bluuebunny/crossref_metadata_2025 huggingface dataset:
Uptake of Immediate Postpartum LARCs and Associated Factors among Mothers Who Gave Birth at Hawassa University Comprehensive Specialized Hospital, Hawassa, Ethiopia
Authors: Tariku Mequanent , Legesse Biruk , Tantu Temesgen , Duko Bereket
Published: July 2022

Background. Postpartum family planning is an effective strategy for reducing maternal and childhood morbidity and mortality by preventing unintended pregnancy and short interpregnancy intervals. Despite the paramount advantages of long-acting reversible contraceptives (LARC), their uptake remains low in Ethiopia. Therefore, the aim of this study was to assess the uptake of immediate postpartum LARC methods and its associated factors among women who gave birth in Hawassa University Comprehensive Specialized Hospital, Hawassa city, Southern Ethiopia. Methods. An institution-based cross-sectional study was conducted among 418 eligible mothers who were in the immediate postpartum period. Data were collected using a pretested structured questionnaire before their discharge from the hospital and analyzed by using SPSS version 20. The statistical significance was declared atPvalue less than 0.05. Results. The uptake LARCs among immediate postpartum mothers was 25.4%. The most commonly reported reasons for not using LARC were preference to start contraception after six weeks of delivery (43.3%) and the need to use other methods of contraception (26%). Having unplanned birth (AOR: 1.97; 95% CI: 1.04-3.71) and receiving family planning counselling on LARCs during the postpartum period (AOR: 21.1; 95% CI: 6.49-68.66) were factors significantly associated with immediate postpartum LARC use. Conclusion. Low utilization of immediate postpartum LARC uptake was found in the current study setting. There was increased utilization of immediate postpartum LARC among mothers who received family planning counselling during the postpartum period. Therefore, strengthening family planning counselling during the immediate postpartum period is crucial to enhance postpartum LARC use.
Significance of TP53 Mutational Status-Associated Signature in the Progression and Prognosis of Endometrial Carcinoma
Authors: Chen Ying , Zhao Wancheng , Bi Fangfang , Pan Xue , Yin Lili , Zhao Chengzhi
Published: July 2022

Background. TP53 mutations are associated with poor outcome for patients with endometrial carcinoma (EC). However, to date, there have been no studies focused on the construction of TP53 mutational status-associated signature in EC. In this study, we aim to conduct a TP53 mutation-associated prognostic gene signature for EC. Methods. Hence, we explored the mutational landscape of TP53 in patients with EC based on the simple nucleotide variation data downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and least absolute shrinkage and selection operator (LASSO)–Cox analysis was used to establish TP53 mutation-associated prognostic gene signature. The overall survival rate between the high-risk and low-risk groups was compared by the Kaplan–Meier (K-M) method. Results. We found that the TP53 mutation was associated with poor outcome, older age, lower BMI, and higher grade and stage of EC in patients. A TP53 mutational status-associated signature was established based on transcriptome profiling data. Moreover, the patients in TCGA database were categorized into high- and low-risk groups. Kaplan–Meier (K-M) analysis indicated that the patients in the high-risk group have poor survival outcome. Furthermore, receiver operating characteristic (ROC) curves confirmed the robust prognostic prediction efficiency of the TP53 mutational status-associated signature. Finally, the prognostic ability was successfully verified in the other two datasets from cBioPortal database as well as in 60 clinical specimens. Univariate (hazard ratio

HR

=
1.041

,

95
%
CI
=
1.031
–
1.051

,

p
<
0.001

) and multivariate (hazard ratio

HR

=
1.029

,

95
%
CI
=
1.018
–
1.040

,

p
<
0.001

) Cox regression analyses indicated that the TP53 mutational status-associated signature could be used as an independent prognostic factor for EC patients. Conclusion. In summary, our research constructed a powerful TP53 mutational status-associated signature that could be a potential novel prognostic biomarker and therapeutic target for EC.
Mesenchymal Stem Cells Alleviate Renal Fibrosis and Inhibit Autophagy via Exosome Transfer of miRNA-122a
Authors: Li Dawei , Qu Junwen , Yuan Xiaodong , Zhuang Shaoyong , Wu Haoyu , Chen Ruoyang , Wu Jiajin , Zhang Ming , Ying Liang
Published: July 2022

