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As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

This paper presents a Cosine Similarity-Based Knowledge Distillation (CSKD) for robust, lightweight object detectors. Knowledge Distillation (KD) has been effective in enhancing the performance of compact models in image classification by leveraging deep CNN models. However, the complex and multifaceted nature of object detection, characterized by its modular design and multitasking requirements, poses significant challenges for traditional KD techniques. These challenges are further compounded by the conventional reliance on the Mean Squared Error (MSE) loss function and the limited application of enhanced feature representations to the training phase. Addressing these limitations, the proposed CSKD method combines cosine similarity guidance with MSE loss to facilitate a more effective knowledge transfer from the teacher model to the student model. This is achieved by distilling both intermediate features and prediction outputs, aided by an assistant prediction branch designed to learn directly from the teacher's predictions. This dual-faceted distillation strategy enables the student model to better mimic the teacher model's behavior, leading to improved performance. The proposed method demonstrates versatility and robustness across various object detector architectures without the need for additional feature enhancement layers during training. Notably, employing ResNet-50 as the teacher model and ResNet-18 as the student model, we achieve new benchmarks in KD for object detection across several architectures, including Faster-RCNN, RetinaNet, FCOS, and GFL, with respective mAP scores of 36.6, 35.2, 35.9, and 38.9. These results highlights the effectiveness of CSKD in advancing the state-of-the-art in KD for object detection, offering a compelling solution to the challenges previously faced by traditional KD methods in this domain. The code of the proposed CSKD is available at https://github.com/swkdn16/CSKD. [ABSTRACT FROM AUTHOR]

Transparent solar cells (TSCs) hold substantial potential as continuous energy generators, enabling their use in situations where conventional devices may not be feasible. However, research aimed at modularizing TSCs for the purpose of regulating the overall voltage and current they produce, a critical step toward practical application, is still in its nascent stages. In this study, we explored a custom-designed, all-back-contact (ABC) configuration, which situates all electrical contacts on the rear side, to create glass-like transparent crystalline silicon (c-Si) solar cells and seamless modules. The ABC design not only demonstrates high power conversion efficiency (PCE) in solar cells but also ensures unobstructed visibility through transparent solar modules. Notably, ABC-transparent c-Si solar cells achieved a peak PCE of 15.8% while maintaining an average visible transmittance of 20%. Through seamlessly interconnecting the unit cells, the output voltage and power were systematically tuned from 0.64 V and 15.8 mW (for a 1 cm²-sized unit cell) to 10.0 V and 235 mW (for a 16 cm²-sized module). Furthermore, we successfully demonstrated the photocharging of a smartphone using a transparent ABC solar module. [ABSTRACT FROM AUTHOR]

We report a series of coordination cages that incorporate peptide chains at their vertices, prepared through subcomponent self‐assembly. Three distinct heterochiral tripeptide subcomponents were incorporated, each exhibiting an L−D−L stereoconfiguration. Through this approach, we prepared and characterized three tetrahedral metal‐peptide cages that incorporate thiol and methylthio groups. The gelation of these cages was probed through the binding of additional metal ions, with the metal‐peptide cages acting as junctions, owing to the presence of sulfur atoms on the peripheral peptides. Gels were obtained with cages bearing cysteine at the C‐terminus. Our strategy for developing functional metal‐coordinated supramolecular gels with a modular design may result in the development of materials useful for chemical separations or drug delivery. [ABSTRACT FROM AUTHOR]

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single cells. However, conventional fluorescent protein (FP) modifications used to discriminate single cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and nondeleterious nuclear localization signal (NLS) tag strategy, called "Arginine-rich NLS" (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes and in response to both local and systemic brain-wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances machine learning-automated segmentation of single cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single cells at scale and paired with behavioral procedures. [ABSTRACT FROM AUTHOR]

Sequential NanoPrecipitation (SNaP) is a nascent controlled precipitation process for the tunable formation of polymeric particles for drug delivery and bioimaging. While SNaP utilizes the same self‐assembly principles as one‐step Flash NanoPrecipitation, SNaP is a two‐step assembly process in which the particle core formation is initiated during a first mixing step followed by particle stabilization in a second mixing step. Current SNaP experimental set‐ups use commercial millifluidic mixers connected in series, which have several limitations, including the inability to access short inter‐mixer delay times (Td). A robust, 3D‐printed, modular mixer design that enables access to short Td's (〈 25 ms) not previously accessible is reported. For the first time, it is demonstrated that decoupling the assembly steps improves control over particle size, expanding the attainable size range to include both nanoparticles and microparticles. It is empirically proven that inter‐mixer Td is a key parameter for particle size control and that particle size scales with Td in agreement with Smoluchowski's model of diffusion‐limited growth. The formation of particles ranging in size from 160 nm to 1.2 µm is shown. Finally, the applicability of the new mixers is established by encapsulating fluorophores and therapeutics into particles for the first time via SNaP. [ABSTRACT FROM AUTHOR]

