bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Tea Seed Oil on In Vitro Rumen Fermentation, Nutrient Degradability, and Microbial Profile in Water Buffalo
Authors: Xie Huade , Zeng Fanquan , Guo Yanxia , Peng Lijuan , Luo Xianqing , Yang Chengjian
Published: August 2023

Tea seed oil (TSO) was investigated for its effects on rumen fermentation and in vitro parameters of bacterial communities in water buffalo diets containing Siraitia grosvenorii and soybean residues. TSO was added at rates of 0% (control group (CT)), 0.5% (T1), 1% (T2), and 2% (T3) of the in vitro fermentation substrate weight (dry matter (DM) basis). T2 and T3 had significantly lower acetate and total volatile fatty acid contents but a significantly higher microbial crude protein content than CT. The lowest NH3-N content was observed in T1 and T2. Treatment significantly increased DM digestibility, with the highest percentage observed in T2. T2 showed significantly higher crude protein digestibility than CT. TSO supplementation significantly increased the C18:2n6c, C18:2 trans-10, cis-12, and C20:4n6 concentrations compared to those in CT. The total number of bacteria was significantly lower in T2 than in CT. TSO supplementation decreased the total bacteria, fungi, and methanogen populations but increased rumen microorganism diversity and richness. In conclusion, TSO can regulate the number and flora of rumen microorganisms through antimicrobial activity, thereby affecting rumen fermentation patterns, reducing methane production, and improving nutrient digestibility, and an optimal supplementation rate appears to be achieved with 1% TSO (DM basis).
Prevalence and Association of Campylobacter spp., Salmonella spp., and Blastocystis sp. in Poultry
Authors: Guyard-Nicodème Muriel , Anis Nagham , Naguib Doaa , Viscogliosi Eric , Chemaly Marianne
Published: August 2023

Poultry and poultry meat are considered the most important sources of human campylobacteriosis and salmonellosis. However, data about the occurrence of Campylobacter and Salmonella concomitantly with intestinal protozoa such as Blastocystis sp. in poultry remain very scarce. Therefore, this study aimed to investigate the presence and possible interactions between these three microorganisms in fecal samples from 214 chickens collected either on farms or from live bird markets in Egypt. The results obtained showed that Campylobacter spp., Salmonella spp., and Blastocystis sp. were present in 91.6% (196/214), 44.4% (95/214), and 18.2% (39/214) of tested samples, respectively, highlighting an active circulation of these microorganisms. Moreover, a significant positive correlation was reported between the occurrence of Campylobacter spp. and Blastocystis sp. together with a significant negative correlation between Blastocystis sp. and Salmonella spp. This study confirms the association reported previously between Blastocystis sp. and Campylobacter spp. while disclosing an association between Blastocystis sp. and Salmonella spp.; it also highlights the need to improve studies on the interactions between bacteria and eukaryotes in the gut microbiota of poultry.
Unveiling the Structural Characteristics and Bioactivities of the Polysaccharides Extracted from Endophytic Penicillium sp.
Authors: Naveen Kumar Vishven , Sathiyaseelan Anbazhagan , Mandal Sumana , Han Kiseok , Wang Myeong-Hyeon
Published: August 2023

Polysaccharides are abundantly present in fungi and are gaining recognition for their exceptional bioactivities. Hence, the present study aimed to extract intracellular polysaccharides (IPS-1 and IPS-2) from the endophytic Penicillium radiatolobatum and compare their physicochemical and bioactive attributes. The monosaccharide composition analysis revealed the existence of galactose, glucose, and mannose in both the IPS, while a trace amount of xylose was found in IPS-1. Further, FT-IR, 1H NMR, and 13C NMR analysis suggested that the IPS-2 was mainly composed of the β-(1→4)-D-Galactose and β-(1→4)-D-Glucose as the main chain, with the β-(1→6)-D-mannose as branched chains. Compared to IPS-1, the IPS-2 showed higher antioxidant activities with an IC50 value of 108 ± 2.5 μg/mL, 272 ± 4.0 μg/mL, and 760 ± 5.0 μg/mL for ABTS+ scavenging, DPPH radical scavenging, and ferric reducing power, respectively. In addition, the IPS-2 inhibited the viability of prostate cancer (PC-3) cells (IC50; 435 ± 3.0 μg/mL) via apoptosis associated with mitochondrial membrane potential collapse and altered morphological features, which was revealed by cellular staining and flow cytometric analysis. Moreover, no apparent cytotoxic effects were seen in IPS-2-treated (1000 μg/mL) non-cancerous cells (HEK-293 and NIH3T3). Overall, the findings of this study suggest that P. radiatolobatum could be a potent source of polysaccharides with promising antioxidant and anticancer activity.
Performance Exploration of Ni-Doped MoS2 in CO2 Hydrogenation to Methanol
Authors: Yuan Yongning , Qi Liyue , Gao Zhuxian , Guo Tuo , Zhai Dongdong , He Yurong , Ma Jingjing , Guo Qingjie
Published: August 2023

