bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review
Authors: Qu Hao-Ran , Su Wen-Hao
Published: February 2024

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.
Comparison of Umbilical Cord Mesenchymal Stem Cells and Fibroblasts as Donor Nuclei for Handmade Cloning in Sheep Using a Single-Cell Transcriptome
Authors: Li Weijian , Liu Yalan , Zhou Guizhen , Li Zhuo , Wang Zhen , Wang Li , Ma Xiuling , Wang Xuguang
Published: February 2024

Oocytes are efficient at reprogramming terminally differentiated cells to a totipotent state. Nuclear transfer techniques can exploit this property to produce cloned animals. However, the overall efficiency is low. The use of umbilical cord mesenchymal stem cells (UC-MSCs) as donor nuclei may increase blastocyst rates, but the exact reasons for this remain unexplored. A single-cell transcriptomic approach was used to map the transcriptome profiles of eight-cell embryos that were in vitro-fertilized and handmade-cloned using umbilical cord mesenchymal stem cells and fibroblasts as nuclear donors. Differences were examined at the chromatin level, the level of differentially expressed genes, the level of histone modifications and the level of DNA methylation. This research provides critical information regarding the use of UC-MSCs as a preferred donor nucleus for nuclear transfer techniques. It also offers unique insights into the mechanism of cellular reprogramming.
Advancements in Genetic Marker Exploration for Livestock Vertebral Traits with a Focus on China
Authors: Khan Muhammad Zahoor , Chen Wenting , Huang Bingjian , Liu Xiaotong , Wang Xinrui , Liu Yihong , Chai Wenqiong , Wang Changfa
Published: February 2024

In livestock breeding, the number of vertebrae has gained significant attention due to its impact on carcass quality and quantity. Variations in vertebral traits have been observed across different animal species and breeds, with a strong correlation to growth and meat production. Furthermore, vertebral traits are classified as quantitative characteristics. Molecular marker techniques, such as marker-assisted selection (MAS), have emerged as efficient tools to identify genetic markers associated with vertebral traits. In the current review, we highlight some key potential genes and their polymorphisms that play pivotal roles in controlling vertebral traits (development, length, and number) in various livestock species, including pigs, donkeys, and sheep. Specific genetic variants within these genes have been linked to vertebral development, number, and length, offering valuable insights into the genetic mechanisms governing vertebral traits. This knowledge has significant implications for selective breeding strategies to enhance structural characteristics and meat quantity and quality in livestock, ultimately improving the efficiency and quality of the animal husbandry industry.
Evaluation of Genetic Parameters and Comparison of Stress Tolerance Traits in Different Strains of Litopenaeus vannamei
Authors: Shi Miao , Jiang Song , Shi Jianzhi , Yang Qibin , Huang Jianhua , Li Yundong , Yang Lishi , Zhou Falin
Published: February 2024

Litopenaeus vannamei stands out globally in aquaculture for its fast growth, broad salt tolerance, disease resistance, and high protein levels. Selective breeding requires the precise estimation of the variance components and genetic parameters for important traits. This study formed lineages from 20 full sibling families of L. vannamei, with progenitors from Thailand and the USA. We then assessed the genetic resilience traits of juvenile shrimp from these families to high ammonia-N, high pH, and low salinity by performing a 96 h acute toxicity test. Mortality rates for the families under 96 h exposure to high ammonia-N, high pH, and low salinity were 19.52–92.22%, 23.33–92.22%, and 19.33–80.00%, respectively, showing significant variance in stress tolerance among families (p < 0.05). Survival heritability estimates, using threshold male and female models, were 0.44 ± 0.12 in high ammonia-N, 0.41 ± 0.12 in high pH, and 0.27 ± 0.08 in low salinity, respectively. Genetic correlations between growth and stress resistance traits varied from 0.0137 ± 0.2406 to 0.8327 ± 0.0781, and phenotypic correlations ranged from 0.0019 ± 0.0590 to 0.6959 ± 0.0107, indicating a low-to-high positive correlation significant at (p < 0.05). It was found that the survival rate of families No. 2 and No. 9 was higher under high ammonia-N and high pH stresses, while the survival rate of family No. 10 was higher under low salinity stress after comparing two selection criteria, the breeding values and phenotypic values. Thus, these three families are identified as potential breeding program candidates. Through the creation of a genetic parameter estimation model, the genetic variances across mating combinations for stress resistance traits were obtained and families with heightened stress resistance were identified, laying the groundwork for enhanced genetic selection of L. vannamei.
Antibiotic Consumption 2017–2022 in 30 Private Hospitals in France: Impact of Antimicrobial Stewardship Tools and COVID-19 Pandemic
Authors: Roger Pierre-Marie , Lesselingue Diane , Gérard Anouk , Roghi Jacques , Quint Pauline , Un Sophie , Chincholle Agnès , Assi Assi , Bouchard Odile , Javaudin Véronique , Denes Eric
Published: February 2024

