bluuebunny/crossref_metadata_2025 huggingface dataset:
Agricultural Chambers in the Process of Transfer of Knowledge and Innovations for the Development of Agriculture and Rural Areas in Poland
Authors: Kasprzyk Anna , Walenia Alina , Kusz Dariusz , Kusz Bożena
Published: December 2023

Agricultural chambers together with agricultural advisory centers and other entities are part of the European Union Agricultural Knowledge and Innovation System (AKIS). The system consists of a network of entities whose activities are focused on the introduction of new products, technologies, and organizational forms for use in agriculture. The AKIS is also defined as a network of research institutes, advisory centers, agricultural chambers, agricultural schools, and other non-governmental organizations that help farmers to upgrade the innovativeness and competitiveness of their farms and solve agriculture-related problems. In Poland, the activities of self-government in the agricultural sector are specified by the Act on Agricultural Chambers of 14 December 1995. An important research problem is the assessment of the activities of agricultural chambers in terms of the transfer of knowledge and innovations in agriculture in Poland and the determination of their place in the AKIS. This study of the impact of agricultural self-government on the transfer of knowledge and innovations in agriculture was carried out through the analysis of selected agricultural chambers in various regions of Poland taking into account the differences in the average surface area of agricultural land belonging to one farm. The research shows that agricultural chambers’ activities had a substantial impact on the rate of absorption of EU funds under the Rural Development Program in the analyzed regions. The comparison of the effectiveness of the agricultural chambers in Poland with the activities of chambers in the other EU Member States indicates a weak influence of the former on developmental processes in agriculture. Agricultural chambers should actively participate in the formulation of the national agricultural policy, take greater measures to ensure the stability of agricultural income and the flow of information, and be an important partner for farmers in representing their interests.
Morpho-Molecular Features and Phylogenetic Relationships of Metorchis butoridi Oschmarin, 1963 (Trematoda: Opisthorchiidae) from East Asia
Authors: Solodovnik Daria Andreevna , Tatonova Yulia Viktorovna , Besprozvannykh Vladimir Vladimirovich
Published: December 2023

Adult trematodes of the genus Metorchis were found in the gallbladders of ducklings that had been experimentally fed freshwater fishes of the genera Rhynchocypris and Rhodeus that were naturally infected by Metorchis metacercariae. Some of the trematodes were identified as Metorchis ussuriensis, whose morphology of developmental stages and molecular data had previously been described in detail. The other trematodes were confirmed as species Metorchis butoridi on the basis of morphological features: subterminal oral sucker, vitelline follicles with interrupted bands, and rosette-shaped testes. An analysis of phylogenetic relationships within Opisthorchiidae using nuclear and mitochondrial markers confirmed that the obtained trematodes were actually from the genus Metorchis. The morphological and molecular features indicated that a number of trematodes found in East Asia and described as Metorchis orientalis belong to M. butoridi. Also, the M. orientalis individuals from Europe are, in fact, representatives of another Metorchis species.
Effects of the Individual and Pair Housing of Calves on Long-Term Heifer Production on a UK Commercial Dairy Farm
Authors: Mahendran Sophie A. , Wathes D. Claire , Booth Richard E. , Blackie Nicola
Published: December 2023

Pair housing of pre-weaning dairy calves has previously demonstrated positive impacts on their growth, health and behaviour, but longer-term effects on production are still relatively unknown. This study followed a cohort of 431 Holstein heifers, recruited from a single UK commercial dairy farm, from weaning until either culling or the end of their first lactation. All animals were allocated to either individual or pair housing as a pre-weaning calf. Following weaning, all heifers were similarly managed through group housing, feeding with total mixed rations, the use of automatic heat detection for artificial insemination and weighing every two months until conception. Farm staff identified disease occurrences, which were treated following standard operating procedures. First-lactation monthly milk recording was used to measure milk yields and somatic cell counts. Overall mortality (voluntary and involuntary) was 26.6%, with a decreased hazard of exiting the herd if the heifer was pair housed as a calf (HR 0.70; p = 0.067). The voluntary cull rate was highest in the post-insemination period (13.0%) due to poor fertility. Heifers that were pair housed as calves had significantly increased odds of developing udder health issues as a primiparous cow (OR = 1.93, p = 0.022). Despite this, the 305-day milk yields were not associated with the housing group. However, the total milk produced per calf recruited into the original study was greater for pair-housed compared with individually housed calves (8088 kg vs. 7115 kg; p = 0.071), which is likely due to the significantly higher hazard of individually housed calves exiting the herd prematurely.
Bats at an Altitude above 2000 m on Pirin Mountain, Bulgaria
Authors: Dundarova Heliana , Popov Vasil V.
Published: December 2023

