bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Risk of African Swine Fever Virus Transmission at the Interface between Feral and Domestic Pigs in Lombardy, with a View to Establishing Preventive Measures for Domestic Pigs
Authors: Calò Stefania , Tironi Marco , Cappa Veronica , Scaburri Alessandra , Perna Stefano Francesco , Chiari Mario , Marracci Massimo , Bellini Silvia
Published: December 2023

African swine fever (ASF) continues to spread worldwide, and has reached multiple countries across Asia, the Caribbean, Europe, and the Pacific, representing a serious economic burden threatening pig health and welfare, as well as food security. The disease affects domestic pigs and wild boar, and in several European countries the disease is endemic in wild boars. The lack of vaccines or effective treatments highlights the importance of effective control measures used to keep domestic and feral pigs separated to prevent the spread of the virus. However, the study of the livestock–wildlife interface is quite complex and has many aspects to consider, including the uncertainty of wild-boar population data. In this study, we determined the risk of spread of the ASF virus at the interface between domestic pigs and wild boars using indicators that can indirectly indicate the presence of wild boars in order to target specific control measures in the highest risk areas. The results of the study were compared with those obtained by Pittiglio, in which the population data for wild boars was estimated using a geostatistical method and similar results were obtained. However, the present study used specific information relating to the wild-boar population and this allowed us to use fewer variables.
At-Home Foscarnet Administration in Patients with Cytomegalovirus Infection Post-Allogeneic Stem Cell Transplantation: A Unicentric, Safe, and Feasible Program
Authors: Ruiz-Boy Sonia , Pedraza Alexandra , Prat Marta , Salas Maria Queralt , Carcelero Esther , Riu-Viladoms Gisela , Suárez-Lledó María , Monge-Escartín Inés , Rodríguez-Lobato Luis Gerardo , Martínez-Roca Alexandra , Rovira Montserrat , Martínez Carmen , Gallego Cristina , Urbano-Ispizua Álvaro , Sánchez Joan , Marcos María Ángeles , Fernández-Avilés Francesc
Published: December 2023

Cytomegalovirus (CMV) infection is a relevant cause of morbimortality in patients receiving allogeneic stem cell transplantation (allo-HCT). Foscarnet (FCN) is an effective drug against CMV administered intravenously and usually on an inpatient basis. The Home Care Unit (HCU) for hematologic patients at our hospital designed an at-home FCN administration model to avoid the hospitalization of patients requiring FCN treatment. This study analyzes whether the at-home administration of FCN is as safe and effective as its hospital administration. We collected and compared demographic, clinical, analytical, and economic data of patients with CMV infection post-allo-HCT who received FCN in the hospital (n = 16, 17 episodes) vs. at-home (n = 67, 88 episodes). The proportions of patients with cured CMV infections were comparable between the two groups (65.9% vs. 76.5%, p = 0.395). The median duration of FCN treatment was 15 (interquartile range [IQR] 9–23) and 14 (IQR 11–19) days in the HCU and inpatient cohorts, respectively (p = 0.692). There were no significant differences in the FCN toxicities between groups except for hypocalcemia (26.1% vs. 58.8%, p = 0.007), which was more prevalent in the inpatient cohort. A significant cost-effectiveness was found in the HCU cohort, with a median savings per episode of EUR 5270. It may be concluded that home administration of FCN is a safe, effective, and cost-efficient therapeutic option for patients with CMV infection and disease.
SARS-CoV-2 Fusion Peptide Conjugated to a Tetravalent Dendrimer Selectively Inhibits Viral Infection
Authors: Zannella Carla , Chianese Annalisa , Monti Alessandra , Giugliano Rosa , Morone Maria Vittoria , Secci Francesco , Sanna Giuseppina , Manzin Aldo , De Filippis Anna , Doti Nunzianna , Galdiero Massimiliano
Published: December 2023

Fusion is a key event for enveloped viruses, through which viral and cell membranes come into close contact. This event is mediated by viral fusion proteins, which are divided into three structural and functional classes. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein belongs to class I fusion proteins, characterized by a trimer of helical hairpins and an internal fusion peptide (FP), which is exposed once fusion occurs. Many efforts have been directed at finding antivirals capable of interfering with the fusion mechanism, mainly by designing peptides on the two heptad-repeat regions present in class I viral fusion proteins. Here, we aimed to evaluate the anti-SARS-CoV-2 activity of the FP sequence conjugated to a tetravalent dendrimer through a classical organic nucleophilic substitution reaction (SN2) using a synthetic bromoacetylated peptide mimicking the FP and a branched scaffold of poly-L-Lysine functionalized with cysteine residues. We found that the FP peptide conjugated to the dendrimer, unlike the monomeric FP sequence, has virucidal activity by impairing the attachment of SARS-CoV-2 to cells. Furthermore, we found that the peptide dendrimer does not have the same effects on other coronaviruses, demonstrating that it is selective against SARS-CoV-2.
Vacuum Interaction of Topological Strings at Short Distances
Authors: Grats Yuri V. , Spirin Pavel
Published: December 2023

