward and inverse model of the system to be controlled. Both models can be replaced by a combination of mathematical and neural topologies, the network portion of which is trained on-line to compensate for the discrepancies between the linear model _ and nonlinear system. Since the network has no dynamic ·capacity, .former system outputs are used as inputs to the forward and inverse model. Due to this direct feedback, the trained structure can be tuned to perform within limits not achievable using a conventional linear system. As mentioned previously the IMC structure uses both forward and inverse models. Since the control law requires that these models are exact inverses, an iterative inversion algorithm has to be used to improve the values produced by the inverse combination model. Due to deadtimes and right-half-plane zeroes, many systems are furthermore not directly invertible. Whilst such unstable elements can be removed from mathematical models, the inverse network is trained directly from the forward model and can not be compensated. These problems could be overcome by a control structure for which only a forward model is required. The neural predictive controller (NPC) presents such a topology. Based on the optimal control philosophy, this structure uses a model to predict several future outputs. The errors between these and the desired output are then collected to form the cost function, which may also include other factors such as the magnitude of the change in input. The input value that optimally fulfils all the objectives used to formulate the cost function, can then be found by locating its minimum. Since the model in this structure includes a neural network, the optimization can not be formulated in a closed mathematical form and has to be performed using a numerical method. For the NPC topology, as for the neural network IMC structure, former system outputs are fed back to the model and again the trained network approach produces results not achievable with a linear model. Due to the single network approach, the NPC topology furthermore overcomes the limitations described for the neural network IMC structure and can be extended to include multivariable systems. This study shows that the nonlinear modelling capability of neural networks can be exploited to produce learning control structures with improved responses for nonlinear systems. Many of the difficulties described are due to the computational burden of these networks and associated algorithms. These are likely to become less significant due to the rapid development in computer technology and advances in neural network hardware. Although neural network based control structures are unlikely to replace the well understood linear topologies, which are adequate for the majority of applications, they might present a practical alternative where (due to nonlinearity or modelling errors) the conventional controller can not achieve the required control action

Trends in regulatory expectations and their impact on compliance management in companies

Compliance requirements for companies are growing, especially in the fields of ESG (Environmental, Social, and Corporate Governance) and data privacy. The phenomenon can be observed not only within the EU, but also many other areas of the world. Within the regulatory environment, fostering ESG practices has long since developed from a voluntary commitment to a “real” compliance issue which lawmakers are driving forward with serious sanctions and which courts are also shaping within the framework of the evolving laws. These laws are very complex, often unclear, and intrude deeply into the areas of risk analysis and risk management, which traditionally represent a core responsibility of companies. Many regulations emphasize development and implementation of internal processes within companies. This greatly reduces companies’ discretionary powers, since responsible use of leeway is a core area of entrepreneurial decision-making governed by the business judgment rule. Structurally, we are seeing increased legalization of risks, through which the legislator de facto takes away companies' leeway to make entrepreneurial decisions. Also, the threat of severe fines and uncertainty about the interpretation of legal terms makes it difficult for companies to decide what needs to be done to meet the laws’ requirements and to avoid risk. Looking at the char acter of the regulations, we see value-driven and symbolically-charged laws. However, these laws are anything but “dead letters” - they intervene deeply in companies’ risk management, aim at changing behavior, and have sharp “teeth” in the form of sanctions. The EU may be a particularly fertile source of symbolic legislation, which can serve to create political identity. Companies can, however, choose different ways to deal with these challenges, and they are free to find the right path. Even if lawmakers are increasingly intervening in the way companies carry out risk analyses and the priorities they set in that context, companies should defend their leeway and use it wisely. It is of utmost importance to know the real risks well and to use leeway responsibly. A diligent risk analysis, carefully aligned to a company’s circumstances and needs, is always a good starting point. Perfect knowledge of applicable laws and the company’s operations is a prerequisite for a professional risk assessment and building an effective Compliance Management System (CMS). There is always room for balanced decision-making regarding risk assessment and prioritization in accordance with the business judgment rule and entrepreneurial responsibility

Simultaneous effects on parvalbumin-positive interneuron and dopaminergic system development in a transgenic rat model for sporadic schizophrenia

To date, unequivocal neuroanatomical features have been demonstrated neither for sporadic nor for familial schizophrenia. Here, we investigated the neuroanatomical changes in a transgenic rat model for a subset of sporadic chronic mental illness (CMI), which modestly overexpresses human full-length, non-mutant Disrupted-in-Schizophrenia 1 (DISC1), and for which aberrant dopamine homeostasis consistent with some schizophrenia phenotypes has previously been reported. Neuroanatomical analysis revealed a reduced density of dopaminergic neurons in the substantia nigra and reduced dopaminergic fibres in the striatum. Parvalbumin-positive interneuron occurrence in the somatosensory cortex was shifted from layers II/III to V/VI, and the number of calbindin-positive interneurons was slightly decreased. Reduced corpus callosum thickness confirmed trend-level observations from in vivo MRI and voxel-wise tensor based morphometry. These neuroanatomical changes help explain functional phenotypes of this animal model, some of which resemble changes observed in human schizophrenia post mortem brain tissues. Our findings also demonstrate how a single molecular factor, DISC1 overexpression or misassembly, can account for a variety of seemingly unrelated morphological phenotypes and thus provides a possible unifying explanation for similar findings observed in sporadic schizophrenia patients. Our anatomical investigation of a defined model for sporadic mental illness enables a clearer definition of neuroanatomical changes associated with subsets of human sporadic schizophrenia