Exosomes derived from mesenchymal stem cell (MSC) alleviate kidney damage through autophagy. This study determined whether MSCs relieve renal fibrosis and inhibit autophagy by exosome transfer of miRNA-122a. The gene expression involved in the mTOR signaling pathway and autophagy was assessed in TGF-β1-treated human renal tubular epithelial cells (HK-2) and unilateral ureteral obstruction (UUO) mice before and after MSC-derived exosomes and miRNA-122a mimic treatment. Small RNA (sRNA) next-generation sequencing was also performed on TGF-β1-treated HK-2 cells. MSC-derived exosomes relieve fibrosis caused by TGFβ in HK-2 via regulation of the mTOR signaling pathway and downstream autophagy. Furthermore, we found that MSC-derived exosomes mediate miRNA-122a to relieve renal fibrosis in HK-2 cells in response to TGF-β1 through the regulation of mTOR signaling and autophagy. In the UUO mouse model, miRNA-122a mimic-transfected MSC treatment and its combination with 3-MA both recapitulated the same results as the in vitro experiments, along with reduced expansion of renal tubule, interstitial expansion, and preservation of kidney architecture. The antifibrotic activity of MSC-derived exosomes after renal fibrosis occurs partially by autophagy suppression via excreted exosomes containing mainly miRNA-122a. These findings indicate that the export of miRNA-122a via MSC-derived exosomes represents a novel strategy to alleviate renal fibrosis.
Enterprise Precision Marketing Effectiveness Model Based on Data Mining Technology
Authors: Li Xiutian , Meng Tan
Published: July 2022

Due to the explosive growth of massive consumption data, the traditional marketing model of enterprises has become stretched. The rapid advancement of data mining technology has given new impetus to the innovation of the marketing strategies of enterprises, promoting the progressive transformation of enterprises from traditional passive marketing to precise and refined marketing. Data mining technology’s basic task is to analyze data, acquire insight into cause and effect, and then predict the future. As a result, the technology is coupled with the enterprises’ marketing activities, and a precise marketing model is built using big data consumption. This will aid in the comprehensive and three-dimensional description of customers, as well as the analysis of potential customers’ attributes, to give a scientific basis for the formulation and implementation of precise data-driven decision-making for enterprises. As a result, our study enhances and optimizes the K-means algorithm in combination with the artificial bee colony algorithm aiming at fixing the issue that the K-means algorithm is sensitive to cluster center initialization and improving the enterprise precision marketing model’s clustering performance. In the precise marketing scenario of the telecom business, the improved K-means clustering model is utilized to realize the analysis and prediction of telecom customers, as well as to carry out precise marketing based on the predicted findings. Finally, the optimized K-means clustering algorithm can objectively and comprehensively reflect the characteristics of telecom customer value segmentation, efficiently mining future clients and preventing blind marketing by enterprises, based on the model's actual verification results. Simultaneously, it provides substantial data support for telecom enterprises’ resource planning, as well as pointing out the next step in increasing market share.
Isothermal Drying Kinetic Study of Spent Coffee Grounds Using Thermogravimetric Analysis
Authors: Bejenari Victoria , Lisa Cătălin , Cernătescu Corina , Mămăligă Ioan , Lisa Gabriela
Published: July 2022

Drying coffee grounds involves high energy consumption and represents an important step in using coffee waste materials as green energy. This study analyzes the drying process using thermogravimetric analysis. The kinetics of the drying process of coffee grounds, containing equal proportions of Arabica and Robusta, were evaluated under isothermal conditions at various temperatures: 50, 60, 70, and 80°C, with sample layer thicknesses of 0.6, 1.2, 1.8, and 2.4 mm. The thermogravimetric curves obtained for the coffee grounds samples under conditions of constant temperature allowed the tracing of the drying curves (moisture content—drying time). The influence of the furnace air velocity on the drying and water evaporation process was analyzed. The drying rate has been established to be about 2.6 times slower than the rate of water evaporation under the same conditions; as a result the overall process is controlled by the effective water diffusion in the layer. This aspect is important in industrial practice for sizing tubular dryers. The activation energy was found to be influenced by the thickness of the coffee grounds layer subjected to the drying process and had values between 8.3 kJ/mol and 10.3 kJ/mol.
The Impact of Entrepreneurial Orientation on Firm Performance: The Multiple Mediating Roles of Competitive Strategy and Knowledge Creation Process
Authors: Yang Liping , Aumeboonsuke Vesarach
Published: July 2022

The strategy and knowledge creation process help entrepreneurial SME managers achieve sustainable competitive advantage and firm performance. This study aims to investigate the multiple mediating roles of competitive strategies and knowledge creation processes implemented by Chinese SMEs in Thailand between their entrepreneurial orientation and firm performance. Structural equation model is used to analyze data collected from Chinese SMEs in Thailand. Results showed that (1) there is a positive relationship between perceptions of entrepreneurial orientation and perceptions of firm performance, (2) competitive strategy and knowledge creation process individually play a mediating role between entrepreneurial orientation and firm performance, and (3) competitive strategy and knowledge creation process play a chain mediating effect between entrepreneurial orientation and firm performance. This hypothesis is verified by a chained multiple mediation model, and this model has theoretical significance for understanding the relationship between perceived entrepreneurial orientation and firm performance. The competitive strategy has positive effects on SME performance and knowledge creation expands the firm’s valuable resources and actively updates firm performance.
Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model
Authors: Liu Li , Yong Seechen
Published: July 2022