The paper provides an extended overview of recent results obtained by the authors in the process of studying the vacuum interaction of topological cosmic strings at short distances, taking into account their transverse size a and the mass m of the quantized field. We consider the case of a massive real-valued scalar field with minimal coupling. It is shown that at the interstring distances significantly larger than the Compton length, lc=1/m, the Casimir effect is damped exponentially. On the other hand, at distances smaller than lc but much larger than the typical string width, the field-mass influence becomes insignificant. In this case, the partial contribution of a massive field to the Casimir energy is of the same order as the contribution of a massless one. At these distances, the string’s transverse size is insignificant also. However, at the interstring distances of the same order as a string radius, the energy of the vacuum interaction of thick strings may significantly surpass the one for two infinitely thin strings with the same mass per unit length.
Dielectric and Viscoelastic Behavior of Polyvinyl Butyral Films
Authors: Puente-Córdova Jesús G. , Rentería-Baltiérrez Flor Y. , López-Walle Beatriz , Aguilar-Garib Juan A.
Published: December 2023

Dielectric and thermal properties of polyvinyl butyral (PVB) were studied in this work, using dynamic electrical analysis (DEA) at frequencies from 100 Hz to 1 MHz and temperatures from 293 K to 473 K. Two electrical relaxation processes were investigated: glass transition and interfacial polarization. Above the glass transition temperature (~343 K), interfacial polarization dominates conductive behavior in polyvinyl butyral. The framework of the complex electric modulus was used to obtain information about interfacial polarization. The viscoelastic behavior was analyzed through dynamic mechanical analysis (DMA), where only the mechanical manifestation of the glass transition is observed. The experimental results from dielectric measurements were analyzed with fractional calculus, using a fractional Debye model with one cap-resistor. We were successful in applying the complex electric modulus because we had a good correlation between data and theoretical predictions. The fractional order derivative is an indicator of the energy dissipated in terms of molecular mobility, and the calculated values close to 1 suggest a conductive behavior at temperatures above the glass transition temperature of PVB.
Use of Bio-Epoxies and Their Effect on the Performance of Polymer Composites: A Critical Review
Authors: Capretti Monica , Giammaria Valentina , Santulli Carlo , Boria Simonetta , Del Bianco Giulia
Published: December 2023

This study comprehensively examines recent developments in bio-epoxy resins and their applications in composites. Despite the reliability of traditional epoxy systems, the increasing demand for sustainability has driven researchers and industries to explore new bio-based alternatives. Additionally, natural fibers have the potential to serve as environmentally friendly substitutes for synthetic ones, contributing to the production of lightweight and biodegradable composites. Enhancing the mechanical properties of these bio-composites also involves improving the compatibility between the matrix and fibers. The use of bio-epoxy resins facilitates better adhesion of natural composite constituents, addressing sustainability and environmental concerns. The principles and methods proposed for both available commercial and especially non-commercial bio-epoxy solutions are investigated, with a focus on promising renewable sources like wood, food waste, and vegetable oils. Bio-epoxy systems with a minimum bio-content of 20% are analyzed from a thermomechanical perspective. This review also discusses the effect of incorporating synthetic and natural fibers into bio-epoxy resins both on their own and in hybrid form. A comparative analysis is conducted against traditional epoxy-based references, with the aim of emphasizing viable alternatives. The focus is on addressing their benefits and challenges in applications fields such as aviation and the automotive industry.
Evaluation of Chemical and Morphological Properties of Spruce Wood Stored in the Natural Environment
Authors: Čabalová Iveta , Bélik Michal , Kučerová Viera , Jurczyková Tereza , Bubeníková Tatiana
Published: December 2023

