Operational Demands Research Articles

A APLICABILIDADE DO TREINAMENTO FÍSICO MILITAR (TFM), DENTRO DO TURNO DE SERVIÇO, PARA A TROPA DA RADIOPATRULHA AUTO (RPA) DA POLÍCIA MILITAR DO PARANÁ

The present article aims to provide a theoretical deepening, detailing how the Commanders of Operational Units, members of the Military Police of Paraná (PMPR), can optimize the management of their respective personnel in order to enable the practice of Military Physical Training during their service shift, specifically for the members of the Radiopatrulha Auto teams. The proposal seeks to fully comply with the provisions of the General Command Ordinance No. 222, dated March 9, 2022, which establishes guidelines for physical activity schedules within the Corporation. However, this guidance faces significant challenges, as the effective implementation of physical activity rarely reaches the ordinary troops, given that the operational service, which is prioritized, precedes any activity related to Physical Education. Thus, this study aims to propose alternatives and solutions that enable a balance between operational demands and the need to ensure physical health and proper preparation of military police officers, with the goal of ensuring compliance with the regulations set forth in the ordinance and improving physical training conditions within the PMPR.

Nghiên cứu hiệu quả hoạt động của nghề câu tay xa bờ tại tỉnh Khánh Hòa

This study employs an output-oriented Data Envelopment Analysis (DEA) approach to evaluate the operational efficiency of the offshore handline fishery in Khanh Hoa province during 2023/2024. Findings reveal that the average operational efficiency of the sampled fleet is 0.79, indicating that output could increase by up to 26% without additional input if vessels operated at optimal efficiency. Analysis shows that vessel size negatively impacts efficiency, with large-capacity vessels (over 450 CV)—despite receiving the most state support—exhibiting the lowest performance. This inefficiency is primarily attributed to the labor force's technical skills falling short of operational demands. Therefore, policies supporting the expansion of large-capacity offshore vessels must be integrated with strategies to enhance the skills and competencies of the fisheries workforce to ensure sustainable development.

A Three-Granularity Pose Estimation Framework for Multi-Type High-Voltage Transmission Towers Using Part Affinity Fields (PAFs)

At present, Unmanned Aerial Vehicles (UAVs) combined with deep learning have become an important means of transmission line inspection; however, the current approach has the problems of high demand for manual operation, low inspection efficiency, inspection results that do not reflect the distribution of defects on transmission towers, and the need for a large number of manually annotated captured images. In order to achieve the UAV understanding the structure of transmission towers and identifying the defects in the parts of transmission towers, a three-granularity pose estimation framework for multi-type high-voltage transmission towers using Part Affinity Fields (PAFs) is presented here. The framework classifies the structural critical points of high-voltage transmission towers and uses PAFs to provide a basis for the connection between the critical points to achieve the pose estimation for multi-type towers. On the other hand, a three-fine-grained prediction incorporating an intermediate supervisory mechanism is designed so as to overcome the problem of dense and overlapping keypoints of transmission towers. The dataset used in this study consists of real image data of high-voltage transmission towers and complementary images of virtual scenes created through the fourth-generation Unreal Engine (UE4). In various types of electrical tower detection, the average keypoint identification AF of the proposed model exceeds 96% and the average skeleton connection AF exceeds 93% at all granularities, which demonstrates good results on the test set and shows some degree of generalization to electricity towers not included in the dataset.

An Improved Whale Optimization Algorithm for the Integrated Scheduling of Automated Guided Vehicles and Yard Cranes

With the rapid development of global trade, the cargo throughput of automated container terminals (ACTs) has increased significantly. To meet the demands of large-scale, high-intensity, and high-efficiency ACT operations, the seamless integration of various terminal facilities has become crucial, particularly the collaboration between yard cranes (YCs) and automated guided vehicles (AGVs). Therefore, an integrated scheduling problem for YCs and AGVs (YAAISP) is proposed and formulated in this paper, considering stacking containers and bidirectional transport of AGVs. As the YAAISP is an NP-hard problem, an Improved Whale Optimization Algorithm (IWOA) is proposed in which a reverse learning strategy is used for the population to enhance population diversity; a random difference variation strategy is employed to improve individual exploration capabilities; and a nonlinear convergence factor alongside an adaptive weighting mechanism to dynamically balance global exploration and local exploitation. For container tasks of size 100, the objective function value (OFV) of the IWOA was reduced by 9.25% compared to the standard Whale Optimization Algorithm. Comparisons with other algorithms, such as the Genetic Algorithm, Particle Swarm Optimization, and Grey Wolf Optimizer, showed an OFV reduction of 9.61% to 11.75%. This validates the superiority of the proposed method.