The preparation of methanol chemicals through CO2 and H2 gas is a positive measure to achieve carbon neutrality. However, developing catalysts with high selectivity remains a challenge due to the irreversible side reaction of reverse water gas shift (RWGS), and the low-temperature characteristics of CO2 hydrogenation to methanol. In-plane sulfur vacancies of MoS2 can be the catalytic active sites for CH3OH formation, but the edge vacancies are more inclined to the occurrence of methane. Therefore, MoS2 and a series of MoS2/Nix and MoS2/Cox catalysts doped with different amounts are prepared by a hydrothermal method. A variety of microscopic characterizations indicate that Ni and Co doping can form NiS2 and CoS2, the existence of these substances can prevent CO2 and H2 from contacting the edge S vacancies of MoS2, and the selectivity of the main product is improved. DFT calculation illustrates that the larger range of orbital hybridization between Ni and MoS2 leads to CO2 activation and the active hydrogen is more prone to surface migration. Under optimized preparation conditions, MoS2/Ni0.2 exhibits relatively good methanol selectivity. Therefore, this strategy of improving methanol selectivity through metal doping has reference significance for the subsequent research and development of such catalysts.
Chemo-Enzymatic Synthesis and Biological Assessment of p-Coumarate Fatty Esters: New Antifungal Agents for Potential Plant Protection
Authors: Thaeder Cyrian , Stanek Juliette , Couvreur Julien , Borrego Célia , Brunissen Fanny , Allais Florent , Flourat Amandine L. , Cordelier Sylvain
Published: August 2023

One trend in agriculture is the replacement of classical pesticides with more ecofriendly solutions, such as elicitation, which is a promising approach consisting of stimulating the natural immune system of a plant to improve its resistance to pathogens. In this fashion, a library of p-coumaric-based compounds were synthesized in accordance with as many principles of green chemistry as possible. Then, these molecules were tested for (1) the direct inhibition of mycelium growth of two pathogens, Botrytis cinerea and Sclerotinia sclerotiorum, and (2) plasma membrane destabilization in Arabidopsis and rapeseed. Finally, the protective effect was evaluated on an Arabidopsis/B. cinerea pathosystem. Total inhibition of the growth of both fungi could be achieved, and significant ion leakage was observed using dihydroxylated fatty p-coumarate esters. A direct effect on plants was also recorded as a ca. three-fold reduction in the necrosis area.
A Fast Star-Detection Algorithm under Stray-Light Interference
Authors: Lu Kaili , Li Huakang , Lin Ling , Zhao Renjie , Liu Enhai , Zhao Rujin
Published: August 2023

The interference caused by stray light leads to the invalid attitude of star sensors in orbit, thus affecting the attitude control of satellites. In order to overcome this problem, this paper proposes a fast star-detection algorithm with strong stray-light suppression ability. The first step in the proposed method is stray-light suppression. The highlighted pixels are unified and then erosion and dilation operations based on a large template are performed. Using the background image only, which is filled with stray light, the cleaner star image is obtained by subtracting the background from the unified image. The second step in the proposed method is binarization. The binary star image is obtained by using a line-segment strategy combined with a local threshold. The third step in the proposed method is star labeling. It comprises connected-domain labeling based on the preordering of pixels and the calculation of centroid coordinates of stars in each connected domain. The experimental results show that the proposed algorithm extracts the stars stably under the interference of different stray lights. The proposed method consumes less resources, and the output delay is only 18.256 us. Moreover, the successful identification rate is 98% and the attitude accuracy of the X and Y axes is better than 5″(3σ) when the star sensor works at the speed of zero.
Dielectric Performance of UHMWPE-MgFe2O4 Composites Depending on Polymer Crystallinity, and the Concentration and Size of Mechanochemically Synthesized Ferrite Particles
Authors: Kiseleva Tatiana , Grigoreva Tatiana , Kovaliova Svetlana , Il’in Maxim , Yakuta Ekaterina , Devyatkina Evgeniya , Malyshkina Inna , Ivanenko Ilya , Vosmerikov Sergey , Lyakhov Nikolay
Published: August 2023

