bluuebunny/crossref_metadata_2025 huggingface dataset:
Benchmarking a Hemodynamics Application on Intel Based HPC Systems
Authors: Auricchio Ferdinando  , Fedele Marco  , Ferretti Marco  , Lefieux Adrien  , Romarowski Rodrigo  , Santangelo Luigi  , Veneziani Alessandro
Published: February 2025

Three different INTEL based HPC systems are used to benchmark an application of the LifeV library for running simulations of patient-specific cardiovascular hemodynamics. The targeted INTEL architectures rely on the Hashwell-Broadwell family of processors. Running times and scalability measures are collected with two real-size experiments. A third small-size test case is used to profile the code, exposing the effect of compiler vectorization, MPI efficiency and memory footprint. Profiling showed an unexpected low degree of floating point functional units usage, and a low percentage of effective vectorization. Extensive code redesign is likely necessary to best exploit the architectural features available in INTEL Knight Landing processors.
High-Level Parallel Implementation of Swarm Intelligence-Based Optimization Algorithms with Algorithmic Skeletons
Authors: Wrede Fabian  , De Melo Menezes Breno Augusto  , De Araujo Pessoa Luis Filipe  , Hellingrath Bernd  , Buarque De Lima Neto Fernando  , Kuchen Herbert
Published: February 2025

Swarm Intelligence (SI)-based metaheuristics are frequently used to solve complex optimization problems, which are too hard to be solved by classic exact algorithms. Inspired by nature, SI particles move through a search space in pursuit of good solutions. Even using SI, solving some large problems still takes a lot of time, e.g., due to the high number of dimensions and large search spaces. In order to over-come this, parallel implementations of SI algorithms have been investigated. They are typically based on low-level approaches for parallelism, such as MPI, OpenMP, and CUDA, which are tedious and error-prone to use. To overcome these issues, frameworks for high-level parallel programming such as the Muenster Skeleton Library (Muesli) can be used. We show how two SI algorithms, namely Particle Swarm Optimization (PSO) and Fish School Search (FSS), can be implemented in Muesli easily. Experimental results demonstrate the obtained performance and good scalability.
Pipelined Streaming Computation of Histogram in FPGA OpenCL
Authors: Hosseinabady Mohammad  , Nunez-Yanez Jose Luis
Published: February 2025

The emergence of High-Level Synthesis (HLS) techniques and tools, along with new features in high-end FPGAs such as multi-port memory interfaces, has enabled designers to utilize FPGAs not only for compute-bound but also for memory-bound tasks. This paper explains how to efficiently parallelise histogram, as a memory-bound task, utilizing the OpenCL framework running on FPGA. We have run our implementation on three high-end FPGAs including Alpha Data 7v3, Alpha Data ADM-PCIE-KU3 and Xilinx KU115. The 256 fixed-width bins histogram running on 7v3, KU3 and KU115 platforms shows 8.38, 15.29 and 38.57 Giga bin Update Per Second (GUPS), respectively. The best result, i.e., 38.57 GUPS on KU115 platform defeats the Nvidia GeForce 1060 GPU with 31.36 GUPS. In addition, it shows better performance than the one obtained in the dual socket 8-core Intel Xeon E5-2690 with 13 GUPS and 60-core Intel Xeon Phi 5110P coprocessor with 18 GUPS. The proposed implementation is not sensitive to locally invariant (LI) data sets, while the performance of GPU and CPU implementations drops with LI data. Processing locally invariant data sets shows that our FPGA implementation can be up to 91.4% and 44.9% faster than that of the GeForce 1060 and 1080 GPUs, respectively. The source codes of the designs are available at https://github.com/Hosseinabady/histogram_sdaccel.
Implementation of the K-Means Algorithm on Heterogeneous Devices: A Use Case Based on an Industrial Dataset
Authors: Xu Ying hao  , Vidal Miquel  , Arejita Beñat  , Diaz Javier  , Alvarez Carlos  , Jiménez-González Daniel  , Martorell Xavier  , Mantovani Filippo
Published: February 2025

This paper presents and analyzes a heterogeneous implementation of an industrial use case based on K-means that targets symmetric multiprocessing (SMP), GPUs and FPGAs. We present how the application can be optimized from an algorithmic point of view and how this optimization performs on two heterogeneous platforms. The presented implementation relies on the OmpSs programming model, which introduces a simplified pragma-based syntax for the communication between the main processor and the accelerators. Performance improvement can be achieved by the programmer explicitly specifying the data memory accesses or copies. As expected, the newer SMP+GPU system studied is more powerful than the older SMP+FPGA system. However the latter is enough to fulfill the requirements of our use case and we show that uses less energy when considering only the active power of the execution.
Simultaneous Multiprocessing on a FPGA+CPU Heterogeneous System-On-Chip
Authors: Nunez-Yanez Jose  , Hosseinabady Mohammad  , Rodríguez Andrés  , Asenjo Rafael  , Navarro Angeles  , Gran-Tejero Rubén  , Suárez-Gracia Darío
Published: February 2025

