Search Results (22)

Membrane-based CO₂ capture has emerged as a promising technology for industrial decarbonization, offering advantages in energy efficiency, modularity, and environmental performance. This review presents a comprehensive assessment of membrane processes applied across major emission-intensive sectors, including power generation, cement, steelmaking, and biogas upgrading. Drawing from pilot-scale demonstrations and simulation-based studies, we evaluate how flue gas characteristics, such as CO₂ concentration, pressure, temperature, and impurity composition, govern membrane selection, process design, and operational feasibility. Case studies highlight the technical viability of membrane systems under a wide range of industrial conditions, from low-CO₂ NGCC flue gas to high-pressure syngas and CO₂-rich cement emissions. Despite these advances, this review discusses the key remaining challenges for the commercialization of membrane-based CO₂ capture and includes perspectives on process design and techno-economic evaluation. The insights compiled in this review are intended to support the design of application-specific membrane systems and guide future efforts toward scalable and economically viable CO₂ capture across industrial sectors. Full article

This paper explores a modular pipeline architecture that integrates ChatGPT, a Large Language Model (LLM), to automate the detection and refactoring of data clumps—a prevalent type of code smell that complicates software maintainability. Data clumps refer to clusters of code that are often repeated and should ideally be refactored to improve code quality. The pipeline leverages ChatGPT’s capabilities to understand context and generate structured outputs, making it suitable for addressing complex software refactoring tasks. Through systematic experimentation, our study not only addresses the research questions outlined but also demonstrates that the pipeline can accurately identify data clumps, particularly excelling in cases that require semantic understanding—where localized clumps are embedded within larger codebases. While the solution significantly enhances the refactoring workflow, facilitating the management of distributed clumps across multiple files, it also presents challenges such as occasional compiler errors and high computational costs. Feedback from developers underscores the usefulness of LLMs in software development but also highlights the essential role of human oversight to correct inaccuracies. These findings demonstrate the pipeline’s potential to enhance software maintainability, offering a scalable and efficient solution for addressing code smells in real-world projects, and contributing to the broader goal of enhancing software maintainability in large-scale projects. Full article

Explosive ordnance disposal (EOD) operations are hazardous due to the volatile and sensitive nature of these devices. EOD robots have improved these tasks, but their high cost limits accessibility for security institutions that do not have sufficient funds. This article presents the design, implementation, and validation of a low-cost EOD robot named JVC-02, specifically designed for use in explosive hazardous environments to safeguard the safety of police officers of the Explosives Disposal Unit (UDEX) of Arequipa, Peru. To achieve this goal, the essential requirements for this type of robot were compiled, referencing the capabilities of Rescue Robots from RoboCup. Additionally, the Quality Function Deployment (QFD) methodology was used to identify the needs and requirements of UDEX police officers. Based on this information, a modular approach to robot design was developed, utilizing commercial off-the-shelf components to facilitate maintenance and repair. The JVC-02 was integrated with a 5-DoF manipulator and a two-finger mechanical gripper to perform dexterity tasks, along with a tracked locomotion mechanism, which enables effective movement, and a three-camera vision system to facilitate exploration tasks. Finally, field tests were conducted in real scenarios to evaluate and experimentally validate the capabilities of the JVC-02 robot, assessing its mobility, dexterity, and exploration skills. Additionally, real EOD missions were carried out in which UDEX agents intervened and controlled the robot. The results demonstrate that the JVC-02 robot possesses strong capabilities for real EOD applications, excelling in intuitive operation, low cost, and ease of maintenance. Full article