The value of research articles is increasingly contingent on complex data analysis results which substantiate their claims. Compared to data production, data analysis more readily lends itself to a higher standard of transparency and repeated operator-independent execution. This higher standard can be approached via fully reexecutable research outputs, which contain the entire instruction set for automatic end-to-end generation of an entire article from the earliest feasible provenance point. In this study, we make use of a peer-reviewed neuroimaging article which provides complete but fragile reexecution instructions, as a starting point to draft a new reexecution system which is both robust and portable. We render this system modular as a core design aspect, so that reexecutable article code, data, and environment specifications could potentially be substituted or adapted. In conjunction with this system, which forms the demonstrative product of this study, we detail the core challenges with full article reexecution and specify a number of best practices which permitted us to mitigate them. We further show how the capabilities of our system can subsequently be used to provide reproducibility assessments, both via simple statistical metrics and by visually highlighting divergent elements for human inspection. We argue that fully reexecutable articles are thus a feasible best practice, which can greatly enhance the understanding of data analysis variability and the trust in results. Lastly, we comment at length on the outlook for reexecutable research outputs and encourage re-use and derivation of the system produced herein. [ABSTRACT FROM AUTHOR]

The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python‐based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high‐level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication‐ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability. Highlights: JCVI is a Python‐based library that enables genomic workflows through a collection of simple reusable tools.The JCVI library is modular with basic functionalities separated into bioinformatics format parsing, assembly and annotation‐related tools, comparative genomics, and graphics generation.Embedded algorithms like MCscan, ALLMAPS, and other tools within JCVI are now widely used in the community and power a wide array of use cases. [ABSTRACT FROM AUTHOR]

Soft electromagnetic artificial muscles (SEAMs) that use electric currents are reported as their power sources. The proposed actuator consists of fully soft components: microfluidic coils, stretchable magnets, ferromagnetic silicone, and stretchable housings. The soft coils are fabricated by directly printing room‐temperature liquid metal on a stretchable substrate, enabling the generation of high‐density electromagnetic fields. Based on design optimization through modeling and simulation, the proposed actuators have a characteristic of bistability following the relationships of the forces acting on the components. Depending on the design configurations, the proposed actuators generate contraction and expansion motions as well as vibrations in a bidirectional manner, enabled by electromagnetic actuation. The main advantages of the proposed actuators are fully compliant structures, compact form factors, and short response times, which have not been observed in existing polymer‐based artificial muscles. Another advantage is the self‐detection of the actuation states by measuring the inductance change in the coils. Last, the modular design fully packaged with a coil and magnets in a soft housing makes it possible to easily resize and reconfigure the robotic systems with multiple actuator modules for different applications. Examples of applications demonstrated are a modular crawling robot, energy‐efficient grippers, a multi‐degrees of freedom (DOF) soft manipulator, and a high‐frequency swimming robot. [ABSTRACT FROM AUTHOR]

Over the past fifty years, the form and function of space habitat modules have remained largely unchanged. While cylindrical modules are relatively simple to transport and deploy, their limited size poses a challenge to their efficiency and usability. In contrast, large space habitat construction projects are costly and present logistical difficulties. The development of polyhedral modules could bridge the gap between these two approaches, allowing for modular, polyhedral units to be assembled and linked together to create larger habitats more efficiently and with less risk. However, identifying the most suitable polyhedron for use in space remains a critical question. Previous research has explored the construction of polyhedral modules, but little rigor has been applied to identifying the most optimal form. This is a crucial issue because the first module to be used will likely set the standard for subsequent modules and any disadvantages present at that time will be perpetuated. Therefore, it is essential to identify the optimal form prior to constructing prototypes. This research paper employs multi-criteria decision analysis and sensitivity analysis to compare various candidate polyhedra across several evaluation metrics, including number of faces, volume to surface area ratio, and joint stress, among several other quantitative and qualitative metrics. The results demonstrate that the Rhombic Dodecahedron is a particularly suitable candidate compared to other forms analyzed. Thus, the Rhombic Dodecahedron should be considered the standard polyhedral form for future research involving the development of polyhedral modules. • Rhombic Dodecahedron identified as optimal polyhedron for space habitat design. • Polyhedral modules surpass cylindrical counterparts in multiple criteria. • Novel multi-criteria analysis method allows cross-metric comparison. [ABSTRACT FROM AUTHOR]

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Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper develops algorithms to solve strong-substitutes product-mix auctions: it finds competitive equilibrium prices and quantities for agents who use this auction's bidding language to truthfully express their strong-substitutes preferences over an arbitrary number of goods, each of which is available in multiple discrete units. Our use of the bidding language and the information it provides contrasts with existing algorithms that rely on access to a valuation or demand oracle. We compute market-clearing prices using algorithms that apply existing submodular minimization methods. Allocating the supply among the bidders at these prices then requires solving a novel constrained matching problem. Our algorithm iteratively simplifies the allocation problem, perturbing bids and prices in a way that resolves tie-breaking choices created by bids that can be accepted on more than one good. We provide practical running time bounds on both price finding and allocation and illustrate experimentally that our allocation mechanism is practical. Funding: E. Baldwin and P. Klemperer were supported by the Economic and Social Research Council [Grant ES/L003058/1]. P. W. Goldberg and E. Lock were supported by a JP Morgan faculty fellowship during the work on the final version of the paper. Supplemental Material: The online companion is available at https://doi.org/10.1287/moor.2019.0248. [ABSTRACT FROM AUTHOR]