Mechanochemically synthesized particles of two types of magnesium ferrites, one of which with structural distortions and an average size of 170 nm, and another that is highly crystalline with an average size of 900 nm, were introduced into a matrix of ultra-high-molecular-weight polyethylene via the milling processing. The final material has been formed by hot pressing mechanocomposites based on ultra-high-molecular-weight polyethylene and magnesium ferrite particles of various fineness and concentration. Structural characteristics were studied using scanning electron microscopy, differential scanning calorimetry and X-ray diffraction analysis. The dielectric properties of the obtained composites were analyzed by testing the frequency dependence of the permeability, dielectric losses, and conductivity. The effect of filler concentration and particle size, as well as the crystallinity of the polymer, on the dielectric properties of the composite material were studied.
Soy Protein Nanofibers Obtained by Solution Blow Spinning
Authors: Penconek Agata , Kasak Dorota , Moskal Arkadiusz
Published: August 2023

The climate crisis, growing pollution of the environment with waste, and ubiquitous microplastics have encouraged the search for new methods and new opportunities to use natural materials in the least harmful processes. Replacing synthetic materials with raw materials is not only a matter of “fashion”, but also a necessity. Therefore, this study aimed to produce fibers from an aqueous solution containing the highest possible concentration of soy protein isolate (SPI) through solution-blowing. As the aqueous solution of SPI has no fiber-forming potential, polyethylene oxide (PEO) was used as the carrier/matrix. The rheology of the aqueous PEO solution and PEO/SPI blends (flow curves, loss modulus, and storage modulus) was then analyzed. The proportions of the PEO/SPI mixtures and the process parameters were determined on this basis. As a result of the conducted research, nanofibers were produced from water solutions of PEO/SPI with a soy protein content of up to 7.88%, with a PEO share of 1.25%. The average diameter of the obtained fibers was 225–495 nm, depending on the process parameters and SPI content—as the SPI content in the PEO/SPI mixture increased, the average fiber diameter decreased. The production of nanofibers with a high soy protein content increases their application possibilities. Firstly, due to a much larger surface area, and secondly, due to the presence of numerous functional groups in the protein structure, which can be a place of attachment for additional compounds that give the obtained nanofibers the desired properties.
Failure Severity Prediction for Protective-Coating Disbondment via the Classification of Acoustic Emission Signals
Authors: Rahman Noor A’in A. , May Zazilah , Jaffari Rabeea , Hanif Mehwish
Published: August 2023

Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.
Intelligent Industrial Process Control Systems
Authors: Grobelna Iwona
Published: August 2023

The widespread realization of Industry 4 [...]
Transformers for Energy Forecast
Authors: Oliveira Hugo S. , Oliveira Helder P.
Published: August 2023

Forecasting energy consumption models allow for improvements in building performance and reduce energy consumption. Energy efficiency has become a pressing concern in recent years due to the increasing energy demand and concerns over climate change. This paper addresses the energy consumption forecast as a crucial ingredient in the technology to optimize building system operations and identifies energy efficiency upgrades. The work proposes a modified multi-head transformer model focused on multi-variable time series through a learnable weighting feature attention matrix to combine all input variables and forecast building energy consumption properly. The proposed multivariate transformer-based model is compared with two other recurrent neural network models, showing a robust performance while exhibiting a lower mean absolute percentage error. Overall, this paper highlights the superior performance of the modified transformer-based model for the energy consumption forecast in a multivariate step, allowing it to be incorporated in future forecasting tasks, allowing for the tracing of future energy consumption scenarios according to the current building usage, playing a significant role in creating a more sustainable and energy-efficient building usage.
SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization
Authors: Clotet Eduard , Palacín Jordi
Published: August 2023

The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.
Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques
Authors: Abbas Farkhanda , Zhang Feng , Ismail Muhammad , Khan Garee , Iqbal Javed , Alrefaei Abdulwahed Fahad , Albeshr Mohammed Fahad
Published: August 2023

