bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eco-friendly strategy for CO2 enrichment performance in commercial greenhouses based on the CO2 spatial distribution and photosynthesis
Authors: Zhang Yue , Yasutake Daisuke , Hidaka Kota , Kimura Kensuke , Okayasu Takashi , Kitano Masaharu , Hirota Tomoyoshi 
Published: October 2023

AbstractCO2 enrichment is an essential environmental control technology due to its significantly enhancing effect on crop production capacity. Despite being a key energy consumer in protected agriculture (i.e. greenhouse systems), CO2 enrichment remains at a low energy use efficiency level, highlighting the need for developing more energy-efficiency strategies for CO2 enrichment. Therefore, this study employed the computational fluid dynamics (CFD) simulation method to replicate the CO2 diffusion process resulting from CO2 enrichment in three commercial strawberry greenhouses with varying geometric characteristics. Based on the CFD-simulated CO2 concentration distributions, the leaf photosynthetic rate was calculated using a mathematical model group. The CO2 enrichment efficiency was then analysed by calculating the ratio of increased photosynthesis across the cultivation area to the amount of energy (in CO2 equivalent) used. The efficiency peaked when the average CO2 concentration was approximately 500 μmol mol−1, thereby providing guidance for determining the target concentration of CO2 enrichment in production. Although this study is limited as the CFD simulation only considered a typical short-period CO2 enrichment event, future research will provide a broader analysis by considering changes throughout the day.
Comparison of heart, grace and TIMI scores to predict major adverse cardiac events from chest pain in a Spanish health care region
Authors: Arispe Iris N. San Román , Sol Joaquim , Gil Ana Celma , Trujillano Javier , Bravo Marta Ortega , Torres Oriol Yuguero 
Published: October 2023

AbstractAcute non-traumatic chest pain (ANTCP) is the second cause of consultation in the Emergency department (ED). About 70% of all Acute Myocardial Infarctions present as non persistent ST-elevation acute coronary syndrome (NSTE-ACS) in the electrocardiogram. Our aim was to compare whether the HEART risk score is more effective than the GRACE and TIMI scores for the diagnosis and prognosis of Major Adverse Cardiac Events (MACE) at six weeks in patients with ANTCP and NSTE-ACS. A prospective cohort study was conducted with patients with ANTCP that attended an ED and a Primary Care Emergency Center (PCEC) from April 2018 to December 2020. The primary outcome was MACE at six weeks. Diagnostic performance was calculated for each scale as the Area under the Receiver Operating Characteristic (ROC) curve (AUC), sensitivity (SE), specificity (SP), and predictive values (PV). Qualitative variables were compared using the Chi-square test, and continuous variables were compared using the nonparametric Kruskal–Wallis test. We adjusted a logistic regression for risk groups, age, and gender to determine the effect of the HEART, GRACE, and TIMI scores on MACE. The degree of agreement (kappa index) was calculated in the categorical classification of patients according to the three risk scales. Cox proportional hazards regressions were performed for each scale and were compared using partial likelihood ratio tests for non-nested models. From a sample of 317 patients with ANTCP, 14.82% had MACE at six weeks. The AUC was 0.743 (95% CI 0.67–0.81) for the HEART score, 0.717 (95% CI 0.64–0.79) for the TIMI score, and 0.649 (95% CI 0.561–0.738) for the GRACE score. The HEART scale identified low-risk patients with a higher SE and negative PV than the GRACE and TIMI scores. The HEART scale was better than the GRACE and TIMI scores at diagnosing and predicting MACE at six weeks in patients with ANTCP and probable NSTE-ACS. It was also a reliable tool for risk stratification in low-risk patients. Its application is feasible in EDs and PCECs, avoiding the need for complementary tests and their associated costs without compromising patient health.
Customer churn prediction using composite deep learning technique
Authors: Khattak Asad , Mehak Zartashia , Ahmad Hussain , Asghar Muhammad Usama , Asghar Muhammad Zubair , Khan Aurangzeb 
Published: October 2023

