Search Results (53)

To address longstanding challenges in sea buckthorn harvesting—such as the absence of effective harvesting principles, inefficient traditional manual and semi-mechanised methods, and rising labour costs—this study developed a self-propelled harvester equipped with a reciprocating cutter. The harvester featured an optimised double-support reciprocating cutter driven by a swing ring mechanism, with its kinematic parameters and cutting speed determined through analytical analysis. A coordinated transport system, consisting of an arc-shaped branch dial wheel, a conveying device, and a hydraulic system, was also designed. Field experiments were conducted employing a three-factor, three-level Box–Behnken design of Response Surface Methodology (RSM), which enabled the establishment of a predictive mathematical model for harvesting performance. Numerical optimisation via the model yielded the optimal operational parameters: harvesting forward speed of 0.6 m·s⁻¹, a cutting speed of 1.2 m·s⁻¹, and a conveyor belt linear speed of 0.8 m·s⁻¹. With this parameter combination, the missed cutting rate was 6.72%, fruit breakage rate 4.06%, and conveyor failure rate 7.79%, all meeting mechanised harvesting standards. This research provides the essential theoretical foundation and technical solutions for harvesting equipment in the sea buckthorn industry, accelerating its mechanisation process. Full article

Background: Broccoli contains diverse phytochemicals, including glucosinolates and their hydrolysis products, with potential nutritional and bioactive properties. Accurate metabolite profiling requires optimized sample preparation and comprehensive databases. Methods: A rapid enzymatic deactivation method with 70% methanol, implemented prior to cryogrinding, was evaluated for processing freeze-dried and fresh broccoli florets, which were compared as plant materials. A widely targeted, organ-resolved metabolite database was constructed by integrating over 612 reported phytochemicals with glucosinolate degradation products. LC-HRMS combined with MS-DIAL and GNPS was employed for metabolite detection and annotation. Results: Freeze-dried samples yielded nearly twice the number of glucosinolates, isothiocyanates, and nitriles compared with standard-processed fresh tissue. Methanol pre-treatment preserved metabolite integrity in fresh samples, achieving comparable sensitivity to freeze-dried material. Using the integrated database, 998 metabolites were identified or tentatively characterized, including amino acids, carboxylic acids, phenolics, alkaloids, terpenoids, and glucosinolate derivatives. Cross-platform reproducibility was improved and false positives reduced. Conclusions: Optimized sample preparation combined with a curated metabolite database enables high-confidence, comprehensive profiling of broccoli florets phytochemicals. The resulting dataset provides a valuable reference for studies on genotype–environment interactions, nutritional quality, and functional bioactivity of cruciferous vegetables. Full article

We present a tunable diode-laser absorption spectroscopy (TDLAS) system operating at 1.5711 µm for CO₂ gas concentration measurements. The system can operate in either a traditional direct-mode (dTDLAS) sawtooth wavelength scan or a recently demonstrated wavelength-toggled single laser differential-absorption lidar (WTSL-DIAL) mode using precompensated current pulses. The use of such precompensated pulses offsets the slow thermal constants of the diode laser, leading to fast toggling between ON and OFF-resonance wavelengths. A short measurement time is indeed pivotal for atmospheric sensing, where ambient factors, such as turbulence or mechanical vibrations, would otherwise deteriorate sensitivity, precision and accuracy. Having a system able to operate in both modes allows us to benchmark the novel experimental procedure against the well-established dTDLAS method. The theory behind the new WTSL-DIAL method is also expanded to include the periodicity of the current modulation, fundamental for the calculation of the OFF-resonance wavelength. A two-detector scheme is chosen to suppress the influence of laser intensity fluctuations in time (1/f noise), and its performance is eventually benchmarked against a one-detector approach. The main difference between dTDLAS and WTSL-DIAL, in terms of signal processing, lies in the fact that while the former requires time-consuming data processing, which limits the maximum update rate of the instrument, the latter allows for computationally simpler and faster concentration readings. To compare other performance metrics, the update rate was kept at 2 kHz for both methods. To analyze the dTDLAS data, a four-parameter Lorentzian fit was performed, where the fitting function comprised the six main neighboring absorption lines centered around 1.5711 µm. Similarly, the spectral overlap between the same lines was considered when analyzing the WTSL-DIAL data in real time. Our investigation shows that, for the studied time intervals, the WTSL-DIAL approach is 3.65 ± 0.04 times more precise; however, the dTDLAS-derived CO₂ concentration measurements are less subject to systematic errors, in particular pressure-induced ones. The experimental results are accompanied by a thorough explanation and discussion of the models used, as well as their advantages and limitations. Full article