In this paper, we investigate how to enhance an existing software-defined framework to reduce overheads and enable the parallel utilization of all the programmable processing resources present in systems that include FPGA-based hardware accelerators. To remove overheads, a new hardware platform is created based on interrupts, which removes spin-locks and frees the processing resources. Additionally, instead of simply using the hardware accelerator to offload a task from the CPU, we propose a scheduler that dynamically distributes the tasks among all the resources to minimize load unbalance. The experimental evaluation shows that the interrupt-based heterogeneous platform increases performance by up 22% while reducing energy requirements by 15%. Additionally, we measure between 50% to 25% reduction in execution time when the CPU cores assist FPGA execution at the same level of energy requirements depending on hardware speed-ups.
Multi-Node Advanced Performance and Power Analysis with Paraver
Authors: Mantovani Filippo  , Calore Enrico
Published: February 2025

Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a preliminary performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. Moreover we show how the same analysis techniques are applicable on different architectures, analyzing the same HPC application running on two clusters, based respectively on Intel Haswell and Arm Cortex-A57 CPUs.
The Brain on Low Power Architectures
Authors: Ammendola Roberto  , Biagioni Andrea  , Capuani Fabrizio  , Cretaro Paolo  , De Bonis Giulia  , Lo Cicero Francesca  , Lonardo Alessandro  , Martinelli Michele  , Paolucci Pier Stanislao  , Pastorelli Elena  , Pontisso Luca  , Simula Francesco  , Vicini Piero
Published: February 2025

Efficient brain simulation is a scientific grand challenge, a parallel/distributed coding challenge and a source of requirements and suggestions for future computing architectures. Indeed, the human brain includes about 1015synapses and 1011neurons activated at a mean rate of several Hz. Full brain simulation poses Exascale challenges even if simulated at the highest abstraction level. The WaveScalES experiment in the Human Brain Project (HBP) has the goal of matching experimental measures and simulations of slow waves during deep-sleep and anesthesia and the transition to other brain states. The focus is the development of dedicated large-scale parallel/distributed simulation technologies. The ExaNeSt project designs an ARM-based, low-power HPC architecture scalable to million of cores, developing a dedicated scalable interconnect system, and SWA/AW simulations are included among the driving benchmarks. At the joint between both projects is the INFN proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine. DPSNN can be configured to stress either the networking or the computation features available on the execution platforms. The simulation stresses the networking component when the neural net — composed by a relatively low number of neurons, each one projecting thousands of synapses — is distributed over a large number of hardware cores. When growing the number of neurons per core, the computation starts to be the dominating component for short range connections. This paper reports about preliminary performance results obtained on an ARM-based HPC prototype developed in the framework of the ExaNeSt project. Furthermore, a comparison is given of instantaneous power, total energy consumption, execution time and energetic cost per synaptic event of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server platforms.
On Parallel Performance and Numerical Stability of Pipelined Conjugate Gradients
Authors: Cools Siegfried  , Vanroose Wim
Published: February 2025

Pipelined Krylov subspace methods achieve significantly improved parallel scalability compared to standard Krylov methods on present-day HPC hardware. However, this typically comes at the cost of a reduced maximal attainable accuracy. This paper presents and compares several stabilized versions of the communication-hiding pipelined Conjugate Gradients method. The main novel contribution of this work is the reformulation of the multi-term recurrence pipelined CG algorithm by introducing shifts in the recursions for specific auxiliary variables. These shifts reduce the amplification of local rounding errors on the residual. Given a proper choice for the shift parameter, the amplification of local rounding errors is reduced and the resulting algorithm improves the attainable accuracy. Numerical results on a variety of SPD benchmark problems compare different stabilization techniques for the pipelined CG algorithm, showing that the stabilized algorithms are able to attain a high accuracy while displaying excellent parallel performance.
Flexible Distributed Computing Across End-Devices, the Edge and the Cloud
Authors: Patras Alexandros  , Lalis Spyros  , Antonopoulos Christos D.
Published: February 2025

The emergence of the edge/fog computing paradigm has increased the programming complexity of applications so that they can work seamlessly in the new distributed and heterogeneous system landscape. In this paper, we investigate a structured dataflow approach which simplifies application development and offers great flexibility regarding the deployment of the application across end-devices, edge computing infrastructure and remote cloud systems. Our prototype is built on top of the Node-RED framework, with extensions in order to support the transparent deployment and distributed execution of application flows. We use a real-world application example to illustrate our approach as well as to explore the performance trade-offs for different deployment scenarios on a real distributed computing setup.
Design Towards Modern High Performance Numerical LA Library Enabling Heterogeneity and Flexible Data Formats
Authors: Imamura Toshiyuki  , Mukunoki Daichi  , Hirota Yusuke  , Yamada Susumu  , Machida Masahiko
Published: February 2025

