Search Results (240)

The main aim of this study is the concept of the magnet holder for the THz undulator utilized in the PolFEL superradiant light source. To achieve maximum flux at high K values (radiation frequencies ranging from 0.5 THz to 5 THz, and K values exceeding 3), it is necessary to use large permanent magnets with dimensions of 100 × 100 × 39.9 mm. For the above assumptions and parameters and specific requirements for magnet positioning, existing design solutions in the literature were found to be insufficient. The main challenges in the design of this holder included the following: (a) the unusually large size of the magnets, (b) requirements of wide-range calibration, and (c) large magnetic forces acting on each magnet, which can approach almost 4 kN. Taking into consideration these challenges, the prototype of the magnet holder was developed and manufactured. The paper presents the findings from both numerical and experimental studies aimed at validating the mechanical behavior and deformation of the proposed magnet holder. The measurements were conducted using two methods—traditional with the use of dial indicators and a novel approach based on the application of the Digital Image Correlation. The results from these numerical and experimental studies indicate that all specified requirements have been satisfactorily met. The study confirms the capability for accurate magnet positioning, demonstrating stable deformation of the holder under a magnetic load. Additionally, it was proved that the positioning of the magnets is both linear and repeatable, with calibration achievable within a range of at least ±0.25 mm. Full article

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)GPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 $\mathsf{\mu }$Wh per day on average). Full article

Water conservation in arid and semi-arid regions faces significant challenges due to low and irregular rainfall, worsened by climate change, which negatively affects rain-fed crop productivity. Various techniques, including supplemental irrigation using runoff harvesting ponds, aim to address these issues but often suffer from water loss due to infiltration, influenced by the pond liner type. This study uses a factorial analysis to assess the farmers’ perceptions of four pond sealing techniques. Using the Waso-2 method, a survey conducted in 2022 among 41 rainwater harvesting pond owners across three regions of Burkina Faso revealed that farmers prioritized impermeability and ease of maintenance over cost and availability. Concrete, scoring 16/20, was the most preferred, chosen by over 75% of farmers for its durability and resistance to weathering, despite its high cost. Geomembrane, with a score of 12/20, was valued for its waterproofing properties but had durability concerns. Clay, although cheap and available, scored 8/20 due to poor waterproofing on unstable ground. Bitumen, the least favored with a score of 6/20, was hindered by scarcity and lack of familiarity. To enhance supplemental irrigation in Burkina Faso and similar regions, waterproof concrete or durable geomembrane liners are recommended. Further research into improving bitumen and clay liners is also suggested. These findings provide key insights into farmers’ preferences, offering guidance for developing effective water conservation strategies to boost agricultural productivity and address food security challenges in the context of climate change. Full article

We aimed to understand how risk (trauma history, health problems, financial problems, family problems) and protective (friend support, family support) factors influenced daily functioning (e.g., self-care, mobility, social participation) among Tunisian adults during the COVID-19 pandemic, directly and through their impact on depression, with a focus on gender differences. We recruited a representative sample of 2014 participants (1024 males, 990 females) using random digit dialing of mobile phone numbers across all 24 governorates in Tunisia. Females reported higher depression, greater impaired functioning, and a higher likelihood of having had COVID-19. Path analysis showed a good fit to the model when paths for males and females were allowed to vary, providing evidence for gender differences. Associations between trauma exposure and depression and depression and age with functioning were stronger among females. Social support from friends was a protective factor for males only. For males, all study variables were associated with functioning indirectly through their association with depression, except for support from friends. For females, family responsibilities and health problems had both direct and indirect effects, whereas other study variables were only linked with functioning through depression. Findings provide insights into factors that can be targeted in interventions aimed at reducing depression and improving daily functioning for males and females. Full article