This paper focuses on the changes in chemical structure and fiber morphological properties of spruce wood during 15 months of its storage in an open forest woodshed. From the chemical composition, the extractives, cellulose, holocellulose, and lignin content were determined. The pH value was measured on the wood surface using a contact electrode. Acetic and formic acid, saccharides (glucose, xylose, galactose, arabinose and mannose), and polymerization degree (PD) of cellulose were analyzed using the HPLC method. Fiber length and width were determined using a fiber tester analyzer. After 15 months of storage the content of both cellulose (determined by the Seifert method) and lignin did not change; the quantity of hemicelluloses decreased by 13.2%, due to its easier degradation and less stability compared to cellulose; and the pH value dropped by one degree. HPLC analyses showed a total decrease in the cellulose DP of 9.2% and in saccharides of 40.2%, while the largest decreases were recorded in the quantity of arabinose, by 72%, in the quantity of galactose, by 61%, and in the quantity of xylose, by 43%. Organic acids were not detected due to their high volatility during wood storage. The total decrease in average fiber length was 38.2% and in width was 4.8%. An increase in the proportion of shorter fibers, and a decrease in the proportion of longer fibers, was recorded. It can be concluded that fundamental changes occurred in the wood, which could affect the quality of further products (e.g., chips, pulp, paper, particleboards).
Manufacture and Combustion Characteristics of Cellulose Flame-Retardant Plate through the Hot-Press Method
Authors: Hwang Jeo , Park Dongin , Rie Dongho
Published: December 2023

This study focuses on the increased risk of high heat release and asphyxiation (toxic gas poisoning) in the event of a fire involving polyurethane (PU)- and MDF-based building materials, which are commonly used in buildings. Among them, polyurethane (PU) building materials are very commonly used in buildings, except in Europe and some other countries, due to their excellent thermal insulation performance. Still, problems of short-term heat release and the spread of toxic gases in the event of a fire continue to occur. To overcome these problems, researchers are actively working on introducing various flame retardants into building materials. Therefore, in this study, we produced a laboratory-sized (500 mm × 500 mm) plate-like flame-retardant board that can be utilized as a building material with a lower heat release rate and a lower toxicity index. The material was made by mixing expanded graphite and ceramic binder as flame retardants in a material that is formulated based on the cellulose of waste paper, replacing the existing building materials with a hot-press method. According to the ISO-5660-1 test on the heat release rate of the plate-like flame-retardant board, the Total Heat Release (THR) value was 2.9 (MJ/m2) for 10 min, showing an effect of reducing the THR value by 36.3 (MJ/m2) compared to the THR value of 39.2 (MJ/m2) of the specimen made using only paper. In addition, the toxicity index of the flame-retardant board was checked through the NES (Naval Engineering Standards)-713 test. As a result, the test specimen showed a toxicity index of 0.7, which is 2.4 lower than the toxicity index of 3.1 of MDF, which is utilized as a conventional building material. Based on the results of this study, the cellulose fire-retardant board showed the effect of reducing the heat release rate and toxicity index of building materials in a building fire, which reduces the risk of rapid heat spread and smoke toxicity. This has the potential to improve the evacuation time (A-SET) of evacuees in fires. It is also important to show that recycling waste paper and utilizing it as the main material for building materials can be an alternative in terms of sustainable development.
Heritage Sites, Devotion, and Quality Enhancement in Tourism: The Promotion and Management of Ancient Marian Places of Worship along the Appian Way in Puglia and Basilicata
Authors: Oliva Luigi , Trono Anna
Published: December 2023

Religious tourism is a significant and growing field of tourism that overlaps with cultural tourism. It has the potential to improve the quality of life of those who live in places of faith or along routes of spiritual interest. Religious tourism involves a complex interplay of spiritual and economic motivations. Effective religious tourism management requires respect for spiritual values, partnerships, local engagement, and quality assessment. Devotional practices have evolved from medieval spiritual care to communal expressions and periodic rituals. This paper specifically analysed the characteristics of the Marian cult and pilgrimage flows to places of Marian faith. It examined their value potential from a religious and cultural perspective and their role as a particular attractor of experiential and quality tourism generated by the territorial context. The area of reference is the region of Puglia, which has often played the role of cultural bridge with the eastern coasts of the Mediterranean in the past. The second part of the paper focuses on the proposed itinerary along the Appian Way in its final route between Puglia and Basilicata. Marian shrines were sometimes the cause and sometimes the evidence of the cultural and economic poles that characterised the medieval and modern variants of this ancient road route. The study outlines a serial path that integrates the usual settlement or infrastructural levels of territorial knowledge with the Marian theme, which was analysed diachronically. An operational track in the contemporary territorial dimension emerged from the correlation of both the stratigraphic reading of the landscape and the interpretation of material and immaterial cultural heritage. This track aims to aggregate and promote the sustainable rediscovery of those places, which are largely cut off from the routes of mass tourism, in adherence to the most recent European and local cultural and landscape guidelines.
Complex Validation of Weather Research and Forecasting—Chemistry Modelling of Atmospheric CO2 in the Coastal Cities of the Gulf of Finland
Authors: Nerobelov Georgii , Timofeyev Yuri , Foka Stefani , Smyshlyaev Sergei , Poberovskiy Anatoliy , Sedeeva Margarita
Published: December 2023