Our aim was to determine the impact of antimicrobial stewardship tools (ASTs) and the COVID-19 pandemic on antibiotic consumption (AC). We used the national software Consores® to determine AC in DDD/1000 days of hospitalization from 2017 to 2022 in voluntary private hospitals in France. The ASTs considered were: 1. internal guidelines; 2. the list of antibiotics with restricted access; 3. the presence of an antibiotic referent or 4. an ID specialist; and 5. proof of an annual meeting on antimicrobial resistance. Institutions with dedicated units for COVID-19 patients were specified. In 30 institutions, the total AC varied from (means) 390 to 405 DDD/1000 DH from 2017 to 2022. Fluoroquinolones and amoxicillin/clavulanate consumption decreased from 50 to 36 (p = 0.003) and from 112 to 77 (p = 0.025), respectively, but consumption of piperacillin/tazobactam increased from 9 to 21 (p < 0.001). Over the study period, 10 institutions with ≤2 AST had lower AC compared to 20 institutions with ≥3 AST (p < 0.01). COVID-19 units opened in 10 institutions were associated with a trend toward higher macrolide consumption from 15 to 25 from 2017 to 2020 (p = 0.065) and with an acceleration of piperacillin/tazobactam consumption from 2020 to 2022 (p ≤ 0.003). Antibiotic consumption in 30 private hospitals in France was inversely related to the number of AST. The COVID-19 pandemic was associated with limited impact on AC, but special attention should be paid to piperacillin/tazobactam consumption.
Carbon Nanodots Inhibit Tumor Necrosis Factor-α-Induced Endothelial Inflammation through Scavenging Hydrogen Peroxide and Upregulating Antioxidant Gene Expression in EA.hy926 Endothelial Cells
Authors: Chavez Jessica , Khan Ajmal , Watson Kenna R. , Khan Safeera , Si Yaru , Deng Alexandra Y. , Koher Grant , Anike Mmesoma S. , Yi Xianwen , Jia Zhenquan
Published: February 2024

Carbon nanodots (CNDs) are a new type of nanomaterial with a size of less than 10 nanometers and excellent biocompatibility, widely used in fields such as biological imaging, transmission, diagnosis, and drug delivery. However, its potential and mechanism to mediate endothelial inflammation have yet to be explored. Here, we report that the uptake of CNDs by EA.hy926 endothelial cells is both time and dose dependent. The concentration of CNDs used in this experiment was found to not affect cell viability. TNF-α is a known biomarker of vascular inflammation. Cells treated with CNDs for 24 h significantly inhibited TNF-α (0.5 ng/mL)-induced expression of intracellular adhesion molecule 1 (ICAM-1) and interleukin 8 (IL-8). ICAM-1 and IL-8 are two key molecules responsible for the activation and the firm adhesion of monocytes to activated endothelial cells for the initiation of atherosclerosis. ROS, such as hydrogen peroxide, play an important role in TNF-α-induced inflammation. Interestingly, we found that CNDs effectively scavenged H2O2 in a dose-dependent manner. CNDs treatment also increased the activity of the antioxidant enzyme NQO1 in EA.hy926 endothelial cells indicating the antioxidant properties of CNDs. These results suggest that the anti-inflammatory effects of CNDs may be due to the direct H2O2 scavenging properties of CNDs and the indirect upregulation of antioxidant enzyme NQO1 activity in endothelial cells. In conclusion, CND can inhibit TNF-α-induced endothelial inflammation, possibly due to its direct scavenging of H2O2 and the indirect upregulation of antioxidant enzyme NQO1 activity in endothelial cells.
Enrichment of Whole-Grain Breads with Food-Grade Extracted Apple Pomace Bioactives Enhanced Their Anti-Inflammatory, Antithrombotic and Anti-Oxidant Functional Properties
Authors: Tsoupras Alexandros , Moran Donal , Shiels Katie , Saha Sushanta Kumar , Abu-Reidah Ibrahim M. , Thomas Raymond H. , Redfern Shane
Published: February 2024

