bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy, Repeatability, and Reproducibility of a Hand-Held Structured-Light 3D Scanner across Multi-Site Settings in Lower Limb Prosthetics
Authors: Cutti Andrea Giovanni , Santi Maria Grazia , Hansen Andrew H. , Fatone Stefania
Published: April 2024

The aim of this work was to assess the accuracy, repeatability, and reproducibility of a hand-held, structured-light 3D scanner (EINScan Pro 2X Plus with High Definition Prime Pack, SHINING 3D Tech. Co., Ltd., Hangzhou, China), to support its potential use in multi-site settings on lower limb prosthetics. Four limb models with different shapes were fabricated and scanned with a metrological 3D scanner (EINScan Laser FreeScan 5X, SHINING 3D Tech. Co., Ltd., Hangzhou, China) by a professional operator (OP0). Limb models were then mailed to three sites where two operators (OP1, OP2) scanned them using their own structured-light 3D scanner (same model). OP1 scanned limb models twice (OP1-A, OP1-B). OP0, OP1-A, and OP2 scans were compared for accuracy, OP1-A and OP1-B for repeatability, and OP1-A and OP2 for reproducibility. Among all comparisons, the mean radial error was <0.25 mm, mean angular error was <4°, and root mean square error of the radial distance was <1 mm. Moreover, limits of agreement were <3.5% for perimeters and volumes. By comparing these results with respect to clinically-relevant thresholds and to the literature available on other 3D scanners, we conclude that the EINScan Pro 2X Plus 3D Scanner with High Definition Prime Pack has good accuracy, repeatability, and reproducibility, supporting its use in multi-site settings.
An Adaptive Temporal Convolutional Network Autoencoder for Malicious Data Detection in Mobile Crowd Sensing
Authors: Owoh Nsikak , Riley Jackie , Ashawa Moses , Hosseinzadeh Salaheddin , Philip Anand , Osamor Jude
Published: April 2024

Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or data poisoning attempts where threat actors can inject malicious data into the system. There is a need for a detection system that mitigates malicious sensor data to maintain the integrity and reliability of the collected information. This paper addresses this issue by proposing an adaptive and robust model for detecting malicious data in MCS scenarios involving sensor data from mobile devices. The proposed model incorporates an adaptive learning mechanism that enables the TCN-based model to continually evolve and adapt to new patterns, enhancing its capability to detect novel malicious data as threats evolve. We also present a comprehensive evaluation of the proposed model’s performance using the SherLock datasets, demonstrating its effectiveness in accurately detecting malicious sensor data and mitigating potential threats to the integrity of MCS systems. Comparative analysis with existing models highlights the performance of the proposed TCN-based model in terms of detection accuracy, with an accuracy score of 98%. Through these contributions, the paper aims to advance the state of the art in ensuring the trustworthiness and security of MCS systems, paving the way for the development of more reliable and robust crowdsensing applications.
Toward Better Pedestrian Trajectory Predictions: The Role of Density and Time-to-Collision in Hybrid Deep-Learning Algorithms
Authors: Korbmacher Raphael , Tordeux Antoine
Published: April 2024

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2–2.2 ped/m2). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments, but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance.
Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation
Authors: Dippold Elisabeth Johanna , Tsai Fuan
Published: April 2024

The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.
Multi-Task Foreground-Aware Network with Depth Completion for Enhanced RGB-D Fusion Object Detection Based on Transformer
Authors: Pan Jiasheng , Zhong Songyi , Yue Tao , Yin Yankun , Tang Yanhao
Published: April 2024

Fusing multiple sensor perceptions, specifically LiDAR and camera, is a prevalent method for target recognition in autonomous driving systems. Traditional object detection algorithms are limited by the sparse nature of LiDAR point clouds, resulting in poor fusion performance, especially for detecting small and distant targets. In this paper, a multi-task parallel neural network based on the Transformer is constructed to simultaneously perform depth completion and object detection. The loss functions are redesigned to reduce environmental noise in depth completion, and a new fusion module is designed to enhance the network’s perception of the foreground and background. The network leverages the correlation between RGB pixels for depth completion, completing the LiDAR point cloud and addressing the mismatch between sparse LiDAR features and dense pixel features. Subsequently, we extract depth map features and effectively fuse them with RGB features, fully utilizing the depth feature differences between foreground and background to enhance object detection performance, especially for challenging targets. Compared to the baseline network, improvements of 4.78%, 8.93%, and 15.54% are achieved in the difficult indicators for cars, pedestrians, and cyclists, respectively. Experimental results also demonstrate that the network achieves a speed of 38 fps, validating the efficiency and feasibility of the proposed method.
Distributed Temperature Sensing through Network Analysis Frequency-Domain Reflectometry
Authors: Zahoor Rizwan , Vallifuoco Raffaele , Zeni Luigi , Minardo Aldo
Published: April 2024

