bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Evolution Characteristics of the Gear Meshing Lubrication for Vehicle Transmission System
Authors: Gu Yihong , Zheng Gaoan
Published: February 2023

The transmission in automobiles is the core component to ensure operational stability. Heat accumulation in the meshing process will reduce the transmission efficiency and affect the service life. Here, the essential physical process to improve transmission heat dissipation is the dynamic evolution process and the thermal transfer characteristics of lubricating oil fields during gear meshing. This paper presents a modeling and solving method for gear meshing lubrication and thermal transfer features based on the volume of the fluid model and piecewise linear interface construction (VOF-PLIC). The dynamic mesh technique combines spring smoothing and reconstruction to optimize the numerical solution process. The dynamic evolution law of gear meshing lubrication and thermal transfer is obtained by analyzing the lubrication evolution process under different speed/steering conditions. The results show that the proposed modeling and solving method could well reveal the lubrication and thermal transfer laws of the gear meshing. The temperature of the gear meshing regions was higher than that in the other regions, and the lubrication temperature showed an increasing linear trend with the stirring process. As the gear speed increased, the meshing resistance moment increased, the transmission power loss increased, and the lubrication oil temperature was larger than that of the gearbox. The power loss under the clockwise rotation of the driving gear G4 was higher than that under the counterclockwise rotation of the driving gear. The relevant results can provide theoretical references for the dynamic analysis of automotive transmission lubrication and technical support for gear profile design and lubrication optimization.
Characterization of Sludge Resulting from Chemical Coagulation and Electrocoagulation of Pumping Water from Fishmeal Factories
Authors: Aguilar-Ascón Edwar , Solari-Godiño Armando , Cueva-Martínez Miguel , Neyra-Ascón Walter , Albrecht-Ruíz Miguel
Published: February 2023

In the fishmeal industry, seawater is used to transport fish to the factories. Due to this, “pumping water” with high concentrations of organic matter is generated that is treated via chemical coagulation before letting it into the sea. The objective of this study is to characterize and compare the sludge obtained from conventional chemical coagulation and the sludge from the electrocoagulation process. A pilot electrocoagulation plant was built next to a chemical coagulation plant. The sludge obtained from both methodologies was analyzed for its proximal composition, its iron and aluminum content, and the fatty acid profile in its contained fat. Electrocoagulation was found to produce sludge with a higher concentration of lipids and ash, which indirectly confirmed that it removes more organic pollutants and salts than chemical coagulation. The contents of aluminum and iron in the sludge obtained by electrocoagulation were 4.2% and 0.025%, respectively, while those in the sludge obtained from chemical coagulation were 0.01% and 4.8%, respectively. Aluminum comes from the sacrificial electrode of the electrocoagulation tank, while iron comes from the salts used in chemical coagulation. The sum of w-3 fatty acid values (EPA + DHA) was 12.5% and 18.8% for sludges from the electrocoagulation and chemical coagulation processes, respectively, so we can assume that electrocoagulation is a more oxidizing process than chemical coagulation. Due to their high organic load, both sludges must be assessed as an alternative feed ingredient.
Assessing the Sorting Efficiency of Plastic Packaging Waste in an Italian Material Recovery Facility: Current and Upgraded Configuration
Authors: Gadaleta Giovanni , De Gisi Sabino , Todaro Francesco , D’Alessandro Giuseppe , Binetti Silvio , Notarnicola Michele
Published: February 2023

The first step in reintroducing plastic waste into the recycling cycle is to use material recovery facilities (MRFs). However, while the composition and types of plastic waste are changing over time, the layout of MRFs does not always adapt to this change. In this paper, an existing MRF in Southern Italy was chosen as a reference to evaluate its current performance and to estimate possible improvements in sorting through a specific upgrade. First, an analysis of the amount, composition, and sources (in terms of type of waste and distance from the MRF) of the input waste was conducted. The composition of the input waste was then compared with the amount of selected output waste streams in order to calculate the current sorting efficiency of each stream and compare it with the values obtained from the upgrade. Lastly, the current performance of the plant was compared with a previous assessment of the same MRF in order to highlight possible variation. Results showed how the incoming waste was mainly composed of packaging plastic waste, and that some plastic waste not yet selected by the plant ended up in specific output streams. Therefore, the current performance of the MRF resulted high for PET and PE bottles (80.2% and 92.8%, respectively), in contrast to mixed or flexible packaging, where the efficiency achieved lower values (55–50%). These values were caused by a weakness in the 2D flow sorting line, which the upgrade mostly addressed. The upgraded configuration increased the production of recyclable waste from 34.32% to 50.39%, especially due to the recovery of small flexible packaging films in PE and biopolymers.
Crusade: The Arising of a Concept Based on Portuguese Written Records of Three Military Campaigns (1147–1217)
Authors: Costa Paula Pinto , Lencart Joana
Published: February 2023