The study describes a pilot survey on bats in the highest areas of Pirin Mountain. The methods included examining subfossil bone remains, mist-netting, and recording echolocation calls. The study was conducted in August 2002 and 2013 and from 2019 to 2020. While in general, bat diversity tends to decrease with increasing altitude due to harsher environmental conditions, the present study, despite a short period, reveals high diversity. Twenty species, more than half of the Bulgarian bat fauna, were detected. The recording and analysis of vocal signatures proved to be the best way to inventory bat diversity. At least 13 species were detected by this method. Vespertilio murinus and Tadarida teniotis together make up more than 60% of all reliably determined echolocation sequences. Significant activity was found for Myotis myotis/blythii, Plecotus auritus, Eptesicus serotinus, and E. nilssonii. The registration of the latter species is of considerable faunistic interest. It was previously only known from a single specimen at one location in the country. The sex and age structure of the bat assemblage suggests that it is likely a swarming assemblage. The area is the highest swarming location in Europe. The results provide valuable information on bat ecology and behaviour, which can be used to inform management and protection efforts.
Correlation of 20 Single-Nucleotide Polymorphisms with Weight and Wool Traits in Alpine Merino Sheep
Authors: Xiao Tong , Li Yuhang , Yue Lin , Lu Zengkui , Yuan Chao , Song Yali , Yang Bohui , Liu Jianbin , Guo Tingting
Published: December 2023

SNPs associated with important traits of fine-wool sheep that were previously obtained through genome-wide association analysis screening were verified and analyzed. A total of 20 SNPs related to birth weight, bundle strength, cleaning rate, and fiber diameter were screened using whole-genome resequencing, and the SNPshot assay was used to detect and analyze polymorphisms. This study found that, among the 20 SNPs associated with important traits in Alpine Merino sheep, 8 were monomorphic and 12 were polymorphic, of which 6 showed moderate polymorphisms and 6 showed low polymorphisms. The heterozygosity of the 12 polymorphic loci ranged from 0.10 to 0.49, the effective number of alleles ranged from 1.11 to 1.98, and the polymorphic information content ranged from 0.09 to 0.37. The chi-square test showed that only RHPN2:g.42678119T>G and RALYL:g.90030866A>G were in Hardy–Weinberg disequilibrium (p < 0.05); the other loci were in equilibrium (p > 0.05). These SNPs associated with important traits in Alpine Merino sheep provide a theoretical basis for genomic selection and molecular design breeding.
Home Range, Movement, and Nest Use of Hedgehogs (Erinaceus europaeus) in an Urban Environment Prior to Hibernation
Authors: Korslund Lars Mørch , Floden Marius Stener , Albertsen Milla Mona Sophie , Landsverk Amalie , Løkken Karen Margrete Vestgård , Johansen Beate Strøm
Published: December 2023

The West European hedgehog (Erinaceus europaeus) is in decline, and it is important to identify its challenges. We used VHF-telemetry to monitor pre-hibernation space use, nest use, and hibernation sites in a suburban area in Norway. Based on nine adult hedgehogs tracked between August and November 2002, we found that home range size was not dependent on individual sex or weight and that home ranges overlapped between individuals regardless of sex. The distance moved was not dependent on individual sex, but there was a tendency for increased movement before dawn. The number of nests used per individual (0–10) and the number of nest switches (0–14) varied greatly and did not differ significantly between sexes. Out of 28 nest sites, 16 were linked to buildings and 12 to vegetation, and nesting material was most often grass and leaves. Three hedgehogs monitored until hibernation established winter nests under tree roots in natural forest patches in September, and this suggests that establishing or maintaining forest patches in urban areas is important to ensure suitable hibernation habitat for hedgehogs. Our study was limited by a low sample size, and additional research is required to gain a deeper understanding of the challenges hedgehogs face in urban environments.
A COVID-19 Silver Lining—Decline in Antibiotic Resistance in Ischemic Leg Ulcers during the Pandemic: A 6-Year Retrospective Study from a Regional Tertiary Hospital (2017–2022)
Authors: Rerkasem Amaraporn , Thaichana Pak , Bunsermvicha Nuttida , Nopparatkailas Rawee , Arwon Supapong , Orrapin Saranat , Reanpang Termpong , Apichartpiyakul Poon , Orrapin Saritphat , Siribumrungwong Boonying , Lumjuan Nongkran , Rerkasem Kittipan , Derraik José G. B.
Published: December 2023