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to ≈3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal formed by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of attaining high measurand resolution (≈50 mK at 3-cm spatial resolution) thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, unlike the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser and therefore is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double-sideband or single-sideband modulated probe light. The results confirm the validity of the proposed approach.
β-Sitosterol Mediates Gastrointestinal Smooth Muscle Relaxation Induced by Coccoloba uvifera via Muscarinic Acetylcholine Receptor Subtype 3
Authors: Aguirre-Crespo Francisco J. , Aragón-Gastélum José L. , Gutiérrez-Alcántara Eduardo J. , Zamora-Crescencio Pedro , Gómez-Galicia Diana L. , Alatriste-Kurzel Diego R. , Alvarez Guzman , Hernández-Núñez Emanuel
Published: April 2024

Coccoloba uvifera is a Mayan medicinal plant, and these leaves are used as antidiarrheal and diuretic agents. In the present work, we develop in-vitro, ex-vivo, in-vivo, and in-silico strategies to evaluate several aqueous extracts of C. uvifera leaves. In vitro tests showed that decoction extract (CuDe) presented the best yield and chlorophyll, phenol, and flavonoid content; however, CuDe showed low antioxidant activity (DPPH model). All aqueous extracts exert spasmolytic and vasorelaxant activity in a concentration-dependent manner (ex vivo), and in vivo tests showed that CuDe exerts the best antiperistaltic and diuretic effects. The in-silico analysis suggests that C. uvifera triterpenes act as a ligand of GPCR, and β-sitosterol could act as an antagonist of muscarinic acetylcholine receptor subtype 3 (m3AChR). In the context of aqueous extracts of C. uvifera, β-sitosterol and their heterosides were identified by FTIR and 1H-NMR spectroscopy. The concerted binding of β-sitosterol and other triterpenes within the m3AChR binding site may be relevant for the induction of relaxant effects at the gastrointestinal smooth muscle level. In this context, C. uvifera is a high-value plant species that requires analytical and pharmacological studies to confirm traditional medicinal use.
Optimizing Biogas Production and Digestive Stability through Waste Co-Digestion
Authors: Ahmad Rao Muhammad , Javied Sabiha , Aslam Ambreen , Alamri Saud , Zaman Qamar uz , Hassan Ayesha , Noor Nabeela
Published: April 2024

This study aimed to enhance the nutrient balance of municipal solid waste (MSW), characterized by a high carbon-to-nitrogen (C/N) ratio, which is a critical factor in the anaerobic digestion process. The investigation involved the addition of MSW, which is rich in carbon content, to food waste (FW) with high nitrogen content. The goal was to determine an optimal co-substrate mixing ratio of MSW and FW for anaerobic co-digestion at mesophilic temperatures, aiming to improve process stability and performance to achieve higher biogas yield. The co-digestion experiments encompassed five mixing ratios of MSW and FW with C/N ratios of 20, 25, 30, 35, and 40 under mesophilic conditions in a laboratory. The results indicated that the highest specific biogas yield, reaching 827 L/kg VS, was attained when the co-substrate feedstock had a balanced C/N ratio of 20, surpassing the 520 L/kg vs. obtained from MSW digestion alone. As the proportion of MSW increased in the co-substrate mixing feedstock, the biogas production rate decreased. Additionally, the study explored the optimal substrate-to-inoculum (S/I) ratio, focusing on the co-substrate feedstock with a C/N ratio of 20. Four S/I ratios (0.5, 1.0, 1.5, and 2.0) were examined, revealing that the highest specific biogas yield, at 642 L/kg VS, occurred at an S/I ratio of 0.5. An accumulation in volatile fatty acids (VFAs) was observed at higher S/I ratios, attributed to the lower abundance of inoculum microorganisms in the anaerobic digestion process. Overall, the findings suggested that the optimum C/N ratio for co-digestion of MSW and FW falls within the range of 20–25/1, while the preferred S/I ratio is 0.5.
A New Direction for the Green, Environmentally Friendly and Sustainable Bioproduction of Aminobenzoic Acid and Its Derivatives
Authors: Xiao Shujian , Zeng Rumei , Wang Bangxu , Zhang Suyi , Cheng Jie , Zhang Jiamin
Published: April 2024