Talent is an indispensable and important factor for the development and growth of an enterprise. Therefore, human resources have become an important resource of enterprises, and performance appraisal in human resource management has also become one of the important contents of modern enterprise management. Without a set of scientific and standardized performance appraisal systems, it will make it difficult for the daily human resource performance management of enterprises to achieve the expected goals. This study analyzes the actual needs, business processes, and existing problems of an enterprise in performance management and uses the multiobjective decision-making mathematical model to obtain an optimization plan for human resources performance assessment. Finally, a digital management system for enterprise performance appraisal is designed. The main work of this study is as follows: (1) based on the work characteristics of a certain enterprise, the key indicators of enterprise performance evaluation are analyzed; (2) a multiobjective decision-making mathematical model is constructed for the optimization of human resources performance evaluation; (3) the Hungarian algorithm is used to solve the construction The mathematical model of multiobjective decision-making can obtain the optimal work arrangement. This study combines performance appraisal with practical work and uses the proposed multiobjective decision-making mathematical model to obtain the optimization results of human resources, guide practical work, and improve the efficiency of enterprise operation.
Overexpression of BRINP3 Predicts Poor Prognosis and Promotes Cancer Cell Proliferation and Migration via MAP4 in Osteosarcoma
Authors: Zeng Wenchao , Xu Huiya , Wei Tian , Liang Huishan , Ma Xiaokun , Wang Fen
Published: July 2022

Osteosarcoma (OS) is a primary malignant bone tumor most commonly affecting children and adolescents and is characterized by loss of differentiation. Bone morphogenetic protein/retinoic acid inducible neural-specific 3 (BRINP3) has been reported to regulate the differentiation of osteoblasts. However, the role that BRINP3 plays in the progression of osteosarcoma remains unknown. We found in this study that BRINP3 was highly expressed in 64.13% of human osteosarcoma tissues and it was associated with histological grade, tumor recurrence, and poor clinical prognosis of osteosarcoma. In vitro, downregulation of BRINP3 was able to inhibit the proliferation and invasion of osteosarcoma cell lines. Furthermore, BRINP3 interacted with microtubule-associated protein 4 (MAP4) at the protein level, and overexpression of MAP4 could partially reverse the inhibitory effect of downregulated BRINP3 on the proliferation and invasion of osteosarcoma cells, which indicates that downregulation of BRINP3 might suppress the proliferation and invasion of osteosarcoma cells by inhibiting MAP4 expression. Overall, our results demonstrate that BRINP3 functions as an oncogene within osteosarcoma through MAP4 and could therefore be used as a potential biomarker for osteosarcoma diagnostics and therapeutics.
Hsa-let-7d-5p Promotes Gastric Cancer Progression by Targeting PRDM5
Authors: Gao Xiang , Liu Huiqi , Wang Rong , Huang Mingyu , Wu Qiong , Wang Yang , Zhang Wei , Liu Yongnian
Published: July 2022

Gastric cancer (GC) is a common malignant tumor in the digestive system and a significant health burden worldwide. In this study, we found that hsa-let-7d-5p was upregulated in GC cells, promoted GC cell proliferation, migration, and invasion, and reduced apoptosis. Moreover, we found that the expression of PRDM5 (PR domain protein 5) was downregulated in GC cells and upregulated in GC cells treated with hsa-let-7d-5p inhibitor. Further investigation showed that hsa-let-7d-5p was the target of PRDM5, and the functions of hsa-let-7d-5p on GC progression were rescued by PRDM5 overexpression in GC cells. Collectively, our findings suggested that hsa-let-7d-5p promoted the development of GC by targeting PRDM5, indicating that hsa-let-7d-5p could be a promising therapeutic molecule for the treatment of gastric cancer.
Herpes Virus Pseudotumor in a Patient with HIV Immunosuppression
Authors: York Daniel , Patel Pavan , Saikumar Smera
Published: July 2022

Background. In rare cases, HSV infections can present as pseudotumors that are often mistaken as malignancies in patients with an uncontrolled HIV infection. Herpes simplex virus type 2 (HSV-2) infection rates range from 60% to 90% in individuals coinfected with HIV. Case Presentation. A 48-year-old patient presented with a large fungating mass near her right inferior vulva with a hardness of surrounding tissues. The mass was

4
 
cm
×
3
 
cm

in size and was excised in the operating room. The pathology was negative for malignancy; however, it showed lymphoplasmacytic proliferation with immunostaining positive for HSV virus. Conclusion. Atypical HSV pseudotumors should be considered in the differential diagnosis for an immunosuppressed patient who presents with a genital mass lesion.
Optimization of the Process of Metal NanoCalcium Oxide Based Biodiesel Production through Simulation Using SuperPro Designer
Authors: Surakasi Raviteja , Gogulamudi Balakrishna , Naveen Krishna Alla , M Raja Ambethkar , Patil Pravin P. , Jayappa Pradeep
Published: July 2022

