bluuebunny/crossref_metadata_2025 huggingface dataset:
A Non-Fungible Token and Blockchain-Based Cotton Lint Traceability Solution
Authors: Wang Lixin , Sun Wenlei , Zhao Jintao , Zhang Xuedong , Lu Cheng , Luo Hao
Published: February 2024

As a critical raw material for the textile industry, cotton lint provides various types of cotton yarns, fabrics and finished products. However, due to the complexity of the supply chain and its many links, information records are often missing, inaccurate or lagging, resulting in low transparency and traceability. In the traditional cotton lint supply chain, the data of each link are stored in isolation; due to the lack of an effective sharing mechanism and the formation of “information silos”, complete traceability is challenging to achieve. In addition, the completeness and authenticity of documents such as lint quality reports and certificates of origin must be rapidly strengthened. Otherwise, quality problems may arise. To solve the above problems, this study proposes a cotton lint supply chain traceability system based on blockchain and non-fungible tokens (NFTs), covering the whole cotton lint production process from harvesting to selling. We use an NFT as an asset token to digitise seed cotton, cotton lint and quality inspection reports and allow participants to store and manage these assets on the blockchain. The system design includes architecture diagrams, sequence diagrams and Ethernet smart contract development based on the ERC721 standard. In addition, the integration of Interplanetary File System (IPFS) technology solves the problem of storing large files on the chain and ensures that the data are permanently preserved and cannot be tampered with. We provide a diagram of the interactions between the system components and the four core algorithms’ design, testing and verification process. We present an in-depth analysis of the solution regarding the transaction costs and smart contract security. We confirm the solution’s security, reliability and applicability through a cost evaluation and security analysis.
Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review
Authors: Arias-Portela Claudia Yohana , Mora-Vargas Jaime , Caro Martha
Published: February 2024

The conceptual framework for assessing the situational awareness (SA) of drivers consists of three hierarchical levels: perception of the elements of the environment, comprehension of the elements, and decision-making in the near future. A common challenge in evaluating SA is the determination of the available subjective and objective techniques and their selection and integration into methodologies. Among the objective techniques, eye tracking is commonly used, considering the influence of gaze behavior on driving. This review is presented as an innovative approach to the subject matter, introducing physiological metrics based on eye tracking and investigating their application in assessing the SA of drivers. In addition, experiments and methodologies that revealed patterns at the three levels of SA were identified. For this purpose, databases were searched, and 38 papers were considered. Articles were clustered according to prevalent themes such as eye-tracking metrics, eye-tracking devices, experiment design, and the relationship between SA and eye-tracking. This review summarizes the main metrics and key findings for each article and reveals a wide relationship between the eye-tracking metrics and SA. The influence of appropriately calibrated equipment, refined data collection protocols, and adequate selection of the eye-tracking metrics was examined. Further reviews are needed to systematically collect more evidence.
Pretreatments Applied to Wheat Straw to Obtain Bioethanol
Authors: Rusănescu Carmen Otilia , Ciobanu Maria , Rusănescu Marin , Dinculoiu Raluca Lucia
Published: February 2024

This work is a comprehensive study focusing on various methods for processing wheat straw to enhance its suitability for bioethanol production. It delves into mechanical, physical, chemical, and biological pretreatments, each aimed at improving the enzymatic hydrolysis and fermentation processes necessary for bioethanol production. Mechanical and physical pretreatments involve reducing the size of wheat straw to improve enzymatic hydrolysis. Physical methods include heating and irradiation, which alter the structural properties of wheat straw. Chemical pretreatments involve using acids, alkalis, and organic solvents to remove lignin and hemicellulose, making cellulose more accessible for hydrolysis. Biological pretreatments utilize microorganisms and fungi to degrade lignin and other complex compounds, enhancing the breakdown of cellulose. The study presents data on the effectiveness of these treatments in terms of lignin removal, sugar yield, and overall bioethanol production efficiency. The research is aligned with the global move towards renewable energy sources and emphasizes the importance of utilizing agricultural waste, like wheat straw, for sustainable energy production.
Strength Reduction Method for the Assessment of Existing Large Reinforced Concrete Structures
Authors: Abra Oumaima , Ben Ftima Mahdi
Published: February 2024

