bluuebunny/crossref_metadata_2025 huggingface dataset:
SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction
Authors: Ryu Ga-Ae , Chuluunsaikhan Tserenpurev , Nasridinov Aziz , Rah HyungChul , Yoo Kwan-Hee
Published: October 2023

In the domain of agricultural product sales and consumption forecasting, the presence of infrequent yet impactful events such as livestock epidemics and mass media influences poses substantial challenges. These rare occurrences, termed Sparse Critical Events (SCEs), often lead to predictions converging towards average values due to their omission from input candidate vectors. To address this issue, we introduce a modified Long Short-Term Memory (LSTM) model designed to selectively attend to and memorize critical events, emulating the human memory’s ability to retain crucial information. In contrast to the conventional LSTM model, which struggles with learning sparse critical event sequences due to its handling of forget gates and input vectors within the cell state, our proposed approach identifies and learns from sparse critical event sequences during data training. This proposed method, referred to as sparse critical event-driven LSTM (SCE-LSTM), is applied to predict purchase quantities of agricultural and livestock products using sharp-changing agricultural time-series data. For these predictions, we collected structured and unstructured data spanning the years 2010 to 2017 and developed the SCE-LSTM prediction model. Our model forecasts monetary expenditures for pork purchases over a one-month horizon. Notably, our results demonstrate that SCE-LSTM provides the closest predictions to actual daily pork purchase expenditures and exhibits the lowest error rates when compared to other prediction models. SCE-LSTM emerges as a promising solution to enhance agricultural product sales and consumption forecasts, particularly in the presence of rare critical events. Its superior performance and accuracy, as evidenced by our findings, underscore its potential significance in this domain.
A Machine Learning Approach for the Estimation of Alfalfa Hay Crop Yield in Northern Nevada
Authors: Quintero Diego , Andrade Manuel A. , Cholula Uriel , Solomon Juan K. Q.
Published: October 2023

Increasing pressure over water resources in the western U.S. is currently forcing alfalfa (Medicago sativa L.) producers to adopt water-saving irrigation techniques. Crop yield forecasting tools can be used to develop smart irrigation scheduling methods that can be used to estimate the future effects of a given irrigation amount applied during a current irrigation event on yield. In this work, a linear model and a random forest model were used to estimate the yield of irrigated alfalfa crops in northern Nevada. It was found that water (rain + irrigation), the occurrence of extreme temperatures, and wind have a greater effect on crop yield. Other variables that accounted for the photoperiod and the dormant period were also included in the model and are also important. The linear model had the best performance with an R2 of 0.854. On the other hand, the R2 value for the random forest was 0.793. The linear model showed a good response to water variability; therefore, it is a good model to consider for use as an irrigation decision support system. However, unlike the linear model, the random forest model can capture non-linear relationships occurring between the crop, water, and the atmosphere, and its results may be enhanced by including more data for its training.
Isolation and Characterization of Cetacean Cell-Derived Extracellular Vesicles
Authors: Moccia Valentina , Centelleghe Cinzia , Giusti Ilaria , Peruffo Antonella , Dolo Vincenza , Mazzariol Sandro , Zappulli Valentina
Published: October 2023

Cetaceans are of scientific interest because they are good candidates as environmental bioindicators. However, in vivo research is arduous and in vitro studies represent a rarely used valid alternative. Extracellular vesicles (EVs) are membrane-bound structures playing roles in cell-to-cell communication. Despite being a promising investigative tool in different fields of science, EVs have been poorly studied in cetaceans. To fill this gap, we describe the preliminary characterization of EVs isolated from a bottlenose dolphin and a Cuvier’s beaked whale cell line. EVs have been isolated with ultracentrifugation (UC) or size exclusion chromatography (SEC) and characterized with nanoparticle tracking analysis (NTA), Western blotting (WB), and scanning transmission electron microscopy (STEM). UC and SEC allowed the isolation of mainly small EVs (<200 nm). A higher number of particles were isolated through UC compared to SEC from both cell lines. At WB, all EVs expressed the EV-markers CD9 and integrin-β. Only EVs isolated with UC were positive for TSG101. In conclusion, we isolated for the first time EVs from a bottlenose dolphin and a Cuvier’s beaked whale cell line using two different techniques. Further studies on cell-derived EVs will be useful to deepen our knowledge on cetacean pathophysiology and health status assessment.
Genetic Variance Estimation over Time in Broiler Breeding Programmes for Growth and Reproductive Traits
Authors: Sosa-Madrid Bolívar Samuel , Maniatis Gerasimos , Ibáñez-Escriche Noelia , Avendaño Santiago , Kranis Andreas
Published: October 2023

Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a “double trait covariances” analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999–2001 to 2020–2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.
Partial Substitution of Whey Protein Concentrate with Spray–Dried Porcine Plasma or Soy Protein Isolate in Milk Replacer Differentially Modulates Ileal Morphology, Nutrient Digestion, Immunity and Intestinal Microbiota of Neonatal Piglets
Authors: Zhang Yuwei , Zhou Qiang , Liu Shiya , Quan Xiang , Fang Zhengfeng , Lin Yan , Xu Shengyu , Feng Bin , Zhuo Yong , Wu De , Che Lianqiang
Published: October 2023

Appropriate protein sources are vital for the growth, development and health of neonates. Twenty–four 2–day–old piglets were randomly divided into three groups and fed isoenergetic and isonitrogenous diets. The experimental diets included a milk replacer with 17.70% whey protein concentrate (WPC group), a milk replacer with 6% spray–dried porcine plasma isonitrogenously substituting WPC (SDPP group), and a milk replacer with 5.13% soy protein isolate isonitrogenously substituting WPC (SPI group). Neonatal piglets were fed milk replacer from postnatal day 2 (PND 2) to day 20 (PND 20). The growth performance, intestinal morphology, activities of digestive enzymes, plasma biochemical parameters, immunity–related genes, short–chain fatty acids (SCFA) and intestinal microbiota in the colonic chyme were determined. The results showed that SDPP–fed piglets had higher final BW (p = 0.05), ADG (p = 0.05) and F/G (p = 0.07) compared with WPC– and SPI–fed piglets, and SDPP–fed piglets had a lower diarrhea index (p < 0.01) from PND 2 to PND 8. SDPP–fed piglets had an increased ileal villus height (p = 0.04) and ratio of villus height to crypt depth (VCR) (p = 0.02), and increased activities of sucrase (p < 0.01), lactase (p = 0.02) and trypsin (p = 0.08) in the jejunum, compared with WPC– and SPI–fed piglets. Furthermore, SPI–fed piglets had an increased mRNA expression of IL-6 (p < 0.01) and concentration of plasma urea (p = 0.08). The results from LEfSe analysis showed that SDPP–fed piglets had a higher abundance of beneficial Butyricicoccus compared with WPC– and SPI–fed piglets, in which higher abundances of pathogenic bacteria such as Marinifilaceae, Fusobacterium and Enterococcus were observed. Moreover, SDPP–fed piglets had an increased concentration of butyric acid (p = 0.08) in the colonic chyme compared with WPC– and SPI–fed piglets. These results suggest that neonatal piglets fed milk replacer with SDPP partially substituting WPC had improved growth performance and intestinal morphology and function, associated with higher digestive enzyme activity and fewer pathogenic bacteria.
Social Network Sentiment Analysis Using Hybrid Deep Learning Models
Authors: Merayo Noemí , Vegas Jesús , Llamas César , Fernández Patricia
Published: October 2023

The exponential growth in information on the Internet, particularly within social networks, highlights the importance of sentiment and opinion analysis. The intrinsic characteristics of the Spanish language coupled with the short length and lack of context of messages on social media pose a challenge for sentiment analysis in social networks. In this study, we present a hybrid deep learning model combining convolutional and long short-term memory layers to detect polarity levels in Twitter for the Spanish language. Our model significantly improved the accuracy of existing approaches by up to 20%, achieving accuracies of around 76% for three polarities (positive, negative, neutral) and 91% for two polarities (positive, negative).
Wind Turbine Blade Cracking Detection under Imbalanced Data Using a Novel Roundtrip Auto-Encoder Approach
Authors: Zhang Yuyan , Zhang Yafeng , Li Hao , Yan Lingdi , Wen Xiaoyu , Wang Haoqi
Published: October 2023

Imbalanced data cause low recognition of wind turbine blade cracking. Existing data-level augmentation methods, including sampling and generative strategies, may yield lots of high-confidence but low-value samples, which fail to improve the detection of blade cracking. Therefore, this paper designs a novel RTAE (roundtrip auto-encoder) method. Based on the idea of the roundtrip approach, we design two generator networks and two discriminator networks to ensure the cycle mapping between cracking samples and latent variables. Further, by leveraging cycle consistency loss, generated samples fit the distribution of historical cracking samples well. Thus, these generated samples effectively realize data augmentation and improve recognition of blade cracking. Additionally, we apply an auto-encoder method to reduce the dimension of historical samples and thus the complexity of the generator network and discriminator network. Through the analysis of real wind turbine blade cracking data, the recognition of cracking samples is improved by 19.8%, 23.8% and 22.7% for precision, recall and F1-score.
An Improved Intuitionistic Fuzzy Decision-Theoretic Rough Set Model and Its Application
Authors: Ali Wajid , Shaheen Tanzeela , Toor Hamza Ghazanfar , Alballa Tmader , Alburaikan Alhanouf , Khalifa Hamiden Abd El-Wahed
Published: October 2023