This work introduces a design proposal towards modernization of high performance numerical library enabling various parallel execution, such as offloading, many-core concurrent execution, heterogeneous hybrid execution. A prototype implementation of Eigen-G2 employs a parallel task model supported by OpenMP, and exclusive control of offloading a GPU device. Also, multiple data formats are available by taking advantage of a metaprogramming support of C++ language. Eigen-G2 exhibits good parallel performance scalability when we use both multicore CPUs and a GPU at the same time.
Targeting Alzheimer's Disease at the Right Time and the Right Place: Validation of a Personalized Approach to Diagnosis and Treatment
Authors: Gauthier Serge  , Pin Ng Kok  , Pascoal Tharick A.  , Zhang Hua  , Rosa-Neto Pedro
Published: February 2025

Cautious optimism is appropriate for a near future (five years) time frame for a number of drugs acting on the different pathophysiological components of Alzheimer's disease (amyloid deposition, tau hyperphosphorylation, neuroin-flammation, vascular changes, to name the most important known so far). Since the relative weight of these components will be different between individuals and will even change over time for each individual, a ‘one drug fit for all’ approach is no longer defensible. Precision medicine using biomarkers in the diagnosis and treatment of Alzheimer's disease is the new strategy.
Targeting Insulin for Alzheimer's Disease: Mechanisms, Status and Potential Directions
Authors: Hyun Lee Jung  , Jahrling Jordan B.  , Denner Larry  , Dineley Kelly T.
Published: February 2025

Insulin resistance can occur when the body is unable to respond to insulin even in excess. In the brain, insulin manages glucose metabolism in regions such as the hippocampus and plays a key role in directly regulating ERK, a kinase required for the type of memory compromised in early Alzheimer's disease (AD). Human imaging studies show that brain glucose utilization declines with age and is notably impaired in subjects with early AD. Likewise, animal models of AD or insulin resistance, or both, demonstrate that dysfunctional insulin signaling and insulin resistance in the brain have reciprocity with neuroinflammation and aberrant accumulation of amyloid-β (Aβ), pathological hallmarks in AD. As such, the association between brain insulin activity and AD has led to clinical trials testing the efficacy of insulin and insulin-sensitizing drugs to intervene in AD. Based on recent inquiries to ClinicalTrials.gov, we evaluated thirty-three clinical studies related to AD and insulin. The search filtered for interventional clinical trials to test FDA-approved drugs or substances that impinge upon the insulin signaling pathway. Insulin, metformin, and thiazolidinediones were the three main interventions assessed. Overall, these strategies are expected to negate the effects of brain insulin resistance by targeting insulin signaling pathways involved in neuroinflammation, metabolic homeostasis, synaptic functional and structural integrity. The goal of this review is to provide an update on insulin and ERK signaling in relation to memory, its decline in early AD, and provide an overview of clinical trials related to insulin for early AD intervention.
Our Tau Tales from Normal to Pathological Behavior
Authors: Alonso Alejandra D.  , Cohen Leah S.
Published: February 2025

The microtubule associated protein tau in a hyperphosphorylated form was identified as the building block of the filamentous aggregates found in the neurons of Alzheimer's disease (AD) patients. In the abnormal state, hyperphosphorylated tau from AD brains (AD P-tau) was unable to promote microtubule assembly and more importantly, it could inhibit the normal activity of tau and other MAPs. AD P-tau was able to disrupt preformed microtubules and, by binding to normal tau, turn the latter into an AD P-tau like molecule. AD P-tau toxic behavior was prevalent in the soluble form and it was lost upon dephosphorylation. Mutations on tau associated with disease, e.g., R406W in frontotemporal dementia with Parkinsonism linked to chromosome 17, altered its conformation to make it a better substrate for kinases. Using phospho-mimetics, it was found that the minimum phospho-sites necessary to acquire such a toxic behavior of tau were at 199, 212, 231 and 262, and tau pseudophosphorylated at those sites in combination with R406W was named Pathological Human Tau (PH-Tau). PH-Tau expressed in cells had similar behavior to AD P-tau: disruption of the microtubule system, change in the normal subcellular localization, and gain of toxic function for cells. In animal models expressing PH-Tau, it was found that two putative mechanisms of neurodegeneration exist depending on the concentration of the toxic protein, both involving cognitive decline, due to synaptic dysfunction at lower concentration and neuronal death at higher. Studies investigating the mechanism of tau pathology and its transmission from neuron to neuron are currently ongoing.
Adaptive Resource Management for Distributed Data Analytics
Authors: Thamsen Lauritz  , Renner Thomas  , Verbitskiy Ilya  , Kao Odej
Published: February 2025

