Search Results (2,631)

Biopharmaceutical medicines represent one of the most dynamic sectors of the pharmaceutical industry, with therapeutic proteins forming the largest and most important group. Their structural complexity and inherent sensitivity to chemical and physical stressors, however, continue to pose major challenges for formulation development and long-term stability. Short peptides have emerged as a promising yet underutilized class of excipients for protein-based drug products. Their modular architecture allows for precise tuning of physicochemical properties such as polarity, charge distribution, and hydrogen-bonding potential, thereby offering advantages over single amino acids. Experimental studies indicate that short peptides can serve multiple functions: stabilizers, antioxidants, viscosity-lowering agents, and as lyo/cryoprotectants or bulking agents in lyophilized formulations. Notably, the relatively small and chemically defined space of short peptides—approximately 400 possible dipeptides and 8000 tripeptides—makes them particularly amenable to systematic screening and computational modeling. This enables rational identification of candidates with tailored excipient functions. This review summarizes current knowledge on the use of short peptides as excipients in parenteral protein formulations, with a focus on their functional versatility and potential for rational design in future development. Full article

Industrial electric drives account for a dominant share of electricity consumption in manufacturing, making their optimal configuration a critical factor for both sustainability and cost reduction. Traditional design approaches based on prototyping and empirical testing are often costly and insufficient for systematically exploring alternative configurations. This study introduces an integrated computational framework that combines digital twin (DT) modeling and virtual commissioning (VC) to enable energy-aware configuration of industrial electric drive systems at early design stages. The methodology employs parameterized component models derived from manufacturer catalog data, implemented in a commercial simulation environment and integrated into an industrial-grade VC platform. Validation is performed on two conveyor-based testbeds, enabling systematic comparison of simulation outputs with physical measurements. The results demonstrate predictive accuracy sufficient to quantify trade-offs in energy consumption, losses, and efficiency across different vendor solutions. Case studies involving belt and strap conveyors highlighted how the framework supports vendor-neutral decision making, revealing nonintuitive optimization trade-offs between minimizing energy consumption and maximizing efficiency. The proposed framework advances sustainable automation by embedding energy analysis directly into commissioning workflows, offering reproducible, scalable, and cross-domain applicability. Its modular design supports transfer to sectors such as renewable energy, transportation, and biomedical mechatronics, where energy efficiency is equally decisive. Full article

This paper presents the design and implementation of a modular cloud-based architecture that enables generative music capabilities in Digital Audio Workstations through a MIDI microservices backend and a user-friendly VST plugin frontend. The system comprises a generative harmony engine deployed as a standalone service, a microservice layer that orchestrates communication and exposes an API, and a VST plugin that interacts with the backend to retrieve harmonic sequences and MIDI data. Among the microservices is a dedicated component that converts textual chord sequences into MIDI files. The VST plugin allows the user to drag and drop the generated chord progressions directly into a DAW’s MIDI track timeline. This architecture prioritizes modularity, cloud scalability, and seamless integration into existing music production workflows, while abstracting away technical complexity from end users. The proposed system demonstrates how microservice-based design and cross-platform plugin development can be effectively combined to support generative music workflows, offering both researchers and practitioners a replicable and extensible framework. Full article

While Horizon Europe is expected to turn the European Union’s Mission-Oriented Innovation Policy (MOIP) into concrete actions, little is known about how coherently its funding calls translate high-level ambitions into effective guidance. To address this, we move beyond the traditional focus on funded projects and offer the first systematic analysis of Horizon Europe call texts as cognitive artefacts of policy design. Using Textual Network Analysis (TNA) on 188 calls of Cluster 5 (“Climate, Energy and Mobility”) in the 2021–2022 Work Programme, we compare Scope and Expected Outcomes texts. We constructed weighted co-occurrence networks and calculated centrality, community structure, and assortativity metrics. Results reveal clear differences between layers: Scope texts show stronger clustering of technical domains (modularity 0.54, assortativity +0.206), while Outcomes present weaker clustering (modularity 0.50, assortativity −0.035), reflecting convergence around high-level impacts. Across both layers, a small set of hubs (“renewable energy”, “climate change”, “emissions”) dominates, with high-betweenness terms bridging siloed domains; peripheral concepts remain weakly linked. The study contributes a novel framework for analysing the architecture of funding calls and demonstrates the utility of centrality metrics for policymakers to identify conceptual gaps and guide future Work Programme design, as well as for applicants optimising their proposal writing. Full article