Many firms vie to attain a strategic bottleneck position in their industry, as it promises superior value appropriation over an extended period. Using a mixed-methods approach, we investigated the power dynamics between bottleneck and challenger segments in the airline ticket distribution sector. Our study of the bottleneck formed by the global distribution system (GDS) firms reveals the mechanisms that these firms employed to maintain power, as well as the mechanisms that airlines in the challenger segment used to attain power. These mechanisms strongly influenced the momentum of power shifts as the industry evolved towards greater modularization; we show how they worked recursively in a process of power distribution dynamics during that evolution. In addition, these mechanisms explain the dynamics at work between the incumbent and challenger segments, with airlines increasing the pressure to modularize and the GDS firms resisting that pressure. Our findings contribute to the literature on industry architectural change and industry evolution by providing a comprehensive understanding of the power dynamics that affect when and how strategic bottlenecks dissolve. [ABSTRACT FROM AUTHOR]

This study details the unique advantages and challenges associated with microreactors. Microreactors offer rapid installation and flexible application capabilities, meeting energy needs in remote and inaccessible areas. Unlike large nuclear power plants, they can be set up and start generating energy within a few days, resulting in significant time and cost savings. Their small size and modular design reduce capital and operational costs while enhancing economic competitiveness. However, some technical and regulatory challenges persist for the widespread adoption of microreactors. Licensing processes designed for large nuclear power plants may delay the widespread adoption of microreactors. Blockchain technology can play a crucial role in overcoming these challenges by providing transparency and reliability in the licensing processes. The operational settings of microreactors should be carefully considered, and regulatory authorities must be effectively designated. Collaboration and coordination are vital in this process. Consequently, the flexibility and innovative solutions offered by microreactors highlight the importance of future research to examine the optimal conditions for their use. [ABSTRACT FROM AUTHOR]

The design of biopharmaceutical processes is predominantly driven by the domain of experimental process design. This approach can be further improved by combining multiple domain information such as experiments, unit models, and flowsheet models. Approaches consisting of methods and flowsheet models provide the framework for exploring, analyzing, and ultimately evaluating the combinatorial space of all possible designs within the molecule‐to‐manufacturing value chain. In recent years, modular process designs are of interest in the pharmaceutical industry because of the shift toward multiproduct, mutiprocess processes. Therefore, a systematic approach for how to evaluate the utilization of the modular plug‐n‐play concept provides metrics that can propel modular design from a viable design alternative to the selected alternative for full‐scale manufacturing. The objective of this paper is to present such an in silico approach for the evaluation of modular designs. The approach is presented as a systematic method and then, is exemplified through the manufacture of an active pharmaceutical ingredient (API). The application of the method shows how to transition from a typical design‐for‐purpose design alternative to a modular design through the utilization of data, modeling, simulation, and uncertainty/sensitivity analyses for quantification of various selection metrics such as process robustness and flexibility. [ABSTRACT FROM AUTHOR]

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios. [ABSTRACT FROM AUTHOR]

Biomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial‐temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two‐stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life‐cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability. [ABSTRACT FROM AUTHOR]

This paper provides universal structural design criteria for heavy industrial projects. Structural design requirements for pipe racks, steel modules, modularized equipment skids, vertical and horizontal vessel supports, and vibrating equipment foundations are discussed. An overview of loading mechanisms of various systems is provided for operational, testing, and lifting conditions. Design recommendations are provided for permanent and removable structures. Several structural idealization concepts are also proposed to ensure accurate load transfer. The theoretical basis and the merits of various methods used in the practice are also discussed. Industrial design examples are provided including multilevel pipe rack and pump foundations. The paper overviews and pieces together the engineering requirements for critical structures that are encountered in heavy industry. [ABSTRACT FROM AUTHOR]

Computer programming is a skill of increasing importance in scientific and technological fields. However, in introductory computer science (CS1) courses in higher education, approximately one in every three students fails. A common reason is that students are overwhelmed by an accelerated and inflexible pace of learning that jeopardizes success. Accordingly, in the computer science education literature it has been suggested that the pedagogical philosophy of 'mastery learning,' which supports students progressing at their own pace, can improve academic outcomes of CS1 courses. Nevertheless, few extended mastery learning implementations in CS1 have been documented in the literature, and there is a lack of guidance and best practices to foster its adoption. In this paper, we present a four-year action research study in which a modular mastery-based CS1 course was designed, evaluated and improved in successive iterations with cohorts of engineering freshmen in a Latin American research university (N = 959). In the first year of the intervention, only 19.3% of students passed the course in their first semester attempting it. In successive iterations, the instructional design, teaching and learning activities, course content, and course management were iteratively improved such that by the fourth year of offering 77.1% of students passed the course in their first semester. Over this period, course attrition was reduced from 25.0% to 3.8% of the cohort, and students' mean time spent in the course decreased from 23.2 weeks (SD = 7.38) to 14.9 (SD = 3.64). Results indicate that modularization for mastery learning is a viable approach for improving academic results in a CS1 course. Practical considerations towards successful implementation of this approach are presented and discussed. [ABSTRACT FROM AUTHOR]