Antibiotic resistance (AR) associated with chronic limb-threatening ischemia (CLTI) poses additional challenges for the management of ischemic leg ulcers, increasing the likelihood of severe outcomes. This study assessed AR prevalence in bacteria isolated from CLTI-associated leg ulcers before (1 January 2017–10 March 2020; n = 69) and during (11 March 2020–31 December 2022; n = 59) the COVID-19 pandemic from patients admitted with positive wound cultures to a regional hospital in Chiang Mai (Thailand). There was a marked reduction in AR rates from 78% pre-pandemic to 42% during the pandemic (p < 0.0001), with rates of polymicrobial infections 22 percentage points lower (from 61% to 39%, respectively; p = 0.014). There were reduced AR rates to amoxicillin/clavulanate (from 42% to 4%; p < 0.0001) and ampicillin (from 16% to 2%; p = 0.017), as well as multidrug resistance (19% to 8%; p = 0.026). Factors associated with increased AR odds were polymicrobial infections (adjusted odds ratio (aOR) 5.6 (95% CI 2.1, 15.0); p = 0.001), gram-negative bacteria (aOR 7.0 (95% CI 2.4, 20.5); p < 0.001), and prior use of antibiotics (aOR 11.9 (95% CI 1.1, 128.2); p = 0.041). Improvements in infection control measures and hygiene practices in the community during the pandemic were likely key factors contributing to lower AR rates. Thus, strategic public health interventions, including community education on hygiene and the informed use of antibiotics, may be crucial in mitigating the challenges posed by AR in CLTI. Further, advocating for more judicious use of empirical antibiotics in clinical settings can balance effective treatment against AR development, thereby improving patient outcomes.
Alternative Assisted Extraction Methods of Phenolic Compounds Using NaDESs
Authors: Coscarella Mario , Nardi Monica , Alipieva Kalina , Bonacci Sonia , Popova Milena , Procopio Antonio , Scarpelli Rosa , Simeonov Svilen
Published: December 2023

A renewed understanding of eco-friendly principles is moving the industrial sector toward a shift in the utilization of less harmful solvents as a main strategy to improve manufacturing. Green analytical chemistry (GAC) has definitely paved the way for this transition by presenting green solvents to a larger audience. Among the most promising, surely DESs (deep eutectic solvents), NaDESs (natural deep eutectic solvents), HDESs (hydrophobic deep eutectic solvents), and HNaDESs (hydrophobic natural deep eutectic solvents), with their unique features, manifest a wide-range of applications, including their use as a means for the extraction of small bioactive compounds. In examining recent advancements, in this review, we want to focus our attention on some of the most interesting and novel ‘solvent-free‘ extraction techniques, such as microwave-assisted extraction (MAE) and ultrasound-assisted extraction (UAE) in relation to the possibility of better exploiting DESs and NaDESs as plausible extracting solvents of the phenolic compounds (PCs) present in different matrices from olive oil components, such as virgin olive pomace, olive leaves and twigs, virgin and extra virgin olive oil (VOO and EVOO, respectively), and olive cake and olive mill wastewaters (OMWW). Therefore, the status of DESs and NaDESs is shown in terms of their nature, efficacy and selectivity in the extraction of bioactive phytochemicals such as secoiridoids, lignans, phenolic acids and alcohols. Related studies on experimental design and processes’ optimization of the most promising DESs/NaDESs are also reviewed. In this framework, an extensive list of relevant works found in the literature is described to consider DESs/NaDESs as a suitable alternative to petrochemicals in cosmetics, pharmaceutical, or food applications.
A Parallel Multimodal Integration Framework and Application for Cake Shopping
Authors: Fang Hui , Weng Dongdong , Tian Zeyu
Published: December 2023