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment’s results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model’s overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.
Low-Cost Sensors Technologies for Monitoring Sustainability and Safety Issues in Mining Activities: Advances, Gaps, and Future Directions in the Digitalization for Smart Mining
Authors: Cacciuttolo Carlos , Guzmán Valentina , Catriñir Patricio , Atencio Edison , Komarizadehasl Seyedmilad , Lozano-Galant Jose Antonio
Published: August 2023

Nowadays, monitoring aspects related to sustainability and safety in mining activities worldwide are a priority, to mitigate socio-environmental impacts, promote efficient use of water, reduce carbon footprint, use renewable energies, reduce mine waste, and minimize the risks of accidents and fatalities. In this context, the implementation of sensor technologies is an attractive alternative for the mining industry in the current digitalization context. To have a digital mine, sensors are essential and form the basis of Industry 4.0, and to allow a more accelerated, reliable, and massive digital transformation, low-cost sensor technology solutions may help to achieve these goals. This article focuses on studying the state of the art of implementing low-cost sensor technologies to monitor sustainability and safety aspects in mining activities, through the review of scientific literature. The methodology applied in this article was carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and generating science mapping. For this, a methodological procedure of three steps was implemented: (i) Bibliometric analysis as a quantitative method, (ii) Systematic review of literature as a qualitative method, and (iii) Mixed review as a method to integrate the findings found in (i) and (ii). Finally, according to the results obtained, the main advances, gaps, and future directions in the implementation of low-cost sensor technologies for use in smart mining are exposed. Digital transformation aspects for data measurement with low-cost sensors by real-time monitoring, use of wireless network systems, artificial intelligence, machine learning, digital twins, and the Internet of Things, among other technologies of the Industry 4.0 era are discussed.
Road Map of Semiconductor Metal-Oxide-Based Sensors: A Review
Authors: Dutta Taposhree , Noushin Tanzila , Tabassum Shawana , Mishra Satyendra K.
Published: August 2023

Identifying disease biomarkers and detecting hazardous, explosive, flammable, and polluting gases and chemicals with extremely sensitive and selective sensor devices remains a challenging and time-consuming research challenge. Due to their exceptional characteristics, semiconducting metal oxides (SMOxs) have received a lot of attention in terms of the development of various types of sensors in recent years. The key performance indicators of SMOx-based sensors are their sensitivity, selectivity, recovery time, and steady response over time. SMOx-based sensors are discussed in this review based on their different properties. Surface properties of the functional material, such as its (nano)structure, morphology, and crystallinity, greatly influence sensor performance. A few examples of the complicated and poorly understood processes involved in SMOx sensing systems are adsorption and chemisorption, charge transfers, and oxygen migration. The future prospects of SMOx-based gas sensors, chemical sensors, and biological sensors are also discussed.
High-Speed Network DDoS Attack Detection: A Survey
Authors: Haseeb-ur-rehman Rana M. Abdul , Aman Azana Hafizah Mohd , Hasan Mohammad Kamrul , Ariffin Khairul Akram Zainol , Namoun Abdallah , Tufail Ali , Kim Ki-Hyung
Published: August 2023

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.
A Vehicle-to-Grid System for Controlling Parameters of Microgrid System
Authors: Sarda Jigar , Raj Yashrajsinh , Patel Arpita , Shukla Aasheesh , Kachhatiya Satish , Sain Mangal
Published: August 2023

The power system for large-scale adoption of hybrid electric vehicles can benefit from a distributed reserve provided by the vehicle-to-grid (V2G) concept. This study suggests a V2G technology that can effectively control frequency on a microgrid throughout a 24-h cycle. When usage is at its lowest in the spring or fall, a microgrid is intended to be large enough to simulate a community of 2000 households. A 1:5 ratio of cars to households is realized by modelling 400 electric vehicles (EVs) as a basic model, indicating a typical case in the future. An in-depth analysis of the voltage, current, reactive, and active power is carried out for a microgrid. By coordinating control of diesel generation, renewable energy source (RES) generation, power exchange, and EV generation, the system frequency of a microgrid can be managed by regulating load demand with V2G devices. The proposed microgrid with V2G effectively manages energy and reduces the uncertain and variable nature of RES power generation with enhanced performance. System parameter variations have been investigated for various operating scenarios, and it has been discovered that error is confined to less than 5%.
LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer
Authors: Zhang Kang , Huang Shu , Liu Eryun , Zhao Heng
Published: August 2023