Deep learning techniques have gradually attracted considerable research interest in numerous application scenarios because of their capacity to simplify and accelerate calculations. Several researchers have adopted deep learning models, primarily end-to-end long short-term memory networks, to predict structural seismic responses in a data-driven manner and have achieved remarkable improvements. However, further research is required to reduce the training cost and complexity while maintaining the prediction accuracy of end-to-end models. In this study, a pre-training strategy was developed to meet these requirements. In brief, a generic base model was pre-trained by embedding the knowledge of calculating the response of single-degree-of-freedom systems, which was used as the basis for building tailored top models for the online prediction of seismic responses of different structures. This strategy modularized and simplified deep-learning-based seismic response prediction for training while maintaining the advantages of the end-to-end method. Proposed method was verified by using both simulation and actual datasets. The results demonstrated that a significantly lower training cost and complexity were required to achieve a prediction accuracy similar to that of the classic end-to-end method. Specifically, there was a reduction in FLOPs ranging from 20% to 80%, and a decrease in training time ranging from 44% to 82%. Moreover, the performance of deep learning with limited actual data was discussed. [ABSTRACT FROM AUTHOR]

The aim of this research was to design and simulate a low-cost, easy-to-maintain molten aluminum alloy casting mechanism. This mechanism was intended to address labor shortages in harsh and high-temperature environments. The functional requirements of the mechanism were as follows: it needed to withstand temperatures exceeding 700 ℃, operate without deformation, and ensure that the ladle’s positioning error during each rotation did not exceed 1 deg. Additionally, the mechanism’s durability had to meet industry standards, with the ladle’s output shaft capable of bearing pressures exceeding 100 kg/cm2. The design should be modular, allowing for the replacement of specific components to accommodate varying specifications in the future. To begin with, a 3D model was constructed using SolidWorks CAD software to verify operational interferences and simulate assembly, ensuring that the design was practical and logically sound. During the design process, mechanical design handbooks and catalogs from major component suppliers were consulted to utilize standard sizes and specifications as much as possible. Once the model was confirmed, structural analyses were conducted using Ansys to ensure that the mechanism could withstand significant loads without failure. Furthermore, the design would focus on ensuring that the mechanism was robust and reliable under hightemperature conditions, reducing the need for frequent maintenance and downtime. The modular design approach not only enhances the flexibility of the mechanism but also simplifies future upgrades and adjustments, ensuring long-term usability and adaptability. [ABSTRACT FROM AUTHOR]

The development and deployment of small nuclear reactors (SMRs) as part of the new generations of nuclear reactors has gained significant importance in recent years. The advantages of SMRs is due not only to their ability to generate low-emission energy, but essentially to their modularity characteristics and their small dimensions. The TRI-structural-ISOtropic (TRISO) fuel, with proven advantages in graphite-gas type nuclear reactors, has been researched for use in PWRs. To obtain high proliferation resistance, extended fuel cycles for SMRs have been proposed. To obtain conceptual designs of SMR cores with a large cycle length, using low fuel enrichment, without shuffle, and with a relatively small core size is a challenge. In this work, a study was carried out to consider improvements in the behavior of the fuel cycles of a conceptual design of a SMR type iPWR, using TRISO fuel. These improvements are related to increasing reactor core power while maintaining extended fuel cycles and, on the other hand, considering different core zones with different fuel compositions, varying the packaging fraction, the last with the aim of to enlarge the fuel cycles and maintaining a flattened radial power distribution. [ABSTRACT FROM AUTHOR]

In the development of the aviation industry, integrating Life Cycle Management (LCM) with Advanced Health Monitoring Systems (AHMSs) and modular design emerges as a pivotal strategy for enhancing sustainability and cost efficiency. This paper examines how AHMSs, using the Internet of Things, artificial intelligence, and blockchain technologies, can transform maintenance operations by providing real-time diagnostics, predictive maintenance, and secure data logging. The study introduces a comprehensive framework that integrates these technologies into LCM, focusing on maximizing the utilization and lifespan of aircraft components. Quantitative models are developed to compare traditional and modern aviation systems, highlighting the substantial life cycle cost savings and operational efficiencies achieved through these integrations. The results demonstrate up to a 30% reduction in maintenance costs and up to a 20% extension in component lifespan, validating the economic and operational benefits of the proposed integrations. The research underscores the potential of these combined strategies to advance the aviation sector's sustainability objectives, and serves as valuable tools for industry stakeholders, offering actionable insights into the implementation of LCM strategies enhanced by AHMSs and modular design, offering a detailed analysis of the practical implementation challenges. [ABSTRACT FROM AUTHOR]