The Decision-Theoretic Rough Set model stands as a compelling advancement in the realm of rough sets, offering a broader scope of applicability. This approach, deeply rooted in Bayesian theory, contributes significantly to delineating regions of minimal risk. Within the Decision-Theoretic Rough Set paradigm, the universal set undergoes a tripartite division, where distinct regions emerge and losses are intelligently distributed through the utilization of membership functions. This research endeavors to present an enhanced and more encompassing iteration of the Decision-Theoretic Rough Set framework. Our work culminates in the creation of the Generalized Intuitionistic Decision-Theoretic Rough Set (GI-DTRS), a fusion that melds the principles of Decision-Theoretic Rough Sets and intuitionistic fuzzy sets. Notably, this synthesis bridges the gaps that exist within the conventional approach. The innovation lies in the incorporation of an error function tailored to the hesitancy grade inherent in intuitionistic fuzzy sets. This integration harmonizes seamlessly with the contours of the membership function. Furthermore, our methodology deviates from established norms by constructing similarity classes based on similarity measures, as opposed to relying on equivalence classes. This shift holds particular relevance in the context of aggregating information systems, effectively circumventing the challenges associated with the process. To demonstrate the practical efficacy of our proposed approach, we delve into a concrete experiment within the information technology domain. Through this empirical exploration, the real-world utility of our approach becomes vividly apparent. Additionally, a comprehensive comparative analysis is undertaken, juxtaposing our approach against existing techniques for aggregation and decision modeling. The culmination of our efforts is a well-rounded article, punctuated by the insights, recommendations, and future directions delineated by the authors.
Water-Level Fluctuation Control of the Trophic Structure of a Yangtze River Oxbow
Authors: Qiu Longhui , Ji Fenfen , Qiu Yuhui , Xie Hongyu , Li Guangyu , Shen Jianzhong
Published: October 2023

Seasonal water-level fluctuations can profoundly impact nutrient dynamics in aquatic ecosystems, influencing trophic structures and overall ecosystem functions. The Tian-e-Zhou Oxbow of the Yangtze River is China’s first ex situ reserve and the world’s first successful case of ex situ conservation for cetaceans. In order to better protect the Yangtze finless porpoise, the effects of water-level fluctuations on the trophic structure in this oxbow cannot be ignored. Therefore, we employed stable isotope analysis to investigate the changes in the trophic position, trophic niche, and contribution of basal food sources to fish during the wet and dry seasons of 2021–2022. The research results indicate that based on stable isotope analysis of the trophic levels of different dietary fish species, fish trophic levels during the wet season were generally higher than those during the dry season, but the difference was not significant (p > 0.05). Fish communities in the Tian-e-Zhou Oxbow exhibited broader trophic niche space and lower trophic redundancy during the wet season (p < 0.05), indicating a more complex and stable food web structure. In both the wet and dry seasons, fish in the oxbow primarily relied on endogenous carbon sources, but there were significant differences in the way they were utilized between the two seasons (p < 0.05). In light of the changes in the trophic structure of the fish during the wet and dry seasons, and to ensure the stable development of the Yangtze finless porpoise population, we recommend strengthening the connectivity between the Tian-e-Zhou Oxbow and the Yangtze River.
A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer’s Disease
Authors: Akkaya Umit Murat , Kalkan Habil
Published: October 2023

Alzheimer’s disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.
An Overview of SARS-CoV-2 Etiopathogenesis and Recent Developments in COVID-19 Vaccines
Authors: Mathew Dona Susan , Pandya Tirtha , Pandya Het , Vaghela Yuzen , Subbian Selvakumar
Published: October 2023

The Coronavirus disease-2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has significantly impacted the health and socioeconomic status of humans worldwide. Pulmonary infection of SARS-CoV-2 results in exorbitant viral replication and associated onset of inflammatory cytokine storm and disease pathology in various internal organs. However, the etiopathogenesis of SARS-CoV-2 infection is not fully understood. Currently, there are no targeted therapies available to cure COVID-19, and most patients are treated empirically with anti-inflammatory and/or anti-viral drugs, based on the disease symptoms. Although several types of vaccines are currently implemented to control COVID-19 and prevent viral dissemination, the emergence of new variants of SARS-CoV-2 that can evade the vaccine-induced protective immunity poses challenges to current vaccination strategies and highlights the necessity to develop better and improved vaccines. In this review, we summarize the etiopathogenesis of SARS-CoV-2 and elaborately discuss various types of vaccines and vaccination strategies, focusing on those vaccines that are currently in use worldwide to combat COVID-19 or in various stages of clinical development to use in humans.
Natural Polyketides Act as Promising Antifungal Agents
Authors: Wang Li , Lu Hui , Jiang Yuanying
Published: October 2023

