bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of the Microbiome on Immunotherapy for Gastroesophageal Cancer
Authors: Dadgar Neda , Edlukudige Keshava Vinay , Raj Moses S. , Wagner Patrick L.
Published: September 2023

Immunotherapy has shown promise as a treatment option for gastroesophageal cancer, but its effectiveness is limited in many patients due to the immunosuppressive tumor microenvironment (TME) commonly found in gastrointestinal tumors. This paper explores the impact of the microbiome on the TME and immunotherapy outcomes in gastroesophageal cancer. The microbiome, comprising microorganisms within the gastrointestinal tract, as well as within malignant tissue, plays a crucial role in modulating immune responses and tumor development. Dysbiosis and reduced microbial diversity are associated with poor response rates and treatment resistance, while specific microbial profiles correlate with improved outcomes. Understanding the complex interactions between the microbiome, tumor biology, and immunotherapy is crucial for developing targeted interventions. Microbiome-based biomarkers may enable personalized treatment approaches and prediction of patient response. Interventions targeting the microbiome, such as microbiota-based therapeutics and dietary modifications, offer the potential for reshaping the gut microbiota and creating a favorable TME that enhances immunotherapy efficacy. Further research is needed to reveal the underlying mechanisms, and large-scale clinical trials will be required to validate the efficacy of microbiome-targeted interventions.
Biocatalysts Based on Immobilized Lipases for the Production of Ethyl Esters of Fatty Acids including Bioactive Gamma-Linolenic Acid from Borage Oil
Authors: Berzal Gonzalo , García-González Martín , Castejón Natalia , García-García Paz , Fernández-Lorente Gloria , Señoráns Francisco J.
Published: September 2023

In the present work, borage oil (Borago officinalis) was used as the main source of gamma linolenic acid (GLA) to obtain ethyl esters by enzymatic ethanolysis using immobilized enzymes for its application in the food industry. Commercial Thermomyces lanuginosus lipase (TLL) was compared to chemical ethanolysis in alkaline medium. In addition, TLL was immobilized by adsorption on hydrophobic porous support (Octadecyl-Sepabeads®) to compare the results. Fatty acid ethyl ester (FAEE) yields of both reactions were compared under the same conditions (25 °C and 200 rpm) and analyzed by GC-MS. Moreover, the conversion yield for borage oil ethanolysis catalyzed by TLL immobilized on C18-Sepabeads® supports was similar to the chemical pathway (93.4% and 99.5%, respectively). When this biocatalyst was used in a solvent-free system (at 40 °C and 200 rpm), it was possible to obtain a high FAEE yield of 84.3% in the first 24 h of reaction. Furthermore, it was possible to re-use the immobilized biocatalyst for the performance of five reaction cycles maintaining 68% of its initial activity. Thus, the use of immobilized enzymes in solvent-free systems is an eco-friendly alternative to obtain GLA ethyl esters for its possible application in cosmetics and food.
Oxide-Derived Copper Nanowire Bundles for Efficient CO2 Reduction to Multi-Carbon Products
Authors: Xu Dong , Wu Minfang , Huang Yan , Gu Yongzheng , Wang Guiwen , Yang Long , Liu Yongping , Gao Tengfei , Li Shoujie , Wei Wei , Chen Wei , Dong Xiao
Published: September 2023

Cu-based catalysts for efficient C2+ production from CO2 electrocatalytic reduction reaction (CO2ERR) exhibit significant promise, but still suffer from ambiguous mechanisms due to the intrinsic structure instability during electroreduction. Herein, we report an oxide-derived copper nanowire bundle (OD-Cu NWB) for efficient CO2ERR to C2+ products. OD-Cu NWBs with a well-preserved nanowire bundle morphology lead to promoted multi-carbon production compared to commercial copper powders. The formation of OD-Cu NWBs shows a great dependence on the precipitation/calcination temperatures and per-reduction potentials, which further influence the ultimate CO2ERR performance correspondingly. The optimized preparation parameters for the formation of a well-ordered nanowire bundle morphology are found, leading to a preferred C2+ production ability. Besides the nanowire bundle morphology, the oxide-derived Cu essence of OD-Cu NWBs with stabilized Cu+ species from per-reduction also promotes the CO2ERR activity and facilitates the C-C coupling of key intermediates for C2+ production. This work provides a facile strategy and inspiration for CO2ERR catalyst developments targeting high-valued multi-carbon products.
Morpholine Radical in the Electrochemical Reaction with Quinoline N-Oxide
Authors: Dolengovski Egor L. , Gryaznova Tatyana V. , Sinyashin Oleg G. , Gavrilova Elena L. , Kholin Kirill V. , Budnikova Yulia H.
Published: September 2023