Recently, targeted degradation has emerged as a powerful therapeutic modality. Relying on "event-driven" pharmacology, proteolysis targeting chimeras (PROTACs) can degrade targets and are superior to conventional inhibitors against undruggable proteins. Unfortunately, PROTAC discovery is limited by warhead scarcity and laborious optimization campaigns. To address these shortcomings, analogous protein-based heterobifunctional degraders, known as bioPROTACs, have been developed. Compared to small-molecule PROTACs, bioPROTACs have higher success rates and are subject to fewer design constraints. However, the membrane impermeability of proteins severely restricts bioPROTAC deployment as a generalized therapeutic modality. Here, we present an engineered bioPROTAC template able to complex with cationic and ionizable lipids via electrostatic interactions for cytosolic delivery. When delivered by biocompatible lipid nanoparticles, these modified bioPROTACs can rapidly degrade intracellular proteins, exhibiting near-complete elimination (up to 95% clearance) of targets within hours of treatment. Our bioPROTAC format can degrade proteins localized to various subcellular compartments including the mitochondria, nucleus, cytosol, and membrane. Moreover, substrate specificity can be easily reprogrammed, allowing modular design and targeting of clinically-relevant proteins such as Ras, Jnk, and Erk. In summary, this work introduces an inexpensive, flexible, and scalable platform for efficient intracellular degradation of proteins that may elude chemical inhibition. Targeted degradation has emerged as a powerful therapeutic modality. In this study, the authors develop a lipid-based platform to deliver recombinant bioPROTACs into cells for targeted protein degradation, providing a platform for efficient intracellular degradation of proteins that may elude chemical inhibition. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to resource shortages, necessitating sustainable approaches in the building industry. This research proposes a preliminary voxel-based modular-architectural design strategy (VMADS), focusing on reusable "H" blocks for component connections and construction. By integrating computational design and robotic fabrication, VMADS enhances precision and efficiency. The framework addresses discrete building theory, prefabrication, and autonomous architecture, emphasizing wood's anisotropic nature for structural integrity. Experimental results validated VMADS through digital simulations and physical tests, demonstrating its potential to create sustainable, reconfigurable structures and revolutionize construction practices. [ABSTRACT FROM AUTHOR]

Quadrupole mass spectrometer is currently one of the most widely used small-scale mass spectrometry instruments. Studying the ions trajectory in a quadrupole field is very important for a deeper understanding of the behavior of ions in quadrupole field, which not only helps to propose new scientific and technological issues in mass spectrometry, but also has practical guidance for the development of instruments. The digital simulation of ions trajectory is an efficient mean for studying the motion of ions in quadrupole field. In this paper, a digital simulation model of quadrupole field, namely Sim-Quaq-COM-V1.0, was established based on COMSOL Multiphysics. The simulation results of the model are in good agreement with the theoretical calculation results, and it can be used as an accurate digital model to study the behavior of ions in quadrupole fields. On this basis, the motion behavior of ions in the hyperbolic quadrupole and the cylindrical quadrupole were compared, and the number of m/z 69 ions passing through the hyperbolic quadrupole field is about 4 times that of the cylindrical quadrupole field under the same conditions. The model has three advantages. Firstly, the self-designed general geometric model and grid were adopted as the built-in model, which can be used to simulate the cylindrical quadrupole field and hyperbolic quadrupole field of various geometric sizes. The modular design is simple and clear. Users only need to modify the geometric parameters and mesh parameters to generate the corresponding geometric model and high-precision mesh. Secondly, the parameters of the model could be configured uniformly in a text file, and the model parameters could be adjusted in batches by modifying the text file. Lastly, the function of multi-core and cluster in COMSOL were utilized to achieve large-scale parallel computing through multi-CPU server and quickly process a large number of ions at the same time. Compared with other models like the default COMSOL model for cylindrical quadrupole field, the simulation speed and accuracy of this model are improved. Through the simulation calculation model, the time of geometric design and modeling is saved, the global parameters can be quickly configured, and the difficulty of calculation is reduced. The model supports parallel computing technology. It is greatly improved in terms of calculation speed and calculation amount, thus can process a large number of ions at the same time, shorten the research cycle, and help the digital development of mass spectrometer design and research technology. [ABSTRACT FROM AUTHOR]

This work quantifies the techno-economic performance of AC and DC residential building distribution. Two methods, utilising software and hardware configurations, are showcased to improve DC distribution: (i) a novel rule-based battery dual-objective operation (DOO) and (ii) a modular Master/Slave design of the grid-tied converter (GC). Both methods use the GC's load-dependent efficiency characteristic, eliminating partial-load operation and enhancing energy efficiency. The work uses measured annual PV and load data to evaluate the performance of the methods compared to AC and DC references. The techno-economic analysis includes the annual net electricity bill and monetised battery degradation. The results show that the DOO eliminates GC partial-load operation at the cost of increased battery usage, resulting in marginal net savings. In contrast, the modular converter design significantly reduces losses: −157 kWh/a (−31%) and −121 kWh/a (−26%), respectively, relative to the DC and AC references. For a parametric sweep of electricity price and discount rate, the Lifetime Operating Cost (LOC) comparison shows savings from DC of up to USD 575 compared to AC. [ABSTRACT FROM AUTHOR]

The rationality of product module partition is crucial to the success of modular design. The correlations between components of complex products are complex, increasing the difficulty of module partition. Thus, many existing methods of module partition have difficulty realizing this process effectively for complex products with a large number of components. This paper proposes a module partition method for complex products based on stable overlapping community detection and overlapping component allocation. The correlations between components are analyzed to obtain a comprehensive correlation strength matrix. The undirected weighted network is used to represent components and the correlations between them. A stable overlapping community detection algorithm based on the improved judgement of within-community Shapley values is proposed to generate multiple preliminary schemes of module partition. Overlapping components among modules are allocated to the most suitable modules by adopting a genetic algorithm (GA). The scheme with the largest modularity measure Q is selected as the final scheme of module partition. The proposed method is applied to a computer numerical control (CNC) grinding machine. The proposed module partition method for complex products is demonstrated to be superior to other effective methods. [ABSTRACT FROM AUTHOR]