This work presents a new developed assessment methodology based on strength reduction and finite element methods which is suitable for existing large reinforced concrete structures commonly used in hydraulic constructions. The methodology is based on a reloading phase of the finite element model and is preceded by an intermediate reduction phase of concrete tensile strength and an initial loading phase up to service level. Rosenblueth’s point estimate method was used to compute a global resistance factor and to deduce a design resistance value of the structure. After validations, the methodology was applied to two existing complex and large hydraulic structures: a spiral case and a draft tube. If compared with existing methodologies using sophisticated non-linear finite element methods, the developed approach is simpler, more practical, and provides results that are on the conservative side. Considering the difficulties in characterizing the tensile peak and post-peak strength of concrete, along with uncertainties regarding the damage conditions of facilities, the developed methodology is deemed robust and well suited for assessing existing critical large reinforced concrete infrastructures.
Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
Authors: Wang Yilei , Hu Yuelin , Xu Wenliang , Zou Futai
Published: February 2024

Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.
Development of a Fault Detection and Localization Model for a Water Distribution Network
Authors: Onukwube Christogonus U. , Aikhuele Daniel O. , Sorooshian Shahryar
Published: February 2024

Water distribution networks are complex systems that aid in the delivery of water to residential and non-residential areas. However, the networks can be affected by different types of faults, which could lead to the wastage of treated water. As such, there is a need to develop a reliable leakage detection and localization system that can detect leak occurrences in the network. This study, using a simulated dataset from EPANET, presents the application of supervised machine learning classifiers for leak detection and localization in the water distribution network of the University of Port Harcourt Choba campus. The study compared three machine learning classification tools that are used in pattern recognition analysis: the support vector machine, k-nearest neighbor, and artificial neural network. The robustness and effectiveness of the proposed approach are compared with those of the performance of the classifiers for leakage detection in the network of the case study. The results show that the support vector machine performs the best, with 79% accuracy, while the respective accuracies for the remaining classifiers are 70% for the k-nearest neighbor and 61% for the artificial neural networks. The high accuracy demonstrated by the models shows that they are able to detect and address issues relating to fault detection in a water distribution network. This model could provide a leakage detection system to be applied to buildings for the efficient management of water in their networks.
A Simplified Radiation Characteristic Analysis Method for Defocus-Fed Parabolic Antennas in a W-Band Communication System
Authors: Kim Kichul , Pyo Seongmin , Jung Jinwoo
Published: February 2024

With increasing interest in the W-band, there is growing focus on parabolic reflector antennas that are known for efficiently inducing high-gain radiation characteristics. There is particular focus on parabolic antennas with diverse defocus-fed applications, including monopulse tracking, multi-beam formation using multiple feeds, and aperture-shared antennas for multi-band operation. Thus, a new simplified method is presented in this paper to analyze the radiation characteristics of defocus-fed parabolic antennas. The presented method is based on the discrete division of a parabolic reflector surface and considers only simplified wave propagation theory and the effect of the scalar function pattern from the feeder. Additionally, array theory is exclusively applied for the analysis of radiation characteristics. Therefore, the presented method uses a very simplified formula to calculate the radiation characteristics of a defocused parabolic reflector antenna. The performance of the presented method was evaluated by comparing the results with commercial EM tools. The results of the analysis confirm the applicability of the presented method for the analysis of defocus-fed parabolic antennas.
Operation Optimization of the Sea Container Fleet Based on the Double-Level Planning Model
Authors: Xiao Ping , Wang Haiyan
Published: February 2024

In response to the optimal operation of ocean container ships, this paper presents a two-level planning model that takes into account carbon tax policies. This model translates the CO2 emissions of ships into carbon tax costs and aims to minimize the overall operation costs of the ships. In top-level planning, the model considers factors such as speed, cargo load, and energy consumption to establish an objective function and optimization strategy. In bottom-level planning, the model involves ship stability and imposes corresponding constraints. By integrating the two levels of planning, a ship operation optimization model that considers multiple factors is obtained. With practical ocean container ships as cases, through numerical examples and sensitivity analysis, the constraint, stability, and structural feasibility of the constructed model are confirmed. The research results of this paper provide a decision-making basis for optimizing the operation of oceanic container ships.
Fractional Heat Conduction with Heat Absorption in a Solid with a Spherical Cavity under Time-Harmonic Heat Flux
Authors: Povstenko Yuriy , Kyrylych Tamara , Woźna-Szcześniak Bożena , Yatsko Andrzej
Published: February 2024

