bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Results of Anodic and Thermal Oxidation Surface Modification on Titanium and Tantalum Implants
Authors: Pinter Gabor Tamas , Trimmel Balint , Kivovics Marton , Huszar Tamas , Nemeth Zsolt , Szabo Gyorgy
Published: April 2023

Tantalum and titanium are two of the most popular materials used in dental implants today. These materials are highly biocompatible, durable, and long-lasting, making them ideal for use in dental and maxillofacial implants. The ceramic oxide layer that covers the surface of titanium and tantalum implants (TiO2,TaO2) is formed through an electrochemical growth from the inside of the metal and subsequently altered through heat treatment. The aim of this retrospective study was to evaluate the long-term survival of the oxide ceramic-coated titanium dental implants. The secondary purpose was to evaluate the production process and the cost of the coated tantalum and titanium implants, and to complete these retrospective investigations with the results of our previous work concerning the titanium oxide coating. The structural, physical, and chemical properties as well as the corrosion resistance of the Ti/TiO2 surface were investigated; XPS, SIMS, and XRD techniques were used. Patients who received tantalum oxide-coated (Ta/TaO2) dental implants, titanium oxide-coated (Ti/TiO2) dental implants, or titanium oxide-coated (Ti/TiO2) osteosynthesis plates for rehabilitation at the Department of Oro-Maxillofacial Surgery and Stomatology, Semmelweis University between 1995 and 2005 were included in this retrospective study. Data collection was performed between June 2021 and December 2021. The cost of the tantalum implant was 25 times that of the titanium implant. Only 21 implants were inserted in 10 patients. The survival rate (min. 16 years) was 95%. Twelve patients with a total of sixty-four TiO2-coated implants were observed. The mean implant survival rate was 95%. Our conclusion was that, regardless of the shape of the implant, the Ti/TiO2 coating proved its excellent durability over the years. The “tantalum issue” is increasingly relevant nowadays, since instead of implants made of pure tantalum metal, implants with a porous tantalum surface have come to the fore.
Unciolidae of Deep-Sea Iceland (Amphipoda, Crustacea)
Authors: Hughes Lauren E. , Lörz Anne-Nina
Published: April 2023

An overview of North Atlantic Unciola Say, 1818 is provided along with the description of two new species, U. conchicola sp. nov. and U. icelandica sp. nov. Both deep-sea species were collected living sympatrically at an almost 2000 m depth in the Iceland Basin, southwest of Iceland, each showing a vertical distribution of more than 1 km. In addition to the wide depth range, U. icelandica sp. nov. also shows a broad geographic distribution of more than 1000 km. Unciola conchicola sp. nov. was seen to attach its tubular domicile to the inner cavity of a gastropod shell; however, no specific lifestyle information is known for the species U. icelandica sp. nov. Both species present novel characters for the genus: in U. conchicola sp. nov., the epimeral plates 1–3 have acute projections angled ventrally, while in U. icelandica sp. nov., the male antenna 2 pedunclar article 2 is developed into a distinct phalange. The large amount of unciolid material allowed for ontogentic studies. The material of U. planipes Norman, 1867 was investigated. Changes from juvenile to adult specimens of Neohela monstrosa (Boeck, 1861) are discussed. A key is provided to the eight North East Atlantic species of Unciola.
Use of Autotransfusion following Percutaneous Thrombectomy for Cardiogenic Shock Due to Pulmonary Embolism in a Single Session—A Case Report
Authors: Haertel Franz , Baez Laura , Franz Marcus , Bogoviku Jurgen , Klein Friederike , Dannberg Gudrun , Schulze P. Christian , Möbius-Winkler Sven
Published: April 2023

A 64-year-old male patient was admitted to the catheterization laboratory with a suspected myocardial infarction and in cardiogenic shock. Upon further investigation, a massive bilateral pulmonary embolism with signs of right heart dysfunction was discovered, leading to a decision to perform a direct interventional treatment with a thrombectomy device for thrombus aspiration. The procedure was successful in removing almost the entirety of the thrombotic material from the pulmonary arteries. The patient’s hemodynamics stabilized and oxygenation improved instantly. The procedure required a total of 18 aspiration cycles. Each aspiration contained approx. 60 mL blood amounting to a total of approx. 1080 mL of blood. During the procedure, a mechanical blood salvage system was used to resupply 50% of the blood via autotransfusion that would otherwise have been lost. The patient was transferred to the intensive care unit for post-interventional care and monitoring. A CT angiography of the pulmonary arteries after the procedure confirmed the presence of only minor residual thrombotic material. The patient’s clinical, ECG, echocardiographic, and laboratory parameters returned to normal or near normal ranges. The patient was discharged shortly after in stable conditions on oral anticoagulation.
Multimodality Medical Image Fusion Using Clustered Dictionary Learning in Non-Subsampled Shearlet Transform
Authors: Diwakar Manoj , Singh Prabhishek , Singh Ravinder , Sisodia Dilip , Singh Vijendra , Maurya Ankur , Kadry Seifedine , Sevcik Lukas
Published: April 2023