The increase of the CO2 content in the atmosphere caused by anthropogenic emissions from the territories of large cities (~70%) is the critical factor in determining the accuracy of emission estimations. Advanced experiment-based methods of anthropogenic CO2 emission estimation are based on the solution of an inverse problem, using accurate measurements of CO2 content and numerical models of atmospheric transport and chemistry. The accuracy of such models decreases the errors of the emission estimations. The aim of the current study is to adapt numerical weather prediction and atmospheric chemistry model WRF-Chem and validate its capability to simulate atmospheric CO2 for the territories of the two large coastal cities of the Gulf of Finland—St. Petersburg (Russia) and Helsinki (Finland). The research has demonstrated that the WRF-Chem model is able to simulate annual variation, as well as the mean seasonal and diurnal variations of the near-surface CO2 mixing ratio, in Helsinki, at a high spatial resolution (2 km). Correlation between the modelled and measured CO2 mixing ratio is relatively high, at ~0.73, with a mean difference and its standard deviation of 0.15 ± 0.04 and 1.7%, respectively. The differences between the WRF-Chem data and the measurements might be caused by errors in the modelling of atmospheric transport and in a priori CO2 emissions and biogenic fluxes. The WRF-Chem model simulates well the column-averaged CO2 mixing ratio (XCO2) in St. Petersburg (January 2019–March 2020), with a correlation of ~0.95 relative to ground-based spectroscopic measurements by the IR–Fourier spectrometer Bruker EM27/SUN. The error of the XCO2 modelling constitutes ~0.3%, and most likely is related to inaccuracies in chemical boundary conditions and a priori anthropogenic CO2 emissions. The XCO2 time series in St. Petersburg by the WRF-Chem model fits well with global CAMS reanalysis and CarbonTracker-modelled data (the differences are less than ~1%). However, due to much higher spatial resolution (2 vs. over 100 km), the WRF-Chem data are in the best agreement with the ground-based remote measurements of XCO2. According to the study, the modelling errors of XCO2 in St. Petersburg during the whole simulated period are sufficiently minimal to fit the requirement of “Error ≤ 0.2%” in 60% of cases. This requirement should be satisfied to evaluate properly the anthropogenic CO2 emissions of St. Petersburg on a city-scale.
Heterogeneous Ship Data Classification with Spatial–Channel Attention with Bilinear Pooling Network
Authors: Tienin Bole Wilfried , Cui Guolong , Mba Esidang Roldan , Talla Nana Yannick Abel , Moniz Moreira Eguer Zacarias
Published: December 2023

The classification of ship images has become a significant area of research within the remote sensing community due to its potential applications in maritime security, traffic monitoring, and environmental protection. Traditional monitoring methods like the Automated Identification System (AIS) and the Constant False Alarm Rate (CFAR) have their limitations, such as challenges with sea clutter and the problem of ships turning off their transponders. Additionally, classifying ship images in remote sensing is a complex task due to the spatial arrangement of geospatial objects, complex backgrounds, and the resolution limitations of sensor platforms. To address these challenges, this paper introduces a novel approach that leverages a unique dataset termed Heterogeneous Ship data and a new technique called the Spatial–Channel Attention with Bilinear Pooling Network (SCABPNet). First, we introduce the Heterogeneous Ship data, which combines Synthetic Aperture Radar (SAR) and optical satellite imagery, to leverage the complementary features of the SAR and optical modalities, thereby providing a richer and more-diverse set of features for ship classification. Second, we designed a custom layer, called the Spatial–Channel Attention with Bilinear Pooling (SCABP) layer. This layer sequentially applies the spatial attention, channel attention, and bilinear pooling techniques to enhance the feature representation by focusing on extracting informative and discriminative features from input feature maps, then classify them. Finally, we integrated the SCABP layer into a deep neural network to create a novel model named the SCABPNet model, which is used to classify images in the proposed Heterogeneous Ship data. Our experiments showed that the SCABPNet model demonstrated superior performance, surpassing the results of several state-of-the-art deep learning models. SCABPNet achieved an accuracy of 97.67% on the proposed Heterogeneous Ship dataset during testing. This performance underscores SCABPNet’s capability to focus on ship-specific features while suppressing background noise and feature redundancy. We invite researchers to explore and build upon our work.
Quantifying the Impact of Hurricane Harvey on Beach−Dune Systems of the Central Texas Coast and Monitoring Their Changes Using UAV Photogrammetry
Authors: Shahtakhtinskiy Aydin , Khan Shuhab D. , Rojas Sara S.
Published: December 2023