Aminobenzoic acid and its derivatives are a class of aromatic compounds that are important foundational chemicals for various dyes, food additives, and pharmaceuticals. Their production relies on chemical synthesis using petroleum-derived substances such as benzene as precursors, but due to the toxicity, environmental pollution, and non-renewable nature of raw materials in chemical synthesis, some suitable alternative methods are gradually being developed. Green, environmentally friendly, and sustainable biosynthesis methods have gradually been favored by researchers, especially after the discovery of the synthetic pathways of aminobenzoic acid and its derivatives in plants and microorganisms. Based on the purpose of protecting the ecological environment, reducing the use of non-renewable resources, and providing theoretical support for industrial green development, this article reviews the biosynthesis pathways of ortho-aminobenzoic acid, meta-aminobenzoic acid, para-aminobenzoic acid, and its derivatives such as catechol, folic acid, etc., and lists some examples of biosynthesis, analyzes their advantages and disadvantages, summarizes and looks forward to the future development direction of biosynthesis of aminobenzoic acid and its derivatives.
The Impact of Country Characteristics on Board Gender Diversity and Sustainability Performance: A Global Perspective
Authors: Trireksani Terri , Djajadikerta Hadrian Geri , Kamran Muhammad , Butt Pakeezah
Published: April 2024

This study assesses the role of country characteristics on the association between board gender diversity and sustainability performance. It evaluates the significance and relevance of country characteristics in capturing the contextual sensitivity of the relationship between board gender diversity and sustainability performance. Using a sample of 5087 firms from 50 countries, the study establishes that the presence of females on corporate boards enhances sustainability performance. However, the strength of this relationship is contingent on the characteristics of the country within which a firm operates. Specifically, the positive relationship between board gender diversity and sustainability performance is more pronounced in countries with higher cultural orientations on individualism, uncertainty avoidance, indulgence, and femininity. More substantial financial development, an application of civil law and legal systems and weaker economic conditions in a country also facilitate female directors in enhancing sustainability performance. The study provides deeper insights into how country factors interact with gender on the board factor in leading the sustainability performance of firms.
Exploring Characteristics of Regenerative Business Models through a Delphi-Inspired Approach
Authors: Drupsteen Linda , Wakkee Ingrid
Published: April 2024

Amidst escalating environmental and social challenges, this study explores regenerative business models’ definition and characteristics. While sustainable models have made considerable strides in research, policy, and practice, the advent of regenerative business models offers a progressive leap forward. Regenerative business models aspire to contribute to ecological restoration and societal well-being. The regenerative business model concept is, however, still in its infancy and lacks a comprehensive definition. Our study aims to expand this knowledge, using a Delphi-inspired approach that builds on the knowledge of academic and business experts. Our approach includes three rounds of surveys: an open-ended survey, a survey for rating and ranking the earlier responses of all participants, and a final survey to select key characteristics. We investigate patterns and distinctions among regenerative, regenerative business, and regenerative business models, and analyze their positioning vis-a-vis circular and net-positive models. Findings underscore that organizations adopting regenerative business models focus on planetary health and societal well-being. They generate value across multiple stakeholder levels, including nature, societies, customers, suppliers, shareholders, and employees. Despite overlapping with circular and net-positive models, regenerative business models also emphasize interdependencies between humans and nature, and provide a more holistic approach, centered on restoration rather than mere mitigation.
Advanced Linguistic Complex T-Spherical Fuzzy Dombi-Weighted Power-Partitioned Heronian Mean Operator and Its Application for Emergency Information Quality Assessment
Authors: Zang Yuqi , Zhao Jiamei , Jiang Wenchao , Zhao Tong
Published: April 2024