Revitalizing interphase in all-solid-state Li metal batteries by electrophile reduction.

All-solid-state lithium metal batteries promise high levels of safety and energy density, but their practical realization is limited by low Li reversibility, limited cell loading and demand for high-temperature and high-pressure operation, stemming from solid-state electrolyte (SSE) low-voltage reduction and high-voltage decomposition, and from lithium dendrite growth. Here we concurrently address these challenges by reporting that a family of reductive electrophiles gain electrons and cations from metal-nucleophile materials (here a Li sulfide SSE) upon contact to undergo electrochemical reduction and form interphase layers (named solid reductive-electrophile interphase) on material surfaces. The solid reductive-electrophile interphase is electron blocking and lithiophobic, prevents SSE reduction, suppresses Li dendrites and supports high-voltage cathodes. Consequently, a reductive-electrophile-treated SSE exhibits high critical capacity and Li reversibility at the anode, and enables Li(1% Mg)/SSE/LiNi0.8Co0.15Al0.05O2 all-solid-state lithium metal batteries to achieve a high coulombic efficiency (>99.9%), long cycle life (~10,000 h) and high loading (>7 mAh cm-2) at 30 °C and 2.5 MPa. This concept also extends to cathodes of other materials (for example, metal oxides), boosting the high-nickel cathode's cycle life and expanding the operational voltage up to 4.5 V. Such solid reductive-electrophile interphase tailoring of material surfaces holds promise to accelerate all-solid-state lithium metal battery commercialization and offer solutions for a wide range of materials.

A Modular and Scalable Approach to Hybrid Battery and Converter Integration for Full-Electric Waterborne Transport

This paper presents a flexible and scalable battery system for maritime transportation, integrating modular converters and hybrid battery technologies that are effectively implemented in real-world scenarios. The proposed system is realized with modular DC-DC converters, which do not require complex design and control or a high number of components and combine high-power (HP) and high-energy (HE) battery cells to optimize the energy and power requirements of vessel operations without oversizing the energy storage system. Moreover, the modular design ensures flexibility and scalability, allowing for easy adaptation to varying operational demands. In particular, the system topology, control mechanisms, and communication protocols are explained in this paper. The concept has been validated through simulations and real-scale laboratory tests, demonstrating its effectiveness. Key results highlight the system’s ability to maintain the DC bus voltage while operating at high efficiency (ranging from 97% to 98%) under different load conditions, supported by reliable and demanding real-time communication using the EtherCAT standard. This real-time capability has been validated, and related results are presented in this paper, showing a synchronization accuracy below 200 ns between two modules and a stable control at a cycle time of 400 µs. This approach offers a promising solution for reducing greenhouse gas emissions in the maritime industry, aligning with global sustainability goals.

EV and PV penetration impact on grid with conservative voltage regulation and reactive voltage compensation.

As global interest grows in renewable energy sources, the impact of combined Electric Vehicle (EV) and PhotoVoltaic (PV) penetration on the power grid stability requires renewed attention, to incorporate new technologies to maintain the power quality under operational constraints. Energy-saving techniques such as Conservation Voltage Reduction (CVR) allow the power utilities to transmit voltage at a lower operation limit, increasing the generation margin to absorb the peak load demands. Increased reverse PV penetration results in grid overvoltage while EV charging absorbs the reactive power causing grid instability. Both overvoltage and loss of reactive power in the grid can be reduced by using CVR and reactive power injection techniques. A power electronic secondary var controller (SVC) can dynamically inject reactive power into selected grid buses. This work compares the voltage stability of an IEEE 33 bus system operating with and without CVR. The simulation studies analyzed the effects of EV penetration level, and PV hosting capacity with SVC compensation paired with and without conservation voltage reduction technique. The analysis results demonstrate that tandem usage of CVR and SVC maintains the grid voltage under operational limits, meets load and EV demand, and increases power efficiency and PV penetration.