An electrochemical reaction between quinoline N-oxides and morpholine was developed by using Cu(OAc)2 as a catalyst, generating products of 4-aminoquinoline N-oxides in CH2Cl2 or 2-aminoquinoline N-oxides in CH3CN in good yields. With an increase in the amount of electricity passed, the product deoxygenates with the formation of aminoquinolines. The advantages of the reaction are mild conditions, room temperature, the use of morpholine rather than its derivatives, and the ability to control the process when the electrolysis conditions change. Bisubstituted quinoline has also been obtained. The redox properties of both individual participants of C–H/N–H cross-coupling and multicomponent systems were established by voltammetry and EPR methods. For the first time, the EPR spectrum of the morpholine radical was recorded at room temperature, and its magnetic resonance parameters were determined in CH2Cl2. Mechanisms for the catalytic reaction have been proposed. This is a simple and easy-to-perform method for introducing a morpholine substituent, important in medicinal chemistry and other fields, by C–H/N–H cross-coupling.
Targeting of AKT1 by miR-143-3p Suppresses Epithelial-to-Mesenchymal Transition in Prostate Cancer
Authors: Armstrong Lee , Willoughby Colin E. , McKenna Declan J.
Published: September 2023

An altered expression of miR-143-3p has been previously reported in prostate cancer where it is purported to play a tumor suppressor role. Evidence from other cancers suggests miR-143-3p acts as an inhibitor of epithelial-to-mesenchymal transition (EMT), a key biological process required for metastasis. However, in prostate cancer the interaction between miR-143-3p and EMT-associated mechanisms remains unclear. Therefore, this paper investigated the link between miR-143-3p and EMT in prostate cancer using in vitro and in silico analyses. PCR detected that miR-143-3p expression was significantly decreased in prostate cancer cell lines compared to normal prostate cells. Bioinformatic analysis of The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) data showed a significant downregulation of miR-143-3p in prostate cancer, correlating with pathological markers of advanced disease. Functional enrichment analysis confirmed the significant association of miR-143-3p and its target genes with EMT. The EMT-linked gene AKT1 was subsequently shown to be a novel target of miR-143-3p in prostate cancer cells. The in vitro manipulation of miR-143-3p levels significantly altered the cell proliferation, clonogenicity, migration and expression of EMT-associated markers. Further TCGA PRAD analysis suggested miR-143-3p tumor expression may be a useful predictor of disease recurrence. In summary, this is the first study to report that miR-143-3p overexpression in prostate cancer may inhibit EMT by targeting AKT1. The findings suggest miR-143-3p could be a useful diagnostic and prognostic biomarker for prostate cancer.
Acute Kidney Injury-Induced Circulating TNFR1/2 Elevations Correlate with Persistent Kidney Injury and Progression to Fibrosis
Authors: Arthanarisami Akshayakeerthi , Komaru Yohei , Katsouridi Charikleia , Schumacher Julian , Verges Deborah K. , Ning Liang , Abdelmageed Mai M. , Herrlich Andreas , Kefaloyianni Eirini
Published: September 2023

Elevated levels of circulating tumor necrosis factor receptors 1 and 2 (cTNFR1/2) predict chronic kidney disease (CKD) progression; however, the mechanisms of their release remain unknown. Whether acute kidney injury (AKI) drives cTNFR1/2 elevations and whether they predict disease outcomes after AKI remain unknown. In this study, we used AKI patient serum and urine samples, mouse models of kidney injury (ischemic, obstructive, and toxic), and progression to fibrosis, nephrectomy, and related single-cell RNA-sequencing datasets to experimentally test the role of kidney injury on cTNFR1/2 levels. We show that TNFR1/2 serum and urine levels are highly elevated in all of the mouse models of kidney injury tested, beginning within one hour post injury, and correlate with its severity. Consistent with this, serum and urine TNFR1/2 levels are increased in AKI patients and correlate with the severity of kidney failure. Kidney tissue expression of TNFR1/2 after AKI is only slightly increased and bilateral nephrectomies lead to strong cTNFR1/2 elevations, suggesting the release of these receptors by extrarenal sources. The injection of the uremic toxin indoxyl sulfate in healthy mice induces moderate cTNFR1/2 elevations. Moreover, TNF neutralization does not affect early cTNFR1/2 elevations after AKI. These data suggest that cTNFR1/2 levels in AKI do not reflect injury-induced TNF activity, but rather a rapid response to loss of kidney function and uremia. In contrast to traditional disease biomarkers, such as serum creatinine or BUN, cTNFR1/2 levels remain elevated for weeks after severe kidney injury. At these later timepoints, cTNFR1/2 levels positively correlate with remaining kidney injury. During the AKI-to-CKD transition, elevations of TNFR1/2 kidney expression and of cTNFR2 levels correlate with kidney fibrosis levels. In conclusion, our data demonstrate that kidney injury drives acute increases in cTNFR1/2 serum levels, which negatively correlate with kidney function. Sustained TNFR1/2 elevations after kidney injury during AKI-to-CKD transition reflect persistent tissue injury and progression to kidney fibrosis.
Flow and Heat Transfer on the Surface of Molten Steel Slag Layer in Continuous Casting Mold
Authors: Li Guohui , Wang Shanjiao , Zheng Gengtao
Published: September 2023