The central-symmetric time-fractional heat conduction equation with heat absorption is investigated in a solid with a spherical hole under time-harmonic heat flux at the boundary. The problem is solved using the auxiliary function, for which the Robin-type boundary condition with a prescribed value of a linear combination of a function and its normal derivative is fulfilled. The Laplace and Fourier sine–cosine integral transformations are employed. Graphical representations of numerical simulation results are given for typical values of the parameters.
Modelling Prefabricated Construction Safety
Authors: Masood Rehan
Published: February 2024

Prefabricated construction is expanding and taking over traditional construction with more intervention of prefabricated building elements. Despite prefabricated construction reducing health and safety risks compared to conventional construction, there is still a risk that needs to be addressed. This article aims to investigate prefabricated construction safety through accident analysis. The accident data was retrieved through governmental resources and covered accident claims, safety costs, vulnerable occupations, and injuries (including type, cause, prior activity, and site of injury). Prefabricated construction safety is then simplistic and predictively modelled. The most common trend has been reported with graphical representation and relevant discussion. Furthermore, the trends are forecasted by using the ARIMA model (p, d, q) based on key performance parameters. The conclusion has been driven by the current status of prefabricated construction safety. This study is a pioneer in modelling prefabricated construction safety to enhance understanding of accidents and forecasting through optimization.
Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach
Authors: Acharya Nirmal , Kar Padmaja , Ally Mustafa , Soar Jeffrey
Published: February 2024

Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.
Nitrogen-Related Defects in Crystalline Silicon
Authors: Sgourou E. N. , Sarlis N. , Chroneos A. , Londos C. A.
Published: February 2024

Defects and impurities play a fundamental role in semiconductors affecting their mechanical, optical, and electronic properties. Nitrogen (N) impurities are almost always present in a silicon (Si) lattice, either unintentionally, due to the growth and processing procedures, or intentionally, as a result of implantation. Nitrogen forms complexes with intrinsic defects (i.e., vacancies and self-interstitials) as well as with other impurities present in the Si lattice such as oxygen and carbon. It is, therefore, necessary to investigate and understand nitrogen-related defects, especially their structures, their energies, and their interaction with intrinsic point defects and impurities. The present review is focused on nitrogen-related defects (for example Ni, Ns, NiNi, NiNs, NsNs); nitrogen–self-interstitial and nitrogen-vacancy-related complexes (for example NsV, (NiNi)Sii, (NsNs)V); nitrogen–oxygen defects (for example NO, NO2, N2O, N2O2); more extended clusters such as VmN2On (m, n = 1, 2); and nitrogen–carbon defects (for example CiN and CiNO). Both experimental and theoretical investigations are considered as they provide complementary information.
SimKG-BERT: A Security Enhancement Approach for Healthcare Models Consisting of Fusing SimBERT and a Knowledge Graph
Authors: Li Songpu , Yu Xinran , Chen Peng
Published: February 2024

Model robustness is an important index in medical cybersecurity, and hard-negative samples in electronic medical records can provide more gradient information, which can effectively improve the robustness of a model. However, hard negatives pose difficulties in terms of their definition and acquisition. To solve these problems, a data augmentation approach consisting of fusing SimBERT and a knowledge graph for application to a hard-negative sample is proposed in this paper. Firstly, we selected 40 misdiagnosed cases of diabetic complications as the original data for data augmentation. Secondly, we divided the contents of the electronic medical records into two parts. One part consisted of the core disease phrases in the misdiagnosed case records, which a medical specialist selected. These denoted the critical diseases that the model diagnosed as negative samples. Based on these core symptom words, new symptom phrases were directly generated using the SimBERT model. On the other hand, the noncore phrases of misdiagnosed medical records were highly similar to the positive samples. We determined the cosine similarity between the embedding vector of the knowledge graph entities and a vector made up of the noncore phrases. Then, we used Top-K sampling to generate text. Finally, combining the generated text from the two parts and the disturbed numerical indexes resulted in 160 enhancement samples. Our experiment shows that the distances between the samples generated using the SimKG-BERT model’s samples were closer to those of the positive samples and the anchor points in the space vector were closer than those created using the other models. This finding is more in line with how hard negatives are defined. In addition, compared with the model without data augmentation, the F1 values in the three data sets of diabetic complications increased by 6.4%, 2.24%, and 5.54%, respectively. The SimKG-BERT model achieves data augmentation in the absence of misdiagnosed medical records, providing more gradient information to the model, which not only improves the robustness of the model but also meets the realistic needs of assisted-diagnosis safety.
Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby–Mori–Tanaka Theory
Authors: Hu Min , Huang Zhangduo , Chen Jiejin , Guo Yipeng , Zhou Yichao , Liu Dashun , Guo Rong
Published: February 2024