This study evaluates improvements made to a biodiesel production process from Chlorella sp. micro algae in a locomotive pilot plant using simulation. Energy and the main variables of the operation such as temperature, reaction time, alcohol molar concentration, vegetable oil, and use of homogeneous and heterogeneous catalysts and their concentration, mixing intensity, and moisture control were collected from operational data, and mass balances were tested in the SuperPro Designer retail package v.9.5. The result was an increase in the efficiency of the process of obtaining company biodiesel from 86% to 92% by volume, the same that were scaled taking into account the species’ production locality, and the results obtained showed that 26% was met by obtaining 10 MM (millions) of liters of biodiesel from the scaled plant.
Awakening Effect of Transcranial Magnetic Stimulation with Multimodal Magnetic Resonance Imaging under Three-Dimensional Reconstruction Algorithm Combined with Wake-Up Nursing on Patients with Massive Cerebral Infarction
Authors: Chen Bocan , Li Li , Ji Xiaoxia
Published: July 2022

This study was aimed to provide arousal treatment for disturbance of consciousness in patients with massive cerebral infarction, using multimodal magnetic resonance imaging (MRI)-assisted transcranial magnetic stimulation (TMS) under three-dimensional reconstruction algorithm combined with wake-up nursing. The application effect was also evaluated. 80 patients with massive cerebral infarction were selected as the research objects. These patients were divided into the control group (routine nursing and TMS) and the experimental group (routine nursing, multisensory stimulation wake-up nursing, and TMS) according to the even- and odd-numbered admission orders. There were 40 cases in each group, and the treatment effects of the two groups were compared and analyzed. The peak signal-to-noise ratio (PSNR) (800 dB) of the bilateral filtering algorithm was higher than that of the wavelet threshold denoising (321 dB) and the nonlocal mean filtering algorithm (455 dB). The segmentation accuracy of the improved region growing method/fuzzy spatial clustering algorithm (96.21% and 97.22%) was higher than that of the unimproved ones (82.11% and 79.99%). The Glasgow Coma Scale (GCS), Coma Recovery Scale-Revised (CRS-R), and Dysfunction Scale (DFS) scores of the experimental group were significantly higher than those of the control group 1 week and 2 weeks after treatment (

P
<
0.05

). The awakening rate of patients in the experimental group (95%) was also significantly higher than that in the control group (72.5%), and the time needed for waking up was (2.28 ± 2.92) hours, lower than that in the control group (4.34 ± 3.49) hours (

P
<
0.05

). The three-dimensional reconstruction algorithm could effectively improve the display effect of MRI images and assist in the examination of diseases. Multisensory stimulation wake-up nursing combined with TMS could promote patients to wake up more quickly and help the recovery of brain function of patients in the treatment of massive cerebral infarction and disturbance of consciousness.
DNA Genetic Algorithm in Digital Intelligent Design of Environmental Art
Authors: Jiang Wanlu
Published: July 2022

In recent years, digital technology has been widely used in various fields, and the concept of environmental art design has naturally changed, moving closer to digital technology. Genetic algorithm is a way to find approximate optimal solutions by simulating natural evolutionary processes and can solve various problems in the environmental art design process. Based on genetic algorithm, this article proposes a digital environmental art design based on DNA genetic theory. Unlike traditional design methods that generally only generate one design scheme, genetic algorithms can generate large-scale schemes and calculate the optimal scheme among many schemes. Through three interrelated experiments ranging from simple to complex, the results demonstrate the feasibility of the model proposed in this article. The construction elements and important links of environmental art design integrated with digital technology provide a good reference for the research on digital environmental art design based on DNA genetic theory.
Influence of Three-Dimensional Visual Reconstruction Technology Combined with Virtual Surgical Planning of CTA Images on Precise Resection of Liver Cancer in Hepatobiliary Surgery
Authors: Zhao Yuanyu , Chen Ting , Wang Hui , Xue Qiang , Guo Wenyuan , Ding Guoshan , Dong Junfeng , Ji Junsong
Published: July 2022