Protective slag is coated on the surface of molten steel during continuous casting, the flow and heat transfer state of the protective slag is a decisive factor affecting the inflow and consumption of liquid slag and is also an important prerequisite for stabilizing and improving the quality of continuous casting billets. Based on the Navier Stokes fluid momentum conservation equation and energy equation, a two-dimensional longitudinal numerical model describing the flow/heat transfer of liquid protective slag on the surface of steel is established. The data comes from the equipment parameters and casting process of an arc shaped slab continuous casting machine in a domestic steel plant. The flow field and temperature field distribution of protective slag are calculated and analyzed, and the effects of factors such as slag layer thickness and shear speed on the flow and heat transfer status of the liquid slag layer are discussed. When the bottom shear velocity increases from 0.005 m/s to 0.2 m/s, the maximum flow velocity of liquid slag from the nozzle to the narrow surface in the center area of the model increases from 0.0012 m/s to 0.0617 m/s, and the average longitudinal flow velocity of liquid slag near the nozzle increases from 0.0012 m/s to 0.0627 m/s. The research results provide reference for investigating the complex metallurgical behavior of protective slag.
Visible Light–Near-Infrared Photodetection on Cys-MoO3−x Nanoparticles for Photothermal Therapy against Papillary Thyroid Carcinoma
Authors: Chen Jinhuan , Liu Xian , Zeng Xin , Yang Ming , Xie Liang
Published: September 2023

The excellent performance of semiconductor nanocrystals as sensitizers for photothermal therapy (PTT) has attracted the attention of many researchers; however, they are hindered by limited bandwidth and complex synthesis. To overcome these limitations, starting with an initial determination of photothermal conductivity, we synthesized and designed molybdenum and Cys-MoO3−x nanoparticles (NPs) for use in the minimally invasive treatment of papillary thyroid carcinoma (PTC), as the NPs are coated only with cysteine molecules. The obtained Cys-MoO2 NPs were used as a PTT reaction drug for topical application to PTC cells. The use of near-infrared photoconductive PTT in combination with low-toxicity biological chemotherapy reached a 90% efficacy for cancer treatment in vitro. The conducted experiments intuitively demonstrate that non-toxic Cys-MoO2 NPs are lethal to the cancer cells under visual (VL, 405 nm) and near-infrared (NIR, 808 nm) laser irradiation and can be precisely controlled. Therefore, this study provides a powerful, safe, and easily modified NP platform for photo-triggered PTC elimination with broad application prospects. Assessment of the ideal damage range indicates a high degree of controllability, allowing the tumor to be precisely targeted while minimizing damage to the surrounding healthy tissue. In conclusion, this study provides a convenient, safe, and powerful NP platform for the near-infrared photo-controlled PTT of PTC cells, which has broad application prospects for the elimination of PTC and other types of cancer.
Synthesis and Properties of a Photocurable Coating Based on Waste Cooking Oil
Authors: Liu Mengyu , Liu Yan , Wang Pengyu , Ying Wanying , Liu Qing , Ding Guanzhi , Chen Shuoping
Published: September 2023

In order to provide a cost-effective solution for photocurable coatings and introduce a novel approach for utilizing waste cooking oil (WCO), a type of photocurable resin based on WCO was synthesized. This WCO-based coating incorporated epoxy waste oil methacrylate (EWOMA) derived from WCO and 2-hydroxyethyl methacrylate (HEMA) as a second monomer. It allowed for easy application and rapid curing when exposed to 405 nm purple light. The cured coating, with an optimized composition of EWOMA: HEMA = 4:1 in mass ratio, exhibited favorable mechanical properties (tensile strength: 9.3 MPa, elongation at break: 38.1%) and demonstrated good coating film performance. The coating film exhibited good adhesion (grade 2), high pencil hardness (2H), excellent impact strength (62 kg·cm−1) and low aldehyde content (0.34 μg·g−1). Moreover, it showcased excellent antismudge properties, with a low percentage of water-based ink residual (6.8%), which could be effortlessly and completely removed by wiping. Additionally, the WCO-based coating demonstrated impressive anticorrosive properties, as evidenced by the intact coating film on the tinplate that remained corrosion-free for over 20 days in brine. This cost-effective WCO-based coating offers a viable alternative to commercial photocurable coatings.
Filter Pruning with Convolutional Approximation Small Model Framework
Authors: Intraraprasit Monthon , Chitsobhuk Orachat
Published: September 2023

Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-tuning pruned models necessitates substantial computing power and a large retraining dataset. To restore model performance after pruning each layer, we propose the Convolutional Approximation Small Model (CASM) framework. CASM involves training a compact model with the remaining kernels and optimizing their weights to restore feature maps that resemble the original kernels. This method requires less complexity and fewer training samples compared to basic fine-tuning. We evaluate the performance of CASM on the CIFAR-10 and ImageNet datasets using VGG-16 and ResNet-50 models. The experimental results demonstrate that CASM surpasses the basic fine-tuning framework in terms of time acceleration (3.3× faster), requiring a smaller dataset for performance recovery after pruning, and achieving enhanced accuracy.
Attitudes towards Digital Educational Technologies Scale for University Students: Development and Validation
Authors: Novikova Irina A. , Bychkova Polina A. , Shlyakhta Dmitriy A. , Novikov Alexey L.
Published: September 2023

Numerous studies of the digitalization of higher education show that university students’ attitudes toward digital educational technologies (DETs) are one of the important psychological factors that can hinder or facilitate the optimal implementation of digital technologies in education. International researchers have developed many tools for diagnosing the attitudes of university students toward various aspects of the digitalization of education; however, until recently, similar scales in Russian have not been developed, which determined the purpose of this present research. The proposed version of the Attitudes towards DETs Scale for University Students (ATDETS-US) includes the cognitive, emotional, and behavioral subscales corresponding components of the attitude according to the ACB Model. The validation sample included 317 (160 females and 157 males) bachelor and master students from different Russian universities. Psychometric testing using Cronbach’s Alpha and McDonald’s Omega coefficients, hierarchical factor analysis, and CFA confirm the high internal consistency, reliability of the ATDETS-US and its subscales, and the good fit of the model. ATDETS-US will be used for obtaining reliable data on the attitudes towards DETs in university students, which should be taken into account when designing programs for their psychological support in the educational process and developing their digital competence.
Ab Initio Study of Structural, Electronic, and Thermal Properties of Pt/Pd-Based Alloys
Authors: Botha Louise Magdalene , Ouma Cecil Naphtaly Moro , Obodo Kingsley Onyebuchi , Bessarabov Dmitri Georgievich , Sharypin Denis Lvovich , Varyushin Pyotr Sergeevich , Plastinina Elizaveta Ivanovna
Published: September 2023

Alloys are beneficial in numerous applications since they combine the desirable properties of different metals. In this regard, Pt/Pd alloys have been investigated as a replacement for Pt, which is the standard catalyst used in various catalytic processes. However, there are still gaps in our understanding of the structural, mechanical, and thermodynamic properties of Pt/Pd alloys. This study was conducted using density functional theory (DFT) calculations to investigate the electronic, elasticity, mechanical, and thermodynamic properties of Pt/Pd alloys and compared them to pristine Pt and Pd structures. The results indicate that the considered Pt/Pd alloy structures, PtPd3, PtPd, Pt3Pd, and Pt7Pd, are energetically favourable based on their formation energies. These structures also satisfy Born’s stability criteria and are elastically stable. The phonon density of states showed that the considered Pt/Pd alloy structures are dynamically stable, with no imaginary modes present. Additionally, the Pt atom dominates at lower frequencies, while the Pd atom dominates at higher frequencies, as seen in the phonon band structure. The electronic density of states revealed that the considered Pt/Pd alloy structures have a metallic character and are non-magnetic. These findings contribute to a better understanding of the properties and stability of Pt/Pd alloy structures that are relevant in various fields, including materials science and catalysis.
Effect of the Spatially-Varied Electron Mean Free Path on Vortex Matter in a Superconducting Pb Island Grown on Si (111)
Authors: González Jesús , González Jader , Durán Fernando , Salas Carlos , Gómez Jorge
Published: September 2023

In this work we report theoretical calculations of a superconducting island in a strong vortex confinement regime. The obtained results reveal the evolution of the superconducting condensate with an applied magnetic field, depending on the spatial profile of the electron mean-free path in the sample. The results of this study provide an insight about the emergent superconducting properties under such conditions, using the Ginzburg-Landau numerical simulations where spatial variation of thickness of the island and the corresponding variation of the mean free path, omnipresent in similar structures of Pb grown on Si (111), are taken into account. These results offer a new route to tailor superconducting circuits by nanoengineered mean free path, using for example the controlled ion-bombardment on thin films, benefiting from the here shown impact of the spatially-varying mean free path on the vortex distribution, phase of superconducting order parameter, and the critical fields.
Sulfonato Complex Formation Rather than Sulfonate Binding in the Extraction of Base Metals with 2,2′-Biimidazole: Extraction and Complexation Studies
Authors: Moleko-Boyce Pulleng , Hosten Eric C. , Tshentu Zenixole R.
Published: September 2023