Black shale, as a type of soft rock, exhibits high strength when freshly exposed. However, it easily disintegrates upon contact with water, making it unsuitable for direct use in roadbed construction. Using it as discarded material not only increases construction costs but also pollutes the environment. Therefore, the reuse and modification of black shale have become particularly important. Based on the theory of composite material equivalent inclusions, this study investigates the strength and water stability characteristics of black shale gravel after being mixed with cement and compacted with clay. The results show that the strength of cemented soil increases linearly with the cement content. The water absorption properties of the modified soils with different amounts of black shale added are similar, with an average water absorption rate of about 2.53%. The strength of black shale modified soil is generally positively correlated with the cement content, although the linear correlation is not significant. The modified black shale soil used in the experiment is suitable for the subgrade of medium- and light-grade secondary roads and below. The recommended mass ratio is Mshale:Mclay:Mcement = 70:21:9. The unconfined compressive strength of the material under 7-day curing is 1.36 MPa. The relationship between the strength of modified soil, clay strength, cement content, and gravel addition has been established, clarifying the physical significance of each parameter. The “drying and soaking” cycle can accelerate the strength degradation of modified soil. It is recommended to strengthen the construction of roadbed drainage facilities during construction to maintain a stable and dry environment for the modified soil as a roadbed filling material. The research results not only provide clear technical indicators for the reuse of discarded black shale in engineering but also serve as a basis for proportion of crushed stone discarded material as roadbed fill.
Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
Authors: Serretiello Enrica , Smimmo Annafrancesca , Ballini Andrea , Parmeggiani Domenico , Agresti Massimo , Bassi Paola , Moccia Giancarlo , Sciarra Antonella , De Angelis Alessandra , Della Monica Paola , Marino Maria Michela , Di Domenico Marina
Published: February 2024

Breast cancer (BC) caused 685,000 deaths globally in 2020, earning the title of the most common type of tumor among females. With a multifactorial genesis, BC is influenced by several factors such as age, genetic and epigenetic predisposition, and an individual’s exposome, and its classification is based on morphological/histological, invasiveness, and molecular futures. Extracellular vesicles (EVs) are cell-derived lipid-bilayer-delimited nanoparticles, which are distinguishable by size, genesis, and the markers expressed in exosomes (40 to 150 nm), microvesicles (40 to 10,000 nm), and apoptotic bodies (100–5000 nm). Produced in physiological and pathological cellular contexts, EVs are shuttles of biological material and are implicated in cell-to-cell communications, thus attracting significant interest in diagnostic and drug delivery research. We report and discuss the latest evidence regarding the important role of EVs in BC, deepening their implication in tumorigenesis and metastatic mechanisms. On the other hand, the use of BC-derived EVs as prognostic biomarkers and therapeutic approaches is undergoing investigation. Hence, EVs have become new weapons in precision medicine; however, only with the support of advanced algorithms such as artificial intelligence (AI) can we develop a wide range of information. Looking ahead, it is possible to see the application of AI in the prognosis and diagnosis of different pathologies.
Analysis of Craniocervical Abnormalities in Osteogenesis Imperfecta during Growth
Authors: De Nova-García Manuel Joaquín , Sola Rafael G. , Burgueño-Torres Laura
Published: February 2024

Osteogenesis Imperfecta (OI) is a genetic disease characterized by osteopenia and bone fragility in which the craniocervical junction is also affected. This is of special relevance due to the high prevalence in anomalies described in the literature as follows: basilar invagination, basilar impression, and platybasia. Methods. We analyzed 19 lateral skull radiographs and 14 magnetic resonance images (MRIs) of 28 patients with OI in which eight linear and five angular measurements were plotted to determine the existence of craniocervical junction anomalies and compare them with 38 lateral skull radiographs and 28 MRIs performed on age-matched healthy controls. Results. From the reference values obtained from the control sample for each age group, we established the limit value at which pathology could be suspected. Some of the variables studied showed a clear trend associated with growth. More than half of the patients (60.71%) presented an anomaly in the skull base. Conclusions. According to the diagnostic criteria used and taking +2.5 SD as the limit value, 10.71% of the patients had basilar invagination, 35.71% had basilar impression, and 39.29% had platybasia, the latter being the most common finding.
Privacy Protection Method for Blockchain Transactions Based on the Stealth Address and the Note Mechanism
Authors: Wei Zeming , Fang Jiawen , Hong Zhicheng , Zhou Yu , Ma Shansi , Zhang Junlang , Liang Chufeng , Zhao Gansen , Tang Hua
Published: February 2024

