Search Results (871)

Existing research on green transformation predominantly emphasizes “hard constraints” such as carbon taxes and environmental regulations, while neglecting “soft constraints” shaped by environmental sentiment expressions from key actors such as the public, financial institutions, media, and government. In particular, the collective influence of these multi-actor environmental sentiments remains insufficiently explored. This study fills that gap by constructing a collaborative governance framework using multi-source heterogeneous data from China spanning 2013–2023, including 330 provincial government work reports, 1862 bank annual reports, 2472 newspaper articles, and 68,519 Weibo posts, matched to 4708 firm-year observations of Chinese A-share energy companies. We quantify environmental sentiment tendencies through natural language processing, calculating the index as (negative word frequency − positive word frequency)/total word frequency at the province-year level, thus higher index value indicates more negative sentiment tendency, while green transformation is proxied by ln(green patent applications + 1). The results reveal the following: (1) More negative environmental sentiment tendencies from financial institutions, media, public, and government significantly promote green transformation in energy enterprises, with stronger effects observed from financial institutions and government. (2) Economic and climate policy uncertainty selectively weaken the impact of financial institutions’ sentiment, while the moderating effects for other actors are statistically insignificant. (3) The effect of multi-actor environmental sentiment is more pronounced for firms located in eastern China, operating under high competition or stricter environmental regulations. This study provides a novel, quantified approach to assessing multi-actor environmental sentiment tendencies, affirms the effectiveness of informal governance, and highlights the importance of stable policy in guiding corporate green transformation in emerging economies. Full article

This paper investigates the effectiveness of a coal mine methane drainage system in hard coal mining, with particular emphasis on coal seam 501 at the Staszic–Wujek coal mine (Polska Grupa Górnicza S.A., Katowice, Poland) in the Upper Silesian Coal Basin (USCB), Poland. The study evaluates methane drainage efficiency considering geo-mechanical conditions governing the optimal location of drainage boreholes. Conventional and long directional boreholes are analyzed. Opposite to conventional static analytical approaches, the proposed integrated analysis framework incorporates multi-physics processes, improving forecasting accuracy and enabling dynamic optimization of methane control in deep coal mines. The framework reproduces the geometry of the mining system and the mechanical properties of the surrounding rock mass, allowing the influence of geo-mechanical processes on methane drainage efficiency to be assessed. The methane content of coal seam 501 and methane sorption kinetics on representative coal samples are analyzed together with key characteristics of the mine ventilation system, including air and pressure distribution in workings and goafs and migration paths of methane–air mixtures within coal panel II/C. Full article

In this work the feasibility of fully autonomous hydrogen homes designed for complete off-grid operation is presented. A detailed mathematical modeling and optimization model is developed to evaluate the technical performance and economic feasibility of hydrogen fuel cell-powered residential systems with no grid connection or fallback. The system integrates primary and standby Proton Exchange Membrane (PEM) fuel cells, multi-day hydrogen storage, advanced power conditioning, and comprehensive controls to achieve reliable year-round power supply. The analysis encompasses a complete 20-year lifecycle cost assessment. The results demonstrate that fully autonomous hydrogen homes achieve 99.85% system availability with 13.1 h of potential downtime annually, providing reliable energy independence. The levelized cost of electricity over the 20-year system lifetime is calculated at 0.4543 US$/kWh at baseline hydrogen prices of 6 US$/${\mathrm{kg}}_{{\mathrm{H}}_2}$, substantially higher than grid-connected alternatives. The analysis identifies critical sensitivity to hydrogen pricing and demonstrates that at hydrogen costs below 3 US$/${\mathrm{kg}}_{{\mathrm{H}}_2}$ (achievable with mature green hydrogen production), competitive payback periods of 12–15 years are possible in high-cost electricity regions. This study concludes that hydrogen-based autonomous homes represent a viable long-term solution for residential energy independence, particularly in remote or off-grid locations where grid connection is impractical or in regions with high electricity tariffs and developing green hydrogen production capacity. Full article

