bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a Small-Sized Urban Cable Conduit Inspection Robot
Authors: You Yiqiang , Zheng Yichen , Huang Kangle , He Yuling , Huang Zhiqing , Zhan Lulin
Published: September 2024

Cable conduits are crucial for urban power transmission and distribution systems. However, current conduit robots are often large and susceptible to tilting issues, which hampers the effective and intelligent inspection of these conduits. Therefore, there is an urgent need to develop a smaller-sized conduit inspection robot to address these challenges. Based on an in-depth analysis of the characteristics of the cable conduit working environment and the associated functional requirements, this study successfully developed a small-scale urban cable conduit inspection robot prototype. This development was grounded in relevant design theories, simulation analyses, and experimental tests. The test results demonstrate that the robot’s bracing module effectively prevents tilting within the conduit. Additionally, the detection module enables comprehensive 360-degree conduit inspections, and the vacuuming module meets the negative pressure requirements for efficient absorption of dust and foreign matter. The robot has met the expected design goals, effectively enhanced the automation of the cable conduit construction process, and improved the quality control of cable laying.
Optimising Sodium Borohydride Reduction of Platinum onto Nafion-117 in the Electroless Plating of Ionic Polymer–Metal Composites
Authors: Manaf Eyman , Fitzpatrick Daniel P. , Higginbotham Clement L. , Lyons John G.
Published: September 2024

The effects of process parameters on the electroless plating of ionic polymer–metal composites (IPMCs) were studied in this work. Specifically, the NaBH4 reduction of platinum onto Nafion-117 was characterised. The effects of the concurrent variation of NaBH4 concentration, stir time and temperature on surface resistance were studied through a full factorial design. The three-factor three-level factorial design resulted in 27 runs. Surface resistance was measured using a four-point probe. A regression model with an R2 value of 97.45% was obtained. Surface resistance was found to decrease with increasing stir time (20 to 60 min) and temperature (20 to 60 °C). These responses were attributed to increased platinisation rates, resulting in more uniform electrode deposition, confirmed by scanning electron microscopy (SEM) and energy-dispersive X-ray (EDAX) analysis. Surface resistance decreased, going from 1% to 5% NaBH4 concentration, but increased from 5% to 10% concentration. This behaviour was attributed to surface morphology: increased grain size inducing porous electrodes, in line with findings in the literature. The maximum tip displacement, measured through a computer vision system, as well as the maximum blocking force, measured through an analytical balance setup, were obtained for all 27 samples. The varying results were discussed with regards to surface and cross-sectional SEMs, alongside EDAX analysis.
Building Safer Workplaces: Unveiling the Impact of Safety Leadership Styles in the Construction Industry
Authors: Senthamizh Sankar S. , Anandh K. S.
Published: September 2024

This study investigates the most effective leadership style for enhancing safety performance in the construction industry. Through a scientometric analysis of 114 research papers and a meta-analysis of 13 studies involving 7220 participants, it uncovers a growing body of research on safety leadership in construction and identifies safety-specific transformational leadership as the most impactful style for improving safety outcomes. The research highlights the increasing importance of safety leadership in the construction sector and reveals a trend towards safety-specific transformational leadership as the preferred approach. This leadership style emphasizes empowering and motivating employees to prioritize safety through shared values, vision, and goals. The study also emphasizes the need for enhanced collaboration among researchers and institutions, and advocates for the inclusion of studies from developing countries to ensure a comprehensive understanding of safety leadership practices globally. Additionally, the findings align with the Sustainable Development Goals 3, 8, and 11, underscoring the significance of fostering safe and secure working environments and promoting sustainable urbanization. This research serves as a valuable resource for construction industry stakeholders seeking to implement effective safety leadership strategies, and it lays the groundwork for further exploration in this critical area.
The Role of Equity Crowdfunding in the Brazilian Entrepreneurial Ecosystem: An Empirical Analysis
Authors: Figueiredo Ronnie , Bendelá Flavia
Published: September 2024

This research provides the first deep and empirical look into Brazil’s increasing equity crowdfunding, or crowdinvest, market, and examines its peculiarities. It is a pioneering work dedicated to understanding which characteristics most impact the success of offers in the country, and whether there is any representative movement pointing to the inclusion of gender, firm age, and location or participation of small investors. We employ descriptive and quantitative research techniques and analyses all the offerings derived through this mechanism from 1 January 2017 to 31 December 2020—totaling 234 operations, considering successful offers (the ones that received investment) and unsuccessful offers (those that failed in raising funds), registered by 24 platforms in the country according to CVM (Comissão de Valores Mobiliários), the Brazilian SEC, under regulation Instruction 588. Furthermore, we use empirical and publicly available data from several sources. The findings reveal that mutual offers are more attractive to investors than those of equity, impacting the dependent variable of success. Besides this, firm location is the only inclusion aspect with representativeness among the independent variables tested. The main contributions of the research lie in providing the first empirical study that (1) consolidates a consistent database of the equity crowdfunding industry in Brazil, (2) reveals the role of crowdinvest in Brazil, related to bank credit to SMEs, and (3) provides recommendations to the main stakeholders that can improve the whole ecosystem.
Intercropping in Coconut Plantations Regulate Soil Characteristics by Microbial Communities
Authors: Tong Chaoqun , Yu Ruoyun , Chen Siting , Hu An , Dong Zhiguo , Tang Longxiang , Lu Lilan , Yang Weibo , Dong Rongshu
Published: September 2024