The historiography of the crusade and reconquest in the Iberian Peninsula, and Portugal in particular, dates from the beginning of the twentieth century. Since the 1920s, it has been assumed that the reconquest was an output of the crusade in the Iberian West due to the so-called “Bull of Crusade” given to Portugal. The “idea of the Crusade” in Portugal was enhanced by Carl Erdmann in the 1930s and 1940s. This interpretation has been endorsed by the very context in which the Kingdom of Portugal emerged and developed throughout the twelfth and thirteenth centuries. From the 1980s onwards, the launching of systematic research on the military orders also reinforced this perspective. The deep affinity between the military orders and the crusades in the context of the reconquest is reflected in this historiography. These concepts—military orders, crusade, reconquest—have been studied without distinction being made between them, adding to the complexity of this analysis. Considering the historiographical achievements regarding the crusade, it is pertinent to rethink the associations between reconquest, crusade and military orders. Reading certain historical narratives is crucial for this analysis, although the written records do not fully deplete the subject. To reinforce the relevance of this approach, we will also consider royal and pontifical diplomas. Tracing the terminology used in these documents and identifying how these historical realities were referred to are the two main goals of this paper. For that purpose, three key moments of the Portuguese reconquest have been chosen: the conquests of Lisbon (1147), Silves (1189), and Alcácer do Sal (1217). These have one feature in common: the presence of crusaders travelling to the Holy Land, which supports the terminological analysis of those discourses. Different perspectives are embodied in these conquest narratives when compared with royal and papal diplomas on the same issue and of a similar chronology. Historiography on Reconquest, crusade, and the military orders is often conditioned by ideology, occasionally revealing a tendency to repeat ideas without debating them. This paper’s analysis is based on the aforementioned written records and is undertaken in order to verify when the word crusade/crusader appeared in Portugal, to assess to what extent the war of conquest in Portuguese territory followed the example of the holy war and to evaluate the commitment of the crown and the Holy See in this complex process.
Whom Do We Serve? Dismantling the Church Industrial Complex in North American Mainline Protestant Churches
Authors: Johnson Sheryl
Published: February 2023

Social justice is often identified as a central commitment in mainline Protestant churches in North America. However, it is often approached as a public-facing issue that engages broader society, rather than as a comprehensive value that also informs internal practices in those same North American Protestant congregations/denominations, particularly in the area of finance. This reality means that a profit orientation often informs and shapes practices, undermining the mission; this reality can be understood as part of an industrial complex. To counter this tendency, I present two themes that are rooted in a liberation-based critical understanding of inequality found in the World Council of Churches’ AGAPE (Alternative Globalization Affecting People and the Earth) statement. These two themes are as follows: that one’s personal financial resources (or lack thereof) are deeply connected to oppressive systemic factors, and that churches are called to exist in economic solidarity and communion with one another. I conclude by asserting that church finance must be rendered more coherent with churches’ own values and commitments to liberating justice as a matter of faith.
A Tailing Dump as Industrial Deposit; Study of the Mineralogical Composition of Tailing Dump of the Southern Urals and the Possibility of Tailings Re-Development
Authors: Shaforostova Elena N. , Kosareva-Volod’ko Olga V. , Belyankina Olga V. , Solovykh Danila Y. , Sazankova Ekaterina S. , Sizova Elena I. , Adigamov Danila A.
Published: February 2023

