bluuebunny/crossref_metadata_2025 huggingface dataset:
Construct the Optimum Process Model for Transistor Gaskets with Six-Sigma DMAIC
Authors: Chen Kuen-Suan , Ye Guo-Ping , Yu Chun-Min , Yu Chun-Hung
Published: June 2023

The improvement process of six-sigma DMAIC refers to the process that the industry performs to enhance process quality via the following five procedures: (1) define, (2) measure, (3) analyze, (4) improve, and (5) control. It is a common tool used in the industry to ameliorate and enhance process quality. In addition, the process capability index is a tool that the industry most frequently uses to measure process quality. Though many studies have discussed six-sigma methods, none of the specific theoretical models could be provided as a reference for the above five DMAIC improvement steps, making it difficult to control the effect. In order to solve this problem, this paper developed the multi-characteristic process capability analysis chart, MPCAC, using the process capability index and applied the method of statistical inference as a tool promoting define, measure, and analyze in the improvement process of six-sigma DMAIC. Additionally, the testing of the orthogonal table in the Taguchi method can efficiently assist process engineers in finding the optimum combination of machining parameters to improve or boost the process quality level of the quality characteristics by reducing the number and the cost of experiments. Therefore, this paper then adopted the testing of the orthogonal table in the Taguchi method as a tool for process improvement in the fourth step. Finally, according to the optimal combination of machining parameters improved in the fourth step, a standard operating procedure for transistor gaskets was established as a tool for process control in the fifth step.
Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis
Authors: Dunn Bryce , Pierobon Mariaelena , Wei Qi
Published: June 2023

Artificial intelligence and emerging data science techniques are being leveraged to interpret medical image scans. Traditional image analysis relies on visual interpretation by a trained radiologist, which is time-consuming and can, to some degree, be subjective. The development of reliable, automated diagnostic tools is a key goal of radiomics, a fast-growing research field which combines medical imaging with personalized medicine. Radiomic studies have demonstrated potential for accurate lung cancer diagnoses and prognostications. The practice of delineating the tumor region of interest, known as segmentation, is a key bottleneck in the development of generalized classification models. In this study, the incremental multiple resolution residual network (iMRRN), a publicly available and trained deep learning segmentation model, was applied to automatically segment CT images collected from 355 lung cancer patients included in the dataset “Lung-PET-CT-Dx”, obtained from The Cancer Imaging Archive (TCIA), an open-access source for radiological images. We report a failure rate of 4.35% when using the iMRRN to segment tumor lesions within plain CT images in the lung cancer CT dataset. Seven classification algorithms were trained on the extracted radiomic features and tested for their ability to classify different lung cancer subtypes. Over-sampling was used to handle unbalanced data. Chi-square tests revealed the higher order texture features to be the most predictive when classifying lung cancers by subtype. The support vector machine showed the highest accuracy, 92.7% (0.97 AUC), when classifying three histological subtypes of lung cancer: adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. The results demonstrate the potential of AI-based computer-aided diagnostic tools to automatically diagnose subtypes of lung cancer by coupling deep learning image segmentation with supervised classification. Our study demonstrated the integrated application of existing AI techniques in the non-invasive and effective diagnosis of lung cancer subtypes, and also shed light on several practical issues concerning the application of AI in biomedicine.
Non-Homologous End-Joining Pathway Genotypes Significantly Associated with Nasopharyngeal Carcinoma Susceptibility
Authors: Tsai Chia-Wen , Shih Liang-Chun , Chang Wen-Shin , Hsu Che-Lun , He Jie-Long , Hsia Te-Chun , Wang Yun-Chi , Gu Jian , Bau Da-Tian
Published: June 2023

Defects in the non-homologous end-joining (NHEJ) DNA repair pathway lead to genomic instability and carcinogenesis. However, the roles of individual NHEJ genes in nasopharyngeal carcinoma (NPC) etiology are not well-understood. The aim of this study was to assess the contribution of NHEJ genotypes, including XRCC4 (rs6869366, rs3734091, rs28360071, rs28360317, rs1805377), XRCC5 (rs828907, rs11685387, rs9288518), XRCC6 (rs5751129, rs2267437, rs132770, rs132774), XRCC7 rs7003908, and Ligase4 rs1805388, to NPC risk, with 208 NPC patients and 416 controls. Genotype–phenotype correlations were also investigated by measuring mRNA and protein expression in adjacent normal tissues and assessing the NHEJ repair capacity in blood lymphocytes from 43 NPC patients. The results showed significant differences in the distributions of variant genotypes at XRCC4 rs3734091, rs28360071, and XRCC6 rs2267437 between the cases and controls. The variant genotypes of these three polymorphisms were associated with significantly increased NPC risks. NPC patients with the risk genotypes at XRCC6 rs2267437 had significantly reduced expression levels of both mRNA and protein, as well as a lower NHEJ repair capacity, than those with the wild-type genotype. In conclusion, XRCC4 rs3734091, rs28360071, and XRCC6 rs2267437 in the NHEJ pathway were associated with NPC susceptibility. XRCC6 rs2267437 can modulate mRNA and protein expression and the NHEJ repair capacity.
Microneedle-Assisted Topical Delivery of Idebenone-Loaded Bioadhesive Nanoparticles Protect against UV-Induced Skin Damage
Authors: Xie Yuan , Ye Jingping , Ouyang Yaqi , Gong Jianing , Li Chujie , Deng Yang , Mai Yang , Liu Yang , Deng Wenbin
Published: June 2023