Blockchain is a distributed ledger technology that possesses characteristics such as decentralization, tamper resistance, and programmability. However, while blockchain ensures transaction openness and transparency, transaction privacy is also at risk of being exposed. Therefore, this paper proposes the blockchain transaction privacy protection method based on the stealth address and the note mechanism to address the privacy leakage risk in blockchain public environments. Firstly, the proposed method generates a random seed known only to the parties involved based on the Diffie–Hellman key exchange protocol, ensuring the privacy of transactions. Then, it utilizes the Note Commitments table to maintain the binding relationship between the stealth address and the corresponding note, enabling efficient transfer and verification of note ownership. The uniqueness of the stealth address is utilized as an invalidation identifier for notes in the Nullifier table, ensuring efficient verification of the correctness of note invalidation identifiers. Additionally, this method employs Pedersen commitment and Bulletproofs range proof to generate proof of the legality of transaction amounts, enabling the concealment of transaction amounts and facilitating private transactions between the parties involved. Finally, this paper presents a detailed performance analysis, implementation, and testing of the method. From the results, it can be concluded that the method proposed can effectively prevent fraudulent behavior by various transaction participants and ensure the security, privacy, and integrity of the transaction. Critical processes consume only milliseconds, and the related commitments and proofs are also minimal, which is crucial for controlling transaction costs. At the same time, this method achieves a completely decentralized privacy transaction solution.
Design of a Trusted Content Authorization Security Framework for Social Media
Authors: Han Jiawei , Li Qingsa , Xu Ying , Zhu Yan , Wu Bingxin
Published: February 2024

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools are accompanied by a series of significant challenges, the most critical of which is that it may cause AI information pollution on social media and mislead the public. Traditional network security models have shown their limitations in dealing with today’s complex network threats, so ensuring that generated content published on social media accurately reflects the true intentions of content creators has become particularly important. This paper proposes a security framework called “secToken”. The framework adopts multi-level security and privacy protection measures. It combines deep learning and network security technology to ensure users’ data integrity and confidentiality while ensuring credibility of the published content. In addition, the framework introduces the concept of zero trust security, integrates OAuth2.0 ideas, and provides advanced identity authentication, fine-grained access control, continuous identity verification, and other functions, to comprehensively guarantee the published content’s reliability on social media. This paper considers the main issues of generative content management in social media and offers some feasible solutions. Applying the security framework proposed in this paper, the credibility of generated content published on social media can be effectively ensured and can help detect and audit published content on social media. At the operational level, when extracting key information summaries from user-generated multimodal artificial intelligence-generated content and binding them to user identity information as a new token to identify user uniqueness, it can effectively associate user identity information with the current network status and the generated content to be published on the platform. This method significantly enhances system security and effectively prevents information pollution caused by generative artificial intelligence on social media platforms. This innovative method provides a powerful solution for addressing social and ethical challenges and network security issues.
A Dual-Branch Speech Enhancement Model with Harmonic Repair
Authors: Jia Lizhen , Xu Yanyan , Ke Dengfeng
Published: February 2024

Recent speech enhancement studies have mostly focused on completely separating noise from human voices. Due to the lack of specific structures for harmonic fitting in previous studies and the limitations of the traditional convolutional receptive field, there is an inevitable decline in the auditory quality of the enhanced speech, leading to a decrease in the performance of subsequent tasks such as speech recognition and speaker identification. To address these problems, this paper proposes a Harmonic Repair Large Frame enhancement model, called HRLF-Net, that uses a harmonic repair network for denoising, followed by a real-imaginary dual branch structure for restoration. This approach fully utilizes the harmonic overtones to match the original harmonic distribution of speech. In the subsequent branch process, it restores the speech to specifically optimize its auditory quality to the human ear. Experiments show that under HRLF-Net, the intelligibility and quality of speech are significantly improved, and harmonic information is effectively restored.
Data Placement Using a Classifier for SLC/QLC Hybrid SSDs
Authors: Cho Heeseong , Kim Taeseok
Published: February 2024