Against the background of a major change in the world unseen in a century, emergencies with high complexity and uncertainty have had serious impacts on economic security and sustainable social development, making emergency management an important issue that needs to be urgently resolved, and the quality assessment of emergency information is a key link in emergency management. To effectively deal with the uncertainty of emergency information quality assessment, a new fuzzy multi-attribute assessment method is proposed in this paper. First, we propose the linguistic complex T-spherical fuzzy set (LCT-SFS), which can deal with two-dimensional problems and cope with situations in which assessment experts cannot give quantitative assessments. Then, the advanced linguistic complex T-spherical fuzzy Dombi-weighted power-partitioned Heronian mean (ALCT-SFDWPPHM) operator, which incorporates the flexibility of Dombi operations, is proposed. The partitioned Heronian mean (PHM) operator can consider attribute partitioning and attribute correlation, the power average (PA) operator can eliminate the effect of evaluation singularities, and the advanced operator can circumvent the problem of consistent or indistinguishable aggregation results, which provides a strong comprehensive advantage in the evaluating information aggregation. Finally, a fuzzy multi-attribute assessment model is constructed by combining the proposed operator with the WASPAS method and applied to the problem of assessing the quality and sensitivity of emergency information; qualitative and quantitative comparison analyses are carried out. The results show the method proposed in this paper has strong feasibility and validity and can represent uncertainty assessment more flexibly while providing reasonable and reliable results. The method can provide new ideas and methods for the quality assessment of emergency information, and promoting sustainable, efficient, and high-quality development of emergency management.
Sustainability and the Food Industry: A Bibliometric Analysis
Authors: Şimşek Erkan Kadir , Kara Mikail , Kalıpçı Mehmet Bahadır , Eren Ramazan
Published: April 2024

The food industry has significantly expanded and become globalized due to the growth of the economies of many countries and an increasing world population. The industry is consequently facing major sustainability challenges. Food, which is critical to the existence of humanity and is affected by the world’s ecosystems and human intervention, is a fundamental issue within academic research, especially in terms of sustainability. Using a bibliometric approach, this paper analyzed studies on sustainability in the food industry in the context of the most cited articles, trends in the number of articles, most influential journals, most influential authors, most productive and influential institutions and countries, and future research directions. The software tools Bibliometrix 3.1.4 R-package and VOSviewer 1.6.18.0 were used together to analyze the dataset, which was obtained from the Web of Science database. The results indicated that Sustainability, Journal of Cleaner Production, and Frontiers in Sustainable Food Systems are the most important journals in this field, with the greatest number of articles published. Analyses of the network and overlay visualization of keyword co-occurrences identified six different research clusters. Since climate change and food security issues are in alignment with each other, sustainable food chains and social aspects are an important cross-cutting area of research. The results showed that future research should be multi-disciplinary and involve adjacent fields, including agriculture, agroecology, and climate science. There is also a need for research in the field of economic and social sustainability in the food industry. Studying the research topics found in the thematic map analysis, such as the effect of a sustainable diet on the food chain and the relationships among the agri-food system, sustainable agriculture, sustainable food systems, and sustainability of the food chain, is recommended for future research.
Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity
Authors: Yuan Changwei , Zhu Jinrui , Zhang Shuai , Zhao Jiannan , Zhu Shibo
Published: April 2024

From 2008 to 2021, this study analyzed the spatial correlation characteristics between provincial transportation carbon emission intensity and explored ways to reduce transportation carbon emissions. This study used the modified gravity model, social network analysis (SNA) method, and temporal exponential random graph model (TERGM) to analyze the spatial correlation network evolution characteristics and driving mechanism of China’s transportation carbon emission intensity. This study found that China’s transportation carbon emission intensity and spatial correlation network have unbalanced characteristics. The spatial correlation network of transportation carbon emission intensity revealed that Shanghai, Beijing, Tianjin, Guangdong, Fujian, and other provinces were at the center of the network, with significant intermediary effects. The spatial correlation of transportation carbon emission intensity was divided into four functional plates: “two-way spillover”, “net benefit”, “broker”, and “net spillover”. The “net benefit” plate was mainly located in developed regions, and the “net spillover” plate was primarily located in underdeveloped regions. Endogenous structural and exogenous mechanism variables were the main factors affecting the evolution of the spatial correlation network of provincial transportation carbon emission intensity.
Environmental Impact Assessment of Organic Wheat Cracker Value Chains with and without Nettle Powder as a Natural Additive: A Case of Sweden
Authors: Bosona Techane
Published: April 2024