Ultraviolet (UV) radiation can penetrate the basal layer of the skin and induce profound alterations in the underlying dermal tissues, including skin pigmentation, oxidative stress, photoaging, glycation, and skin cancer. Idebenone (IDB), an effective antioxidant that suppresses melanin biosynthesis and glycation, can protect the skin from UV-induced damage, accounting for its use in commercial anti-aging formulations. Ideally, IDB formulations should retain IDB inside the skin for a sufficient period, despite disturbances such as sweating or swimming. Herein, we present an IDB topical formulation based on Tris (tris(hydroxymethyl)-aminomethane)-modified bioadhesive nanoparticles (Tris-BNPs) and microneedle-assisted delivery. We found that Tris-BNPs loaded with IDB (IDB/Tris-BNPs) effectively reached the basal layer of the skin and were retained for at least 4 days with a slow and continuous drug release profile, unlike non-bioadhesive nanoparticles (NNPs) and bioadhesive nanoparticles (BNPs) of similar sizes (ranging from 120–142 nm) and zeta-potentials (above −20 mV), which experienced a significant reduction in concentration within 24 h. Notably, IDB/Tris-BNPs showed superior performance against UV-induced damage relative to IDB/NNPs and IDB/BNPs. This effect was demonstrated by lower levels of reactive oxygen species and advanced glycation end-products in skin tissues, as well as suppressed melanogenesis. Therefore, the proposed IDB delivery strategy provided long-term protective effects against UV-induced skin damage.
Sertoli Cells Express Accommodation, Survival, and Immunoregulatory Factors When Exposed to Normal Human Serum
Authors: Washburn Rachel L. , Martinez-Marin Dalia , Sniegowski Tyler , Korać Ksenija , Rodriguez Alexis R. , Miranda Jonathan M. , Chilton Beverly S. , Bright Robert K. , Pruitt Kevin , Bhutia Yangzom D. , Dufour Jannette M.
Published: June 2023

Transplantation is a clinical procedure that treats a variety of diseases yet is unattainable for many patients due to a nationwide organ shortage and the harsh side effects of chronic immune suppression. Xenografted pig organs are an attractive alternative to traditional allografts and would provide an endless supply of transplantable tissue, but transplants risk rejection by the recipient’s immune system. An essential component of the rejection immune response is the complement system. Sertoli cells, an immunoregulatory testicular cell, survive complement as xenografts long term without any immune suppressants. We hypothesized that exposure to the xenogeneic complement influences Sertoli cell gene expression of other accommodation factors that contribute to their survival; thus, the purpose of this study was to describe these potential changes in gene expression. RNA sequencing of baseline neonatal pig Sertoli cells (NPSC) as compared to NPSC after exposure to normal human serum (NHS, containing complement) revealed 62 significantly differentially expressed genes (DEG) that affect over 30 pathways involved in immune regulation, cell survival, and transplant accommodation. Twelve genes of interest were selected for further study, and Sertoli cell protein expression of CCL2 and the accommodation factor A20 were confirmed for the first time. Functional pathway analyses were conducted in NPSC and three biological clusters were revealed as being considerably affected by NHS exposure: innate immune signaling, cytokine signaling, and T cell regulation. Better understanding of the interaction of Sertoli cells with complement in a xenograft environment may reveal the mechanisms behind immune-privileged systems to increase graft viability.
The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place
Authors: Kim Byeongjun , Kwon Gunam , Park Chaneun , Kwon Nam Kyu
Published: June 2023

This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. One of the two reaching tasks is approaching the object, and the other is reaching the place position. These two reaching tasks are carried out using each optimal policy of the agents which are trained using Soft Actor-Critic (SAC). Different from the two reaching tasks, the grasping is implemented via simple logic which is easily designable but may result in improper gripping. To assist the grasping task properly, a dedicated reward system for approaching the object is designed through using individual axis-based weights. To verify the validity of the proposed method, wecarry out various experiments in the MuJoCo physics engine with the Robosuite framework. According to the simulation results of four trials, the robot manipulator picked up and released the object in the goal position with an average success rate of 93.2%.
Graphene-Based Electrodes for Monitoring of Estradiol
Authors: Musa Auwal M. , Kiely Janice , Luxton Richard , Honeychurch Kevin C.
Published: June 2023