The Pulsed Electric Field (PEF) technique represents a modern technology for treating and processing food and agricultural raw materials. The application of high-voltage electric pulses has been shown to modify macrostructure, improve extractability, and enhance the microbial safety of the treated matrix. In this study, we investigated metabolomic changes occurring during the individual technological steps of malting following PEF treatment. Methanolic extracts of technological intermediates of malting barley were analyzed using metabolomic fingerprinting performed with UHPLC-HRMS/MS. For data processing and interpretation, the freely available MS-DIAL—MS-CleanR—MS-Finder software platform was used. The metabolomes of the treated and untreated barley samples revealed significant changes. Tentatively identified PEF-related biomarkers included 1,2-diacylglycerol-3-phosphates, triacylglycerols, linoleic acids and their derivatives, octadecanoids, N-acylserotonins, and very long-chain fatty acids, and probably reflect abiotic stress response. Monitoring of the profiles of selected biomarkers in PEF malting batch indirectly revealed a potential enhancement of enzymatic activity after the PEF treatment. These results contribute to fundamental knowledge regarding the influence of PEF on final malt from a metabolomic perspective. Full article

For decades, analog dial gauges have been essential for measuring and monitoring data at various industrial instruments including production machines and laboratory equipment. Among them, we focus on the instrument for creep test in a mechanical engineering laboratory, which evaluates material strength under sustained stress. Manual reading of gauges imposes significant labor demands, especially in long-duration tests. This burden further increases under low-lighting environments, where poor visibility can lead to misreading data points, potentially compromising the accuracy of test results. In this paper, to address the challenges, we implement a creep test assisting system that possesses the following features: (1) to save the installation cost, a web camera and Raspberry Pi are employed to capture images of the dial gauge and automate the needle reading by image processing in real time, (2) to ensure reliability under low-lighting environments, a smart lighting mechanism is integrated to turn on a supplementary light when the dial gauge is not clearly visible, and (3) to allow a user to stay in a distant place from the instrument during a creep test, material break is detected and the corresponding message is notified to a laboratory staff using LINE automatically. For evaluations, we install the implemented system into a material strength measuring instrument at Okayama University, Japan, and confirm the effectiveness and accuracy through conducting experiments under various lighting conditions. Full article

Fusarium fungi are widespread pathogens of food crops, primarily associated with the formation of mycotoxins. Therefore, effective mitigation strategies for these toxicogenic microorganisms are required. In this study, the potential of pulsed electric field (PEF) as an advanced technology of increasing use in the food processing industry was investigated to minimize the viability of Fusarium pathogens and to characterize the PEF-induced changes at the metabolomic level. Spores of four Fusarium species (Fusarium culmorum, Fusarium graminearum, Fusarium poae, and Fusarium sporotrichioides) were treated with PEF and cultured on potato dextrose agar (PDA) plates. The viability of the Fusarium species was assessed by counting the colony-forming units, and changes in the mycotoxin content and metabolomic fingerprints were evaluated by using UHPLC-HRMS/MS instrumental analysis. For metabolomic data processing and compound identification, the MS-DIAL (v. 4.80)–MS-CleanR–MS-Finder (v. 3.52) software platform was used. As we found out, both fungal viability and the ability to produce mycotoxins significantly decreased after the PEF treatment for all of the species tested. The metabolomes of the treated and untreated fungi showed statistically significant differences, and PEF-associated biomarkers from the classes oxidized fatty acid derivatives, cyclic hexapeptides, macrolides, pyranocoumarins, carbazoles, and guanidines were identified. Full article