With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%.
The Use of Artificial Intelligence in the Assessment of User Routes in Shared Mobility Systems in Smart Cities
Authors: Kubik Andrzej
Published: August 2023

The use of artificial intelligence in solutions used in smart cities is becoming more and more popular. An example of the use of machine learning is the improvement of the management of shared mobility systems in terms of assessing the accuracy of user journeys. Due to the fact that vehicle-sharing systems are appearing in increasing numbers in city centers and outskirts, and the way vehicles are used is not controlled by operators in real mode, there is a need to fill this research gap. The article presents a built machine learning model, which is a supplement to existing research and is updated with new data from the existing system. The developed model is used to determine and assess the accuracy of trips made by users of shared mobility systems. In addition, an application was also created showing an example of using the model in practice. The aim of the article is therefore to indicate the possibility of correct identification of journeys with vehicles from shared mobility systems. Studies have shown that the prediction efficiency of the data generated by the model reached the level of 95% agreement. In addition, the research results indicate that it is possible to automate the process of evaluating journeys made in shared mobility systems. The application of the model in practice will facilitate management and, above all, it is open to further updates. The use of many machine learning models will allow solving many problems that will occur in an increasing number of smart cities.
The Construction and Practice of a TPACK Development Training Model for Novice University Teachers
Authors: Zhang Suiling , Zhou Aibao
Published: August 2023

The smart age has created new goals, and offers novel approaches for teachers’ professional development. The acquisition of technological pedagogical content knowledge (TPACK) has become essential for novice university teachers’ professional development. With the aim of developing teachers’ TPACK, this study constructed a BOPPPS–TPACK training model by following four key design principles: smart-tool-based, pedagogy-driven, microteaching-integrated, and creatively enhanced. This paper describes a practical study involving 145 teachers that was carried out using questionnaire surveys, interviews, and electronic record analyses. The results showed that the BOPPPS–TPACK training process could achieve the effective coupling of BOPPPS and TPACK and enhance novice university teachers’ ability to engage in in-depth learning and TPACK transfer, creating a method and system to guide teachers’ professional development. Measures such as optimizing the construction of smart environments for teachers’ professional development, enriching pedagogical content of knowledge-based representations, and extending feedback collection to practical settings can help to enhance TPACK development.
Unravelling the Formation Mechanism of Sustainable Underground Pedestrian Systems: Two Case Studies in Shanghai
Authors: Peng Cheng , Ma Chenxiao , Dong Yunhao
Published: August 2023

The development of subterranean non-motorized traffic infrastructure, commonly referred to as the underground pedestrian system (UPS), has become increasingly necessary in densely populated megacities worldwide as a means of advancing the sustainable development goal 11, which aims to promote sustainable cities and communities. To improve the overall spatial performance, it is imperative to decipher the fundamental formation mechanism of sustainable underground pedestrian systems (SUPSs) that is simultaneously influenced by spatial morphology and pedestrian behaviors. Thereby, two representative case studies, namely the Wujiaochang UPS and the Loushanguanlu UPS located in Shanghai, were selected for an in-depth investigation. This study employed correlation and regression analysis to examine the impact of spatial configuration variables and spatial attribute factors on pedestrian flow distributions in distinct SUPSs. The findings indicate that the variables of betweenness, as measured by both Euclidean and Angular metrics, along with the presence of metro station locations and commercial space connected by the UPS, are the three most significant factors influencing pedestrian behaviors in both scenarios. The disclosure has been made that the Wujiaochang UPS is seamlessly integrated into a comprehensive three-dimensional pedestrian network both above and below ground. By contrast, it appears that the Loushanguanlu UPS exhibits a greater degree of self-sufficiency as an underground system. This study aims to elucidate the mechanism underlying the development of SUPSs, thus offering effective guidance for the implementation of three-dimensional walking systems in cities that prioritize sustainability.
Global Collaboration in Technology Sectors during the COVID-19 Pandemic: A Patent Review
Authors: Jeon Eunsook , Kim Kyungkook , Park Hyunjeong , Cho Keuntae
Published: August 2023

