bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Bovine Bone Marrow Derived Mesenchymal Stem Cells and Immunostaining of Differentiated Neurospheres
Authors: Dikmen T , Özden Akkaya Ö , Nawaz S , Erdoğan M
Published: April 2024

Stem cell differentiation has been a key element for the therapeutical applications for various cases. The novel anti-inflammatory and angiogenic findings and potential applications for both autologous and allogenic studies have emphasized about the usage of mesenchymal stem cells in the clinical trials. The neurosphere differentiation studies and their role for peripheral nerve injuries have gained momentum. Following study emphasized on the isolation of stem cells from bovine bone marrow, their characterization by PCR and their differentiation into mesodermal lineages and into neurospheres under specific induction media. The generated neurospheres were durther investigated for their potential expressions of neuroprogenitor markers Sox2, Nestin and neurofilament protein β-III Tubulin by immunofluorescence staining. The results of following study highlight the use of bone marrow derived mesenchymal stem cells for translational research and regenerative therapy.
Impact of stocking density and dietary nano-zinc supplementation on stress indicators, immunity, and DNA damage in broiler chickens
Authors: Sevim Ö , Boyacıoğlu M , Yılmaz ÖB , Aydın AK , Özdemir ÖS , Reman Temiz A , Khamseh EK , Ahsan U , Kuter E , Tatli O , Yıldırım EK
Published: April 2024

Effects of stocking density (SD) and nano zinc on oxidative status, immune function, and DNA damage in broilers were examined in the current study. Totally 480 one-day-old male broilers (Ross 308) were randomly divided into 4 treatments each contained 8 replicates.  A 2 ´ 2 factorial arrangement with two groups for dietary zinc (Zn) form (inorganic or nano) and two groups for SD (low = 12 birds/m2 and high = 18 birds/m2) was conducted. Basal diets based on the corn-soybean meal were formulated. Relative liver weight was lower (P<0.05) in broiler subjected high stocking density (HSD) but other lymphoid organ weights did not affect. Relative lymphoid organ weights were not affected by dietary nano zinc. HSD increased the serum corticosterone level of broiler (P<0.05) but did not affect the blood heterophil:lymphocyte ratio, serum glutathione, superoxide dismutase, and malondialdehyde levels. Dietary nano zinc had no effect on these stress indicators. Serum interferon-gamma, interleukin 12, and interleukin 18 levels were not affected by HSD. Dietary nano zinc tended to reduce the interferon-gamma level (P=0.05) but did not affect interleukin 12, and interleukin 18 levels in broiler. Eeither HSD or nano zinc had no effect on DNA damage in lymphocyte. In conclusion, HSD may decrease the relative lymphoid organ weights and increase the stress indicators but did not affect the immunity and DNA damage. Generally, dietary nano zinc had no effect on stress indicators, immunity, and DNA damage in broiler.
Impact of Dietary Supplementation of Vitamins C and E on the Productive Traits, Egg Quality and Immune Response of Guinea Fowl in Al-Ahsa Province, Saudia Arabia
Authors: Abdulmohsen M , Al-Rasheedb MI , Shawky M , Abdel-Raheem SM
Published: April 2024

Guinea fowl production makes significant contributions to animal protein through producing cheap meat and eggs, which serve as a buffer to shortages of poultry products. This study aims to e the effect of vitamin C, vitamin E solely and together on laying performance, egg quality and some blood parameters of Guinea fowl. A total of 180 black guinea fowls at eight months of age were equally divided into four groups, 45 birds, each.  Each group was subdivided into 3 replicates, each one consists of 15 birds. The dietary treatments include the control group (basal diet); basal diet plus 500 mg /kg vitamin C (T1); basal diet plus 200 mg/kg vitamin E  and basal diet plus 500 mg vitamin C and 200 mg vitamin E /kg feed. The results revealed that, the overall production characters including livability of laying guinea fowl were significantly improved (P<0.05) by the dietary inclusion of vitamin C and E singly or in combinations. The hen day egg production %, egg mass, albumen height, albumen weight, egg length, egg width and egg shell thickness were higher (P < 0.01) in the group supplemented with the combination of vitamin C and E in comparison with other treatment groups. In addition, supplementation of the dietary combination of vitamin C and E significantly increased (P<0.01) the Newcastle vaccine antibody titer and blood metabolites than single supplementation of both vitamins. In conclusion, the combined dietary supplementation of 500 mg /kg vitamin C and 200 mg/kg vitamin E had proved to be successful in improving the productive performance, egg quality, immune responses and the general health of black guinea fowl under present experimental conditions. This study proposes the synergistic beneficial action between vitamin C and vitamin E when supplemented together to Guinea fowl.
Hepatoprotective effect of Artichoke (Cynara scolymus) in laying hens
Authors: Ürüşan H
Published: April 2024