Hepatobiliary malignancies, such as hepatocellular carcinoma (HCC) and biliary tract cancers, namely, gallbladder carcinoma and cholangiocarcinoma, are linked to a high rate of morbidity and mortality, depending on the phase of the disease. The intricate hepatobiliary anatomy and the need for accurate peroperative management, especially in patients with advanced liver disease, make these tumors difficult to treat. Surgical resection is a notable therapy for hepatobiliary cancers. Unnecessary or excessive liver excision influences patient rehabilitation, normal liver function, and postoperative complications. Hepatobiliary operations must therefore include accurate liver removal. The present advancements in imaging technology are aimed at improving the diagnostic efficacy of liver injury even more. Three-dimensional visual reconstruction is becoming more important in the diagnosis as well as treatment of a variety of disorders. In this paper, we proposed a novel three-dimensional visual reconstruction technology using enhanced nonuniform rational basis spline (ENURBS) combined with virtual surgical planning of Computed Tomography Angiography (CTA) images for precise liver cancer resection. The purpose of this project is to rebuild 2D CTA scan images of liver cancer into a 3D reconstructed model for efficient visualization and diagnosis of liver cancer and to prepare an effective preoperative surgical plan for precise liver excision based on a 3D recreated liver model. This method’s performance is compared to that of 2D planning in terms of accuracy and time taken to complete the plan. It is concluded that our proposed technique outperforms the planning technique based on 2D images.
Effects of Combining Cognitive Behavioral Therapy with Bilateral Upper Limb Training in Stroke Patients: A Randomized Controlled Trial
Authors: Choi SoEun , Kim DeokJu
Published: July 2022

CBT has a beneficial effect on depression and anxiety; however, the number of cases where therapy was used in patients with stroke was rare. In addition, there is still a lack of research on the effects of occupation-based training and the effects of bilateral upper limb training that provides an intervention based on patients’ state of hemiplegic upper limb function. This study investigated the effects of combining CBT and occupation-based bilateral upper limb training on the depression, anxiety, upper limb function, and occupational performance. The experimental group was given 30-min cognitive behavioral therapy and occupation-based bilateral upper limb training, while the control group was given 30-min conventional occupational therapy and occupation-based bilateral upper limb training. For both groups, the intervention was given as a 30-min session once a day and five times a week for 4 weeks. Following intervention, the experimental group showed significant within-group variance for automatic thoughts, depression, anxiety, upper limb function, and occupational performance only (

p
<
0.01

). For between-group variance, a significant difference was found for automatic thoughts, depression, anxiety, and occupational performance (

p
<
0.01

), however, not for upper limb function (

p
>
0.05

). In this study, it is meaningful that this author provided good guidelines for therapists and caregivers by organizing and providing actual programs in a very rare situation where cognitive behavioral therapy was applied to stroke patients.
A Novel Short-Term Power Load Forecasting Method Based on TSNE-EEMD-LSTM
Authors: Jiang Mingkun , Jiang He , Zhao Yan , Hu Chenjia , Xu Jian
Published: July 2022

In this paper, a novel power load forecasting model is proposed to fully extract the periodic characteristics of short-term load at various time scales and explore the potential correlations between influencing factors and characteristics of load components. Firstly, the t-distributed stochastic neighbor embedding algorithm is used to map sample points of high-dimensional load influencing factors to low-dimensional space, and the ensemble empirical mode decomposition algorithm is employed to split the historical load curve into multiple signal components with different frequencies. Then, several long short-term memory networks including nonlinear mapping and time series models are established to mine the relationship between low-dimensional comprehensive influencing factors and each intrinsic mode function component by utilizing different inputs. Finally, the effectiveness of the hybrid model is verified via using the short-term load dataset of 3-hour data granularity in a certain region, and the influence of key parameters of the model on the forecasting effect is discussed.
A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images
Authors: Nazir Muhammad Saquib , Khan Usman Ghani , Mohiyuddin Aqsa , Al Reshan Mana Saleh , Shaikh Asadullah , Rizwan Muhammad , Davidekova Monika
Published: July 2022

Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct diagnoses. The purpose of this study is to develop a unique hybrid technique to identify breast cancer mass pictures as benign or malignant. The combination of two networks helps accelerate the categorization process. This study proposes a novel-based hybrid approach, CNN-Inception-V4, based on the fusing of these two networks. Mass images are used in this research from the CBIS-DDSM dataset. 450 images are taken for benign, and 450 images are used for malignant. The images are first cleaned by removing pectoral muscles, labels, and white borders. Then, CLAHE is used to these images to improve their quality in order to produce promising classification results. Following preprocessing, our model classifies cancer in mammography pictures as benign or malignant abnormalities. Our proposed model’s accuracy is 99.2%, with sensitivity of 99.8%, specificity of 96.3%, and F1-score of 97%. We also compared our proposed model to CNN, Inception-V4, and ResNet-50. Our proposed model outperforms existing classification models, according to the results.
Description and Dynamic Analyses of the 1935 Luchedu Rock Avalanche in Sichuan, China
Authors: Cui Jie , Gao Chunyu , Zhang Zhilong , Xiang Guifu
Published: July 2022

The Luchedu rock avalanche (LRA) that occurred in 1935 at the junction of Sichuan and Yunnan in Southwest China is a disaster chain of the rock slide, debris avalanche, and river blocking induced by heavy rainfall. The rock slide originated from the wedge formed by sheet metamorphic rocks on the fault zone on the left bank of the Jinsha River. After breaking away from the slope, the wedge disintegrated and cracked rapidly, forming debris and causing them to flow in fluidization along a 7.3 km path. After the obvious entrainment process and curve superelevation in the proximal area, the deposition reached