Imaging data fusion is becoming a bottleneck in clinical applications and translational research in medical imaging. This study aims to incorporate a novel multimodality medical image fusion technique into the shearlet domain. The proposed method uses the non-subsampled shearlet transform (NSST) to extract both low- and high-frequency image components. A novel approach is proposed for fusing low-frequency components using a modified sum-modified Laplacian (MSML)-based clustered dictionary learning technique. In the NSST domain, directed contrast can be used to fuse high-frequency coefficients. Using the inverse NSST method, a multimodal medical image is obtained. Compared to state-of-the-art fusion techniques, the proposed method provides superior edge preservation. According to performance metrics, the proposed method is shown to be approximately 10% better than existing methods in terms of standard deviation, mutual information, etc. Additionally, the proposed method produces excellent visual results regarding edge preservation, texture preservation, and more information.
Salivary ATR-FTIR Spectroscopy Coupled with Support Vector Machine Classification for Screening of Type 2 Diabetes Mellitus
Authors: Caixeta Douglas Carvalho , Carneiro Murillo Guimarães , Rodrigues Ricardo , Alves Deborah Cristina Teixeira , Goulart Luís Ricardo , Cunha Thúlio Marquez , Espindola Foued Salmen , Vitorino Rui , Sabino-Silva Robinson
Published: April 2023

The blood diagnosis of diabetes mellitus (DM) is highly accurate; however, it is an invasive, high-cost, and painful procedure. In this context, the combination of ATR-FTIR spectroscopy and machine learning techniques in other biological samples has been used as an alternative tool to develop a non-invasive, fast, inexpensive, and label-free diagnostic or screening platform for several diseases, including DM. In this study, we used the ATR-FTIR tool associated with linear discriminant analysis (LDA) and a support vector machine (SVM) classifier in order to identify changes in salivary components to be used as alternative biomarkers for the diagnosis of type 2 DM. The band area values of 2962 cm−1, 1641 cm−1, and 1073 cm−1 were higher in type 2 diabetic patients than in non-diabetic subjects. The best classification of salivary infrared spectra was by SVM, showing a sensitivity of 93.3% (42/45), specificity of 74% (17/23), and accuracy of 87% between non-diabetic subjects and uncontrolled type 2 DM patients. The SHAP features of infrared spectra indicate the main salivary vibrational modes of lipids and proteins that are responsible for discriminating DM patients. In summary, these data highlight the potential of ATR-FTIR platforms coupled with machine learning as a reagent-free, non-invasive, and highly sensitive tool for screening and monitoring diabetic patients.
Deep Neural Network Augments Performance of Junior Residents in Diagnosing COVID-19 Pneumonia on Chest Radiographs
Authors: Feng Yangqin , Sim Zheng Ting Jordan , Xu Xinxing , Bee Kun Chew , Ong Tien En Edward , Irawan Tan Wee Jun Hendra , Ting Yonghan , Lei Xiaofeng , Chen Wen-Xiang , Wang Yan , Li Shaohua , Cui Yingnan , Wang Zizhou , Zhen Liangli , Liu Yong , Siow Mong Goh Rick , Tan Cher Heng
Published: April 2023

Chest X-rays (CXRs) are essential in the preliminary radiographic assessment of patients affected by COVID-19. Junior residents, as the first point-of-contact in the diagnostic process, are expected to interpret these CXRs accurately. We aimed to assess the effectiveness of a deep neural network in distinguishing COVID-19 from other types of pneumonia, and to determine its potential contribution to improving the diagnostic precision of less experienced residents. A total of 5051 CXRs were utilized to develop and assess an artificial intelligence (AI) model capable of performing three-class classification, namely non-pneumonia, non-COVID-19 pneumonia, and COVID-19 pneumonia. Additionally, an external dataset comprising 500 distinct CXRs was examined by three junior residents with differing levels of training. The CXRs were evaluated both with and without AI assistance. The AI model demonstrated impressive performance, with an Area under the ROC Curve (AUC) of 0.9518 on the internal test set and 0.8594 on the external test set, which improves the AUC score of the current state-of-the-art algorithms by 1.25% and 4.26%, respectively. When assisted by the AI model, the performance of the junior residents improved in a manner that was inversely proportional to their level of training. Among the three junior residents, two showed significant improvement with the assistance of AI. This research highlights the novel development of an AI model for three-class CXR classification and its potential to augment junior residents’ diagnostic accuracy, with validation on external data to demonstrate real-world applicability. In practical use, the AI model effectively supported junior residents in interpreting CXRs, boosting their confidence in diagnosis. While the AI model improved junior residents’ performance, a decline in performance was observed on the external test compared to the internal test set. This suggests a domain shift between the patient dataset and the external dataset, highlighting the need for future research on test-time training domain adaptation to address this issue.
Prevalence and Risk Factors of Hypertension in Hargeisa, Somaliland: A Hospital-Based Cross-Sectional Study
Authors: Nooh Faisal , Ali Mohamed I. , Chernet Afona , Probst-Hensch Nicole , Utzinger Jürg
Published: April 2023