It is found that regions with depleted, or on the verge of depletion, of mineral resources are subject to additional pressures due to intensifying social and environmental problems. This paper proposes the development of the mining industry, reducing the dependence of the economy on the sharp volatility of the raw materials market in a period of global shocks by addressing social and environmental problems in regions with a depleting mineral resource base. It is assumed that the geotechnology development from simple mineral extraction to technologies providing a full cycle of georesources development with industrial waste recycling contributes to the resource provision of sustainable development. The material and mineralogical composition of the four tailing dumps (Uchalinskiy, Buribayskiy, Sibayskiy and Gaiskiy Ore-Processing plants have been studied) united with the similarity of the processed raw materials, and as a consequence, the similar enrichment technology has been studied and established. An approximate estimate of valuable components left in industrial wastes was made. The possibility of valuable component extraction (e.g., gold) from tailings using double agitation cyanidation was substantiated. There is no necessity of obligatory grinding of tailings to increase the recovery rate of valuable components. It was experimentally determined that the extraction of gold from tailings is 75.9–82.14% and depends on the investigated technogenic raw material. It has been proved that industrial waste can be recycled for the purpose of the resource provision of sustainable development. The need for further, more detailed studies of industrial formations has been identified. This will help to identify patterns of valuable component distribution in the industrial mass and to study its extraction possibilities in more details.
A Novel Hyperspectral Image Classification Method Using Class-Weighted Domain Adaptation Network
Authors: Wang Haoyu , Cheng Yuhu , Wang Xuesong
Published: February 2023

With the development of science and technology, hyperspectral image (HSI) classification has been studied in depth by researchers as one of the important means of human cognition in living environments and the exploration of surface information. Nevertheless, the shortage of labeled samples is a major difficulty in HSI classification. To address this issue, we propose a novel HSI classification method called class-weighted domain adaptation network (CWDAN). First, the convolutional domain adaption network (ConDAN) is designed to align the marginal distributions and second-order statistics, respectively, of both domains via multi-kernel maximum mean discrepancy (MK-MMD) and CORAL loss. Then, the class-weighted MMD (CWMMD) is defined to simultaneously consider the conditional distribution discrepancy and changes of class prior distributions, and the CWMMD-based domain adaptation term is incorporated into the classical broad learning system (BLS) to construct the weighted conditional broad network (WCBN). The WCBN is applied to reduce the conditional distribution discrepancy and class weight bias across domains, while performing breadth expansion on domain-invariant features to further enhance representation ability. In comparison with several existing mainstream methods, CWDAN has excellent classification performance on eight real HSI data pairs when only using labeled source domain samples.
Fabrication of Efficient and Non-Enzymatic Electrochemical Sensors for the Detection of Sucrose
Authors: Asghar Nazia , Mustafa Ghulam , Jabeen Nawishta , Dawood Asadullah , Rida  , Jabeen Zeenat , Malik Qaiser Hameed , Khan Muhammad Asad , Khan Muhammad Usman
Published: February 2023

Molecularly imprinted polymers have been used for the creation of an electrochemical sensor for the detection of sucrose, which are modified by using functionalized graphene (fG). Using AIBN as the free radical initiator and sucrose as the template, imprinted polymers are synthesized. The monomer, 4,4′-diisocyanatodiphenylmethane (DPDI), has both proton donor groups (N-H or O-H) and lone-pair donor groups (C=O). By creating H-bonds with electron donor groups (C=O), the proton donor group in this polymer may interact with the sugar molecule serving as its template. The sensor signals have improved as a result of the interaction between the monomer and the template. Thermogravimetric and differential thermal analysis (TGA/DTA) curves, scanning electron microscopy (SEM), and FT-IR spectroscopy have been employed to characterize the fabricated receptors. The fabricated sensor has exhibited a limit of detection of 16 ppb for the target analyte that is highly sensitive, linear, reversible, regenerative, and selective. Moreover, the sensor’s stability, reproducibility, and reusability have been evaluated for six months, following the device’s manufacturing, and the results revealed similar responses with the percentage error of less than 1%. Most importantly, this sensor has demonstrated a quick response time, which is very sensitive, stable, and selective.
A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information
Authors: Milani Rudy , Moll Maximilian , De Leone Renato , Pickl Stefan
Published: February 2023

