The main goal of Process Network Synthesis is usually to find the lowest-cost process for a given problem. Since the model is not able to account for every parameter of an industrial realisation, the decision makers prefer to have alternatives, which can be provided when generating the n-best solutions. This, however, comes with another issue, specifically that several of the near-optimal solutions are almost identical to the optimal one, and only differ in one or two operating units. Thus, the next step to improve the generation of feasible and performant alternatives is to provide process designs with meaningful differences from the optimum. Meaningful differences between designs have to be defined by the decision makers. These are differences that the decision makers consider as major strategic questions, while other changes in the process constitute fine details where simply selecting the lowest cost option is enough. The current work describes a branch-and-bound algorithm that is able to generate the n-best strategically different process designs. The difference between considering and ignoring strategic differences when generating n-best solutions is illustrated via a case study.

Besides traditional modelling approaches, machine learning surrogate models have become widespread. In addition to existing concepts, this work investigates the use of neural networks (NNs) to replace dynamic equations in multibody systems. While physics-informed neural networks (PINNs) dominate recent literature, their constraint enforcement via cost-function penalties poses tuning challenges. This paper proposes an alternative: training NNs on data with intentional constraint violations to implicitly learn stabilisation, avoiding PINN limitations. The proposed concept is applied to the inverse dynamics control of underactuated systems, where the task is defined by servo-constraints. Using Scikit-learn’s MLP Regressor, we demonstrate NN-based surrogate modeling in three levels: 1) forward dynamics of minimum-coordinate models, 2) constrained models, and 3) the inverse dynamics control of underactuated multibody systems via servo-constraints, which is a classical approach not yet combined with NNs. The neural network model is used to represent the inverse dynamics model, for which training data are generated through forward dynamic simulations. The study demonstrates that low-degree-of-freedom planar systems can be approximated by middle-scale neural network models of a few hundred perceptrons, requiring training times of minutes on a personal computer. The proposed control architecture can stabilise the servo-constraints and track trajectories, even for non-collocated underactuated systems where traditional methods might fail. The results highlight a simple, industry-friendly path for NN-based MBD control.

Recent advances on laser technology have enabled the generation of ultrashort (fs) high power (PW) laser systems. For such large scale laser facilities there is an imperative demand for high repetition rate operation in symbiosis with beamlines or end-stations. In such extreme conditions the generation of electromagnetic pulses (EMP) during high intense laser target interaction experiments can tip the scale for the good outcome of the campaign. The EMP effects are several including interference with diagnostic devices and actuators as well as damage of electrical components. The EMP issue is quite known in the picosecond (ps) pulse laser experiments but no systematic study on EMP issues at multi-Joule fs-class lasers has been conducted thus far. In this paper we report the first experimental campaign for EMP-measurements performed at the 200 TW laser system (VEGA 2) at CLPU laser center. EMP pulse energy has been measured as a function of the laser intensity and energy together with other relevant quantities such as (i) the charge of the laser-driven protons and their maximum energy, as well as (ii) the X-ray Kα emission coming from electron interaction inside the target. Analysis of experimental results demonstrate (and confirm) a direct correlation between the measured EMP pulse energy and the laser parameters such as laser intensity and laser energy in the ultrashort pulse duration regime. Numerical FEM (Finite Element Method) simulations of the EMP generated by the target holder system have been performed and the simulations results are shown to be in good agreement with the experimental ones.

Related to microelectronics’ reliability, lifetime estimation methods have gained importance, especially for surface-mounted devices. The virtual testing of electronic assemblies necessitates the geometry modeling and finite element analysis of the solder joint. The effect of the simplification of the solder geometry on the predicted lifetime is an open question. Furthermore, there is still not yet straightforward guidance for the choice of the material model and fatigue lifetime model. In this study, the impact of the geometry input method, the material model and the lifetime model choice is investigated on two different surface-mounted capacitors in a simulation-based benchmark analysis under thermal cyclic loading. Four different types of solder geometry modeling approaches are compared, among which one is a physics-based approach. Ten different fatigue models founded on plastic and viscoplastic material models are benchmarked. The results show that the component standoff height and the solder volume have a positive effect on the lifetime, while the capacitor size has a slightly negative effect on the lifetime. The results also suggest that approximate geometries can be used to replace the physics-based model with a restriction for the minimum standoff height.