Unmanned aerial vehicles (UAVs) have gained significant popularity across various domains, but their proliferation also raises concerns about security, public safety, and privacy. Consequently, the detection and tracking of UAVs have become crucial. Among the UAV-monitoring technologies, those suitable for urban Internet-of-Things (IoT) environments primarily include radio frequency (RF), acoustic, and visual technologies. In this article, we provide a comprehensive review of passive UAV surveillance technologies, encompassing RF-based, acoustic-based, and vision-based methods for UAV detection, localization, and tracking. Our research reveals that certain lightweight UAV depth detection models have been effectively downsized for deployment on edge devices, facilitating the integration of edge computing and deep learning. In the city-wide anti-UAV, the integration of numerous urban infrastructure monitoring facilities presents a challenge in achieving a centralized computing center due to the large volume of data. To address this, calculations can be performed on edge devices, enabling faster UAV detection. Currently, there is a wide range of anti-UAV systems that have been deployed in both commercial and military sectors to address the challenges posed by UAVs. In this article, we provide an overview of the existing military and commercial anti-UAV systems. Furthermore, we propose several suggestions for developing general-purpose UAV-monitoring systems tailored for urban environments. These suggestions encompass considering the specific requirements of the application scenario, integrating detection and tracking mechanisms with appropriate countermeasures, designing for scalability and modularity, and leveraging advanced data analytics and machine learning techniques. To promote further research in the field of UAV-monitoring systems, we have compiled publicly available datasets comprising visual, acoustic, and radio frequency data. These datasets can be employed to evaluate the effectiveness of various UAV-monitoring techniques and algorithms. All of the datasets mentioned are linked in the text or in the references. Most of these datasets have been validated in multiple studies, and researchers can find more specific information in the corresponding papers or documents. By presenting this comprehensive overview and providing valuable insights, we aim to advance the development of UAV surveillance technologies, address the challenges posed by UAV proliferation, and foster innovation in the field of UAV monitoring and security. Full article

Digital designs complexity has exponentially increased in the last decades. Heterogeneous Systems-on-Chip integrate many different hardware components which require a reliable and scalable verification environment. The effort to set up such environments has increased as well and plays a significant role in digital design projects, taking more than 50% of the total project time. Several solutions have been developed with the goal of automating this task, integrating various steps of the Very Large Scale Integration design flow, but without addressing the exploration of the design space on both the software and hardware sides. Early in the co-design phase, designers break down the system into hardware and software parts taking into account different choices to explore the design space. This work describes the use of a framework for automating the verification of such choices, considering both hardware and software development flows. The framework automates compilation of software, cycle-true simulations and analyses on synthesised netlists. It accelerates the design space exploration exploiting the GNU Make tool, and we focus on ensuring consistency of results and providing a mechanism to obtain reproducibility of the design flow. In design teams, the last feature increases cooperation and knowledge sharing from single expert to the whole team. Using flow recipes, designers can configure various third-party tools integrated into the modular structure of the framework, and make workflow execution customisable. We demonstrate how the developed framework can be used to speed up the setup of the evaluation flow of an Elliptic-Curve-Cryptography accelerator, performing post-synthesis analyses. The framework can be easily configured taking approximately 30 min, instead of few days, to build up an environment to assess the accelerator performance and its resistance to simple power analysis side-channel attacks. Full article

Citizen science platforms, social media and smart phone applications enable the collection of large amounts of georeferenced images. This provides a huge opportunity in biodiversity and ecological research, but also creates challenges for efficient data handling and processing. Recreational and small-scale fisheries is one of the fields that could be revolutionised by efficient, widely accessible and machine learning-based processing of georeferenced images. Most non-commercial inland and coastal fisheries are considered data poor and are rarely assessed, yet they provide multiple societal benefits and can have substantial ecological impacts. Given that large quantities of georeferenced fish images are being collected by fishers every day, artificial intelligence (AI) and computer vision applications offer a great opportunity to automate their analyses by providing species identification, and potentially also fish size estimation. This would deliver data needed for fisheries management and fisher engagement. To date, however, many AI image analysis applications in fisheries are focused on the commercial sector, limited to specific species or settings, and are not publicly available. In addition, using AI and computer vision tools often requires a strong background in programming. In this study, we aim to facilitate broader use of computer vision tools in fisheries and ecological research by compiling an open-source user friendly and modular framework for large-scale image storage, handling, annotation and automatic classification, using cost- and labour-efficient methodologies. The tool is based on TensorFlow Lite Model Maker library, and includes data augmentation and transfer learning techniques applied to different convolutional neural network models. We demonstrate the potential application of this framework using a small example dataset of fish images taken through a recreational fishing smartphone application. The framework presented here can be used to develop region-specific species identification models, which could potentially be combined into a larger hierarchical model. Full article