Apple pomace (AP) is a bio-waste product of apples that is co-produced as a by-product during apples’ processing for making apple-based products, mainly apple juice, cider and vinegar. AP is a rich source of several bioactives that can be valorized as ingredients for developing novel functional foods, supplements and nutraceuticals. Within the present study, food-grade extracts from AP with different tannin contents were found to contain bioactive polar lipids (PLs), phenolics and carotenoids with strong anti-oxidant, antithrombotic and anti-inflammatory properties. The extract from the low-in-tannins AP showed stronger anti-inflammatory potency in human platelets against the potent thrombo-inflammatory mediator platelet-activating factor (PAF), while it also exhibited considerable anti-platelet effects against the standard platelet agonist, adenosine diphosphate (ADP). The infusion of 0.5–1.0 g of this bioactive AP extract as functional ingredients for whole-grain bread-making resulted in the production of novel bio-functional bread products with stronger anti-oxidant, antithrombotic and anti-inflammatory potency against both PAF and ADP in human platelets, compared to the standard non-infused control breads. Structural analysis by LCMS showed that the PL-bioactives from all these sources (AP and the bio-functional breads) are rich in bioactive unsaturated fatty acids (UFA), especially in the omega-9 oleic acid (OA; 18:1n9), the omega-3 alpha linolenic acid (ALA; 18:n3) and the omega-6 linoleic acid (LA; 18:2n6), which further supports their strong anti-inflammatory and antithrombotic properties. All food-grade extracted AP including that infused with AP-bioactives novel functional breads showed higher hydrophilic, lipophilic and total phenolic content, as well as total carotenoid content, and subsequently stronger antioxidant capacity. These results showed the potential of appropriately valorizing AP-extracts in developing novel bio-functional bakery products, as well as in other health-promoting applications. Nevertheless, more studies are needed to fully elucidate and/or validate the anti-inflammatory, antithrombotic and antioxidant potential of novel bio-functional products across the food and cosmetic sectors when infused with these AP bioactives.
Melatonin Use during Pregnancy and Lactation Complicated by Oxidative Stress: Focus on Offspring’s Cardiovascular–Kidney–Metabolic Health in Animal Models
Authors: Tain You-Lin , Hsu Chien-Ning
Published: February 2024

Cardiovascular–kidney–metabolic (CKM) syndrome has emerged as a major global public health concern, posing a substantial threat to human health. Early-life exposure to oxidative stress may heighten vulnerability to the developmental programming of adult diseases, encompassing various aspects of CKM syndrome. Conversely, the initiation of adverse programming processes can potentially be thwarted through early-life antioxidant interventions. Melatonin, originally recognized for its antioxidant properties, is an endogenous hormone with diverse biological functions. While melatonin has demonstrated benefits in addressing disorders linked to oxidative stress, there has been comparatively less focus on investigating its reprogramming effects on CKM syndrome. This review consolidates the current knowledge on the role of oxidative stress during pregnancy and lactation in inducing CKM traits in offspring, emphasizing the underlying mechanisms. The multifaceted role of melatonin in regulating oxidative stress, mediating fetal programming, and preventing adverse outcomes in offspring positions it as a promising reprogramming strategy. Currently, there is a lack of sufficient information in humans, and the available evidence primarily originates from animal studies. This opens up new avenues for novel preventive intervention in CKM syndrome.
Variations in Proline Content, Polyamine Profiles, and Antioxidant Capacities among Different Provenances of European Beech (Fagus sylvatica L.)
Authors: Kebert Marko , Stojnić Srđan , Rašeta Milena , Kostić Saša , Vuksanović Vanja , Ivanković Mladen , Lanšćak Miran , Markić Anđelina Gavranović
Published: February 2024