50.5
×

10

6

 m3 in the distal area. The sedimentary structure of clasts has typical stratification characteristics. The types of sedimentary facies include carapace facies, body facies, and mixed facies. Inside the sedimentary structure, dense shear zones, liquefied intercalations, jigsaw structures, and directional arrangement of particles are observed. Through a detailed geological survey and DEM spatial analysis, the avalanche entrainment rate of LRA was determined as 1.2. The morphological fluctuation of basement lithology and gully path plays an important role in the long-distance movement. The rock fragments formed by metamorphic rocks with a primary schistosity structure can effectively reduce the energy loss in the internal shear process and significantly promote the laminar flow movement in the distal region of the avalanche. Therefore, as a giant rock avalanche formed by the evolution of specific metamorphic rocks, LRA results from the combination of macrohydrodynamics and microrock failure properties.
Implementation of Artificial Intelligence Image Emotion Detection Mechanism Based on Python Architecture for Industry 4.0
Authors: Jinnuo Zhu , Goyal S. B. , Tesfayohanis Miretab , Omar Yahye
Published: July 2022

Since the beginning of the 21st century, with the development of information technology, researchers in various fields have gradually increased their research on human emotion and behavior. The current research mechanism used in emotion and behavior research is artificial intelligence technology. Through the literature survey and data analysis in related fields, it is found that the acquisition of human emotions and behaviors will be carried out through facial feature algorithm for point capture and combined with machine learning for output detection and analysis. Among them, the detection process requires machine learning of artificial intelligence first. This paper firstly analyzes and summarizes the advantages of Python programs at this stage and completes the preliminary work of system construction by setting and installing platform parameters. In the research process, this paper uses the existing algorithm to apply the σE value algorithm to the samples and conducts preliminary tests. The overall detection values in the test data are relatively average, and there are still differences in the samples. At the same time, we compare the UE and TE detection algorithms according to the output Y value of the algorithm in the machine learning. The detection rate of some emoticons in the UE algorithm is high, but the detection rate of other emoticons is low. Finally, according to the limitation of the output method in the mathematical formula, a new algorithm σx of taking the weighted sum and taking the logarithm and then taking the square root is proposed again. According to the statistical analysis, the overall average value of the final algorithm has been improved, and the overall detection rate is about 80%; compared with the TE and UE algorithms, the overall detection frequency fluctuates less. The σx algorithm in the frequency fluctuation data table in the paper is also superior to the existing algorithms in machine learning, sample testing, and data in the frequency fluctuation. Our next direction will be to use the Python main program to perform AI automatic facial emotion detection work by combining the new algorithm σx with the V value, DWT, and CNN algorithm in the facial recognition feature through machine learning.
Reputation Detection for Information Diffusion in Social Network Systems
Authors: Zhou Yifeng , Yu Fang
Published: July 2022

Reputation detection in traditional distributed systems (e.g., electronic commerce systems and peer‐to‐peer systems) relies heavily on the factor of interaction reputation, which can be derived from direct interactions between agents through bidirectional relationships. However, in the current information diffusion in social network systems (SNS) (e.g., Twitter), the characteristic of the unidirectional relationship between agents and the decay property of diffusion will result in lacking direct interactions; therefore, interaction reputations will be difficult to be obtained by agents in a distributed manner. To solve this problem, a novel distributed reputation detection model following the pattern “from path to individual” (FPTI) is proposed, which can provide a new reputation factor as an alternative to interaction reputation in such environments. The main idea is that the positive (or negative) observation of an information diffusion process increases (or decreases) the belief of the corresponding diffusion path, which further increases (or decreases) the reputation of each involved agent. Thus, the reputation of a target agent can be assessed by the superimposition of reputations of multiple paths on which this agent has participated in past information diffusion processes. Furthermore, being aware of agent’s limited capacity for reputation detection in SNS, we then propose theenhanced FPTI model(eFPTI), which simplifies the detection source to reduce detection costs and achieve the approximate performance as FPTI. Theoretical analyses and experimental evaluations validate the efficiency and effectiveness of our models and also show several properties of the models, for example, the robustness for dynamic environments.
Enhanced Biodegradation of Battery-Contaminated Soil Using Bacillus sp. (MZ959824) and Its Phytotoxicity Study
Authors: Raag Harshavardhan P. , Subbaiyan Anandakumar , Vasavi U. , Thirumoorthy Pradeep , Periyasamy Mageshkumar , Jesteena Johney J. , Ragunathan R. , Pichaipillai Sudha , Velusamy Sampathkumar , Balamoorthy Dhivya
Published: July 2022