Intercropping is a commonly employed agricultural technique that offers numerous advantages, such as increasing land productivity, enhancing soil health, and controlling soil-borne pathogens. In this study, Artemisia argyi, Dioscorea esculenta, and Arachis pintoi were intercropped with coconuts and compared with naturally growing weeds (Bidens pilosa), respectively. The regulatory mechanism of intercropping was examined by analyzing the variability in soil properties and microbial community structure across different intercropping modes and soil depths (0–20 cm, 20–40 cm, and 40–60 cm). The results indicate that intercropping can increase the diversity of soil bacteria and fungi. Moreover, as soil depth increases, the changes in microbial communities weaken. Intercropping reduced soil SOM and increased pH, which is directly related to the changes in the abundance of Acidobacteria in the soil. In various intercropping systems, the disparities resulting from intercropping with A. pintoi are particularly pronounced. Specifically, intercropping with A. pintoi leads to an increase in soil potassium and phosphorus levels, as well as an enhancement in the abundance of Bacillus sp., which plays a crucial role in the suppression of plant pathogenic fungi within the soil ecosystem. The results of the correlation analysis and structural equation modeling (SEM) suggest that the impacts of three intercropping systems on microbial composition and soil indicators exhibit considerable variation. However, a common critical factor influencing these effects is the soil phosphorus content. Furthermore, our findings indicate that intercropping resulted in lower soil nitrogen levels, exacerbating nitrogen deficiency and masking its impact on the microbial community composition.
Named Entity Recognition for Crop Diseases and Pests Based on Gated Fusion Unit and Manhattan Attention
Authors: Tang Wentao , Wen Xianhuan , Hu Zelin
Published: September 2024

Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and enhances it using bidirectional long short-term memory (BiLSTM) to extract features from the context. It uses a gated unit to perform weighted fusion between the outputs of RoBERTa and BiLSTM, thereby enriching the information flow. The fused output is then fed into a novel Manhattan attention mechanism to capture the long-range dependencies. The global optimum tagging sequence is obtained using the conditional random fields layer. To enhance the model’s robustness, we incorporate adversarial training using the fast gradient method. This introduces adversarial examples, allowing the model to learn more disturbance-resistant feature representations, thereby improving its performance against unknown inputs. GatedMan achieved F1 scores of 93.73%, 94.13%, 93.98%, and 96.52% on the AgCNER, Peoples_daily, MSRA, and Resume datasets, respectively, thereby outperforming the other models. Experimental results demonstrate that GatedMan accurately identifies entities related to crop diseases and pests and exhibits high generalizability in other domains.
Biological and Physiological Changes in Spodoptera frugiperda Larvae Induced by Non-Consumptive Effects of the Predator Harmonia axyridis
Authors: Fan Zeyun , Kong Weizhen , Ran Xiaotong , Lv Xiaolu , Ma Chongjian , Yan He
Published: September 2024

The effects of predatory natural enemies on their prey or hosts involve both consumption and non-consumptive effects. This study investigated the non-consumptive effects of the predator, Harmonia axyridis (Coleoptera: Coccinellidae) on 1st, 2nd and 3rd instar larvae of Spodoptera frugiperda. We exposed larvae of different instars to the predator and assessed various parameters using a combination of biological and biochemical methods. Exposure to the predator significantly affected the growth and development of the S. frugiperda caterpillars. Firstly, the developmental duration of S. frugiperda larvae in the 1st–3rd instars and the pupal stage were notably prolonged. Moreover, we observed significant effects on pupal mass, pupal abnormality rate and emergence rate. These non-consumptive effects were gradually weakened with an increase in the larval stage exposed to the predator. Antioxidant enzyme activities including catalase (CAT) peroxidase (POD) and superoxide dismutase (SOD) activity increased significantly. Additionally, organismal triglyceride, trehalose and glycogen content were significantly altered by non-consumptive effects, while protein content showed no significant change. Spodoptera frugiperda larvae increased the activity of antioxidant enzymes in response to potential predators to mitigate oxidative stress and reduce cellular and tissue damage. This resources redistribution towards survival may inhibit growth and development of the species and further exacerbate these non-consumptive effects. These findings highlight the importance of considering non-consumptive effects in pest-management strategies to optimize control measures in agricultural systems.
Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN
Authors: Khairunniza-Bejo Siti , Ibrahim Mohd Firdaus , Hanafi Marsyita , Jahari Mahirah , Ahmad Saad Fathinul Syahir , Mhd Bookeri Mohammad Aufa
Published: September 2024

Counting planthoppers manually is laborious and yields inconsistent results, particularly when dealing with species with similar features, such as the brown planthopper (Nilaparvata lugens; BPH), whitebacked planthopper (Sogatella furcifera; WBPH), zigzag leafhopper (Maiestas dorsalis; ZIGZAG), and green leafhopper (Nephotettix malayanus and Nephotettix virescens; GLH). Most of the available automated counting methods are limited to populations of a small density and often do not consider those with a high density, which require more complex solutions due to overlapping objects. Therefore, this research presents a comprehensive assessment of an object detection algorithm specifically developed to precisely detect and quantify planthoppers. It utilises annotated datasets obtained from sticky light traps, comprising 1654 images across four distinct classes of planthoppers and one class of benign insects. The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. The results indicated that Faster R-CNN VGG 16 outperformed other models, achieving a mean average precision (mAP) score of 97.69% and exhibiting exceptional accuracy in classifying all planthopper categories. The correctness of the model was verified by entomologists, who confirmed a classification and counting accuracy rate of 98.84%. Nevertheless, the model fails to recognise certain samples because of the high density of the population and the significant overlap among them. This research effectively resolved the issue of low- to medium-density samples by achieving very precise and rapid detection and counting.
Exploring Community-Supported Agriculture through Maslow’s Hierarchy: A Systematic Review of Research Themes and Trends
Authors: Tian Xiaofan , Zhang Ruifang , Wang Zifan , Kang Xinna , Yang Zhixin
Published: September 2024

Community-supported agriculture (CSA) has emerged as a pivotal model for sustainable and humanistic agricultural practices, emphasizing the symbiotic relationship between food production, consumption, and sustainable ecosystems. Despite the growing interest, a comprehensive analysis of research themes and trends within the CSA framework remains sparse. This paper undertakes a systematic review of CSA literature from 1999 to 2023, identifying evolving research hotspots, dominant themes, and prospective directions by keyword analysis to corroborate Maslow’s hierarchy of needs theory. The research analysis location is categorized into four temporal phases, revealing a geographical expansion from North America to encompass Asia, Africa, and other continents. This expansion corroborates Maslow’s theory, illustrating a global shift from fulfilling basic physiological needs towards recognizing sustainable practices, particularly in developing regions. The results of temporal trends (5 phases) and the hotspots of keyword analysis support each other by showing a societal shift from basic sustenance to a deeper understanding of nutrition and diet. Most of the recent research keywords are grouped into the “environment” and “health and education” categories, indicating an increasing emphasis on transforming the food system and nutrition education. This review suggests conducting an integrated analysis that links the various stages of the food supply chain with the criteria outlined in the Sustainable Agriculture Matrix (SAM). It highlights that the “environment” theme is a stage of building up esteem and self-realization that needs to be unfolded in the future, given that most research on community-supported agriculture (CSA) focuses on the “economy and society” aspect and consumption stage, which burnish self-morality in the theory of Maslow. Overall, this review proposes an analysis of the relevance among different subject categories and between food supply chain stages, which reveals that the trend of research under CSA development is accorded to the theory of Maslow’s hierarchy of needs and calls for a more holistic approach to agricultural research that considers ecological, health, and social imperatives.
Integrated Scale-Adaptive Adjustment Factor-Enhanced BlendMask Method for Pineapple Processing System
Authors: Wang Haotian , Zhang Haojian , Zhang Yukai , Deng Jieren , Liu Chengbao , Tan Jie
Published: September 2024

This study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, we developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a Pose Correction Planning (PCP) method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.
A Hydroponic System to Study the Effects of Root and Meristem Night Temperature on Growth, Photosynthesis Carbon Balance, and Antioxidant Enzymes in Rice
Authors: Pieters Alejandro J. , Stürz Sabine , Asch Julia , Asch Folkard
Published: September 2024

Nocturnal root and meristem temperature (RMT) can have a strong effect on rice growth and yield. However, underlying mechanisms are not well understood. To investigate the effects of night-time RMT on photosynthesis biomass allocation and activities of antioxidant enzymes, we designed a hydroponic system that maintained the following daily patterns of day/night temperature: 18/28 °C (HNT) or 28/18 °C (LNT). Rice plants cv. IR64 were grown in the greenhouse and subjected to either HNT or LNT. HNT stimulated growth and tillering but did not affect biomass allocation. HNT plants increased total biomass by 16 and 35%, depending on time of exposure. HNT increased rates of photosynthesis (Pn) compared to LNT plants in leaves of different ages. Overnight carbohydrate remobilisation was larger in HNT than in LNT plants, particularly at 16 days after treatment (dat), when Pn and relative growth rates were highest. Leaf soluble protein concentrations and specific leaf area were not affected by RMT, indicating higher photosynthetic nitrogen use efficiency in HNT plants. Super Oxide Dismutase, Ascorbate Peroxidase, and Glutathione Reductase activities did not respond to RMT, indicating no change in the production of reactive oxygen species in LNT plants despite lower photosynthesis rates. HNT increased sink demand by stimulating tillering, the increased sink demand upregulated the source activity through a larger leaf area per plant and a higher Pn throughout the canopy. The hydroponic system described here was able to control the temperature of the nutrient solution effectively, the installation of a second pump directly circulating the nutrient solution from and back to the reservoir through the cooling system allowed reaching the target temperature within 1 h. This system opens new opportunities to characterise plant responses to RMT alone or in combination with other environmental drivers.
Scientific and Technological Innovation Effects on High-Quality Agricultural Development: Spatial Boundaries and Mechanisms
Authors: Qin Shuai , Chen Hong
Published: September 2024

This study investigates the spatial boundaries and mechanisms of the effect of scientific and technological innovation (STI) on high-quality agricultural development (HQA) to enhance agricultural practices. By employing a double-fixed spatial Durbin model and analyzing panel data from 167 prefectural-level cities in major grain-producing regions spanning from 2004 to 2021, we revealed significant spatiotemporal variations in the impact of STI on HQA in both local and adjacent cities. Our findings remained robust after rigorous testing. The study identified the spillover range of STI to be 420 km, displaying a distinctive inverted U-shaped trend around 170 km. Mechanism analysis indicates that both agricultural industry upgrades and human capital levels within 420 km amplify the influence of STI on local HQA, with only the latter demonstrating spillover effects. Within 170 km, both factors effectively regulate HQA in adjacent cities, while beyond this distance, only human capital regulatory impact continues to exhibit spillover effects. These insights offer theoretical guidance for designing effective agricultural scientific and technology promotion policies aimed at elevating the quality of HQA.
A Lightweight Real-Time Recognition Algorithm for Tomato Leaf Disease Based on Improved YOLOv8
Authors: Liu Wenbo , Bai Chenhao , Tang Wei , Xia Yu , Kang Jie
Published: September 2024

To address the real-time detection challenge of deploying deep learning-based tomato leaf disease detection algorithms on embedded devices, an improved tomato leaf disease detection algorithm based on YOLOv8n is proposed in this paper. It is able to achieve the efficient, real-time detection of tomato leaf diseases while maintaining model’s lightweight requirements. The algorithm incorporated the LMSM (lightweight multi-scale module) and ALSA (Attention Lightweight Subsampling Module) to improve the ability to extract lightweight and multi-scale semantic information for the specific characteristics of tomato leaf disease, which include irregular spot size and lush tomato leaves. The head network was redesigned utilizing partial and group convolution along with a parameter-sharing method. Scalable auxiliary bounding box and loss function optimization strategies were introduced to further enhance performance. After undergoing the pruning technique, computation decreased by 61.7%, the model size decreased by 55.6%, and the FPS increased by 44.8%, all while a high level of accuracy was maintained. A detection speed of 19.70FPS on the Jetson Nano was obtained after undergoing TensorRT quantization, showing a 64.85% improvement compared to the initial detection speed. This method met the high real-time performance and small model size requirements for embedded tomato leaf disease detection systems, indirectly reducing the energy consumption of online detection. It provided an effective solution for the online detection of tomato leaf disease.
Geographical Distribution of Mauremys sinensis, Mauremys reevesii, and Their Hybrids in South Korea
Authors: Baek Hae-Jun , Cheong Eujin , Kim Youngha , Koo Kyo Soung , Kim Su-Hwan , Park Chang-Deuk , Yoon Ju-Duk
Published: September 2024

The Chinese striped-necked turtle Mauremys sinensis, introduced into South Korea presumably in 2012, is considered an invasive alien species owing to its devastating impact, including hybridization with the native protected species Reeves’ turtle M. reevesii. Recently, the presence of M. sinensis has been confirmed throughout the country, and several sympatric areas with M. reevesii have been reported. Thus, field surveys were conducted at 47 sites across M. sinensis and M. reevesii habitats in South Korea to determine the extent of hybridization. Five sympatric sites were confirmed, and hybrid individuals were identified at four sites. Genetic analyses (COI and R35) of two individuals from Jeju Island confirmed maternal M. reevesii and paternal M. sinensis lineages. Hybridization presumably does not occur under natural conditions, and the hybrids likely originated from captive breeding. This study identifies for the first time the habitats of M. sinensis and its hybrids in the wild of South Korea. The management measures proposed in the current study could be of value for the conservation of the native species; however, our study did not include reproductive monitoring, and there is a need for such surveys as well as for systematic management of non-native turtles introduced into South Korea.
The Impact of Genetic and Non-Genetic Factors on Lamb Loin Shear Force
Authors: Al-Moadhen Hussein , Lees Jarrod C. , van der Werf Julius H. J. , McGilchrist Peter
Published: September 2024

Shear force is commonly used to evaluate tenderness, one of the most crucial eating quality aspects of sheep meat. The effect size of various factors on tenderness is still unknown. Studies have suggested that both genetic and environmental factors contribute to the variation in meat tenderness, and there are possible interactions between these factors. An extensive data set (n = 23,696) was analyzed to examine genetic and non-genetic influences on the shear force at 5 days postmortem (SF5). SF5 was measured on lamb loins (Longissimus lumborum) taken from lambs reared over 12 years at eight sites across Australia. The results showed that all carcass traits had a significant (p < 0.001) impact on SF5, with the largest effect on SF5 associated with intramuscular fat (IMF %) (f = 1035). There was also a significant effect of sex, cold shortening at 18 °C, sire type and cohort on SF5 (p < 0.001), with a large variation observed between the minimum cohort at 15.9 ± 1.5 N and maximum at 51.2 ± 2.1 N. In conclusion, a complex matrix of production, processing and genetic factors impact lamb tenderness as measured by shear force. This experiment helps identify the size of the contribution of these factors towards lamb tenderness, enabling the sheep industry to enhance consumers’ satisfaction.
The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning
Authors: Niu Ben , Lee Benjamin , Wang Lili , Chen Wen , Johnson Jeffrey
Published: September 2024

Therapeutic antibodies such as monoclonal antibodies (mAbs), bispecific and multispecific antibodies are pivotal in therapeutic protein development and have transformed disease treatments across various therapeutic areas. The integrity of therapeutic antibodies, however, is compromised by sequence liabilities, notably deamidation, where asparagine (N) and glutamine (Q) residues undergo chemical degradations. Deamidation negatively impacts the efficacy, stability, and safety of diverse classes of antibodies, thus necessitating the critical need for the early and accurate identification of vulnerable sites. In this article, a comprehensive antibody deamidation-specific dataset (n = 2285) of varied modalities was created by using high-throughput automated peptide mapping followed by supervised machine learning to predict the deamidation propensities, as well as the extents, throughout the entire antibody sequences. We propose a novel chimeric deep learning model, integrating protein language model (pLM)-derived embeddings with local sequence information for enhanced deamidation predictions. Remarkably, this model requires only sequence inputs, eliminating the need for laborious feature engineering. Our approach demonstrates state-of-the-art performance, offering a streamlined workflow for high-throughput automated peptide mapping and deamidation prediction, with the potential of broader applicability to other antibody sequence liabilities.
A Novel Tetravalent Bispecific Immune Cell Engager Activates Natural Killer Cells to Kill Cancer Cells without Mediating Fratricide
Authors: Yang Ge , Nikkhoi Shahryar Khoshtinat , Owji Hajar , Li Geng , Massumi Mohammad , Cervelli Jessica , Vandavasi Venu Gopal , Hatefi Arash
Published: September 2024

We previously reported the structure, affinity, and anticancer activity of a bivalent bispecific natural killer cell engager (BiKE) composed of one anti-CD16a VHH and one anti-HER2 VHH fused via a linker. In this study, we explored the engineering of a tetravalent BiKE by fusing two anti-CD16a and two anti-HER2 VHHs in tandem, using bivalent BiKE as a template. The tetravalent BiKE was genetically engineered, and its tertiary structure was predicted using in silico modeling. The antigen binding and affinity of the tetravalent BiKE were assessed using ELISA, flow cytometry, and biolayer interferometry. The ability of the BiKEs to kill cancer cells was evaluated through classical and residual antibody-dependent cellular cytotoxicity (ADCC) assays. Additionally, we investigated the potential for NK cell fratricide via CD16a-CD16a crosslinking. Our results revealed that the tetravalent BiKE exhibited at least 100-fold higher affinity toward its target antigens compared to its bivalent counterpart. The residual ADCC assay indicated that the tetravalent BiKE was more effective in killing cancer cells than the bivalent BiKE, attributable to its lower Koff value, which prolonged its binding to NK cell surfaces. Fratricide assays demonstrated that neither the bivalent nor the tetravalent BiKE mediated fratricide. Notably, our findings showed that daratumumab-induced NK fratricide was restricted to CD38-CD38 crosslinking and was not related to ADCC via CD16a-CD38 crosslinking. This study is the first in the literature to show the successful engineering of a tetravalent immune cell engager composed of tandem VHH units, which achieves high affinity and anticancer activity without mediating fratricide.
Analysis of High-Dose Ascorbate-Induced Cytotoxicity in Human Glioblastoma Cells and the Role of Dehydroascorbic Acid and Iron
Authors: Piotrowsky Alban , Burkard Markus , Hammerschmidt Katharina , Ruple Hannah K. , Nonnenmacher Pia , Schumacher Monika , Leischner Christian , Berchtold Susanne , Marongiu Luigi , Kufer Thomas A. , Lauer Ulrich M. , Renner Olga , Venturelli Sascha
Published: September 2024

Several studies have demonstrated, both in vitro and in animal models, the anti-tumor efficacy of high-dose ascorbate treatment against a variety of tumor entities, including glioblastoma, the most common and aggressive primary malignant brain tumor. The aim of this study was to investigate the effects of high-dose ascorbate as well as dehydroascorbic acid on human glioblastoma cell lines and to evaluate different treatment conditions for the combined administration of ascorbate with magnesium (Mg2+) and iron (Fe3+). Intracellular levels of reactive oxygen species and the induction of cell death following ascorbate treatment were also investigated. We demonstrated high cytotoxicity and antiproliferative efficacy of high-dose ascorbate in human glioblastoma cells, whereas much weaker effects were observed for dehydroascorbic acid. Ascorbate-induced cell death was independent of apoptosis. Both the reduction in cell viability and the ascorbate-induced generation of intracellular reactive oxygen species could be significantly increased by incubating the cells with Fe3+ before ascorbate treatment. This work demonstrates, for the first time, an increase in ascorbate-induced intracellular ROS formation and cytotoxicity in human glioblastoma cells by pre-treatment of the tumor cells with ferric iron, as well as caspase-3 independence of cell death induced by high-dose ascorbate. Instead, the cell death mechanism caused by high-dose ascorbate in glioblastoma cells shows evidence of ferroptosis. The results of the present work provide insights into the efficacy and mode of action of pharmacological ascorbate for the therapy of glioblastoma, as well as indications for possible approaches to increase the effectiveness of ascorbate treatment.
Carthamus tinctorius L. (Safflower) Flower Extract Attenuates Hepatic Injury and Steatosis in a Rat Model of Type 2 Diabetes Mellitus via Nrf2-Dependent Hypoglycemic, Antioxidant, and Hypolipidemic Effects
Authors: Alshareef Nuha Saad , AlSedairy Sahar Abdulaziz , Al-Harbi Laila Naif , Alshammari Ghedeir M. , Yahya Mohammed Abdo
Published: September 2024

This study aimed to examine the hepatic and anti-steatotic protective effects of methanolic extract from Carthamus tinctorius (safflower) flowers (SFFE), using a rat model of type 2 diabetes mellitus (T2DM), and to examine the molecular mechanisms underlying these effects. Adult male Wistar rats were used for this study. First, T2DM was induced in some rats by feeding them a high-fat diet (HFD) for 4 weeks, followed by a single dose of streptozotocin (STZ) (35 mg/kg, i.p.). Experimental groups included the following five groups (n = 8 in each): control, control + SFFE, T2DM, T2DM + SFFE, and T2DM + SFFE + brusatol (an Nrf2 inhibitor, 2 mg/kg, i.p.). SFFE was administered at a concentration of 300 mg/kg, and all experiments concluded after 8 weeks. Treatments with SFFE significantly reduced fasting blood glucose levels, free fatty acids (FFAs), cholesterol, triglycerides, and low-density lipoprotein cholesterol in both the control and T2DM rats, but they failed to reduce fasting insulin levels in these groups. SFFE treatments also improved the liver structure and reduced hepatocyte vacuolization and hepatic levels of triglycerides and cholesterol in T2DM rats, in addition to increasing the hepatic mRNA levels of keap1 and the cytoplasmic levels and nuclear activities of Nrf2 in both the control and T2DM rats. SFFE also stimulated the expression levels of PPARα and CPT-1 but reduced the malondialdehyde (MDA), mRNA levels of SREBP1, fatty acid synthase, and acetyl CoA carboxylase in both the control and T2DM rats; meanwhile, it reduced hepatic mRNA and the nuclear activities of NF-κB and increased levels of glutathione, superoxide dismutase, and heme oxygenase-1 in the livers of both groups of treated rats. Furthermore, SFFE suppressed the levels of caspase-3, Bax, tumor necrosis factor-α, and interleukin-6 in the T2DM rats. Treatment with brusatol prevented all of these effects of SFFE. In conclusion, SFFE suppresses liver damage and hepatic steatosis in T2DM through Nrf2-dependent hypoglycemic, antioxidant, anti-inflammatory, and hypolipidemic effects.
Advances in Particle Acceleration: Novel Techniques, Instruments and Applications
Authors: Kutsaev Sergey V.
Published: September 2024

In the last decade, there have been significant advancements in accelerator technologies, driven by both fundamental research and practical applications in various fields, including X-ray science, medical treatments, and industrial processes [...]
Improving Quantum Optimization Algorithms by Constraint Relaxation
Authors: Pecyna Tomasz , Różycki Rafał
Published: September 2024

Quantum optimization is a significant area of quantum computing research with anticipated near-term quantum advantages. Current quantum optimization algorithms, most of which are hybrid variational-Hamiltonian-based algorithms, struggle to present quantum devices due to noise and decoherence. Existing techniques attempt to mitigate these issues through employing different Hamiltonian encodings or Hamiltonian clause pruning, but they often rely on optimistic assumptions rather than a deep analysis of the problem structure. We demonstrate how to formulate the problem Hamiltonian for a quantum approximate optimization algorithm that satisfies all the requirements to correctly describe the considered tactical aircraft deconfliction problem, achieving higher probabilities for finding solutions compared to previous works. Our results indicate that constructing Hamiltonians from an unconventional, quantum-specific perspective with a high degree of entanglement results in a linear instead of exponential number of entanglement gates instead and superior performance compared to standard formulations. Specifically, we achieve a higher probability of finding feasible solutions: finding solutions in nine out of nine instances compared to standard Hamiltonian formulations and quadratic programming formulations known from quantum annealers, which only found solutions in seven out of nine instances. These findings suggest that there is substantial potential for further research in quantum Hamiltonian design and that gate-based approaches may offer superior optimization performance over quantum annealers in the future.
Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
Authors: Niu Yani , Fan Songhua , Cheng Xin , Yao Xinpeng , Wang Zijian , Zhou Jingmei
Published: September 2024

As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method.
Deterministic and Probabilistic Assessment of Failure Mechanisms in Geosynthetic-Reinforced Embankments
Authors: Zhang Shen , Pai Lifang , Yue Rongxue , Shan Yuang , You Renjie , Ma Yaqing , He Xiaojuan
Published: September 2024

Geosynthetic-reinforced embankments are subject to two primary failure mechanisms: bond failure and rupture. Bond failure occurs when the critical slip surface extends beyond the reinforced zone, while rupture occurs when the slip surface intersects the reinforcement. For a specified factor of safety and reinforcement length, there exists a minimum tensile strength of the reinforcement required to ensure bond failure only. Increasing the tensile strength beyond this minimum does not alter the failure mechanism or the factor of safety. Conversely, extending the reinforcement length while keeping the tensile strength below this critical value may lead to rupture failure at the same factor of safety. This study utilizes Monte Carlo simulation to perform a probabilistic stability analysis of these failure mechanisms in embankments with varying soil types and slope angles. The analysis evaluates safety margins in terms of the factor of safety and probability of failure. Furthermore, this study investigates the impact of cross-correlation between soil strength parameters, demonstrating that realistic values of the correlation coefficient can reduce the probability of failure for both failure mechanisms.
Advancing Nighttime Object Detection through Image Enhancement and Domain Adaptation
Authors: Zhang Chenyuan , Lee Deokwoo
Published: September 2024

Due to the lack of annotations for nighttime low-light images, object detection in low-light images has always been a challenging problem. Achieving high-precision results at night is also an issue. Additionally, we aim to use a single nighttime dataset to complete the knowledge distillation task while improving the detection accuracy of object detection models under nighttime low-light conditions and reducing the computational cost of the model, especially for small targets and objects contaminated by special nighttime lighting. This paper proposes a Nighttime Unsupervised Domain Adaptation Network (NUDN) based on knowledge distillation to address these issues. To improve the detection accuracy of nighttime images, high-confidence bounding box predictions from the teacher and region proposals from the student are first fused, allowing the teacher to perform better in subsequent training, thus generating a combination of high-confidence and low-confidence pseudo-labels. This combination of feature information is used to guide model training, enabling the model to extract feature information similar to that of source images in nighttime low-light images. Nighttime images and pseudo-labels undergo random size transformations before being used as input for the student, enhancing the model’s generalization across different scales. To address the scarcity of nighttime datasets, we propose a nighttime-specific augmentation pipeline called LightImg. This pipeline enhances nighttime features, transforming them into daytime features and reducing issues such as backlighting, uneven illumination, and dim nighttime light, enabling cross-domain research using existing nighttime datasets. Our experimental results show that NUDN can significantly improve nighttime low-light object detection accuracy on the SHIFT and ExDark datasets. We conduct extensive experiments and ablation studies to demonstrate the effectiveness and efficiency of our work.
Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
Authors: Aldahmani Emad , Alzubi Ahmad , Iyiola Kolawole
Published: September 2024