Historically, the Texas Gulf Coast has been affected by many tropical storms and hurricanes. The most recent severe impact was caused by Hurricane Harvey, which made landfall in August 2017 on the central Texas coast. We evaluated the impact of Hurricane Harvey on the barrier islands of the central Texas coast, including San Jose Island, Mustang Island, and North Padre Island. We used public data sets, including 1 m resolution bare-earth digital elevation models (DEMs), derived from airborne lidar acquisitions before (2016) and after (2018) Hurricane Harvey, and sub-meter scale aerial imagery pre- and post-Harvey to evaluate changes at a regional scale. Shoreline proxies were extracted to quantify shoreline retreat and/or advance, and DEM differencing was performed to quantify net sediment erosion and accretion or deposition. Unmanned aerial vehicle surveys were conducted at each island to produce high-resolution (cm scale) imagery and topographic data used for morphological and change analyses of beaches and dunes at the local scale. The results show that Hurricane Harvey caused drastic local shoreline retreat, reaching 59 m, and significant erosion levels of beach−dune elements immediately after its landfall. Erosion and recovery processes and their levels were influenced by the local geomorphology of the beach−foredune complexes. It is also observed that local depositional events contributed to their post-storm rebuilding. This study aims to enhance the understanding of major storm impacts on coastal areas and help in future protection planning of the Texas coast. It also has broader implications for coastlines on Earth affected by major storms.
Segment Anything Model (SAM) Assisted Remote Sensing Supervision for Mariculture—Using Liaoning Province, China as an Example
Authors: Ren Yougui , Yang Xiaomei , Wang Zhihua , Yu Ge , Liu Yueming , Liu Xiaoliang , Meng Dan , Zhang Qingyang , Yu Guo
Published: December 2023

Obtaining spatial distribution information on mariculture in a low-cost, fast, and efficient manner is crucial for the sustainable development and regulatory planning of coastal zones and mariculture industries. This study, based on the Segment Anything Model (SAM) and high-resolution remote sensing imagery, rapidly extracted mariculture areas in Liaoning Province, a typical northern province in China with significant mariculture activity. Additionally, it explored the actual marine ownership data to investigate the marine use status of Liaoning Province’s mariculture. The total area of mariculture we extracted in Liaoning Province is 1052.89 km2. Among this, the area of cage mariculture is 27.1 km2, while raft mariculture covers 1025.79 km2. Through field investigations, it was determined that in the western part of Liaodong Bay, cage mariculture predominantly involves sea cucumbers. In the southern end of Dalian, the raft mariculture focuses on cultivating kelp. On the other hand, around the islands in the eastern region, the primary crop in raft mariculture is scallops, showing a significant geographical differentiation pattern. In the planned mariculture areas within Liaoning Province’s waters, the proportion of actual development and utilization is 11.2%, while the proportion approved for actual mariculture is 90.2%. This indicates a suspicion that 9.8% of mariculture is possibly in violation of sea occupation rights, which could be due to the untimely updating of marine ownership data. Based on SAM, efficient and accurate extraction of cage mariculture can be achieved. However, the extraction performance for raft mariculture is challenging and remains unsatisfactory. Manual interpretation is still required for satisfactory results in this context.
Feature Scalar Field Grid-Guided Optical-Flow Image Matching for Multi-View Images of Asteroid
Authors: Zhang Sheng , Xue Yong , Tang Yubing , Zhu Ruishuan , Jiang Xingxing , Niu Chong , Yin Wenping
Published: December 2023

Images captured by deep space probes exhibit large-scale variations, irregular overlap, and remarkable differences in field of view. These issues present considerable challenges for the registration of multi-view asteroid sensor images. To obtain accurate, dense, and reliable matching results of homonymous points in asteroid images, this paper proposes a new scale-invariant feature matching and displacement scalar field-guided optical-flow-tracking method. The method initially uses scale-invariant feature matching to obtain the geometric correspondence between two images. Subsequently, scalar fields of coordinate differences in the x and y directions are constructed based on this correspondence. Next, interim images are generated using the scalar field grid. Finally, optical-flow tracking is performed based on these interim images. Additionally, to ensure the reliability of the matching results, this paper introduces three methods for eliminating mismatched points: bidirectional optical-flow tracking, vector field consensus, and epipolar geometry constraints. Experimental results demonstrate that the proposed method achieves a 98% matching correctness rate and a root mean square error of 0.25 pixels. By combining the advantages of feature matching and optical-flow field methods, this approach achieves image homonymous point matching results with precision and density. The matching method exhibits robustness and strong applicability for asteroid images with cross-scale, large displacement, and large rotation angles.
Oil Well Detection under Occlusion in Remote Sensing Images Using the Improved YOLOv5 Model
Authors: Zhang Yu , Bai Lu , Wang Zhibao , Fan Meng , Jurek-Loughrey Anna , Zhang Yuqi , Zhang Ying , Zhao Man , Chen Liangfu
Published: December 2023