Collaborative filtering (CF) approaches proved to be effective for recommender systems in predicting user preferences in item selection using known user ratings of items. This subfield of machine learning has gained a lot of popularity with the Netix Prize competition started in October 2006. Two major approaches for this problem are matrix factorization (MF) and the neighbor based approach (NB). In this work, we propose various variants of MF and NB that can boost the performance of the usual ensemble based scheme. First, we investigate various regularization scenarios for MF. Second, we introduce two NB methods: one is based on correlation coeficients and the other on linear least squares. At the experimentation part, we show that the proposed approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. We present results of blending the proposed methods. © 2008 ACM.

Purpose – This study examines how the decisions and traits of top managers translate into indirect, symbolic and procedural barriers to organizational learning (OL) and sustainable change within fragmented higher education institutions (HEIs) that are increasingly characterized by digitally enabled, globally disparate online workforces (faculty and students). It addresses the critical issue of whether current managerial selection and development models are sustainable, specifically investigating how non-academic leaders, often ill-equipped for complex intellectual capital management, impede the development required for a modern, global online HEI. Design/methodology/approach – A qualitative analysis was conducted on the Pedagogic Innovation Project, a strategic, digitally relevant change initiative across 10 French campuses. This context provides a rich context for observing managerial practices and outcomes associated with leading highly specialized, distributed workforces through transformation. We employ upper echelons theory (UET) to investigate how the top management team (TMT) acts as a barrier to OL in a fragmented, knowledge-intensive HEI. We move beyond general applications of UET to focus on the indirect, symbolic and procedural forms of managerial influence. By analysing these underreported mechanisms, we contribute novel insights into how the values of TMT shape learning failures and defensive routines at the organizational level. Findings – The findings show that non-academic senior leaders, often recruited via informal networks and overly focused on procedural control, tend to cultivate unsustainable human resource management (HRM) practices that undermine organizational capacity. Such leaders inhibit double-loop learning (DLL) and suppress faculty expertise, which constitutes the core intellectual capital of a global HEI. Their reliance on defensive routines (rebranding top-down mandates) fosters distrust, opposing the autonomy needed for online work. By promoting commodified “plug-and-play” staffing and prioritizing superficial compliance over genuine integration, these leaders fail to implement sustainable performance management or authentic employee well-being during digital transformation, both of which are detrimental to sustainable HRM practices. Their reliance on defensive routines (rebranding top-down mandates) fosters distrust, opposing the autonomy needed for online work. By promoting commodified “plug-and-play” staffing and prioritizing superficial compliance over genuine integration, these leaders fail to implement sustainable performance management or authentic employee well-being during digital transformation, both of which are detrimental to sustainable HRM practices. Research limitations/implications – This study offers fresh insights into top management's role in innovating the business model within a fragmented French HEI, which therefore limits the generalizability to other HE contexts or sectors beyond education. Findings may not hold in systems with stronger academic leadership or less marketized environments. The study reflects only faculty perspectives, omitting direct input from top managers, which limits visibility into executive constraints. Confidentiality also restricted analysis of TMT demographics, narrowing the use of UET. The single-case design reduces comparative scope. Practical implications – HEIs and organizations managing global, digitally enabled workforces must overhaul manager recruitment, prioritizing candidates with expertise in intellectual capital stewardship and sustainable HRM, not just procedural control. They must invest in developing existing managers to champion DLL and authentic employee well-being over superficial compliance. For HR departments, this study signals the immediate need to discontinue unsustainable practices such as “plug-and-play” staffing models that erode expertise, favouring long-term talent development and management that empowers faculty autonomy. Social implications – The managerial suppression of faculty expertise, coupled with the use of “plug-and-play” staffing, actively contributes to the erosion of academic identity and professional morale. This suggests a counter-productive societal trend where bureaucratic control and metric-driven compliance supersede deep intellectual capital, thereby marginalizing practitioner-led innovation. Moreover, the reliance on defensive routines breeds a culture of distrust, undermining the high autonomy necessary for effective knowledge work. Over time, public resources are invariably diverted towards symbolic governance (accreditations) instead of genuine pedagogical investment, raising concerns about the long-term quality and integrity of public service outputs. Originality/value – The originality of this study lies in its extension of established theory and its application to an understudied, non-corporate context. It provides a crucial, non-corporate extension of UET, highlighting the specific mechanisms (namely, indirect, symbolic and procedural managerial influence) by which TMT characteristics actively obstruct organizational adaptation and deep learning in fragmented public sector environments. It offers empirical evidence that top managers' decisions, particularly those rooted in non-academic managerial expertise and focused on procedural/metric compliance, can systematically undermine a university's core mission (teaching and research integration).