Multimodal interaction systems can provide users with natural and compelling interactive experiences. Despite the availability of various sensing devices, only some commercial multimodal applications are available. One reason may be the need for a more efficient framework for fusing heterogeneous data and addressing resource pressure. This paper presents a parallel multimodal integration framework that ensures that the errors and external damages of integrated devices remain uncorrelated. The proposed relative weighted fusion method and modality delay strategy process the heterogeneous data at the decision level. The parallel modality operation flow allows each device to operate across multiple terminals, reducing resource demands on a single computer. The universal fusion methods and independent devices further remove constraints on the integrated modality number, providing the framework with extensibility. Based on the framework, we develop a multimodal virtual shopping system, integrating five input modalities and three output modalities. The objective experiments show that the system can accurately fuse heterogeneous data and understand interaction intent. User studies indicate the immersive and entertaining of multimodal shopping. Our framework proposes a development paradigm for multimodal systems, fostering multimodal applications across various domains.
Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
Authors: Waheeb Samer Abdulateef
Published: December 2023

This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA).
Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution
Authors: Toki Eugenia I. , Tatsis Giorgos , Pange Jenny , Tsoulos Ioannis G.
Published: December 2023

Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.
Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification
Authors: Riaz-ud-din Muhammad , Ghafoor Salman Abdul , Shafait Faisal
Published: December 2023

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.
Designing an Automatic Frequency Stabilization System for an External Cavity Diode Laser Using a Data Acquisition Card in the LabVIEW Platform
Authors: Wu Yueyang , Qin Fangjun , Li Yang , Ding Zhichao , Xu Rui
Published: December 2023

The frequency stability of free-running lasers is susceptible to the influence of environmental factors, which cannot meet the long-term frequency stabilization requirements for atom interferometry precision measurements. To obtain a frequency-stabilized 780 nm laser beam, an automatic frequency stabilization system for an external cavity diode laser (ECDL) based on rubidium (Rb) atomic saturated absorption spectrum was designed using a commercial data acquisition (DAQ) card. The signals acquired by the A/D terminal are processed and analyzed by LabVIEW, which can automatically identify all the locking points and output the piezoelectric ceramic transducer (PZT) scan and digital feedback through the D/A terminal. The experimental results show that the system can lock to six different frequencies separately and realize automatic relocking within 3.5 s after unlocking. The system has a stability of 1.68 × 10−10@1 s and 4.77 × 10−12@1000 s, which meets the laboratory’s requirements for atomic interference experiments.
Digital Content Management Using Non-Fungible Tokens and the Interplanetary File System
Authors: Ko Hoon , Oh Juhee , Kim Sung Uk
Published: December 2023

Non-fungible tokens (NFTs) are digital identifiers containing metadata, such as token number, title, content, and image URL, and are linked to digital assets, which are characterized by the fact that, unlike conventional virtual assets, they have their own unique value and cannot be replaced. NFTs cannot be deleted or forged; therefore, they can be used to authenticate the ownership of digital assets. The metadata of the NFTs are uploaded to the interplanetary file system (IPFS), which is a distributed file system, and converted into unique content identifiers (CIDs) that are stored on the blockchain. Digital content (DC) is divided into multiple pieces; it also has its own unique value and is distributed and stored using the IPFS. This study built an NFT-based IPFS testbed and experimented with the process of generating unique values for DC divided into three groups and sharing them. The results confirmed that each DC had a unique hash value and no duplicates existed.
Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building
Authors: Lawal Kelvin N. , Olaniyi Titus K. , Gibson Ryan M.
Published: December 2023

It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.
IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
Authors: Rodríguez Aguilar Manuel José , Cardiel Ismael Abad , Somolinos José Antonio Cerrada
Published: December 2023

Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect configurations, often introducing complexity in pinpointing the root cause and resulting in financial losses. Furthermore, improper machine maintenance contributes to this situation as well. This article seeks to establish a framework grounded in the contemporary smart factory, the IIoT, the Industry 4.0 paradigm, and Big Data. The proposed system places emphasis on leveraging real-time data analysis for predicting risks, while concurrently conducting a thorough analysis of historical data to monitor trends and enhance bottleneck identification. The defined architecture operates across multiple levels, acquiring real-time information and generating historical data for training and continuous optimization. Predictive results contribute to decision-making and assist in mitigating bottlenecks in manufacturing lines.
Machine Learning Applications and Uncertainty Quantification Analysis for Reflood Tests
Authors: Tiep Nguyen Huu , Kim Kyung-Doo , Jeong Hae-Yong , Xuan-Mung Nguyen , Hoang Van-Khanh , Ngoc Anh Nguyen , Vu Mai The
Published: December 2023