Invasive fungal infections present a significant risk to human health. The current arsenal of antifungal drugs is hindered by drug resistance, limited antifungal range, inadequate safety profiles, and low oral bioavailability. Consequently, there is an urgent imperative to develop novel antifungal medications for clinical application. This comprehensive review provides a summary of the antifungal properties and mechanisms exhibited by natural polyketides, encompassing macrolide polyethers, polyether polyketides, xanthone polyketides, linear polyketides, hybrid polyketide non-ribosomal peptides, and pyridine derivatives. Investigating natural polyketide compounds and their derivatives has demonstrated their remarkable efficacy and promising clinical application as antifungal agents.
Divided Village, Divided Identity? Exploring the Professional Identity of Teachers Amid the Geopolitical Configuration in Al-Ghajar
Authors: Nissim Yonit , Simon Eitan
Published: October 2023

This quantitative research is based on a validated research questionnaire. It presents a preliminary exploratory study examining perspectives of professional identity as reflected in self-reports of the teachers of Al-Ghajar, a village on Israel’s northern border, given its unique geographical and geopolitical configuration. The current study is the first of its kind, clarifying the teachers’ perception of their professional identity in the unique space in which they live and work via a questionnaire completed by 61 teachers of both genders from kindergarten through high school. The findings show that there are strong positive correlations: “love for the profession” and “self-fulfillment” have a very strong positive correlation (0.831). There is also a positive correlation (0.430) between the indicators of “professional skills” and “professional unity” suggesting that the teachers’ perceptions of professional identity are holistic in all examined dimensions. These perceptions are motivated by a strong sense of mission that influences their profession and their sense of satisfaction. Al-Ghajar, as a unique enclave, has created a geopolitical configuration that contributes to the construction of the professional identity of teachers in the village. The research conclusion indicates that the ability to legitimize one’s role has important implications for the quality of teaching, as it can help teachers form familiar, affiliated, and secure identities. These are key traits since a positive sense of professional self is a prerequisite for job satisfaction and resilience.
Three-Dimensional Numerical Analysis on the Influence of Buttress Wall Removal Timing on the Lateral Deformation of Diaphragm Walls during Deep Excavation
Authors: Hsu Chia-Feng , Kuan Chung-Fu , Chen Shong-Loong
Published: October 2023

Urban areas in Taiwan are densely populated with limited land. Excavation often takes place near existing buildings, necessitating protective measures for adjacent properties. Among these measures, cross walls or buttress walls are commonly employed, especially in weak foundation soils, which have seen many successful applications. These mechanisms mainly contribute to reducing lateral deformation of the diaphragm walls, ground subsidence, and excavation face uplift. However, the behavior of these walls is essentially three-dimensional, and common engineering analyses employing one-dimensional elastoplastic beam analysis cannot adequately simulate their mechanical behavior. This study utilized the PLAXIS 3D 2018 software to analyze real-life cases of buttress walls and cross walls. Then, the results of the numerical models were validated against actual field measurements, and the outcomes were satisfactory, and within the regulatory allowable values. The primary objective of this study was to find the influence of different buttress wall removal timings on the lateral deformation of the diaphragm wall. The findings suggest that the gradual removal of buttress walls will effectively control the lateral deformation of diaphragm walls during the excavation of deeper floors like mezzanines.
Advancements and Obstacles of PARP Inhibitors in Gastric Cancer
Authors: Chen Hongjie , Hu Yangchan , Zhuang Zirui , Wang Dingyi , Ye Zu , Jing Ji , Cheng Xiangdong
Published: October 2023

Gastric cancer (GC) is a common and aggressive cancer of the digestive system, exhibiting high aggressiveness and significant heterogeneity. Despite advancements in improving survival rates over the past few decades, GC continues to carry a worrisome prognosis and notable mortality. As a result, there is an urgent need for novel therapeutic approaches to address GC. Recent targeted sequencing studies have revealed frequent mutations in DNA damage repair (DDR) pathway genes in many GC patients. These mutations lead to an increased reliance on poly (adenosine diphosphate-ribose) polymerase (PARP) for DNA repair, making PARP inhibitors (PARPi) a promising treatment option for GC. This article presents a comprehensive overview of the rationale and development of PARPi, highlighting its progress and challenges in both preclinical and clinical research for treating GC.
Correction: Ding et al. MBD3 as a Potential Biomarker for Colon Cancer: Implications for Epithelial–Mesenchymal Transition (EMT) Pathways. Cancers 2023, 15, 3185
Authors: Ding Yuntao , Wang Huizhi , Liu Junqiang , Jiang Han , Gong Aihua , Xu Min
Published: October 2023