This experiment was conducted to investigate the effect of artichoke plant (Cynara scolymus) at different ratios (0, 5, 10 and 15 g/kg) supplementation into high-energy laying hen diet on performance, egg quality parameters, serum lipid profile, some liver enzymes and liver fat ratio. In total, one hundred and twenty 70-week-old Lohman LSL laying hens were randomly assigned to five groups and each group consisted of six subgroups. The control group was fed the basal feed (2650 kcal/kg ME),  and the treatments groups were fed with high energy (2850 kcal/kg ME) including 0, 5, 10 and 15 g/kg artichoke, respectively. The feed and water were given to animals as ad-libitum during the experimental period. Experiment lasted for 8 weeks.The supplementation of 10 g/kg artichoke to the diet improved feed conversion ratio (FCR) and increased egg production. All additive artichoke diets showed the highest eggshell weight, shell breaking strength, shell thickness and yolk color. The addition of artichoke to the high energy diet reduced the serum triglyceride, LDL and VLDL concentration. The GSH, CAT and GPx values which are enzymatic antioxidants in serum were found to be highest in the HE+ artichoke 5 g/kg group. The liver fat content was dramatically lower in laying hens fed on HE+artichoke 10 g/kg diet (18.83 %) compared with those fed on the high energy diet (48.26 %).As s result, dietary supplementation of artichoke could be used in the diets to improve the performance, shell weight, breaking strength, thickness of shell, egg yolk color and reduced to serum triglyceride, LDL and VLDL concentration, liver fat ratio and increased the serum antioxidants enzymes (GSH, CAT and GPx).
Investigation of brucellosis in culled cows and professionals at El-Harrach’s slaughterhouse in Algeria
Authors: Akkou M , Titouche Y , Bentayeb L , Ramdani-Bouguessa N
Published: April 2024

¶In Algeria, despite control program (test/slaughter) applied since 1995, animal and human brucellosis incidence remains high over the last years. Our ¶Ou survey was carried out with, an aim of determining the insufficiencies of the applied prophylaxis plan, and assessing brucellosis hazard on professionals of slaughterhouse. ¶On top of the 31 visits performed at the slaughterhouse of El-Harrach, blood sera and data associated with: age, identification statute, race, type of slaughtering, pregnancy, and metritis presence were collected from 351 cull-cows¶. ¶From the cooperative professionals of the same establishment, 31 sera in conjunction with the socio-epidemiological data were sampled. ¶All sera were analyzed with Rose Bengal Plat Agglutination Test (RBPAT). ¶The obtained results showed 0% prevalence in the slaughterhouse workers, and a global rate of 4.56 % RBPAT positivity in the cull-cows, within 5.55% prevalence in the pregnant ones. A rate of 50% (28.6 to 83.3%) daily frequency appearance of a sero-positive cow at the slaughterhouse of El-Harrach was established. ¶In addition, 4.01% slaughtered cows in the framework of healthiness slaughtering, were positive. ¶While, no difference in prevalence related to race, neither to identification statute, nor for age, were recorded. ¶Indeed, one can expect that the uncontrolled movement of animals can evoke a substantial factor limiting the effectiveness of brucellosis control program.
Clinical and epidemiological profiles and outcome of non-traumatic myelopathies in dogs
Authors: Baka RD , Balla S , Kazakos GM , Patsikas MN , Polizopoulou ZS
Published: April 2024

Myelopathies, the disorders affecting the spinal cord, are classified into traumatic or non-traumatic. In dogs, intervertebral disc disease (IVD) is the most common cause of spinal pain and neurological deficits. The aim of the current study is to present the prevalence, clinical presentation, diagnosis and outcome of non-traumatic myelopathies of various etiologies, in dogs. This was a retrospective study in dogs. The inclusion criteria for the dog population included complete clinical records of the cases (physical, neurological examination, clinicopathological and/or diagnostic imaging), therapy, outcome and re-examinations. A total of 618 dogs were included in the study with a mean age of 6 years and a mean body weight of 12Kg. The neurological deficits appeared acutely in 346 dogs (56%).Paraplegia (201/618, 32.5%), paraparesis (144/618, 23.3%) were the most frequent neurological deficits, followed by spinal pain (61/618, 9.9%). In the majority of cases the lesions (374/618, 60.5%) were localized in the thoracolumbar spinal cord segments. Degenerative disc disease was the predominant diagnosis in the study population (212 dogs, 62.5%), followed by neoplasia (10%), congenital anomalies (6.2%), myelitis (5.9%) and, less frequently, discospondylitis (4.1%), caudal cervical spondylomyelopathy (3.8%) and meningitis (3%). There were few cases with extradural synovial cysts (2.1%), congenital myelopathies (1.2%), Chiari like malformation (0.9%). Regarding outcome, 476/618 (77%) dogs were alive at the end of the study. From the 476 dogs that were still alive, 278/476 dogs (58.4%) improved neurologically, 120/476 dogs (25.2%) were neurologically stable, 22/476 dogs (4.6%) deteriorated. Degenerative disc disease was the most common diagnosis in dogs admitted with paraparesis/ paraplegia. Myelography could diagnose secondary spinal compression caused by disc protrusion/ extrusion or any other cause. Corticosteroids and non-steroidal anti-inflammatory drugs (NSAID) were used commonly by the referring veterinarian in case a compressive myelopathy was suspected. Prognosis in dogs with degenerative disc disease type II is favorable when therapy was started soon after the clinical sign’s onset and the deep pain sensation was intact.
Investigation of Some Heavy Metal Resistance Genes in E.coli Isolated from Shrimp and Mussels
Authors: Celik B , Sipahi N , Kekec I , Halac B
Published: April 2024