This study explores the potential use of graphene-based electrodes in the electrochemical determination of estradiol using amperometric techniques as a simple, enzyme-free approach. Graphene, a carbon-based nanomaterial, has been extensively investigated in materials science as a sensing material. Its remarkable properties, such as its high electron mobility and conductivity, robust mechanical characteristics, and good surface-to-volume ratio, have led to its adoption in numerous applications, including electrochemical sensing. Estradiol is a crucial sex hormone that affects metabolism and reproduction. However, excessive amounts may disrupt endocrine functions. Electrochemical sensors suffer from electrode fouling, leading to passivation that ultimately affects performance. We exploit the inherent properties of various types of graphene-based electrodes, including graphene screen-printed electrodes (GHSPE), electrochemically exfoliated graphene-modified electrodes (EEFGHSPE), and 3D graphene foam screen-printed electrodes (3D-GFSPE), for the amperometric studies. The electrochemical properties and structural characteristics of these sensors are evaluated using cyclic voltammetry and scanning electron microscopy. The analytical performance of these sensors is at an applied potential of +0.65 V (vs. Ag/AgCl) over the concentration range 0.83 to 4.98 μM estradiol. Sensitivities of 0.151 µAµM−1 cm−2, 0.429 µAµM−1 cm−2, and 0.273 µA µM−1 cm−2, with detection limits of 0.0041 µM, 0.097 µM, and 0.018 µM (S/N = 3), are found for GHPSPE, 3D-GFSPE and EEFGHSPE, respectively. The possibility of amperometrically determining the estradiol levels in a potable tap water sample are then investigated over the concentration range 0.83–4.98 µM.
Laser-Induced Graphene on Optical Fibre: Towards Simple and Cost-Effective Electrochemical/Optical Lab-on-Fibre Bioplatforms
Authors: Ferreira Laura L. , Ribeiro Rafael A. , Fernandes António J. S. , Costa Florinda M. , Marques Carlos , Santos Nuno F.
Published: June 2023

A 3D graphene foam made of interconnected multilayer graphene flakes was produced on optical fibres (OF) by laser-induced transformation of a polyimide (PI) film coated on the OF cladding. This material, known as laser-induced graphene (LIG), was explored in the electrochemical detection and quantification of dopamine (DA) at physiologically relevant concentrations in the presence of the most relevant interfering molecules in biological fluids, ascorbic acid (AA) and uric acid (UA). The measured limit of detection is 100 nM, the linear range is 0.1 to 5.0 μM and a maximum sensitivity of 5.0 µA µM−1 cm−2 was obtained for LIG decorated with Pt nanoparticles (NPs). Moreover, immunity to AA and UA interference and to fouling was attained by decorating the LIG electrode with Pt NPs and coating it with Nafion. These figures of merit underline the potential of these sensors for the quantification of physiologically relevant concentrations of DA in biological fluids, paving the way for the development of hybrid electrochemical/optical sensing actuating platforms in a lab-on-fibre configuration, with relevant applications in biomedical engineering. The advantages of this hybrid arrangement include the possibility of in situ counterproofing, extended measuring ranges, photoelectrochemical detection and the probing of inaccessible places. This elegant approach can also provide a simple and cost-effective way to fabricate biomedical devices with extended functionality, such as medical optical probes with added electrochemical capabilities and optogenetics combined with local electrochemical detection, among others.
Synthesis of Thiophene-Based Derivatives and the Effects of Their Molecular Structure on the Mesomorphic Behavior and Temperature Range of Liquid-Crystalline Blue Phases
Authors: Wang Meng , Song He , Wu Chongye , Liu Beiqi , Wang Zichen , Yang Huai
Published: June 2023

The development of blue-phase liquid crystal (BPLC) materials with a wide temperature range is of great significance for practical applications in the optoelectronic field. In the study, bent-core derivatives with a 3-hexyl-2,5-disubstituted thiophene central ring in the λ-shaped molecular structure were designed and synthesized. Their mesomorphic behavior and effect on the blue-phase (BP) temperature range were investigated. Interestingly, a BP was achieved both during the heating and cooling processes by doping with a proper concentration of chiral compound into the thiophene bent-shaped molecule with high rigidity, while derivatives with fluorine atom substitution only exhibited cholesteric phase no matter how many chiral compounds were added. This result proved that BP is highly sensitive to the molecular structures of bent-shaped molecules. Moreover, the BP temperature range was broadened when adding these molecules into a BPLC host, which thus improved the BP temperature range from the initial value, no more than 4 °C, to as much as 24 °C. The experimental phenomena were reasonably explained through molecular simulation calculations. The study may provide some experimental basis and theoretical guidance for the design of novel bent-shaped molecules and BPLC material with a wide temperature range.
Effect of Electrochemical Hydrogen Charging on Blistering and Mechanical Properties Behavior of Q690 Steel
Authors: Ma Heng , Tian Huiyun , Wang Zhongxue , He Kang , Wang Yuexiang , Zhang Qingpu , Liu Deyun , Cui Zhongyu
Published: June 2023