In hybrid SSDs (solid-state drives) consisting of SLC (single-level cell) and QLC (quad-level cell), efficiently using the limited SLC cache space is crucial. In this paper, we present a practical data placement scheme, which determines the placement location of incoming write requests using a lightweight machine-learning model. It leverages information about I/O workload characteristics and SSD status to identify cold data that does not need to be stored in the SLC cache with high accuracy. By strategically bypassing the SLC cache for cold data, our scheme significantly reduces unnecessary data movements between the SLC and QLC regions, improving the overall efficiency of the SSD. Through simulation-based studies using real-world workloads, we demonstrate that our scheme outperforms existing approaches by up to 44%.
Double-Constraint Fuzzy Clustering Algorithm
Authors: Zhu Shiyuan , Zhao Yuwei , Yue Shihong
Published: February 2024

Given a set of data objects, the fuzzy c-means (FCM) partitional clustering algorithm is favored due to easy implementation, rapid response, and feasible optimization. However, FCM fails to reflect either the importance degree of the individual data objects or that of the clusters. Numerous variants of FCM have been proposed to address these issues. However, most of them cannot effectively apply the available information on data objects or clusters. In this paper, a double-constraint fuzzy clustering algorithm is proposed to reflect the importance degrees of both individual data objects and clusters. By incorporating double constraints into each data object and cluster, the objective function of FCM is reformulated and its realization equation is mathematically conducted. Consequently, the clustering accuracy of FCM is improved by applying the available information on both data objects and clusters. Especially, the proposed algorithm effectively addresses the limitations inherent in the existing variants of FCM. The experimental results validate the effectiveness, implementation, and robustness of the new fuzzy clustering algorithm.
Efficacy of Elastodontic Devices in Temporomandibular Disorder Reduction Assessed by Computer Aid Evaluation
Authors: Ortu Eleonora , Di Nicolantonio Sara , Cova Samuele , Pietropaoli Davide , De Simone Lucia , Monaco Annalisa
Published: February 2024

Background: The main objective of this study was to compare the clinical efficacy of two different devices, the Eptamed elastodontic device and a common bite sold in pharmacies, by assessing a computer aid evaluation of patients’ surface electromyography (sEMG) and kinesiography activity of four pairs of masticatory and postural muscles (anterior temporalis, digastricus, masseters and sternocleidomastoids muscles) before and after 6 months of treatment. Materials and Methods: Twelve adult patients with temporomandibular disorders and in need of orthodontic treatment were enrolled in the study and divided into cases and controls. Cases underwent orthodontic treatment with the Eptamed elastodontic device, while controls were treated with a bite sold in pharmacies. Both groups underwent electromyographic and kinesiographic examinations before and after 6 months from the start of treatment. Results: The Eptamed device was found to guarantee an improvement in the electrical activity of the muscles examined. The subjects in the control group, on the other hand, had a general worsening of electrical activity after wearing a splint purchased in a pharmacy. As for the kinesiographic examination, there was no significant improvement in both groups. Conclusions: the use of the Eptamed device in subjects with TMD ensured a greater relaxation of the chewing muscles than a standard bite, effectively reducing the risk of worsening the symptomatology of temporomandibular disorders.
Development of a Firefighting Drone for Constructing Fire-breaks to Suppress Nascent Low-Intensity Fires
Authors: Jin Juan , Kim Seunghan , Moon Jiwon
Published: February 2024

Korean forests are highly vulnerable to forest fires, which can severely damage property and human life. This necessitates the establishment of a rapid response system and the construction of firebreaks to prevent the spread of fires and protect key facilities. The existing firebreak construction methods can be classified into prevention- and response-stage methods. In the prevention stage, the progression and spread of fire are delayed, while in the response stage, primitive manual methods involving tools such as hooks are used, in addition to aerial deployment of water and fire retardants through helicopters. Herein, we propose the use of “fire-extinguishing drones” for firebreak construction during the initial, low-intensity stage of a fire before the deployment of firefighting personnel. We implement a continuous fire-extinguishing module capable of carrying six fire-extinguishing balls to verify its deployment accuracy and stable hovering capabilities. Through the operation of multiple drones using a ground control system and real-time kinematics to precisely generate designated automatic flight paths, we conducted experiments to assess the feasibility of firebreak construction by using fire-extinguishing drones to prevent the spread of wildfires. A firebreak construction field test was conducted to evaluate the accuracy of continuous fire extinguisher deployment, hovering performance during deployment, accuracy of the RTK-designated paths, and GCS performance. The proposed system achieved 100% performance on all indicators, except the accuracy of the RTK-designated paths.
Sample-Based Gradient Edge and Angular Prediction for VVC Lossless Intra-Coding
Authors: Chen Guojie , Lin Min
Published: February 2024