One of the biggest problems in the modern world is environmental pollution. All organisms are affected by the resulting pollution, albeit at different levels. One of the most important causes of pollution is the stable and non-perishable compounds formed due to heavy metals used in a wide range of environments. Heavy metal pollution, especially in the seas, causes cumulative accumulations in aquatic life, food and other products obtained from aquatic life, in living things that consume these products, and related diseases. The most critical sign of heavy metal pollution in water is the resistance formed against heavy metals in bacteria living in these waters. To determine the resistance to heavy metals in bacteria, the presence of resistance to Copper, Mercury and Manganese heavy metals in E. coli isolated from 18 mussels and 16 shrimps was genotypically investigated. For this purpose, the presence of pcoR genes for the determination of copper resistance, merA for the determination of mercury resistance, and mntR for the determination of manganese resistance was investigated in both plasmid and genomic DNA. As a result of the study, resistance genes were detected against heavy metals in 31 (91.17%) of 34 E. coli isolates examined. The presence of the pcoR gene (copper resistance) was found in 2 isolates (5.88%), the merA gene (mercury resistance) in only one isolate (2.94%) and the mntR gene (manganese resistance) in 8 isolates (23.52%). While pcoR and merA genes were not observed together in any isolates, pcoR and mntR genes were detected together in the genetic material of 10 isolates (29.41%). In comparison, mntR and merA genes were detected together in the genetic material of 7 isolates (20.58%). In 3 samples, all of the resistance genes against heavy metals were detected (8.82%).
The effects of laurel (Laurus nobilis L.) leaf powder supplementation on performance, carcass characteristics, meat lipid oxidation and some blood parameters of broiler chicks
Authors: Arslan Duru A , Gölcü AO
Published: April 2024

The purpose of this study was conducted to evaluated the effects of bay laurel (Laurus nobilis L.) leaves powder (LLP) on performance, thigh and breast meat lipid oxidation and chemical composition, body components, digestive tract parts and some blood parameters in broiler chickens. A total of 144 d-old mix-sexed chicks were weighed and randomly assigned to four experimental groups with three replicates of 12 chicks (6 ♀and 6 ♂) each. Treatments were as follows: (i) basal diet-no additive (control), (ii) basal diet+1000 mg/kg LLP, (iii) basal diet+5000 mg/kg LLP and (iv) basal diet+10000 mg/kg LLP. The results revealed that over entire the experimental period body weight and body weight gain increased by LLP supplementation (P<0.01). Feed intake of birds enhanced (P<0.05) whilst feed conversion ratio and chemical composition thigh meat were not significant affected by LLP concentration (P>0.05). Liver improved with 10000 mg/kg LLP (P<0.001) and the weight of ileum + jejunum improved with 5000 and 10000 mg/kg LLP (P<0.01). Breast dry matter (P<0.05), glucose (P<0.05) and with LLP treatment, the TBARS values of breast and thigh meat decreased (P<0.001). As a result, LLP supplementation in broiler diets can be advised because it lowers lipid oxidation in the meat.
The effects of goji berry (Lycium barbarum L.) leaves on performance, meat lipid oxidation, digestive tract parts, and some blood parameters of broiler chickens
Authors: Arslan Duru A , Barıt M
Published: April 2024

The aim of this study was to investigate the effects of supplementing diets with goji berry (Lycium barbarum L.) leaves (GBL) on performance, meat lipid oxidation, meat chemical composition, carcass traits, some internal organs, digestive tract parts and some blood parameters in broiler chickens. A total of 144 d-old mix-sexed chicks (Ross-308) were weighed and randomly allocated to four experimental groups with three replicates of 12 chickens each. Treatments were as follows: (i) basal diet (control), (ii) basal diet+1 g/kg GBL, (iii) basal diet + 5 g/kg GBL and (iv) basal diet+ 10 g/kg GBL. After analysis of one-way ANOVA, the results revealed that 10 g/kg GBL improved body weight (0-6 weeks) (P<0.01), body weight gain (4-6 weeks) and feed intake (4-6 and 0-6 weeks) (P<0.05). Feed conversion ratio did not significantly improve with GBL treatment (P>0.05). In addition, 10 g/kg GBL of the diet increased heart weight and proventriculus length (P<0.05), however, decreased breast meat lipid oxidation on d 21 (P<0.05). Experimental treatments did not affect the total cholesterol, HDL, glucose and triglycerides (P>0.05) but decreased LDL (P<0.05). In conclusion, supplementation of 10 g/kg GBL to broiler chicken diet may improve performance and may decrease breast meat lipid oxidation and blood serum LDL.
The Efficacy of an Engineered Biocarbon in Young Broiler Chicken During an Aflatoxin Exposure
Authors: Bayir HO , Ritz C , Fowler J
Published: April 2024

This study was conducted to evaluate aflatoxin (AFB1) adsorption capacity of a carbonaceous biomass (Bio-C) in vitro and the efficacy of a Bio-C in mitigating the effects of a 0.5 mg/kg dose of aflatoxins (AF) in vivo. In vitro AFB1 affinity of Bio-C were determined by using different AFB1 concentration containing solutions, and these concentrations were measured by a UV-visible spectrophotometry. In in vivo test, a total of 192 Cobb-500 male broilers were obtained on the day of hatch and randomly allocated to one of 32 treatments pens (6 birds/pen). Birds were fed a broiler starter mash diet containing either 0 or 0.5 mg/kg AF, with or without 0.4% of Bio-C, resulting in 4 treatments arranged as a 2x2 full-factorial. Pen weights and feed consumed were recorded weekly, and mortality was recorded daily. On day 21, three birds from each pen were killed by cervical dislocation and the liver, kidney, and spleen were removed and weighed for relative organ weight assessment. Data were analyzed as a 2x2 full-factorial for AF level and Bio-C using the GLM procedures of SPSS. Our data indicates that Bio-C adsorption capacity was ranged from 29% to 70%, with an average of 44%. In vivo results showed that the performance of birds receiving 0.5 mg/kg AF were not significantly different compared to the control group at any point during the 21-day trial. No main effects were seen on the performance parameters by the inclusion of Bio-C in the diet.  There was also no AF/Bio-C interaction. The relative weights of the liver, kidney, and spleen were not significantly different whether birds were fed 0.5 mg/kg AF or 0.4% Bio-C. These data suggest that supplementation of Bio-C has no effect on broiler performance when provided in diets either free of AF or containing up to 0.5 mg/kg.
Effects of Shiitake mushroom (Lentinus edodes) supplementation into quail diets on performance, blood serum parameters and intestine microbial populations
Authors: Ay C , Ülger İ , Kaliber M , Hızlısoy H , Ayasan T
Published: April 2024

This study was conducted to investigate the effects of shiitake mushroom (Lentinus edodes) (SM) supplementations into quail diets on live weight (LW), live weight gain (LWG), feed consumption (FC), feed conversion ratio (FCR), blood serum biochemical parameters (total cholesterol, total protein, triglyceride, glucose) and intestine bacteria populations. A total of 120 Japanese quail (Coturnix coturnix Japonica) were used in present experiments. Quail were separated into 4 groups (control and 3 treatment groups). Control group was fed with a basic ration and treatments groups were fed with 0.5, 1 and 2% shiitake mushroom-supplemented diets. Shiitake mushroom supplementations significantly influenced live weights (P<0.05). Differences in live weight gains, feed consumptions, feed conversion ratios, blood serum parameters and intestine bacteria populations of the treatment groups were not found to be significant (P>0.05). SM supplementations influenced live weight gains and feed conversion ratios in the 4th week of the experiments (P>0.05). SM supplementations also influenced blood serum glucose levels (P<0.05).
The effects of body weight and age on performance, egg quality, blood parameters, and economic production of laying hens
Authors: Ekinci O , Esenbuga N , Dagdemir V
Published: April 2024

This study was conducted to investigate the effects of body weight on performance, egg quality, blood parameters, and economics of production of Lohmann white laying hens. A total of 288 Lohmann white layers, 44 wks of age, were allocated randomly to three groups, each formed 24 replicate cages as subgroups, comprising of four hens. The study was conducted over a period of 36 wks. Feed and water were offered ad libitum in the experiment. Performance parameters were significantly affected by body weight except for cracked egg. Considering the egg production, differences among the groups were significant (P<0.001). Egg production in the light group was higher than that of the medium and the heavy group. However, egg weight was determined to be lower in the light group (66.58 g) than in medium (67.54 g) and heavy hens (68.84 g). Hens in light body weight had lower feed intake and feed conversion ratio (FCR) than hens in heavy body weight. There were no alterations in egg quality parameters in response to increased body weight except for  SS and yolk color. SS decreased linearly (P<0.001) and YC increased with BW. Other egg quality parameters did not change as BW increased. There was no change in the metabolic profile in response to increased BW except for glucose. Heavy hens had greater serum glucose concentrations than light and medium hens. This study emphasized that body weight affected the laying performance, some egg quality parameters, but had no significant effect on metabolic profile except for glucose. As a result, it was found that there was a positive relationship between the egg weight and the body weight of the hens. In this case, it is possible to produce more eggs with less feed by increasing the number of light and medium-weight chickens in the herd for profitable livestock.
Association between white line disease and sole ulcers with certain milk components in Simmental cows
Authors: Ninković M , Žutić J , Arsić S , Zdravković N , Zurovac Sapundžić Z , Glišić D , Bojkovski J , Giadinis ND , Panousis N
Published: April 2024

Lameness is one of the high influence production illnesses in intensive dairy production farming, it reduces milk yield and can also negatively affect the quality of milk. Many factors can affect the production of milk components. In addition race, nutrition, milk yield, various metabolic disorders, and lameness can have an effect on the synthesis of milk components.  White line disease and sole ulcers are widespread hoof diseases of cows in tied-holding systems. Albeit the main cause of lameness, associations between claw disorders of cows and variation of milk components haven’t been widely studied in Simmental cows. The objective of our study was to investigate the effect of white line disease and sole ulcers on the production of milk components of Simmental dairy cows kept in the small households in Mačva locality, Serbia. Cows were selected with a history of lameness. A total of 3 groups of cows: affected by white line disease (n=12), sole ulcers (n=12), and healthy cows (n=12) in the early stage of lactation were selected for analysis of milk components. Milk components (milk protein, fat, and non-fat dry matter) were analyzed using Lactoscan S. Significance of differences in milk component characteristics between white line disease, sole ulcers, and healthy groups were tested using a Kruskal-Wallis multiple comparisons test. The contents of milk fat of cows affected by white line disease and cows affected by sole ulcers were significantly lower than those of non-lame cows: 3.80%, 3.69%, and 4.18%, respectively (both p<0.05). However, differences in the contents of milk protein and the contents of non-fat dry matter cows affected by white line disease, sole ulcers, and in health cows were not significantly different (p>0.05). Our results indicate that hoof diseases of cows namely whitee line disease and sole ulcers, significantly reduce the milk fat production in lame Simmental cows.
Negative Pressure Pulmonary Edema due to Upper Airway Obstruction in Two Dogs
Authors: Choi D , Yang MP , Kang BT , Choi M , Nam H , Chae Y , Koo Y , Yun T , Lee D , Kim H
Published: April 2024

Two dogs were referred because of chronic cough and respiratory distress. One dog (Case 1) showed severe respiratory distress and mild interstitial patterns on the thoracic radiographs after external ring prostheses were placed to support a collapsing cervical trachea. The dog had received 1 mg/kg of furosemide three times daily for 2 days to treat pulmonary edema. Although appropriate treatment was provided to resolve pulmonary edema, persistent pulmonary edema and respiratory distress had been shown. There was no evidence of cardiogenic pulmonary edema based on the physical, radiographic, and echocardiographic examinations. The other dog (Case 2) was diagnosed with tracheal collapse and bronchomalacia. In addition, mild interstitial patterns were detected on the thoracic radiographs. There was no evidence of cardiac disease based on the physical and radiographic examinations. Because these findings were suspected to indicate negative pressure pulmonary edema due to airway obstruction, Case 1 was immediately given dexamethasone for 2 days to relieve postoperative swelling. Case 2 received 0.05 mg/kg of acepromazine for sedation and endotracheal intubation with positive-pressure ventilation for 2 days. All abnormal signs, including pulmonary edema, disappeared in Case 1, but Case 2 was discharged with a mild cough. This report describes the clinical course of negative pressure pulmonary edema due to airway obstruction in two dogs. Clinicians should be aware that airway obstruction can cause fulminant non-cardiogenic pulmonary edema in dogs.
The genome sequence of the Hoary Bell moth, Eucosma cana (Haworth, 1811)
Authors: Boyes Douglas , Wawman Denise C. ,   University of Oxford and Wytham Woods Genome Acquisition Lab,   Darwin Tree of Life Barcoding collective,   Wellcome Sanger Institute Tree of Life Management, Samples and Laboratory team,   Wellcome Sanger Institute Scientific Operations: Sequencing Operations,   Wellcome Sanger Institute Tree of Life Core Informatics team,   Tree of Life Core Informatics collective,   Darwin Tree of Life Consortium
Published: April 2024

We present a genome assembly from an individual female Eucosma cana (the Hoary Bell; Arthropoda; Insecta; Lepidoptera; Tortricidae). The genome sequence is 580.3 megabases in span. Most of the assembly is scaffolded into 28 chromosomal pseudomolecules, including the Z and W sex chromosomes. The mitochondrial genome has also been assembled and is 16.76 kilobases in length.
Hazardous Chemicals Logistics Internet of Vehicles Based on Encryption Algorithm
Authors: Tang Yujian , Chen Ya , Jung Hoekyung
Published: April 2024

With the advancement of information and communication technology, vehicles role in people's lives is not just for transportation but also equipment for the carrier of mobile communication. The new direction is provided by the Internet for the automobiles development and upgrading. Internet of Vehicles (IoVs) includes smart vehicles, Autonomous Vehicles (AVs) as well as roadside units (RSUs) that communicate for providing the enhanced transportation services such as high traffic efficiency and reduced congestion and accidents. In the hazardous chemical logistics sector, the integration of Internet of Vehicles (IoVs) introduces significant security, privacy, and trust challenges. Vulnerabilities to cyberattacks, such as hacking and data manipulation, threaten the integrity of sensitive information regarding hazardous cargo, while concerns about location privacy and data minimization arise due to the tracking of vehicle movements. Ensuring authentication, authorization, and compliance with regulatory standards is essential for building trust within the IoV ecosystem, as any compromise in supply chain integrity could lead to safety hazards or legal liabilities. Robust encryption algorithms like AES and RSA play a crucial role in securing data transmission, but proper implementation and key management practices are necessary to prevent cryptographic weaknesses. Addressing these issues comprehensively is vital for safeguarding the transportation of hazardous chemicals and ensuring the safety of both the environment and the public. IoVs, suffer from security, privacy and trust issues. In order to solve the problem of hazardous chemical logistics of encryption algorithm, the author proposed a research on the information security of the Internet of Vehicles. Firstly, the information characteristics of dangerous chemicals vehicles are analyzed, then analyze the Data Encryption Standard (DES) algorithm. Advanced Encryption Standard (AES) algorithm is analyzed in the symmetric cryptosystem and then RSA algorithm is analyzed in the public key cryptosystem. Finally, the performance of the algorithm and the characteristics of vehicle information are comprehensively analyzed. The security of Internet of Vehicle's data transmission is efficiently improved by the proposed method.
Target Image Processing based on Super-resolution Reconstruction and Machine Learning Algorithm
Authors: Liu Chunmao
Published: April 2024

This article proposes a target image processing method based on super-resolution reconstruction and machine learning algorithms, which solves the low-resolution problem in medical images during imaging. This method uses nonlocal autoregressive learning based on a medical image super-resolution reconstruction method. The autoregressive model is introduced into the sparse representation-based medical image super-resolution reconstruction model by utilizing medical image data inherent nonlocal similarity characteristics. At the same time, a clustering algorithm is used to obtain a classification dictionary, improving experimental efficiency. The experimental results show that ten randomly selected CT/MR images are used as test images, and each image's peak signal-to-noise ratio and structural similarity values are calculated separately. Compared with other methods, the method proposed in this paper is significantly better and can achieve ideal results, with the highest value being 31.49. This method demonstrates the feasibility of using super-resolution reconstruction and machine learning algorithms in medical image resolution.
Enhancing IoT Security in Russian Language Teaching: A Improved BPNN and Blockchain-Based Approach for Privacy and Access Control
Authors: Jia Qi
Published: April 2024

Russian language instruction emerges as a pivotal course in tertiary education, necessitating novel approaches to maintain instructional quality and efficacy. This study introduces a novel approach to Russian language teaching that combines the robustness of Machine Learning with the security framework of Blockchain technology and is tailored to the unique needs of the Internet of Things (IoT) environment. At its core, the study creates an advanced back-propagation deep neural network enriched with a deep noise-reducing auto-encoder and a support vector machine to improve privacy and access control in IoT-based educational platforms. The proposed model employs a polynomial kernel function and a one-error penalty factor in a single hidden layer, resulting in a system that is not only efficient in handling small-scale data samples but also adept at processing larger data volumes, a common scenario in IoT settings. This design effectively overcomes the problems of overfitting and slow convergence that are common in traditional models. Furthermore, the incorporation of blockchain technology ensures a decentralized and secure data handling framework, reinforcing the privacy and access control aspects that are critical in the digital education domain. The combination of these technologies yields a more rational, scientifically based evaluation system, propelling the standardization and enhancement of Russian language instruction forward. This method not only improves language teaching quality, but it also paves the way for more secure, scalable, and efficient IoT applications in educational settings.
Blockchain Enhanced Student Physical Performance Analysis using Machine Learning-IoT and Apriori Algorithm in Physical Education Network Teaching
Authors: Li Jianing , Quan Zheping , Song Weijia
Published: April 2024

In the digital era, particularly with the rise of online teaching, traditional approaches to college physical education face challenges in adequately monitoring and enhancing students’ physical fitness. This study introduces a novel approach that integrates blockchain technology with a Machine Learning-IoT framework to evaluate and improve students' physical performance. Utilizing the Apriori algorithm, enhanced with particle swarm optimization and an improved K-means methodology, this system offers a robust tool for correlating student behavior with sports performance in a secure and decentralized manner. The proposed system uses blockchain for safe data management and IoT for real-time data collection, ensuring privacy as well as efficiency. The algorithm's accuracy, recall, and F1 values on the Iris dataset are 0.947, 0.931, and 0.928, respectively, with a considerable Calinski Harabasz score of more than 240. When applied to university student behavior data, the blockchain-enhanced system successfully mined association rules with a maximum confidence level of 0.923.
Remote Intelligent Medical Monitoring Data Transmission Network Optimization based on Deep Learning
Authors: Wang Run
Published: April 2024

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the Autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi-source heterogeneity in multi-source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.
E-Commerce Data Mining Analysis based on User Preferences and Assiciation Rules
Authors: Fan Zhiying
Published: April 2024

Improving the sales of e-commerce platforms is the primary goal of this paper. This paper studies the data of e-commerce product recommendations from the perspective of user preference and association rules. The characteristics of positive and reverse association rules in data mining are analyzed. Then, a multi-dimension association rule calculation method is proposed. Create a data attribute unit set. By analyzing each attribute's weighted coefficient and similarity, the attribute confidence degree is obtained, and the data is preprocessed. An example is given to verify the effectiveness of the proposed method. The recommendation engine based on user preferences and association rules significantly improves the accuracy, recall rate and prediction coverage of e-commerce recommendation systems.
Classification of Covid-19 using Differential Evolution Chaotic Whale Optimization based Convolutional Neural Network
Authors: Manoj Kumar D.P. , N Patil Sujata , Bidare Divakarachari Parameshachari , Falkowski-Gilski Przemysław , Suganthi R.
Published: April 2024

COVID-19, also known as the Coronavirus disease-2019, is an transferrable disease that spreads rapidly, affecting countless individuals and leading to fatalities in this worldwide pandemic. The precise and swift detection of COVID-19 plays a crucial role in managing the pandemic's dissemination. Additionally, it is necessary to recognize COVID-19 quickly and accurately by investigating chest x-ray images. This paper proposed a Differential Evolution Chaotic Whale Optimization Algorithm (DECWOA) based Convolutional Neural Network (CNN) method for identifying and classifying COVID-19 chest X-ray images. The DECWOA based CNN model improves the accuracy and convergence speed of the algorithm. This method is evaluated {by} Chest X-Ray (CXR) dataset and attains better results in terms of accuracy, precision, sensitivity, specificity, and F1-score values of about 99.89}%, 99.83%, 99.81%, 98.92%, and 99.26% correspondingly. The result shows that the proposed DECWOA based CNN model provides accurate and quick identification and classification of COVID-19 compared to existing techniques like ResNet50, VGG-19, and Multi-Model Fusion of Deep Transfer Learning (MMF-DTL) models.
Deep Machine Learning-based Analysis for Intelligent Phonetic Language Recognition
Authors: Liu Yumei , Luo Qiang
Published: April 2024

Modern speech generating systems can produce results that are almost as visually realistic as actual sounds. They still require further production management. This research presents a paradigm for managing prosodic output using explicit, unambiguous, and understandable parameters. We utilize this strategy to emphasize key words and provide a variety of architectural possibilities based on a richness of labelled resources. In an objective voice, we compare the options for producing data with or without labels. We assess them using listening tests that demonstrate our ability to retain the same level of naturalness while effectively attaining regulated concentration over a specific area.
Localization of Dielectric Anomalies with Multi-level Outlier Detection through Membership Function and Ensemble Classification Framework
Authors: Md Najumunnisa , ASCS Sastry , B T P Madhav
Published: April 2024

This research presents an innovative method for real-time detection of dielectric anomalies, with a primary focus on evaluating apple quality and ripeness using dielectric tomography. The study involves the development of an advanced tomography system within an anechoic chamber, harnessing electromagnetic wave technology and sophisticated antenna systems for data acquisition. The proposed framework encompasses critical stages, including data collection, range bounds computation, threshold determination, class membership assignment, and ensemble classification. By seamlessly integrating statistical methods, density-based clustering, and ensemble learning, this approach significantly enhances precision and reliability in anomaly detection. The integration of available statistical methods, density-based clustering, and ensemble learning may demand substantial computational resources, limiting the scalability and real-time applicability of the proposed framework. Empirical results demonstrate the superior performance of the method, with an accuracy rate of 98.9%, precision of 0.989, F-measure of 0.989, dielectric anomaly recall rate of 0.99, and a low error rate of 0.18. Overall, this research introduces an advanced approach with the potential to revolutionize apple quality assessment and industrial processes across various sectors.
Research on High-performance Computing Network Search System based on Computer Big Data
Authors: Chen Xiaogang , Liu Dongmei
Published: April 2024

An efficient computing system for massively parallel systems is established. The stochastic Petri net abstracts and models the high-performance computer work scheduling system. The IB switch is modeled. Stochastic Petri nets are used for performance analysis. Finally, the proposed method is combined with InfiniBand interconnection architecture to evaluate the system's delay. The experimental results prove the feasibility of this algorithm.
Database Access Information Security Management Simulation under Big Data Platform
Authors: Li Zhaocui , Wang Dan
Published: April 2024

When people perform database access information security management, the traditional method cannot accurately verify the identity of the visitor, the credibility of the identity information, and the security management of the access information. With the widespread application of big data technology, the amount of data in databases is rapidly increasing, which brings new challenges to information security management. The main purpose of this study is to explore how to more effectively manage the security of database access information on big data platforms. Therefore, the trusted computing platform is established to implement database access information security management under the data platform. The method determines the user behavior is credible by establishing a behavior chain of behavior based on the user identity and measuring user operation behavior. For the user's private data, the encryption/decryption module is used for security protection, preventing data from being leaked through illegal copying. A trusted metric model based on the USB Key user identity is established and a trusted platform is established. By improving the ELGamal algorithm, the IMC/IMV metrics architecture is utilized to measure platform security attributes. In the first round of anonymous authentication, the identity authentication of the platform is completely completed, and the database access information security management under the big data platform is completed. The simulation results show that in 10 experiments, the transmission time delay of TCP/IP protocol is less than 200ms, and the security of database access information is enhanced after the encryption system is established in the database. This has certain theoretical enlightenment for the improvement of database security and the optimization of information security management.
Hospital Medical Behaviour Supervision and Operational Efficiency Evaluation Method based based on Big Data Platform
Authors: Liu Yi , Zhang Yi
Published: April 2024

The application of cloud computing and big data core technologies and concepts to medical informatization can improve its flexibility and efficiency, and achieve the overall deployment and intensive management of the system. In the era of big data, the use of information management platform can optimize and standardize clinical diagnosis and treatment process, improve the quality and efficiency of diagnosis and treatment services, and improve the quality and level of scientific research, which meets the requirements of medical reform for fine management of hospitals and precise medical treatment in the era of evidence-based medicine. This paper introduces the development and application of big data analysis in the field of information technology. Through the research on the supervision of hospital medical service behavior, the top-level design is used to standardize the content of medical behavior supervision, and the effectiveness of supervision is discussed to achieve the purpose of reducing clinical paperwork. Based on the needs, the medical service behavior supervision system is proposed and constructed. Strengthen hospital medical behavior supervision through hospital big data analysis and knowledge base system support. This system can provide management with an effective monitoring and management tool, enabling them to promptly identify and solve problems that arise in medical services. By analyzing a large amount of medical data, hospitals can help predict the trend of disease occurrence in advance, thereby taking preventive and control measures in advance, and reducing the occurrence and spread of diseases. Through deep learning and analysis of patient data, personalized treatment plans can be provided for each patient to improve treatment effectiveness.
Design and Application of Automatic English Translation Grammar Error Detection System based on BERT Machine Vision
Authors: Qing Yu
Published: April 2024

Given the traditional handwritten English fonts, the accuracy of grammar error detection is unsatisfactory. This attribute leads to poor grammar error correction. Based on the optimized BERT machine vision model, an automatic English translation grammar error detection system is proposed in this paper. First, the basic architecture of the Transformer model and BERT model is considered, and a mixed attention module is discussed into the Transformer model to capture the features of the target in space and channel dimensions and realize the modeling of the context dependence of the target features. The feature maps are sampled by multiple parallel only if then cavity convolutions with different void rates to obtain multi-scale features and enhance local feature representation. Then, the input words of the BERT model are weighted by TFIDF to improve the feature extraction ability of the BERT model and construct the TF-BERT model. A database query rewriting model based on BERT and Transformer is proposed. The construction details of the model are described from the aspects of encoding processing, table embedding, and decoding processing respectively. Based on the principles of English translation, we extract grammatical features and build a grammar error detection method. TF-BERT model is selected as the basic framework. Combined with the hybrid attention mechanism, an automatic error correction model of English grammar is constructed. Finally, it is found that the loss value of the traditional system is as high as 0.7411, and the accuracy rate is 75%, while the loss value of the English grammar error detection system proposed in this paper is 0.2639, and the accuracy rate is 100%, which is 25% higher than that of the traditional system, and the performance is remarkable.
Optimization of Internal Control for Budget Operations in Public Institutions Based on Random Forest Algorithm
Authors: Jin Yang
Published: April 2024

In order to promote the construction of internal control in public institutions and improve work efficiency, the author proposes an optimization of internal control in public institution budget business based on random forest algorithm. We have constructed a big data audit framework for internal control of A Maritime Bureau based on the financial cloud platform and sorted out its audit process. By using the random forest algorithm to identify suspicious points in the internal control audit of administrative institutions at the data level, an example analysis is conducted using some data from A Maritime Bureau's assets, budget, revenue and expenditure, infrastructure, and contract business. The results indicate that the design of the internal control big data audit plan for administrative institutions will promote the innovation of audit information technology and application in A Maritime Bureau, provide theoretical guidance for the internal control big data audit carried out by administrative institutions, and effectively solve the problems of high workload and low work efficiency when A Maritime Bureau conducts internal control big data audits, thereby improving audit efficiency.
Research on the Construction of a Higher Education Knowledge Management Model Based on the Integration of Shadow Teams
Authors: Xia Tian
Published: April 2024

With the advancement of technology and the development of globalization, higher education institutions are facing increasingly complex knowledge management challenges. Shadow team usually refers to an informal and flexible team, where different departments or fields work together to promote knowledge exchange and integration, and stimulate innovative thinking. In order to enhance the knowledge management capabilities and competitiveness of higher education knowledge management institutions, this study will integrate shadow teams, construct a knowledge management model for higher education training enterprises, and use Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method to construct an evaluation model for knowledge management capabilities of higher education training enterprises. The research results indicate that compared with traditional knowledge management models, the knowledge management model of higher education training enterprises integrating shadow teams collects 253 and 164 pieces of intelligence information at the 60th second, respectively; When the intelligence information is 300 pieces, the effectiveness of the analysis of the two models is 87.2\% and 39.5\%, respectively. An empirical analysis was conducted on a certain postgraduate entrance examination institution, and it was found that compared to students who reviewed independently, most of the students who participated in the training had significantly higher grades than those who did not receive training. The higher education knowledge management model that integrates shadow teams has stronger knowledge management capabilities and higher competitiveness.