The purpose of this work is to study the effect of charging conditions on hydrogen damage. The effects of electrochemical hydrogen charging current density and time on hydrogen-induced blistering (HIB), cracking behavior, and mechanical properties of Q690 steel are studied by electrochemical hydrogen charging, microstructure observation, and slow strain rate tensile (SSRT) tests. The results show that HIB and internal cracks occur when the Q690 steel is charged at different current densities. The charging conditions have a significant effect on the HIB characteristics of the material and the morphology, number, size, and location of internal cracks. The geometrical parameters of blisters on the surface of Q690 steel are quantitatively evaluated, and deeper cracks are found at higher hydrogen concentrations. At high hydrogen charging current density (50 mA/cm2), due to the accumulation of a large number of hydrogen atoms and the precipitation of hydrogen, the active sites on Q690 steel surface increase dramatically, leading to the initiation of a large number of blisters. At this time, high current density is responsible for the initiation of blisters. The relationship between hydrogen charging current density and mechanical properties of Q690 steel is studied, and the change in the fracture morphology is observed. The Q690 steel was damaged and failed due to an internal crack caused by excessive hydrogen pressure. On the other hand, electrochemical hydrogen charging leads to the degradation of mechanical properties and the transition from ductile fracture to brittle fracture.
The White-Tailed Eagle, the Apex Predator, Adjusts Diet towards Larger Prey in Suboptimal Territories
Authors: Mirski Paweł , Komar Ervin
Published: June 2023

The White-tailed eagle, an apex predator, is currently recovering its populations across Europe and has already reached high numbers in many countries. This led to the saturation of eagles in optimal habitats and their encroachment on suboptimal ones. We aimed to compare the diet of White-tailed eagles in optimal and suboptimal conditions in northeastern Poland to investigate how population development affected prey composition, which is expected to be lacking in suboptimal eagle territories. We have monitored eagle nests with trail cameras to investigate their diet objectively and precisely. In order to compare territories of different quality, we have conducted modeling of habitat suitability using data on nest locations prior to their saturation. Using recorded photos of the prey, we measured their size and estimated their weight to check if the size and biomass of the prey are comparable between optimal and suboptimal territories. We found that eagles in the latter conditions were not limited by prey biomass but turned to alternative prey and brought larger prey. The alternative prey were large birds such as White storks and Common cranes, but also chicks of other avian predators that were robbed from their nests. Most probably, eagles cope with a lack of optimal prey by ranging farther and exploring non-optimal foraging habitats. We conclude that the diet flexibility of White-tailed eagle enables him to still increase its numbers despite already high densities. Our study also shows that this species might possibly impact the White stork population, as seen in the case of the Black stork and some seabird species.
Machine Learning for Precision Agriculture Using Imagery from Unmanned Aerial Vehicles (UAVs): A Survey
Authors: Zualkernan Imran , Abuhani Diaa Addeen , Hussain Maya Haj , Khan Jowaria , ElMohandes Mohamed
Published: June 2023

Unmanned aerial vehicles (UAVs) are increasingly being integrated into the domain of precision agriculture, revolutionizing the agricultural landscape. Specifically, UAVs are being used in conjunction with machine learning techniques to solve a variety of complex agricultural problems. This paper provides a careful survey of more than 70 studies that have applied machine learning techniques utilizing UAV imagery to solve agricultural problems. The survey examines the models employed, their applications, and their performance, spanning a wide range of agricultural tasks, including crop classification, crop and weed detection, cropland mapping, and field segmentation. Comparisons are made among supervised, semi-supervised, and unsupervised machine learning approaches, including traditional machine learning classifiers, convolutional neural networks (CNNs), single-stage detectors, two-stage detectors, and transformers. Lastly, future advancements and prospects for UAV utilization in precision agriculture are highlighted and discussed. The general findings of the paper demonstrate that, for simple classification problems, traditional machine learning techniques, CNNs, and transformers can be used, with CNNs being the optimal choice. For segmentation tasks, UNETs are by far the preferred approach. For detection tasks, two-stage detectors delivered the best performance. On the other hand, for dataset augmentation and enhancement, generative adversarial networks (GANs) were the most popular choice.
Impact of Climate Variability on Rainfall Characteristics in the Semi-Arid Shashe Catchment (Botswana) from 1981–2050
Authors: Matenge Ronny G. , Parida Bhagabat P. , Letshwenyo Moatlhodi W. , Ditalelo Gofetamang
Published: June 2023

Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region.
Short Text Classification Based on Hierarchical Heterogeneous Graph and LDA Fusion
Authors: Xu Xinlan , Li Bo , Shen Yuhao , Luo Bing , Zhang Chao , Hao Fei
Published: June 2023

The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical heterogeneous graph with latent Dirichlet allocation (LDA) fusion. Our method first models the short text dataset as a hierarchical heterogeneous graph, which incorporates more syntactic and semantic information through a word graph, parts-of-speech (POS) tag graph, and entity graph. We then connected the representation of these three feature maps to derive a comprehensive feature vector for the text. Finally, we used the LDA topic model to adjust the feature weight, enhancing the effectiveness of short text extension. Our experiments demonstrated that our proposed approach has a promising performance in English short text classification, while in Chinese short text classification, although slightly inferior to the LDA + TF-IDF method, it still achieved promising results.
Application Layer-Based Denial-of-Service Attacks Detection against IoT-CoAP
Authors: Almeghlef Sultan M. , AL-Ghamdi Abdullah AL-Malaise , Ramzan Muhammad Sher , Ragab Mahmoud
Published: June 2023

