Search

We analyze the existing safety standard, ISO 26262, for automotive applications, determining the certifiability of machine learning (ML) approaches used in low-risk automotive applications. This can help assuring the security and safety of ML-based autonomous driving systems, gaining the trust of regulators, certification agencies, and stakeholders.

Failure Mode and Effects Analysis is a core tool for the systems engineering design assurance, facilitating the identification of design risks and their management throughout the lifecycle of the system. However, the development and management of the FMEA within a systems engineering context faces many challenges, including effectiveness of the process and integrity of knowledge capture, and the lack of a consistent integration with the systems lifecycle. The research underpinning this contribution aims to establish an intelligent FMEA environment supported by an ontology as the basis for knowledge representation of complex systems function failure underpinning the FMEA analysis and documentation. We propose to extend the Function-Behaviour-Structure (FBS) ontology of system design, with representation of function failure reasoning supported by a function failure taxonomy. This provides a consistent ontological basis underpinning the FMEA analysis of a system. We also discuss the expansion of the ontology with the levels of design decomposition of a complex system, to support the management of traceability of the FMEA across the design process. We illustrate the deployment of the proposed ontology framework, implemented in Protégé environment.

Natural Language Processing is increasingly used in different areas of design and product development with varied objectives, from enhancing productivity to embedding resilience into systems. In this paper, we introduce a framework that draws on NLP algorithms and expert knowledge for the automotive engineering domain, to extract actionable insight for system reliability improvement from data available from the operational phase of the system. Specifically, we are looking at the systematic exploration and exploitation of automotive heterogeneous data sources, including both closed-source (such as warranty records) and open-source (e.g., social networks, chatrooms, recall systems) data, to extract and classify information about faults, with predictive capability for early detection of issues. We present a preliminary NLP-based framework for enhancing system knowledge representation to increase the effectiveness and robustness of information extraction from data, and discuss the temporal alignment of data sources and insight to improve prediction ability. We demonstrate the effectiveness of the proposed framework using real-world automotive data in a recall study for a vehicle lighting system and a particular manufacturer: four recall campaigns were identified leading to corrective actions by the warranty experts.

While the development of autonomous driving systems (ADS) offers significant potential safety benefits, this is conditional upon addressing the challenges associated with increased complexity and multidisciplinarity, including the introduction of artificial intelligence (AI) embedded components. This increases the importance of assuring the robustness of these systems during their operations where possible hazards can arise from both the malfunctioning behavior of safety-related in-vehicle systems and functional insufficiencies or performance limitations of a system that is free from faults. Robustness is underpinned by the systematic consideration of failure modes which indicate how a system can potentially fail to deliver the intended function/performance and associated requirements. The introduction of AI components has made the identification for failures of ADSs challenging due to increased use of sensors and wireless communications which provide the backbone of core software functions of perception, planning and control. Current literature on failure modes mostly focuses on “physical” systems, and do not readily support the analysis of safety-critical systems which include AI components. This paper discusses the applicability of current failure mode taxonomies and their effectiveness in supporting the identification of failure modes within the context of design methodologies (such as FMEA) that underpin an effective approach to robustness of multidisciplinary autonomous driving systems.

This paper proposes a novel approach for the reliability cost-based optimization of Distribution Systems (DS), considering tie line-based network reconfiguration method with integration of Distributed Energy Resources (DER). An optimal Energy not Supplied (ENS) index is proposed, where the capacity is handled by curtailment devices in the network such as sectionalizers and the energy supplied by DERs which considers Flexibility Services (FS) within a market environment. The decision variables include the investment and operation of tie-lines and buying regulation services from DER such as Distributed Generation (DG) and Battery Energy Storage Systems (BESS). The results validate the cost-effectiveness of the proposed method through implementation of these technologies to improve the reliability of the DS, within a comprehensive set of case-study scenarios for a 16-bus UK generic distribution system (UKGDS). The case study results indicate that significant savings can be achieved through the proposed method, ranging between 36%–71% depending on the level of automation in tie-line operations in combination with the settlement price for the power-balance of FS. This illustrates that the proposed DS reliability cost-based optimization method could have a significant impact for real world DG and BESS applications.

Chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic pulmonary vascular disease (CTED) are rare manifestations of venous thromboembolism. Presumably, CTEPH and CTED are variants of the same pathophysiological mechanism. CTEPH and CTED can be near-cured by pulmonary endarterectomy, balloon pulmonary angioplasty, and medical treatment with Riociguat or subcutaneous treprostinil, which are the approved drugs.

ABSTRACTIntroductionDespite an increasingly older pulmonary hypertension (PH) population, data on PH treatments in these patients are limited because there exist no clinical studies dedicated to geriatric groups. Furthermore, elderly patients with comorbidities have been systematically excluded from clinical trials, limiting the evidence base for drugs approved for pulmonary arterial hypertension (PAH).Areas coveredThis review is focused on the diagnosis and treatment of pulmonary hypertension (PH) in the elderly, which is a hot topic today. Areas covered by the authors include current changes in demographics, clinical characteristics, diagnoses, and risk assessment in the geriatric PAH population. A central part of this review is dedicated to the therapeutic challenges in elderly patients with PAH. The literature search is focused on sorting out post-capillary conditions in the elderly, and on current treatment strategies for PAH and for chronic thromboembolic pulmonary hypertension (CTEPH).Expert opinion Current data indicate that despite more severe disease in elderly patients, the concept of hit hard and early is less used. For example, double upfront oral combination, a common strategy for younger patients, or early parenteral prostacyclins, are less used in the elderly, purporting worse outcomes for these patients.

Adopting multi-carrier energy systems (MES) is vital for a sustainable and eco-friendly energy future. MES integration connects diverse energy networks, prompting the deployment of large-scale multi-energy storage systems (MESS) to support interconnected multi-energy distribution systems. This paper proposes a bi-level mixed-integer linear programming (MILP) model to optimise MESS sizing and location in a peer-to-peer (P2P) supported integrated energy distribution network with energy hubs. The MESS, operated by an energy storage system operator (ESSO), stores various energy carriers and exchanges energy with electricity, gas, and heating networks, enabling cross-product arbitrage in P2P and day-ahead markets. The bi-level methodology aims to minimise costs (maximise profit) for ESSO and energy hubs at the upper-level while reducing network power losses at the lower level. By applying Karush-Kuhn-Tucker (KKT) optimality conditions and the big-M approach, the bi-level problem transforms into a single-level MILP. Case studies on an integrated energy network based on the 16-bus 33 kV UK generic distribution system validate the proposed model and methodology, demonstrating its effectiveness in integrated energy networks.

This paper introduces a novel method referred to as Enhanced Sequence Diagram (ESD) to support rigorous functional modelling of complex multidisciplinary systems. The ESD concept integrates an exchanges based functional requirements reasoning based on a coherent graphical schema, integrated with the system operational analysis based on a sequence diagram. The effectiveness of the method to support generic function modelling of complex multidisciplinary systems at the early conceptual design stages is discussed in conjunction with an electric vehicle powertrain example, followed by an assessment of potential impact for broader application of the method in the industry.

This paper considers the importance of applying sound instructional systems design to the development of a learning intervention aimed at developing skills for the effective deployment of an enhanced methodology for engineering systems design analysis within a Product Development context. The leading features of the learning intervention are summarised including the content and design of a training course for senior engineering management which is central to the intervention. The importance of promoting behavioural change by fostering meaningful learning as a collaborative process is discussed. Comparison is made between the instructional design of the corporate learning intervention being developed and the systems engineering based product design process which is the subject of the intervention.

This paper aims documentary research of theoretical and experimental statistical process control in industrial engineering. The most effective means to reduce process variations against the customer requirements is to implement a quality management system based on statistical process control tools. Cutting regime changes in turning processes are based on careful monitoring of vibration. As soon as it is found in the milling excessive noise or vibration the measure to which it resort is to reduce one or more parameters (speed, feed and depth of cut) to remove the effect. Finally, the measure has a negative impact on performance.