Nowadays, Artificial Intelligence systems have expanded their competence field from research to industry and daily life, so understanding how they make decisions is becoming fundamental to reducing the lack of trust between users and machines and increasing the transparency of the model. This paper aims to automate the generation of explanations for model-free Reinforcement Learning algorithms by answering “why” and “why not” questions. To this end, we use Bayesian Networks in combination with the NOTEARS algorithm for automatic structure learning. This approach complements an existing framework very well and demonstrates thus a step towards generating explanations with as little user input as possible. This approach is computationally evaluated in three benchmarks using different Reinforcement Learning methods to highlight that it is independent of the type of model used and the explanations are then rated through a human study. The results obtained are compared to other baseline explanation models to underline the satisfying performance of the framework presented in terms of increasing the understanding, transparency and trust in the action chosen by the agent.
Microstructure Turbulence Measurement in the Northern South China Sea from a Long-Range Hybrid AUV
Authors: Nie Yunli , Luan Xin , Huang Yan , Du Libin , Song Dalei , Liu Xiuyan
Published: February 2023

This study describes the development of a long-range hybrid autonomous underwater vehicle (AUV) for ocean turbulence measurement. It is a unique instrument, combining the characteristics of the conventional AUV and the buoyancy-driven glider, with a variety of flexible motion modes, such as cruise mode, glider mode, drift mode, and combination of multiple motion modes. The hybrid AUV was used for continuous turbulence measurement in the continental slope of the northern South China Sea in 2020. A total of ten continuous profiles were completed covering a horizontal span of 25 Km and a depth of 200 m. The hybrid AUV was operated in the combined glider and cruise mode. The hybrid AUV’s flight performance was stable and satisfied the requirement for turbulence observation. The measured velocity shears from both probes were in good agreement, and the noise-reduced shear spectra were in excellent agreement with the Nasmyth spectrum. The water column in the study area was highly stratified, with a thick thermocline. The dissipation rate (ε) varied from 1.41 × 10−10 to 4.18 × 10−7 W·kg−1. In the surface mixed layer, high values of ε (10−9∼10−8 W·kg−1) were observed toward the water surface. In the thermocline, ε was 10−9.5∼10−9 W·kg−1, which was smaller than the level of the surface mixed layer. This result was mainly because of the strong “barrier”-like thermocline, which damped the transmission of wind and heat energy from the surface mixed layer to the deep layer. Overall, this study demonstrates the utility of hybrid AUVs for collecting oceanic turbulence measurements. They are a powerful addition to traditional turbulence instruments, as they make it possible to survey large areas to obtain high-quality and high-resolution data in both vertical and horizontal directions over long durations.
The Total Ionizing Dose Effects on Perovskite CsPbBr3 Semiconductor Detector
Authors: Ma Wuying , Liu Linyue , Qin Haoming , Gao Runlong , He Baoping , Gou Shilong , He Yihui , Ouyang Xiaoping
Published: February 2023

Perovskite CsPbBr3 semiconductors exhibit unusually high defect tolerance leading to outstanding and unique optoelectronic properties, demonstrating strong potential for γ-radiation and X-ray detection at room temperature. However, the total dose effects of the perovskite CsPbBr3 must be considered when working in a long-term radiation environment. In this work, the Schottky type of perovskite CsPbBr3 detector was fabricated. Their electrical characteristics and γ-ray response were investigated before and after 60Co γ ray irradiation with 100 and 200 krad (Si) doses. The γ-ray response of the Schottky-type planar CsPbBr3 detector degrades significantly with the increase in total dose. At the total dose of 200 krad(Si), the spectral resolving ability to γ-ray response of the CsPbBr3 detector has disappeared. However, with annealing at room temperature for one week, the device’s performance was partially recovered. Therefore, these results indicate that the total dose effects strongly influence the detector performance of the perovskite CsPbBr3 semiconductor. Notably, it is concluded that the radiation-induced defects are not permanent, which could be mitigated even at room temperature. We believe this work could guide the development of perovskite detectors, especially under harsh radiation conditions.
Ultra-High Sensitivity Terahertz Microstructured Fiber Biosensor for Diabetes Mellitus and Coronary Heart Disease Marker Detection
Authors: Xue Jia , Zhang Yani , Guang Zhe , Miao Ting , Ali Zohaib , Qiao Dun , Yao Yiming , Wu Kexin , Zhou Lei , Meng Cheng , Copner Nigel
Published: February 2023