The reflooding phase, a crucial recovery process after a loss of coolant accident (LOCA) in reactors, involves cooling overheated fuel rods with subcooled water. Its complex nature, notably in its flow regime and heat transfer, makes prediction challenging, resulting in high uncertainty and computation cost. In this study, we utilized the data assimilation (DA) technique to enhance the prediction of reflooding phenomena and subsequently deployed machine learning models to predict the accuracy of the safety and performance analysis code (SPACE) simulation. To generate the dataset for the machine learning model, we employed the sampling method for highly nonlinear system uncertainty analysis (STARU), providing a high-quality dataset for a complex problem such as a reflooding simulation. In this dataset, the physical models were assimilated under their selected uncertainty bands and utilized the effective sampling approach of STARU, generating the high-quality output and efficient enhancement of SPACE predictions. Consequently, the implemented machine learning model can be used to enhance model development and uncertainty quantification (UQ) analysis using the system code.
Design Optimization of the Lift Mechanism in the Robotic Walking Training Device Using the Engineering Design Methodology
Authors: Bourgeois Austin , Rice Brian , Goh Chung-Hyun
Published: December 2023

Partial paralysis caused by spinal cord injury (SCI) or stroke are two of the most prevalent forms of physical disability. Through proper gait training, people with incomplete SCI have more potential to retain or regain the ability to walk than those with complete SCI. To help patients who have these disabilities regain the function of walking unassisted, the robotic walking training device (RWTD) has been developed to perform gait rehabilitation. This research plays a pivotal role in advancing medical robotic technology and gait rehabilitation by conducting a comprehensive evaluation and comparison of three lift mechanisms. Specifically, the lift mechanisms are designed to reposition a patient, using the RWTD, from a supine to a vertical position. Addressing a crucial gap in supporting and placing patients in gait rehabilitation devices, design optimization was performed using the engineering design process. This approach utilizes sophisticated techniques, including CAD modeling, motion analysis, structural analysis using finite element analysis, and a Pugh decision matrix. The findings offer valuable insights for optimizing lift mechanisms for the RWTD, contributing to the enhancement of patient-centric care. This research ensures a focus on safety, efficiency, and comfort in the gait rehabilitation process, with broader implications for the evolution of medical robotic devices.
Real-Time Machine Learning for Human Activities Recognition Based on Wrist-Worn Wearable Devices
Authors: Alexan Alexandru Iulian , Alexan Anca Roxana , Oniga Stefan
Published: December 2023

Wearable technologies have slowly invaded our lives and can easily help with our day-to-day tasks. One area where wearable devices can shine is in human activity recognition, as they can gather sensor data in a non-intrusive way. We describe a real-time activity recognition system based on a common wearable device: a smartwatch. This is one of the most inconspicuous devices suitable for activity recognition as it is very common and worn for extensive periods of time. We propose a human activity recognition system that is extensible, due to the wide range of sensing devices that can be integrated, and that provides a flexible deployment system. The machine learning component recognizes activity based on plot images generated from raw sensor data. This service is exposed as a Web API that can be deployed locally or directly in the cloud. The proposed system aims to simplify the human activity recognition process by exposing such capabilities via a web API. This web API can be consumed by small-network-enabled wearable devices, even with basic processing capabilities, by leveraging a simple data contract interface and using raw data. The system replaces extensive pre-processing by leveraging high performance image recognition based on plot images generated from raw sensor data. We have managed to obtain an activity recognition rate of 94.89% and to implement a fully functional real-time human activity recognition system.
Vehicle Activity Dataset: A Multimodal Dataset to Understand Vehicle Emissions with Road Scenes for Eco-Routing
Authors: Jendoubi Firas , Pradeep Vishnu , Khemmar Redouane , Berradia Tahar , Rossi Romain , Sibbille Benjamin , Fourre Jérémy , Ohayon Avigaël , Jouni Mohammad
Published: December 2023

