bluuebunny/crossref_metadata_2025 huggingface dataset:
Studies of biochemical parameters of the blood of laboratory animals when studying the subchronic toxicity of the drug L-carnitine
Authors: Sabirzyanova L. I. , Lunegov A. M. , Konovalova G. V. , Turner V. V.
Published: January 2024

L-carnitine is a vitamin-like compound that is synthesized in the human or animal body from the amino acids methionine and lysine. Today, L-carnitine is used in cardiology, neurology, gastroenterology and is a substance necessary for the functioning of the body. It normalizes metabolic processes, stimulates cellular energy exchange, eliminates energy deficiency, strengthens the immune system, relieves overwork and fatigue, increases the body's adaptive capabilities, and reduces muscle weakness. Protected L-carnitine is used in dairy farming for metabolic diseases, but only as a feed additive. The purpose of our research is to study the injectable form of L-carnitine, which has not previously been registered in the Russian Federation. Toxicity studies were conducted on outbred rats in October 2021 in the vivarium of the St. Petersburg State University of Veterinary Medicine. The study involved females weighing 190-210 grams, purchased from the Federal State Unitary Enterprise “Nursery of Laboratory Animals “RAPPOLOVO”. When studying subchronic toxicity when administered subcutaneously, L-carnitine was administered at 2 dose levels. Doses were determined based on the results of the acute toxicity experiment: 1/5 and 1/10 of the maximum tolerated dose. The first experimental group (n=10) received the drug subcutaneously at a dose of 0.08 mg/kg (1/5 of 2000 mg/kg). The second experimental group (n=10) received the drug subcutaneously at a dose of 0.04 mg/kg (1/10 of 2000 mg/kg). The control group (n=10) received subcutaneous sodium chloride 0.09% at a dose of 1/5 of 2000 mg/kg. The drug was administered subcutaneously daily for 42 days. Slaughter and sampling of biological material from 5 animals from each group were carried out the next day after the end of the drug administration (day 43), and 10 days after the last drug administration (day 53). As a result of studies of subchronic toxicity on laboratory animals of the drug L-carnitine for veterinary use when administered subcutaneously, it was established that a dosage of 1/5 of the maximum tolerated, and a dosage of 1/10 of the maximum tolerated, does not cause external signs of toxicosis and death in rats . No significant changes were found in the biochemical parameters of the blood of animals in the experimental and control groups.
System of antiepizootic and preventive measures against Dirofilariasis of dogs in the Ararat region of Armenia
Authors: Slobodyanik R. V. , Zykova S. S. , Shcherbakov O. V. , Lunegov A. M.
Published: January 2024

The article analyzes the spectrum of diagnostic methods for establishing the invasion of dirofilariasis, describes the mechanisms of development of the epizootological situation of dirofilariasis, the complication of which is facilitated by the climatic features of the region and the lack of a clear algorithm for prevention. In order to increase the effectiveness of preventive treatments against the main vectors (mosquitoes), the calculation of the season of maximum infection with dirofilariae in the Ararat region of Armenia was carried out. Entomological exploration was carried out in order to establish the species composition of mosquitoes and the main vector of Aedes caspius was determined. A system of antiepizootic and preventive measures for dirofilariasis of dogs in the Ararat region of Armenia has been developed, which includes two lines of protection: the first line of protection includes preventive deworming with drugs from the group of macrocyclic lactones, the second group of protection consists of repellent agents (pyrethroids, cyflutrin, piperonylbutoxide and diflubenzuron).
Service Level Agreement Enforcement Model with Human Factor for Electronic Health Record
Authors: Mohamed Amir Mohamed Talib , Atan Rodziah , Alshammari Abdulaziz , Alsahli Abdulaziz , Rozami Mohammad Nasrollah
Published: January 2024

Service Level Agreement (SLA) is a document contract between the service provider and service recipient which is the expected services to be delivered and received. SLA includes all the information about the services provided and their performance. The SLA identified the level of services performance such as penalties, priorities, compensation and resolution time. If the quality of service does not meet the SLA usage then the service provider need to pay penalties also known as SLA violation. SLA violation occurred might be from software or hardware but another factor such as human factor also involved. The performance of the system and the quality of services requires a human interference to enforce the SLA. In this research work, the human factor such as user willingness, skill/knowledge, information sharing, Staff adequacy was being investigated. The method survey was implemented to find the relationship between human factor and SLA usage. Respondents in IT department are selected to fill in survey form. 11 respondents are used for pilot study to find the reliability of instrument and 24 respondents are used for actual data. The result show there is positive significant value in relationship between human factor and SLA usage.
Early Detection of Diabetes Using Random Forest Algorithm
Authors: Noviyanti Cindy Nabila , Alamsyah Alamsyah
Published: January 2024