Human–machine interaction (HMI) is becoming increasingly important, especially in the automotive industry, where advancements in automated driving and driver assistance systems are key to enhancing driver safety and convenience. Among the many HMI interfaces, tactile sensing has been widely used in automotive applications as it enables instant and direct interactions with drivers. An area that remains underexplored among the tactile HMI interfaces is the application of haptic feedback to gear shifter modules. Therefore, this study investigates the design optimization of a dial gear shifter by analyzing the vibrations transmitted to the knob surface from an integrated haptic actuator. Specifically, we first tuned the mechanical properties of the haptic actuator (in terms of the resonance frequency and vibration level) in a simulation model by referring to experimental results. Next, a numerical model of a dial gear shifter was constructed, integrated with a haptic actuator, and tuned with the experimental results. The model was further optimized based on the design of the experiment and sensitivity analyses. The optimized design yielded a 24.5% improvement in the vibration level compared with the reference design, exceeding the minimum threshold (>~2.5 m/s² at 200 Hz) required for tactile sensing. The vibration enhancement (>22.x%) was also confirmed under the simulated hand-grabbing condition. This study is technically significant as it demonstrates that the haptic vibration in a dial gear shifter can be efficiently optimized through numerical analyses. This research will be used for the actual prototyping of a dial gear shifter to provide a safe driving experience for drivers. Full article

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively. Full article

Smart electric vehicles (SEVs) hold significant potential in alleviating the energy crisis and environmental pollution. The augmented reality (AR) dashboard, a key feature of SEVs, is attracting considerable attention due to its ability to enhance driving safety and user experience through real-time, intuitive driving information. This study innovatively integrates Kansei engineering, factor analysis, fuzzy systems theory, analytic hierarchy process, grey relational analysis, and factorial experimentation to evaluate AR dashboards’ visual imagery and subjective preferences. The findings reveal that designs featuring blue planar and unconventional-shaped dials exhibit the best performance in terms of visual imagery. Subsequent factorial experiments confirmed these results, showing that drivers most favor blue-dominant designs. Furthermore, in unconventional-shaped dial designs, the visual effect of vertical 3D is more popular with drivers than horizontal 3D, while the opposite is true in round dials. This study provides a scientific evaluation method for assessing the emotional experience of AR dashboard interfaces. Additionally, these findings will help reduce the subjectivity in interface design and enhance the overall competitiveness of SEV vehicles. Full article

Identification of features with high levels of confidence in liquid chromatography–mass spectrometry (LC–MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC–MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS² spectra. This highlights the critical importance of validation across positive and negative LC–MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors. Full article

Recently, in China, during the period of transition between spring and summer, the combination of sandstorms and ozone (O₃) pollution has posed a significant challenge to the strategy of coordinated control of fine particulate matters (PM_2.5) and O₃. On the one hand, the dust invasion brings many primary aerosols and causes a large range of transboundary transport. On the other hand, the high concentration of aerosol causes a severe disturbance to the distribution of O₃. Traditionally, high-resolution assessments of the spatial distribution of aerosols and O₃ can be carried out using LiDAR technology. However, the negligence of the influence of aerosols in the process of O₃ retrieval in traditional differential absorption lidar (DIAL) leads to an error in the accuracy of ozone concentration. Especially when dust transit occurs, the errors become bigger. In this study, a self-customized four-wavelength differential-absorption LiDAR system was used to synchronously obtain the accurate vertical distributions of ozone and high-concentration aerosol. The wavelength index of concentrated aerosol was inverted and applied to the differential equation framework for O₃ calculation. This novel approach to retrieving the vertical profile of O₃ was proposed and verified by applying it to a dust pollution event that occurred from April to May 2021 in Anyang City Henan Province, which is located in Northern China. It was found that the extinction coefficient of aerosol reached 2.5 km⁻¹ during the dust period, and O₃ was mainly distributed between 500 m and 1500 m. The O₃ error exceeded over 10% arising from the high-concentration aerosol below 1.5 km during the dust storm event. By employing the inversion algorithm while considering the aerosol effects, the ozone concentration error was improved by over 10% compared with the error recorded without considering the aerosol influence especially in dust events. Through this study, it was found that the algorithm could effectively realize the synchronous and accurate inversion of high-concentration aerosols and O₃ and can provide key technical support for air pollution control in China in the future. Full article

In order to improve the reading efficiency of pointer meter, this paper proposes a reading method based on LinkNet. Firstly, the meter dial area is detected using YOLOv8. Subsequently, the detected images are fed into the improved LinkNet segmentation network. In this network, we replace traditional convolution with partial convolution, which reduces the number of model parameters while ensuring accuracy is not affected. Remove one pair of encoding and decoding modules to further compress the model size. In the feature fusion part of the model, the CBAM (Convolutional Block Attention Module) attention module is added and the direct summing operation is replaced by the AFF (Attention Feature Fusion) module, which enhances the feature extraction capability of the model for the segmented target. In the subsequent rotation correction section, this paper effectively addresses the issue of inaccurate prediction by CNN networks for axisymmetric images within the 0–360° range, by dividing the rotation angle prediction into classification and regression steps. It ensures that the final reading part receives the correct angle of image input, thereby improving the accuracy of the overall reading algorithm. The final experimental results indicate that our proposed reading method has a mean absolute error of 0.20 and a frame rate of 15. Full article