The application of a bidentate aromatic N,N’-donor ligand, 2,2′-biimidazole (BIIMH2), as an extractant in the form of 1-octyl-2,2′-biimidazole (OBIIMH) and related derivatives in the solvent extraction of base metal ions (Mg2+, Mn2+, Fe3+, Fe2+, Co2+, Ni2+, Cu2+ and Zn2+) from an acidic sulfonate medium using dinonylnaphthalene disulfonic acid (DNNDSA) as a synergist was investigated. OBIIMH with DNNDSA as a co-extractant showed a lack of selectivity for base metals ions (Mg2+, Mn2+, Fe3+, Fe2+, Co2+, Ni2+, Cu2+ and Zn2+) despite its similarity with a related bidentate aromatic ligand, 2,2′-pyridylimidazole, which showed preference for Ni(II) ions. The nickel(II) specificity, through stereochemical “tailor-making”, was not achieved as expected and the extracted species were isolated to study the underlying chemistry. The homemade metal sulfonate salts, M(RSO3)2·6H2O (R = Toluene and M2+ = Co2+, Ni2+, Cu2+ and Zn2+), were used as precursors of the metal complexes of BIIMH2 using toluene-4-sulfonic acid as the representative sulfonate. Spectroscopic analysis and single-crystal X-ray analysis supported the formation of similar neutral distorted octahedral sulfonato complexes through the bis coordination of BIIMH2 and two sulfonate ions rather than the formation of cationic complex species with anion coordination of sulfonates. We attributed the observation of similar complex species and the similar stability constants of the bis-complexes in solution as the cause for the lack of pH-metric separation of the later 3d metal ions.
Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT
Authors: Schwarz Ricarda , Bier Georg , Wilke Vera , Wilke Carlo , Taubmann Oliver , Ditt Hendrik , Hempel Johann-Martin , Ernemann Ulrike , Horger Marius , Gohla Georg
Published: September 2023

(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88–92% followed by the more distal MCA and basilar artery with 67–69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available.
An Exploratory Review on the Potential of Artificial Intelligence for Early Detection of Acute Kidney Injury in Preterm Neonates
Authors: Kandasamy Yogavijayan , Baker Stephanie
Published: September 2023

A preterm birth is a live birth that occurs before 37 completed weeks of pregnancy. Approximately 15 million babies are born preterm annually worldwide, indicating a global preterm birth rate of about 11%. Up to 50% of premature neonates in the gestational age (GA) group of <29 weeks’ gestation will develop acute kidney injury (AKI) in the neonatal period; this is associated with high mortality and morbidity. There are currently no proven treatments for established AKI, and no effective predictive tool exists. We propose that the development of advanced artificial intelligence algorithms with neural networks can assist clinicians in accurately predicting AKI. Clinicians can use pathology investigations in combination with the non-invasive monitoring of renal tissue oxygenation (rSO2) and renal fractional tissue oxygenation extraction (rFTOE) using near-infrared spectroscopy (NIRS) and the renal resistive index (RRI) to develop an effective prediction algorithm. This algorithm would potentially create a therapeutic window during which the treating clinicians can identify modifiable risk factors and implement the necessary steps to prevent the onset and reduce the duration of AKI.
A Visual Odometry Pipeline for Real-Time UAS Geopositioning
Authors: Wei Jianli , Yilmaz Alper
Published: September 2023

The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes.
Living Skin Substitute Tissue—Is a Replacement for the Autograft Possible?
Authors: Gibson Angela L. F.
Published: September 2023

The ideal living tissue skin substitute for use in burn injury does not yet exist. The currently available alternatives to autologous skin grafting require an understanding of their characteristics and limitations to make an informed decision of surgical treatment options. In this review, living tissue substitutes are categorized by autologous and allogeneic cell sources and epidermal-only versus bilayered tissue options. A short summary of the tissue composition, clinical data, and indications is provided. Finally, the gap in technology is defined and future potential areas of research are explored.
Lane-Level Map Generation and Management Framework Using Connected Car Data
Authors: Kim Jungseok , Moon Jeongmin , Moon Changjoo
Published: September 2023

This study proposes a lane-level map generation and management framework using connected sensor data to reduce the manpower and time required for producing and updating high-definition (HD) maps. Unlike previous studies that relied on the onboard processing capabilities of vehicles to collect map-constructing elements, this study offloads computing for map generation to the cloud, assigning vehicles solely the role of transmitting sensor data. For efficient data collection, we divide the space into a grid format to define it as a partial map and establish the state of each map and its transition conditions. Lastly, tailored to the characteristics of the road elements composing the map, we propose an automated map generation technique and method for selectively collecting data. The map generation method was tested using data collected from actual vehicles. By transmitting images with an average size of 350 KB, implementation was feasible even with the current 5G upload bandwidth. By utilizing 12,545 elements, we were able to achieve a position accuracy and regression RMSE of less than 0.25 m, obtaining 651 map elements to construct the map. We anticipate that this study will help reduce the manpower and time needed for deploying and updating HD maps.
A Software-Defined Distributed Architecture for Controlling Unmanned Swarm Systems
Authors: An Xuyang , Yu Xuewei , Song Weilong , Han Le , Yang Tingting , Li Zhaodong , Su Zhibao
Published: September 2023