In modern robotics, actuators are crucial for achieving effective movement and ensuring robustness. Although different applications demand specific actuator qualities, an actuator with built-in compliance and high torque density is generally preferred. Recently, harmonic gearboxes have become widely used in robotics for actuation due to their zero-backlash, lightweight design, flexibility, and high torque density. However, the intricate and precise machining required for these gearboxes makes them economically unviable in some cases. This work presents the RM-Act, a novel Radial Modular Actuator that employs synchronous belts as a harmonic speed reducer. The RM-Act retains the advantages of the harmonic principle, making it a promising candidate for robotic actuation. This paper describes the novel actuation principle and its validation through a prototype, along with a model identification to define its characteristics. The actuator demonstrates a nominal torque density of 10.08 N·m/kg, indicating its potential for efficient robotic applications. Full article

Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints. Full article

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

This study introduces a novel adaptive Mechanical Wave Energy Converter (MWEC) designed to efficiently capture nearshore wave energy for sustainable electricity generation along the southeast surf coast of NEOM (135° longitude). The MWEC system features a polyvinyl chloride (PVC) cubic buoy integrated with a mechanical power take-off (PTO) mechanism, optimized for deployment in shallow waters for a depth of around 1 m. Three buoy volumes, V1: 6000 cm³, V2: 30,000 cm³, and V3: 72,000 cm³, were experimentally evaluated under consistent PTO and spring tension configurations. The findings reveal a direct relationship between buoy volume and force output, with larger buoys exhibiting greater energy capture potential, while smaller buoys provided faster and more stable response dynamics. The energy retention efficiency of the buoy–PTO system was measured at 20% for V1, 14% for V2, and 10% for V3, indicating a trade-off between responsiveness and total energy capture. Notably, the largest buoy (V3) generated a peak power output of 213 W at an average wave amplitude of 65 cm, confirming its suitability for high-energy conditions along NEOM’s surf coast. In contrast, the smaller buoy (V1) performed more effectively during periods of reduced wave activity. Wave climate data collected during November and December 2024 support a hybrid deployment strategy, utilizing different buoy sizes to adapt to seasonal wave variability. These results highlight the potential of modular, wave-adaptive mechanical systems for scalable, site-specific renewable energy solutions in coastal environments like NEOM. The proposed MWEC offers a promising path toward low-cost, low-maintenance wave energy harvesting in shallow waters, contributing to Saudi Arabia’s sustainable energy goals. Full article

On-demand urban delivery increasingly relies on electric delivery bicycles (EDBs), yet their limited battery capacity creates coupled challenges of routing efficiency and energy replenishment. We study a novel battery swapping cabinet location-routing problem (BSC-LRP) with multiple depots, which jointly optimizes routing and modular energy infrastructure deployment under time-window and battery constraints. To address the problem’s complexity, we design an improved branch-and-price algorithm enhanced with adaptive heuristic-exact labeling (IBP-HL) and a robust arc-based branching scheme. This hybrid framework accelerates column generation while preserving exactness, representing a methodological advancement over standard B&P approaches. Computational experiments on modified Solomon instances show that IBP-HL consistently outperforms Gurobi in both runtime and solution quality on small cases, and achieves substantial speedups and improved bounds over baseline B&P on medium and large cases. These results demonstrate not only the scalability of IBP-HL but also its practical relevance: the framework provides decision support for operators and planners in designing cost-efficient, reliable, and sustainable last-mile delivery systems with battery-swapping infrastructure. Full article

This paper presents the design, development, and preliminary validation of a haptic virtual reality simulator for Shielded Metal Arc Welding (SMAW) at the Pontificia Universidad Católica de Valparaíso, Chile, aimed at enhancing psychomotor training for mechanical engineering students in line with Industry 4.0 demands. The system integrates Unity 3D, a commercial haptic device, and a custom 3D-printed electrode holder replicating the welding booth, enabling interaction through visual, auditory, and tactile feedback. Thirty students with minimal welding experience and seven experts participated in usability and realism assessments. The results showed that 80% of students perceived motor skill improvement, 60% rated realism as adequate, and 90% preferred hybrid training (simulator + workshop). The prototype was practically implemented at the mechanical engineering school, requiring only a mid-range workstation, the Touch haptic device, and the developed software, demonstrating feasibility in real academic settings. The findings indicate potential to build confidence, support motor coordination, and provide a safe, resource-efficient training environment, while experts emphasized the need for automated feedback and improved haptic fidelity. The modular architecture allows scalability, extension to other welding processes, and adaptation for inclusive education. This prototype demonstrates how locally developed immersive technologies can modernize technical education while promoting sustainability, accessibility, and skill readiness. Full article