Due to the growing global population and consequent increased demand for food, the global production of cereal crops has increased. Wheat is one of the most important food crops in the world, as its products, e.g., bread and crackers, have served as important sources of nutrition for many years. However, the environmental impacts of wheat-derived food products are not frequently explored. This study presents an environmental impact assessment of organic wheat crackers within the context of Swedish winter wheat production using both primary and literature-based data. A cradle-to-consumer gate life cycle analysis (LCA) approach using the functional unit (FU) of 1 kg of crackers was applied while considering two cracker value chains: (i) without additives and (ii) using nettle powder as a natural additive. Four environmental impact categories—cumulative energy demand (CED), climate change impact (GWP), acidification, and eutrophication—were explored, with a particular focus on CED and GWP. The analysis results indicated that the total CED values were about 13 MJ/FU and 14 MJ/FU for crackers without and with the additive, respectively. Similarly, the total GWP values were 379 g CO2 eq/FU and 464 g CO2 eq/FU, respectively. The post-harvest processing and handling stage was an environmental hot spot in both cases. The introduction of the nettle additive has increased the quantified values of all four of the investigated impact categories. These insights will enable food processors and policy makers to communicate the environmental impacts and make informed decisions to improve the sustainability of wheat crackers. This paper contributes to a database of the environmental impacts of wheat products, specifically LCA data of organic wheat crackers and the LCA method for further LCA studies of snacks and other wheat products with plant-based functional additives.
AHiLS—An Algorithm for Establishing Hierarchy among Detected Weak Local Reflection Symmetries in Raster Images
Authors: Podgorelec David , Kolingerová Ivana , Lovenjak Luka , Žalik Borut
Published: April 2024

A new algorithm is presented for detecting the local weak reflection symmetries in raster images. It uses contours extracted from the segmented image. A convex hull is constructed on the contours, and so-called anchor points are placed on it. The bundles of symmetry line candidates are placed in these points. Each line splits the plane into two open half-planes and arranges the contours into three sets: the first contains the contours pierced by the considered line, while the second and the third include the contours located in one or the other half-plane. The contours are then checked for the reflection symmetry. This means looking for self-symmetries in the first set, and symmetric pairs with one contour in the second set and one contour in the third set. The line which is evaluated as the best symmetry line is selected. After that, the symmetric contours are removed from sets two and three. The remaining contours are then checked again for symmetry. A multi-branch tree representing the hierarchy of the detected local symmetries is the result of the algorithm.
Res-DFNN: An NN-Based Device Fingerprint Extraction Method Using Network Packet Data
Authors: Zhong Yinan , Pan Mingyu , Chen Yanjiao , Xu Wenyuan
Published: April 2024

The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios.
Novel Phenomena of the Hartle–Hawking Wave Function
Authors: Kang Subeom , Park Wan-il , Yeom Dong-han
Published: April 2024

We find a novel phenomenon in the solution to the Wheeler–DeWitt equation by solving numerically the equation assuming O(4)-symmetry and imposing the Hartle–Hawking wave function as a boundary condition. In the slow-roll limit, as expected, the numerical solution gives the most dominant steepest-descent that describes the probability distribution for the initial condition of a universe. The probability is consistent with the Euclidean computations, and the overall shape of the wave function is compatible with analytical approximations, although there exist novel differences in the detailed probability computation. Our approach gives an alternative point of view for the no-boundary wave function from the wave function point of view. Possible interpretations and conceptual issues of this wave function are discussed.
Bipartite (P6,C6)-Free Graphs: Recognition and Optimization Problems
Authors: Quaddoura Ruzayn , Al-Qerem Ahmad
Published: April 2024

The canonical decomposition of a bipartite graph is a new decomposition method that involves three operators: parallel, series, and K⨁ S. The class of weak-bisplit graphs is the class of totally decomposable graphs with respect to these operators, and the class of bicographs is the class of totally decomposable graphs with respect to parallel and series operators. We prove in this paper that the class of bipartite (P6,C6)-free graphs is the class of bipartite graphs that are totally decomposable with respect to parallel and K⨁S operators. We present a linear time recognition algorithm for (P6,C6)-free graphs that is symmetrical to the linear recognition algorithms of weak-bisplit graphs and star1,2,3-free bipartite graphs. As a result of this algorithm, we present efficient solutions in this class of graphs for two optimization graph problems: the maximum balanced biclique problem and the maximum independent set problem.
Improved Weed Detection in Cotton Fields Using Enhanced YOLOv8s with Modified Feature Extraction Modules
Authors: Ren Doudou , Yang Wenzhong , Lu Zhifeng , Chen Danny , Shi Houwang
Published: April 2024