Filter-feeding invertebrates are found in almost all of the animal classes that are represented in the sea, where they are the necessary links between suspended food particles (phytoplankton and free-living bacteria) and the higher trophic levels in the food chains. Their common challenge is to grow on the dilute concentrations of food particles. In this review, we consider examples of sponges, jellyfish, bryozoans, polychaetes, copepods, bivalves, and ascideans. We examine their growth with the aid of a simple bioenergetic growth model for size-specific growth, i.e., in terms of dry weight (W), µ = (1/W) dW/dt = aW^b, which is based on the power functions for rates of filtration (F ≈ W^b1) and respiration (R ≈ W^b2). Our theory is that the exponents have (during the evolution) become near equal (b₁ ≈ b₂), depending on the species, the stage of ontogeny, and their adaptation to the living site. Much of the compiled data support this theory and show that the size-specific rate of growth (excluding spawning and the terminal phase) may be constant (b = 0) or decreasing with size (b < 0). This corresponds to the growth rate that is exponential or a power function of time; however, with no general trend to follow a suggested 3/4 law of growth. Many features are common to filter-feeding invertebrates, but modularity applies only to bryozoans and sponges, implying exponential growth, which is probably a rather unique feature among the herein examined filter feeders, although the growth may be near exponential in the early ontogenetic stages of mussels, for example. Full article

Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research. Full article

With the burgeoning transition toward electrified automobile fleets, electric-vehicle batteries (EVBs) have become one of the critical aspects to be considered to avoid resources issues while achieving necessary climate goals. This paper compiles and syntheses reported barriers, enablers, involved stakeholders, and business models of Circular Economy (CE) implementation of the EVBs based on a systematic literature review (SLR). Findings indicate that inefficient and inadequate government policy, lack of safety standards, and high recycling costs are the three most reported barriers. The barriers have interconnections with each other, implying the necessity for simultaneous strategies. Based on the barriers-enablers analysis, the key strategies establishing the CE for the EVBs are innovative business models, economic incentives, EVB standards, legal environmental responsibilities, and certification, whereas the optimized supply-chain operations can be realized through eco-design of the EVBs, battery modularization, proper technology for checking, diagnosing, tracking, information sharing, extensive collaboration, alignment of supply-chain stakeholders, innovative business model, and certification. A conceptual framework presenting the required strategies for both establishing the CE and optimizing the circular supply chain system of the EVBs was then proposed. Potential future research directions are also discussed. Full article

Data science and machine learning are buzzwords of the early 21st century. Now pervasive through human civilization, how do these concepts translate to use by researchers and clinicians in the life-science and medical field? Here, we describe a software toolkit, just large enough in scale, so that it can be maintained and extended by a small team, optimised for problems that arise in small/medium laboratories. In particular, this system may be managed from data ingestion statistics preparation predictions by a single person. At the system’s core is a graph type database, so that it is flexible in terms of irregular, constantly changing data types, as such data types are common during explorative research. At the system’s outermost shell, the concept of ’user stories’ is introduced to help the end-user researchers perform various tasks separated by their expertise: these range from simple data input, data curation, statistics, and finally to predictions via machine learning algorithms. We compiled a sizable list of already existing, modular Python platform libraries usable for data analysis that may be used as a reference in the field and may be incorporated into this software. We also provide an insight into basic concepts, such as labelled-unlabelled data, supervised vs. unsupervised learning, regression vs. classification, evaluation by different error metrics, and an advanced concept of cross-validation. Finally, we show some examples from our laboratory using our blood sample and blood clot data from thrombosis patients (sufferers from stroke, heart and peripheral thrombosis disease) and how such tools can help to set up realistic expectations and show caveats. Full article

Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3$ϵ$ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX${}^{+}$ server. Full article

Integrated development environments (IDEs) provide many useful tools such as a code editor, a compiler, and a debugger for creating software. These tools are highly sophisticated, and their development requires a significant effort. Traditionally, an IDE supports different programming languages via plugins that are not usually reusable in other IDEs. Given the high complexity and constant evolution of popular programming languages, such as C++ and even Java, the effort to update those plugins has become unbearable. Thus, recent work aims to modularize IDEs and reuse the existing parser implementation directly in compilers. However, when IDE debugging tools are insufficient at detecting performance defects in large and multithreaded systems, developers must use tracing and trace visualization tools in their software development process. Those tools are often standalone applications and do not interoperate with the new modular IDEs, thus losing the power and the benefits of many features provided by the IDE. The structure and use cases of tracing tools, with the potentially massive execution traces, significantly differ from the other tools in IDEs. Thus, it is a considerable challenge, one which has not been addressed previously, to integrate them into the new modular IDEs. In this paper, we propose an efficient modular client–server architecture for trace analysis and visualization that solves those problems. The proposed architecture is well suited for performance analysis on Internet of Things (IoT) devices, where resource limitations often prohibit data collection, processing, and visualization all on the same device. The experimental evaluation demonstrated that our proposed flexible and reusable solution is scalable and has a small acceptable performance overhead compared to the standalone approach. Full article

Non-causal modelling is a powerful approach to modelling physical systems in a variety of domains from science and engineering. Non-causal modelling languages enable a high-level and modular approach to modelling. However, it is hard to compile non-causal languages modularly (in the sense of separate compilation). This causes difficulties when simulating large models for which code generation takes a long time, or structurally singular models in which parts of the model are allowed to change at runtime. In this work, we introduce a technique we call order-parametric differentiation to allow truly modular compilation. The idea is to generate (machine) code that can compute derivatives of any order of an expression as needed, thus allowing for ahead-of-time modular compilation of a hybrid non-causal language. We also develop a compilation scheme that enables using partial models as first-class objects in a seamless way and simulating them without the need for just-in-time compilation, even in the presence of structural dynamism. We present a performance evaluation of the scheme we used and study its shortcomings and possible improvements, demonstrating that it is a feasible complement to existing implementation techniques for cases where true modular compilation is a primary objective. Full article

In this paper, the approach and main advances made in multi-process hybrid production cells (HyProCell) for rapid individualised laser-based production are compiled and discussed, including highlights and achievements. HyProCell constructs automated manufacturing platforms that integrate highly flexible laser-based additive build processes with more conventional yet precise subtractive machining processes and include novel solutions like automatic powder removal system/machines and robot arms in integrated multi-process production cells. The HyProCell approach can either build parts additively from scratch and finish them in a coherent production single line/cell or prepare parts by machining and add laser-based additive features, achieving otherwise impossible shapes. In addition to producing new parts, existing parts can be repaired or improved by adding new details with the HyProCell hybrid concept. The research work includes the design of pilot cell facilities, the development of the, and a new modular architecture including a middleware and integration layer to ensure automation with improved pallet handling systems. Finally, the MES and data management methodologies for future improvements and pilot facility implementation were made. Full article

Coupling metrics that count the number of inter-module connections in a software system are an established way to measure internal software quality with respect to modularity. In addition to static metrics, which are obtained from the source or compiled code of a program, dynamic metrics use runtime data gathered, e.g., by monitoring a system in production. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We study weighted dynamic coupling that takes into account how often a connection (e.g., a method call) is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. To compare the different metrics, we use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses. Full article