In the published paper [...]
Strategies to Make Telemedicine a Friend, Not a Foe, in the Provision of Accessible and Equitable Cancer Care
Authors: Calton Brook A. , Nouri Sarah , Davila Carine , Kotwal Ashwin , Zapata Carly , Bischoff Kara E.
Published: October 2023

Telemedicine has the potential to improve access to cancer care, particularly for patients with functional limitations, high symptom burdens, or financial or geographic constraints. However, there is also a risk that telemedicine can widen healthcare disparities among patients facing systemic disadvantages like those with technological barriers, poor digital literacy, older age, or non-English language preferences. To optimize telemedicine usage, we must implement practical strategies like video onboarding programs, user-friendly technology platforms, optimizing the clinician’s environment, and best practices for using interpreters. Policy changes such as state licensing requirements, controlled substance prescribing requirements, and payment parity are also crucial. This Perspective highlights these practical strategies and policy recommendations to ensure accessible and equitable cancer care augmented by telemedicine.
FAM111B Acts as an Oncogene in Bladder Cancer
Authors: Huang Ning , Peng Lei , Yang Jiaping , Li Jinqian , Zhang Sheng , Sun Mingjuan
Published: October 2023

Bladder cancer (BLCA) is a prevalent malignancy of the urinary system, associated with a high recurrence rate and poor prognosis. FAM111B, which encodes a protein containing a trypsin-like cysteine/serine peptidase domain, has been implicated in the progression of various human cancers; however, its involvement in BLCA remains unclear. In this study, we investigated the expression of FAM111B gene in tumor tissues compared to para-tumor tissues using immunohistochemistry and observed a significantly higher FAM111B gene expression in tumor tissues. Furthermore, analysis of clinical characteristics indicated that the increased FAM111B gene expression correlated with lymphatic metastasis and reduced overall survival. To investigate its functional role, we employed FAM111B-knockdown BLCA cell models and performed cell proliferation, wound-healing, transwell, and flow cytometry assays. The results showed that decreased FAM111B gene expression inhibited proliferation and migration but induced apoptosis in BLCA cells. In vivo experiments further validated that FAM111B knockdown suppressed tumor growth. Overall, our findings suggest that FAM111B acts as an oncogene in BLCA, playing a critical role in tumorigenesis, progression, and metastasis of BLCA. In conclusion, we have demonstrated a strong correlation between the expression of FAM111B gene and the development, progression, and metastasis of bladder cancer (BLCA). Thus, FAM111B is an oncogene associated with BLCA and holds promise as a molecular target for future treatment of this cancer.
A Novel Organic/Inorganic Dual Z-Scheme Photocatalyst with Visible-Light Response for Organic Pollutants Degradation
Authors: Yu Taoming , Wang Doudou , Li Lili , Song Wenjing , Pang Xuan , Liang Ce
Published: October 2023

The design of highly efficient organic/inorganic photocatalysts with visible-light response has attracted great attention for the removal of organic pollutants. In this work, the polyacrylonitrile (PAN) worked as the matrix polymer, while polyaniline (PANI) and Sb2S3–ZnO were used as organic/inorganic photocatalysts. The heterojunction PAN/PANI–Sb2S3–ZnO photocatalyst was prepared using electrospinning and surface ultrasound. PAN/PANI–Sb2S3–ZnO exhibited an excellent visible-light absorption intensity in the wavelength range of 400–700 nm. The maximum removal efficiencies of PAN/PANI–Sb2S3–ZnO for four organic dyes were all greater than 99%. The mechanism study showed that a dual Z-scheme could be constructed ingeniously because of the well-matched bandgaps between organic and inorganic components in the photocatalyst, which achieved efficient separation of photogenerated carriers and reserved photogenerated electrons (e−) and holes (h+) with strong redox ability. The active species •OH and •O2− played an important role in the photocatalytic process. The composite photocatalyst also had excellent stability and reusability. This work suggested a pathway for designing novel organic/inorganic composite photocatalysts with visible-light response.
Sustainable New Technology for the Improvement of Metallic Materials for Future Energy Applications
Authors: Jovičević-Klug Patricia , Rohwerder Michael
Published: October 2023

