Search Results (485)

This paper describes two low-cost, modular instruments developed for rapid room-temperature characterization of mainly thermoelectrics. The first instrument measures the Seebeck coefficient across diverse sample geometries and incorporates a four-point probe configuration for simultaneous electrical conductivity measurement, including disk-shaped samples. The second instrument implements the Van der Pauw method, enabling detailed investigation of charge carrier behavior within materials. Both devices prioritize accessibility, constructed primarily from 3D-printed components, basic hardware, and readily available instrumentation, ensuring ease of reproduction and modification. A unique calibration protocol using pure elemental disks and materials with well-established properties was employed for both instruments. Validation against comparable systems confirmed reliable operation. Control and data acquisition software for both devices was developed in-house and is fully documented and does not require an experienced operator. We demonstrate the utility of these instruments by characterizing the electronic properties of polycrystalline SnSe thermoelectric materials doped with Bi, Ag, and In. The results reveal highly complex charge carrier behavior significantly influenced by both dopant type and concentration. Full article

The multi-objective optimization of an electric prototype racing vehicle is addressed in this study. The goal was to identify the optimal combination of battery type, pilot weight, and power mode to maximize operational time and distance while minimizing energy consumption. A structured $2\times 3\times 3$ factorial design was implemented, and the resulting data were analyzed through Response Surface Methodology (RSM) in combination with the Desirability Function Approach (DFA). The experimental design included two battery configurations, three weight levels, and three power settings, while data acquisition was performed through a custom Arduino-based system validated against commercial instruments. The results revealed that the configuration with the smallest battery, the lowest weight (66 kg), and the lowest power mode (N5) achieved the most efficient performance, yielding an operating time of 1.12 h, a travel distance of 24.63 km, and an energy performance index of 2.90 km/Ah. The integration of RSM with DFA provided a robust framework for identifying optimal multiparameter conditions under competition constraints. Unlike previous studies that examined these variables in isolation, this work advances the state of the art by demonstrating the feasibility of multiparameter optimization in real-world racing contexts, offering methodological and practical insights for sustainable electric mobility. Full article

Hybrid Aerial Underwater Vehicle (HAUV) is a new type of unmanned system that can operate both in air and water, and complete underwater and air operations tasks by carrying corresponding sensors. Owing to this dual-medium operational capability, HAUVs hold significant promise for coordinated air–sea surveillance and monitoring efforts. Optical methods enable high-resolution sampling across both spatial and temporal scales, offering enhanced contextual information for the interpretation of discrete observational data. In order to evaluate the feasibility of ocean optical profiling systems based on HAUVs, this paper reviews the design features of current HAUV models and summarizes advanced techniques that support their cross-medium mobility. Subsequently, we summarized the types of commercial optical instruments commonly used for underwater observation and compared the field deployment methods. By analyzing the underwater motion performance of HAUVs and the requirements for optical observation platforms, we believe that multi-rotor HAUVs can provide new observation methods for future underwater optical acquisition due to their smooth entry and exit characteristics and the ability to maintain a controlled orientation during underwater operation. Finally, the paper explores prospective applications and outlines key obstacles to be overcome in the advancement of amphibious platforms for ocean optical profiling. Full article

This study presents a novel approach to analysing the early stages of the combustion process by measuring the surface temperature of a kerosene droplet from its point of ignition through to its evaporation. An indium arsenide antimonide (InAsSb) photodiode-based infrared radiation thermometer (IRT), operating between 3 μm and 11 μm in wavelength, was designed to enable non-contact, low-temperature sensing with an acquisition time of 500 μs. Integrated with a data acquisition unit (DAQ), the instrument captures the transient combustion stages occurring below the droplet’s boiling point of 300 °C. The instrument was assessed against industry standards and demonstrated a measurement uncertainty of ±2 °C, confirming suitability within the performance bounds of commercial instrumentation. The IRT was deployed to measure the temperature of a kerosene droplet within an enclosed combustion chamber upon ignition, in direct comparison with a contact thermocouple. The instrument demonstrated its capability to measure the droplet’s surface temperature changes throughout its early-stage combustion. Furthermore, the wavelength specificity of the IRT eliminates thermal interference from the subsequent flame, a capability which contact thermocouples lack, thereby enabling measurement of the droplet’s temperature in isolation. This study focuses on single-droplet Jet A kerosene combustion under controlled conditions, using a transferable methodology adaptable to other fuels and environments. It supports the use of IRT for non-contact temperature measurement of fuel droplets and early-stage combustion, aiding fuel characterisation and the development of future fuels such as SAF. Full article