Diabetes Mellitus (DM) and Coronary Heart Disease (CHD) are among top causes of patient health issues and fatalities in many countries. At present, terahertz biosensors have been widely used to detect chronic diseases because of their accurate detection, fast operation, flexible design and easy fabrication. In this paper, a Zeonex-based microstructured fiber (MSF) biosensor is proposed for detecting DM and CHD markers by adopting a terahertz time-domain spectroscopy system. A suspended hollow-core structure with a square core and a hexagonal cladding is used, which enhances the interaction of terahertz waves with targeted markers and reduces the loss. This work focuses on simulating the transmission performance of the proposed MSF sensor by using a finite element method and incorporating a perfectly matched layer as the absorption boundary. The simulation results show that this MSF biosensor exhibits an ultra-high relative sensitivity, especially up to 100.35% at 2.2THz, when detecting DM and CHD markers. Furthermore, for different concentrations of disease markers, the MSF exhibits significant differences in effective material loss, which can effectively improve clinical diagnostic accuracy and clearly distinguish the extent of the disease. This MSF biosensor is simple to fabricate by 3D printing and extrusion technologies, and is expected to provide a convenient and capable tool for rapid biomedical diagnosis.
Emergency Management in the Event of Radiological Dispersion in an Urban Environment
Authors: Cavalieri d’Oro Edoardo , Malizia Andrea
Published: February 2023

Dispersion of a radiological source is a complex scenario in terms of first response, especially when it occurs in an urban environment. The authors in this paper designed, simulated, and analyzed the data from two different scenarios with the two perspectives of an unintentional fire event and a Radiological Dispersal Device (RDD) intentional explosion. The data of the simulated urban scenario are taken from a real case of orphan sources abandoned in a garage in the center of the city of Milan (Italy) in 2012. The dispersion and dose levels are simulated using Parallel Micro Swift Spray (PMSS) software, which takes into account the topographic and meteorological information of the reference scenarios. Apart from some differences in the response system of the two scenarios analyzed, the information provided by the modeling technique used, compared to other models not able to capture the actual urban and meteorological contexts, make it possible to modulate a response system that adheres to the real impact of the scenario. The authors, based on the model results and on the evidence provided by the case study, determine the various countermeasures to adopt to mitigate the impact for the population and to reduce the risk factors for the first responders.
Estimating Volumetric Water Content in Soil for IoUT Contexts by Exploiting RSSI-Based Augmented Sensors via Machine Learning
Authors: Bertocco Matteo , Parrino Stefano , Peruzzi Giacomo , Pozzebon Alessandro
Published: February 2023

This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33%, a virtual sensor of 8.67%, and an augmented sensor of 1.84%, which improves down to 1.53% if filtered in post-processing.
Provably Secure Mutual Authentication and Key Agreement Scheme Using PUF in Internet of Drones Deployments
Authors: Park Yohan , Ryu Daeun , Kwon Deokkyu , Park Youngho
Published: February 2023

Internet of Drones (IoD), designed to coordinate the access of unmanned aerial vehicles (UAVs), is a specific application of the Internet of Things (IoT). Drones are used to control airspace and offer services such as rescue, traffic surveillance, environmental monitoring, delivery and so on. However, IoD continues to suffer from privacy and security issues. Firstly, messages are transmitted over public channels in IoD environments, which compromises data security. Further, sensitive data can also be extracted from stolen mobile devices of remote users. Moreover, drones are susceptible to physical capture and manipulation by adversaries, which are called drone capture attacks. Thus, the development of a secure and lightweight authentication scheme is essential to overcoming these security vulnerabilities, even on resource-constrained drones. In 2021, Akram et al. proposed a secure and lightweight user–drone authentication scheme for drone networks. However, we discovered that Akram et al.’s scheme is susceptible to user and drone impersonation, verification table leakage, and denial of service (DoS) attacks. Furthermore, their scheme cannot provide perfect forward secrecy. To overcome the aforementioned security vulnerabilities, we propose a secure mutual authentication and key agreement scheme between user and drone pairs. The proposed scheme utilizes physical unclonable function (PUF) to give drones uniqueness and resistance against drone stolen attacks. Moreover, the proposed scheme uses a fuzzy extractor to utilize the biometrics of users as secret parameters. We analyze the security of the proposed scheme using informal security analysis, Burrows–Abadi–Needham (BAN) logic, a Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. We also compared the security features and performance of the proposed scheme and the existing related schemes. Therefore, we demonstrate that the proposed scheme is suitable for IoD environments that can provide users with secure and convenient wireless communications.
In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements
Authors: Wang Yi , Fu Boya , Fu Longwen , Xia Chunlei
Published: February 2023

Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.
Turn-On Fluorescent pH Probes for Monitoring Alkaline pHs Using Bis[2-(2′-hydroxyphenyl)benzazole] Derivatives
Authors: Lee Hyuna , Lee Suji , Han Min Su
Published: February 2023

For surveilling human health, industries, and the environment, pH monitoring is important. Numerous studies on fluorescent probes have been conducted to monitor various pH ranges. However, fluorescent probes that are capable of sensing alkaline regions are rare. In this study, we propose turn-on-type fluorescent probes for detecting alkaline pHs using bis[2-(2′-hydroxyphenyl)benzazole] (bis(HBX)) derivatives. These probes have high pKa values (from 9.7 to 10.8) and exhibit strong fluorescence intensity and color changes at alkaline pHs. Probes derived from bis(HBX) exhibit good photostability, reversibility, and anti-interference toward pH variations, which can be identified as a certain fluorescence change toward a basic pH. Therefore, compounds would be advantageous to use fluorescent probes for monitoring alkaline pH changes.
Design of an Integrated Controller for a Sweeping Mechanism of a Low-Dust Almond Pickup Machine
Authors: Serajian Reza , Sun Jian-Qiao , Ehsani Reza
Published: February 2023

California is the world’s biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard. The system includes a feedback control system to control the sweeper brushes’ height and their angular velocity by adjusting the forward velocity of the harvester and the brushes’ rotational speeds to avoid any extra overlapping sweeping, which increases dust generation. The governing kinematic equations for sweepers’ angular velocity and vehicle forward speed were derived. The feedback controllers for synchronizing these speeds were designed to optimize brush/dust contact to minimize dust generation. The sweepers’ height controller was also designed to stabilize the gap between the brushes and the orchard floor and track the road trajectory. Controllers were simulated and tuned for a fast response for agricultural applications with less than a second response delay. Results showed that the designed system has acceptable performance and generates low amounts of dust within the acceptable range of California ambient air quality standards.
Interaction with a Hand Rehabilitation Exoskeleton in EMG-Driven Bilateral Therapy: Influence of Visual Biofeedback on the Users’ Performance
Authors: Cisnal Ana , Gordaliza Paula , Pérez Turiel Javier , Fraile Juan Carlos
Published: February 2023

The effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation.
Study of Single and Multipass f–rGO Inkjet-Printed Structures with Various Concentrations: Electrical and Thermal Evaluation
Authors: Apostolakis Apostolos , Barmpakos Dimitris , Pilatis Aggelos , Belessi Vassiliki , Pagonis Dimitrios-Nikolaos , Jaber Fadi , Aidinis Konstantinos , Kaltsas Grigoris
Published: February 2023

Reduced graphene oxide (rGO) is a derivative of graphene, which has been widely used as the conductive pigment of many water-based inks and is recognized as one of the most promising graphene-based materials for large-scale and low-cost production processes. In this work, we evaluate a custom functionalised reduced graphene oxide ink (f–rGO) via inkjet-printing technology. Test line structures were designed and fabricated by the inkjet printing process using the f–rGO ink on a pretreated polyimide substrate. For the electrical characterisation of these devices, two-point (2P) and four-point (4P) probe measurements were implemented. The results showed a major effect of the number of printed passes on the resulting resistance for all ink concentrations in both 2P and 4P cases. Interesting results can be extracted by comparing the obtained multipass resistance values that results to similar effective concentration with less passes. These measurements can provide the ground to grasp the variation in resistance values due to the different ink concentrations, and printing passes and can provide a useful guide in achieving specific resistance values with adequate precision. Accompanying topography measurements have been conducted with white-light interferometry. Furthermore, thermal characterisation was carried out to evaluate the operation of the devices as temperature sensors and heaters. It has been found that ink concentration and printing passes directly influence the performance of both the temperature sensors and heaters.
Can Satellite Remote Sensing Assist in the Characterization of Yeasts Related to Biogeographical Origin?
Authors: Castrillo David , Blanco Pilar , Vélez Sergio
Published: February 2023