AbstractCustomer churn, a phenomenon that causes large financial losses when customers leave a business, makes it difficult for modern organizations to retain customers. When dissatisfied customers find their present company's services inadequate, they frequently migrate to another service provider. Machine learning and deep learning (ML/DL) approaches have already been used to successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results for detecting client churn. Previous research on estimating customer churn revealed unexpected forecasts when utilizing machine learning classifiers and traditional feature encoding methodologies. Deep neural networks were also used in these efforts to extract features without taking into account the sequence information. In view of these issues, the current study provides an effective method for predicting customer churn based on a hybrid deep learning model termed BiLSTM-CNN. The goal is to effectively estimate customer churn using benchmark data and increase the churn prediction process's accuracy. The experimental results show that when trained, tested, and validated on the benchmark dataset, the proposed BiLSTM-CNN model attained a remarkable accuracy of 81%.
Genome-wide identification of candidate copy number polymorphism genes associated with complex traits of Tibetan-sheep
Authors: Tian Dehong , Sun De , Ren Qianben , Zhang Pei , Zhang Zian , Zhang Wenkui , Luo Haizhou , Li Xue , Han Buying , Liu Dehui , Zhao Kai 
Published: October 2023

AbstractCopy number variation (CNV) is a genetic structural polymorphism important for phenotypic diversity and important economic traits of livestock breeds, and it plays an important role in the desired genetic variation. This study used whole genome sequencing to detect the CNV variation in the genome of 6 local Tibetan sheep groups. We detected 69,166 CNV events and 7230 copy number variable regions (CNVRs) after merging the overlapping CNVs, accounting for 2.72% of the reference genome. The CNVR length detected ranged from 1.1 to 1693.5 Kb, with a total length of 118.69 Mb and an average length of 16.42 Kb per CNVR. Functional GO cluster analysis showed that the CNVR genes were mainly involved in sensory perception systems, response to stimulus, and signal transduction. Through CNVR-based Vst analysis, we found that the CACNA2D3 and CTBP1 genes related to hypoxia adaptation, the HTR1A gene related to coat color, and the TRNAS-GGA and PIK3C3 genes related to body weight were all strongly selected. The findings of our study will contribute novel insights into the genetic structural variation underlying hypoxia adaptation and economically important traits in Tibetan sheep.
The value of serum creatinine as biomarker of disease progression in spinal and bulbar muscular atrophy (SBMA)
Authors: Blasi Lorenzo , Sabbatini Daniele , Fortuna Andrea , Querin Giorgia , Martinelli Ilaria , Vianello Sara , Bertolin Cinzia , Pareyson Davide , Pennuto Maria , Pegoraro Elena , Bello Luca , Sorarù Gianni 
Published: October 2023

AbstractSerum creatinine has been indicated as a potential marker of motor function in SBMA and results form previous longitudinal studies pointed to its decline over time. This is a longitudinal retrospective study investigating creatinine changes over a 36-month-period in 73 patients with SBMA. Severity and progression of the disease was assessed according to serum creatine kinase (CK) values, manual muscle testing (MMT), SBMA functional rating scale (SBMAFRS) score, 6-min-walk test (6MWT) value, and spirometry (forced vital capacity, fVC%) obtained at the baseline and at each of the annual follow-up visits. Baseline serum creatinine concentrations positively correlated with 6MWT, the MMT megascore score of both the upper (ULM) and lower (LLM) limbs and SBMAFRS. No correlation was found with CK or fVC% values. Similar correlation results were achieved at all the subsequent time points. Longitudinal assessments conducted by the generalized estimating equations (GEE) method returned significant changes for SBMAFRS (− 1.41 points per year, p < 0.001), ULM and LLM (− 0.69, p = 0.01; and − 1.07, p < 0.001, respectively), 6MWT (− 47 m, p < 0.001) but not for creatinine (− 0.82, p > 0.05). We also observed that creatinine levels at baseline did not correlate with changes in the other measures from baseline at each annual visit. Our data do not support a role for serum creatinine as sensitive biomarker of disease progression, and possibily prognosis, in SBMA.
Pharmarcomechanical thrombectomy combined with transluminal balloon angioplasty for treating transplant renal vein thrombosis
Authors: Wu Shao-Jie , Zhang Chi , Wu Min , Ruan Dan-dan , Zhang Yan-ping , Lin Bin , Tang Yi , Chen Xin , Wang Chen , Pan Hong-hong , Zhu Qing-guo , Luo Jie-wei , Ye Lie-fu , Fang Zhu-ting 
Published: October 2023