This study presents a flame detection model that is based on real experimental data that were collected during turbopump hot-fire tests of a liquid rocket engine. In these tests, a MEMRECAM ACS-1 M40 high-speed camera—serving as an optical sensor within the test instrumentation system—captured plume images for analysis. To detect abnormal flame phenomena in the plume, a Gaussian support vector machine (SVM) model was developed using image features that were derived from both color and gradient information. Six representative frames containing visible flames were selected from a single test failure video. These images were segmented in the YCbCr color space using the k-means clustering algorithm to distinguish flame and non-flame pixels. A 10-dimensional feature vector was constructed for each pixel and then reduced to five dimensions using the Maximum Relevance Minimum Redundancy (mRMR) method. The reduced vectors were used to train the Gaussian SVM model. The model achieved a 97.6% detection accuracy despite being trained on a limited dataset. It has been successfully applied in multiple subsequent engine tests, and it has proven effective in detecting ablation-related anomalies. By combining real-world sensor data acquisition with intelligent image-based analysis, this work enhances the monitoring capabilities in rocket engine development. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Beam diagnostics are essential tools for monitoring the performance of charged particle beams and the safe operation of particle accelerators. The performance of an accelerator is determined by evaluating the properties of beam particles, such as energy, charge, spatial, and temporal density distributions, which require very specific instruments. Faraday Cups (FCs) have emerged as important beam diagnostic devices because of their ability to accurately measure the beam charge and, in some cases, the charge distribution, which can be subsequently used to reconstruct transverse beam profiles. This paper aims to provide a detailed review of FCs, their principles, and their design challenges. FCs have applications in various scientific disciplines that include the measurement of beam current/intensity in particle accelerators, in addition to those for mass spectrometry, beam profiles/total beam currents for broad ion beams, thermonuclear fusion, and antimatter experiments. This review also covers and discusses the versatility of FCs in various scientific disciplines, along with showcasing the technological advancements that include improved collector materials, novel designs, enhanced measurement techniques, and developments in electronics and data acquisition (D.A.Q). A summary of the challenges faced while working with the FCs, such as sensitivity, calibration, and potential errors, is included in this review. Full article

Line laser measurement, as a typical method of laser triangulation, makes the acquisition of 3D tooth-surface data more accurate, efficient, and informative. Thus, a line laser 3D measurement model of gears is established, and a specialized polyhedral artifact with specific geometric features is invented to determine the pose parameters of the line laser sensor in measuring space. Based on this, a single-spindle gear-measuring instrument is developed and a series of experimental studies are conducted for gears with different module and flank directions in this instrument, including profile deviation, helix deviation, pitch deviation, topological deviation, etc. A comparative experiment with traditional contact measurement methods validates the correctness of the methods mentioned in this paper for the accurate evaluation of tested gears. In further research, the mining and utilization of big data obtained from the line laser 3D measurement of gears will be an important topic. Full article

In this study, we examine the impact of mergers and acquisitions (M&As) on firm environmental performance, aiming to address the gap in research and guide firms, investors, and policymakers toward more environmentally conscious decision-making in M&A. Using panel data from Chinese A-share listed firms (2008–2022), we estimate a two-way fixed effect model. The Propensity Score Matching and the instrumental variable method address potential endogeneity concerns, and robustness checks validate the findings. We found that M&As have a significantly positive effect on firm environmental performance, with heterogeneous impacts across regions, industries, and M&A types. The environmental benefits are most pronounced in heavily polluting industries and hybrid M&A deals. Eastern China shows more modest improvements. The results of mechanism tests revealed that M&As enhance environmental performance primarily by boosting total factor productivity and fostering innovation. This study offers a novel perspective by linking M&A activities to environmental sustainability, enriching the literature on both M&As and corporate environmental performance. We show that even conventional M&A deals (not sustainability-focused) can improve environmental performance through operational synergies. Expanding beyond polluting industries, we reveal how sector characteristics shape M&A’s environmental impacts. We identify practical mechanisms through which standard M&A activities can advance sustainability goals, helping firms balance economic and environmental objectives. It provides empirical evidence from China, an emerging market with distinct institutional and regulatory contexts. The findings offer guidance for firms engaging in M&A to strategically improve sustainability performance. Policymakers can leverage these insights to design incentives for M&A in pollution-intensive industries, aligning economic growth with environmental goals. By demonstrating that M&As can enhance environmental outcomes, this study supports the potential for market-driven mechanisms to contribute to broader societal sustainability objectives, such as reduced industrial pollution and greener production practices. Full article

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

This study examines the performance of two diffuser configurations—a trumpet-shaped and a semi-diagonal design—for application in micro gas turbine engines, aiming to assess their suitability in terms of efficiency and operational flexibility. Both diffusers were initially evaluated using steady-state CFD simulations with the k-omega SST turbulence model, followed by experimental testing on an actual engine across the start-up sequence from idle to 70% of nominal speed. Performance was mapped over four constant-speed lines for each configuration. Results showed that the trumpet-shaped diffuser offered a greater choke margin but suffered from increased aerodynamic losses, whereas the semi-diagonal diffuser demonstrated higher efficiency but required closer alignment with the target operating point. The k-omega SST model showed strong predictive accuracy, with 5.13% agreement across all instrumented parameters for all investigated speed lines. These findings suggest that while the trumpet diffuser provides better stability, the semi-diagonal design is more efficient when properly targeted. Future work will focus on extending the analysis to higher speed ranges and transient regimes using harmonic balance CFD methods and enhanced data acquisition techniques. Full article

This study presents the design and implementation of a novel, sensor-based falling-sphere viscometer specifically tailored for measuring the viscosity of engine oil. The equipment utilizes a metallic sphere and two strategically placed sensors to determine the travel time over a predetermined distance within an oil-filled tube. By applying fundamental principles of fluid dynamics, including Stokes’ law, the system accurately calculates the dynamic viscosity based on the sphere’s velocity and the oil’s density. Experimental validation at particular temperature demonstrates the device’s sensitivity and reliability, which are critical for assessing oil degradation and engine performance. The simplicity and low cost of the design make it an attractive alternative to conventional, more complex viscometers. Furthermore, the automated data acquisition system reduces human error and enhances reproducibility of results. Overall, the developed instrument shows great promise for both laboratory research and practical maintenance applications in the automotive industry. Full article

In recent years, there has been an increasing transition from 1D point-based to 3D point-cloud-based data acquisition for monitoring applications and deformation analysis tasks. Previously, many studies relied on point-to-point measurements using total stations to assess structural deformation. However, the introduction of terrestrial laser scanning (TLS) has commenced a new era in data capture with a high level of efficiency and flexibility for data collection and post processing. Thus, a robust understanding of both data acquisition and processing techniques is required to guarantee high-quality deliverables to geometrically separate the measurement uncertainty and movements. TLS is highly demanding in capturing detailed 3D point coordinates of a scene within either short- or long-range scanning. Although various studies have examined scanner misalignments under controlled conditions within the short range of observation (scanner calibration), there remains a knowledge gap in understanding and characterizing errors related to long-range scanning (scanning calibration). Furthermore, limited information on manufacturer-oriented calibration tests highlights the motivation for designing a user-oriented calibration test. This research focused on investigating four primary sources of error in the generic error model of TLS. These were categorized into four geometries: instrumental imperfections related to the scanner itself, atmospheric effects that impact the laser beam, scanning geometry concerning the setup and varying incidence angles during scanning, and object and surface characteristics affecting the overall data accuracy. This study presents previous findings of TLS calibration relevant to the four error sources and mitigation strategies and identified current challenges that can be implemented as potential research directions. Full article

The real-time acquisition of an accurate underwater sound velocity profile (SSP) is crucial for tracking the propagation trajectory of underwater acoustic signals, making it play a key role in ocean communication positioning. SSPs can be directly measured by instruments or inverted leveraging sound field data. Although measurement techniques provide a good accuracy, they are constrained by limited spatial coverage and require a substantial time investment. The inversion method based on the real-time measurement of acoustic field data improves operational efficiency but loses the accuracy of SSP estimation and suffers from limited spatial applicability due to its stringent requirements for ocean observation infrastructures. To achieve accurate long-term ocean SSP estimation independent of real-time underwater data measurements, we propose a semi-transformer neural network (STNet) specifically designed for simulating sound velocity distribution patterns from the perspective of time series prediction. The proposed network architecture incorporates an optimized self-attention mechanism to effectively capture long-range temporal dependencies within historical sound velocity time-series data, facilitating an accurate estimation of current SSPs or prediction of future SSPs. Through the architectural optimization of the transformer framework and integration of a time encoding mechanism, STNet could effectively improve computational efficiency. For long-term forecasting (using the Pacific Ocean as a case study), STNet achieved an annual average RMSE of 0.5811 m/s, outperforming the best baseline model, H-LSTM, by 26%. In short-term forecasting for the South China Sea, STNet further reduced the RMSE to 0.1385 m/s, demonstrating a 51% improvement over H-LSTM. Comparative experimental results revealed that STNet outperformed state-of-the-art models in predictive accuracy and maintained good computational efficiency, demonstrating its potential for enabling accurate long-term full-depth ocean SSP forecasting. Full article

Malondialdehyde (MDA), a mutagenic and carcinogenic compound, is widely studied in the meat industry and lipid peroxidation research due to its implications for food quality and safety. Current methods for quantifying MDA in solid tissues are labor-intensive, requiring multiple instruments and approximately two hours to complete. This study presents an ultrafast kinetic fluorogenic method for quantifying MDA in ground beef, utilizing 2-thiobarbituric acid (TBA) as a fluorogenic probe. The total assay time is significantly shortened to 6 min from sample preparation to data acquisition. The assay’s robustness against matrix interference was validated using sample volume variation and standard addition calibration methods. Additionally, the effects of ambient exposure to air, washing, and cooking on MDA content in raw ground beef were quantified. While both ambient exposure to air and cooking increased MDA levels, washing raw ground beef and decanting cooked ground beef broth effectively reduced MDA levels in the ground beef. This simple and rapid assay can be adopted both in food research and industry. Moreover, insights from our study on the relationship between ground beef treatment and MDA concentration will help consumers make informed decisions about ground beef handling and consumption to lower their intake of MDA. Full article