Internet of Things (IoT) is a massive network based on tiny devices connected internally and to the internet. Each connected device is uniquely identified in this network through a dedicated IP address and can share the information with other devices. In contrast to its alternatives, IoT consumes less power and resources; however, this makes its devices more vulnerable to different types of attacks as they cannot execute heavy security protocols. Moreover, traditionally used heavy protocols for web-based communication, such as the Hyper Text Transport Protocol (HTTP) are quite costly to be executed on IoT devices, and thus specially designed lightweight protocols, such as the Constrained Application Protocol (CoAP) are employed for this purpose. However, while the CoAP remains widely-used, it is also susceptible to attacks, such as the Distributed Denial-of-Service (DDoS) attack, which aims to overwhelm the resources of the target and make them unavailable to legitimate users. While protocols, such as the Datagram Transport Layer Security (DTLS) and Lightweight and the Secure Protocol for Wireless Sensor Network (LSPWSN) can help in securing CoAP against DDoS attacks, they also have their limitations. DTLS is not designed for constrained devices and is considered as a heavy protocol. LSPWSN, on the other hand, operates on the network layer, in contrast to CoAP which operates on the application layer. This paper presents a machine learning model, using the CIDAD dataset (created on 11 July 2022), that can detect the DDoS attacks against CoAP with an accuracy of 98%.
Residual Depth Feature-Extraction Network for Infrared Small-Target Detection
Authors: Wang Lizhe , Zhang Yanmei , Xu Yanbing , Yuan Ruixin , Li Shengyun
Published: June 2023

Deep-learning methods have exhibited exceptional performance in numerous target-detection domains, and their application is steadily expanding to include infrared small-target detection as well. However, the effect of existing deep-learning methods is weakened due to the lack of texture information and the low signal-to-noise ratio of infrared small-target images. To detect small targets in infrared images with limited information, a depth feature-extraction network based on a residual module is proposed in this paper. First, a global attention guidance enhancement module (GAGEM) is used to enhance the original infrared small target image in a single frame, which considers the global and local features. Second, this paper proposes a depth feature-extraction module (DFEM) for depth feature extraction. Our IRST-Involution adds the attention mechanism to the classic Involution module and combines it with the residual module for the feature extraction of the backbone network. Finally, the feature pyramid with self-learning weight parameters is used for feature fusion. The comparative experiments on three public datasets demonstrate that our proposed infrared small-target detection algorithm exhibits higher detection accuracy and better robustness.
Camouflaged Object Detection with a Feature Lateral Connection Network
Authors: Wang Tao , Wang Jian , Wang Ruihao
Published: June 2023

We propose a new framework for camouflaged object detection (COD) named FLCNet, which comprises three modules: an underlying feature mining module (UFM), a texture-enhanced module (TEM), and a neighborhood feature fusion module (NFFM). Existing models overlook the analysis of underlying features, which results in extracted low-level feature texture information that is not prominent enough and contains more interference due to the slight difference between the foreground and background of the camouflaged object. To address this issue, we created a UFM using convolution with various expansion rates, max-pooling, and avg-pooling to deeply mine the textural information of underlying features and eliminate interference. Motivated by the traits passed down through biological evolution, we created an NFFM, which primarily consists of element multiplication and concatenation followed by an addition operation. To obtain precise prediction maps, our model employs the top-down strategy to gradually combine high-level and low-level information. Using four benchmark COD datasets, our proposed framework outperforms 21 deep-learning-based models in terms of seven frequently used indices, demonstrating the effectiveness of our methodology.
Enhanced Exhaust after-Treatment Warmup in a Heavy-Duty Diesel Engine System via Miller Cycle and Delayed Exhaust Valve Opening
Authors: Basaran Hasan Ustun
Published: June 2023

The exhaust after-treatment (EAT) threshold temperature is a significant concern for highway vehicles to meet the strict emission norms. Particularly at cold engine start and low loads, EAT needs to be improved above 250 °C to reduce the tailpipe emission rates. Conventional strategies such as electrical heating, exhaust throttling, or late fuel injection mostly need a high fuel penalty for fast EAT warmup. The objective of this work is to demonstrate using a numerical model that a combination of the Miller cycle and delayed exhaust valve opening (DEVO) can improve the tradeoff between EAT warmup and fuel consumption penalty. A relatively low-load working condition (1200 RPM speed and 2.5 bar BMEP) is maintained in the diesel engine model. The Miller cycle via retarded intake valve closure (RIVC) is noticeably effective in increasing exhaust temperature (as high as 55 °C). However, it also dramatically reduces the exhaust flow rate (over 30%) and, thus, is ineffective for rapid EAT warmup. DEVO has the potential to enhance EAT warmup via increased exhaust temperature and increased exhaust flow rate. However, it considerably decreases the brake thermal efficiency (BTE)—by up to 5%—due to high pumping loss in the system. The RIVC + DEVO combined technique can elevate the exhaust temperature above 250 °C with improved fuel consumption—up to 10%—compared to DEVO alone as it requires a relatively lower rise in pumping loss. The combined method is also superior to RIVC alone. Unlike RIVC alone, the RIVC + DEVO combined mode does not need the extreme use of RIVC to increase engine-out temperature above 250 °C and, thus, provides relatively higher heat transfer rates (up to 103%) to the EAT system through a higher exhaust flow rate. The RIVC + DEVO combined method can be technically more difficult to implement compared to other methods. However, it has the potential to maintain accelerated EAT warmup with improved BTE and, thus, can keep emission rates at low levels during cold start and low loads.
Cybersecurity in Cyber–Physical Power Systems
Authors: Ribas Monteiro Luiz Fernando , Rodrigues Yuri R. , Zambroni de Souza A. C.
Published: June 2023