Hypertension is the leading risk factor for cardiovascular diseases and represents a major public health challenge worldwide. There is a paucity of information regarding the hypertension status of adults in Somaliland. We aimed to assess the magnitude of, and factors associated with, hypertension among adult patients seeking care at Hargeisa group hospital in Hargeisa city, Somaliland. We conducted a health facility-based cross-sectional study enrolling adult outpatients. We used the World Health Organization (WHO) STEPwise surveillance approach to obtain patient information. A total of 319 participants (54.2% males; mean age 40.4 ± 14.0 years) had complete data records. The prevalence of hypertension was 22.6% (95% confidence interval; 18.2–27.6%). The prevalence of hypertension increased with age and was higher in males (24.9%) than in females (19.9%). Age, cholesterolaemia and obesity were significantly associated with hypertension. Separate analyses for females and males revealed that obesity was significantly associated with hypertension in females but not in males. On the contrary, cholesterolaemia was significantly associated with hypertension in males but not in females. We found a high prevalence of hypertension and multiple risk factors for non-communicable diseases (NCDs) in outpatients seeking care in Hargeisa. Our findings emphasise the need for enhanced focus on the prevention and management of NCDs in Somaliland.
A Network Model Approach to International Aid
Authors: Scattergood Joe , Bishop Steven
Published: April 2023

Decisions made by international aid donors regarding the allocation of their aid budgets to recipients can be mathematically modelled using network theory. The many countries and multilateral organisations providing developmental aid, mostly to developing countries, have numerous competing or conflicting interests, biases and motivations, often obscured by a lack of transparency and confused messaging. Using network theory, combined with other mathematical methods, these inter-connecting and inter-dependent variables are identified, revealing the complicated properties and dynamics of the international aid system. Statistical techniques are applied to the vast amount of available, open data to first understand the complexities and then identify the key variables, focusing principally on bilateral aid flows. These results are used to create a weighted network model which is subsequently adapted for use by a hypothetical aid recipient. By incorporating modern portfolio theory into this weighted network model and taking advantage of a donor’s reasons for allocating their aid budgets to that recipient, a simulation is carried out treating the problem as an optimal investment portfolio of aid determinant ‘assets’ which illustrates how a recipient can maximise their aid receipts. Suggestions are also made for further uses and adaptations of this weighted network model.
DARE: Distill and Reinforce Ensemble Neural Networks for Climate-Domain Processing
Authors: Xiang Kun , Fujii Akihiro
Published: April 2023

Natural-language processing is well positioned to help stakeholders study the dynamics of ambiguous Climate Change-related (CC) information. Recently, deep neural networks have achieved good results on a variety of NLP tasks depending on high-quality training data and complex and exquisite frameworks. This raises two dilemmas: (1) the networks are highly reliant on powerful hardware devices and processing is time-consuming, which is not only inconducive to execution on edge devices but also leads to resource consumption. (2) Obtaining large-scale effective annotated data is difficult and laborious, especially when it comes to a special domain such as CC. In this paper, we propose a CC-domain-adapted BERT distillation and reinforcement ensemble (DARE) model for tackling the problems above. Specifically, we propose a novel data-augmentation strategy which is a Generator-Reinforced Selector collaboration network for countering the dilemma of CC-related data scarcity. Extensive experimental results demonstrate that our proposed method outperforms baselines with a maximum of 26.83% on SoTA and 50.65× inference time speed-up. Furthermore, as a remedy for the lack of CC-related analysis in the NLP community, we also provide some interpretable conclusions for this global concern.
Smartphone-Based Indoor Localization Systems: A Systematic Literature Review
Authors: Naser Rana Sabah , Lam Meng Chun , Qamar Faizan , Zaidan B. B.
Published: April 2023

These recent years have witnessed the importance of indoor localization and tracking as people are spending more time indoors, which facilitates determining the location of an object. Indoor localization enables accurate and reliable location-based services and navigation within buildings, where GPS signals are often weak or unavailable. With the rapid progress of smartphones and their growing usage, smartphone-based positioning systems are applied in multiple applications. The smartphone is embedded with an inertial measurement unit (IMU) that consists of various sensors to determine the walking pattern of the user and form a pedestrian dead reckoning (PDR) algorithm for indoor navigation. As such, this study reviewed the literature on indoor localization based on smartphones. Articles published from 2015 to 2022 were retrieved from four databases: Science Direct, Web of Science (WOS), IEEE Xplore, and Scopus. In total, 109 articles were reviewed from the 4186 identified based on inclusion and exclusion criteria. This study unveiled the technology and methods utilized to develop indoor localization systems. Analyses on sample size, walking patterns, phone poses, and sensor types reported in previous studies are disclosed in this study. Next, academic challenges, motivations, and recommendations for future research endeavors are discussed. Essentially, this systematic literature review (SLR) highlights the present research overview. The gaps identified from the SLR may assist future researchers in planning their research work to bridge those gaps.
Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement
Authors: Fang Zheng , Wang Tingjun
Published: April 2023