Batteries that have been used and thrown away are potential threats to the environment. The aim of the present study is to explore the bacterial bioremediation of the battery-contaminated soil. The battery contaminated soil sample was collected from the municipal compost yard, Vellalore, Coimbatore, Tamil Nadu, India. The Bacillus sp was isolated by the serial dilution method. The Bacillus strain was identified based on the colony morphology as well as the 16s ribosomal ribonucleic acid partial gene sequence and was designated the name HVRCBNR. It was deposited in the GenBank under the accession number Bacillus sp MZ959824. The bacterial growth was evaluated by measuring the optical density of the media (OD600), while the degradation was determined by FTIR analysis. The phytotoxic analysis was performed using Trigonella foenum-graecum to assess the toxicity of the battery waste before and after bacterial treatment. The spectroscopic study showed that the strain HVRCBNR achieved 83.6% degradation. The growth indexes of Trigonella foenum-graecum showed that the biodegraded soil was nonphytotoxic in comparison with the control. This study supports the degradability of the strain HVRCBNR, and this could pave a way for sustainable solution to battery contaminated soil treatment.
Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision
Authors: Yang Ruizhu , Wang Ximing , Yu Miao , Hao Ping , Cao Xu , Liu Quan
Published: July 2022

To solve the problems existing in the traditional algorithm, such as image quality, the ability to predict the coordinate of the motion fat reduction image, the Intersection over Union (IOU) of the estimated coordinate of the landmark, the peak signal-to-noise ratio (PSNR) of the image, and the recognition accuracy of the image landmark, an automatic recognition algorithm for fat reduction motion image landmark using computer vision is proposed. The OV7725 camera is used to obtain the fat reduction motion image, and the histogram equalization method is used to enhance the fat reduction motion image. After that, the estimated coordinates of the landmark are obtained by the automatic recognition model of the landmarks of the fat reduction motion image. According to the coordinates of the landmark, after using a field programmable gate array to segment the image of the fat reduction motion image, the incremental updated freeman chain code extracts the contour of the landmark of the fat reduction motion image through 8-neighborhood retrieval, so as to realize the automatic recognition of landmark. The results show that the fat reduction motion image enhanced by the algorithm is clear and the contrast is obvious. The ability to predict the coordinates of the landmark of the fat reduction image is strong. The IOU of the estimated landmark coordinates is about 0.96. The processed image has a high PSNR, which can effectively recognize the landmark of the fat reduction motion image, and the recognition accuracy is about 97%, which has certain value in the field of image recognition and can improve the fat reduction movement normality.
Signature for Prostate Cancer Based on Autophagy-Related Genes and a Nomogram for Quantitative Risk Stratification
Authors: Wen Chenghao , Ge Qintao , Dai Bangshun , Li Jiawei , Yang Feixiang , Meng Jialin , Gao Shenglin , Fan Song , Zhang Li
Published: July 2022

Background. Prostate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare. Method. We obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the “maftools” R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016. Result. Six autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures. Conclusion. The autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.
Risk Evaluation and Forecast Behavior Analysis of Supply Chain Financing Based on Blockchain
Authors: Rao Qixuan , Li Weibin
Published: July 2022

With the deepening of globalization and the improvement of the fineness of production division and cooperation, various forms of suppliers coexist, and the supply chain network becomes complex and fragmented. Traditional supply chain management mode and purchasing mechanism cannot respond to the changes of the times in time, and enterprises and suppliers cannot connect. As a new type of financial service, supply chain finance plays an important role in solving the financing difficulties and expensive problems of SMEs. However, in recent years, traditional supply chain finance has encountered many problems, and it has gradually become a trend to empower traditional supply chain finance with blockchain technology. This paper proposes a blockchain supply chain financing risk assessment and behavior prediction algorithm. By analyzing the development status of supply chain business, it puts forward corresponding preventive measures to effectively improve the efficiency of supply chain financing. Firstly, the paper puts forward the double-chain management system and constructs the double-chain management architecture and operation flow; Thirdly, the weighting method of enterprise financing risk evaluation index based on variance homogeneity test is put forward to explain; Finally, five enterprises are tested on the data set, and the performance of TPR, FPR, Precision, Recall, F-Measure, and Accuracy is compared. Different enterprises have the problem of supply chain financing risk.
Indoor Art Display System under Computer Virtual Reality Visual Video Communication Transmission Signal
Authors: He Ping , Li Guijun , Hou Chenglong
Published: July 2022

This paper proposes a network video transmission system for indoor art display systems and visual video monitoring. Due to the problems of high mean square error, poor signal capture performance, and long timing jitter in current optical transmission signals, this paper proposes a low-rate narrowband IoT long-distance communication synchronization capture algorithm and optimizes the algorithm. The algorithm improves the visual center processing algorithm with high computational complexity into a simple look-up table calculation. This can simplify the calculation process and improve the system’s operating speed. Finally, the paper implements a video communication indoor art display system with compatible multiple network interfaces based on high-speed multimedia DSP.
Fruit Target Detection Based on BCo-YOLOv5 Model
Authors: Yang Ruoli , Hu Yaowen , Yao Ye , Gao Ming , Liu Runmin
Published: July 2022