This study aims to identify the international technology trends and convergence structures that emerged during the coronavirus disease (COVID-19) pandemic by analyzing COVID-19-related patents. Accordingly, network analysis was performed using data drawn from COVID-19-related patent applications submitted to the World Intellectual Property Organization (WIPO) between 2020 and 2022. The results showed that patent applications were submitted in 21 countries, with 97% of all applications coming from the intellectual property 5 (IP5) countries (US, Korea, China, Japan, and Europe). Technology convergence has occurred between the fields of physics and biology or between different biotechnology sectors. Owing to the nature of government-initiated development processes, technologies related to infectious diseases may exhibit a correlation between national patents and disease control policies. This study is significant since it empirically analyzes the convergence structure and development direction of global technologies engaged in overcoming the COVID-19 pandemic by analyzing new patent applications after the COVID-19 outbreak. The findings of this study will help to establish new directions for overcoming other infectious diseases that may emerge in the future.
Return to Work Following Hip Arthroscopy for Femoroacetabular Impingement Syndrome: A Case Series
Authors: Li Zachary I. , Shankar Dhruv S. , Garra Sharif , Hughes Andrew J. , Triana Jairo , Blaeser Anna M. , Youm Thomas
Published: August 2023

The expected rate and timing of return to work (RTW) following hip arthroscopy is an important consideration for patients. The purpose of this study was to evaluate the rates of RTW following hip arthroscopy, the time required to RTW, and factors correlated with successful RTW. A retrospective review was performed of patients who underwent hip arthroscopy for the treatment of femoroacetabular impingement syndrome (FAIS) from 2018–2021. Data were collected regarding pre-injury employment, work activity level, workers’ compensation (WC) status, ability to return to work, the pre- and postoperative pain visual analog score (VAS), and the Nonarthritic Hip Score (NAHS). Overall, 76 patients (90.5%) returned to their pre-surgical occupation at an average of 9.3 weeks (range: 0.14–56 weeks) postoperatively. Of those who returned, 62 patients (81.6%) returned to full work duties. The NAHSs improved significantly from the preoperative period to follow-up at 1 year (64.7 vs. 83.7, p < 0.001). A linear regression found elevated BMI to be associated with an increased time to return (β = 0.757, p = 0.012). On average, patients with WC status returned to work 9 weeks later than non-WC patients, though this difference did not reach statistical significance (18.4 vs. 9.9 weeks, p = 0.130). There is a high rate of return to full work duties at an average of 9.3 weeks following hip arthroscopy for FAIS.
Power Supply Technologies for Drones and Machine Vision Applications: A Comparative Analysis and Future Trends
Authors: Pekias Antonios , Maraslidis George S. , Tsipouras Markos G. , Koumboulis Fotis N. , Fragulis George F.
Published: August 2023

The field of Unmanned Aerial Vehicles (UAVs), or drones, is encountering quick development in the areas of air transportation and computerization. Progress in innovation has prompted more noteworthy capacities and highlights in UAVs, which are currently broadly involved by the military and flying industry for an assortment of high-end generally safe errands. Highly advanced UAVs that can be controlled remotely via a controller, mobile phone, or ground station cockpit have been developed through the integration of automation technology and machine vision, which includes thermal imaging, cameras, sensors, and other sensors. The three primary characteristics of UAVs will be investigated in this study, namely power-source technology, deep-learning neural networks for computer vision, and some of the applications that are used the most. The goal is to thoroughly examine these characteristics and offer suggestions for addressing some of the difficulties of optimizing UAV performance and also exploring potential future trends.
Independent and Combined Associations of Blood Manganese, Cadmium and Lead Exposures with the Systemic Immune-Inflammation Index in Adults
Authors: Zhong Qiya , Zhou Wenxin , Lin Jiaqi , Sun Wen , Qin Yao , Li Xiang , Xu Huadong
Published: August 2023