Biogeography is a key concept associated with microbial terroir, which is responsible for the differentiation and uniqueness of wines. One of the factors influencing this microbial terroir is the vegetation, which in turn is influenced by climate, soil, and cultural practices. Remote sensing instruments can provide useful information about vegetation. This study analyses the relationship between NDVI, calculated using Sentinel-2 and Landsat-8 satellite images of different veraison dates, and microbial data obtained in 2015 from 14 commercial (organic and conventional) vineyards belonging to four Designations of Origin (DOs) from Galicia (northwest Spain). Microbial populations in grapes and musts were identified using PCR techniques and confirmed by sequencing. Statistical analyses were made using PCA, CCA, TB-PLS, and correlation analyses. This study confirms that the NDVI is positively correlated with the diversity of yeasts, both in grapes’ surface and must samples. Moreover, the results of this study show: (i) Sentinel-2 images, as well as Landsat-8 images, can establish differences in NDVI related to yeast terroir in grapes and musts, as it is the most relevant DO factor, (ii) Sentinel-2 NDVI and yeast biogeography are moderately to strongly correlated, (iii) Sentinel-2 achieved a better delimitation of the DOs than Landsat-8 and can establish more accurate differences in NDVI–yeast terroir correlations, and (iv) a higher NDVI was associated with the yeast biogeographical patterns of the DOs with higher species richness (S) consisting of weakly fermenting yeasts (Hanseniaspora uvarum, Pichia spp., Starmerella bacillaris, and Zygosaccharomyces spp). However, NDVI values did not correlate well with biogeographic patterns of yeasts previously studied at frequency level (proportion or percentage of each species) in each particular DO. This study suggests that satellite imagery has the potential to be a valuable tool for wine quality management and a decision-making instrument for DO regulators and winegrowers.
Sparse Component Analysis (SCA) Based on Adaptive Time—Frequency Thresholding for Underdetermined Blind Source Separation (UBSS)
Authors: Hassan Norsalina , Ramli Dzati Athiar
Published: February 2023

Blind source separation (BSS) recovers source signals from observations without knowing the mixing process or source signals. Underdetermined blind source separation (UBSS) occurs when there are fewer mixes than source signals. Sparse component analysis (SCA) is a general UBSS solution that benefits from sparse source signals which consists of (1) mixing matrix estimation and (2) source recovery estimation. The first stage of SCA is crucial, as it will have an impact on the recovery of the source. Single-source points (SSPs) were detected and clustered during the process of mixing matrix estimation. Adaptive time–frequency thresholding (ATFT) was introduced to increase the accuracy of the mixing matrix estimations. ATFT only used significant TF coefficients to detect the SSPs. After identifying the SSPs, hierarchical clustering approximates the mixing matrix. The second stage of SCA estimated the source recovery using least squares methods. The mixing matrix and source recovery estimations were evaluated using the error rate and mean squared error (MSE) metrics. The experimental results on four bioacoustics signals using ATFT demonstrated that the proposed technique outperformed the baseline method, Zhen’s method, and three state-of-the-art methods over a wide range of signal-to-noise ratio (SNR) ranges while consuming less time.
Load Estimation of Moving Passenger Cars Using Inductive-Loop Technology
Authors: Marszalek Zbigniew , Duda Krzysztof , Piwowar Piotr , Stencel Marek , Zeglen Tadeusz , Izydorczyk Jacek
Published: February 2023