Establishing modular binders as diagnostic detection agents represents a cost-and time-efficient alternative to the commonly used binders that are generated one molecule at a time. In contrast to these conventional approaches, a modular binder can be designed in silico from individual modules to, in principle, recognize any desired linear epitope without going through a selection and hit-validation process, given a set of preexisting, amino acid-specific modules. Designed armadillo repeat proteins (dArmRP) have been developed as modular binder scaffolds, and we report here the generation of highly specific dArmRP modules by yeast surface display selection, performed on a rationally designed dArmRP library. A selection strategy was developed to distinguish the binding difference resulting from a single amino acid mutation in the target peptide. Our reverse-competitor strategy introduced here employs the designated target as a competitor to increase the sensitivity when separating specific from cross-reactive binders that show similar affinities for the target peptide. With this switch in selection focus from affinity to specificity, we found that the enrichment during this specificity sort is indicative of the desired phenotype, regardless of the binder abundance. Hence, deep sequencing of the selection pools allows retrieval of phenotypic hits with only 0.1% abundance in the selectivity sort pool from the next-generation sequencing data alone. In a proof-of-principle study, a binder was created by replacing all corresponding wild-type modules with a newly selected module, yielding a binder with very high affinity for the designated target that has been successfully validated as a detection agent in western blot analysis. [ABSTRACT FROM AUTHOR]

On-orbit servicing and on-orbit assembly represent very appealing mission concepts that could facilitate the exploitation of the space environment. Autonomy is a critical requirement for systems aimed at the execution of these operations. The development of enabling technologies for this type of missions is a focal research topic for the global space community. The AUTOMA project (University of Padua, Italy) aims at the development of technologies to enable the on-orbit assembly of a standardized modular unit by means of a robotic arm. The goal is the development of a capture interface, composed of a gripper mechanism and a suite of close-range navigation sensors (a navcam, four time-of-flight sensors, two custom matrix sensors based on a set of phototransistors), and the mock-up of a modular assembly unit. The paper prevalently focuses on the conspicuous series of tests that has been performed at subsystem level for the characterization of the two custom matrix sensors, both in terms of their resolution and range of application. In particular, the test campaign has proven how the in-plane matrix sensor provides information about lateral misalignment and relative distance with a resolution of a few millimetres while the roll matrix sensor provides information about the angular roll misalignment with a resolution dependent on the distance between the two bodies. In addition, an analytical relation for the computation of the relative distance through the information provided by the in-plane matrix has been investigated. • Capture interface composed of a gripper mechanism and a suite of close-range navigation sensors. • Design of a standardized unit mock-up for modular assembly. • Custom sensors based on a matrix of phototransistors activated by an infrared LED beacon counterpart. • Testing campaign aimed at the characterization of the custom matrix sensors. • Analysis of a relation for the retrieval of the relative distance based on the number of phototransistors activated. [ABSTRACT FROM AUTHOR]

The modular construction industry struggles to implement the modularization strategy appropriately due to a poor understanding of the differences between stick-built and modular construction methods and unique modular challenges. Among many modular challenges, transporting prefinished volumetric modules is one of the underinvestigated key challenges—the conventional methods for module transportation [e.g., truck, self-propelled modular transporter (SPMT), and so on), although fitting for projects comprising multiple modules, are not ideal for projects with fewer modules and limited jobsite space. This study investigates hydraulic jacks and trailer bogie as the potential alternative transporting and module lifting/offloading methods by examining how the prefinished volumetric housing module is affected during the lifting, transporting (on-road and jobsite), and offloading phases of transportation. A sensor was installed on the volumetric housing module to record transportation-induced shocks in front-to-back tilt and left-to-right roll, deformation, and impact. The peaks and lows recorded for tilt and roll during each transportation phase were 42, 57, 1, and 0, and 346, 70, 6, and 14, respectively. The results indicated that impacts to the module were more directly associated with left-to-right rolls than front-to-back tilt, because high rolls primarily reflected the module making wide turns, whereas no deformation or impact was recorded. This study contributes to the body of knowledge by highlighting the hydraulic jack and trailer bogie as potential alternatives to the conventional crane and truck under certain conditions through the assessment of the transportation-induced impact on the prefinished volumetric housing module. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Automotive Digest is the property of Automotive Digest Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses a solution to the relevant task of analyzing and designing modular buildings made of blocks to be used in industrial and civil engineering. A block that represents a container is a combination of plate and beam systems. The criteria for its failure include both the strength of the individual elements and the loss of stability in a corrugated web. Methods of engineering analysis are hardly applicable to this system. Numerical analysis based on the finite element method is time-consuming, and this fact limits the number of design options for modular buildings made of blocks. Adjustable machine learning models are proposed as a solution to these problems. Decision trees are made and clustered into a single ensemble depending on the values of the design parameters. Key parameters determining the structures of decision trees include design steel resistance values, types of loads and the number of loadings, and ranges of rolled sheet thickness values. An ensemble of such models is used to take into account the nonlinear strain of elements. Piecewise approximation of the dependencies between components of the stress–strain state is used for this purpose. Linear regression equations are subjected to feature binarization to improve the efficiency of nonlinearity projections. The identification of weight coefficients without laborious search optimization methods is a distinguishing characteristic of the proposed models of steel blocks for modular buildings. A modular building block is used to illustrate the effectiveness of the proposed models. Its purpose is to accommodate a gas compressor of a gas turbine power plant. These machine learning models can accurately spot the stress–strain state for different design parameters, in particular for different corrugated web thickness values. As a result, ensemble models predict the stress–strain state with the coefficient of determination equaling 0.88–0.92. [ABSTRACT FROM AUTHOR]