After the birth of deep learning, artificial intelligence has entered a vigorous period of rapid development. In this process of rising and growing, we have made one achievement after another. When deep learning is applied to fruit target detection, due to the complex recognition background, large similarity between models, serious texture interference, and partial occlusion of fruits, the fruit target detection rate based on traditional methods is low. In order to solve these problems, a BCo-YOLOv5 network model is proposed to recognize and detect fruit targets in orchards. We use YOLOv5s as the basic model for feature image extraction and target detection. This paper introduces BCAM (bidirectional cross attention mechanism) into the network and adds BCAM between the backbone network and the neck network of the YOLOv5s basic model. BCAM uses weight multiplication strategy and maximum weight strategy to build a deeper position feature relationship, which can better assist the network in detecting fruit targets in fruit images. After training and testing the network, the map BCo-YOLOv5 network model reaches 97.70%. In order to verify the detection ability of the BCo-YOLOv5 network to citrus, apple, grape, and other fruit targets, we conducted a large number of experiments BCo-YOLOv5 network. The experimental results of the BCo-YOLOv5 network show that this method can effectively detect citrus, apple, and grape targets in fruit images, and the fruit target detection method based on BCo-YOLOv5 network is better than most orchard fruit detection methods.
Molecular Characterization and Antimicrobial Resistance of Pathogenic Escherichia coli Strains in Children from Wolaita Sodo, Southern Ethiopia
Authors: Wolde Amanuel , Deneke Yosef , Sisay Tesfaye , Mathewos Mesfin
Published: July 2022

Introduction. Pathogenic Escherichia coli strains cause diarrheal infection in children due to their virulence factors. A nonanalytical observational study followed by a purposive sampling technique was conducted from October 2017 to June 2018, to determine the antimicrobial susceptibility patterns and molecularly detect pathogenic Escherichia coli strains in under-five children at Wolaita Sodo town using molecular and the Kirby-Bauer disc diffusion method. Result. In the current investigation, out of 110 stool samples, Escherichia coli was isolated in 68 (61.8%) (95% CI: 52.1–70.9%). Out of 68 Escherichia coli isolates, 61.9% of Escherichia coli isolates were resistant, 9.4% were intermediately resistant, and 28.7% were susceptible. Among the antimicrobial agents, 91.2% of Escherichia coli isolates were highly sensitive to ciprofloxacin followed by norfloxacin (86.8%). Virulence genes were detected in 55.9% (38/68) (95% CI 52.1%–70.9%) of isolates. The following genes were detected: stx1 (6 (8.8%)), stx2 (4 (5.9%)), eaeA (5 (7.4%)), eaat (14 (20.5%)), and St (4 (5.9%)). Out of 68 Escherichia coli isolates, 43 (63.2%) isolates had shown multidrug resistance patterns. Furthermore, 11.8% of Escherichia coli isolates had shown resistance to eight different drugs. The multidrug resistance index value of Diarrheagenic Escherichia coli pathotypes was greater than or equal to 0.4, which indicates the high risk of resistance. Conclusion. This study demonstrated important pathogenic Escherichia coli strains and multidrug resistance in isolates containing virulence genes. Wise use of antimicrobials and improving the hygienic practices amongst parents of children reduce its occurrence. Therefore, appropriate usage of antimicrobial agents should also be highly practiced in hospitals.
Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A Systematic Review and Open Research Challenges
Authors: Reegu Faheem Ahmad , Abas Hafiza , Jabbari Abdoh , Akmam Rudzidatul , Uddin Mueen , Wu Chih-Ming , Chen Chin-Ling , Khalaf Osamah Ibrahim
Published: July 2022

A distributed ledger system blockchain proves to be worthy in the domain of healthcare due to its enormous applications and benefits. Peer-to-peer devolved transactions in an allocated way make the blockchain an efficient and modern tool to be utilized in healthcare as a solution to problems and challenges. The traditional healthcare system utilizes classical approaches to manage and maintain the EHR and cross-domain implementation. Therefore, a systematic investigation is necessary to find the current research trends, challenges, and solutions to implement blockchain to address the challenges. The motivation of this study is to pave the way for future research to find more problem-specific solutions by implementing blockchain to make the healthcare system more robust. The presented systematic survey provides the visualization and graphical representation of current methodologies, challenges, and future directions. The bibliometric analysis has been performed on the published studies in Scopus from 2017 to 2021. The publication published in the first three months of 2021 in the domain of healthcare using blockchain has also been reviewed to find the latest trends in the blockchain. The study covers multiple challenges in the presented systematic literature review, especially the interoperability of blockchain-based systems in the healthcare domain. The presented study results show the top trending research topics, top-ranked authors, and top institutes focusing on blockchain around the world. The proposed study provides a baseline for future challenges and solutions related to blockchain implementation in healthcare.