Conversational recommender systems (CRSs) have garnered increasing attention for their ability to provide personalized recommendations through natural language interactions. Although large language models (LLMs) have shown potential in recommendation systems owing to their superior language understanding and reasoning capabilities, extracting and utilizing implicit user preferences from conversations remains a formidable challenge. This paper proposes a method that leverages LLMs to extract implicit preferences and explicitly incorporate them into the recommendation process. Initially, LLMs identify implicit user preferences from conversations, which are then refined into fine-grained numerical values using a BERT-based multi-label classifier to enhance recommendation precision. The proposed approach is validated through experiments on three comprehensive datasets: the Reddit Movie Dataset (8413 dialogues), Inspired (825 dialogues), and ReDial (2311 dialogues). Results show that our approach considerably outperforms traditional CRS methods, achieving a 23.3% improvement in Recall@20 on the ReDial dataset and a 7.2% average improvement in recommendation accuracy across all datasets with GPT-3.5-turbo and GPT-4. These findings highlight the potential of using LLMs to extract and utilize implicit conversational information, effectively enhancing the quality of recommendations in CRSs. Full article

An economic assessment of an innovative solar thermal system called Application to Solar Thermal Energy to Processes (ASTEP) was conducted. It considered its three main subsystems: a novel rotary Fresnel SunDial, Thermal Energy Storage (TES) and Control System. Current Fresnel collectors are unable to provide thermal energy above 150 °C in high-latitude locations. Therefore, the key contribution of this study is the assessment of the economic performance of the ASTEP system used to provide high-temperature process heat up to 400 °C for industries located at low and high latitudes. The ASTEP system is installed at two end-users: Mandrekas (MAND), a dairy factory located in Greece at a latitude of 37.93 N and ArcelorMittal (AMTP), a manufacturer of steel tubes located in Romania at a latitude of 47.1 N. The life cycle costs (LCC), levelised cost of energy (LCOE), energy cost savings, EU carbon cost savings and benefit–cost ratio (BCR) of the ASTEP system were assessed. The results showed that AMTP’s ASTEP system had higher LCC and LCOE than MAND. This can be attributed to the use of two TES tanks and a double-axis solar tracking system for AMTP’s ASTEP system due to its high latitude location, compared to a single TES tank and single-axis solar tracking system used for MAND at low latitude. The total financial savings of the ASTEP system were EUR 249,248 for MAND and EUR 262,931 for AMTP over a period of 30 years. This study demonstrates that the ASTEP system offers financial benefits through its energy and EU carbon cost savings for industries at different latitudes while enhancing their environmental sustainability. Full article

With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images. To address these challenges, we propose an end-to-end PMRR method based on a decoupled circle head detection algorithm (YOLOX-DC) and a Unet-like pure Transformer segmentation network (PM-SwinUnet). First, according to the characteristics of the pointer dial, the YOLOX-DC detection algorithm is designed based on the exceeding you only look once detector (YOLOX). The decoupled circle head of YOLOX-DC detects the pointer meter dial more accurately than the commonly used rectangular detection head. Second, the window multi-head attention of the PM-SwinUnet network enhances the feature extraction ability of pointer meter images and solves problems of missed scale detection and incomplete pointer segmentation. Additionally, the scale and pointer fitting module is introduced into the PM-SwinUnet to locate the accurate position of the scale and pointer. Finally, through the angle relationship between the pointer and the first two main scale lines, the pointer meter reading is accurately calculated by the improved angle method. Experimental results demonstrate the effectiveness and superiority of the proposed end-to-end method across three-pointer meter datasets. Furthermore, it provides a rapid and robust approach to the digital transformation of manufacturing systems. Full article