The need for a more sustainable and accessible source of energy is increasing as human society advances. The use of different metallic materials and their challenges in current and future energy sectors are the primary focus of the first part of this review. Cryogenic treatment (CT), one of the possible solutions for an environmentally friendly, sustainable, and cost-effective technology for tailoring the properties of these materials, is the focus of second part of the review. CT was found to have great potential for the improvement of the properties of metallic materials and the extension of their service life. The focus of the review is on selected surface properties and corrosion resistance, which are under-researched and have great potential for future research and application of CT in the energy sector. Most research reports that CT improves corrosion resistance by up to 90%. This is based on the unique oxide formation that can provide corrosion protection and extend the life of metallic materials by up to three times. However, more research should be conducted on the surface resistance and corrosion resistance of metallic materials in future studies to provide standards for the application of CT in the energy sector.
Ag3PO4/Bi2WO6 Heterojunction Photocatalyst with Remarkable Visible-Light-Driven Catalytic Activity
Authors: Wang Li , Wang Junbo , Fei Yanfei , Cheng Heping , Pan Hua , Wu Chunfeng
Published: October 2023

Novel Ag3PO4/Bi2WO6 catalysts with enhanced visible-light performance were synthesized using a hydrothermal method and characterized to investigate their morphology, microscopic structure, and binding energies. Photoluminescence spectrum (PL) and electrochemical impedance spectroscopy (EIS) data demonstrate that the formed Ag3PO4/Bi2WO6 heterojunction effectively promotes hole (h+)–electron (e−) separation and transfer efficiency, resulting in the enhancement of photocatalytic activity. Ag3PO4/Bi2WO6 displays higher photocatalytic activity than pure Bi2WO6 or Ag3PO4 alone. Photogenerated holes (h+), ·O2−, and ·OH were found to be the main active species for the degradation of malachite green (MG), methylene blue (MB), and Rhodamine B (RhB). The DFT calculation explains the photostability of Ag3PO4/Bi2WO6 from the perspective of electronic structure. The bandgap of Ag3PO4/Bi2WO6 between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) is 1.41 eV, compared with that of Ag3PO4 at 0.91 eV and Bi2WO6 at 2.59 eV. Ag–O–Bi hybridization and the wide HOMO–LUMO bandgap lead to difficulty in electron transfer. As a consequence, Ag+ is difficult to obtain electrons and difficult to convert into Ag0, which makes the catalyst stable.
First-Principles Study of the Effect of Sn Content on the Structural, Elastic, and Electronic Properties of Cu–Sn Alloys
Authors: Zhang Lingzhi , Li Yongkun , Zhou Rongfeng , Wang Xiao , Wang Qiansi , Xie Lingzhi , Li Zhaoqiang , Xu Bin
Published: October 2023

In order to explore the mechanism of the influence of Sn contents on the relevant properties of Cu–Sn alloys, the structure, elasticity, electronic, and thermal properties of Cu–Sn alloys doped with different proportions of Sn (3.125 at%, 6.25 at%, and 9.375 at%) were established using the first-principles calculation based on density functional theory. Firstly, their lattice constants and Sn concentration comply with Vegard’s Law. From the mixing enthalpy, it can be seen that Sn atoms can be firmly dissolved in the Cu matrix, and the structure is most stable when the Sn content is 3.125 at%. In addition, the introduction of mismatch strain characterized their solid solution strengthening effect. The elastic and electronic properties showed that when the Sn content is 6.25 at%, the Cu–Sn alloy has the best plasticity and the highest elastic anisotropy; when the Sn content is 3.125 at%, the Cu–Sn alloy is the most stable and has stronger bulk and shear modulus, which was mainly due to a stronger Cu-Cu covalent bond. Finally, the Debye temperature, thermal conductivity, and melting point were calculated. It is estimated that the thermal conductivity of Cu–Sn alloy is relatively good when the Sn content is low.
A Comprehensive Review of Factors That Influence the Accuracy of Intraoral Scanners
Authors: Alkadi Lubna
Published: October 2023

Intraoral scanners (IOSs) have become increasingly popular in the field of dentistry for capturing accurate digital impressions of patients’ teeth and oral structures. This study investigates the various factors influencing their accuracy. An extensive search of scholarly literature was carried out via PubMed, utilizing appropriate keywords. Factors evaluated in the included studies were categorized into three primary divisions: those related to the operator, the patient, and the IOS itself. The analysis demonstrated that the accuracy of intraoral scanning is influenced by various factors such as scanner selection, operator skill, calibration, patient’s oral anatomy, ambient conditions, and scanning aids. Maintaining updated software and understanding factors beyond scanner resolution are crucial for optimal accuracy. Conversely, smaller IOS tips, fast scanning speeds, and specific scanning patterns compromise the accuracy and precision. By understanding these factors, dental professionals can make more informed decisions and enhance the accuracy of IOSs, leading to improved final dental restorations.
The Association between Nonsuicidal Self-Injury and Perfectionism in Adolescence: The Role of Mental Disorders
Authors: Gyori Dora , Farkas Bernadett Frida , Komaromy Daniel , Horvath Lili Olga , Kollarovics Nora , Garas Peter , Balazs Judit
Published: October 2023