A conceptual framework for ergonomic innovations in logistics: enhancing workplace safety through data-driven design

This paper presents a conceptual framework for ergonomic innovations in logistics, emphasizing the integration of ergonomic principles, data-driven design, and stakeholder collaboration to enhance workplace safety, productivity, and employee well-being. It explores the theoretical underpinnings of workplace ergonomics, highlighting the connection between safety, efficiency, and worker satisfaction. The proposed framework outlines four core components: ergonomic assessment, data collection, design innovation, and implementation strategies. It also emphasizes the importance of leveraging data analytics to inform decision-making, adapting to dynamic operational demands, and fostering collaboration among employees, safety experts, and management. The framework’s implementation offers significant organizational benefits, including reduced workplace risks, improved employee health, and measurable economic value through cost savings and increased efficiency. Moreover, the broader applicability of this framework extends to other industries, demonstrating its potential to transform workplace practices globally. The paper concludes with actionable recommendations for practitioners and policymakers to adopt and sustain ergonomic innovations in logistics, ensuring safer and more productive work environments. Keywords: Ergonomic Innovations, Logistics Safety, Data-Driven Design, Workplace Productivity, Employee Well-Being, Occupational Health.

Two-Dimensional Shear-Wave Elastography: Accuracy in Liver Fibrosis Staging Using Magnetic Resonance Elastography as the Reference Standard.

Background: The accurate staging of liver fibrosis is crucial for managing chronic liver disease (CLD). Although magnetic resonance elastography (MRE) is the reference standard for noninvasive fibrosis assessment, its cost, specialized hardware, and operational demands restrict accessibility. In contrast, two-dimensional shear-wave elastography (2D-SWE) is more affordable, accessible, and widely integrated into routine ultrasound systems. Objective: Our aim was to determine the optimal 2D-SWE cut-offs for detecting significant fibrosis (≥F2) and evaluate its diagnostic performance across fibrosis stages. Methods: In this prospective study, 71 patients with suspected CLD underwent same-day MRE and 2D-SWE. MRE-defined cut-offs categorized fibrosis stages (≥3.5 kPa for significant fibrosis). Sensitivity, specificity, area under the receiver operating characteristic curve (AUROC), and likelihood ratios were calculated for various 2D-SWE thresholds. Results: At a 2D-SWE cut-off of 7.0 kPa, sensitivity for detecting ≥F2 fibrosis was 100% with a specificity of 85.7% and a positive likelihood ratio (LR+) of 7.0. Increasing the threshold to 8.0 kPa improved specificity to 91.8% while maintaining a sensitivity of 86.4% and achieving an AUROC of 0.89. For cirrhosis, a cut-off of 11.0 kPa achieved 100% sensitivity and 96.9% specificity. A 5.0 kPa cut-off reliably excluded abnormal stiffness with 89.1% sensitivity. Conclusions: Two-dimensional SWE is a reliable method for staging liver fibrosis. Thresholds of 7.0 kPa for screening significant fibrosis, 8.0 kPa for confirmation, and 11.0 kPa for diagnosing cirrhosis demonstrate high diagnostic accuracy. A 5.0 kPa cut-off effectively excludes abnormal liver stiffness.

Operant demand and public health.

Operant demand and public health.

An Approach to Predicting Energy Demand Within Automobile Production Using the Temporal Fusion Transformer Model

The increasing share of renewable energies within energy systems leads to an increase in complexity. The growing complexity is due to the diversity of technologies, ongoing technological innovations, and fluctuating electricity production. To continue to ensure a secure, economical, and needs-based energy supply, additional information is needed to efficiently control these systems. This impacts public and industrial supply systems, such as vehicle factories. This paper examines the influencing factors and the applicability of the Temporal Fusion Transformer (TFT) model for the weekly energy demand forecast at an automobile production site. Seven different TFT models were trained for the weekly forecast of energy demand. Six models predicted the energy demand for electricity, heat, and natural gas. Three models used a rolling day-ahead forecast, and three models predicted the entire week in one step. In the seventh model, the rolling day-ahead forecast was used again, with the three target values being predicted in the same model. The analysis of the models shows that the rolling day-ahead forecasting method with a MAPE of 13% already delivers good results in predicting the electrical energy demand. The prediction accuracy achieved is sufficient to use the model outcomes as a basis for weekly operational planning and energy demand reporting. However, further improvements are still required for use in automated control of the energy system to reduce energy procurement costs. The models for forecasting heat and natural gas demands still show too high deviations, with a MAPE of 62% for heat demand and a MAPE of 39% for natural gas demand. To accurately predict these demands, further factors must be identified to explain the demand.