The construction industry is embracing digital technologies, particularly generative or algorithm-aided design principles. However, integrating these digital tools into design processes while ensuring compliance with rules and regulations remains a significant challenge. This study aims to address this challenge by identifying the essential design parameters and constraints required for semi-automated building model design in the early design stages, with a specific focus on a use case of modular multi-story housing in Vienna. To achieve this, we investigate which parameters are fundamental, how constraints must be formulated, and how these aspects can be covered effectively in the design process. Our research provides a parameter catalog in natural language format to be used for scripting algorithms to generate parametric models. We delineate crucial housing-specific design parameters and identify constraints derived from legal, technical, evaluative, and expert knowledge sources. These constraints ensure that the designs comply with regulations and standards. The findings are organized into eight thematic clusters, each detailing specific conditions, and their interdependencies, thus offering a guideline for scripting algorithm-aided design processes. In conclusion, we propose a conceptual model for translating natural language design parameters into visual programming language. [ABSTRACT FROM AUTHOR]

Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches. [ABSTRACT FROM AUTHOR]

Unmanned vehicles (UVs) have become increasingly important in various scenarios of civil and military operations. The present work aims at the conceptual design of a modular Amphibious Unmanned Ground Vehicle (A-UGV) that can be easily adapted for different types of land and/or water missions with low monetary cost (EUR < 5 k, without sensors). Basing the design on the needs highlighted in the 2021 review of the Strategic Directive of the Portuguese Navy, the necessary specifications and requirements are established for two mission scenarios. Then, a market research analysis focused on vehicles currently available and their technological advances is conducted to identify existing UV solutions and respective characteristics/capabilities of interest to the current work. To study and define the geometry of the hull and the configuration of the A-UGV itself, preliminary computational structural and fluid analyses are carried out to ensure it complies with the specifications initially established. As a result, one obtains a fully electric vehicle with approximate dimensions of 1050 × 670 × 450 mm (length–width–height), enabled with 6 × 6 traction capable of reaching 20 km/h on land, which possesses amphibious capabilities of independent propulsion in water up to 8 kts and an estimated autonomy of over 60 min. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The surrounding geological conditions and supporting structures of underground engineering are often updated during construction, and these updates require repeated numerical modeling. To improve the numerical modeling efficiency of underground engineering, a modularized and parametric modeling cloud server is developed by using Python codes. The basic framework of the cloud server is as follows: input the modeling parameters into the web platform, implement Rhino software and FLAC3D software to model and run simulations in the cloud server, and return the simulation results to the web platform. The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters. The main modules of the modeling program include modeling the 3D geological structures, the underground engineering structures, and the supporting structures as well as meshing the geometric models. In particular, various cross-sections of underground caverns are crafted as parametricmodules in themodeling program. Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station. This complicatedmodel is rapidly generated for the simulation, and the simulation results are reasonable. Thus, this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses. [ABSTRACT FROM AUTHOR]

How tissue-level information encoded by fields of regulatory gene activity is translated into the patterns of cell polarity and growth that generate the diverse shapes of different species remains poorly understood. Here, we investigate this problem in the case of leaf shape differences between Arabidopsis thaliana, which has simple leaves, and its relative Cardamine hirsuta that has complex leaves divided into leaflets. We show that patterned expression of the transcription factor CUP-SHAPED COTYLEDON1 in C. hirsuta (ChCUC1) is a key determinant of leaf shape differences between the two species. Through inducible genetic perturbations, time-lapse imaging of growth, and computational modeling, we find that ChCUC1 provides instructive input into auxin-based leaf margin patterning. This input arises via transcriptional regulation of multiple auxin homeostasis components, including direct activation of WAG kinases that are known to regulate the polarity of PIN-FORMED auxin transporters. Thus, we have uncovered a mechanism that bridges biological scales by linking spatially distributed and species-specific transcription factor expression to cell-level polarity and growth, to shape diverse leaf forms. [ABSTRACT FROM AUTHOR]

This study developed inclusive apparel design (IAD) framework to systematically analyse diverse clothing needs of people with different capabilities in reach, dexterity, and mobility, including people living with disabilities. The research critically examined two accessible design approaches, inclusive design framework and universal design framework, focusing on their similarities and distinctions as well as suitability for developing apparel products that cater to the needs of people with different capabilities in reach, dexterity, and mobility. Based on the analysis, three levels of difficulty in using clothing were proposed: (a) from no to minimal difficulty; (b) moderate difficulty; and (c) severe difficulty. For the three levels of difficulty, the IAD framework outlines three respective design strategies: (a) user-aware design, (b) modular design, and (c) special-purpose design. IAD framework can be used to guide future research and practitioner applications to meet the clothing needs of ability-diverse consumers. [ABSTRACT FROM AUTHOR]