International provenance trials are a hot topic in forestry, and in light of climate change, the search for more resilient beech provenances and their assisted migration is one of the challenges of climate-smart forestry. The main aim of the study was to determine intraspecific variability in European beech (Fagus sylvatica L.) among 11 beech provenances according to total antioxidant capacities estimated by various assays, such as DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonic) acid), FRAP (ferric reducing antioxidant power) assay, and radical scavenging capacity against nitric oxide (RSC-NO assays), as well as osmolyte content, primarily individual polyamines (putrescine, spermidine, and spermine), and free proline content. Polyamine amounts were quantified by using HPLC coupled with fluorescent detection after dansylation pretreatment. The highest values for radical scavenger capacity assays (ABTS, DPPH, and FRAP) were measured in the German provenances DE47 and DE49. Also, the highest NO inhibition capacity was found in the provenance DE49, while the highest content of proline (PRO), total phenolic content (TPC), and total flavonoid content (TFC) was recorded in DE47. The Austrian AT56 and German provenance DE49 were most abundant in total polyamines. This research underlines the importance of the application of common antioxidant assays as well as osmolyte quantification as a criterion for the selection of climate-ready beech provenances for sustainable forest management.
Derivatives of Betulin and Betulinic Acid Containing a Phosphonate Group—In Silico Studies and Preliminary In Vitro Assessment of Antiviral Activity
Authors: Bębenek Ewa , Pęcak Paweł , Kadela-Tomanek Monika , Orzechowska Beata , Chrobak Elwira
Published: February 2024

Viral diseases affecting both humans and animals are a serious public problem. Chemical modifications of the structure of compounds of natural origin, e.g., betulin, seem to be a promising model in the search for new antiviral agents. The subject of our work was to conduct preliminary tests on the antiviral activity of phosphonic derivatives of betulin and betulinic acid and to assess the pharmacokinetic profile of target compounds. Human (HHV-1, HAdV-5) and animal viruses (BEV, VSV) were used in the in vitro tests. Additionally, this paper presents the results of research using in silico methods (ADMET and molecular docking). Two compounds (betulin 29-phosphonate 3 and 3-(3′,3′-dimethylsuccinyl)betulin acid 29-phosphonate 8a) showed antiviral activity against BEV, and compound 3 was also active against HAdV-5. For compound 3, which showed advantageous pharmacokinetic parameters, molecular docking was performed to determine possible interactions with the cellular target HAdV-5 endopeptidase, which plays an important role in various functions of the virus. Selecting the most active derivatives makes it possible to plan tests on an animal model.
Exergy as Lyapunov Function for Studying the Dynamic Stability of a Flow, Reacting to Self-Oscillation Excitation
Authors: Skiba Dmitriy Vladimirovich , Zubrilin Ivan Alexandrovich , Yakushkin Denis Vladimirovich
Published: February 2024

This article introduces a new, physically-based Lyapunov function definition that can be used as acoustic energy for finite-amplitude pressure oscillations in reacting systems, especially in combustion chambers. Reacting flow is seen as an open, non-equilibrium (parameters are distributed unevenly locally) thermodynamic system. This Lyapunov function is defined as the maximum mechanical work that could be extracted by a heat engine from the studied system if this system were disconnected from the inlet and outlet and from any other surrounding environment and the engine could transfer to the surrounding environment only mechanical work.
Privacy-Preserving Electricity Trading for Connected Microgrids
Authors: Alàs Oriol , Sebé Francesc
Published: February 2024

The electricity market is evolving from the traditional unidirectional model into a bidirectional one in which households also generate and sell energy. This new scenario requires technology able to manage the available energy and guarantee that all the participants pay or are paid appropriately. Unfortunately, fine-grained monitoring of energy production and consumption makes it possible to infer sensitive information about confidential aspects of people’s private life. In this paper, we propose a system designed for privacy-preserving electricity trading in a connected microgrid. The system guarantees that at the end of a billing period, the distribution system operator can compute the quantity to be charged or paid to each household while being unable to trace its consumption details.
Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
Authors: Kraprayoon Jittisa , Pham Austin , Tsai Timothy J.
Published: February 2024

Dynamic time warping estimates the alignment between two sequences and is designed to handle a variable amount of time warping. In many contexts, it performs poorly when confronted with two sequences of different scale, in which the average slope of the true alignment path in the pairwise cost matrix deviates significantly from one. This paper investigates ways to improve the robustness of DTW to such global time warping conditions, using an audio–audio alignment task as a motivating scenario of interest. We modify a dataset commonly used for studying audio–audio synchronization in order to construct a benchmark in which the global time warping conditions are carefully controlled, and we evaluate the effectiveness of several strategies designed to handle global time warping. Among the strategies tested, there is a clear winner: performing sequence length normalization via downsampling before invoking DTW. This method achieves the best alignment accuracy across a wide range of global time warping conditions, and it maintains or reduces the runtime compared to standard usages of DTW. We present experiments and analyses to demonstrate its effectiveness in both controlled and realistic scenarios.
Exploring the Effectiveness of Evaluation Practices for Computer-Generated Nonverbal Behaviour
Authors: Wolfert Pieter , Henter Gustav Eje , Belpaeme Tony
Published: February 2024

This paper compares three methods for evaluating computer-generated motion behaviour for animated characters: two commonly used direct rating methods and a newly designed questionnaire. The questionnaire is specifically designed to measure the human-likeness, appropriateness, and intelligibility of the generated motion. Furthermore, this study investigates the suitability of these evaluation tools for assessing subtle forms of human behaviour, such as the subdued motion cues shown when listening to someone. This paper reports six user studies, namely studies that directly rate the appropriateness and human-likeness of a computer character’s motion, along with studies that instead rely on a questionnaire to measure the quality of the motion. As test data, we used the motion generated by two generative models and recorded human gestures, which served as a gold standard. Our findings indicate that when evaluating gesturing motion, the direct rating of human-likeness and appropriateness is to be preferred over a questionnaire. However, when assessing the subtle motion of a computer character, even the direct rating method yields less conclusive results. Despite demonstrating high internal consistency, our questionnaire proves to be less sensitive than directly rating the quality of the motion. The results provide insights into the evaluation of human motion behaviour and highlight the complexities involved in capturing subtle nuances in nonverbal communication. These findings have implications for the development and improvement of motion generation models and can guide researchers in selecting appropriate evaluation methodologies for specific aspects of human behaviour.
Comparison of the Effects of Ketorolac and Acetaminophen on RANK-L Levels in the Gingival Crevicular Fluid during Orthodontic Tooth Movement: A Pilot Study
Authors: Rodríguez-Montaño Ruth , Ponce-Gómez Yesenia Isahy , Lomelí-Martínez Sarah Monserrat , Sifuentes-Franco Sonia , Ruiz-Gutiérrez Alondra del Carmen , Bayardo-González Rubén Alberto , Martínez-Rodríguez Vianeth María del Carmen , Meléndez-Ruíz José Luis , Gómez-Sandoval Juan Ramón
Published: February 2024

Background: Patients usually present pain due to the release of different inflammatory mediators such as prostaglandin E2 and RANK-L. Analgesics such as acetaminophen and ketorolac can inhibit RANK-L expression and this can affect orthodontic treatment by decreasing bone remodeling and slowing orthodontic dental movement. Several studies have reported a decrease in dental movement after administering some non-steroidal anti-inflammatory drugs. Proposal: The objective was to evaluate the RANK-L levels and a possible modulation by administering acetaminophen and ketorolac in patients starting orthodontic treatment. Methodology: A double-blind, randomized clinical trial was carried out with 24 subjects divided into three study groups: calcined magnesia as a placebo, acetaminophen, and ketorolac. Gingival crevicular fluid was obtained at four time points: before pharmacological intervention, at 24 h, at 48 h, and on the 5th day. RANK-L concentrations were evaluated through ELISA analysis. Also, interproximal space generated by the elastic separator at the end of the study was recorded in the different study groups using the visual analog scale. Results: An increase in RANK-L at 24 h was observed in the placebo group compared to the ketorolac and acetaminophen groups. However, no significant differences were observed in the interproximal space at day 5 in the three study groups. Conclusion: Patients who do not take analgesics at the start of orthodontic treatment have higher levels of RANK-L. Therefore, the use of ketorolac or acetaminophen could decrease bone remodeling and interfere with orthodontic dental movement.
Tunable Ultralow-Frequency Bandgaps Based on Locally Resonant Plate with Quasi-Zero-Stiffness Resonators
Authors: Guo Zhiwei , Xie Buliang , Sheng Meiping , Zeng Hao
Published: February 2024

In order to suppress the transverse vibration of a plate, a quasi-zero-stiffness (QZS) resonator with tunable ultralow frequency bandgaps was introduced and analyzed. The resonator was designed by introducing the quasi-zero-stiffness systems into mass-in-mass resonators. The plane wave expansion method was employed to derive the bandgap characteristics of the locally resonant (LR) plate with QZS resonators, and corresponding simulations were carried out by finite element method (FEM). The results show that an LR plate with a QZS resonator can provide two bandgaps, and the ranges of the bandgaps agree well with the vibration attenuation bands calculated by FEM. Owing to the introduction of the QZS system, the bandgaps can be easily transferred to a lower frequency or even an ultralow frequency. The damping of the QZS resonators can effectively broaden the vibration attenuation bands. In addition, the differentiated design of the bandgap frequencies can be realized to obtain broadband low-frequency transverse wave suppression performance. Finally, a mechanical structure design scheme was proposed in order to achieve flexible adjustment of the bandgap frequency, which significantly increases the engineering applicability of QZS resonators.
Enhanced Soft Error Rate Estimation Technique for Aerospace Electronics Safety Design via Emulation Fault Injection
Authors: Lee Dongmin , Nam Taehyeong , Park Daeseon , Kim Yeju , Na Jongwhoa
Published: February 2024

In this paper, we propose Automatic Configuration Memory Fault Injection (ACMFI), a tool that calculates the architectural vulnerability factor (AVF) and soft error rate (SER) using the emulation fault injection technique. SER, which is essential for the safety design of aerospace electronics, can be obtained by experiments (beam tests) performed in a beam facility equipped with high-energy radiation facilities. However, SER calculation using beam tests has the disadvantage of a high cost and a long waiting time, making it difficult to use in the conceptual design stage, which is the aerospace system development stage and the initial HW/SW development stage. Using the emulation fault injection method, it is possible to estimate the SER, which can be used in the system safety design phase. This paper describes the ACMFI tool, which automatically performs emulation fault injection in SRAM-based FPGAs, which are widely used in aerospace electronic hardware. Unlike the existing methods, the proposed method has the advantage of minimizing the side effects by injecting faults into a dedicated SRAM area. In other words, the SER obtained by the proposed method can be estimated more accurately than the SER result obtained by the existing method. To prove the accuracy of the proposed test method, the SER calculated by performing an emulation fault injection test on the same FPGA was compared with the SER results tested at the beam facility. The method of obtaining SER using the proposed ACMFI gave results that were closer to the SER obtained by testing at the beam facility than the method of obtaining SER using the existing EMFI. The proposed method is used to calculate the failure rate, which is a key variable in determining the development assurance level when performing safety design tests in aerospace system development, enabling the development of safer systems and lower cost/higher quality aerospace electronic equipment than before.
Machine Vision—Moving from Industry 4.0 to Industry 5.0
Authors: Tzampazaki Maria , Zografos Charalampos , Vrochidou Eleni , Papakostas George A.
Published: February 2024

The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans’ daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity.
Controlling Traffic Congestion in a Residential Area via GLOSA Development
Authors: Jawad Yahya Kadhim , Nitulescu Mircea
Published: February 2024

The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society’s constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities’ traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour.
Lean Manufacturing Assessment: Dimensional Analysis with Hesitant Fuzzy Linguistic Term Sets
Authors: Chitiva-Enciso William Alexander , Pérez-Domínguez Luis Asunción , Romero-López Roberto , Luviano-Cruz David , Pérez-Olguín Iván Juan Carlos , Méndez-González Luis Carlos
Published: February 2024

Lean Manufacturing has become, in recent years, one of the most important philosophies for improving production and organizational systems. The literature shows that Hesitant Fuzzy Linguistic Terms Sets (HFLTSs) are highly capable of manipulating the uncertainty that the judgments made by evaluators carry and that they are subject to their perception, especially when used in combination with multicriteria decision making (MCDM) for the measurement of indicators in this type of system, as well as their general performance. However, it is still of interest to researchers to develop techniques and instruments that facilitate the measurement of the results obtained after applying this philosophy in organizations. This article proposes a model for the evaluation of the Lean Manufacturing performance through the Analytic Hierarchy Process (AHP) and Dimensional Analysis (DA) using HFLTSs. The results obtained show that the proposed model is a solid tool for the evaluation of Lean Manufacturing systems from a different perspective and that it can be integrated into the issuance of evaluations in a better way by considering human subjectivity. At the same time, it offers a strategy to create priorities in the action plans that Lean system managers propose after evaluating. However, it is important to apply the proposed model to multiple organizations and analyze the results obtained to maximize its benefits.
Influence of Material on the Density of a Point Cloud Created Using a Structured-Light 3D Scanner
Authors: Kaiser Jiří , Dědič Martin
Published: February 2024

Global digitization affects all sectors, including construction. Indeed, 3D scanning and digital photogrammetry methods are increasingly being used to obtain 3D data of buildings. The data obtained by these methods are a cloud of points, and our research is focused on this cloud’s density. From the literature and our own previous research, it is known that different materials have different properties that are manifested in the structured-light 3D scanning of the surface of the measured object. We have selected materials with the assumption that their properties would negatively affect the density of the point cloud. The article describes the methodology of how the measurement of selected materials was performed and suggests material surface treatment possibilities to improve the properties of the materials for structured-light 3D data acquisition. The influence of suggested surface treatments on objects and/or materials was not investigated. Each intended case of using the suggested surface treatments needs to be considered individually to avoid object deterioration and/or material deterioration. Thanks to this research, it is possible to estimate the problem areas in terms of the materials during the reconnaissance of the measured object. The results of our experiments show that the treatments used can improve the accuracy of the measured object model and reduce the need to manually complete the model or scan the measured object several times.
Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting
Authors: D’Elia Alessandro , Alfakes Boulos , Alkhazaleh Anas , Banchi Leonardo , Beretta Matteo , Carrazza Stefano , Chiarello Fabio , Di Gioacchino Daniele , Giachero Andrea , Henrich Felix , Piedjou Komnang Alex Stephane , Ligi Carlo , Maccarrone Giovanni , Macucci Massimo , Palumbo Emanuele , Pasquale Andrea , Piersanti Luca , Ravaux Florent , Rettaroli Alessio , Robbiati Matteo , Tocci Simone , Gatti Claudio
Published: February 2024

In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, such as the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on a single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript, we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favoring applications like axion dark matter searches.
Mathematical Analysis and Real-Time Control of a Novel 5-DOF Robotic System with a Parallel Kinematics Structure for Additive Manufacturing Technologies
Authors: Dumlu Ahmet , Ayten Kagan Koray , Mahboubkhah Mehran , Kalınay Gurkan , Golcugezli Sadrettin , Akhbari Sina
Published: February 2024

The systems that use parallel kinematic structures in additive manufacturing technology are particularly notable for their ability to provide exceptional precision and efficiency in the fabrication of intricate geometrically shaped items. This study introduces a novel system paradigm with five degrees of freedom, specifically developed to tackle existing additive manufacturing issues. In the proposed design, by incorporating rotational motions along the x and z axes, contributions were added to the efficiency of typical three-degrees-of-freedom (3-DOF) systems, resulting in a total of five degrees of freedom. In this way, it is aimed at increasing product durability, improving surface integrity, and saving production time. In this study, the conceptual design of the system was defined. Mathematical analyses were then used to determine the kinematic and dynamic models of the system, and a proposed model-based control technique was revealed. To evaluate the axis movement performance of the system, two different control techniques were used, and real-time test studies were conducted. The first control technique was the proportional–integral–derivative (PID) controller, and the second method was the sliding mode control (SMC) method, which was used to increase the performance of the system during trajectory tracking. The experimental results showed that the SMC method provides a reasonably good trajectory tracking response and a steady-state error compared to the classical PID controller.
Mechanical Properties of Rock Specimens Containing Pre-Existing Cracks with Different Dip Angles Based on Energy Theory and Cohesive Element Method
Authors: Tian Limei , Feng Zhiming , Wu Zhide , Liu Bingbing , Zhang Jinghua , Pan Jiliang
Published: February 2024

To investigate the influence of the crack dip angle on the strength of rock specimens, uniaxial compression tests were conducted on granite specimens containing pre-existing cracks. The strain energy evolution during the loading process was analyzed, and the loading-induced cracking process was simulated using the cohesive element method. Both the experimental and numerical results indicate that cracks significantly impact the plastic-yielding stage of the stress–strain curve more than the initial and elastic deformation stages. When the crack dip angle is less than 45°, the stress concentration near the crack is significant, which is an important factor affecting the strength and elastic strain energy distribution of rock specimens. When the crack dip angle is greater than 45°, the degree of stress concentration decreases, and the uniformity of the elastic strain energy distribution and the possibility of crack bifurcation increase. Combining the energy theory with the cohesive element method helps comprehensively understand the initiation, propagation, and coalescence of microcracks near pre-existing crack tips. These research results can provide a reference for geotechnical engineering design and structural stability assessment.
A Second-Order Generalized Total Variation with Improved Alternating Direction Method of Multipliers Algorithm for Electrical Impedance Tomography Reconstruction
Authors: Zhao Ruwen , Xu Chuanpei , Mo Wei , Zhu Zhibin
Published: February 2024