AbstractRenal vein thrombosis (RVT) is a rare vascular complication that occurs after renal transplantation and usually results in irreversible kidney damage and graft loss. We report the case of a patient who underwent right iliac fossa allogeneic kidney transplantation and developed RVT combined with ipsilateral thrombosis from the popliteal to the femoral veins, with extension to the common iliac veins, 4 months after transplantation. Under unfractionated heparin anticoagulation, an Aegisy (Life Tech Scientific Co., Ltd., Shenzhen, China) vena cava filter was placed to prevent pulmonary embolism. Percutaneous mechanical thrombectomy combined with balloon angioplasty was performed to aspirate the thrombus and successfully dilate the narrow venous lumen. The patient’s renal function was restored postoperatively. Ultrasonography showed the allograft and ipsilateral lower extremity deep veins to be fluent and patent. To conclude, in patients with RVT after renal transplantation, percutaneous mechanical thrombectomy in conjunction with balloon angioplasty can be performed with desirable outcomes and no severe adverse effects. This method reduces the risk of bleeding from exposure to systemic intravenous thrombolysis and avoids surgery-associated trauma.
General and central obesity prevalence in young adult: a study based on the Rafsanjan youth cohort study
Authors: Abbasifard Mitra , Bazmandegan Gholamreza , Ostadebrahimi Hamid , Amiri Mahsa , Kamiab Zahra 
Published: October 2023

AbstractGrowing prevalence of obesity among youth would have adverse consequences and increased risk of developing chronic diseases at older ages. This study explored the prevalence of obesity and its association with relevant risk factors in the Rafsanjan youth cohort population. This cross-sectional study was done on 3006 individuals from the 15–35-year-old population included in the Rafsanjan youth cohort study. The data were extracted from the youth cohort databases, which had been collected through in-person interview and standard questionnaires. Definition of general obesity was considered as body mass index ≤ 30 and that of central obesity as waist to hip ratio (WHR) ≥ 0.9 for men and ≥ 0.85 for women. Multivariate stepwise proportional odds model and multivariable stepwise logistic regression models were done to explore the factors associated with general obesity and central obesity. The mean age was 25.78 ± 6.06 years with 56% (n = 1683) female. The prevalence of general obesity was 15.80% (95% CI 14.50–17.11) and central obesity was 28.41% (95% CI 26.80–30.02). The risk of general obesity increased with increasing age (OR = 1.053, P < 0.0001), being married (OR = 1.658, P < 0.0001), history of diabetes (OR = 1.609, P = 0.0185), history of hypertension (OR = 1.609, P < 0.0001), elevated triglyceride (OR = 1.007, P < 0.0001) and LDL (OR = 1.015, P < 0.0001), while decreasing with being employed (OR = 0.748, P = 0.0002) and elevated HDL (OR = 0.975, P < 0.0001). Prevalence of obesity was high in study population. Marital status, increasing age, and history of chronic diseases were associated with obesity. Preventing programs should be developed against obesity and for promoting healthy habits in young adult especially during education at schools.
Research on fire resistance and economy of basalt fiber insulation mortar
Authors: Ding Chen , Xue Kaixi , Yi Guangsheng 
Published: October 2023