THE IMPACT OF MATHEMATICAL MODELING ON AIR TRAFFIC CONTROL EFFICIENCY: A COMPREHENSIVE STRUCTURED REVIEW

Air travel is crucial for global connectivity, yet it faces increasing challenges due to rising air traffic volumes and complex operational demands. Mathematical modeling has emerged as a transformative solution in air traffic control (ATC), enhancing efficiency and safety across the aviation sector. This review delves into the application and outcomes of mathematical models in ATC by analyzing recent literature that incorporates various computational techniques for optimizing air traffic flow, predicting and mitigating conflicts, and improving decision-making processes. In order to attain this, we thoroughly searched scholarly articles from well-known databases, for instance, Scopus as well as IEEE articles published in the last decade, focusing on studies that demonstrated clear numerical results regarding the effectiveness of mathematical modeling in ATC operations. The flow of the study is based on the PRISMA framework. By analyzing a comprehensive selection using an advanced searching approach on Scopus and IEEE database found (n=22), the final primary data was analyzed. The review highlights significant advancements, such as the development of algorithms capable of optimizing flight paths, reducing airspace congestion, and enhancing fuel efficiency, which collectively contributes to more sustainable aviation practices. The findings are divided into four themes, which are (1) optimization of ATC operations, (2) route and flight scheduling optimization, (3) resilience and robustness in atm systems, and (4) technological advances and their integration in air traffic management (ATM). Conclusively, mathematical models have proven indispensable for maximizing ATC efficiency and are pivotal in guiding strategic planning and policy-making to accommodate future air traffic growth while maintaining high safety standards.

Rewinding of 3 Phase Induction Motor Double Speed

A double-speed motor is a type of asynchronous AC motor designed with two or more windings. The presence of two separate windings causes three-phase double-speed motors to have a significantly larger physical size compared to three-phase single-speed motors of the same power rating. Numerous studies have investigated the impact of the rewinding process on the efficiency of single-speed induction motors. However, limited attention has been given to double-speed induction motors. Addressing this research gap, the present study focuses on two primary objectives: first, to analyze the impact of rewinding on the performance characteristics of double-speed induction motors; and second, to evaluate the operational performance of these motors after undergoing the rewinding process. In this study, the rewinding process utilized copper wire with a diameter of 0.50 mm, wound using a mold to create a total of 52 windings. Performance testing revealed the following results: under no-load conditions with slow rotation, the motor exhibited a current of 1.3 A, a frequency of 50.45 Hz, a power factor (cos φ) of 0.86, and a speed of 1515 RPM. When a load was applied under fast rotation, the motor demonstrated a current of 1.9 A, a frequency of 50.29 Hz, a power factor (cos φ) of 0.997, and a speed of 2949 RPM. The experimental results showed minimal variation in current and frequency between loaded and unloaded conditions, with significant differences primarily observed in rotational speed between slow and fast modes. This behavior is characteristic of double-speed motors, which are capable of operating at two distinct speeds. In fast rotation mode, the speed can reach approximately twice that of slow rotation, highlighting the design's capability to adapt to varying operational demands.

How to achieve sustainable operation of transport infrastructure development: a case study of 30 transport infrastructure projects in China

Transport infrastructure projects are distinguished by their high levels of investment, lengthy construction periods, and reliance on government subsidies during operation. These factors have far-reaching impacts, not only on the sustainable operation and success of the projects themselves, but also on the orderly functioning of society and the sustainable development of the economy. As such, it is of paramount importance to thoroughly investigate the sustainable operation of transport infrastructure. This paper examines the impact of sustainable operation on transport infrastructure by analysing 30 projects in China’s transport infrastructure sector. The study employs the qualitative comparative analysis method. To achieve the aim of this paper, we identified five dimensions for assessing a project’s sustainability. These include the economic environment of the construction site, operational market demand, government support capacity, and enterprise management level. This analysis offers valuable guidance for future engineering projects. Finally, the study identified seven strategies for achieving sustainable operation of the project. The findings revealed that the economic development status, population size, and government subsidies of the construction site do not effectively promote sustainable operation of the project. Rather, they can hinder enterprises’ market competitiveness. The study’s results can assist decision-makers in determining the feasibility of implementing sustainable management practices in the new project. Examine the factors that influence the achievement of sustainable business while enhancing the operational efficiency of ongoing initiatives. This research aims to produce a valuable addition to risk assessments in sustainable operation research and the advancement of transportation infrastructure reduction.