An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex missions, making it crucial in military, transportation, intelligent traffic, and other fields. However, the coupling between the hardware and software of a heterogeneous unmanned swarm can indeed have significant implications for system flexibility, software development and deployment, and hardware maintenance. Over the years, there has been a significant shift from traditional hardware-focused control systems to a greater emphasis on the core software layer. In this paper, a distributed network architecture is proposed to solve this problem, in which hardware resources are abstracted and represented to accomplish standardization and unification by defining a consistent and uniform set of data formats, and a resource pool of hardware data is constructed to realize the function that the number and scale of platforms is irrelevant, the task module can be plug-and-play at any time, and the software can be configured on demand. The resource scheduling of a single platform is achieved through process and thread communication using shared memory, while the resource scheduling of a cross platform is achieved through a network using request and response and subscription and notification. As a result, it can satisfy the development of functional modules in a software-defined mode and gradually improve the intelligence capability of an unmanned swarm. Based on the above architecture, the overall framework of the autonomous navigation system and the collaborative control system has been successfully established. Finally, a hardware-in-the-loop simulation environment is constructed, and the integration and verification of the proposed distributed architecture is carried out by the cooperative formation experiment, which proves the feasibility of this proposal.
Edge Collaborative Online Task Offloading Method Based on Reinforcement Learning
Authors: Sun Ming , Bao Tie , Xie Dan , Lv Hengyi , Si Guoliang
Published: September 2023

With the vigorous development of industries such as self-driving, edge intelligence, and the industrial Internet of Things (IoT), the amount and type of data generated are unprecedentedly large, and users’ demand for high-quality services continues to increase. Edge computing has emerged as a new paradigm, providing storage, computing, and networking resources between traditional cloud data centers and end devices with solid timeliness. Therefore, the resource allocation problem in the online task offloading process is the main area of research. It is aimed at the task offloading problem of delay-sensitive customers under capacity constraints in the online task scenario. In this paper, a new edge collaborative online task offloading management algorithm based on the deep reinforcement learning method OTO-DRL is designed. Based on that, a large number of simulations are carried out on synthetic and real data sets, taking obstacle recognition and detection in unmanned driving as a specific task and experiment. Compared with other advanced methods, OTO-DRL can well realize the increase in the number of tasks requested by mobile terminal users in the field of edge collaboration while guaranteeing the service quality of task requests with higher priority.
Local-Aware Hierarchical Attention for Sequential Recommendation
Authors: Hu Jiahao , Liu Qinxiao , Zhao Fen
Published: September 2023

Modeling the dynamic preferences of users is a challenging and essential task in a recommendation system. Taking inspiration from the successful use of self-attention mechanisms in tasks within natural language processing, several approaches have initially explored integrating self-attention into sequential recommendation, demonstrating promising results. However, existing methods have overlooked the intrinsic structure of sequences, failed to simultaneously consider the local fluctuation and global stability of users’ interests, and lacked user information. To address these limitations, we propose LHASRec (Local-Aware Hierarchical Attention for Sequential Recommendation), a model that divides a user’s historical interaction sequences into multiple sessions based on a certain time interval and computes the weight values for each session. Subsequently, the calculated weight values are combined with the user’s historical interaction sequences to obtain a weighted user interaction sequence. This approach can effectively reflect the local fluctuation of the user’s interest, capture the user’s particular preference, and at the same time, consider the user’s general preference to achieve global stability. Additionally, we employ Stochastic Shared Embeddings (SSE) as a regularization technique to mitigate the overfitting issue resulting from the incorporation of user information. We conduct extensive experiments, showing that our method outperforms other competitive baselines on sparse and dense datasets and different evaluation metrics.
AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition
Authors: Chen Xiaojuan , Cui Wenbo , Zhang Tiantong
Published: September 2023

The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM.
YOLO-SSFS: A Method Combining SPD-Conv/STDL/IM-FPN/SIoU for Outdoor Small Target Vehicle Detection
Authors: Gu Zhenchao , Zhu Kai , You Shangtao
Published: September 2023