Oil wells play an important role in the extraction of oil and gas, and their future potential extends beyond oil and gas exploitation to include the development of geothermal resources for sustainable power generation. Identifying and detecting oil wells are of paramount importance given the crucial role of oil well distribution in energy planning. In recent years, significant progress has been made in detecting single oil well objects, with recognition accuracy exceeding 90%. However, there are still remaining challenges, particularly with regard to small-scale objects, varying viewing angles, and complex occlusions within the domain of oil well detection. In this work, we created our own dataset, which included 722 images containing 3749 oil well objects in Daqing, Huatugou, Changqing oil field areas in China, and California in the USA. Within this dataset, 2165 objects were unoccluded, 617 were moderately occluded, and 967 objects were severely occluded. To address the challenges in detecting oil wells in complex occlusion scenarios, we propose the YOLOv5s-seg CAM NWD network for object detection and instance segmentation. The experimental results show that our proposed model outperforms YOLOv5 with F1 improvements of 5.4%, 11.6%, and 23.1% observed for unoccluded, moderately occluded, and severely occluded scenarios, respectively.
Large Scale Optical Projection Tomography without the Use of Refractive-Index-Matching Liquid
Authors: Stavroulakis Petros Ioannis , Ganetsos Theodore , Zabulis Xenophon
Published: December 2023

The practical, rapid, and accurate optical 3D reconstruction of transparent objects with contemporary non-contact optical techniques, has been an open challenge in the field of optical metrology. The combination of refraction, reflection, and transmission in transparent objects makes it very hard to use common off-the-shelf 3D reconstruction solutions to accurately reconstruct transparent objects in three dimensions without completely coating the object with an opaque material. We demonstrate in this work that a specific class of transparent objects can indeed be reconstructed without the use of opaque spray coatings, via Optical Projection Tomography (OPT). Particularly, the 3D reconstruction of large thin-walled hollow transparent objects can be achieved via OPT, without the use of refractive-index-matching liquid, accurately enough for use in both cultural heritage and beverage packaging industry applications. We compare 3D reconstructions of our proposed OPT method to those achieved by an industrial-grade 3D scanner and report average shape differences of ±0.34 mm for ‘shelled’ hollow objects and ±0.92 mm for ‘non-shelled’ hollow objects. A disadvantage of using OPT, which was noticed on the thicker ‘non-shelled’ hollow objects, as opposed to the ‘shelled’ hollow objects, was that it induced partial filling of hollow areas and the deformation of embossed features.
A Novel Spinel Ferrite-Hexagonal Ferrite Composite for Enhanced Magneto-Electric Coupling in a Bilayer with PZT
Authors: Saha Sujoy , Acharya Sabita , Popov Maksym , Sauyet Theodore , Pfund Jacob , Bidthanapally Rao , Jain Menka , Page Michael R. , Srinivasan Gopalan
Published: December 2023

The magnetoelectric effect (ME) is an important strain mediated-phenomenon in a ferromagnetic-piezoelectric composite for a variety of sensors and signal processing devices. A bias magnetic field, in general, is essential to realize a strong ME coupling in most composites. Magnetic phases with (i) high magnetostriction for strong piezomagnetic coupling and (ii) large anisotropy field that acts as a built-in bias field are preferred so that miniature, ME composite-based devices can operate without the need for an external magnetic field. We are able to realize such a magnetic phase with a composite of (i) barium hexaferrite (BaM) with high magnetocrystalline anisotropy field and (ii) nickel ferrite (NFO) with high magnetostriction. The BNx composites, with (100 − x) wt.% of BaM and x wt.% NFO, for x = 0–100, were prepared. X-ray diffraction analysis shows that the composites did not contain any impurity phases. Scanning electron microscopy images revealed that, with an increase in NFO content, hexagonal BaM grains become prominent, leading to a large anisotropy field. The room temperature saturation magnetization showed a general increase with increasing BaM content in the composites. NFO rich composites with x ≥ 60 were found to have a large magnetostriction value of around −23 ppm, comparable to pure NFO. The anisotropy field HA of the composites, determined from magnetization and ferromagnetic resonance (FMR) measurements, increased with increasing NFO content and reached a maximum of 7.77 kOe for x = 75. The BNx composite was cut into rectangular platelets and bonded with PZT to form the bilayers. ME voltage coefficient (MEVC) measurements at low frequencies and at mechanical resonance showed strong coupling at zero bias for samples with x ≥ 33. This large in-plane HA acted as a built-in field for strong ME effects under zero external bias in the bilayers. The highest zero-bias MEVC of ~22 mV/cm Oe was obtained for BN75-PZT bilayers wherein BN75 also has the highest HA. The Bilayer of BN95-PZT showed a maximum MEVC ~992 mV/cm Oe at electromechanical resonance at 59 kHz. The use of hexaferrite–spinel ferrite composite to achieve strong zero-bias ME coupling in bilayers with PZT is significant for applications related to energy harvesting, sensors, and high frequency devices.
Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams
Authors: Green Adrien , Lawrence Jeremy , Siopsis George , Peters Nicholas A. , Passian Ali
Published: December 2023