Due to their lack of driving controllability, overweight vehicles are a big threat to road safety. The proposed method for a moving passenger car load estimation is capable of detecting an overweight vehicle, and thus it finds its application in road safety improvement. The weight of a car’s load entering or leaving a considered zone, e.g., industrial facility, a state, etc., is also of concern in many applications, e.g., surveillance. Dedicated vehicle weight-in-motion measurement systems generally use expensive load sensors that also require deep intervention in the road while being installed and also are calibrated only for heavy trucks. In this paper, a vehicle magnetic profile (VMP) is used for defining a load parameter proportional to the passenger vehicle load. The usefulness of the proposed load parameter is experimentally demonstrated in field tests. The sensitivity of the VMP to the load change results from the fact that the higher load decreases the vehicle clearance value which in turn increases the VMP. It is also shown that a slim inductive-loop sensors allows the building of a load estimation system, with a maximum error around 30 kg, which allows approximate determination of the number of passengers in the car. The presented proof of concept extends the functionality of inductive loops, already installed in the road, for acquiring other traffic parameters, e.g., moving vehicle axle-to-axle distance measurement, to road safety and surveillance related applications.
An Improved Parameter Identification Algorithm for the Friction Model of Electro-Hydraulic Servo Systems
Authors: Liao Jian , Zhou Fuming , Zheng Jianbo
Published: February 2023

Friction is an inherent nonlinear disturbance that can lead to creeping, jitter, and decreased tracking precision in an electro-hydraulic servo system. In this paper, the LuGre friction model is used to describe the dynamic and static characteristics of the friction force of a servo system comprehensively. Accurate identification of model parameters is key to implementing friction compensation. However, traditional genetic identification algorithms have the shortcomings of a premature solution, slow convergence, and poor accuracy. To address these shortcomings, this paper proposes an improved adaptive genetic identification algorithm. The proposed algorithm selects evolutionary processes adaptively according to the population concentration in the initial stage of population evolution. Moreover, it adjusts the crossover probability and the mutation probability to identify a local optimum accurately and converge to the global optimum rapidly. During the late stage of population evolution, the accuracy of the global optimal solution can be improved by reducing the search range of identification parameters. The simulation results show that the relative error of the model parameter values identified by the proposed algorithm is reduced to less than 1% and the convergence speed is faster. Compared with the existing traditional genetic algorithm and adaptive genetic algorithm, the overall performance of the proposed method is better. This study provides a feasible and highly accurate identification method for parameter identification of friction models used in electro-hydraulic servo systems.
Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library
Authors: Khoma Volodymyr , Khoma Yuriy , Brydinskyi Vitalii , Konovalov Alexander
Published: February 2023

Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are designed with the prerequisite that only one speaker is present in the analyzed audio recording. However, some applications involve the need to identify multiple speakers that interact freely in an audio recording. This paper proposes two architectures of speaker identification systems based on a combination of diarization and identification methods, which operate on the basis of segment-level or group-level classification. The open-source PyAnnote framework was used to develop the system. The performance of the speaker identification system was verified through the application of the AMI Corpus open-source audio database, which contains 100 h of annotated and transcribed audio and video data. The research method consisted of four experiments to select the best-performing supervised diarization algorithms on the basis of PyAnnote. The first experiment was designed to investigate how the selection of the distance function between vector embedding affects the reliability of identification of a speaker’s utterance in a segment-level classification architecture. The second experiment examines the architecture of cluster-centroid (group-level) classification, i.e., the selection of the best clustering and classification methods. The third experiment investigates the impact of different segmentation algorithms on the accuracy of identifying speaker utterances, and the fourth examines embedding window sizes. Experimental results demonstrated that the group-level approach offered better identification results were compared to the segment-level approach, and the latter had the advantage of real-time processing.
Wake-Up Receiver for Underwater Acoustic Communication Using in Shallow Water
Authors: Schmidt Jan H. , Schmidt Aleksander M.
Published: February 2023

A data frame transmitted over the underwater acoustic channel usually begins with a preamble. Therefore, underwater communication systems have a dedicated receiver that constantly listens to the preamble signals. A receiver that is to work effectively in shallow waters must have solutions that effectively reduce the impact of the permanently occurring multipath propagation. The article presents a solution based on complementary broadband signals. Initial tests were carried out using the Watermark simulator to determine its reliability in such a difficult propagation environment. The results of experimental tests carried out in a model pool are also included. Details of the implementation of the wake-up receiver are presented.
A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox
Authors: Liu Hongbin , Zhou Han , Chen Hao , Yan Yong , Huang Jianping , Xiong Ao , Yang Shaojie , Chen Jiewei , Guo Shaoyong
Published: February 2023

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.