As a vital part of autonomous driving, vehicle detection, especially for outdoor small target vehicles, has attracted great attention from researchers during recent years. To ameliorate the difficulty in accurately identifying outdoor small vehicle targets in dense environments, this paper proposes a new structure named YOLO-SSFS, in which SPD-Conv, a small target detection layer (STDL), the Improved Feature Pyramid Network structure (IM-FPN), and the SCYLLA-IoU (SIoU) loss function are introduced. Firstly, the multi-scale fusion module of the original algorithm is improved by adding a detection layer for smaller targets. This detection layer preserves shallow semantic information, which helps to refine the algorithm’s detection accuracy for small targets. Then, a new Convolutional Neural Network (CNN) building block named SPD-Conv is constructed to replace the pooling layers and convolutional layers in the YOLOv5 algorithm, which reduces information loss, ensures the original fine-grained details of the image and improves the learning ability. Afterwards, a new FPN structure is created to retain more information conducive to small target detection during the feature fusion process so as to enhance the robustness of the method. Finally, to speed up the convergence of the loss function, the SIoU loss function is introduced to replace Complete-IoU (CIoU) in the original algorithm. In order to verify the authenticity of the improved algorithm, we conduct a series of experiments on the VisDrone dataset and perform comparative analyses of the results. The results obtained demonstrate that compared with the original YOLOv5, the proposed model performs better in small target detection. The mean average precision (mAP) is 83.07%, which is 7.63% higher than that for YOLOv5, while the detection speed reaches 52 frames per second (FPS), meeting the requirements for real-time detection.
A Reinforcement Learning List Recommendation Model Fused with Graph Neural Networks
Authors: Lv Zhongming , Tong Xiangrong
Published: September 2023

Existing list recommendation methods present a list consisting of multiple items for feedback recommendation to user requests, which has the advantages of high flexibility and direct user feedback. However, the structured representation of state data limits the embedding of users and items, making them isolated from each other, missing some useful infomation for recommendation. In addition, the traditional non-end-to-end learning series takes a long time and accumulates errors. During the model training process, the results of each task can easily affect the next calculation, thus affecting the entire training effect. Aiming at the above problems, this paper proposes a Reinforcement Learning List Recommendation Model Fused with a Graph Neural Network, GNLR. The goal of this model is to maximize the recommendation effect while ensuring that the list recommendation system accurately analyzes user preferences to improve user experience. To this end, firstly, we use an user–item bipartite graph and Graph Neural Network to aggregate neighborhood information for users and items to generate graph structured representation; secondly, we adopt an attention mechanism to assign corresponding weights to neighborhood information to reduce the influence of noise nodes in heterogeneous information networks; finally, we alleviate the problems of traditional non-end-to-end methods through end-to-end training methods. The experimental results show that the method proposed in this paper can alleviate the above problems, and the recommendation hit rate and accuracy rate increase by about 10%.
Incipient Fault Feature Extraction of Rolling Bearing Based on Signal Reconstruction
Authors: Lv Xu , Zhou Fengxing , Li Bin , Yan Baokang
Published: September 2023

In the incipient fault vibration signals of rolling bearings, weak fault features are easily submerged in strong background noise and difficult to be extracted. The sparse decomposition method can perform well in the extraction of weak fault features, but the low signal-to-noise ratio (SNR) would cause excessive decomposition. To enhance the fault features and maintain the time–frequency structure of fault impulses, a novel incipient fault feature extraction of rolling bearing based on signal reconstruction is proposed. Firstly, the Teager energy operator (TEO) is used to obtain the envelope of the impulse components in the vibration signal, which is also sensitive to noise and would be seriously affected by strong noise. Secondly, a Savitzky–Golay (S–Golay) filter based on the particle swarm optimization (PSO) algorithm is adopted to suppress the noise in the TEO envelope and generate a smooth envelope signal. Finally, the fault signal is reconstructed by the multiplication of the filtered TEO envelope signal and the original signal. The reconstructed signal can maintain the structural characteristics of the original fault impact signal and can provide reliable feature enhancement signals for further sparse decomposition, multi-source vibration separation, and other operations. Simulation signals and experiments verify the effectiveness of this method in extracting early fault features under low SNRs.
A Two-Stream 3D-CNN Network Based on Pressure Sensor Data and Its Application in Gait Recognition
Authors: Hu Chunfen , Huan Zhan , Dong Chenhui
Published: September 2023

Accurate diagnosis of Parkinson’s disease (PD) is challenging in clinical medicine. To reduce the diagnosis time and decrease the diagnosis difficulty, we constructed a two-stream Three-Dimensional Convolutional Neural Network (3D-CNN) based on pressure sensor data. The algorithm considers the stitched surface of the feet as an “image”; the geometric positions of the pressure sensors are considered as the “pixel coordinates” and combines the time dimension to form 3D data. The 3D-CNN is used to extract the spatio-temporal features of the gait. In addition, a twin network of 3D-CNN with shared parameters is used to extract the spatio-temporal features of the left and right foot respectively to further obtain symmetry information, which not only extracts the spatial information between the multiple sensors but also obtains the symmetry features of the left and right feet at different spatio-temporal locations. The results show that the proposed model is superior to other advanced methods. Among them, the average accuracy of Parkinson’s disease diagnosis is 99.07%, and the average accuracy of PD severity assessment is 98.02%.
Benchmarking Electrolytes for the Solid Oxide Electrolyzer Using a Finite Element Model
Authors: Srinivas Sriram , Dhanushkodi Shankar Raman , Chidambaram Ramesh Kumar , Skrzyniowska Dorota , Korzen Anna , Taler Jan
Published: September 2023