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility’s control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.
Supervised Manifold Learning Based on Multi-Feature Information Discriminative Fusion within an Adaptive Nearest Neighbor Strategy Applied to Rolling Bearing Fault Diagnosis
Authors: Wang Hongwei , Yao Linhu , Wang Haoran , Liu Yu , Li Zhiyuan , Wang Di , Hu Ren , Tao Lei
Published: December 2023

Rolling bearings are a key component for ensuring the safe and smooth operation of rotating machinery and are very prone to failure. Therefore, intelligent fault diagnosis research on rolling bearings has become a crucial task in the field of mechanical fault diagnosis. This paper proposes research on the fault diagnosis of rolling bearings based on an adaptive nearest neighbor strategy and the discriminative fusion of multi-feature information using supervised manifold learning (AN-MFIDFS-Isomap). Firstly, an adaptive nearest neighbor strategy is proposed using the Euclidean distance and cosine similarity to optimize the selection of neighboring points. Secondly, three feature space transformation and feature information extraction methods are proposed, among which an innovative exponential linear kernel function is introduced to provide new feature information descriptions for the data, enhancing feature sensitivity. Finally, under the adaptive nearest neighbor strategy, a novel AN-MFIDFS-Isomap algorithm is proposed for rolling bearing fault diagnosis by fusing various feature information and classifiers through discriminative fusion with label information. The proposed AN-MFIDFS-Isomap algorithm is validated on the CWRU open dataset and our experimental dataset. The experiments show that the proposed method outperforms other traditional manifold learning methods in terms of data clustering and fault diagnosis.
Silicon Nanowire Phototransistor Arrays for CMOS Image Sensor Applications
Authors: Jun Hyunsung , Choi Johyeon , Hwang Jinyoung
Published: December 2023

This paper introduces a new design of silicon nanowire (Si NW) phototransistor (PT) arrays conceived explicitly for improved CMOS image sensor performance, and comprehensive numerical investigations clarify the characteristics of the proposed devices. Each unit within this array architecture features a top-layer vertical Si NW optimized for the maximal absorption of incoming light across the visible spectrum. This absorbed light generates carriers, efficiently injected into the emitter–base junction of an underlying npn bipolar junction transistor (BJT). This process induces proficient amplification of the output collector current. By meticulously adjusting the diameters of the NWs, the PTs are tailored to exhibit distinct absorption characteristics, thus delineating the visible spectrum’s blue, green, and red regions. This specialization ensures enriched color fidelity, a sought-after trait in imaging devices. Notably, the synergetic combination of the Si NW and the BJT augments the electrical response under illumination, boasting a quantum efficiency exceeding 10. In addition, by refining parameters like the height of the NW and gradient doping depth, the proposed PTs deliver enhanced color purity and amplified output currents.
Graphene-Based Chemiresistor Sensors for Drinking Water Quality Monitoring
Authors: McGarrity Mason , Zhao Feng
Published: December 2023

Monitoring the quality of drinking water is a crucial responsibility for all water infrastructure networks, as it guarantees access to clean water for the communities they serve. With water infrastructure deteriorating due to age and neglect, drinking water violations are on the rise in the US, underscoring the need for improved monitoring capabilities. Among the different sensor technologies, graphene-based chemiresistors have emerged as a promising technology for water quality monitoring due to advantages such as simple design, sensitivity, and selectivity. This review paper provides an overview of recent advances in the development of graphene-based chemiresistors for water quality monitoring, including principles of chemiresistive sensing, sensor design and functionalization, and performance of devices reported in the literature. The paper also discusses challenges and opportunities in the field and highlights future research directions. The development of graphene-based chemiresistors has the potential to revolutionize water quality monitoring by providing highly sensitive and cost-effective sensors that can be integrated into existing infrastructure for real-time monitoring.
A Dynamic IIoT Framework Based on the Publish–Subscribe Paradigm
Authors: Ungurean Ioan , Gaitan Nicoleta Cristina
Published: December 2023