Manganese (Mn), cadmium (Cd) and lead (Pb) have toxic effects on the immune system. However, their independent and combined effects on immune-inflammation responses are unclear. In recent years, the systemic immune-inflammation index (SII) has been developed as an integrated and novel inflammatory indicator. A retrospective cross-sectional study of 2174 adults ≥20 years old from the National Health and Nutrition Examination Survey (NHANES) 2015–2016 was conducted. Generalized linear models were used to evaluate the independent and combined associations of SII with blood Mn, Cd and Pb levels. As continuous variables, both blood Cd and Mn showed dose-dependent relationships with the SII before and after adjusting for all potential confounding factors. Metal concentrations were then converted into categorical variables. Compared with the adults in the lowest Cd or Mn tertile, those in the highest tertile had higher risks of elevated SII. Furthermore, co-exposure to Mn and Cd also showed a positive relationship with the SII after adjusting for all confounding factors. However, the single effect of Pb exposure and the joint effect of Pb and other metal exposures on the SII were not observed. This study provides important epidemiological evidence of the associations of SII with single and co-exposure effects of blood Mn, Cd, and Pb.
Genotoxicity of Occupational Pesticide Exposures among Agricultural Workers in Arab Countries: A Systematic Review and Meta-Analysis
Authors: Sherif Moustafa , Makame Khadija , Östlundh Linda , Paulo Marilia , Nemmar Abderrahim , Ali Bassam , Al-Rifai Rami , Nagy Károly , Ádám Balázs
Published: August 2023

Exposure to pesticides in Arab countries is a significant public health concern due to extensive agricultural activity and pesticide use. This systematic review aimed to evaluate the genotoxic effects of agricultural pesticide exposure in the region, identify research gaps, and assess methodological limitations. Following the PRISMA guidelines, a comprehensive search yielded five relevant studies conducted in Egypt, Syria, and Jordan. Various genotoxicity assays were employed, revealing a higher level of DNA damage in exposed compared to non-exposed individuals. Farmers exposed to pesticides exhibited a significantly higher occurrence of chromosomal translocation (t(14;18)), micronuclei, and chromosomal aberrations. However, only two studies assessed cytotoxicity indirectly. The studies predominantly focused on male participants, with variations in sample size and pesticide types. The lack of detailed exposure data necessitates cautious interpretation. This review underscores the need for further research on the genotoxicity of occupational pesticide exposure in the Middle East. Future studies should adopt robust study designs, collect biological and environmental samples, conduct repeated sampling, analyze seasonal variations, and encompass diverse study sites associated with specific crop groups.
Mission Target: Tetraquark Mesons of Flavour-Cryptoexotic Type
Authors: Lucha Wolfgang
Published: August 2023

Currently, flavour-cryptoexotic tetraquarks form the most common sort of all experimentally established exotic multiquark hadrons. This note points out a few promising concepts that should help improve theoretical (but, for several reasons, not quite straightforward) analyses of this kind of states; among others, their scope of application encompasses the strong interactions in the limit of (arbitrarily) large numbers of colours, and equally analytical and nonperturbative approaches to multiquark states.
Litters of Various-Sized Mummies (LVSM) and Stillborns after Porcine Reproductive and Respiratory Syndrome Virus Type 1 Infection—A Case Report
Authors: Unterweger Christine , Kreutzmann Heinrich , Buenger Moritz , Klingler Eva , Auer Angelika , Rümenapf Till , Truyen Uwe , Ladinig Andrea
Published: August 2023

Diverse origins and causes are described for papyraceous mummifications of porcine foetuses, but the porcine reproductive and respiratory syndrome virus (PRRSV) is not one of them. In contrast, PRRSV is unlikely to cause mid-term placental transmission but may cause late-term abortions and weakness of piglets. This case report describes a sudden occurrence of mummified foetuses of various sizes and stillborns and delayed birth (>115 days) in more than 50% of sows from one farrowing batch, while newborn piglets were mostly vital. Neither increased embryonic death nor infertility was reported. Three litters with mummies, autolysed piglets and stillborn piglets were investigated, and infections with porcine parvoviruses, porcine teschoviruses, porcine circoviruses, encephalomyocarditis virus, Leptospira spp. and Chlamydia spp. were excluded. Instead, high viral loads of PRRSV were detected in the thymus pools of piglets at all developmental stages, even in piglets with a crown–rump length between 80 and 150 mm, suggesting a potential mid-term in utero transmission of the virus. Genomic regions encoding structural proteins (ORF2–7) of the virus were sequenced and identified the virulent PRRSV-1 strain AUT15-33 as the closest relative. This case report confirms the diversity of PRRSV and its potential involvement in foetal death in mid-gestation.
Analysis of Fecal Microbial Changes in Young Calves Following Bovine Rotavirus Infection
Authors: Kim Seon-Ho , Choi Youyoung , Miguel Michelle A. , Lee Shin-Ja , Lee Sung-Sill , Lee Sang-Suk
Published: August 2023