Increasingly large datasets make scalable and distributed data analytics necessary. Frameworks such as Spark and Flink help users in efficiently utilizing cluster resources for their data analytics jobs. It is, however, usually difficult to anticipate the runtime behavior and resource demands of these distributed data analytics jobs. Yet, many resource management decisions would benefit from such information.
High-Performance Massive Subgraph Counting Using Pipelined Adaptive-Group Communication
Authors: Chen Langshi  , Peng Bo  , Ossen Sabra  , Vullikanti Anil  , Marathe Madhav  , Jiang Lei  , Qiu Judy
Published: February 2025

Subgraph counting aims to count the number of occurrences of a subgraph T (aka as a template) in a given graph G. The basic problem has found applications in diverse domains. The problem is known to be computationally challenging – the complexity grows both as a function of T and G. Recent applications have motivated solving such problems on massive networks with billions of vertices.
Parallel Motion Estimation Based on GPU and Combined GPU-CPU
Authors: Zamanshoar Rashid  , Mansouri Azadeh
Published: February 2025

Parallel video coding has recently received considerable attention since there is a strong need for real time applications. To resolve the extremely high computational complexity of Motion Estimation as a most time consuming part of the video coding, suitable parallel algorithms need to be developed. Recently, the use of Graphics Processing Units (GPUs) for parallel tasks has become popular. Motion estimation as a most important part of the video coding standard is readily parallelized. Thus, this task is well-suited for GPU based parallel implementation. In this paper, three GPU based parallel approaches of motion estimation are introduced. In all of these algorithms, the motion information is analyzed and dynamic search areas are employed. In another approach, we make use of the combined CPUs and GPU system in order to reach more effective results especially in the case of real time applications for devices with limited computational resources. The results of the combined method are superior especially for medium amount of motion. The experimental results show appropriate performance of the presented approaches in both PSNR values and speedup.
Reliability-Aware Voltage Scaling of Multicore Processors in Dark Silicon Era
Authors: Nejatollahi Hamid  , Salehi Mostafa E.
Published: February 2025

With the technology scaling, multicore and many-core processors introduced themselves as an alternative to offset performance demands. Power and thermal constraints make remarkable area of chip underutilized, which is called the dark silicon in the literature. In this paper, we model a multicore processor based on Amdahl's Law and meanwhile consider both reliability issues and memory overheads. Then based on the accurate empirical results, we suggest effective voltage and frequency scaling under power constraints and different amount of dark silicon. Since power-efficient small cores result in more active cores in the multicore architecture, we attempt to improve the total performance by introducing more power-efficient multicore architectures along with decreasing the dark silicon percentage. According to the results, voltage scaling of the processor has negligible effect on performance of memory-intensive applications. However, performance of CPU-intensive applications is sensitive to voltage scaling, where for parallel and serial applications performance is improved and diminished, respectively. Energy and performance per watt of all applications are improved by voltage scaling.
Parallel and Distributed Analysis of Microarray Data
Authors: Agapito Giuseppe  , Guzzi Pietro Hiram  , Cannataro Mario
Published: February 2025

Microarrays are extensively used in genomic research and have several ranges of applications in biology and medicine, providing a large amount of data. Several different kinds of microarrays are available, distinguishable by characteristics such as the kind of probes, the surface support used, and the method employed for the target detection. Although microarrays have been developed and applied in many biological contexts, the management and investigation, require advanced computational tools to speed up data analysis and the interpretation of the results. To better deal with microarray data sets with characteristics is the huge dimension, the development of easy to use analysis tools as well as to produce accurate predictions, and comprehensible models arise. The object of this paper is to provide a review of software tool easy to use even from not expert of the domain, as well as able to efficiently deal with microarray data to derive a set of information to discriminate and identify SNPs, associated with genes related with particular drug response, phenotypes and complex diseases developing.
CBIR on Big Data by Use of Deep Learning
Authors: Saeidi Mahmoud  , Ahmadi Ali
Published: February 2025

Learning effective feature representations and similarity measures are critical to the performance of a CBIR. Although various techniques have been proposed, it remains one of the most challenging problems in CBIR, which is mainly due to “semantic gap” issue that exists between low-level image pixels captured by machine and high-level semantic concepts perceived by human. One of the most important advances in machine learning is known as “deep learning” that attempts to model high-level abstractions in data by employing deep architectures composed of multiple non-linear transformations. We can improve CBIR using the state-of-the-art deep learning techniques for learning feature representations and similarity measures. Deep Neural Networks have recently shown great performance on image classification.
Protecting the Civilian Population in Urban Combat Areas
Authors: Erzse Akos
Published: February 2025

In today's complex operational and threat environment, the protection of civilians in urban military operations represents a daunting tactical, operational and strategic challenge, which non-state armed groups using terrorism as a tactic will exploit to its fullest. Nevertheless, given the observable trends in urbanization and armed conflicts, it is one that commanders will inevitably have to face more frequently in the coming years. The integration of gender perspective is one tool forces will have to rely on to provide efficient protection and mitigate harm caused to the civilian population.
Counter Terrorism and National Resilience
Authors: Bachar Avi
Published: February 2025