Weed detection plays a crucial role in enhancing cotton agricultural productivity. However, the detection process is subject to challenges such as target scale diversity and loss of leaf symmetry due to leaf shading. Hence, this research presents an enhanced model, EY8-MFEM, for detecting weeds in cotton fields. Firstly, the ALGA module is proposed, which combines the local and global information of feature maps through weighting operations to better focus on the spatial information of feature maps. Following this, the C2F-ALGA module was developed to augment the feature extraction capability of the underlying backbone network. Secondly, the MDPM module is proposed to generate attention matrices by capturing the horizontal and vertical information of feature maps, reducing duplicate information in the feature maps. Finally, we will replace the upsampling module of YOLOv8 with the CARAFE module to provide better upsampling performance. Extensive experiments on two publicly available datasets showed that the F1, mAP50 and mAP75 metrics improved by 1.2%, 5.1%, 2.9% and 3.8%, 1.3%, 2.2%, respectively, compared to the baseline model. This study showcases the algorithm’s potential for practical applications in weed detection within cotton fields, promoting the significant development of artificial intelligence in the field of agriculture.
An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning
Authors: Yang Wenqiang , Yuan Ying , Zhang Donghua , Zheng Liyuan , Nie Fuquan
Published: April 2024

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately.
Nanoframes as a Resilient Candidate for a Broader Spectra of Catalysis Challenges
Authors: Ahmad Fawad , Ain Qurat ul , Zahid Shafaq , Akitsu Takashiro
Published: April 2024

Metal nanoframes have gained tremendous attention in the domain of modern research and development due to their distinctive 3D spatial structure, efficient physiochemical properties, and comparatively good activity. Different strategies have been implicated by the researchers to design nanoframes of varying chemical natures and shapes. Most of the synthetic protocols being adopted to design nanoframes consist of two main steps: nucleation and the growth of solid particles and, secondly, excavation of the interiors. In this context, many synthetic methods are overviewed. To show their unprecedented performance or activity, a few applications in catalysis, biomedicine, theranostics, SERS, the sensing of different materials, the reduction of CO2, etc., are also discussed.
Formation of Singularity for Isentropic Irrotational Compressible Euler Equations
Authors: Liu Jianli , Qin Ziyi , Yuen Manwai
Published: April 2024

The domain of science and engineering relies heavily on an in-depth comprehension of fluid dynamics, given the prevalence of fluids such as water, air, and interstellar gas in the universe. Euler equations form the basis for the study of fluid motion. This paper is concerned with the Cauchy problem of isentropic compressible Euler equations away from the vacuum. We use the integration method with the general test function f=f(r), proving that there exist the corresponding blowup results of C1 irrotational solutions for Euler equations and Euler equations with time-dependent damping in Rn (n≥2), provided the density-independent initial functional is sufficiently large. We also provide two simple and explicit test functions f(r)=r and f(r)=1+r, to demonstrate the blowup phenomenon in the one-dimensional case. In particular, our results are applicable to the non-radial system.
Roadblocks in Integrating Lean Six Sigma and Industry 4.0 in Small and Medium Enterprises
Authors: Alsaadi Naif
Published: April 2024

To survive in highly competitive markets, industries focus on enhancing product quality and customer satisfaction. One specific iteration of this approach sees industries striving to optimize processes to improve product quality while generating minimum waste. This has encouraged companies to begin adopting management practices such as lean six sigma (LSS) and kaizen alongside their implementation of digital technologies of automating processing. This study aims to identify and analyze the barriers to integrating LSS practices with Industry 4.0 technologies for small and medium enterprises. Upon shortlisting fifteen barriers from an exhaustive literature review, the Grey-Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to analyze those shortlisted barriers and establish causal relations between barriers and outcomes. The findings identify the lack of available infrastructure, lack of training on LSS, and lack of consultants in the field as the most dominant barriers in the cause group. Furthermore, the influence map of barriers produced demonstrates the relationship between cause-and-effect barriers. This study’s findings will ensure that small and medium enterprises formulate business strategies that mitigate the barriers to integrating LSS with Industry 4.0. This study offers insights into overcoming the identified challenges by proposing strategies to enhance product quality and the accuracy of enterprise decision-making, thereby facilitating a successful integration and fostering sustainable growth in SMEs.
Diagnosing the Causes of Failing Waste Collection in Belize, Bolivia, the Dominican Republic, Ecuador, Panama, and Paraguay Using Dynamic Modeling
Authors: Breukelman Hans , Krikke Harold , Löhr Ansje
Published: April 2024