Understanding platypus health and disease is made challenging by the cryptic nature of this unique and iconic species. The retrospective medical record review methodology offers a valuable tool to better understand platypus health. A multi-institution review was performed, with 21 organisations and individuals contributing veterinary and pathology records spanning 34 years and 5 Australian states and territories. In total, records were reviewed from 278 wild platypuses and 40 zoo-housed platypuses, with a combined total of 383 presentations. Data from these were extracted and analysed, providing information on demography (age, sex), geographic location, season, reason for presentation, outcome of presentation, and clinical and pathological findings. For wild platypuses, key findings included that the juvenile age class was disproportionately represented in Queensland and New South Wales, and that the peak in juvenile presentations corresponded with weaning. For both wild and zoo-housed platypuses, novel reports of neoplasia were identified, and in wild platypuses, the first reports of neural angiostrongyliasis. For zoo-housed platypuses, an area identified for future research is the high prevalence of presentations for skin lesions. This study contributes to our understanding of platypus health and disease and should be used to guide further work to improve both conservation and welfare outcomes for one of the world’s most unique mammals. Full article

Climate chambers enable repeatable indoor boundary conditions and are increasingly used to study multi-domain IEQ. However, thermal comfort and IAQ are still often evaluated separately, limiting evidence on their coupled behavior and potential trade-offs under different ventilation and air-cleaning strategies. The present study was carried out in the climate chamber located in the laboratory facilities of the University of Western Macedonia to quantify thermal comfort and IAQ simultaneously across different experimental scenarios that vary ventilation mode, heating operation, and occupancy. The results show a correlation between subjective and objective measurements, with the comfort temperature varying around 22.2 °C, as estimated by the Griffiths model, while ventilation mainly affects the stability of the thermal environment. CO₂ levels scaled with occupancy and ventilation rate, while PM removal was strongly strategy-dependent: after a controlled smoke event, mechanical ventilation plus air purification achieved the fastest decay and recovery toward near-background concentrations. Overall, this work represents a first step toward coupled IEQ research by jointly quantifying thermal comfort and IAQ in a climate chamber, enabling systematic comparison of ventilation strategies in terms of both perceived comfort and pollutant exposure. Full article

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S²SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported. Full article

Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of precisely locating the target, as is often the case in lung biopsies. This study presents the design and implementation of an autonomous image processing algorithm included in a closed-loop controller that drives the activity of a multi-degree-of-freedom (six) robotic manipulator that performs emulated tissue biopsies. A realistic lung motion emulator, based on a two-degree-of-freedom robotic device with a photon emitter (to simulate radiopharmaceutical identification of cancerous tissue), was used to test the proposed automatic biopsy collector. Applying image processing to detect cancer tissue enables the identification of the centroid and tumor boundaries. Using the detected centroid coordinates, the reference trajectory of the end effector (biopsy needle) was automatically determined. A finite-time convergent controller was implemented to guide the robotic manipulator’s motion towards the tumor position within a specified time window. The controller was evaluated using a digital twin representation of the entire robotic system and using an experimental device working on the simulated mobile tumor emulator. Evaluation of simulated tumor detection and reference trajectory tracking effectiveness was used to validate the operation of the proposed automatic robotic lung biopsy sampler. The application of the controller allows one to track the position of the emulated tumor with a deviation of 0.52 mm and a settling time of less than 1 s. Full article

Tilting pad journal bearings are critical components in high-speed turbomachinery. The use of sensors within the bearing is crucial to ensure operational safety and to validate computational models. The objective of this study is to improve the experimental investigation of the performance of a tilting pad journal bearing by enhancing the selection and placement of conventional and non-conventional sensors based on the results of a thermohydrodynamic model. The multi-sensor system measures film pressure and pad temperature at multiple locations, as well as pad tilt and film thickness. Redundant measurements are also performed to evaluate the performance of new induction coils capable of detecting magnetic flux variations due to vibrations. This work contributes to the discussion of bearing instrumentation by proposing a synergic sensor system comprising a suitable number of appropriately located conventional sensors together with non-conventional, non-invasive sensors. The experimental results obtained with the refined conventional sensor system agree with the predicted results, with differences that can be attributed to manufacturing and assembly tolerances of the bearing and simplified assumptions in the model. The results of the non-conventional sensor device, although promising, need further investigation. Full article