Lossless coding is a compression method in the Versatile Video Coding (VVC) standard, which can compress video without distortion. Lossless coding has great application prospects in fields with high requirements for video quality. Since the current VVC standard is mainly designed for lossy coding, the compression efficiency of VVC lossless coding makes it hard to meet people’s needs. In order to improve the performance of VVC lossless coding, this paper proposes a sample-based intra-gradient edge detection and angular prediction (SGAP) method. SGAP utilizes the characteristics of lossless intra-coding to employ samples adjacent to the current sample as reference samples and performs prediction through sample iteration. SGAP aims to improve the prediction accuracy for edge regions, smooth regions and directional texture regions in images. Experimental results on the VVC Test Model (VTM) 12.3 reveal that SGAP achieves 7.31% bit-rate savings on average in VVC lossless intra-coding, while the encoding time is only increased by 5.4%. Compared with existing advanced sample-based intra-prediction methods, SGAP can provide significantly higher coding performance gain.
What’s in Your Fruit Juice?—Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
Authors: Aykas Didem P. , Rodriguez-Saona Luis
Published: February 2024

Fruit juices (FJ) have gained widespread global consumption, driven by their perceived health benefits. The accuracy of nutrition information is essential for consumers assessing FJ quality, especially with increasing concerns about added sugars and obesity risk. Conversely, ascorbic acid (Vitamin C), found in nature in many fruits and vegetables, is often lost due to its susceptibility to light, air, and heat, and it undergoes fortification during FJ production. Current analytical methods for determining FJ components are time-consuming and labor-intensive, prompting the need for rapid analytical tools. This study employed a field-deployable portable FT-IR device, requiring no sample preparation, to simultaneously predict multiple quality traits in 68 FJ samples from US markets. Using partial least square regression (PLSR) models, a strong correlation (RCV ≥ 0.93) between FT-IR predictions and reference values was obtained, with a low standard error of prediction. Remarkably, 21% and 37% of FJs deviated from nutrition label values for sugars and ascorbic acid, respectively. Portable FT-IR devices offer non-destructive, simultaneous, simple, and high-throughput approaches for chemical profiling and real-time prediction of sugars and acid levels in FJs. Their handiness and ruggedness can provide food processors with a valuable “out-of-the-laboratory” analytical tool.
Anodic Oxidation of 3D Printed Ti6Al4V Scaffold Surfaces: In Vitro Studies
Authors: de Sousa Talita Kathleen Correia , Maia Fátima Raquel , Pina Sandra , Reis Rui L. , Oliveira Joaquim Miguel , Carobolante João Pedro Aquiles , Escada Ana Lúcia do Amaral , Longhitano Guilherme Arthur , Alves Ana Paula Rosifini
Published: February 2024

This study focuses on the surface modification of Ti6Al4V scaffolds produced through additive manufacturing using the Powder-Bed Fusion Electron-Beam Melting (PBF-EB) technique. From our perspective, this technique has the potential to enhance implant osseointegration, involving the growth of a layer of titanium dioxide nanotubes (TiO2) on surfaces through anodic oxidation. Scaffolds with anodized surfaces were characterized, and the formation of a nanoporous and crystalline TiO2 layer was confirmed. The analysis of cell morphology revealed that cells adhered to the anodized surfaces through their filopodia, which led to proliferation during the initial hours. However, it was observed that the adhesion of Saos-2 cells was lower on anodized scaffolds compared to both built and chemically polished scaffolds throughout the cell culture period. The results obtained here suggest that while anodic oxidation is effective in achieving a nanoporous surface, cell adhesion and interaction were affected by the weak adhesion of cell filopodia to the surface. Thus, combining surface treatment techniques to create micro- and nanopores may be an effective alternative for achieving a favorable cellular response when the objective is to enhance the performance of porous titanium scaffolds in the short term.
Key Issues and Solutions in the Study of Quantitative Mechanisms for Tropical Islands Zero Carbon Buildings
Authors: Wang Qiankun , Zhu Ke , Guo Peiwen , Zhang Jiaji , Xiong Zhihua
Published: February 2024