The meter reading work in the power distribution room of a mine is traditionally carried out by manual inspection, which requires several workers and often lacks timeliness and efficiency. Owing to the widespread use of quadruped robots, this paper proposes a Mining Automatic Meter Reading System, named MAMRS, to identify and read pointer and digital meters in mine distribution rooms using quadruped robots. The new method consists of three stages. Initially, this technique identifies the type of meter using the ResNet18 convolutional neural network model, then proceeds to extract the dial area image using the YOLOv5 object detection algorithm, and finally reads the pointer and digital meters’ data using the U2-net algorithm and SVM, respectively. Experimental results show that the recognition accuracy rates for meter classification, pointer meter reading, and digital meter reading are 99.87%, 85.35%, and 90.73%, respectively. The MAMRS fulfills the need for fully automated and intelligent inspection of mine distribution rooms, resulting in significantly enhanced accuracy, flexibility, and innovation in inspection processes. Moreover, it reduces labor costs and improves inspection efficiency. The findings of this study serve as a reliable reference for intelligent inspection practices in the power distribution rooms of metal mines. Full article

Background: The rise of drug-resistant Leishmania strains presents a significant challenge in the treatment of Leishmaniasis, a neglected tropical disease. Extracellular vesicles (EVs) produced by these parasites have gained attention for their role in drug resistance and host–pathogen interactions. Methods: This study developed and applied a novel lipidomics workflow to explore the lipid profiles of EVs from three types of drug-resistant Leishmania infatum strains compared to a wild-type strain. EVs were isolated through ultracentrifugation, and their lipid content was extracted using a modified Matyash protocol. LC-MS analysis was performed, and data processing in MS-DIAL enabled lipid identification and quantification. Statistical analysis in MetaboAnalyst revealed strain-specific lipid alterations, highlighting potential links between lipid composition and drug resistance mechanisms. Results: Our results show distinct alterations in lipid composition associated with drug resistance. Specifically, drug-resistant strains exhibited reduced levels of phosphatidylcholine (PC) and phosphatidylglycerol (PG), particularly in the amphotericin B-resistant strain LiAmB1000.1. Sterol and glycerolipid species, including cholesteryl ester (CE) and triacylglycerol (TG) were also found to be diminished in LiAmB1000.1. These changes suggest significant lipid remodeling under drug pressure, potentially altering the biophysical properties of EV membranes and their capacity for molecule transfer. Furthermore, the lipidomic profiles of EVs from the other resistant strains, LiSb2000.1 and LiMF200.5, also displayed unique alterations, underscoring strain-specific adaptations to different drug resistance mechanisms. Conclusions: These significant alterations in lipid composition suggest potential lipid-based mechanisms underlying drug resistance in Leishmania, providing new avenues for therapeutic intervention. 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

Multivalent pneumococcal vaccines have been developed successfully to combat invasive pneumococcal diseases (IPD) and reduce the associated healthcare burden. These vaccines employ pneumococcal capsular polysaccharides (PnPs), either conjugated or unconjugated, as antigens to provide serotype-specific protection. Pneumococcal capsular polysaccharides used for vaccine often contain residual levels of cell wall polysaccharides (C-Ps), which can generate a non-serotype specific immune response and complicate the desired serotype-specific immunity. Therefore, the C-P level in a pneumococcal vaccine needs to be controlled in the vaccine process and the anti C-P responses need to be dialed out in clinical assays. Currently, two types of cell-wall polysaccharide structures have been identified: a mono-phosphocholine substituted cell-wall polysaccharide C-Ps1 and a di-phosphocholine substituted C-Ps2 structure. In our effort to develop a next-generation novel pneumococcal conjugate vaccine (PCV), we have generated a monoclonal antibody (mAb) specific to cell-wall polysaccharide C-Ps2 structure. An antibody-enhanced HPLC assay (AE-HPLC) has been established for serotype-specific quantification of pneumococcal polysaccharides in our lab. With the new anti C-Ps2 mAb, we herein extend the AE-HPLC assay to the quantification and identification of C-Ps2 species in pneumococcal polysaccharides used for vaccines. Full article