The proliferation of distributed photovoltaic (PV) generation introduces significant operational challenges for distribution networks, including voltage instability and elevated technical losses. While modern PV inverters capable of static synchronous compensator (STATCOM) functionality—forming PV-STATCOM systems—offer a promising solution, their optimal integration remains a complex mixed-integer non-linear programming (MINLP) problem. This paper addresses this gap by proposing a novel hybrid evaluator–optimizer framework for the optimal daily placement and sizing of PV-STATCOM devices. The framework synergistically integrates the metaheuristic crow search algorithm (CSA) for global exploration of discrete device locations with a high-fidelity, multi-period optimal power flow (OPF) model—implemented efficiently in Julia with the Ipopt solver—for continuous operational evaluation and constraint validation. The methodology incorporates realistic 24 h load and solar irradiance profiles. Extensive validation on standard IEEE 33- and 69-bus test systems demonstrates the efficacy of the proposed approach. The results indicate substantial reductions in daily energy losses—by up to 70.4% and 72.9% for the 33- and 69-bus systems, respectively—and corresponding operational costs, outperforming recent state-of-the-art metaheuristic and convex optimization methods reported in the literature. The CSA also exhibits robust convergence and repeatability across multiple independent runs. This work contributes a computationally efficient, open-source planning tool that leverages modern optimization solvers, providing a scalable and effective strategy for enhancing the power quality and economic performance of PV-rich distribution networks. Full article

Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature. Full article

The high expansion rate of industrial-scale photovoltaic (PV) systems in emerging economies requires proper performance prediction models that consider particular climatic variabilities. Although the theoretical potential of solar energy in South Asia is well documented, there still exists a gap in the validation of simulation models to operational data over long periods in subtropical monsoon climates. Unlike prior studies, this work combines multi-year operational data with dynamic TRNSYS simulations to quantify both technical and environmental performance of a 1 MW PV system under subtropical monsoon conditions. This paper provides a detailed performance evaluation of a 1 MW grid-connected PV system located in Punjab, Pakistan. The actual performance of the system is compared with a dynamic simulation model that is created in the Transient System Simulation Tool (TRNSYS) using three years of operational data. Four different scenarios are analyzed: (1) Ideal Theoretical Operation, (2) Actual Field Data, (3) Simulated Operation with Maximum Power Point Tracking (MPPT), and (4) Simulated Operation without MPPT. The results reveal that the real system produced an average of 1342 MWh/year, whereas the MPPT-enabled simulation predicted 1664 MWh/year, indicating a performance difference of 19.3%. Statistical validation revealed a strong correlation ($R^2=0.84$) between the model and reality, yet identified a normalized Root Mean Square Error (nRMSE) of 26.8%. This deviation represents a performance gap which is deconvoluted into agricultural soiling losses and grid curtailment. The research work quantifies the technical effect of MPPT where a 27% operational advantage is realized in comparison to fixed-voltage cases, proving its necessity in climates with high diffuse radiation during monsoon seasons. Economic analysis demonstrates a Levelized Cost of Energy (LCOE) of $0.0378/kWh of the existing system, and a Simple Payback Time (SPBT) of 4.74 years at the current industrial tariffs. Sensitivity analysis also indicates that in case of an increase in grid tariffs to 50 PKR/kWh, Internal Rate of Return (IRR) increases to 18.8%. Environmental analysis confirms a carbon emission reduction of 765 tons/year. These results validate the techno-economic feasibility of large-scale PV in the area and provide an important understanding of the critical yield losses in monsoon seasons, which offers an effective robust benchmark for future industrial energy policy in developing economies. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

Modern cyber threats demand coordinated defensive strategies that extend beyond centralized security mechanisms. However, existing multi-agent platforms exhibit critical limitations in explicit communication and real-time coordination for cyberdefense operations. This work proposes a hierarchical multi-agent architecture for autonomous cyberdefense that addresses these limitations through structured inter-agent communication and distributed coordination. The architecture integrates a centralized monitor agent with specialized defensive swarms deployed across operational hosts. It is implemented using SPADE 4.1 (Smart Python Agent Development Environment) to enable XMPP-based (Extensible Messaging and Presence Protocol) communication with low-latency messaging and location transparency. Four specialized swarms—Network Defender, Host Defender, Anomaly Detection, and Forensic and Recovery—perform autonomous defensive tasks. A secure authentication mechanism ensures trusted communication between monitor and deployer agents. The system was evaluated in a controlled virtualized environment using the Network Defender Swarm as an illustrative case. The experimental results focus on internal coordination behavior, messaging efficiency, and end-to-end detection time across increasing levels of parallelism. A scan agent scalability analysis shows that moderate parallelism (2–16 agents) yields the lowest Total Detection Time (12.88 s across the full TCP port range), while excessive agent counts degrade performance. Results demonstrate how the proposed architecture supports low-latency communication, efficient coordination, and parallel task execution. Message latency benchmarks show improvements compared to classical agent frameworks such as JADE. These findings provide initial evidence that communication-centric multi-agent architectures can facilitate coordinated and adaptive cyberdefense operations, while serving as a platform for further experimental evaluation. Full article

Automated visual inspection of safety-critical metal assemblies such as automotive door lock strikes remains challenging due to their complex three-dimensional geometry, highly reflective surfaces, and scarcity of defect samples. While 3D sensing technologies are often constrained by cost and speed, traditional 2D optical methods struggle with severe imaging artifacts and poor generalization under few-shot conditions. This work constructs a complete system integrating defect imaging, generation, and detection. It proposes an integrated framework through the co-design of an image acquisition system and deep generative models to holistically enhance defect perception capability. First, we develop an imaging system using dome illumination and a small-aperture lens to acquire high-quality images of non-planar metal surfaces. Subsequently, we introduce a dual-stage generation strategy: stage one employs an improved FastGAN with Dynamic Multi-Granularity Fusion Skip-Layer Excitation (DMGF-SLE) and perceptual loss to efficiently generate high-quality local defect patches; stage two utilizes Poisson image editing and an optimized loss function to seamlessly fuse defect patches into specified locations of normal images. This strategy avoids modeling the complete complex background, concentrating computational resources on creating realistic defects. Experiments on a dedicated dataset demonstrate that our method can efficiently generate realistic defect samples under few-shot conditions, achieving 11–24% improvement in Fréchet Inception Distance (FID) scores over baseline models. The generated synthetic data significantly enhances downstream detection performance, increasing YOLOv8’s mAP@50:95 from 50.4% to 60.5%. Beyond proposing individual technical improvements, this research provides a complete, synergistic, and deployable system solution—from physical imaging to algorithmic generation—delivering a computationally efficient and practically viable technical pathway for defect detection in highly reflective, non-planar metal components. Full article

Wire-directed energy deposition (W-DED) produces steep thermal gradients and rapid heating-cooling cycles due to the moving heat source, where modest variations in process parameters significantly alter heat input per unit length and therefore the full thermal history. This sensitivity makes process tuning by trial-and-error or repeated FE sweeps expensive, motivating inverse analysis. This work proposes an inverse thermal process design framework that couples single-track experiments, a calibrated finite element (FE) thermal model, and a parametric physics-informed neural network (PINN) surrogate. By using experimentally calibrated heat-loss physics to define the training constraints, the PINN learns a parameterized thermal response from physics alone (no temperature data in the PINN loss), enabling inverse design without repeated FE runs. Thermocouple measurements are used to calibrate the convection film coefficient and emissivity in the FE model, and those parameters are used to train a parametric PINN over continuous ranges of arc power (1.5–3.0 kW) and travel speed (0.005–0.015 m/s) without using temperature data in the loss function. The trained PINN model was validated against the calibrated FE model at 3 probe locations with different power and travel speed combinations. Across these benchmark conditions, the mean absolute errors are between 6.5–17.4 °C, with cooling-tail errors ranging from 1.8–12.1 °C. The trained surrogate is then embedded in a sampling-based inverse optimization loop to identify power-speed combinations that achieve prescribed interlayer temperatures at a fixed dwell time. For target interlayer temperatures of 100, 130, and 160 °C with a 10 s dwell time, the optimized solutions remain within 3.3–5.6 °C of the target according to the PINN, while FE verification is within 4.0–6.6 °C. The results demonstrate that a physics-only parametric PINN surrogate enables inverse thermal process design without repeated FE runs while establishing a single-track baseline for extension to multi-track and multi-layer builds. Full article