The use of the Internet of Things (IoT) technologies and principles in industrial environments is known as the Industrial Internet of Things (IIoT). The IIoT concept aims to integrate various industrial devices, sensors, and actuators for collection, storage, monitoring, and process automation. Due to the complexity of IIoT environments, there is no one-size-fits-all solution. The main challenges in developing an IIoT solution are represented by the diversity of sensors and devices, connectivity, edge/fog computing, and security. This paper proposes a distributed and customized IioT (Industrial Internet of Things) framework for the interaction of things from the industrial environment. This framework is distributed on the fog nodes of the IIoT architecture proposed, and it will have the possibility to interconnect local things (with low latency) or global things (with a latency generated by the Internet network). To demonstrate the functionality of the proposed framework, it is included in the fog nodes presented in other paper. These fog nodes allow the integration of CANOpen networks into an IioT architecture. The most important advantages of the proposed architecture are its customizability and the fact that it allows decision operations to be carried out at the edge of the network to eliminate latency due to the Internet.
Development of a High-Efficiency Device for Thermal Neutron Detection Using a Sandwich of Two High-Purity 10B Enriched Layers
Authors: Provenzano Chiara , Marra Marcella , Caricato Anna Paola , Finocchiaro Paolo , Amaducci Simone , Longhitano Fabio , Martino Maurizio , Poma Gaetano Elio , Quarta Gianluca
Published: December 2023

The shortage of 3He, a crucial element widely used as a neutron converter in neutron detection applications, has sparked significant research efforts aimed at finding alternative materials, developing appropriate deposition methods, and exploring new detector architectures. This issue has required the exploration of novel approaches to address the challenges faced in neutron detection. Among the available conversion materials, 10B has emerged as one of the most promising choices due to its high neutron-capture cross-section and relatively high Q value. In our previous papers, we delved into the possibility of depositing neutron conversion layers based on 10B using Pulsed Laser Deposition (PLD). We investigated and evaluated the performance of these layers based on various factors, including deposition conditions, substrate properties, and film thickness. Moreover, we successfully developed and tested a device that employed a single conversion layer coupled with a silicon particle detector. In this current study, we present the development of a new device that showcases improved performance in terms of efficiency, sensitivity, and discrimination against γ background signals. The background signals can arise from the environment or be associated with the neutron field. To achieve these advancements, we considered a new detection geometry that incorporates the simultaneous use of two 10B conversion layers, each with a thickness of 1.5 μm, along with two solid-state silicon detectors. The primary objective of this design was to enhance the overall detection efficiency when compared to the single-layer geometry. By employing this novel setup, our results demonstrate a significant enhancement in the device’s performance when exposed to a neutron flux from an Am-Be neutron source, emitting a flux of approximately 2.2 × 106 neutrons per second. Furthermore, we established a noteworthy agreement between the experimental data obtained and the simulation results.
Maritime Infrared Small Target Detection Based on the Appearance Stable Isotropy Measure in Heavy Sea Clutter Environments
Authors: Wang Fan , Qian Weixian , Qian Ye , Ma Chao , Zhang He , Wang Jiajie , Wan Minjie , Ren Kan
Published: December 2023

Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods.
A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor
Authors: Alemayoh Tsige Tadesse , Lee Jae Hoon , Okamoto Shingo
Published: December 2023

In this study, we introduce a novel framework that combines human motion parameterization from a single inertial sensor, motion synthesis from these parameters, and biped robot motion control using the synthesized motion. This framework applies advanced deep learning methods to data obtained from an IMU attached to a human subject’s pelvis. This minimalistic sensor setup simplifies the data collection process, overcoming price and complexity challenges related to multi-sensor systems. We employed a Bi-LSTM encoder to estimate key human motion parameters: walking velocity and gait phase from the IMU sensor. This step is followed by a feedforward motion generator-decoder network that accurately produces lower limb joint angles and displacement corresponding to these parameters. Additionally, our method also introduces a Fourier series-based approach to generate these key motion parameters solely from user commands, specifically walking speed and gait period. Hence, the decoder can receive inputs either from the encoder or directly from the Fourier series parameter generator. The output of the decoder network is then utilized as a reference motion for the walking control of a biped robot, employing a constraint-consistent inverse dynamics control algorithm. This framework facilitates biped robot motion planning based on data from either a single inertial sensor or two user commands. The proposed method was validated through robot simulations in the MuJoco physics engine environment. The motion controller achieved an error of ≤5° in tracking the joint angles demonstrating the effectiveness of the proposed framework. This was accomplished using minimal sensor data or few user commands, marking a promising foundation for robotic control and human–robot interaction.
A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization
Authors: Yan Suqing , Xu Xiaoyue , Luo Xiaonan , Xiao Jianming , Ji Yuanfa , Wang Rongrong
Published: December 2023

Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN–Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.