AbstractThe construction sector has become the most critical source of carbon emissions, but the existing thermal insulation materials such as thermal insulation mortar have obvious limitations, so it is urgent to develop building thermal insulation materials with superior performance and low cost. Aiming at the problem of poor bond strength of foam thermal insulation mortar, this research team selected basalt fiber as admixture to verify the influence of basalt fiber content on its performance and the economic feasibility of thermal insulation mortar. The main finding is that basalt fiber as an additive can obviously improve the crack resistance of thermal insulation mortar. When the content of basalt fiber increases from 0 to 2.5%, the compressive strength of mortar increases at first and then decreases, and the bond strength increases nonlinearly, but the thermal conductivity and dry density also increase. Therefore, the optimal content of basalt fiber is 1.5%. The improvement effect of fire resistance of thermal insulation mortar with 1.5% basalt fiber content is better. After curing for 28 days, the mass loss rate of the sample is reduced by about 11.1% after high temperature, and the relative compressive strength is increased by about 9.71% after high temperature. The raw material cost of the new fireproof thermal insulation mortar improved by basalt fiber is lower, and the cost of the finished product is reduced by 16.98%, 28.18%, 33.05% and 38.96%, respectively, compared with the four types of thermal insulation mortar already used in the market. More importantly, the economic recovery period of the new fireproof and thermal insulation mortar is undoubtedly shorter than that of alternative thermal insulation or energy storage materials, which not only achieves low emission and environmental protection, but also satisfies the economic feasibility.
Rapid and visual detection of Toxoplasma gondii oocyst in cat feces using loop-mediated isothermal amplification (LAMP) assay
Authors: Sheng Lingwei , Xue Qiqi , Xu Sijia , Can Fang , Yao Ning , Zou Minghui , Teng Qiao , Li Yuanyuan , El-Ashram Saeed , Ji Yongsheng , Zhao Jinhong 
Published: October 2023

AbstractToxoplasma gondii is an obligate parasitic protozoon that transmits to animals and humans via ingested food. Cats that act as T. gondii’s final hosts play a critical role in T. gondii transmission by shedding millions of oocysts. Timely diagnosis of infected cats is essential for preventing toxoplasmosis because oocysts are a putative T. gondii source in epidemiology. We developed a new visual LAMP assay targeting the B1 gene to analyze single oocysts in cat feces in this study. The amplification result could be visually estimated based on the color change. LAMP assay analytical sensitivity was 101 copies/µL for the B1 gene plasmid, which was tenfold better than the PCR reaction. There were no cross-reactions with other parasites. The LAMP assay can detect a single T. gondii oocyst in 200 mg of cat feces. The LAMP assay detected a single oocyst in 200 mg cat feces at a higher rate than the PCR assay (83.3% vs. 50.0%).
A digital solution framework for enabling electric vehicle battery circularity based on an ecosystem value optimization approach
Authors: Kumar Amit , Huyn Pierre , Vennelakanti Ravigopal 
Published: October 2023

AbstractA circular economy for batteries is crucial for building a sustainable battery value chain, as end-of-life electric vehicle batteries can be given a second life or valuable raw materials can be harvested to make new batteries. However, significant challenges remain in forecasting availability, predicting remaining value, minimizing reverse logistics costs, and maximizing value recovery from end-of-life batteries. Here we devise an ecosystem value optimization approach powered by a digital solution framework, consisting of innovative analytical models and a trusted data platform, to optimize five key value drivers for battery circularity—safety, regulatory compliance, carbon footprint reduction, quality, and financials. The envisioned solution can help reduce average transportation costs of end-of-life batteries by 11% to 44% compared to current shipping practices, estimate battery health with error rates less than 1%, and improve value recovery by 52% to 60% by routing batteries with good health to second-life application providers.
Rheological study of nanoemulsions with repulsive and attractive interdroplet interactions
Authors: Abbasian Chaleshtari Zahra , Foudazi Reza 
Published: October 2023

As the potential well between droplets becomes deeper, nanoemulsions show higher and σy. By superposition of rheological properties of nanoemulsions, a master curve is constructed for dynamic moduli at different volume fractions and interactions.
Switching threshold event‐triggered critic algorithm for optimal orbit tracking and formation motion
Authors: Yu Rui , Chen Yang‐Yang , Zhang Ya 
Published: October 2023