Disrupted-in-schizophrenia 1 overexpression disrupts hippocampal coding and oscillatory synchronization

Aberrant proteostasis of protein aggregation may lead to behavior disorders including chronic mental illnesses (CMI). Furthermore, the neuronal activity alterations that underlie CMI are not well understood. We recorded the local field potential and single‐unit activity of the hippocampal CA1 region in vivo in rats transgenically overexpressing the Disrupted‐in‐Schizophrenia 1 (DISC1) gene (tgDISC1), modeling sporadic CMI. These tgDISC1 rats have previously been shown to exhibit DISC1 protein aggregation, disturbances in the dopaminergic system and attention‐related deficits. Recordings were performed during exploration of familiar and novel open field environments and during sleep, allowing investigation of neuronal abnormalities in unconstrained behavior. Compared to controls, tgDISC1 place cells exhibited smaller place fields and decreased speed‐modulation of their firing rates, demonstrating altered spatial coding and deficits in encoding location‐independent sensory inputs. Oscillation analyses showed that tgDISC1 pyramidal neurons had higher theta phase locking strength during novelty, limiting their phase coding ability. However, their mean theta phases were more variable at the population level, reducing oscillatory network synchronization. Finally, tgDISC1 pyramidal neurons showed a lack of novelty‐induced shift in their preferred theta and gamma firing phases, indicating deficits in coding of novel environments with oscillatory firing. By combining single cell and neuronal population analyses, we link DISC1 protein pathology with abnormal hippocampal neural coding and network synchrony, and thereby gain a more comprehensive understanding of CMI mechanisms

DISC1-dependent Regulation of Mitochondrial Dynamics Controls the Morphogenesis of Complex Neuronal Dendrites

The DISC1 protein is implicated in major mental illnesses including schizophrenia, depression, bipolar disorder, and autism. Aberrant mitochondrial dynamics are also associated with major mental illness. DISC1 plays a role in mitochondrial transport in neuronal axons, but its effects in dendrites have yet to be studied. Further, the mechanisms of this regulation and its role in neuronal development and brain function are poorly understood. Here we have demonstrated that DISC1 couples to the mitochondrial transport and fusion machinery via interaction with the outer mitochondrial membrane GTPase proteins Miro1 and Miro2, the TRAK1 and TRAK2 mitochondrial trafficking adaptors, and the mitochondrial fusion proteins (mitofusins). Using live cell imaging, we show that disruption of the DISC1-Miro-TRAK complex inhibits mitochondrial transport in neurons. We also show that the fusion protein generated from the originally described DISC1 translocation (DISC1-Boymaw) localizes to the mitochondria, where it similarly disrupts mitochondrial dynamics. We also show by super resolution microscopy that DISC1 is localized to endoplasmic reticulum contact sites and that the DISC1-Boymaw fusion protein decreases the endoplasmic reticulum-mitochondria contact area. Moreover, disruption of mitochondrial dynamics by targeting the DISC1-Miro-TRAK complex or upon expression of the DISC1-Boymaw fusion protein impairs the correct development of neuronal dendrites. Thus, DISC1 acts as an important regulator of mitochondrial dynamics in both axons and dendrites to mediate the transport, fusion, and cross-talk of these organelles, and pathological DISC1 isoforms disrupt this critical function leading to abnormal neuronal development

Disrupted in Schizophrenia 1 regulates the processing of reelin in the perinatal cortex

Disrupted in Schizophrenia 1 (DISC1) is a prominent gene in mental illness research, encoding a scaffold protein known to be of importance in the developing cerebral cortex. Reelin is a critical extracellular protein for development and lamination of the prenatal cortex and which has also been independently implicated in mental illness. Regulation of reelin activity occurs through processing by the metalloproteinases ADAMTS-4 and ADAMTS-5. Through cross-breeding of heterozygous transgenic DISC1 mice with heterozygous reeler mice, which have reduced reelin, pups heterozygous for both phenotypeswere generated. Fromthese,we determine that transgenic DISC1 leads to a reduction in the processing of reelin, with implications for its downstream signalling element Dab1. An effect of DISC1 on reelin processing was confirmed in vitro, and revealed that intracellular DISC1 affects ADAMTS-4 protein, which in turn is exported and affects processing of extracellular reelin. In transgenic rat cortical cultures, an effect of DISC1 on reelin processing could also be seen specifically in early, immature neurons, but was lost in calretinin and reelin-positive mature neurons, suggesting cell-type specificity. DISC1 therefore acts upstream of reelin in the perinatal cerebral cortex in a cell type/time specific manner, leading to regulation of its activity through altered proteolytic cleavage. Thus a functional link is demonstrated between two proteins, each of independent importance for both cortical development and associated cognitive functions leading to behavioural maladaptation and mental illness