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria. Here, the authors suggest that bacterial proteomes and transcriptomes can be modularized similarly, revealing previously uncharacterized relationships and opening up the possibility of predicting proteome allocation from transcriptomic data. [ABSTRACT FROM AUTHOR]

The global decline of pollinators, particularly insects, underscores the importance of enhanced monitoring of their populations and habitats. However, monitoring some pollinator habitat is challenging due to widespread species distributions and shifts in habitat requirements through seasons and life stages. The monarch butterfly (Danaus plexippus), a migratory insect pollinator that breeds widely throughout North America, presents a unique case study for testing a sampling framework to overcome these challenges. Monarchs exhibit discrete resource needs across life stages (e.g., larval requirement for milkweed, adult requirement for floral nectar), utilizing many land use types across their extensive geographic range during breeding and migration seasons. The Integrated Monarch Monitoring Program (IMMP) uses a standardized protocol with a generalized random tessellation stratified (GRTS) sampling design to gather spatially balanced and ecologically representative information on monarch habitats within the United States. The IMMP is applicable to various land use types and habitats used by breeding monarchs and may be extended to sites outside of the GRTS design to collect data on non-random sites of interest, such as legacy or conservation sites. Additionally, the IMMP's modular design and publicly available training allows for broad participation, including involvement from community scientists. Here, we summarize habitat metrics (milkweed and floral resources) across 1,233 sites covering much of the monarch's breeding range. We examine variation in milkweed density and floral resource availability on probabilistic (random) and non-probabilistic (convenience) samples and among land use types (site types). Additionally, we assess resource availability within core geographies for monarch breeding and migration, specifically within the U.S. Fish and Wildlife Service's Monarch Conservation Units (western, northern, and southern United States). Milkweed density, floral frequency, and floral richness were higher on nonrandom sites and in the North region. Among site types, milkweed density was highest on Rights-of-Way and Unclassified Grassland, while floral frequency was lowest on Rights-of-Way. The IMMP represents the first field-based habitat monitoring program of this scale for monarchs, yielding a robust dataset on monarchs and their habitats across their breeding range and offering a framework for surveying the habitat of insect species with diverse habitat requirements or widespread distributions. [ABSTRACT FROM AUTHOR]

This paper highlights the development and validation of two innovative modular cantilever (MC) designs for railway overhead applications. Existing MCs are heavy, complex, and prone to failures. Addressing these shortcomings, the proposed designs leverage topology optimization and TRIZ principles to achieve significant improvements: weight reduction: Up to 28% lighter than existing MCs due to optimized material distribution. Enhanced robustness: Improved structural efficiency and strength, reducing failure risk. Simplified assembly and maintenance: fewer components and modular design facilitate easier handling and installation. Environmental resistance: increased durability against fatigue cracking, corrosion, and wear and tear. Numerical calculations and finite element analysis validate the performance of the new designs, confirming their ability to withstand dynamic loading conditions equivalent to traditional MCs. These lightweight, robust, and adaptable solutions offer tremendous potential for reducing costs, improving safety, and optimizing performance in railway overhead applications. [ABSTRACT FROM AUTHOR]

This paper explores participatory design methods for the interconnection of digital recording techniques, like digital photogrammetry and Gaussian splatting, with emerging domains such as the metaverse and blockchain technology. The focus lies in community engagement and the economic growth of urban and rural areas through blockchain integration, utilizing low-cost digitalization methods to create Web3 environments mirroring real settlements. Through a case study of an Italian village, the potential of participatory design and community-led development strategies in revitalizing neglected areas are explored, and the use of low-cost drone-based photogrammetry and Gaussian splatting in digitization are compared, highlighting their advantages and drawbacks considering the aim of this work, i.e., the creation of an interactive metaverse space. Ultimately, the study underscores the transformative role of digital technologies in reshaping design processes and fostering community development through a workflow, stressing collaborative decision-making and blockchain-driven economy, manufacturing, and maintenance through self-ownership models and performance-based smart contracts. [ABSTRACT FROM AUTHOR]

Wind power exhibits frequent fluctuations, which can impact the stability of the power grid. The impact of wind power on grid stability can be mitigated by using energy storage system whose battery lifespan can be extended by control strategies. This study focuses on the design of a modular multilevel hybrid energy storage system that incorporates both battery packs and supercapacitors. This system aims to effectively smooth the fluctuation of wind power and prolong the lifespan of battery packs. The power distribution between the battery packs and the supercapacitors is achieved through a first-order low-pass filter. The low-frequency power variations are smoothed using the battery packs, while the time constant of the filter is adjusted based on changes in the state of charge of battery packs. This approach prevents overcharging and overdischarging of the battery packs, thereby increasing their lifespan. The remaining high-frequency power variations are handled using the supercapacitors. Simulation results and a comparison with fixed time constant low-pass filtering demonstrate that the grid-connected power meets national standards following the implementation of the energy storage configuration. The proposed control strategy effectively allocates high- and low-frequency energy storage power, resulting in an 18.34% increase in battery pack lifespan. [ABSTRACT FROM AUTHOR]

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP's modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles. [ABSTRACT FROM AUTHOR]