Terrorism is about instability and fear. Today, countering terrorism is more complex than before. Asymmetric threats and extensive use of technology created a difficult environment for law enforcement agencies to deal with. Therefore, any organization should understand the importance of a comprehensive and holistic doctrine at national level for emergency and crisis, which will be followed by the concept of an all hazard approach. Seamlessly integrating technology, human resources and procedures as integral components of the preparation, response and recovery.
How Can We Be Radical in Combating Radicalism and Terrorism? What Europe Can Learn from Some Southern Neighbours
Authors: Capezzuto Dario
Published: February 2025

Terrorism is not a new phenomenon in itself, but compared to the past it has become an extraterritorial threat, and due to the new conditions it can only be effectively countered by having a global response. The religious aspect of radicalisation must be properly addressed, as it is what gives today's terrorism its global nature and what makes it extremely dangerous. The Muslim world, which is the one which suffers the most from the current situation, is the best placed to provide us with the right answers. Morocco in particular established a comprehensive counterterrorism strategy which includes specific training for Imams and spiritual guides, and opened it to foreign countries. This is an element that should be considered by all western counterterrorism strategies, because it represents a fundamental component of the fight against the root causes of terrorism and we alone do not have the knowledge and the competences to be successful in this field. However, while it is essential that national counterterrorism strategies also address the religious aspect, it is equally important that they are coordinated among themselves, and the work of the UN, NATO, EU, OSCE and all other relevant organisations is crucial from this point of view.
Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
Authors: Garofalo Martina  , Pellegrino Maria Angela  , Altabba Abdulrahman  , Cochez Michael
Published: February 2025

Industry is evolving towards Industry 4.0, which holds the promise of increased flexibility in manufacturing, better quality and improved productivity. A core actor of this growth is using sensors, which must capture data that can used in unforeseen ways to achieve a performance not achievable without them. However, the complexity of this improved setting is much greater than what is currently used in practice. Hence, it is imperative that the management cannot only be performed by human labor force, but part of that will be done by automated algorithms instead. A natural way to represent the data generated by this large amount of sensors, which are not acting measuring independent variables, and the interaction of the different devices is by using a graph data model. Then, machine learning could be used to aid the Industry 4.0 system to, for example, perform predictive maintenance. However, machine learning directly on graphs, needs feature engineering and has scalability issues. In this paper we discuss methods to convert (embed) the graph in a vector space, such that it becomes feasible to use traditional machine learning methods for Industry 4.0 settings.
Cyber Platforms for Adaptive Cyber Defence in Industry 4.0 and Logistics Systems Based on Intelligent Fault Diagnosis
Authors: Dimitrov Konstantin
Published: February 2025

Some advanced concepts and methods for intelligent Fault Diagnosis (FD) are proposed to be implemented in industrial cyber physical platforms (CPP), respectively with capacities to prevent and/or to neutralize cyber attacks in Industry 4.0 complexes and logistics systems.
Industry 4.0: Global Analysis of Energy Considerations and Cyber-Security Constraints
Authors: El Abbassi I.  , Hamaci S.  , Labadi K.  , Darcherif M.
Published: February 2025

The present work deals with the main challenges and constraints inherent to the factories of the future (FoF), especially those related to resilience and hacking risks. After a brief historical review of the industry evolution through the ages, the authors focus on the technical and technological specificities of the so-called industry 4.0 and propose an original Security Simulator Platform for cyber-attacks evaluation in real life industrial production systems, based on the CPS concept (Cyber-Physical System). For this, a graphic architecture is proposed to study the different flows and exchanges between the physical part (manufacturing system) and its ecosystem (Customers, suppliers, stocks, etc.). Then, a numerical model under Witness software, is built, in order to analyze the behavior of the global system (Soft and Hardware) in the event of possible integrity breach and cyber-attacks. The developed model is based on a set of mathematical algorithms and numerical techniques to ensure an intelligent cyber control of the resilience the industrial system.
BigDedup: A Big Data Integration Toolkit for Duplicate Detection in Industrial Scenarios
Authors: Gagliardelli Luca  , Zhu Song  , Simonini Giovanni  , Bergamaschi Sonia
Published: February 2025

Duplicate detection aims to identify different records in data sources that refer to the same real-world entity. It is a fundamental task for: item catalogs fusion, customer databases integration, fraud detection, and more. In this work we present BigDedup, a toolkit able to detect duplicate records on Big Data sources in an efficient manner. BigDedup makes available the state-of-the-art duplicate detection techniques on Apache Spark, a modern framework for distributed computing in Big Data scenarios. It can be used in two different ways: (i) through a simple graphic interface that permit to the user to process structured and unstructured data in a fast and effective way; (ii) as a library that provides different components that can be easily extended and customized. In the paper we show how to use BigDedup and its usefulness through some industrial examples.
Enacted Affordance when Doing Transdisciplinary Engineering
Authors: Mathiasen John Bang  , Bækgaard Lars  , Nielsen Jacob Steendahl
Published: February 2025