The current energy transition combined with the modernization of power systems has provided meaningful transformations in the transmission, distribution, operation, planning, monitoring, and control of power systems. These advancements are heavily dependent on the employment of new computing and communications technologies, which, combined with traditional physical systems, lead to the emergence of cyber–physical systems (CPSs). In this sense, besides the traditional challenges of keeping a reliable, affordable, and safe power grid, one must now deal with the new vulnerabilities to cyberattacks that emerge with the advancement of CPSs. Aware of this perspective and the severity of the ongoing challenges faced by the industry due to cyberattacks, this paper aims to provide a comprehensive survey of the literature on cybersecurity in cyber–physical power systems. For this, clear definitions, historical timelines, and classifications of the main types of cyberattacks, including the concepts, architectures, and basic components that make up, as well as the vulnerabilities in managing, controlling, and protecting, a CPS are presented. Furthermore, this paper presents defense strategies and future trends for cybersecurity. To conduct this study, a careful search was made in relevant academic and industrial databases, leading to a detailed reporting of key works focused on mitigating cyberattacks and ensuring the cybersecurity of modern CPSs. Finally, the paper presents some standards and regulations that technical and international institutions on cybersecurity in smart grids have created.
Investigation of the Tendency of Carbon Fibers to Disintegrate into Respirable Fiber-Shaped Fragments
Authors: Meyer-Plath Asmus , Kehren Dominic , Große Anna , Naumann Romy , Hofmann Marcel , Schneck Tanja , Ota Antje , Hermanutz Frank , Dziurowitz Nico , Thim Carmen , Plitzko Sabine , Bäger Daphne
Published: June 2023

Recent reports of the release of large numbers of respirable and critically long fiber-shaped fragments from mesophase pitch-based carbon fiber polymer composites during machining and tensile testing have raised inhalation toxicological concerns. As carbon fibers and their fragments are to be considered as inherently biodurable, the fiber pathogenicity paradigm motivated the development of a laboratory test method to assess the propensity of different types of carbon fibers to form such fragments. It uses spallation testing of carbon fibers by impact grinding in an oscillating ball mill. The resulting fragments were dispersed on track-etched membrane filters and morphologically analyzed by scanning electron microscopy. The method was applied to nine different carbon fiber types synthesized from polyacrylonitrile, mesophase or isotropic pitch, covering a broad range of material properties. Significant differences in the morphology of formed fragments were observed between the materials studied. These were statistically analyzed to relate disintegration characteristics to material properties and to rank the carbon fiber types according to their propensity to form respirable fiber fragments. This tendency was found to be lower for polyacrylonitrile-based and isotropic pitch-based carbon fibers than for mesophase pitch-based carbon fibers, but still significant. Although there are currently only few reports in the literature of increased respirable fiber dust concentrations during the machining of polyacrylonitrile-based carbon fiber composites, we conclude that such materials have the potential to form critical fiber morphologies of WHO dimensions. For safe-and-sustainable carbon fiber-reinforced composites, a better understanding of the material properties that control the carbon fiber fragmentation is imperative.
Processing of Fresh-Cut Potato Using Plasma-Activated Water Prepared by Decreasing Discharge Frequency
Authors: Aihaiti Aihemaitijiang , Maimaitiyiming Ruxianguli , Wang Liang , Wang Jiayi
Published: June 2023

As a novel non-thermal processing method, the concentration of active compounds in plasma-activated water (PAW) is usually adjusted by changing the voltage and preparation time. We recently adjusted the discharge frequency and found that the PAW properties were improved. In this study, fresh-cut potato was selected as a model, and PAW was prepared using a frequency of 200 Hz (200 Hz-PAW). Its efficacy was compared with that of PAW prepared using 10 kHz. The results showed that the ozone, hydrogen peroxide, nitrate, and nitrite concentrations in 200 Hz-PAW were 5.00-, 3.62-, 8.05-, and 1.48-fold higher than those of 10 kHz-PAW. PAW inactivated the browning-related enzymes polyphenol oxidase and peroxidase, lowering the browning index and inhibiting browning; 200 Hz-PAW exhibited the lowest of these parameters during storage. In addition, PAW induced PAL to promote phenolic synthesis and increase antioxidant activity to delay malondialdehyde accumulation; 200 Hz-PAW exhibited the highest of these parameters. Moreover, 200 Hz-PAW had the lowest weight loss and electrolyte leakage rates. Furthermore, microbial analysis showed that the lowest aerobic mesophilic, mold, and yeast counts during storage were observed in the 200 Hz-PAW group. These results suggest that frequency-controlled PAW has the potential to treat fresh-cut produce.
Effect of Previous Frozen Storage and Coating Medium on the Essential Macroelement and Trace Element Content of Canned Mackerel
Authors: Prego Ricardo , Cobelo-García Antonio , Martínez Beatriz , Aubourg Santiago P.
Published: June 2023