Previous evidence has drawn attention to the fact that maladaptive perfectionism is a risk factor for engagement in nonsuicidal self-injury (NSSI). Until now, few studies have examined this topic, especially among community adolescents. The aim of this study was to explore the relationship between perfectionism dimensions and NSSI functions to examine the potential mediating effect of mental disorders. Altogether, 146 Hungarian community adolescents (ages 13–18 years) were involved. All participants completed the Hungarian adaptation of the Inventory of Statements about Self-Injury (ISAS), the Frost Multidimensional Perfectionism Scale (FMPS), and the Mini International Neuropsychiatric Interview Kid. To analyse the interrelationships among NSSI, perfectionism, and mental disorders, we conducted regression and network analysis. Of the 146 adolescents, 90 (61.64%, girls: 71.11%) engaged in NSSI. The Concern over Mistakes and Doubts about Action scales of the FMPS significantly and positively predicted both NSSI intrapersonal and interpersonal motivation, with comparable effect sizes, and this association was fully mediated by anxiety disorders. There was a significant direct negative relationship between the FMPS Organisation dimension and both main NSSI functions. This study draws attention to an increasing trend and the extremely high NSSI prevalence rate among community adolescents. Adolescents with perfectionistic concerns are at heightened risk for anxiety disorders, which can increase their vulnerability to NSSI engagement.
Deform2NeRF: Non-Rigid Deformation and 2D–3D Feature Fusion with Cross-Attention for Dynamic Human Reconstruction
Authors: Xie Xiaolong , Guo Xusheng , Li Wei , Liu Jie , Xu Jianfeng
Published: October 2023

Reconstructing dynamic human body models from multi-view videos poses a substantial challenge in the field of 3D computer vision. Currently, the Animatable NeRF method addresses this challenge by mapping observed points from the viewing space to a canonical space. However, this mapping introduces positional shifts in predicted points, resulting in artifacts, particularly in intricate areas. In this paper, we propose an innovative approach called Deform2NeRF that incorporates non-rigid deformation correction and image feature fusion modules into the Animatable NeRF framework to enhance the reconstruction of animatable human models. Firstly, we introduce a non-rigid deformation field network to address the issue of point position shift effectively. This network adeptly corrects positional discrepancies caused by non-rigid deformations. Secondly, we introduce a 2D–3D feature fusion learning module with cross-attention and integrate it with the NeRF network to mitigate artifacts in specific detailed regions. Our experimental results demonstrate that our method significantly improves the PSNR index by approximately 5% compared to representative methods in the field. This remarkable advancement underscores the profound importance of our approach in the domains of new view synthesis and digital human reconstruction.
Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration
Authors: Zhang Zhen , Zhao Yan , Bo Wen , Wang Donglai , Zhang Dong , Shi Jiaqi
Published: October 2023

Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences.
Electromagnetic Safety of Short-Range Radio Frequency Identification Systems
Authors: Musial Slawomir , Firlej Andrzej , Kubiak Ireneusz , Dalecki Tomasz
Published: October 2023

The intensive development of information and telecommunications technology has a very large impact on society. On the one hand, it greatly facilitates many activities in everyday life, such as searching for information, establishing contacts with other people, or controlling household appliances at a distance. On the other hand, it poses serious threats to our personal data, the information we process, or our property. One of the examples of such threats may be radio identification systems, enabling the registration of working time, entry into restricted zones in workplaces and offices, or warehouse data. Copying data from an identification card may allow unauthorized persons access to premises and data that they should not have. The article presents the principles of operation, the results of the conducted research, and their analysis in the aspect of security of the short-range radio frequency identification systems used in relation to the RFID 125 kHz system, broadly used in access control systems or time and attendance systems. Particular attention has been paid to the possibility of unauthorized acquisition of information contained in the identification card in order to, for example, copy it and gain access to specific protected zones. An analysis of the security of such systems was carried out not only in relation to the data carriers themselves but also to complete access control systems installed in buildings. The research focused especially on the ability to determine the range of information penetration, i.e., the distance of remote information acquisition using electromagnetic radiated emissions.
Operational Stress Injury
Authors: Rudnick Abraham , Shaheen Andrea , Lefurgey Sarah , Nolan Dougal
Published: October 2023