The demand for green hydrogen is increasing, as it is estimated to reduce ten percent of total global green-house-gas emissions from fossil fuel. The solid oxide electrolysis cell (SOEC) is an electrochemical energy-conversion device (EECD) that produces green hydrogen via steam electrolysis. It is preferred to other EECDs for clean hydrogen production owing to its high efficiency, robust kinetics, and lack of precious-metal requirements for cell construction. Herein, we report a Multiphysics model describing the transport phenomena in the SOEC. The governing equations used in the model include a thorough description of the electrode kinetics and of the behavior of the three electrode–electrolyte interfaces in the cell. For the first time, the effect of the scandium-doped zirconia (SCGZ), yttrium-stabilized zirconia (YSZ), and gadolinium-doped ceria (GDC) electrolytes was modeled at different temperatures and pressures. By linking the convection and diffusion equations with the Butler–Volmer at shorter scales, a true representation of the cell operation was simulated. Our models show a R2 value of over 0.996 in predicting the cell-polarization curves and electrochemical properties at the given operating conditions. The impedance of the SCGZ was 0.0240 Ohm.cm2. This value was two- and four-fold lower than the values of the YSZ and GDC, respectively. Furthermore, our theoretical findings of both the polarization data and the impedance were in good agreement with the experimental data.
Performance Evaluation of a Fuel Cell mCHP System under Different Configurations of Hydrogen Origin and Heat Recovery
Authors: Gabana Pedro , Tinaut Francisco V. , Reyes Miriam , Domínguez José Ignacio
Published: September 2023

Motivated by the growing importance of fuel cell systems as the basis for distributed energy-generation systems, this work considers a micro-combined heat and power (mCHP) generation system based on a fuel cell integrated to satisfy the (power and thermal) energy demands of a residential application. The main objective of this work is to compare the performance of several CHP configurations with a conventional alternative, in terms of primary energy consumption, greenhouse gas (GHG) emissions and economic viability. For that, a simulation tool has been developed to easily estimate the electrical and thermal energy generated by a hydrogen fuel cell, and all associated results related to the hydrogen production alternatives: excess or shortfall of electrical and thermal energy, CO2 emission factor, overall performance, operating costs, payback period, etc. A feasibility study of different configuration possibilities of the micro-CHP generation system has been carried out considering different heat-to-power ratios (HPRs) in the possible demands, and analyzing primary energy savings, CO2 emissions savings and operating costs. An extensive parametric study has been performed to analyze the effect of the fuel cell’s electric power and number of annual operation hours as parameters. Finally, a study of the influence of the configuration parameters on the final results has been carried out. Results show that, in general, configurations using hydrogen produced from natural gas save more primary energy than configurations with hydrogen production from electricity. Furthermore, it is concluded that the best operating points are those in which the generation system and the demand have similar HPR. It has also been estimated that a reduction in renewable hydrogen price is necessary to make these systems profitable. Finally, it has been determined that the most influential parameters on the results are the fuel cell electrical efficiencies, hydrogen production efficiency and hydrogen cost.
Improving the Energy Performance of a Household Using Solar Energy: A Case Study
Authors: Mârza Carmen , Moldovan Raluca , Corsiuc Georgiana , Chisăliță Gelu
Published: September 2023

In the global context of energy transition from fossil fuels to renewable sources of energy, solar energy plays a key role in electricity generation, having the highest annual growth in the last years. This case study focuses on improving the overall energy efficiency of a household through thermal retrofitting, harnessing solar energy with photovoltaic (PV) systems and using air-source (ASHP) or ground-source (GSHP) heat pumps. The electricity generated by the PV systems is used to power the heat pumps and all other electricals of the household. The simulations for the ASHP and GSHP systems were conducted with GeoT*SOL software, while for electricity generation by PV systems, PV*SOL Premium software was used. The comparative results show: a decrease in the heating load to about 51.56%; an annual heating requirement of 53.88% of the normed one; an energy consumption index of about 58.34 kWh/m2·year; an annual energy consumption reduction of 88% for ASHP and 91% for GSHP, compared with the current gas heating system; 34% of the household consumption was covered by the PV system in the case of using ASHP and 36% for GSHP; and lower operating costs by 47% for the PV system with ASHP and 53% for the PV system with GSHP.