The effect of previous frozen storage (−18 °C for 6 months) and different coating media (aqueous: water and brine; oily: sunflower, refined olive, and extra-virgin olive oils) on the essential macroelement and trace element content of canned Atlantic mackerel (Scomber scombrus) was studied. Previous frozen storage led to an increased (p < 0.05) content of canned samples of K (oil-coated samples) and Ca (all coating conditions) and to a decreased (p < 0.05) content of P (aqueous-coating samples) and S (water- and oil-coated samples). For trace elements, a content increase (p < 0.05) in Cu and Se (brine-canned samples) and Mn (water- and refined-olive-oil-coated samples) was detected in canned fish muscle with frozen storage. Concerning the coating effect, aqueous-coating samples showed lower (p < 0.05) Mg, P, S, K, and Ca contents than their corresponding oil-coated samples. For trace elements, lower average contents were found for Co, Cu, Mn, Se, and Fe in aqueous-coating fish muscle when compared to their counterparts coated in oily media. Content changes in the different elements in canned fish muscle are discussed based on interactions with other tissue constituents and modifications that such constituents undergo during processing (i.e., protein denaturation, liquor losses from the muscle, lipid changes).
Influence of Sodium and Potassium Chloride on Rennet Coagulation and Curd Firmness in Bovine Milk
Authors: Oštarić Fabijan , Kalit Samir , Curik Ino , Mikulec Nataša
Published: June 2023

One of the salting methods in cheese production implies salting the milk before coagulation used in making Domiati-type cheeses and a variety of autochthonous “Lički Škripavac” cheese. The most used sodium replacer is potassium. This study investigated the influence of different added salt concentrations (1%, 1.5%, and 2%) and NaCl to KCl ratios (100%, 50:50%, 25:75%) on the rennet coagulation and curd firmness in bovine milk. The milk coagulation parameters were determined with a computerized renneting meter, Lactodinamograph. The results showed significant interactions between the salt concentrations and NaCl to KCl ratios (p < 0.0001, α = 0.05) by prolonging the beginning of coagulation (10–20 min) and curd firming rate (1–5 min) by an increase in salt concentration for all treatments. The 50:50 treatment values (RCT, k20, a30, a60, amax) were closest to the control (without salt) and had the best results among all treatments in the lower (1%) and medium (1.5%) salt concentration (p > 0.0001, α = 0.05) while in the highest salt concentration (2%) the treatment effect was nonsignificant (p > 0.05). These results should help future studies make a lower sodium product appealing to consumers without losing quality.
Comparison of the Main Constituents in Two Varieties of Proso Millet Using GC–MS
Authors: Pexová Kalinová Jana , Tříska Jan , Hořejší Karel
Published: June 2023

Proso millet (Panicum miliaceum) is neglected in human nutrition. Thanks to the composition of the grains, millet is suitable for people with celiac disease and it is also useful in the prevention of cardiovascular diseases. For screening the substances in all plant parts of millet via GC–MS, two varieties, Hanacká Mana and Unicum, were used. Substances from the group saccharides, amino acids, fatty acids, carboxylic acids, phytosterols and others were identified in the roots, leaves, stems, and seeds. The highest level of saccharides was found in the stems (83%); amino acids in the roots (6.9%); fatty acids in the seeds (24.6%); carboxylic acids in the roots (3%), phytosterols in the seeds (10.51%); other substances, such as tetramethyl-2-hexadecenol (1.84%) and tocopherols (2.15%), in the leaves; retinal in the roots (1.30%) and squalene in the seeds (1.29%). Saccharides were the dominant group in all plant parts of proso millet followed by fatty acids. The dominant saccharides in all parts of the millet plant were sucrose, fructose and psicose. On the contrary, turanose, trehalose, glucose and cellobiose belonged to the least represented sugars. Additionally, amyrin, miliacin, campesterol, stigmasterol, β-sitosterol, and others were identified. Varietal variability can be assumed, e.g., in retinal, miliacin or amyrin content.
Deriving an Electric Wave Equation from Weber’s Electrodynamics
Authors: Li Qingsong , Maher Simon
Published: June 2023