Industrial CT is useful for defect detection, dimensional inspection and geometric analysis, while it does not meet the needs of industrial mass production because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT-reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor and observe the objects in different angles of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time this source-detector layout strategy has been adopted. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. Our experiment shows that the reconstruction resulting in undersampled projections is highly enhanced using a deep-learning neural network which meets the demand of non-destructive testing. Meanwhile, our proposed system structure can perform quick scans and reconstructions on larger objects. It solves the pain points of limited scan size and slow scanning speed of existing industrial CT scans.
An Edge Intelligent Method for Bearing Fault Diagnosis Based on a Parameter Transplantation Convolutional Neural Network
Authors: Ding Xiang , Wang Hang , Cao Zheng , Liu Xianzeng , Liu Yongbin , Huang Zhifu
Published: April 2023

A bearing is a key component in rotating machinery. The prompt monitoring of a bearings’ condition is critical for the reduction of mechanical accidents. With the rapid development of artificial intelligence technology in recent years, machine learning-based intelligent fault diagnosis (IFD) methods have achieved remarkable success in the field of bearing condition monitoring. However, most algorithms are developed based on computer platforms that focus on analyzing offline, rather than real-time, signals. In this paper, an edge intelligence diagnosis method called S-AlexNet, which is based on a parameter transplantation convolutional neural network (CNN), is proposed. The method deploys the lightweight IFD method in a low-cost embedded system to monitor the bearing status in real time. Firstly, a lightweight IFD algorithm model is designed for embedded systems. The model is trained on a PC to obtain optimal parameters, such as the model’s weights and bias. Finally, the optimal parameters are transplanted into the embedded system model to identify the bearing status on the edge side. Two datasets were used to validate the performance of the proposed method. The validation using the CWRU dataset shows that the proposed method achieves an average prediction accuracy of 94.4% on the test set. The validation using self-built data shows that the proposed method can identify bearing operating status in embedded systems with an average prediction accuracy of 99.81%. The results indicate that the proposed method has the advantages of high recognition accuracy, low model complexity, low cost, and high portability, which allow for the simple and effective implementation of the edge IFD of bearings in embedded systems.
Registered Data-Centered Lab Management System Based on Data Ownership Safety Architecture
Authors: Zheng Xuying , Miao Fang , Udomwong Piyachat , Chakpitak Nopasit
Published: April 2023

University and college laboratories are important places to train professional and technical personnel. Various regulatory departments in colleges and universities still rely on traditional laboratory management in research projects, which are prone to problems such as untimely information and data transmission. The present study aimed to propose a new method to solve the problem of data islands, explicit ownership, conditional sharing, data safety, and efficiency during laboratory data management. Hence, this study aimed to develop a data-centered lab management system that enhances the safety of lab data management and allows the data owners of the labs to control data sharing with other users. The architecture ensures data privacy by binding data ownership with a person using a key management method. To achieve data flow safely, data ownership conversion through the process of authorization and confirmation was introduced. The designed lab management system enables laboratory regulatory departments to receive data in a secure form by using this platform, which could solve data sharing barriers. Finally, the proposed system was applied and run in different server environments by implementing data security registration, authorization, confirmation, and conditional sharing using SM2, SM4, RSA, and AES algorithms. The system was evaluated in terms of the execution time for several lab data with different sizes. The findings of this study indicate that the proposed strategy is safe and efficient for lab data sharing across domains.
Optimal Layout of Electric Vehicle Charging Station Locations Considering Dynamic Charging Demand
Authors: Li Yongjing , Pei Wenhui , Zhang Qi , Xu Di , Ma Hao
Published: April 2023

This paper proposes an optimization method for electric vehicle charging station locations considering dynamic charging demand. Firstly, the driving characteristics and charging characteristics of the electric vehicle are obtained based on the driving trajectory of the electric vehicle, and the charging demand is predicted using a Monte Carlo simulation. Then a mathematical model with the goal of minimizing the overall cost is constructed, and the impact on carbon emissions is considered in the model. In order to better solve the location model, an improved whale optimization algorithm based on a hybrid strategy is proposed. Finally, the location problem of Shenzhen electric taxi charging stations is analyzed as an example. The results show that when the number of charging stations is set to 19, the comprehensive cost is the smallest and the energy saving and emission reduction effect is good. The improved whale optimization algorithm also has higher solution accuracy and convergence speed than other classical algorithms.
A Family of Automatic Modulation Classification Models Based on Domain Knowledge for Various Platforms
Authors: Wei Shengyun , Wang Zhenyi , Sun Zhaolong , Liao Feifan , Li Zhen , Zou Li , Mi Haibo
Published: April 2023