An operational stress injury (OSI) is a term used most often to describe mental disorders which result from, or are exacerbated by, military or police service. In the Canadian context, this most often refers to active or former members of the Canadian Armed Forces (CAF) or the Royal Canadian Mounted Police (RCMP). The most common diagnoses within this term include post-traumatic stress disorder (PTSD), anxiety disorders, depression, and substance use disorders.
Evaluation of Forestry Component Survival in Plots of the Program “Sembrando Vida” (Sowing Life) Using Drones
Authors: Gallardo-Salazar José Luis , Sáenz-Romero Cuauhtémoc , Lindig-Cisneros Roberto A. , Blanco-García Arnulfo , Osuna-Vallejo Verónica
Published: October 2023

Reforestation is one of the main actions undertaken to mitigate the effects of climate change. In Mexico, the Federal Government program “Sembrando Vida” (Sowing Life) is currently the most important reforestation effort. It aims to recoup forest cover and achieve food self-sufficiency through the establishment of agroforestry systems. The evaluation of tree survival in reforested areas helps to identify achievements and failures, as well as aspects of the program that require improvement. However, given the magnitude of this program, evaluation using traditional methodologies is labor-intensive and costly. In this context, drones equipped with high-resolution cameras are a promising tool. The objective of this study was to evaluate the feasibility of using drones to monitor tree survival in reforested areas. This study was conducted in 12 randomly chosen plots, benefited by the “Sembrando Vida” program, located on the Purépecha Plateau in the state of Michoacán, in central–western Mexico. Field surveys with GPS were conducted to record the total number of live and dead forest-tree seedlings. Simultaneously, high-resolution images were captured using a DJI Phantom 4 Pro drone equipped with an RGB camera for subsequent visual interpretation in a geographic information system to determine the status of each seedling and calculate the rates of survival. ANOVA was performed to compare the survival calculated using the drone images compared to that recorded in the field. No significant difference was found between survival estimated using the drone and that recorded directly in the field in any of the study plots, although the drone overestimated survival by an average of 6%, mostly due to the presence of dead seedlings that had already lost their foliage and were thus missed when scoring the RGB image. It is therefore concluded that the estimation of survival using drones is a reliable method. For future research, it is recommended to evaluate machine-learning algorithms in terms of detecting both living and dead trees in reforested sites. It is also recommended to use multispectral thermal cameras and LiDAR technology to broaden the knowledge of the different levels of vigor/stress present in the vegetation.
Conservation Effectiveness Assessment of the Three Northern Protection Forest Project Area
Authors: Shao Yakui , Liu Yufeng , Ma Tiantian , Sun Linhao , Yang Xuanhan , Li Xusheng , Wang Aiai , Wang Zhichao
Published: October 2023

The Three-North Shelterbelt Project is the largest ecological engineering initiative in China to date, distinguished by its immense scale, extended construction period, and widespread benefits for the population. The gross ecosystem product (GEP) serves as a crucial indicator for assessing ecological benefits. This study focuses on the Three Northern Protection Forest Project Area, utilizing GEP calculations for the years 2000 to 2020. This study evaluates variations in the production values of different ecosystem services to reflect the ecological conservation benefits of the restoration project. Additionally, it analyzes the spatiotemporal evolution and trends of the GEP calculations, offering data references and decision support for the enduring efficacy of ecological restoration projects. The findings are as follows. (i) Between 2000 and 2020, the GEP of the Three-North region exhibited significant growth with continuous enhancement of various ecosystem service functions; the most substantial rate of change was observed in the water conservation function, followed by carbon sequestration and oxygen release, soil retention, windbreak and sand fixation, flood regulation, and environmental purification functions. (ii) The per-unit area value of different ecosystem types generally increased; the forest ecosystem displayed the largest growth rate at 61.18%, followed by shrubland ecosystems at 49.84%. (iii) The spatial distribution of ecosystem service in the Three-North region displayed a clustering trend alongside notable spatial heterogeneity. High-high clustering zones were identified in areas such as the Tianshan Mountains, Altai Mountains, Qilian Mountains, and Greater and Lesser Khingan Mountains. Conversely, low-low clustering areas were scattered, forming patchy distributions in regions like the Tarim Basin, northern Qinghai-Tibet Plateau, and the Hexi Corridor. This study, by analyzing the gross ecosystem product of the Three-North Shelterbelt Project region, unveils the spatial distribution characteristics, trends, and variations in ecosystem service values over the past two decades. It provides data support and decision guidance for the long-term efficacy of future ecological conservation and restoration projects. This study incorporates the GEP accounting method into the assessment of the effectiveness of major conservation projects. Compared to the traditional methods of effectiveness assessment, this represents a significant exploration and innovation.