This paper presents an overview of existing function-based conceptual design expert (CDE) systems with a view to identifying their scope in the contexts of functional modeling knowledge representation and conceptual design process. A number of heterogeneous function-based CDE systems are identified and listed. A key comprehensive benchmarking framework is developed in this paper that helps in identifying the trend, gaps and scope of current function-based CDE systems. Accordingly, the gaps in the aforementioned contexts are discussed which are to date have not been summarized and highlighted for a well-balanced system development for a product conceptual design.

This paper presents the development and implementation of an analytical target cascading (ATC) multi-disciplinary design optimisation (MDO) framework for the steady state engine calibration optimisation problem. A case study on variable valve timing calibration for a gasoline engine was considered to illustrate the analysis and mathematical formulation of the calibration optimisation as an ATC framework, and its Matlab implementation with gradient based and evolutionary optimisation algorithms. The results and performance of the ATC are discussed comparatively with the conventional two-stage approach to steady state calibration optimisation. The conclusion is that ATC offers a powerful and efficient approach for engine calibration optimisation, delivering better solutions at both ‘Global’ and ‘Local’ levels. The ATC framework is flexible and scalable to the complexity of the calibration problem, and enables calibrator preference to be incorporated a priori in the optimisation problem formulation, delivering important time saving for the overall calibration development process.

Between regularity of production, selling goods and regularity of supply units need to be a perfect timing and a perfect collaboration. The paper presents the policies that can occur in each production systems, and the benefits that can be obtained from the collaboration between the systems elements. The competitive advantage is ensured through the harmonization of the three processes of the company and through the integration of the logistics chain of all upstream and downstream organizations in order to ensure a high level of consumer service at economical costs.

The main purpose of the paper is to develop a neural network application destined to the raw material stock management, as an performant alternative to the classical models of costs stock management. Stocks of goods that manufacturers would classify as raw materials stocks are, in a special sense, goods in early stages of the production process. The testing was made for three companies from the automotive industry, but it could be applied to any kind of Romanian organization.

The main purpose of the paper is to develop a neural network application destined to the workspace generation of a parallel mechanism, as an performant alternative to the workspace representation based on inverse kinematic model. The paper describes both algorithms. The initial testing was made for a parallel mechanism with two degrees of freedom that could be applied for the orientation of different systems like a TV satellite dish antennas, sun trackers, telescopes, cameras, radars etc.

An association between low nephron number and subsequent development of hypertension in later life has been demonstrated. The underlying pathomechanisms are unknown, but glomerular and postglomerular changes have been discussed. We investigated whether such changes are already present in prehypertensive “glial cell line-derived neurotrophic growth factor” heterozygous mice (GDNF+/−) with lower nephron number. Twenty-six-week-old mice [22 GDNF+/−, 29 C57B6 wild-type control (wt)] were used for in vivo experiments with intra-arterial and tail cuff blood pressure measurements. After perfusion fixation, kidneys were investigated with morphological, morphometric, stereological, and immunohistochemical techniques and TaqMan PCR analysis. As expected at this age, blood pressure was comparable between GDNF+/− and wt. Nephron number per kidney was significantly lower in GDNF+/− than in wt (−32.8%, P< 0.005), and mean glomerular volume was significantly higher (+49.5%, P< 0.001). Renal damage scores, glomerular and tubular proliferation, analysis of intrarenal arteries and peritubular capillaries, expression of relevant tubular transporter proteins, as well as gene expression of profibrotic, proinflammatory, or prohypertensive markers were not significantly different between GDNF+/− and wt. Compensatory glomerular hypertrophy in GDNF+/− was accompanied by higher numbers of endothelial and mesangial cells as well as PCNA-positive glomerular cells, whereas podocyte density was significantly reduced. Further electron microscopic analysis showed marked thickening of glomerular basement membrane. In conclusion, lower nephron number is associated with marked early glomerular structural changes, in particular lower capillary supply, reduced podocyte density, and thickened glomerular basement membrane, that may predispose to glomerular sclerosis.