Neuropeptide precursor VGF is genetically associated with social anhedonia and underrepresented in the brain of major mental illness: its downregulation by DISC1

In a large Scottish pedigree, disruption of the gene coding for DISC1 clearly segregates with major depression, schizophrenia and related mental conditions. Thus, study of DISC1 may provide a clue to understand the biology of major mental illness. A neuropeptide precursor VGF has potent antidepressant effects and has been reportedly associated with bipolar disorder. Here we show that DISC1 knockdown leads to a reduction of VGF, in neurons. VGF is also downregulated in the cortices from sporadic cases with major mental disease. A positive correlation of VGF single-nucleotide polymorphisms (SNPs) with social anhedonia was also observed. We now propose that VGF participates in a common pathophysiology of major mental disease

Mechanisms underlying the role of DISC1 in synaptic plasticity

Disrupted in schizophrenia 1 (DISC1) is an important hub protein, forming multimeric complexes by self‐association and interacting with a large number of synaptic and cytoskeletal molecules. The synaptic location of DISC1 in the adult brain suggests a role in synaptic plasticity, and indeed, a number of studies have discovered synaptic plasticity impairments in a variety of different DISC1 mutants. This review explores the possibility that DISC1 is an important molecule for organizing proteins involved in synaptic plasticity and examines why mutations in DISC1 impair plasticity. It concentrates on DISC1's role in interacting with synaptic proteins, controlling dendritic structure and cellular trafficking of mRNA, synaptic vesicles and mitochondria. N‐terminal directed mutations appear to impair synaptic plasticity through interactions with phosphodiesterase 4B (PDE4B) and hence protein kinase A (PKA)/GluA1 and PKA/cAMP response element‐binding protein (CREB) signalling pathways, and affect spine structure through interactions with kalirin 7 (Kal‐7) and Rac1. C‐terminal directed mutations also impair plasticity possibly through altered interactions with lissencephaly protein 1 (LIS1) and nuclear distribution protein nudE‐like 1 (NDEL1), thereby affecting developmental processes such as dendritic structure and spine maturation. Many of the same molecules involved in DISC1's cytoskeletal interactions are also involved in intracellular trafficking, raising the possibility that impairments in intracellular trafficking affect cytoskeletal development and vice versa. While the multiplicity of DISC1 protein interactions makes it difficult to pinpoint a single causal signalling pathway, we suggest that the immediate‐term effects of N‐terminal influences on GluA1, Rac1 and CREB, coupled with the developmental effects of C‐terminal influences on trafficking and the cytoskeleton make up the two main branches of DISC1's effect on synaptic plasticity and dendritic spine stability

Trends in regulatory expectations and their impact on compliance management in companies

Compliance requirements for companies are growing, especially in the fields of ESG (Environmental, Social, and Corporate Governance) and data privacy. The phenomenon can be observed not only within the EU, but also many other areas of the world. Within the regulatory environment, fostering ESG practices has long since developed from a voluntary commitment to a “real” compliance issue which lawmakers are driving forward with serious sanctions and which courts are also shaping within the framework of the evolving laws. These laws are very complex, often unclear, and intrude deeply into the areas of risk analysis and risk management, which traditionally represent a core responsibility of companies. Many regulations emphasize development and implementation of internal processes within companies. This greatly reduces companies’ discretionary powers, since responsible use of leeway is a core area of entrepreneurial decision-making governed by the business judgment rule. Structurally, we are seeing increased legalization of risks, through which the legislator de facto takes away companies' leeway to make entrepreneurial decisions. Also, the threat of severe fines and uncertainty about the interpretation of legal terms makes it difficult for companies to decide what needs to be done to meet the laws’ requirements and to avoid risk. Looking at the char acter of the regulations, we see value-driven and symbolically-charged laws. However, these laws are anything but “dead letters” - they intervene deeply in companies’ risk management, aim at changing behavior, and have sharp “teeth” in the form of sanctions. The EU may be a particularly fertile source of symbolic legislation, which can serve to create political identity. Companies can, however, choose different ways to deal with these challenges, and they are free to find the right path. Even if lawmakers are increasingly intervening in the way companies carry out risk analyses and the priorities they set in that context, companies should defend their leeway and use it wisely. It is of utmost importance to know the real risks well and to use leeway responsibly. A diligent risk analysis, carefully aligned to a company’s circumstances and needs, is always a good starting point. Perfect knowledge of applicable laws and the company’s operations is a prerequisite for a professional risk assessment and building an effective Compliance Management System (CMS). There is always room for balanced decision-making regarding risk assessment and prioritization in accordance with the business judgment rule and entrepreneurial responsibility