Faced with the challenges of global climate change, zero-carbon buildings (ZCB) serve as a crucial means to achieve carbon peak and carbon neutrality goals, particularly in the development of tropical island regions. This study aims to establish a ZCB technology system suitable for the unique climatic conditions of tropical islands. By employing methods such as energy flow boundaries, parametric design, and data-driven optimization algorithms, the research systematically analyzes the integrated mechanisms and optimization solutions for energy utilization, energy conservation, energy production, and intelligent systems. The study identifies and addresses key technical challenges faced by ZCB in tropical island regions, including the accurate identification of system design parameters, the precise quantification of the relationship between design parameters and building performance, and the comprehensive optimization of technical and economic goals for zero-carbon operational design solutions. The research results not only provide a comprehensive theoretical framework, promoting the development of architectural design theory, but also establish a practical framework for technology and methods, advancing the integration and application of ZCB technology. The study holds significant practical implications for the green transformation of the tropical island construction industry and the realization of national dual-carbon strategic goals. Future research should further explore the applicability of the technology system and the economic feasibility of optimized design solutions, promoting continuous innovation and development in ZCB technology.
Effects of Packaging Material Type, Storage Time and Lipid Content on Phthalate Migration in Smoked Fish Meat
Authors: Đurić Boban , Kartalović Brankica , Habschied Kristina , Novakov Nikolina , Vranešević Jelena , Brkić Boris , Mastanjević Krešimir
Published: February 2024

The objective of this study is an investigation of the influence of six different plastic packages (polyethylene terephthalate, high-density polyethylene, biodegradable high-density polyethylene, low-density polyethylene, polypropylene and polyamide polyethylene) on the migration of phthalate residues in smoked carp, trout and salmon stored at −18 °C for three and six months. Six phthalate residues concentrations were determined using the gas chromatography-mass spectrometry method. Diisobutyl phthalate (DIBP) and dibutyl phthalate (DBP) migrated the most into salmon meat from PAPE packaging after six months of storage, reaching 73.77 μg/kg and 78.45 μg/kg, respectively. The highest concentrations of bis(2-ethylhexyl) phthalate (DEHP) after six months of storage were present in salmon meat packed in polyamide polyethylene (253.56 μg/kg) and the lowest in carp meat packages in polypropylene (157.72 μg/kg). Phthalate residues in all the samples showed higher levels after three and six months of storage compared to the control sample. Among the investigated phthalates, polypropylene was the material with the lowest migration into fish meat. A further amount of DEHP migration in the fish was detected with a higher fat content. We acknowledge that levels of phthalates should be monitored, and research in this field should be continued, especially since there are no legal restrictions regarding the maximum level of phthalates in food.
Evaluation of Recycled Paperboard Properties and Characteristics
Authors: Tikhomirova Ekaterina , Aleksandrov Denis , Tofanica Bogdan-Marian , Mikhailidi Aleksandra
Published: February 2024

Paperboard boxes represent a sought-after class of packaging products, where the use of recycled fibers offers a cost-effective and environmentally friendly alternative to virgin fibers. The presence of a significant proportion of recycled fibers in the paperboard leads to a number of limitations associated with the deterioration of its characteristics. In this study, the properties of coated and uncoated paperboards containing recycled fibers, wood pulp, and virgin cellulose fibers were investigated using a laboratory sample of paperboard produced from 100% recycled fibers without any chemical additives for comparison. Properties such as smoothness, bulk density, absorbency, and tensile strength, as well as colorimetric characteristics, were determined for the recycled paperboards; for the latter test, charts were printed using inkjet printing and UV-curable inks. Whiteness was calculated by three formulae (CIE, Berger, and Stensby), and all the paperboards had a relatively low whiteness (not exceeding 90 CIE), with a yellowish tint due to recycled fibers and mechanical pulp in the composition. The absence of optical brightening agents in the paperboards was experimentally confirmed. The color gamuts of the paperboards were in direct ratio to their whiteness, with the highest ones demonstrated by the coated paperboards. Color reproduction, according to the tone value increase curves, also depended on the whiteness. Uncoated paperboards demonstrated smaller color shifts than the coated ones, with the greatest increase in tone for yellow color. Coated recycled paperboards are suitable for packaging printing under the same conditions as virgin fiber boards, while uncoated boards are the recommended choice for digital printing with UV inks.
Improved Performance and Cost Algorithm for Scheduling IoT Tasks in Fog–Cloud Environment Using Gray Wolf Optimization Algorithm
Authors: Alsamarai Naseem Adnan , Uçan Osman Nuri
Published: February 2024

Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm.