bluuebunny/crossref_metadata_2025 huggingface dataset:
A Review on Trust-Based Resource Allocation in Cloud Environment: Issues Toward Collaborative Cloud
Authors: Pol Pooja Shashank , Pachghare Vinod K.
Published: September 2022

Due to the exponential rise in the usage of the internet and smart devices, there is a demand for enhanced network efficiency and user satisfaction in a cloud computing environment. Moreover, moving to the cloud systems, it mainly focuses on storage, computation and resources. Due to copious growth, there exist more challenges as well. Among those, resource allocation in cloud computing is the main study, which is essential to determine the QoS and improved performance concerning reliability, confidentiality, trust, security, user satisfaction, profits, etc. This paper plans to prepare a detailed review on trust-based resource allocation in the collaborative cloud. The cloud industry has been assessed in terms of trust-based and other important factors to produce a road plan for resource allocation. Many papers are reviewed here and give a substantial evaluation of cloud resources and their resource allocation models using machine learning and optimization models. First, this survey provides an elaborated study concerning the various cloud resources considering the performance and QoS. Eventually, it extends the research based on trust-based approaches, with the intention of motivating the researchers to focus on trust-based resource allocation on collaborative cloud computing (CCC) atmosphere.
AR Sightseeing: Comparing Information Placements at Outdoor Historical Heritage Sites using Augmented Reality
Authors: Matviienko Andrii , Günther Sebastian , Ritzenhofen Sebastian , Mühlhäuser Max
Published: September 2022

Augmented Reality (AR) has influenced the presentation of historical information to tourists and museum visitors by making the information more immersive and engaging. Since smartphones and AR glasses are the primary devices to present AR information to users, it is essential to understand how the information about a historical site can be presented effectively and what type of device is best suited for information placements. In this paper, we investigate the placement of two types of content, historical images and informational text, for smartphones and AR glasses in the context of outdoor historical sites. For this, we explore three types of placements: (1) on-body, (2) world, and (3) overlay. To evaluate all nine combinations of text and image placements for smartphone and AR glasses, we conducted a controlled experiment (N = 18) at outdoor historical landmarks. We discovered that on-body image and text placements were the most convenient compared to overlay and world for both devices. Furthermore, participants found themselves more successful in exploring historical sites using a smartphone than AR glasses. Although interaction with a smartphone was more convenient, participants found exploring AR content using AR glasses more fun.
Ubiquitous Machinery Monitoring - A Field Study on Manufacturing Workers' User Experience of Mobile and Wearable Monitoring Apps
Authors: Müller Sebastian , Baldauf Matthias , Seeliger Arne
Published: September 2022

Fueled by ongoing digitization efforts, manufacturing is currently undergoing a transformational process towards interconnected machinery and workforce, which enables a wide range of interactive monitoring and controlling applications. Whereas existing user-centered work addressed remote monitoring from office workplaces, it remains unclear how manufacturing workers experience and adopt machinery monitoring apps on mobile and wearable devices. To close this gap, we conducted a four-week field study in a running factory to study workers' overall user experience and acceptance of such monitoring apps, the subjective impact on their work routines, and their preferred device type. Under productive operation, 11 manufacturing workers used functional application prototypes on smartphones and smartwatches to receive notifications of machine incidents. In 22 individual interviews and two focus groups, we collected the participants' impressions and assessments. Based on these results, we derive a set of recommendations for designing and deploying machinery monitoring apps for manufacturing workers.
Electrochemical Study of the Electrodeposition of a Dense Nanocrystalline Fe Film from Fe(II) Ions Dissolved in an Ionic Liquid
Authors: Li Jian-Ru , Xu Cun-Ying , Lu Jing-Ling , Wang Shu-Xian , Chen Xiao , Xiang Qin-Qin , Hua Yi-Xin
Published: September 2022

The electrodeposition behavior of Fe was studied in phenyltrimethylammonium chloride-ethylene glycol eutectic-based ionic liquids (TMPAC-EG EILs) containing FeCl2 through electrochemical measurement and the deposit characterization. Some kinetic parameters (standard rate constants, transfer coefficients and activation energy for standard rate constants) as well as transport properties were obtained and discussed as function of temperature. The values of transfer coefficient (0.32–0.39) are significantly lower than conventional value (0.5). Electrodeposition of Fe follows three-dimensional progressive nucleation and growth mode, which is unaffected by temperature. Besides, it is found that deposition temperature plays a central role in controlling surface morphology and structure of the resultant Fe coatings. At low temperature (333–353 K), smooth and dense iron coating can be obtained. The phase structure changes from a nanocrystalline α-Fe to amorphous phase (metallic glass) as the deposition temperature increases from 333 K to 353 K.
Application of Mobile Information Technology in High-Intensity Resistance Training System for Cross-Country Skiing
Authors: Kang Jianxin , Zhang Peng
Published: September 2022

Cross-country skiing is one of the most important competitive events in the Winter Olympics, and it is also the earliest competitive sports event in China’s winter snow events. However, due to the influence of factors such as economic development, climatic conditions, and regional resources, cross-country skiing has developed slowly in China and is still in a relatively backward position compared with developed countries such as Europe and the United States. In this paper, the modeling and analysis of the attitude calculation system is proposed, and the attitude recognition technology is applied to the high-intensity resistance training system of cross-country skiing. Several training elements such as special endurance training goals, training content, training methods, and training monitoring are presented. It demonstrates the rationality and feasibility of the high-intensity resistance training system for excellent cross-country skiers in cross-country skiing and arranges the strengthening training of the upper and lower limbs and waist and abdominal muscles of the athletes in a targeted manner. For 6–9 times a week, each endurance training is about 1–4 hours or strength training is about 15–90 minutes. The fastest time for men is 31 minutes 29 seconds 21, which is 1 second 88 higher than that before training, and the average score has improved by 6 seconds 80; the women’s fastest time was 24 minutes 06 seconds 21, which was 4 seconds 22 higher than that before training, and the average score increased by 7 seconds 70.
Sensor Technology and Intelligent Systems in Anorexia Nervosa: Providing Smarter Healthcare Delivery Systems
Authors: Almenara Carlos A. , Cimino Silvia , Cerniglia Luca
Published: September 2022

Ubiquitous technology, big data, more efficient electronic health records, and predictive analytics are now at the core of smart healthcare systems supported by artificial intelligence. In the present narrative review, we focus on sensing technologies for the healthcare of Anorexia Nervosa (AN). We employed a framework inspired by the Interpersonal Neurobiology Theory (IPNB), which posits that human experience is characterized by a flow of energy and information both within us (within our whole body), and between us (in the connections we have with others and with nature). In line with this framework, we focused on sensors designed to evaluate bodily processes (body sensors such as implantable sensors, epidermal sensors, and wearable and portable sensors), human social interaction (sociometric sensors), and the physical environment (indoor and outdoor ambient sensors). There is a myriad of man‐made sensors as well as nature‐based sensors such as plants that can be used to design and deploy intelligent systems for human monitoring and healthcare. In conclusion, sensing technologies and intelligent systems can be employed for smarter healthcare of AN and help to relieve the burden of health professionals. However, there are technical, ethical, and environmental sustainability issues that must be considered prior to implementing these systems. A joint collaboration of professionals and other members of the society involved in the healthcare of individuals with AN can help in the development of these systems. The evolution of cyberphysical systems should also be considered in these collaborations.
A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children
Authors: Fang Yuting
Published: September 2022

The music development matching model and the quantifiable planning model have an undesirable fit between the dance produced by the model and the music self in terms of music-driven dance development age (e.g., generated dance development is lacking, and long-distance dance arrangements are lacking in perfection and discernment). The traditional methodology cannot produce new dance moves or other associated concerns. To address these concerns, we are working on a dance age estimation based on technological developments and neural networks that will eliminate the need for voice and development planning. The first stage uses the prosody elements and sound beat highlights extracted from music as music highlights, while the second stage uses the directions of essential human body issues derived from dance recordings as movement highlights. The model’s generating module acknowledges the vital planning of music and dance advancements to build a smooth dance posture in the next stage; the discriminator module acknowledges the autoencoder module agent has improved sound characteristics and the consistency of dance and music. In the third and final step, the model’s transformed form changes the dance act succession into a good diversity of dance. Finally, a reasonable rendition of the dance that matches the music has been found (e.g., trial data is gathered from online dance recordings, and the exploratory outcomes are examined from five perspectives: poor work esteem, correlation of various baselines, assessment of grouping age influence, client examination, and genuine dance recording quality assessment). The proposed dance age model has a reasonable impact on converting into actual dance recordings, according to the results.
The Optimum Lipid Level for the Juvenile Redclaw Crayfish Cherax quadricarinatus: Practical Diets with Soybean Oil as the Lipid Source
Authors: Chen Chengzhuang , Xu Chang , Yang Xiaolong , Jia Yongyi , Gu Zhimin , Li Erchao
Published: September 2022

As a new species in aquaculture, the lipid nutrition requirement for the juvenile redclaw crayfish Cherax quadricarinatus on a dietary basis on a practical formula needs to be evaluated accurately. In this study, the optimum dietary lipid level was explained by analyzing the growth performance, antioxidant state, lipid metabolism, and gut microbiota of C. quadricarinatus after an eight-week cultivation trial. Six diets with different soybean oil levels (named L0, L2, L4, L6, L8, and L10) were fed to C. quadricarinatus (

11.39
±
0.28
 
g

). The results indicated that the specific growth rate and weight gain of crayfish fed the L4 and L6 diets were significantly higher than those of the other groups (

P
<
0.05

). By the analysis of a second-order polynomial regression model according to growth performance (weight gain rate), the optimum lipid level in a practical diet for juvenile C. quadricarinatus was 9.67%. The survival, condition factor, and hepatosomatic index of crayfish were not significantly affected by dietary oil levels (

P
>
0.05

). As the level of dietary lipids increased, the total antioxidant capacity and glutathione peroxidase activity in serum showed a tendency to rise and then fall and the enzyme activity was highest in crayfish fed the L6 diet. Gut lipase and pepsin activities showed the highest value in crayfish fed the L6 diet. There was no significant difference in acetyl-CoA carboxylase and carnitine palmitoyltransferase-1 contents in crayfish among all the groups (

P
>
0.05

). The relative abundance of Proteobacteria in the phylum and Citrobacter in the genus showed a significant decrease in crayfish of the L10 diet, while the relative abundance of Firmicutes in the phylum markedly increased compared to that of the other groups (

P
<
0.05

). In summary, the results indicated that the 10.39% (L6 diet) dietary lipid level could induce better growth performance, antioxidant ability, and digestive enzyme activity. Most of the fatty acid composition of muscle is not closely related to the fatty acid composition of the diet. Moreover, the composition and diversity of the gut microbiota of C. quadricarinatus were changed by high dietary lipid levels.
Analysis of College Students’ Psychological Education Management in Public Emergencies Based on Big Data
Authors: Li Xiaoyu
Published: September 2022

In recent years, college students’ psychological problems have occurred frequently, and the early warning of college students’ psychological crisis has received social attention. Artificial intelligence and big data, as emerging technologies that have attracted much attention in recent years, have broad application and development space in improving the development of intelligent and refined education in colleges and universities. Applying artificial intelligence and big data to the practice of college students’ mental health education plays a very positive role in accurately finding and scientifically solving college students’ mental health problems. This paper combs the current application and research of artificial intelligence and big data in college students’ mental health education and then clarifies the problems existing in the practical application. Finally, on the basis of in‐depth analysis of the characteristics of college students’ psychological crisis, the paper designs college students’ psychological crisis early warning data collection system from six aspects, including the educational administration system and the access control system. And from the aspects of establishing a multilevel linkage feedback early warning system, building a team of big data technical personnel and mental health education personnel, it puts forward countermeasures for college students’ psychological crisis, so as to provide theoretical and methodological support for college mental health management.
The Quality and Composition of Iranian Low-Salt UF-White Cheese
Authors: Lavasani Alireza Shahab
Published: September 2022

In cheese, the reduction of salt is still a challenging task, as sodium chloride exerts multiple and fundamental functions. Salt favors the drainage of the residual whey; enhances the taste and the aroma profile; regulates the texture, the final pH, and the water activity; and affects the microbial growth. Hence the impact of partial replacement of NaCl by KCl on the ripening characteristics of Iranian UF (ultrafiltration) cheese during storage was monitored. To produce low-salt cheese, different mixtures of UF white cheese were treated with NaCl : KCl ratios of (a) 3% NaCl (control), (b) 1.50% NaCl+1.50% KCl, (c) 1.00% NaCl+2.00% KCl, and (d) 0.75% NaCl+2.25% KCl by dry salting. ADV (acid degree value) results showed significant differences (

P
<
0.05

) in all treatments after 15, 30, 40, and 50 days of ripening. No significant differences were observed in the GC (gas chromatography) results in the samples’ free fatty acid (FFA) profile except for C18 : 0 in all treatments. KCl did not affect the moisture, dry matter, fat, TN (total nitrogen)/dry matter, and WSN (water-soluble nitrogen) contents of cheeses considerably. The evaluation of force to fracture showed that there were significant differences (

P
<
0.05

) between treatment (d) as a control cheese and treatments (b) and (c). Sensory evaluations showed as the concentration of KCl increased, the cheese gradually became less acceptable and treatments with higher potassium chloride content were crumblier and less firm. Results of the aroma evaluation of cheese samples showed that unlike acetaldehyde, ethanol, acetoin, and diacetyl amounts decreased significantly (

P
<
0.05

) during the storage period. Results also indicated that a reduction of sodium by up to 50% did not significantly affect the quality and composition of Iranian low-salt UF-white cheese except for sensory evaluation, texture analysis, and aroma characteristics.
A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques
Authors: Al-Fahaidy Farouk A. K. , Al-Fuhaidi Belal , AL-Darouby Ishaq , AL-Abady Faheem , AL-Qadry Mohammed , AL-Gamal Abdurhman
Published: September 2022

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.
The Prevalence of Chronic Pain in the Adult Population in Israel: An Internet-Based Survey
Authors: Sharon Haggai , Greener Hila , Hochberg Uri , Brill Silviu
Published: September 2022

Background. Chronic pain (CP) prevalence in different studies has been inconsistent, ranging from 12% in Spain to 42% in the UK. Purpose. We conducted an internet-based survey in a representative cohort of Israeli adults assembled by a large professional survey company in order to probe the prevalence of CP in Israel. Methods. 8,300 Israeli adults comprising a representative cohort of the Israeli population were asked whether they were suffering from pain lasting over 3 months. 1647 participants responded (19.8% response rate). Of these, 515 (31.3%) had CP. Participants with CP were then asked a series of follow-up questions regarding their chronic pain. Statistical weights were used to correct for the distribution of the Israeli population based on sociodemographic characteristics. Results. CP patients were significantly older than respondents without pain. The average daily pain was 5.8/10 on a numerical rating scale. Common pain locations were axial skeleton and headaches. However, over half of patients reported pain in multiple body areas, and around a fifth had an undiagnosed chronic pain syndrome. Around 40% of pain patients reported to have visited a specialized pain clinic, and the same proportion has consulted several specialists. Despite this, a sizable proportion of high pain intensity patients were still left with no or inefficient treatment to alleviate their pain. Conclusions. This is the first internet survey conducted in Israel to estimate the incidence of CP, and the high CP prevalence documented is in agreement with previous reports from Europe and the USA. It also reaffirms the widespread existence of multifocal or widespread pain in clinical chronic pain and the correlation between pain intensity, impact on patients’ quality of life and disability, and pain intractability. These data reaffirm the similarly major health burden CP presents across different countries and cultures.
Efficacy of Yishen Huashi Granules Combined with Linagliptin Tablets on Blood Glucose and Renal Function in Patients with Type 2 Diabetic Nephropathy
Authors: Zhang Panke , Meng Jingxi , Duan Mingliang , Li Dan , Wang Ruixin
Published: September 2022

Objective. To probe into the efficacy of Yishen Huashi granules combined with linagliptin tablets in the treatment of type 2 diabetic nephropathy (DN) and its effect on blood glucose and renal function in patients. Methods. 70 patients with type 2 DN at our hospital between May 2020 and May 2022 were chosen as the research objects and separated into the control group and the research group based on their treatments. With 35 cases in each group, the patients treated with initial therapy and linagliptin tablets were enrolled in the control group, and those who received the above treatments and also Yishen Huashi granules were included in the research group. Their clinical indexes such as blood glucose and renal function were compared with both groups after treatment. Results. After treatment, the research group had remarkably lower fasting blood glucose (FPG), 2 h-postprandial blood glucose (2 h-PBG), and glycosylated hemoglobin A1c (HbA1c) levels than those in the control group (

P
<
0.05

). After treatment, the research group had remarkably lower levels of total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) (

P
<
0.05

) and higher high-density lipoprotein (HDL) levels (

P
<
0.05

) than those in the control group. After treatment, the urinary microalbumin (u-mALB) level was remarkably lower in both groups (

P
<
0.05

) and was distinctly lower in the research group than in the control group (

P
<
0.05

). After treatment, the research group had remarkably lower renal function indexes such as serum creatinine (SCr), blood urea nitrogen (BUN), urinary protein (UPro), and urinary albumin excretion rate (UAER) (

P
<
0.05

) and a higher estimated glomerular filtration rate (eGFR) level (

P
<
0.05

) than those in the control group. The efficacy was evaluated by the traditional Chinese medicine (TCM) syndrome score after treatment. There were no patients in complete remission between both the groups, where slight differences were found in the proportion of significant remission (

P
>
0.05

), with the total effective rate of the research group remarkably higher than that of the control group (

P
<
0.05

). Conclusion. The combination of Yishen Huashi granules and linagliptin tablets can reduce the blood glucose and blood lipid levels in patients with type 2 DN and lower UPro and protect renal function at the same time, which provides a new idea and a method for clinical treatment of type 2 DN with integrated traditional Chinese and Western medicine.
[Retracted] Chemical Structures and Pharmacological Properties of Typical Bioflavonoids in Polygonati Rhizoma (PGR)
Authors: Luo Min , Hu Zongren , Zhong Zixuan , Liu Lumei , Lin Chengxiong , He Qinghu
Published: September 2022

Most medicines are coming with toxic and detrimental side effects. In addition, microbials are resisting the medicine. Therefore, alternative drugs with low toxic and side effects and low microbial resistance are needed. Plants offer good potential candidates due to a broad range of chemicals they contain. These chemicals have been studied, and research is still going on to probe chemical properties of plant chemicals. In China, traditional Chinese medicine is practised, whereby plant extracts are obtained, and then sold in packages for reasons like memory enhancement, cancer treatment, boosting immune system, and so on. Among the herbs cultivated in China is Polygonati rhizoma (PGR). This plant contains various bioflavonoids such as diosgenin, kaempferol, catechin, daidzein, and 3′‐methoxydaidzein. In this review, we discussed the pharmacological effects of these chemicals, including luteolin antimicrobial activity in a manner that it circumvents antibiotic resistance; rutin antivenom property; kaempferol as an agent that mitigates neuropathic pain; genistein anticancer property; isorhamnetin’s ability to alleviate chronic obstructive pulmonary diseases (COPD); proanthocyanidins’ ability to deal with diabetic neuropathy and analgesic property of catechin.
Effects of Circ_0109046 Regulating Mir-338-3p on the Malignant Behavior of A2780 Cells
Authors: Zhao Yue , Diao Haidan , Li Jiangning , Guan Xin , Tian Xiaofang , Guo Wei , Zhang Baoxi , Hao Dan , Yang Jian
Published: September 2022

Objective. The objective is to explore the action and mechanism of circ_0109046 on the malignant phenotypes of ovarian cancer cells. Methods. Circ_0109046 and miR-338-3p expression were detected by quantitative real-time polymerase chain reaction (qRT-PCR). In vitro assays were conducted to investigate the action of circ_0109046 and miR-338-3p on ovarian cancer cell growth and metastasis. Western blotting was utilized to investigate the contents of apoptosis-related markers. The binding between circ_0109046 and miR-338-3p was validated using dual-luciferase reporter assay. Results. Circ_0109046 was increased, while miR-338-3p content was decreased in ovarian cancer tissues. Deficiency of circ_0109046 or the upregulation of miR-338-3p was observed to weaken cell proliferative, migratory, and invasive abilities and elevated cell apoptosis rate in ovarian cancer. Circ_0109046 targetedly suppressed miR-338-3p. Down-regulation of miR-338-3p was able to reverse the repressing impacts of circ_0109046 silencing on ovarian cancer growth and mobility. Conclusion. Circ_0109046 silencing impaired the proliferation, migration, and invasion of ovarian cancer cells through negatively regulating miR-338-3p in vitro, indicating the potential implication of circ_0109046 in ovarian cancer progression.
Asymmetric Price Volatility Transmission in Agricultural Supply Chains: Evidence from the Chinese Pork Market
Authors: Wan Xiangrong , Li Cuixia
Published: September 2022

The asymmetric price volatility transmission issue in agricultural supply chains has been ignored in the previous literature. This paper applies an asymmetrical MGARCH-BEKK model to investigate the asymmetric price volatility transmission in agricultural supply chains with an application to the Chinese pork market. Additionally, we use the Zivot–Andrews unit root test with a structural break to examine whether the piglet, hog, and pork prices have structural breaks. The results show that pork’s market prices have a structural breakpoint in 2007M03 and support the existence of the asymmetric volatility transmission in Chinese pork supply chains. Furthermore, the volatility spillover effects are different before and after 2007M03.
Effect of Amplitude and Confining Pressure on Granite Failure under Ultrahigh Frequency (UHF) Impact Based on Radiation Temperature
Authors: Zhang Cheng , Zhang Shulei , Zhao Dajun
Published: September 2022

Ultrahigh frequency impact technology has great potential to break hard rocks with lower energy consumption. Based on the infrared nondestructive testing technology, the effects of load (i.e., amplitude) and boundary (i.e., confining pressure) conditions on granite damage under ultrahigh frequency impact have been investigated to promote the application of this technology in rock engineering. Experimental results demonstrate that the evolution law of the maximum radiation temperature on granite surface reflects the granite damage state. Under ultrahigh impact, granite specimens are damaged effectively with the impact amplitude exceeding 32 μm, and the failure mode of the specimen is changed by the confining pressure. Depending on Griffith’s theory, the extension angle of the fatigue crack changes from 60 to 30 degrees with increasing confining pressure. And the transverse fracture occurs in the upper part of the specimen under ultrahigh frequency impact subjected to confining pressure. This research determines the amplitude threshold of the ultrahigh frequency impact load and verifies the effectiveness of ultrahigh frequency impact technology for granite failure subjected to confining pressure.
[Retracted] A Comprehensive Assessment of Cultivation Environment of Top Innovative High‐Level Talents Based on Deep Learning Algorithm
Authors: Zhu Wei , Qin Jin
Published: September 2022

The quality of talent has increased across all fields due to the constant growth of different industries and the growing job saturation. Real‐time job information on recruitment platforms can, therefore, accurately reflect the demand for talent from businesses, serving as a basis for the creation of training policies in schools. In international competition, the development of talents, especially top‐level talents, will become more and more crucial. Growing in importance is China’s economy and social development. The evaluation of higher vocational and technical talents, however, should also be assessed from a variety of angles, given the diversification of talent training objectives and teaching methods, as well as the expansion of teaching functions. An emerging machine learning technology called deep learning (DL) has been developed to bring machine learning closer to the goals of artificial intelligence. This essay offers a thorough evaluation of the depth of deep learning as it relates to the development of innovative talent in schools. The entire school must be strengthened. It is demonstrated that the average execution time is slashed by 0.0024 s, and the learning sample size error of the DL model is reduced by 0.05276 when compared to the Apriori method. As a result, implementing and researching the DL model can significantly improve both the overall teaching quality of schools and their capacity for innovation.
The Symmetry Effect: Symmetrical Shapes Increase Consumer’s Health Perception of Food
Authors: Li Shan , Liang Jiarong , Zhou Shoujiang , Kang Qi
Published: September 2022

Focusing on the physical appearance of the food itself, and limiting the focus on shape to the concept of symmetry, this research investigated how food shape influences consumer perceptions of healthiness and naturalness and their subsequent food preferences. By conducting three empirical studies involving self-reported preference and trade-off choices, this research verified that it is a two-staged process of naturalness and healthiness that mediates the main effect of symmetry on foods preference. Furthermore, the incremental perceived unhealthiness of food would mitigate the positive effect of food symmetry. These findings are meaningful for food marketing managers and policymakers when making food-related decisions.
Assessment of Bacterial Diversity of Industrial Poultry Wastewater by Denaturing Gradient Gel Electrophoresis (DGGE) and the Cultivation Method in Order to Inform Its Reuse in Agriculture
Authors: Oueslati Amira , Hassen Wafa , Ellafi Ali , Alibi Sana , Jaziri Ahlem , Bachkouel Sarra , Oueslati Imen , Snoussi Mejdi , Adnan Mohd , Patel Mitesh , Elasbali Abdelbaset Mohamed , Mansour Hedi Ben
Published: September 2022

Effluents discharged by poultry meat industries are heavily polluted with raw materials, such as fat, blood residues, and proteins. Thus, untreated effluents directly discharged into the environment may constitute a public health threat. This study aims to evaluate the bacterial diversity of three water qualities: industrial poultry wastewater (PWW), tap water (TW), and PWW diluted with TW (50 : 50) (V/V) (TWPWW) by the combination of culture‐independent and culture‐dependent approaches. The total bacterial DNA was extracted using phenol/chloroform method. The hypervariable 16S rRNA region V3‐V5 was amplified by PCR using universal primers. The amplicons were separated by vertical electrophoresis on a polyacrylamide gel of increasing denaturing gradient according to their richness in GC bases. Selected bands were reamplified and sequenced. Pure isolated bacteria from nutrient agar medium were characterized according to their morphological and biochemical characteristics. Genomic DNA from pure strains was extracted by boiling method, and a molecular amplification of the 16S–23S ITS region of the 16S rRNA gene was performed using the universal primers. Selected isolates were identified by sequencing. Results showed a high bacterial load and diversity in PWW in comparison with TW and TWPWW. A collection of 44 strains was obtained, and 25 of them were identified by sequencing. Proteobacteria represented 76% of isolated bacteria Gamma-Proteobacteria was the predominate isolate (68%). Other isolates were Firmicutes (8%), Bacteroidetes (12%), and Actinobacteria (8%). These isolates belong to different genera, namely, Pseudomonas, Acinetobacter, Proteus, Empedobacter, Corynebacterium, Enterobacter, Comamonas, Frondibacter, Leclercia, Staphylococcus, Atlantibacter, Klebsiella, and Microbacterium.
The Influencing Factors of Spatial Layout of SKI Venues in China and the Influence of Internet of Things Technology on Their Structural Optimization
Authors: Zheng Jiaqi , Bai Yunchao , Lin Xianpeng
Published: September 2022

Skiing is currently the most popular winter outdoor recreation in China, thanks to the progressive growth of the ski sector. The construction of ski resorts would assist mountain communities economically. The Chinese ski sector is now booming, with the number of ski resorts expanding considerably. The geographical structure and driving causes for these ski regions, however, are poorly understood. Applying nearest neighbor ratio (NNR) analysis and the Spatial Lorentz Curve, this research examines the spatial patterns of these venues in detail. Kernel density estimation is used to identify hotspots (KDE). Furthermore, a regression analysis was used to discover the characteristics that influence the spatial distribution. We also look at the impact of the Internet of Things (IoT) on venue structural optimization. While China’s ski regions were regionally distributed, the results show that ski areas are much a higher chance of being found at latitudes (northeast and northwest China) than at latitudes (central and south China). The key elements that affect the distribution of resorts vary by location and ski resort type. The implications for the ski resorts sector are explored, including the varied practices for cold and hot spot regions of China’s ski areas, as well as the ski industry’s future development orientation.
Abandon Policies for Two Types of Multiattempt Missions
Authors: Feng Yuejiao
Published: September 2022

Systems that perform critical missions will often be affected by internal degradation until they reach a failure state. For safety critical systems that perform certain tasks, failure of the systems will have serious consequences. In such scenarios, the survival of the systems has a higher priority than the completion of the task. The task can be suspended at an appropriate time and a rescue procedure can be initiated to reduce the risk of system failures. When the task is important, the systems can try to execute the task multiple times after the task is abandoned and the rescue is completed, to improve the probability of task completion. This study further expands the existing research on multiple task abandon strategy by proposing degradation‐based multicriteria mission abandon policies considering multiattempts and two types of task success criteria. The task is abandoned dynamically based on the degradation level and time in the mission in each attempt. Under the dynamic abandon policies, mission reliability, and systems survivability are evaluated using the recursive method. The optimal abandon thresholds are investigated numerically.
Study on Health Care Service Standard of Chronic Disease Management Based on Process Reengineering and the Delphi Method
Authors: Zhao Jing
Published: September 2022

The Delphi method is also called an expert consultation method, scientifically measuring the objectivity, practicality, and authority of the metric. This study aims to build a standard plan for chronic disease management care services in hospitals and guide the development of chronic disease management care services. This article proposes a chronic disease management nursing experiment, further revises the current hospital chronic disease management nursing service, redesigns the program, compiles the chronic disease management hospital nursing standard list according to the new program, and uses expert suggestions to modify the standard program items. It adopts a preliminary expert inquiry table, submits Delphi expert inquiry, and constructs a chronic patient prevention strategy for clinical nursing practice to learn from the reference for guiding the actual specific situation of nursing, providing evidence basis and practice guidance. More than 40 initial standards were listed, which were reduced to 30 after revision. The importance of the indicators to different experts was 3.56 to 4.85 points. The coordination coefficient is 0.180~0.356. Finally, after discussion by all parties, more than 40 specific indicators including the size of the new standard have been determined, which provides a great theoretical basis for the establishment of a new type of hospital chronic disease management and nursing service standard in my country. Based on process reconstruction and the Delphi method determined by the hospital chronic disease management nursing service standards, clear normative and operability can provide basis for the development of chronic disease management nursing services.
Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting
Authors: Li WenQiang , Zhang Chang
Published: September 2022

Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.
Analysis of Professional Psychological Adaptability of Students Majoring in Hotel Management and Digital Operation for Higher Vocational Education under Deep Learning
Authors: Li Juan
Published: September 2022

Under the background of rapid and comprehensive development of society and technology, the accommodation industry has shown a relatively prosperous development trend, which has promoted the reform and development of the hotel industry. The scale, quantity, and mode of hotel enterprises have undergone great changes, and there has been a structural surplus. Among them, the most obvious change is the integration of digital technology, which integrates technology and hotel services to form a new hotel format. Combined with a series of policy encouragement trends promulgated by the state, the hotel management and digital operation majors in higher vocational education are more targeted for the cultivation of talents, which can cultivate talents with innovative ability and industry skills in the hotel industry. In this context, it is very important to carry out an adaptive analysis on the professional psychology of students majoring in hotel management and digital operations, which can tap their potential and promote the improvement of teaching quality. Aiming at this subject, this work proposes a neural network (PPANet) for analyzing the psychological adaptability of students in this major. Aiming at the problem that CNN cannot capture the periodicity and trend of data, this work proposes a multiscale deep residual network. The network adds convolution kernels with different scales to learn data features and enhances generalization ability and improves accuracy through residual short-circuit structure and deep structure. Furthermore, based on ReLU, this work proposes an improved IReLU activation function with better learning properties. The systematic experiment verifies the feasibility of applying PPANet to the analysis of professional psychological adaptability of students majoring in hotel management and digital operations.
Potential Functional Food Products and Molecular Mechanisms of Portulaca Oleracea L. on Anticancer Activity: A Review
Authors: de Souza Pâmela Gomes , Rosenthal Amauri , Ayres Ellen Mayra Menezes , Teodoro Anderson Junger
Published: September 2022

Portulaca oleracea Linn. (P. oleracea L.) has recently gained attention as a functional food due to the chemical composition of this plant regarding bioactive compounds. The special attention to the use of P. oleracea as an ingredient in functional food products is also due to the promotion of sustainable food. It is an unconventional food plant, and its consumption may contribute to preserving biodiversity due to its cultivation in a polyculture system. Food sovereignty may be achieved, among other strategies, with the consumption of unconventional food plants that are more resistant in nature and easily cultivated in small places. P. oleracea grows spontaneously and may be found in streets and sidewalks, or it may be cultivated with seeds and cuttings propagation. The culinary versatility of P. oleracea opens up opportunities to explore the development of sustainable, functional food products. This mini-review shows that functional food products developed from P. oleracea are already available at the research level, but it is expected that more scientific literature focusing on the development of P. oleracea functional products with proven anticancer activities may be released in the near future. Polysaccharides, some phenolic compounds, alkaloids, and cerebrosides are associated with the inhibition and prevention of carcinogenesis through in vitro and in vivo investigations. The anticancer activities of P. oleracea, its bioactive compounds, and the involved molecular mechanisms have been reported in the literature. The importance of further elucidating the cancer inhibition mechanisms is in the interest of forthcoming applications in the development of food products with anticancer properties for implementation in the human diet.
Influence Analysis of Hotel and Tourism Economic Development Based on Computational Intelligence
Authors: Wei Canhui , Li Jiayu , Guo Xiya
Published: September 2022

As the main infrastructure of the tourism industry, the hotel industry is an important part of the tourism industry and one of the important symbols of the development level of the tourism industry. In recent years, with the prosperity and development of the hotel industry, it can not only effectively solve many social work problems but also contribute to the further development of the region’s economy. In this paper, the convergence model and fitness function of the GSO-MCM algorithm are evaluated, and the optimal adaptive threshold of the particle swarm is given. The impact of computational intelligence on the development of hotel and tourism economy is analyzed, and the accuracy of computational intelligence method, data mining method, and fuzzy statistical method are evaluated and compared. The computational intelligence method shows better performance. The domestic tourism revenue is predicted using computational intelligence and compared with the actual value, which shows a good prediction effect. The hotel industry, an important tourism infrastructure, is an important symbol of the level of development of the tourism industry. In recent years, the rise of the hotel industry has not only effectively addressed many of the company’s labor problems but also made great contributions to the region's economic growth. With this in mind, we are conducting pilot studies on data collection.
Statistical Inference to the Parameter of the Inverse Power Ishita Distribution under Progressive Type‐II Censored Data with Application to COVID‐19 Data
Authors: Elnagar Kariema A. , Ramadan Dina A. , El-Desouky Beih S.
Published: September 2022

The goal of the article is the inference about the parameters of the inverse power ishita distribution (IPID) using progressively type‐II censored (Prog–II–C) samples. For IPID parameters, maximum likelihood and Bayesian estimates were obtained. Two bootstrap “confidence intervals” (CIs) are also proposed in addition to “approximate confidence intervals” (ACIs). In addition, Bayesian estimates for “squared error loss” (SEL) and LINEX loss functions are provided. The Gibbs within Metropolis–Hasting samplers process is used to provide Bayes estimators of unknown parameters also “credible intervals” (CRIs) of them by using the “Markov Chain Monte Carlo” (MCMC) technique. Then, an application of the suggested approaches is considered a set of real‐life data this data set COVID‐19 data from France of 51 days recorded from 1 January to 20 February 2021 formed of mortality rate. To evaluate the quality of the proposed estimators, a simulation study is conducted.