The objective of the present study was to identify changes in fecal microbiota and predict the functional features of healthy calves and those infected with rotavirus over time. Six Holstein calves (average body weight 43.63 ± 1.19 kg, age-matched within 5–7 d) were randomly selected and distributed into two groups which contained three calves each. Fecal samples were taken 3 days before inoculation and on days 1 and 7 post-inoculation. The 16S rRNA gene amplicon sequencing was performed. Bacterial diversity tended to decrease in the rota group, as indicated by the alpha (evenness, p = 0.074 and Shannon, p = 0.055) and beta (Bray–Curtis dissimilarity, p = 0.099) diversity at 1 day post-inoculation. Differences in the bacterial taxa between healthy and rota-infected calves were detected using a linear discriminant analysis effect size (LDA > 2.0, p < 0.05). Rota calves had a higher abundance of certain bacterial taxa, such as Enterococcus, Streptococcus, and Escherichia-Shigella, and a lower abundance of bacteria that contribute to the production of short-chain fatty acids, such as Alistipes, Faecalibacterium, Pseudoflavonifractor, Subdoligranulum, Alloprevotella, Butyricicoccus, and Ruminococcus, compared to the healthy calves. The observed changes in the fecal microbiota of the rota-infected group compared to the healthy group indicated potential dysbiosis. This was further supported by significant differences in the predicted functional metagenomic profiles of these microbial communities. We suggest that calves infected with bovine rotavirus had bacterial dysbiosis, which was characterized by lower diversity and fewer observed genera than the fecal microbiota of healthy calves.
Uncertainty Propagation and Global Sensitivity Analysis of a Surface Acoustic Wave Gas Sensor Using Finite Elements and Sparse Polynomial Chaos Expansions
Authors: Hamdaoui Mohamed
Published: August 2023

The aim of this work is to perform an uncertainty propagation and global sensitivity analysis of a surface acoustic wave (SAW) gas sensor using finite elements and sparse polynomial chaos. The SAW gas sensor is modeled using finite elements (FEM) under COMSOL, and the sensitivity to DCM of its Sezawa mode is considered to be the quantity of interest. The importance of several geometrical (width and PIB thickness), material (PIB Young’s modulus and density), and ambient (pressure, temperature, and concentration) parameters on the sensor’s sensitivity is figured out by means of Sobol’ indices using sparse polynomial chaos expansions. It is shown that when the variability of the input parameters is low (inferior to 5%), the only impacting parameter is the cell width. However, when the variability of the input parameters reaches medium levels (around 10%), all the input parameters except the ambient temperature are impacting the sensor’s sensitivity. It is also reported that in the medium variability case, the sensor’s sensitivity experiences high variations that can lead to a degradation of its performances.
September 2020-Printed Issue
Authors:   Patient Safety Authority
Published: August 2023

The fall 2020 issue of Patient Safety is available to read and download at no charge.

Each issue features the latest evidence-based research, thoughtful commentaries and editorials, interviews with healthcare leaders and safety advocates, and powerful patient stories.

An interview with Dr. Tejal Gandhi and Dr. Jeff Brady about the recently released National Action Plan for Patient Safety in the United States

A global look at innovative solutions to patient safety falls

A timely examination of how process breakdowns contribute to delays in recognizing, treating, and preventing the spread of respiratory infections, like the flu and COVID-19

A social worker raises awareness of <a title=“https://patientsafetyj.com/index.php/patientsaf/article/view/intimate-partner-violence-pandemic” href=“https://patientsafetyj.com/index.php/patientsaf/article/view/intimate-partner-violence-pandemic”>intimate partner violence, a serious problem that gets worse during periods of isolation

A survivor of COVID-19 shares his hospital experience and recovery from the disease

A researcher takes a close look at nonsuicidal self-injury among children and adolescents

Thank you to our authors, peer reviewers, editorial board members, and Patient Safety Authority staff, who all play a part in the success of our journal and in advancing patient safety everywhere. Patient Safety is fully open access (no subscription fees or author fees), so please consider sending us your next original manuscript and sharing our journal with your colleagues, students, families, friends, and patients. Together we save lives.