Range-resolved CH₄ concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH₄ concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH₄ concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH₄ profile by 85.54%. The standard deviation of CH₄ concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH₄ concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH₄ concentration at observation sites. Full article

Dynamic dial-a-ride problems (DDARPs) involve designing routes and schedules for customers with specific origins and destinations. While the optimization of DDARPs has been extensively examined, these analyses often focus solely on economic decisions. The recent literature emphasizes the inclusion of social and environmental factors in addition to economic considerations for a sustainable transportation system. This paper provides a conceptual review that identifies and classifies the most common DDARP objectives in the three dimensions of the Triple-Bottom-Line (3BL) approach of sustainability: environmental, economic, and social. This study analyzes the interconnections among different objectives and provides insights into multi-objective approaches used in transportation problems. The findings demonstrate the interconnectedness of objectives from different dimensions and highlight the involvement of various stakeholders in decision-making. The results show that optimizing one objective may have implications for other objectives, suggesting a trade-off to be considered. The results reveal that social objectives boost the economic dimension by improving service quality; however, environmental objectives negatively impact the economic dimension. Additionally, a geographical analysis was conducted, which revealed continent-wise variations in research focus and contributions. Future studies should focus more on the social and environmental dimensions to promote a sustainable transportation system. Full article

Differential absorption lidar is an advanced tool for investigating tropospheric ozone transport and development. High-quality differential absorption lidar data are the basis for studying the temporal and spatial evolution of ozone pollution. We assessed the quality of the ozone data generated via differential absorption lidar. By correcting the ozone lidar profile in real-time with an atmospheric correction term and comparing the lidar data to ozone data collected using an unmanned aerial vehicle (UAV), we quantified the statistical error of the ozone lidar data in the vertical direction and determined that the data from the two instruments were generally in agreement. To verify the reliability of the ozone lidar system and the atmospheric correction algorithm, we conducted a long-term comparison experiment using data from the Canton Tower. Over the two months, the UAV and lidar data were consistent with one another, which confirmed the viability of the ozone lidar optomechanical structure and the atmospheric correction algorithm, both in real-time and over a given time duration. In addition, we also quantified the relationship between statistical error and signal-to-noise ratio. When the SNR is less than 10, the corresponding statistical error is about 40%. The statistical error was less than 15% when the signal-to-noise ratio was greater than 20, and the statistical error was mostly less than 8% when the signal-to-noise ratio was greater than 40. In general, the statistical error of the differential absorption lidar data was inversely proportional to the signal-to-noise ratio of each echo signal. Full article

The tractable preparation of Phase I drug metabolites is a critical step to understand the first-pass behaviour of novel chemical entities (NCEs) in drug discovery. In this study, we have developed a structure–electroactivity relationship (SeAR)-informed electrochemical reaction of the parent 2-chlorophenothiazine and the antipsychotic medication, chlorpromazine. With the ability to dial-in under current controlled conditions, the formation of S-oxide and novel S,S-dioxide metabolites has been achieved for the first time on a multi-milligram scale using a direct batch electrode platform. A potential rationale for the electrochemical formation of these metabolites in situ is proposed using molecular docking to a cytochrome P₄₅₀ enzyme. Full article

As customers’ expectations continue to rise, advanced on-demand transport services face the challenge of meeting new requirements. This study addresses a specific transportation issue belonging to dial-a-ride problems, including constraints aimed at fulfilling customer needs. In order to provide more efficient on-demand transportation solutions, we propose a new hybrid evolutionary computation method. This method combines customized heuristics including two exchanged mutation operators, a crossover, and a tabu search. These optimization techniques have been empirically proven to support advanced designs and reduce operational costs, while significantly enhancing service quality. A comparative analysis with an evolutionary local search method from the literature has demonstrated the effectiveness of our approach across small-to-large-scale problems. The main results show that service providers can optimize their scheduling operations, reduce travel costs, and ensure a high level of service quality from the customer’s perspective. Full article