This study of transdisciplinary engineering draws on the concept of affordance with the purpose of bridging the across boundary interactions research and technical interactions research. Affordance, possibilities for actions, is enacted in the relationship between an engineer's experience and artefacts when transforming a perceived indeterminacy into a determinate situation. We use three examples from an ethnographic study to analyse the extent to which perceived indeterminacy influences affordance. Three types of affordances are conceptualised; shared affordance if low perceived indeterminacy, collective affordance if increasing perceived indeterminacy and pragmatic affordance if high indeterminacy. Cues from an “Interface Diagram model” artefact are sufficient to enact shared affordance, thus possibilities for actions across boundaries. Cues from a “Product Architecture model” artefact are adequate to enact collective affordance, thus possibilities for actions across boundaries. The multifarious cues from a “Quality Function Deployment matrix” artefact pave the way for pragmatic affordance, thus a common ground in terms of which possibilities for actions across boundary should be brought to the fore, supressed or even omitted.
Evaluating Supply Chain Risks in a Capability-Based Sustainment Environment
Authors: Mo John P.T.  , Shankar Sindhuja
Published: February 2025

In recent years, the Australian Defence Force (ADF) acquisition strategy is evolving from a technology/platform-based to a capability-based. Many of the ADF's legacy platforms were purchased ‘out-right’ where the procurement included the technology/platform itself and an initial delivery of some support systems such as simulators, training devices, technical publications, design documentation and spare parts. This technology/platform-based acquisition approach gave the ADF the flexibility to contract sustainment activities but at the same time took on full responsibility of asset performance. A capability-based acquisition approach on the other hand limits the ADF's ability to independently manage sustainment activities but draws on expertise that are otherwise not available to ADF. The aim of this study is to evaluate the supply chain risks that the ADF faces as a result of adopting new capability-based platform acquisitions that are delivered with their own ‘specialised logistic support systems that integrate into bespoke global supply chains’. The research identified 15 supply chain risks. Local suppliers to ADF were surveyed to analyse and evaluate the risks. Most risks were found to be a medium to high levels and prompt attention to mitigate these risks would be required for both the ADF and industry partners urgently.
Investigating into the Risks of Forming Alliance
Authors: Cook Matthew  , Mo John P.T.
Published: February 2025

Many large and highly complex engineering projects present enormous technical and financial risk to organisations. This is especially true in the defence industry where budgets can potentially run into the billions and the project lifecycle may extend over many years. In frequent cases, such projects are too much for a single organisation to undertake. One option that is becoming ubiquitous within contemporary defence projects is to spread the risk by forming an alliance between several organisations. Unfortunately, forming an alliance between several competing organisations brings its own set of challenges and risks. The operating conditions of the business environment are characterised by frequent changes in products, services, processes, organisations, markets, supply and distribution networks. The partners need to work together as an entity to achieve a goal but the relationships within the alliance are often disrupted by the established practices and culture of the individual companies. This paper starts by examining how risks can multiply when an alliance is formed and what potential impacts these risks have on success. A novel 3PE method for modelling the structure of an alliance with the three elements being product, people, process, and their interactions is proposed within an alliance environment. By examining the relationships among the elements, risks are identified, and the key drivers are exposed. Finally, a case study is presented that focused on risks relating to People and behavioural risks exhibited within an alliance.
Unstable Approach in Aviation: Examine Air Traffic Controllers' Involvement from Situation Awareness and Shared-Situation Awareness
Authors: Lai Hsueh-Yi  , Chen Chun-Hsien  , Khoo Li-Pheng
Published: February 2025

This research aims to investigate how air traffic controllers (ATCOs) contribute to landing instability from the perspective of situation awareness and shared situation awareness. Seven (7) air traffic controllers were involved in semi-structured interviews to study how their decisions were made during the landing phase. Based on the results obtained, the themes identifed were articulated respectively under both proactive and reactive scenarios. In proactive scenarios, ATCOs will make a plan to expedite the air traffic flow to manage the workload in the future, which may decrease the time and the distance for pilots to operate. Thus, the task-related Shared-Mental Model (SMM) enables ATCOs to expedite the landing process within acceptable margins. In addition, the team-related SMM allows ATCOs to intervene in the unstabilized landing state in advance. On the other hand, as for reactive scenarios, unexpected weather and pilots' improper performance due to fuel conservation are the most prominent scenarios for ATCOs to be reactive. To tackle unexpected changes, the task-related SMM facilitates ATCOs in providing extra time and distance for pilots to operate the aircraft. To conclude, for a team in a complex system to achieve the required performance, proper SMM is compulsory to mitigate requirements conflicts between different operators. In this case, it enables ATCOs to achieve a proactive mangement in ways which can be accepted by pilots. Furthermore, proper SMM also helps ATCOs to actively provide extra instructions for pilots under contingencies.
How Can Green Supply Chain Management Contribute to the Product Development Process
Authors: Canciglieri Junior Osiris  , Reche Alda Yoshi Uemura  , Estorilio Carla Cristina Amodio
Published: February 2025