Many traffic accidents are caused by unforeseen and unexpected events in a site that was hidden from the driver's eyes. Road design parameters determining required visibility are based on relationships formulated decades ago. It is worth reviewing them from time to time in the light of technological developments. In this paper, sight distances for stopping and crossing situations are studied in relation to the assumed visual abilities of autonomous vehicles. Current sight distance requirements at unsignalized intersections are based among others on speeds on the major road and on accepted gaps by human drivers entering or crossing from the minor road. Since these requirements vary from country to country, regulations and sight terms of a few selected countries are compared in this study. From the comparison it is remarkable that although the two concepts, i.e. gap acceptance on the minor road and stopping on the major road have different backgrounds, but their outcome in terms of required sight distances are similar. Both distances are depending on speed on the major road: gap sight distances show a linear, while stopping sight distances a parabolic function. In general, European SSD values are quite similar to each other. However, the US and Australian guidelines based on gap acceptance criteria recommend higher sight distances. Human capabilities and limitations are considered in sight field requirements. Autonomous vehicles survey their environment with sensors which are different from the human vision in terms of identifying objects, estimating distances or speeds of other vehicles. This paper compares current sight field requirements based on conventional vehicles and those required for autonomous vehicles. Visibility requirements were defined by three vision indicators: distance, angle of view and resolution abilities of autonomous cars and human drivers. These indicators were calculated separately for autonomous vehicles and human drivers for various speeds on the main road and for intersections with 90° and 60° angles. It was shown that the required sight distances are 10 to 40 meters shorter for autonomous vehicles than for conventional ones.

This study reviews Internet of Things (IoT) research in supply chain management (SCM) and logistics. A thorough review and bibliometric analysis were conducted to analytically and objectively unearth the knowledge development in IoT research within the context of SCM and logistics. The analysis started with the selection of 807 journal articles published over a two-decade period. Then, the articles were analyzed according to bibliometric parameters such as year of publication, sources, authors, and institutions. A keyword co-occurrence network was used to cluster the pertinent literature. Results of the review and bibliometric analysis reveal that IoT research has attracted significant attention from the SCM and logistics community. Three leading journals published widely on IoT and the fifteen most productive authors are identified. Based on the keyword co-occurrence clustering, the IoT literature in SCM and logistics is focalized on RFID technology, Industry 4.0 technologies, reverse logistics, and additionally covers various industries, such as the food, retailing, construction, and the pharmaceutical sector. The study provides researchers with a better understanding of IoT research in SCM and logistics and existing knowledge gaps for further research. Practitioners may benefit from the review to keep abreast of the current discussions and applications of IoT in diverse industrial sectors. To the best of the authors’ knowledge, the current review is one of the few attempts to investigate IoT research in SCM and logistics using a comprehensive set of articles published during the past two decades.

Additive technologies have several advantages over conventional manufacturing, such as the freedom of geometry of the products and internal structures. There are also some limitations and problems, deriving from stopping the process during the production. By restarting the process, the building often continues with a thicker starting layer due to the deposition of two or more layers. The effect of skipped melting of layers is investigated in this paper. Maraging steel powder (MS1) was used in direct metal laser sintering (DMLS) process to produce samples with increased thickness of melted layers. The layer thickness was increased in 20 μm steps up to 160 μm with 0.5 mm offset between the increased thickness layers. Porosity caused by the uneven melting was measured by optical microscope, mechanical tests were carried out to quantify the effect of skipped layers and fractured surfaces were observed under SEM. We have found that the yield strength and tensile strength are not affected if the layer thickness is slightly increased locally in the laser sintered part, while even a small increase in porosity greatly reduces the total elongation of the specimen. The decrease of impact energy due to the porosities shows similar correlation with the decrease of percentage elongation at break. However, the Charpy impact test is much more sensitive to layer skipping, the lack of melted layers lowers the impact strength significantly.