The issue of Electrical Impedance Tomography (EIT) is a well-known inverse problem that presents challenging characteristics. In order to address the difficulties associated with ill-conditioned inverses, regularization methods are typically employed. One commonly used approach is total variation (TV) regularization, which has shown effectiveness in EIT. In order to meet the requirements of real-time tracking, it is essential to acquire fast and reliable algorithms for image reconstruction. Therefore, we present a modified second-order generalized regularization algorithm that enables more-accurate reconstruction of organ boundaries and internal structures, to reduce EIT artifacts, and to overcome the inability of the conventional Tikhonov regularization method in solving the step effect of the medium boundary. The proposed algorithm uses the improved alternating direction method of multipliers (ADMM) to tackle this optimization issue and adopts the second-order generalized total variation (SOGTV) function with strong boundary-preserving features as the regularization generalization function. The experiments are based on simulation data and the physical model of the circular water tank that we developed. The results showed that SOGTV regularization can improve image realism compared with some classic regularization.
Adaptive Method for Exploring Deep Learning Techniques for Subtyping and Prediction of Liver Disease
Authors: Hendi Ali Mohammed , Hossain Mohammad Alamgir , Majrashi Naif Ali , Limkar Suresh , Elamin Bushra Mohamed , Rahman Mehebubar
Published: February 2024

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.
Unary Quantum Finite State Automata with Control Language
Authors: Mereghetti Carlo , Palano Beatrice , Raucci Priscilla
Published: February 2024

We study quantum finite automata with control language (qfcs), a theoretical model for finite memory hybrid systems coupling a classical computational framework with a quantum component. We constructively show how to simulate measure-once, measure-many, reversible, and Latvian qfas by qfcs, emphasizing the size cost of such simulations. Next, we prove the decidability of testing the periodicity of the stochastic event induced by a given qfc. Thanks to our qfa simulations, we can extend such a decidability result to measure-once, measure-many, reversible, and Latvian qfas as well. Finally, we focus on comparing the size efficiency of quantum and classical finite state automata on unary regular language recognition. We show that unary regular languages can be recognized by isolated cut point qfcs for which the size is generally quadratically smaller than the size of equivalent dfas.
Stable Low-Rank CP Decomposition for Compression of Convolutional Neural Networks Based on Sensitivity
Authors: Yang Chenbin , Liu Huiyi
Published: February 2024

Modern convolutional neural networks (CNNs) play a crucial role in computer vision applications. The intricacy of the application scenarios and the growing dataset both significantly raise the complexity of CNNs. As a result, they are often overparameterized and have significant computational costs. One potential solution for optimizing and compressing the CNNs is to replace convolutional layers with low-rank tensor decomposition. The most suitable technique for this is Canonical Polyadic (CP) decomposition. However, there are two primary issues with CP decomposition that lead to a significant loss in accuracy. Firstly, the selection of tensor ranks for CP decomposition is an unsolved issue. Secondly, degeneracy and instability are common problems in the CP decomposition of contractional tensors, which makes fine-tuning the compressed model difficult. In this study, a novel approach was proposed for compressing CNNs by using CP decomposition. The first step involves using the sensitivity of convolutional layers to determine the tensor ranks for CP decomposition effectively. Subsequently, to address the degeneracy issue and enhance the stability of the CP decomposition, two novel techniques were incorporated: optimization with sensitivity constraints and iterative fine-tuning based on sensitivity order. Finally, the proposed method was examined on common CNN structures for image classification tasks and demonstrated that it provides stable performance and significantly fewer reductions in classification accuracy.
Minimizing the Limitations in Improving Historical Aerial Photographs with Super-Resolution Technique
Authors: Incekara Abdullah Harun , Alganci Ugur , Arslan Ozan , Seker Dursun Zafer
Published: February 2024

Compared to natural images in artificial datasets, it is more challenging to improve the spatial resolution of remote sensing optical image data using super-resolution techniques. Historical aerial images are primarily grayscale due to single-band acquisition, which further limits their recoverability. To avoid data limitations, it is advised to employ a data collection consisting of images with homogeneously distributed intensity values of land use/cover objects at various resolution values. Thus, two different datasets were created. In line with the proposed approach, images of bare land, farmland, residential areas, and forested regions were extracted from orthophotos of different years with different spatial resolutions. In addition, images with intensity values in a more limited range for the same categories were obtained from a single year’s orthophoto to highlight the contribution of the suggested approach. Training of two different datasets was performed independently using a deep learning-based super-resolution model, and the same test images were enhanced individually with the weights of both models. The results were assessed using a variety of quality metrics in addition to visual interpretation. The findings indicate that the suggested dataset structure and content can enable the recovery of more details and effectively remove the smoothing effect. In addition, the trend of the metric values matches the visual perception results.