SSTar-TransGAN: Transformer-based Unsupervised Multi-modality Hyperspectral Digital Staining Network

Immunohistochemistry (IHC) plays an important role in accurate cancer screening and diagnosis, the clinical application of which has been restricted by complex operation process, high costs and demand of professional skills from pathologists. Digital staining methods based on deep learning provide the possibility for Hematoxylin & Eosin (H&E) stained images to be converted into IHC stained images, but it needs to train multiple staining networks for various modalities of IHC images from different cancers, thus weakening the versatility and convenience of digital staining in the process of practical application. At the same time, microscopic hyperspectral imaging technology can provide abundant spectral information for pathological images, which has been proved effective in digital staining tasks to transform staining modalities from one to many, but microscopic hyperspectral imaging has also been troubled by time-consuming acquisition process and huge data storage. In order to overcome the above challenges, we propose SSTar-TransGAN network for digital staining tasks. With the inhabitation of StarGAN structure, SSTar-TransGAN transfers the training burden of generators into lightweight style encoders between different modalities, while in addition to the introduction of transformer structure into the encoder, the application of spectral super-resolution Swin-Spectral Transformer U-Net (SSTU) network enables the convertion from H&E stained RGB images into hyperspectral images as well, which ensure the spatial structure and color information of IHC images under multiple staining modalities and the further conversion between different staining modalities. Qualitative and quantitative experiments prove the performance of SSTar-TransGAN on digital staining tasks superior to other state-of-the-art digital staining methods.

A Data-Driven Approach to Analyzing Fuel-Switching Behavior and Predictive Modeling of Liquefied Natural Gas and Low Sulfur Fuel Oil Consumption in Dual-Fuel Vessels

International shipping is responsible for approximately 2.7% of the global greenhouse gas emissions, a share expected to rise by as much as 250% by 2050. In response, the International Maritime Organization (IMO) has set ambitious targets to reduce these emissions to near-zero by 2050, focusing on alternative fuels like LNG. This study examines the energy consumption patterns of dual-fuel engines powered by LNG and develops machine learning models using LightGBM to predict fuel usage for both fuel oil (FO) and gas (GAS) modes. The methodology involved analyzing operational data to identify patterns in fuel usage across different voyage conditions. The FO mode was found to be predominantly used for rapid propulsion during speed changes or directional shifts, while the GAS mode was optimized for stable conditions to maximize fuel efficiency. Additionally, a mixed mode of FO and GAS was occasionally applied on complex routes to balance safety and efficiency. Using these insights, LightGBM models were trained to predict fuel consumption in each mode, achieving high accuracy with R2 scores of 0.94 for the GAS mode and 0.98 for the FO mode. This model enables ship operators to optimize fuel decisions in response to varying voyage conditions, resulting in reduced overall fuel consumption and lower CO2 emissions. By applying the predictive model, operators can adjust fuel usage strategies to match operational demands, potentially achieving notable cost savings and meeting stricter environmental regulations. Furthermore, the accurate estimation of fuel usage supports CO2 emissions management, aligning with the Carbon Intensity Indicator (CII) and providing ship operators with actionable data for fleet management optimization. This research provides essential data to support carbon emission compliance, improves fuel efficiency, and offers practical insights into fuel management strategies. The predictive model serves as a valuable resource for ship operators to optimize fuel use and aligns with the IMO’s environmental targets, aiding the maritime industry’s transition toward carbon neutrality.

Understanding deviations from original equipment manufacturers’ maintenance recommendations: reasons, barriers, and benefits

Understanding why maintenance professionals deviate from OEM maintenance recommendations is essential. These recommendations are crucial for ensuring equipment reliability, safety, and performance over its operational lifecycle. However, deviations from these guidelines are common and can occur for various reasons. This study seeks to enhance maintenance practices by analyzing the reasons for deviations (RFD) from OEM maintenance recommendations, identifying the barriers to adherence (BTA), and exploring the potential benefits (PB) of such deviations. To achieve this objective, a survey was conducted in Port Harcourt, Southern Nigeria, involving 105 maintenance personnel from three maintenance-intensive sectors: Oil and Gas, Energy, and Petrochemicals, the study achieved response rates of, 78%, 82%, and 92% respectively. Through qualitative evaluation of the response data, the research identified cost considerations and operational constraints as primary reasons for non-adherence to OEM guidelines. Respondents highlighted benefits such as cost savings and enhanced equipment availability, citing limited budgets and operational demands as significant barriers to compliance. Comparative analysis across the three deviant factors (RFD, BTA, and PB) underscores the dominance of cost-related factors and operational constraints in driving deviations, alongside technological influences, that both facilitate and impede adherence. Environmental and organizational factors, though influential, exhibit comparatively lesser impact. These findings highlight the significance of aligning maintenance practices with OEM recommendations. This alignment not only enhances equipment reliability and reduces operational risks but also has the potential to improve maintenance practices, foster innovation in the industry, and ultimately optimize equipment performance and reliability in industrial settings.