AbstractThis paper deals with the orbit tracking and formation motion problems with optimal energies and reduced computational cost. First, the orbit tracking and formation motion are decomposed into movements in the normal and tangent directions of level orbits, respectively, and simultaneously, the optimal value functions in both directions are defined. Then, to reduce the computational cost, a switching threshold event‐triggered (STET) mechanism is designed. Based on the STET mechanism, the optimal value functions are constructed to evaluate the optimal energies of orbit tracking and formation motion. Critic neural networks are then designed to approximate the optimal value functions, which yield the optimal policies along the normal and tangent directions of desired orbits, that is, a so‐called switching threshold event‐triggered critic algorithm (STET‐C). Theoretical analysis of system convergence is given in detail. Finally, two comparison simulations are given. The former intends to verify the optimal energy of STET‐C compared to the feedback controllers. The latter shows that STET‐C significantly reduces the computational cost in contrast with the non‐triggered actor‐critic algorithm, non‐triggered critic algorithm, and the relative threshold event‐triggered critic algorithm.
Representation constraint‐based dual‐channel network for face antispoofing
Authors: Li Zuhe , Cui Yuhao , Wang Fengqin , Liu Weihua , Yang Yongshuang , Yu Zeqi , Jiang Bin , Chen Hui 
Published: October 2023

AbstractAlthough multimodal face data have obvious advantages in describing live and spoofed features, single‐modality face antispoofing technologies are still widely used when it is difficult to obtain multimodal face images or inconvenient to integrate and deploy multimodal sensors. Since the live/spoofed representations in visible light facial images include considerable face identity information interference, existing deep learning‐based face antispoofing models achieve poor performance when only the visible light modality is used. To address the above problems, the authors design a dual‐channel network structure and a constrained representation learning method for face antispoofing. First, they design a dual‐channel attention mechanism‐based grouped convolutional neural network (CNN) to learn important deceptive cues in live and spoofed faces. Second, they design inner contrastive estimation‐based representation constraints for both live and spoofed samples to minimise the sample similarity loss to prevent the CNN from learning more facial appearance information. This increases the distance between live and spoofed faces and enhances the network's ability to identify deceptive cues. The evaluation results indicate that the framework we designed achieves an average classification error rate (ACER) of 2.37% on the visible light modality subset of the CASIA‐SURF dataset and an ACER of 2.4% on the CASIA‐SURF CeFA dataset, outperforming existing methods. The proposed method achieves low ACER scores in cross‐dataset testing, demonstrating its advantage in domain generalisation.
Short‐term wind power prediction based on combined long short‐term memory
Authors: Zhao Yuyang , Li Lincong , Guo Yingjun , Shi Boming , Sun Hexu 
Published: October 2023

AbstractWind power is an exceptionally clean source of energy; its rational utilization can fundamentally alleviate the energy, environment, and development problems, especially under the goals of ‘carbon peak’ and ‘carbon neutrality’. A combined short‐term wind power prediction based on long short‐term memory (LSTM) artificial neural network has been studied aiming at the non‐linearity and volatility of wind energy. Due to the large amount of historical data required to predict the wind power precisely, the ambient temperature and wind speed, direction, and power are selected as model input. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise has been introduced as data preprocessing to decompose wind power data and reduce the noise. And the Particle Swarm Optimization is conducted to optimize the LSTM network parameters. The combined prediction model with high accuracy for different sampling intervals has been verified by the wind farm data of Chongli Demonstration Project in Hebei Province. The results illustrate that the algorithm can effectively overcome the abnormal data influence and wind power volatility, thereby providing a theoretical reference for precise short‐term wind power prediction.
Vulnerable node identification method for distribution networks based on complex networks and improved TOPSIS theory
Authors: Jiang Enyu , Zhang Wentao , Xue Ang , Lin Shunfu , Mi Yang , Li Dongdong 
Published: October 2023

AbstractA method for identifying vulnerable nodes in distribution networks is proposed, which is based on complex networks and optimized TOPSIS. This method aims to address the issues of one‐sided evaluation indicators and inaccurate indicator weights that are present in existing methods for identifying vulnerable nodes in distribution networks. Based on the theory of complex networks, a comprehensive set of vulnerability indicators for distribution network nodes is constructed by considering both the topology structure and system operation status of the distribution network. The TOPSIS comprehensive evaluation model for optimization is proposed to enhance the selection process of optimal and worst indicator values. The advantages and disadvantages of each indicator are characterized using Mahalanobis distance. The calculation of proximity is optimized by establishing a virtual negative ideal solution, which makes the identification of vulnerable nodes more reasonable. The simulation results demonstrate that this method is more effective in identifying vulnerable nodes in the power grid compared to traditional methods, and has significant practical applications.