In the field of smart mobility, Artificial Intelligence (AI) approaches are influential and can make a highly beneficial contribution. Our project aims to develop a real-time ecological map of road traffic. This map will allow electric vehicles (EVs) and thermal vehicles (TVs) to display the cost of energy consumption and CO2 emissions on different road sections. In urban environments, road traffic emissions are a significant contributor to environmental pollution, with vehicle emissions being a major component. Addressing these impacts requires a thorough understanding of the operational behavior of vehicles on different road infrastructures within the region. This paper presents a novel, comprehensive dataset, the Vehicle Activity Dataset (VAD), designed to assess the emissions and fuel consumption characteristics of vehicles about their actual operating environment. Constructed from a large number of real-world driving scenarios, VAD incorporates emission data collected by an industrial Portable Emission Measurement System (PEMS), road scenes captured by an RGB camera, and the detection of different object classes within these images. The primary objective of VAD is to provide a comprehensive understanding of the relationship between vehicle emissions and the diverse range of objects present on the road. Experimental results in real road traffic environments through different studies demonstrate the robustness of the developed dataset.
Analysis of the Influence of Surrounding Rock State on Working Performance of Cutting Head in Metal Mining
Authors: Xu Weipeng , Wan Lirong , Gao Kuidong , Bu Yu , Xu Meng , Ma Shenghao , Jiang Kao
Published: December 2023

Continuous mining is one of the development goals for metal mines, and the application of coal mining equipment represented by shearers provides a reference for continuous mining. However, rock in metal mines is generally harder than coal, making cutting difficult. Improving the surrounding rock conditions is an important way to improve the applicability of the drum for hard rock cutting. Therefore, this article explores the correlation between drum-cutting performance and surrounding rock boundary conditions, aiming to obtain surrounding rock boundary conditions that can help improve drum-cutting performance. To achieve the goal, a model of a shearer drum and hard rock is established using finite element software. With the model, hard rock cutting was simulated and the stress distribution on rock mass, deformation of rock mass, and drum cutting force during the cutting process under different confining pressures were analyzed. Relations between drum cutting force and confining pressure on rock mass were obtained. Then, drum cutting force under different free surfaces of rock mass are studied and the positions of free surface on rock mass that help to reduce the drum cutting force were summarized. According to the research, when the rock mass is under uniaxial compression, drum cutting force increases with the confining pressure on the rock mass; In addition, the free surfaces on the rock mass are proved to be helpful to reduce the drum cutting force. The research content lays the foundation for the boundary conditions required to reduce drum-cutting force in metal mining.
Study of Modified Primary Compression Index Tests and a Regression Model for Municipal Solid Waste Considering the Temperature Effect
Authors: Zhou Yaji , Wu Xun , Yang Yang
Published: December 2023

The modified primary compression index (MPCI) of municipal solid waste (MSW) is an important parameter for describing the primary consolidation settlement of MSW. It is beneficial to calculate the settlement and storage capacity of landfills accurately by calculating the MPCI of MSW correctly. This article studies the primary consolidation compression characteristics and influencing factors of MSW through a consolidation experiment considering temperature control. The experimental results show that the MPCI increases with an increase in temperature, decreases with an increase in dry weight, increases with an increase in water content, and increases with an increase in organic matter content. According to the above findings, an MPCI regression fitting model was established. The reliability and accuracy of the model was validated by the experimental results of the indoor bioreactor test considering temperature control.
Triangular Norm-Based Elements on Bounded Lattices
Authors: Yılmaz Şerife , İşçi Rabia
Published: December 2023

In this study, we introduce the notion of the T-irreducible element as a generalization of the notion of the meet-irreducible element in complete lattices. We derive some related properties of these elements and T-prime elements. We prove that T-irreducible elements and T-prime elements are preserved under the isomorphism that is generated by the same t-norm. We discuss the relationship between the sets of T-prime elements and co-atoms under some conditions. We illustrate this discussion with some examples. We also give some characterizations for the sets of T-irreducible elements and T-prime elements on the direct product of lattices. Then, we show that Theorem 2 given by Karaçal and Sağıroğlu is false by giving some counterexamples. We present a necessary and sufficient condition for the mentioned theorem to be correct.
A Novel Method for State of Charge Estimation in Lithium-Ion Batteries Using Temporal Convolutional Network and Multi-Verse Optimization
Authors: Li Yuanmao , Liu Guixiong , Deng Wei
Published: December 2023

This study presents a novel data-driven method for state-of-charge estimation in lithium-ion batteries. It integrates a temporal convolutional network with multi-verse optimization to enhance the accuracy of predicting the state of charge. The temporal convolutional network possesses advantages such as an extended memory window and efficient parallel computation, exhibiting exceptional performance in time-series tasks for state of charge estimation. Its hyperparameters are optimized by adopting multi-verse optimization to obtain better model performance. The driving model utilizes various measurable data as inputs, including battery terminal voltage, current, and surface temperature. To validate the effectiveness of the proposed method, extensive datasets from diverse dynamic working conditions at different ambient temperatures are employed for model training, validation, and testing. The numerical outcomes provide evidence of the proposed method’s superior performance compared to the other two methods, providing a more robust and accurate solution for the state of charge estimation in lithium-ion batteries.
Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model
Authors: Orellana Marcos , García Patricio Santiago , Ramon Guillermo Daniel , Zambrano-Martinez Jorge Luis , Patiño-León Andrés , Serrano María Verónica , Cedillo Priscila
Published: December 2023

Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative and Alternative Communication (AAC) methods are widely used to support this population segment. Moreover, with Artificial Intelligence (AI), and specifically, machine learning algorithms, AAC can be improved. Although there have been several studies in this field, it is interesting to analyze common phrases used by seniors, depending on their context (i.e., slang and everyday expressions typical of their age). This paper proposes a semantic analysis of the common phrases of older adults and their corresponding meanings through Natural Language Processing (NLP) techniques and a pre-trained language model using semantic textual similarity to represent the older adults’ phrases with their corresponding graphic images (pictograms). The results show good scores achieved in the semantic similarity between the phrases of the older adults and the definitions, so the relationship between the phrase and the pictogram has a high degree of probability.
Measurement of the Growth of the Main Commercial Rays (Raja clavata, Raja brachyura, Torpedo marmorata, Dipturus oxyrinchus) in European Waters Using Intercalibration Methods
Authors: Bellodi Andrea , Carbonara Pierluigi , MacKenzie Kirsteen M. , Agus Blondine , Bekaert Karen , Greenway Eleanor S. I. , Follesa Maria C. , Madia Manfredi , Massaro Andrea , Palmisano Michele , Romano Chiara , Sinopoli Mauro , Ferragut-Perello Francesca , Mahé Kélig
Published: December 2023

The intercalibration of age readings represents a crucial step in the ageing procedure; the use of different sampling methods, structures, preparation techniques, and ageing criteria can significantly affect age and growth data. This study evaluated the precision and accuracy of ageing for the most important North Atlantic (NA) and Mediterranean (M) ray species, Raja clavata, Raja brachyura, Torpedo marmorata, and Dipturus oxyrinchus, through exchange exercises carried out by readers from different laboratories. In addition, growth parameters were estimated from the obtained data. A total of 663 individual batoids were analysed. R. clavata and R. brachyura samples were obtained from both the NA and the M, while vertebral centra of T. marmorata and D. oxyrinchus were only available for the M. High reading variability was observed for all four evaluated species in terms of CV, APE, and PA. D. oxyrinchus and T. marmorata showed relatively slow growth and the von Bertalanffy model with fixed t0 and Gompertz’s model were, respectively, the most precise models for each of these species. In R. brachyura, females had a faster growth rate compared to combined sexes. The vbt0p proved the most precise model for describing growth in this species, and no statistical differences were found between the NO and the M. For R. clavata, the best-fitting model was the vbt0p for females and males in the NO and for females from the M, while the best-fitting model for males from the M and sexes combined for both areas was log.p. Distinct growth patterns were observed between the two study areas.
Biomaterials in Traumatic Brain Injury: Perspectives and Challenges
Authors: Aqel Sarah , Al-Thani Najlaa , Haider Mohammad Z. , Abdelhady Samar , Al Thani Asmaa A. , Kobeissy Firas , Shaito Abdullah A.
Published: December 2023

Traumatic brain injury (TBI) is a leading cause of mortality and long-term impairment globally. TBI has a dynamic pathology, encompassing a variety of metabolic and molecular events that occur in two phases: primary and secondary. A forceful external blow to the brain initiates the primary phase, followed by a secondary phase that involves the release of calcium ions (Ca2+) and the initiation of a cascade of inflammatory processes, including mitochondrial dysfunction, a rise in oxidative stress, activation of glial cells, and damage to the blood–brain barrier (BBB), resulting in paracellular leakage. Currently, there are no FDA-approved drugs for TBI, but existing approaches rely on delivering micro- and macromolecular treatments, which are constrained by the BBB, poor retention, off-target toxicity, and the complex pathology of TBI. Therefore, there is a demand for innovative and alternative therapeutics with effective delivery tactics for the diagnosis and treatment of TBI. Tissue engineering, which includes the use of biomaterials, is one such alternative approach. Biomaterials, such as hydrogels, including self-assembling peptides and electrospun nanofibers, can be used alone or in combination with neuronal stem cells to induce neurite outgrowth, the differentiation of human neural stem cells, and nerve gap bridging in TBI. This review examines the inclusion of biomaterials as potential treatments for TBI, including their types, synthesis, and mechanisms of action. This review also discusses the challenges faced by the use of biomaterials in TBI, including the development of biodegradable, biocompatible, and mechanically flexible biomaterials and, if combined with stem cells, the survival rate of the transplanted stem cells. A better understanding of the mechanisms and drawbacks of these novel therapeutic approaches will help to guide the design of future TBI therapies.
Antinociceptive Effects of Cannabichromene (CBC) in Mice: Insights from von Frey, Tail-Flick, Formalin, and Acetone Tests
Authors: Raup-Konsavage Wesley M. , Sepulveda Diana E. , Wang Jian , Dokholyan Nikolay V. , Vrana Kent E. , Graziane Nicholas M.
Published: December 2023