Identifying the modulation type of radio signals is challenging in both military and civilian applications such as radio monitoring and spectrum allocation. This has become more difficult as the number of signal types increases and the channel environment becomes more complex. Deep learning-based automatic modulation classification (AMC) methods have recently achieved state-of-the-art performance with massive amounts of data. However, existing models struggle to achieve the required level of accuracy, guarantee real-time performance at edge devices, and achieve higher classification performance on high-performance computing platforms when deployed on various platforms. In this paper, we present a family of AMC models based on communication domain knowledge for various computing platforms. The higher-order statistical properties of signals, customized data augmentation methods, and narrowband convolution kernels are the domain knowledge that is specifically employed to the AMC task and neural network backbone. We used separable convolution and depth-wise convolution with very few residual connections to create our lightweight model, which has only 4.61k parameters while maintaining accuracy. On the four different platforms, the classification accuracy and inference time outperformed those of the existing lightweight models. Meanwhile, we use the squeeze-and-excitation attention mechanism, channel shuffle module, and expert feature parallel branch to improve the classification accuracy. On the three most frequently used benchmark datasets, the high-accuracy models achieved state-of-the-art average accuracies of 64.63%, 67.22%, and 65.03%, respectively. Furthermore, we propose a generic framework for evaluating the complexity of deep learning models and use it to comprehensively assess the complexity strengths of the proposed models.
Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN
Authors: Logeshwaran Jaganathan , Kiruthiga Thangavel , Kannadasan Raju , Vijayaraja Loganathan , Alqahtani Ali , Alqahtani Nayef , Alsulami Abdulaziz A.
Published: April 2023

In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability.
Passive IoT Optical Fiber Sensor Network for Water Level Monitoring with Signal Processing of Feature Extraction
Authors: Lee Hoon-Keun , Kim Youngmi , Park Sungbaek , Kim Joonyoung
Published: April 2023

This paper presents a real-time remote water level monitoring system based on dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network for the application of the Internet of Things (IoT). This network employs a broadband light source based on amplified spontaneous emission (ASE) as a seed light. This ASE light is spectrum-sliced by an athermal type arrayed waveguide grating (200 GHz × 16 channel), then distributed towards multiple sensing units (SU). Here, 16 SUs are installed vertically at the specified height in the water pool according to the design specification (i.e., spatial resolution). Then, each SU reflects an optical spectrum having a different reflection coefficient depending on the surrounding medium (e.g., air or water). By measuring these reflected optical spectra with an optical spectrum analyzer, the water level can be easily recognized in real time. However, as the sensing distance increases, system performance is severely degraded due to the Rayleigh Back-Scattering of the ASE light. As a result, the remote sensing capability is limited at a short distance (i.e., <10 km). To overcome this limitation, we propose a simple signal processing technique based on feature extraction of received optical spectra, which includes embedding a peak detection algorithm with a signal validation check. For the specific, the proposed signal processing performs the peak power detection, signal quality monitoring, and determination/display of the actual water level through three function modules, i.e., data save/load module, signal processing module, and Human–Machine Interface display module. In particular, the signal quality of the remote sensing network can be easily monitored through several factors, such as the number of spectral peaks, the wavelength spacing between neighboring peaks and the pattern of detected peak power. Moreover, by using this validation check algorithm, it is also possible to diagnose various error types (such as peak detection error, loss of data and so on) according to the pattern of measured optical spectra. As a result, the IoT sensor network can recognize 17 different level statuses for the water level measurement from a distance of about 25 km away without active devices such as optical amplifiers (i.e., passive remote sensing).
The Factors Affecting Digital Transformation in Vietnam Logistics Enterprises
Authors: Le Viet Ha , Dang Quoc Huu
Published: April 2023

In the context of the industrial revolution 4.0 that is firmly taking place globally, the digital transformation process is considered a revolution that changes the operating and business model. In Vietnam, logistics is one of eight areas that need to be prioritized in the national conversion program to 2025, the orientation towards 2030 by the Prime Minister under Decision No. 749/QĐ-TTg. Digital transformation is an essential solution that helps businesses improve their competitiveness, increase labor productivity, sustainably develop businesses, and integrate with the global economy. This study analyzes the influencing factors of digital transformation and the situation in Vietnam’s logistics enterprises. This paper used a qualitative research method carried out through direct interviews with 20 digital transformation experts in the field of logistics about the current situation, adjusting models and scales, and discussing research results. Quantitative research was conducted online through 258 survey questionnaires of logistics enterprises in the country. The authors performed descriptive statistics, tested the scale, analyzed EFA using SPSS software, and tested the research hypotheses. Research results indicate that five factors—managers, digital transformation human resources, information technology, investment cost, and support services for digital transformation—affect the digital conversion activity in logistics enterprises. Afterward, the research team proposed solutions to promote this operation in Vietnam’s logistics enterprises, contributing to implementing critical tasks of the government’s digital transformation.
Radar Signal Processing Architecture for Early Detection of Automotive Obstacles
Authors: Petrović Nikola , Petrović Marija , Milovanović Vladimir
Published: April 2023

With the mass adoption of automotive vehicles, road accidents have become a common occurrence. One solution to this problem is to employ safety systems that can provide early warning for potential accidents. These systems alert drivers to brake or take active control of a vehicle in order to make braking safer and smoother, thereby protecting drivers and all other road traffic participants. Most such safety systems utilize millimeter-wave radar as primary sensors, and one of the main challenges is real-time data processing from multiple sensors integrated into a single passenger car. When an obstacle is too close to a vehicle, often there is insufficient time to run higher-order digital signal processing algorithms; hence, the decision to brake must be made based on low-level hardware processing only. For that purpose, a hardware generator for the early detection of automotive obstacles that does not impede the operation of higher-order signal processing algorithms is described. The proposed generator is captured in the Chisel hardware design language and a method for reducing the overall ranging latency is presented. The system constraints are calculated using an exemplary radar front-end and the proposed generator parameters. The obtained analytical results are experimentally confirmed with a prototype composed of a typical industrial radar front-end while the signal processing back-end instance of the described generator was implemented on an FPGA board. The measurements demonstrate that with the fast proximity alert, objects can be detected in less than a hundred microseconds, thus considerably reducing the system reaction delay and braking distance.
Verification and Analysis of the Problem-Dependent Multigroup Macroscopic Cross-Sections for Shielding Calculations
Authors: Zhao Xu , Shi Shengchun , Xu Wen , Hu Jiaju , Wu Jun , Zhang Bin
Published: April 2023

Multigroup constants are the foundation of neutron and photon transport problems, and the accuracy of multigroup cross-sections has a significant impact on shielding calculation. Challenges have arisen in generating accurate multigroup macroscopic cross-sections for some problems using the widely used cross-section processing code TRANSX 2.15. We developed the multigroup neutron-photon macroscopic cross-section processing module in the shielding transport code ARES. The module is capable of handling the neutron-photon coupled master libraries in MATXS format and providing the problem-dependent multigroup macroscopic cross-sections tailored to each specific shielding physics problem. The self-designed problems with a single nuclide, as well as the iron and OKTAVIAN experiments, are used to verify and analyze the accuracy of neutron and photon macroscopic cross-sections. Results indicate that the macroscopic cross-sections, neutron flux and neutron-photon leakage spectrum obtained by the MGMXS module are consistent with corresponding reference values. As for the JANUS Phase I fixed source shielding benchmark, the relative differences in the reaction rate between the calculation results and experimental data are within 20%. The module provides better problem-dependent multigroup macroscopic cross-sections for neutron and photon shielding calculations that satisfy the accuracy requirements of engineering applications.
One-Dimensional Fluid Dynamic Modeling of a Gas Bladder Hydraulic Damper for Pump Flow Pulsation
Authors: Casoli Paolo , Vescovini Carlo Maria , Rundo Massimo
Published: April 2023

Positive displacement pumps produce pressure ripple that can be reduced with the attenuation of the generated flow ripple. This paper presents the application of a gas bladder hydraulic damper with the aim of reducing the oscillations of the delivery flow rate of positive displacement machines. This work is focused on the development of a 1D fluid dynamic model of the damper, which is based on the fundamental fluid motion equations applied for a mono-dimensional flow. In order to represent the fluid flow inside the damper, a particular evaluation of the sound speed has been implemented. Experimental tests have been performed involving an axial piston pump with the damper installed in the delivery pipe to validate the model; tests were carried out at different pump working conditions and with different gas precharge pressure of the damper. The test results confirmed the effectiveness of the device, and the comparison with numerical results demonstrated a good agreement. Simulations have been carried out to investigate the influence of various parameters on damper effectiveness.
Integrating Prospective Scenarios in Life Cycle Engineering: Case Study of Lightweight Structures
Authors: Ostermann Moritz , Grenz Julian , Triebus Marcel , Cerdas Felipe , Marten Thorsten , Tröster Thomas , Herrmann Christoph
Published: April 2023

Lightweight design is a common approach to reduce energy demand in the use stage of vehicles. The production of lightweight materials is usually associated with an increase in energy demand, so the environmental impacts of lightweight structures need to be assessed holistically using a life cycle assessment. To estimate the life cycle environmental impacts of a product in its developmental stage, for example, by life cycle engineering, future changes in relevant influencing factors must be considered. Prospective life cycle assessment provides methods for integrating future scenarios into life cycle assessment studies. However, approaches for integrating prospective life cycle assessment into product development are limited. The objective of this work is to provide the methodological foundation for integrating future scenarios of relevant influencing factors in the development of lightweight structures. The applicability of the novel methodology is demonstrated by a case study of a structural component in a steel, aluminium, and hybrid design. The results show that appropriate decarbonisation measures can reduce the life cycle greenhouse gas emissions by up to 95 percent until 2050. We also found that shifts in the environmentally optimal design are possible in future scenarios. Therefore, the methodology and data provided contribute to improved decision-making in product development.
Hydrogen-Containing “Green” Fuels Influence on the Thermal Protection and Formation of Wear Processes Components in Compression-Ignition Engines Modern Injection System
Authors: Balitskii Alexander , Abramek Karol , Osipowicz Tomasz , Eliasz Jacek , Balitska Valentina , Kochmański Paweł , Prajwowski Konrad , Mozga Łukasz
Published: April 2023

The article describes the impact of hydrogen-containing vegetable fuels consumption with modern injection apparatus. The fuel in question is B100 rapeseed oil ethyl ester. The process of atomizing fuel in the engine at high temperature and in a high pressure chamber plays an important role in the combustion processes in the CI engine. The elements responsible for supplying fuel to the engine’s combustion chamber are the injectors and the injection pump. The paper presents the construction and operation of modern injection pumps and fuel injectors, the methods of their diagnosis are discussed, the important role of precision, and the course of their wear phenomenon are indicated. The paper discusses the impact of hydrogen-containing “green” vegetable fuels on the durability and reliability of injection pumps and fuel injectors used in Common Rail systems. In addition, the tests on the operating parameters of the fuel injector and pump operating on conventional fuel and hydrogen-containing “green” biofuel were carried out.
Effects of Zn Exposure on Populus simonii Seedling Growth and Its Resistance to Leaf Rust
Authors: Gao Lulu , Zhang Aoying , Yan Shanchun
Published: April 2023

Heavy metals are serious pollutants that affect the growth and disease resistance of woody plants. Herein, the enrichment characteristics of an essential element (Zn) in Populus simonii seedlings, as well as the effects of Zn stress on seedling growth and resistance to leaf rust, were investigated. Zn contents in roots, stems, and leaves of treatment groups were significantly increased. Zn stress at all concentrations significantly inhibited the biomass of seedlings. Under the low, middle, and high Zn treatments, compared with the control group, CAT activity significantly decreased by 36%, 21%, and 14%; SOD activity significantly decreased by 13%, 8%, and 5%; PPO activity significantly decreased by 27%, 31%, and 47%; TI activity significantly decreased by 48%, 55%, and 61%, and CI activity significantly decreased by 10%, 20%, and 14%, respectively. In the natural environment, we accidentally discovered that P. simonii was infected with leaf rust, and Zn stress significantly increased the rust disease index. The disease index correlated positively with Zn content in leaves and negatively with the chemical defense indexes. Taken together, Zn stress causes a strong growth toxicity in P. simonii seedlings, and the reduced chemical defense under Zn stress increases the susceptibility of seedlings to leaf rust.
Castanea crenata Ginkbilobin-2-like Recombinant Protein Reveals Potential as an Antimicrobial against Phytophthora cinnamomi, the Causal Agent of Ink Disease in European Chestnut
Authors: Colavolpe Maria Belén , Vaz Dias Fernando , Serrazina Susana , Malhó Rui , Lourenço Costa Rita
Published: April 2023

The European chestnut tree (Castanea sativa Mill.) is widely cultivated throughout the world’s temperate regions. In the Mediterranean region, it has a significant economic role mainly because of the high quality of its edible nuts. The Oomycete Phytophthora cinnamomi is one of the most severe pathogens affecting European chestnuts, causing ink disease and significant losses in production. Ginkgobilobin-2 (Gnk2) in Ginkgo biloba is a secreted protein with a plant-specific cysteine-rich motif that functions as a lectin, and its carbohydrate-binding properties are closely related to its antifungal activity. The binding of lectins to mannose residues of the cell wall of Phytophthora species may disturb and disrup the cell wall structure. This work determined that the amino acid sequence has a signal peptide that directs the final protein peptide to the apoplast. The Cast_Gnk2-like expression was performed and optimized, and different in vitro antagonism tests were done against P. cinnamomi using different purified protein concentrations. As a result of one of these assays, Cast_Gnk2-like significantly reduced the mycelia growth of P. cinnamomi in liquid medium as shown by the mycelia weight (g) in control treatments was 377% higher than in the treatments. These insights reveal the potential of Cast_Gnk2-like for agricultural uses and biotechnological developments for the pathosystem chestnut/P. cinnamomi.
Photosynthetic and Antioxidant Responses of Gymnocarpos przewalskii to Simulated Rainfall Changes
Authors: Huang Haixia , Cui Peng , Lu Gang , Wang Xuhu , Jiang Le , Luo Yongzhong
Published: April 2023

Gymnocarpos przewalskii is a rare Tertiary relict species, mainly distributed in desert areas of northwestern China. Changes in rainfall have a significant impact on the physiological characteristics of desert plants. In the present study, the effects of five simulated rainfall levels on the gas exchange parameters, chlorophyll fluorescence characteristics, and antioxidant system of G. przewalskii were studied. The results show that with increased rainfall the net photosynthetic rate (Pn) and transpiration rate increase significantly. The reduction in Pn is caused by stomatal and non-stomatal limitations under decreased rainfall. Decreased rainfall markedly improves the instantaneous water-use efficiency of leaves. With increased rainfall, the maximum photosynthetic rate, apparent quantum efficiency, and light utilization range significantly increase. Under reduced rainfall, the chlorophyll content, maximum photochemical efficiency of PSII, and steady-state optical quantum efficiency decrease and photoinhibition is caused in the PSII system. A rainfall reduction of 30% leads to massive production of superoxide anions and hydrogen peroxide, causing obvious peroxidation damage. Meanwhile, superoxide dismutase, peroxidase, and catalase in the leaves are significantly enhanced to remove excess reactive oxygen species and alleviate the injury to photosynthetic apparatus. Our study reveals the effect of rainfall changes on the photosynthetic characteristics and antioxidant system of G. przewalskii, and can improve understanding of the adaptive strategies of desert plants under future precipitation changes in northwestern China.
Bio-Succinic Acid Production from Palm Oil Mill Effluent Using Enterococcus gallinarum with Sequential Purification of Biogas
Authors: Nagime Pooja Vilas , Upaichit Apichat , Cheirsilp Benjamas , Boonsawang Piyarat
Published: April 2023

Bio-succinic acid production using microorganisms has been interesting as an environmentally friendly process. Palm oil mill effluent (POME) was considered as a cheap substrate to lower the cost of production. It was revealed that 2-fold diluted POME produced more succinic acid than undiluted and 5-fold diluted POME. In addition, the effects of various neutralizing agents on succinic acid production utilized to manage pH and CO2 supply indicated that the utilization of MgCO3 as a neutralizing agent produced succinic acid of 11.5 g/L with a small amount of by-product synthesis. Plackett–Burman Design (PBD) was used to screen the most significant nutrients for bio-succinic acid production from 2-fold diluted POME using E. gallinarum. From the Pareto chart, MgCO3 and peptone presented the highest positive effect on the production of succinic acid. In addition, Box–Behnken Design (BBD) was conducted to increase bio-succinic acid production. Experiments showed the highest production of succinic acid of 23.7 g/L with the addition of 22.5 g/L MgCO3 and 12.0 g/L peptone in 2-fold diluted POME. Moreover, the experiment of replacing MgCO3 with CO2 from biogas resulted in 19.1 g/L of succinic acid, simultaneously creating the high purity of biogas and a higher CH4 content.
Eco-Friendly Extraction of Green Coffee Oil for Industrial Applications: Its Antioxidant, Cytotoxic, Clonogenic, and Wound Healing Properties
Authors: Dias Lucas D. , Carbinatto Fernanda Mansano , Almeida Isabelle da Silveira , Blanco Kate C. , Marquele-Oliveira Franciane , Munari Carla Carolina , Bagnato Vanderlei Salvador
Published: April 2023

The development of natural oil-based cosmetic and pharmaceutical products presents great scientific and commercial interest. Herein, we aimed to extract green coffee oil from Arabic coffee by a sustainable cold-pressing method. Furthermore, this work aimed to characterize the obtained green coffee oil by Fourier-Transform Infrared (FT–IR) and ultraviolet–visible spectroscopies (UV–Vis), peroxide analysis, and fatty acids profile by gas chromatography–mass spectrometry (GC–MS). Moreover, the functional and biological properties of the obtained green coffee oil and a green-coffee oil-based commercial product (Energy up®, Dermociencia) were investigated. The green coffee oil presented linoleic and palmitic acids as the major fatty acids showing 44.8% and 35.4%, respectively. Moreover, this green coffee oil presented an antioxidant activity (EC50 7.64 mg/mL) and an absence of cytotoxic effects in keratinocyte cultures treated with up to 20 mg/mL. The obtained green coffee oil showed wound healing properties as well as clonogenic efficiency, a biological potential to induce the proliferative and migratory capacity of cells of human skin keratinocytes at 2.5 mg/mL. The samples presented high antioxidant activity and the absence of a cytotoxic effect, suggesting that green coffee oil is a promising natural product for cosmetic applications with wound healing properties. These results open new ways for the use of green coffee oil for the development of cosmetic and pharmaceutics natural-based products.