This research presents a uni-regression deep approximate forecasting model for predicting future demand in supply chains, tackling issues like complex patterns, external factors, and nonlinear relationships. It diverges from traditional models by employing a deep learning strategy through recurrent bidirectional long short-term memory (BiLSTM) and nonlinear autoregressive with exogenous inputs (NARX), focusing on regression-based approaches. The model can capture intricate dependencies and patterns that elude conventional approaches. The integration of BiLSTM and NARX provides a robust foundation for accurate demand forecasting. The novel uni-regression technique significantly improves the model’s capability to detect intricate patterns and dependencies in supply chain data, offering a new angle for demand forecasting. This approach not only broadens the scope of modeling techniques but also underlines the value of deep learning for enhanced accuracy in the fluctuating supply chain sector. The uni-regression model notably outperforms existing models in accuracy, achieving the lowest errors: mean average error (MAE) at 1.73, mean square error (MSE) at 4.14, root mean square error (RMSE) at 2.03, root mean squared scaled error (RMSSE) at 0.020, and R-squared at 0.94. This underscores its effectiveness in forecasting demand within dynamic supply chains. Practitioners and decision-makers can leverage the uni-regression model to make informed decisions, optimize inventory management, and enhance supply chain resilience. Furthermore, the findings contribute to the ongoing evolution of supply chain demand forecasting methodologies.
Process-Oriented Tolerance and Variation Management: Review and Classification
Authors: Litzenburger Philipp , Goetz Stefan , Margies Lennard , Bode Christoph , Müller Rainer , Wartzack Sandro
Published: September 2024

In the context of tolerance management, the consideration of manufacturing and assembly processes is becoming increasingly important. The main drivers for this are, above all, short development times and high-quality requirements, leading to tight tolerances. To overcome the resulting challenges, many publications address the process-oriented tolerance management. However, since multiple terms and definitions for describing activities that link tolerance management with the production process exist, it is hard to obtain a comprehensive overview on the topic. Therefore, this paper presents a review of existing approaches. The aim is to identify similarities and differences of existing approaches and present them with the help of a classification. For this purpose, among others, work from the areas of process-oriented tolerance management, stream of variation, state space modeling, and variation propagation in multistation manufacturing and assembly systems is considered. Based on the definition of the summarizing term “process-oriented tolerance and variation management”, a classification of this thematic area will be introduced.
On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
Authors: Varam Dara , Khalil Lujain , Shanableh Tamer
Published: September 2024

This paper aims to explore the possibility of utilizing vision transformers (ViTs) for on-edge medical diagnostics by experimenting with the Kvasir-Capsule image classification dataset, a large-scale image dataset of gastrointestinal diseases. Quantization techniques made available through TensorFlow Lite (TFLite), including post-training float-16 (F16) quantization and quantization-aware training (QAT), are applied to achieve reductions in model size, without compromising performance. The seven ViT models selected for this study are EfficientFormerV2S2, EfficientViT_B0, EfficientViT_M4, MobileViT_V2_050, MobileViT_V2_100, MobileViT_V2_175, and RepViT_M11. Three metrics are considered when analyzing a model: (i) F1-score, (ii) model size, and (iii) performance-to-size ratio, where performance is the F1-score and size is the model size in megabytes (MB). In terms of F1-score, we show that MobileViT_V2_175 with F16 quantization outperforms all other models with an F1-score of 0.9534. On the other hand, MobileViT_V2_050 trained using QAT was scaled down to a model size of 1.70 MB, making it the smallest model amongst the variations this paper examined. MobileViT_V2_050 also achieved the highest performance-to-size ratio of 41.25. Despite preferring smaller models for latency and memory concerns, medical diagnostics cannot afford poor-performing models. We conclude that MobileViT_V2_175 with F16 quantization is our best-performing model, with a small size of 27.47 MB, providing a benchmark for lightweight models on the Kvasir-Capsule dataset.
Response Surface Design Models to Predict the Strength of Iron Tailings Stabilized with an Alkali-Activated Cement
Authors: Caetano Isabela , Rios Sara , Milheiro-Oliveira Paula
Published: September 2024

Tailing storage facilities are very complex structures whose failure generally leads to catastrophic consequences in terms of casualties, serious environmental impacts on local biodiversity, and disruptions in the mineral supply. For this reason, structures at risk must be reinforced or decommissioned. One possible option is its reinforcement with compacted filtered tailings stabilized with binders. Alkali-activated binders provide a more sustainable solution than ordinary Portland cement but require an optimization of the tailing–binder mixture, which, in some cases, can lead to a substantial experimental effort. Statistical models have been used to reduce the number of those experiments, but a rational design methodology is still lacking. This methodology to define the right mixture for a required strength should consider both the mixture components and in situ conditions. In this paper, response surface methods were used to plan and interpret unconfined compression strength test results on an iron tailing stabilized with alkali-activated binders. It was concluded that the fly ash content was the most important parameter, followed by the liquid content and sodium hydroxide concentration. From the obtained results, several statistical models were defined and compared according to the definition of a strength prediction model based on a mixture index parameter. It was interesting to observe that models with the porosity cement index still provide reasonable adjustment even when different tailings’ water contents are considered.