Cannabis sativa contains minor cannabinoids that have potential therapeutic value in pain management. However, detailed experimental evidence for the antinociceptive effects of many of these minor cannabinoids remains lacking. Here, we employed artificial intelligence (AI) to perform compound–protein interaction estimates with cannabichromene (CBC) and receptors involved in nociceptive signaling. Based on our findings, we investigated the antinociceptive properties of CBC in naïve or neuropathic C57BL/6 male and female mice using von Frey (mechanical allodynia), tail-flick (noxious radiant heat), formalin (acute and persistent inflammatory pain), and acetone (cold thermal) tests. For von Frey assessments, CBC dose (0–20 mg/kg, i.p.) and time (0–6 h) responses were measured in male and female neuropathic mice. For tail-flick, formalin, and acetone assays, CBC (20 mg/kg, i.p.) was administered to naïve male and female mice 1 h prior to testing. The results show that CBC (10 and 20 mg/kg, i.p.) significantly reduced mechanical allodynia in neuropathic male and female mice 1–2 h after treatment. Additionally, CBC treatment caused significant reductions in nociceptive behaviors in the tail-flick assay and in both phase 1 and phase 2 of the formalin test. Finally, we found a significant interaction in neuropathic male mice in the acetone test. In conclusion, our results suggest that CBC targets receptors involved in nociceptive signaling and imparts antinociceptive properties that may benefit males and females afflicted with diverse forms of acute or chronic/persistent pain.
The Impact of Biomarkers on the Early Detection of Acute Mesenteric Ischemia
Authors: Zafirovski Aleksandar , Zafirovska Marija , Kuhelj Dimitrij , Pintar Tadeja
Published: December 2023

Background: acute mesenteric ischemia (AMI) is a life-threatening condition that is caused by inadequate blood flow through the mesenteric vessel and is related to high mortality rates due to systemic complications. This study aims to systematically review the available literature concerning the major findings of possible biomarkers for early detection of acute mesenteric ischemia in the human population. Methods: studies that measured the performance of biomarkers during acute mesenteric ischemia were identified with the search of PubMed, Embase, Medline, and Cochrane library. Results: from a total of 654 articles, 46 articles examining 14 different biomarkers were filtered, falling within our inclusion criteria. Intestinal fatty acid-binding protein (I-FABP) was the most commonly researched biomarker regarding AMI, with sensitivity ranging from 61.5% to 100% and specificity ranging from 40% to 100%. The second most commonly researched biomarker was D-dimer, with a sensitivity of 60–100% and a specificity of 18–85.71%. L-lactate had a sensitivity of 36.6–90.91% and a specificity of 64.29–96%. Several parameters within the blood count were examined as potential markers for AMI, including NLR, PLR, MPV, RDW, DNI, and IG. Citrulline, interleukin 6 (IL-6), and procalcitonin (PCT) were the least-researched biomarkers. Conclusion: different biomarkers showed different accuracies in detecting AMI. I-FABP and D-dimer have been the most researched and shown to be valuable in the diagnosis of AMI, whereas L-lactate could be used as an additional tool. Ischemia-modified albumin (IMA), alpha glutathione S-transferase (αGST), interleukin 6 (IL-6), and citrulline showed potential use in their respective studies. However, further research needs to be done on larger sample sizes and with controls to reduce bias. Several studies showed that neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), mean platelet volume (MPV), red-cell distribution width (RDW), delta neutrophil index (DNI), and immature granulocytes (IGs) might be useful, as well at the same time be widely distributed and affordable in combination with other markers presenting higher specificity and sensitivity.