Most developing countries fail to provide waste collection services to all their citizens, which leads to many adverse effects. Nevertheless, research has not yet succeeded in explaining the underlying causes. We drew up a quantitative system dynamics model that can be used to diagnose the complex societal system that is leading to poor waste collection. The model describes demographic, social, economic, financial, participatory, and governance processes that may play a role. It is calibrated against real-life datasets for six Latin American countries. The calibration shows adequate performance of the model. Strong population growth appears to have a dual effect. It leads to an increase in available budgets for collection but also tends to dilute the available budget per inhabitant. Processes on the growth of GDP, government revenues, and quality of governance strongly improve access of citizens to waste collection. They do so separately but also because they reinforce each other. But, there are differences per country. Progress in Belize seems to be hampered mostly by low governance quality. For Bolivia, the hurdle seems to consist of an inability to increase public revenues and absorb new urban citizens. Ecuador and Paraguay would also benefit from increased revenues along with an ability to increase public participation. The Dominican Republic and Panama reveal an overall passivity to improve their services. This model may be useful for decision makers globally to develop effective interventions.
Reducing the Impact of Headache and Allodynia Score in Chronic Migraine: An Exploratory Analysis from the Real-World Effectiveness of Anti-CGRP Monoclonal Antibodies Compared to Onabotulinum Toxin A (RAMO) Study
Authors: Montisano Danilo Antonio , Giossi Riccardo , Canella Mattia , Altamura Claudia , Marcosano Marilena , Vernieri Fabrizio , Raggi Alberto , Grazzi Licia
Published: April 2024

Background: Chronic migraine (CM) is a disabling and hard-to-treat condition, associated with high disability and high cost. Among the preventive treatments, botulinum toxin A (BoNT-a) and monoclonal antibodies against the calcitonin gene-related protein (anti-CGRP mAbs) are the only disease-specific ones. The assessment of the disease burden is complex, and among others, tools such as the allodynia symptoms checklist (ASC-12) and headache impact test (HIT-6) are very useful. This exploratory study analysed the impact of these two therapies on migraine burden. Methods: The RAMO study was a multicentre, observational, retrospective investigation conducted in two headache centres: the Fondazione IRCCS Istituto Neurologico Carlo Besta (Milan) and the Fondazione Policlinico Campus Bio-Medico (Rome). This study involved patients with chronic migraine treated with mAbs or BoNT-A. We conducted a subgroup exploratory analysis on HIT-6 and ASC-12 scores in the two groups. The Wilcoxon rank-sum test, Fisher’s exact test, and ANOVA were performed. Results: Of 126 patients, 36 on mAbs and 90 on BoNT-A had at least one available follow-up. mAbs resulted in a mean reduction of −11.1 and −11.4 points, respectively, in the HIT-6 at 6 and 12 months, while BoNT-A was reduced −3.2 and −3.6 points, respectively; the mAbs arm resulted in mean reductions in ASC-12 at 6 and 12 months of follow-up of −5.2 and −6.0 points, respectively, while BoNT-A showed lesser mean changes of −0.5 and −0.9 points, respectively. The adjusted analysis confirmed our results. Conclusions: In this exploratory analysis, anti-CGRP mAbs showed superior effectiveness for HIT-6 and ASC12 compared to BoNT-A. Reductions in terms of month headache days (MHD), migraine disability assessment test (MIDAS), and migraine acute medications (MAM) were clinically relevant for both treatments.
Search for Extreme Mass Ratio Inspirals Using Particle Swarm Optimization and Reduced Dimensionality Likelihoods
Authors: Zou Xiao-Bo , Mohanty Soumya D. , Luo Hong-Gang , Liu Yu-Xiao
Published: April 2024

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long-lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH, where the latter contribute a time-independent amplitude to the waveforms. Additionally, we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio’s maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with a duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. However, the ranges used by both the LISA Data Challenge (LDC) and Mock LISA Data Challenge (MLDC) to generate their simulated signals are still wider than the those we currently employ in our direct searches. Consequently, we discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency, accuracy, and generality of the EMRI search algorithm.
QED Meson Description of the Anomalous Particles at ∼17 and ∼38 MeV
Authors: Wong Cheuk-Yin
Published: April 2024