Uremic cardiomyopathy of men and rodents is characterized by lower myocardial capillary supply that in rats could be prevented by central and peripheral blockade of the sympathetic nervous system. The underlying pathomechanisms remain largely unknown. We investigated whether alterations of cardiac vascular endothelial growth factor (VEGF) gene and protein expression were involved. In our long-term experiment, we analyzed whether VEGF gene and protein expression was altered in the heart of male Sprague-Dawley rats with either sham operation (sham, n= 10) or subtotal nephrectomy (SNX, n= 10). In our short-term experiment (17 sham, 24 SNX), the effect of a putative downregulation of sympathetic nervous activity by surgical renal denervation (interruption of renal afferent pathways) on cardiac gene expression of VEGF, flt-1, and flk-1 and on myocardial capillary supply was analyzed. In the long-term study, cardiac capillary supply and vascular endothelial growth factor gene and protein expression were significantly lower in SNX than in sham. In the short-term experiment, cardiac VEGF mRNA expression was significantly lower in untreated SNX (4,258 ± 2,078 units) than in both sham groups (11,709 ± 4,169 and 8,998 ± 4,823 units); this decrease was significantly prevented by renal denervation (8,190 ± 3,889, P< 0.05). We conclude that cardiac VEGF gene and protein expression is reduced in experimental renal failure, and this may be considered as one potential reason for impaired myocardial adaptation under the situation of cardiac hypertrophy. The beneficial effect of sympathetic downregulation on cardiac structure and function in renal failure may be at least in part explained by increased cardiac VEGF gene expression.

The transforming growth factor- (TGFB) pathway has substantial impact on cellular functions, cell proliferation, and apoptosis. We used bioinformatics, gene expression, and cell biological assays to evaluate the functionality of frequent inherited germline polymorphisms in the TGFB receptor 1 (TGFBR1).

The inhibitor of apoptosis protein survivin is of critical importance for regulation of cellular division and survival. Published data point to a restricted function of survivin in embryonic development and cancer; thus survivin has been broadly proposed as an ideal molecular target for specific anti-cancer therapy. In contrast to this paradigm, we report here broad expression of survivin in adult differentiated tissues, as demonstrated at the mRNA and protein levels. Focusing on the kidney, survivin is strongly expressed in proximal tubuli, particularly at the apical membrane, which can be verified in rat, mouse, and human kidneys. In the latter, survivin expression seems to be even stronger in proximal tubuli than in adjacent cancerous tissue. Primary and immortalized human renal tubular cells also showed high levels of survivin protein expression, and RNA interference resulted in a partial G2/M arrest of the cell cycle and increased rate of apoptosis. In conclusion, survivin may be of importance for renal pathophysiology and pathology. The predominant apical expression of survivin may indicate a further, yet unknown, function. Interventional strategies to inhibit survivin's function in malignancy need to be carefully (re)evaluated for renal side effects, as well as for other possible organ dysfunctions.

In polycystic kidney disease (PKD), erythropoietin (EPO) production and interstitial vascularization are increased compared with other kidney diseases. EPO and several angiogenic factors are controlled by hypoxia-inducible transcription factors (HIFs), which are composed of a constitutive β-subunit and two alternative α-subunits (HIF-1α, HIF-2α). We hypothesized that cyst expansion may result in pericystic hypoxia and consecutive up-regulation of HIF and thus examined the expression of HIF-α and HIF target genes in human PKD and in a rodent PKD model. HIF-1α and HIF-2α were found to be up-regulated in cyst epithelium and cells of cyst walls, respectively. The distinct expression pattern of the HIF-α isoforms closely resembles the respective pattern in normal kidneys under systemic hypoxia. Pimonidazole staining, a marker for tissue hypoxia, confirmed the existence of regional hypoxia in polycystic kidneys. Immunohistochemistry for selected target genes implicated a role for HIF-1α in vascular endothelial growth factor and Glut-1 activation and HIF-2α in endoglin and EPO stimulation. Polycystin-deficient cells showed physiological, oxygen-dependent HIF-α modulation, excluding a direct influence of polycystin deficiency on HIF-α regulation. In conclusion, HIF accumulation in human and rat PKD seems to be responsible for increased EPO production and pericystic hypervascularity and may have an impact on progression of PKD.