The product development process has the following phases: concept, research, analysis, development and launch. At the same time, the supply chain management needs integrations with suppliers, manufacturers and customers. When a company develops a product, it is necessary to link the product development process to the supply chain management and think about how it can become greener. The aim of this article is to identify the main bibliography references about the product development, oriented towards the green supply chain management. For the literature review, efforts have been made to search the concepts of product development process, supply chain management, green supply chain management, sustainable supply chain management and triple bottom line.
New Trends for Mitigation of Environmental Impacts: A Literature Review
Authors: De Oliveira Kaio Vasconcelos  , Borsato Milton  , Miranda Vitor
Published: February 2025

Accelerated population growth and climate change, consequences of anthropic action, have brought negative impacts to the environment. In order to evaluate and minimize, or even delay these effects, new means have emerged, such as the Biobased economy, Circular economy, and Bioeconomy. This study aims to provide a structured literature review in order to identify the state of the art of the Bioeconomy axis, along with Circular economy and Sustainability. With the aid of the Proknow-C methodology, which approaches the techniques of bibliometric and systemic analysis, 2293 articles were compiled. Among those, 19 articles have been selected for reading and searching for systemic guidelines. From this analysis, it was possible to identify the main points that should guide future research in the field.
Integrating Additive Manufacturing in the Design of Aerospace Components
Authors: Stolt Roland  , Heikkinen Tim  , Elgh Fredrik
Published: February 2025

In the aerospace industry, Additive Manufacturing (AM) is quickly gaining ground. When optimizing the design of an AM component, all life-cycle aspects need to be considered. It is by no means limited to the classic weight/stiffness optimization of the topology alone. The AM component design must comply with an array of requirements on for example assembly, maintenance and inspection. In addition, there are the manufacturability requirements and constraints of the printing procedure itself, including component orientation and support structures. In this paper, a proposal on how to integrate the AM design of components with the design of the complete engine structure is presented. To find how the current design process is conducted, an interview study involving design and manufacturing experts has been made at an aerospace company, forming a base for the proposal. The result is that a primary design procedure for the AM component must be made as a separate step involving a limited set of design considerations prior to making a multidisciplinary evaluation of the proposed engine structure.
Human and User-Centered Design Product Development: A Literature Review and Reflections
Authors: Unruh Gabriela Unger  , Canciglieri Junior Osiris
Published: February 2025

New products are created for humans with the purpose of attending needs in the most varied contexts of their lives, therefore understanding these needs and meeting them through products that generate effectiveness, efficiency, satisfaction and good experiences is essential for business sustainability. Fields such as Ergonomics, Usability, User Experience (UX), Psychology and Anthropology are focused on studying these needs, and they have been applied to different types of products and services. Due to several updates in these areas and recent applications in digital products, there is an opportunity to update and structure the application of human-centered qualitative studies in physical Product Development Processes. The aim of this article is to identify main relevant studies about Human and User-Centered Product Development in the past 10 years and to reflect about new research opportunities. The research was carried out on Capes portal platform (Brazilian Government Scientific Database), which includes 532 databases, as well as through analysis of results, identifying fields of research, methodologies and themes such as the types of products studied. The results show interesting studies and gaps, proving that there are still opportunities to develop structured methods for Human and User-Centered Design for Physical Product Development, and that these developments can contribute for a better business sustainability.
Gesture Recognition from Skeleton Data for Intuitive Human-Machine Interaction
Authors: Brás André  , Simão Miguel  , Neto Pedro
Published: February 2025

Human gesture recognition has assumed a capital role in industrial applications, such as Human-Machine Interaction. We propose an approach for segmentation and classification of dynamic gestures based on a set of handcrafted features, which are drawn from the skeleton data provided by the Kinect sensor. The module for gesture detection relies on a feedforward neural network which performs framewise binary classification. The method for gesture recognition applies a sliding window, which extracts information from both the spatial and temporal dimensions. Then we combine windows of varying durations to get a multi-temporal scale approach and an additional gain in performance. Encouraged by the recent success of Recurrent Neural Networks for time series domains, we also propose a method for simultaneous gesture segmentation and classification based on the bidirectional Long Short-Term Memory cells, which have shown ability for learning the temporal relationships on long temporal scales. We evaluate all the different approaches on the dataset published for the ChaLearn Looking at People Challenge 2014. The most effective method achieves a Jaccard index of 0.75, which suggests a performance almost on pair with that presented by the state-of-the-art techniques. At the end, the recognized gestures are used to interact with a collaborative robot.
Deep Learning-Based Method for Vision-Guided Robotic Grasping of Unknown Objects
Authors: Bergamini Luca  , Sposato Mario  , Peruzzini Margherita  , Vezzani Roberto  , Pellicciari Marcello
Published: February 2025