Power modules in silicon carbide (SiC)-based modular multilevel converters (MMCs) suffer from notably severe thermal imbalance and localized overheating. This paper puts forward an asymmetric SiC power module with direct integration of a vapor chamber (VC), designed to balance the thermal distribution inside MMC SMs. Specifically, the chips on the lower side of the HBSM are soldered onto a VC, which is additionally mounted on the direct bonding copper (DBC). Endowed with merits such as favorable temperature uniformity, exceptional thermal conductivity, compact size, flexible design, high integration level, and reasonable cost, the VC serves as an outstanding heat diffuser significantly expanding the effective thermal conduction area and reducing thermal resistance. Moreover, in this structure, the VC also functions as a conductor for device current. Finite element method (FEM) simulation results reveal that the proposed structure can notably reduce the hotspot temperature (from 109 $°\mathrm{C}$ to 71.8 $°\mathrm{C}$), the maximum temperature difference among chips (from 45 $°\mathrm{C}$ to 13.89 $°\mathrm{C}$), and the low-frequency temperature swing (TSL) (from 68 $°\mathrm{C}$ to 38 $°\mathrm{C}$). Consequently, the issues of localized overheating and thermal imbalance in SiC-MMC SMs are effectively addressed. Lifetime analysis further indicates that the proposed structure can reduce the annual damage rate of the chip solder layer by 92.6%. Full article

This paper introduces a hypergraph-based formalism for modeling kinematic and dynamic structures in robotics, addressing limitations of the existing formats such as Unified Robot Description Format (URDF), MuJoCo-XML, and Simulation Description Format (SDF). Our method represents mechanical constraints and connections as hyperedges, enabling the native description of multi-joint closures, tendon-driven actuation, and multi-physics coupling. We present a tensor-based representation derived via star-expansion, implemented in the Hypergraph Model Cognition Framework (HyMeKo) language. Comparative experiments show a substantial reduction in model verbosity compared to URDF while retaining expressiveness for large-language model integration. The approach is demonstrated on simple robotic arms and a quarter vehicle model, with derived state-space equations. This work suggests that hypergraph-based models can provide a modular, compact, and semantically rich alternative for the next-generation simulation and design workflows. The introduced formalism reaches 50% reduction compared to URDF descriptions and 20% reduction compared to MuJoCo-XML descriptions. Full article

The evolution of Industrial Internet of Things (IIoT) systems demands flexible and intelligent architectures capable of addressing low-latency requirements, real-time analytics, and adaptive resource management. In this context, softwarized edge computing emerges as a key enabler, supporting advanced IoT deployments through programmable infrastructures, distributed intelligence, and seamless integration with cloud environments. This paper presents an extended and publicly available proof of concept (PoC) for a softwarized, data-driven architecture designed to operate across the cloud/edge/IoT continuum. The proposed architecture incorporates containerized microservices, open standards, and ML-based inference services to enable runtime decision-making and on-the-fly network reconfiguration based on real-time telemetry from IIoT nodes. Unlike traditional solutions, our approach leverages a modular control plane capable of triggering dynamic adaptations in the system through RESTful communication with a cloud-hosted inference engine, thus enhancing responsiveness and autonomy. We evaluate the system in representative IIoT scenarios involving multi-agent collaboration, showcasing its ability to process data at the edge, minimize latency, and support real-time decision-making. This work contributes to the ongoing efforts toward building advanced IoT ecosystems by bridging conceptual designs and practical implementations, offering a robust foundation for future research and deployment in intelligent, software-defined industrial environments. Full article

This paper presents a comprehensive comparative analysis of high-voltage, high-frequency pulse generation techniques for Pockels cell drivers. These drivers are critical in electro-optic systems for laser modulation, where nanosecond-scale voltage pulses with amplitudes of several kilovolts are required. The study reviews key design challenges, with particular emphasis on thermal management strategies, including air, liquid, solid-state, and phase-change cooling methods. Different high-voltage, high-frequency pulse generation architectures including vacuum tubes, voltage multipliers, Marx generators, Blumlein structures, pulse-forming networks, Tesla transformers, switching-mode power supplies, solid-state switches, and high-voltage operational amplifiers are systematically evaluated with respect to cost, complexity, stability, and their suitability for driving capacitive loads. The analysis highlights hybrid approaches that integrate solid-state switching with modular multipliers or pulse-forming circuits as offering the best balance of efficiency, compactness, and reliability. The findings provide practical guidelines for developing next-generation high-performance Pockels cell drivers optimized for advanced optical and laser applications. Full article