The Schwinger confinement mechanism stipulates that a massless fermion and a massless antifermion are confined as a massive boson when they interact in the Abelian QED interaction in (1+1)D.If we approximate light quarks as massless and apply the Schwinger confinement mechanism to quarks, we can infer that a light quark and a light antiquark interacting in the Abelian QED interaction are confined as a QED meson in (1+1)D. Similarly, a light quark and a light antiquark interacting in the QCD interaction in the quasi-Abelian approximation will be confined as a QCD meson in (1+1)D. The QED and QCD mesons in (1+1)D can represent physical mesons in (3+1)D when the flux tube radius is properly taken into account. Such a theory leads to a reasonable description of the masses of π0,η, and η′, and its extrapolation to the unknown QED sector yields an isoscalar QED meson at about 17 MeV and an isovector QED meson at about 38 MeV. The observations of the anomalous soft photons, the hypothetical X17 particle, and the hypothetical E38 particle bear promising evidence for the possible existence of the QED mesons. Pending further confirmation, they hold important implications on the properties on the quarks and their interactions.
Abundant Intra-Subtype Reassortment Revealed in H13N8 Influenza Viruses
Authors: Feoktistova Sofia , Sayganova Marya , Trutneva Kseniya , Glazova Olga , Blagodatski Artem S. , Shevkova Liudmila , Navoikova Anna , Anisimov Yuriy , Albert Eugene , Mityaeva Olga , Volchkov Pavel , Deviatkin Andrey
Published: April 2024

Influenza A viruses (IAVs) pose a serious threat to global health. On the one hand, these viruses cause seasonal flu outbreaks in humans. On the other hand, they are a zoonotic infection that has the potential to cause a pandemic. The most important natural reservoir of IAVs are waterfowl. In this study, we investigated the occurrence of IAV in birds in the Republic of Buryatia (region in Russia). In 2020, a total of 3018 fecal samples were collected from wild migratory birds near Lake Baikal. Of these samples, 11 were found to be positive for the H13N8 subtype and whole-genome sequencing was performed on them. All samples contained the same virus with the designation A/Unknown/Buryatia/Arangatui-1/2020. To our knowledge, virus A/Unknown/Buryatia/Arangatui-1/2020 is the first representative of the H13N8 subtype collected on the territory of Russia, the sequence of which is available in the GenBank database. An analysis of reassortments based on the genome sequences of other known viruses has shown that A/Unknown/Buryatia/Arangatui-1/2020 arose as a result of reassortment. In addition, a reassortment most likely occurred several decades ago between the ancestors of the viruses recently collected in China, the Netherlands, the United States and Chile. The presence of such reassortment emphasizes the ongoing evolution of the H13N8 viruses distributed in Europe, North and East Asia, North and South America and Australia. This study underscores the importance of the continued surveillance and research of less-studied influenza subtypes.
Improving Pharmacokinetics of Peptides Using Phage Display
Authors: Asar Mallika , Newton-Northup Jessica , Soendergaard Mette
Published: April 2024

Phage display is a versatile method often used in the discovery of peptides that targets disease-related biomarkers. A major advantage of this technology is the ease and cost efficiency of affinity selection, also known as biopanning, to identify novel peptides. While it is relatively straightforward to identify peptides with optimal binding affinity, the pharmacokinetics of the selected peptides often prove to be suboptimal. Therefore, careful consideration of the experimental conditions, including the choice of using in vitro, in situ, or in vivo affinity selections, is essential in generating peptides with high affinity and specificity that also demonstrate desirable pharmacokinetics. Specifically, in vivo biopanning, or the combination of in vitro, in situ, and in vivo affinity selections, has been proven to influence the biodistribution and clearance of peptides and peptide-conjugated nanoparticles. Additionally, the marked difference in properties between peptides and nanoparticles must be considered. While peptide biodistribution depends primarily on physiochemical properties and can be modified by amino acid modifications, the size and shape of nanoparticles also affect both absorption and distribution. Thus, optimization of the desired pharmacokinetic properties should be an important consideration in biopanning strategies to enable the selection of peptides and peptide-conjugated nanoparticles that effectively target biomarkers in vivo.