Optimizing residential flexibility for sustainable energy management in distribution networks

In search of a sustainable and green economy, many initiatives have been undertaken to promote clean energy and enhance local flexibility. Residential flexibility, achieved through home appliances capable of adjusting their consumption profiles, offers a feasible solution for operators to address challenges such as congestion and balancing in distribution systems. This paper considered an improved approach for aggregators to provide flexibility in distribution systems. By leveraging load flexibility resources, the model facilitates the rescheduling of real-time and shifting appliances to meet the demands of Balance Responsible Parties (BRPs) or Distribution System Operators (DSOs). This study uses a number of approaches to solve the recommended model effectively despite the problem's inherent complexity. An extensive test case with twenty residential houses equipped with seven types of appliances each is run in order to confirm and compare the optimization algorithms' performance. The results show that by rescheduling home appliance loads across 24 hours, the aggregator may effectively accommodate flexibility requests from DSOs/BRPs while optimizing the expenses associated with user compensation. To further improve the optimization process, this study uses a new Reinforced Learning Quantum Inspired Grey Wolf Optimization (RLQIGWO). Through the integration of reinforcement learning and quantum mechanics principles into the original grey wolf optimizer, RLQIGWO achieves better performance in load balancing, resource utilization, and execution of tasks. The findings demonstrate that the proposed RLQIGWO improves the efficacy and competence of flexibility options in distribution networks, paving the way to a more adaptable and strong energy management strategy.

Fog Cloud Computing Based on Lightweight Scheme for Secured Smart Grids

The integration of fog and cloud computing in smart grids presents a promising approach to enhance the security and efficiency of energy management systems. This paper proposes a lightweight cryptographic scheme tailored for fogcloud architectures to secure smart grid communications. The scheme leverages the distributed nature of fog computing to reduce latency while maintaining robust security measures against cyber threats. By incorporating quantum-resistant cryptographic techniques, the proposed solution addresses the unique challenges of data confidentiality, integrity and privacy in smart grids. Our approach is designed to be resource-efficient, making it suitable for deployment in environments with limited computational power, such as smart meters and Internet of Things (IoT) devices. Comprehensive performance evaluations demonstrate that the proposed scheme not only enhances security but also improves the overall efficiency and reliability of smart grid operations. The findings suggest that this lightweight scheme is a viable solution for securing next-generation smart grids, providing a balance between security and operational demands. Performance evaluation demonstrates that the scheme provides secure and efficient authentication while minimising computational and communication overheads.

Solar-powered thermo-pneumatic actuators for passively controlled adaptive shading

Abstract The operational energy demand of buildings plays a significant role in global energy consumption. Especially, the energy required for cooling is predicted to rise due to climate change. Adaptive shading is a promising approach to save operational energy in buildings. State-of-the-art adaptive shading systems employ both active and passive actuation as well as control strategies, to lower energy consumption while maintaining the thermal and visual comfort of building occupants. However, both approaches still must overcome challenges that hinder widespread adoption. Active systems need motors and pumps, dedicated power supplies, and control electronics. These components make the systems heavy and complex to maintain and repair. Passive systems work without dedicated power supplies and control electronics but can face limitations based on the active materials used, e.g. material degradation, toxicity, narrow response windows, and unknown performance in varying conditions. This work presents a solar-powered, thermo-pneumatic actuator for use in adaptive shading applications that works without specialized active materials, electronic actuation and controls. The actuator incorporates elastic bellows to transform the thermal expansion of air into motion and adapts to changes in irradiance. The actuator is built as a functional prototype and its performance in different irradiance scenarios is evaluated. The actuator is capable of considerable motion, moving a rotational mechanism close to 90° powered by an irradiance of 1000 W m−2. The actuator response is sensitive enough to react to hourly changes in irradiance as well as to a simulated solar cycle. The paper concludes with the presentation of a shading device concept incorporating the actuator to highlight its potential for use in adaptive shading applications to reduce operational energy in buildings.