Weber’s electrodynamics presents an alternative theory to the widely accepted Maxwell–Lorentz electromagnetism. It is founded on the concept of direct action between particles, and has recently gained some momentum through theoretical and experimental advancements. However, a major criticism remains: the lack of a comprehensive electromagnetic wave equation for free space. Our motivation in this research article is to address this criticism, in some measure, by deriving an electric wave equation from Weber’s electrodynamics based on the axiom of vacuum polarization. Although this assumption has limited experimental evidence and its validity remains a topic of debate among researchers, it has been shown to be useful in the calculation of various quantum mechanical phenomena. Based on this concept, and beginning with Weber’s force, we derive an expression which resembles the familiar electric field wave equation derived from Maxwell’s equations.
Several Quantum Hermite–Hadamard-Type Integral Inequalities for Convex Functions
Authors: Ciurdariu Loredana , Grecu Eugenia
Published: June 2023

The aim of this study was to present several improved quantum Hermite–Hadamard-type integral inequalities for convex functions using a parameter. Thus, a new quantum identity is proven to be used as the main tool in the proof of our results. Consequently, in some special cases several new quantum estimations for q-midpoints and q-trapezoidal-type inequalities are derived with an example. The results obtained could be applied in the optimization of several economic geology problems.
Documents Publication Evolution (1990–2022) Related to Physical Activity and Healthy Habits, a Bibliometric Review
Authors: Hernández-Beltrán Víctor , Espada Mário C. , Santos Fernando J. , Ferreira Cátia C. , Gamonales José M.
Published: June 2023

This research aims to provide an overview of the growing number of scientific literature publications related to Physical Activity and Healthy Habits. A bibliometric analysis between 1990 and 2022 in the Web of Science was carried out, following the bibliometric analysis law, using Microsoft Excel and VosViewer Software for analysis and data processing. A total of 276 documents (262 primary studies and 14 revisions) related to the topic under study were located. The results reveal an exponential growth of scientific production by 48% between 2006 and 2022. Public Environmental Occupational Health, Kaprio, J., and the USA were the knowledge field, author, and country most productive, respectively. A great thematic diversity was found related to the most used keywords by the authors, such as “physical activity”, “health habits”, “exercise”, and “obesity”. Thus, the research related to this theme is really in an exponential phase, with great interest in the importance of physical activity and healthy habits, implying practical decisions in policies to develop programs to promote physical activity and healthy habits.
Mentally Demanding Work and Strain: Effects of Study Duration on Fatigue, Vigor, and Distress in Undergraduate Medical Students
Authors: Blasche Gerhard , Khanaqa Tav A. K. , Wagner-Menghin Michaela
Published: June 2023

Aims: The impact of the extent of mentally demanding work on the next-day’s strain is largely unknown, as existing studies generally investigate consequences of extended versus normal workdays. The present study sought to fill this gap by investigating how short work periods of mentally demanding academic work impact strain reactions in medical students preparing for an exam, using days of no work as reference category. Method: The observational design involved students repeatedly self-reporting fatigue, vigor, distress, and the preceding day’s study duration. Hours of nocturnal sleep, attending paid work and compulsory classes, gender, and proximity to the exam were controls in the linear model (generalized estimating equations). Forty-nine students provided 411 self-reports (M = 8.6, SD = 7.0 self-reports/student). Results: Engaging in mentally demanding work was associated with increased distress and work periods > 4 h with increased fatigue. Distress, vigor loss, and fatigue increased in proximity to the exam. Conclusion: Despite students’ high control of their schedule, even short periods of mentally highly demanding work may impair next-day’s well-being when task motivation is high. Freelancers and students might require health-promoting scheduling of work and leisure to avoid an accumulation of strain.
An Earth Observation Framework in Service of the Sendai Framework for Disaster Risk Reduction 2015–2030
Authors: Li Boyi , Gong Adu , Liu Longfei , Li Jing , Li Jinglin , Li Lingling , Pan Xiang , Chen Zikun
Published: June 2023

The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) proposed seven targets comprising 38 quantified indicators and various sub-indicators to monitor the progress of disaster risk and loss reduction efforts. However, challenges persist regarding the availability of disaster-related data and the required resources to address data gaps. A promising way to address this issue is the utilization of Earth observation (EO). In this study, we proposed an EO-based disaster evaluation framework in service of the SFDRR and applied it to the context of tropical cyclones (TCs). We first investigated the potential of EO in supporting the SFDRR indicators, and we then decoupled those EO-supported indicators into essential variables (EVs) based on regional disaster system theory (RDST) and the TC disaster chain. We established a mapping relationship between the measurement requirements of EVs and the capabilities of EO on Google Earth Engine (GEE). An end-to-end framework that utilizes EO to evaluate the SFDRR indicators was finally established. The results showed that the SFDRR contains 75 indicators, among which 18.7% and 20.0% of those indicators can be directly and indirectly supported by EO, respectively, indicating the significant role of EO for the SFDRR. We provided four EV classes with nine EVs derived from the EO-supported indicators in the proposed framework, along with available EO data and methods. Our proposed framework demonstrates that EO has an important contribution to supporting the implementation of the SFDRR, and that it provides effective evaluation solutions.