Collaborative robots must operate safely and efficiently in ever-changing unstructured environments, grasping and manipulating many different objects. Artificial vision has proved to be collaborative robots' ideal sensing technology and it is widely used for identifying the objects to manipulate and for detecting their optimal grasping. One of the main drawbacks of state of the art robotic vision systems is the long training needed for teaching the identification and optimal grasps of each object, which leads to a strong reduction of the robot productivity and overall operating flexibility. To overcome such limit, we propose an engineering method, based on deep learning techniques, for the detection of the robotic grasps of unknown objects in an unstructured environment, which should enable collaborative robots to autonomously generate grasping strategies without the need of training and programming. A novel loss function for the training of the grasp prediction network has been developed and proved to work well also with low resolution 2-D images, then allowing the use of a single, smaller and low cost camera, that can be better integrated in robotic end-effectors. Despite the availability of less information (resolution and depth) a 75% of accuracy has been achieved on the Cornell data set and it is shown that our implementation of the loss function does not suffer of the common problems reported in literature. The system has been implemented using the ROS framework and tested on a Baxter collaborative robot.
BPMN Applied to the Automation of an Integrated Product Development Process Oriented to the R&D Projects of the Brazilian Electrical Sector
Authors: do Val da Fonseca Maria Carolina  , Canciglieri Junior Osiris
Published: February 2025

The lack of models for the development of products oriented to the R&D projects of the Brazilian Electrical Sector generated the need of specific knowledge for this purpose. Aiming to close this gap Pereira and Canciglieri Júnior (2014) have structured the Integrated Product Development Model-Oriented to the Research and Development Projects (MOP&D), a conceptual model, that uses the principals of concurrent engineering, to support the R&D projects foreseen by the Brazilian Electric Energy National Agency's (ANEEL) Innovation Chain composed of six modules. The model has been validated through case studies but has no user interface for the application. Thus, to make this features tangible, this article proposes the use of the tools from the Business Process Modeling and Notation (BPMN), considered the de-facto standard for the representation of the process, for the modeling and automation of the MOP&D. To reach this objective it was proposed the automation of the three last modules of the chain. At first, it was made a Systematic Bibliographic Review regarding the theme, then the modeling of the modules process was developed, followed by its automation using an appropriate engine. The Systematic Bibliographic Review showed that the application of BPMN for modeling Product Development Process is scarce but suitable. The automation proposed in this article provides the multidisciplinary team a consistent vehicle for the correct execution of the modules as structured in the MOP&D, with a decision support system based on process integration taking in to account the concurrent engineering parameters stated in the model.
The Firm's Critical Dimensions in the Open Innovation Arena
Authors: Sivam Ashwin  , Dieguez Teresa  , Ferreira Luís Pinto  , Silva F.J.G.
Published: February 2025

The purpose of this paper is to examine settings for the Open Innovation Arena. More particularly, the paper examines the ways in which a firm can generate the necessary conditions to harness outside performers' knowledge at the crossing point between the firm's boundary and that of the outside world. This process consists of creating a physical location to encourage collaboration and allow the firm to use accessible information remotely and in a compelling manner. Such physical locations imply Open Innovation Arenas, a subject that has not been addressed in many studies by researchers and scholars. This paper incorporates open innovation literature, which examines current standings on factors considered to impact firm's capacity to embrace and practice open innovation, namely conditions, resources and processes. The work also presents the results of a survey conducted among 25 researchers from INESC TEC, The Portuguese Institute for Systems and Computer Engineering, Technology, and Science. The study concludes what conditions are the main drivers, with culture constituting one of the most important factors to achieve open innovation.
Embedding Customers' Viewpoint in FMEA: Application in Service
Authors: Charoenchokdilok Thuangporn  , Koomsap Pisut
Published: February 2025

Failure mode and effects analysis (FMEA) is a popular risk management tool that has been applied in various industries, including in service sector, to prevent possible failures from happening. The potential failure modes are assessed and reflected via a risk priority number (RPN). The RPN calculation is simple, straightforward, and easy to understand. However, in practice FMEA is internal activity done by a multidisciplinary team. Customers who are a stakeholder group and will be directly affected if any failure modes occur, do not involve. In fact, their viewpoint should be seriously considered because they may see things different from the FMEA team. Customer-oriented FMEA that was not received much attention has been improved recently in order to better reflect customers' point of view on the failure modes. Customer dissatisfaction has been integrated directly into this new approach for risk assessment. Kano model has been applied also to identify how customers perceive failure mode effects. Presented in this paper is the illustration of its application in service and comparison with the conventional approach as well as the previous customer-oriented approach. Movie theater services have been selected for this illustration.