Diabetes is one of the most chronic and deadly diseases. According to data from WHO in 2021, there were approximately 422 million adults living with diabetes worldwide, and this number is expected to continue to increase in the future due to various factors. Many studies have been conducted for early detection of diabetes by focusing on improving accuracy. However, a big problem in diabetes prediction is the selection of the right classification algorithm. This study aims to improve the accuracy of early detection of diabetes by implementing the Random Forest algorithm model. This research was conducted with the stages of data collection, data preprocessing, split data, modeling, and evaluation. This research uses the Pima Indian Diabetes data set. The results showed that the diabetes early detection model using the Random Forest algorithm produced an accuracy of 87%. This research shows that by using the Random Forest algorithm model, the performance of early detection of diabetes can be improved. However, there is still room for optimization of this performance, which is recommended for further research to carry out feature selection, data balancing, more complex model building, and exploring larger data.
Effects of Intergenerational Trauma on African-Americans and Interventions
Authors: Duyilemi Femi
Published: January 2024

Trauma is the response to a deeply distressing or disturbing event that overwhelms an individual’s coping ability with such a situation. Trauma causes helplessness and diminishes an individual’s sense of self and the ability to feel the full range of emotions and experiences. Intergenerational trauma is about the effects of trauma suffered or undergone by one generation which impacts future generations' lives and mental health. The problem has been pervasive among African-Americans, with law enforcement worsening the situation through racial bias and profiling that increases police brutality. This article describes the effects of intergenerational trauma as related to African-Americans and suggests trauma-informed interventions in U.S. law enforcement. The primary effects of intergenerational trauma among African-Americans include healthcare disparities, family problems, violence and abuse, and feelings of inferiority. The interventions to consider include offering continuous training lessons to law enforcement officers on police psychology, ensuring strict adherence to ethical conduct and enhancing healthcare and education sectors. Officers dismissed from the force for misconduct should not be rehired to increase public trust and to discourage the repetition of misconduct(s).
Modelling of Cyclic Shear Modulus and Factor of Safety in Clay Soil
Authors: Ganiyu Oluwaseun Mohammed , Kennedy Charles
Published: January 2024

The liquefaction potential of Niger Delta soil was studied through formulated models based on cyclic shear modulus and factor of safety (FS). Data from the experiment were fitted into models to predict the cyclic shear modulus and Factor of Safety. The test analysis shows effective prediction of cyclic shear modulus for a given number of cycles (1–40) and cyclic shear strain (0.01–5 %). Comparison of results shows no significant differences between the measured and predicted cyclic shear modulus, especially from 0.1% shear strain and above. Similarly, the values of factor of safety predicted by the model were very close to those obtained from the experiment; the predicted FS obtained at depths close to 30 m across the sites were slightly greater than 1.0, as against the observed results. Despite this slight variation, the FS model still shows a high degree of prediction. Therefore, the formulated models can be utilised in the study of liquefaction potential, especially in the Niger Delta region.
DAMPAK SOSIAL TERHADAP KEBERADAAN PETERNAKAN AYAM PETELUR DI KELURAHAN BENTENG KECAMATAN BARANTI KABUPATEN SIDENRENG RAPPANG
Authors: Yasir Muhammad , Nugraha Angga , Mansur Musdalifa
Published: January 2024

Penelitian ini bertujuan untuk mengetahui dampak sosial yang terhadap keberadaan peternakan ayam petelur di Kelurahan Benteng Kecamatan Baranti Kabupaten Sidenreng Rappang. Metode penelitian yang digunakan yaitu metode penelitian desktriptif kualitatif, dengan menggunakan kuisioner/angket, observasi, wawancara dan dokumentasi untuk pengumpulan data. Populasi dalam penelitian ini adalah masyarakat Kelurahan Benteng Kecamatan Baranti yang  berjumlah 2.991 orang, dan sampelnya adalah 25 orang yang mewakili setiap rumah yang berada di sekitaran lokasi penelitian dan diambil secara responden. Teknik pengambilan sampel adalah purpove sampling diambill dengan pertimbangan minimal 500 meter dan maksimal 1 kilometer dari lokasi penelitian. Berdasarkan hasil penelitian yang dilakukan terhadap penelitian tentang dampak sosial terhadap keberadaan peternak ayam petelur di Kelurahan Benteng Kecamatan Baranti yang ditinjau dari beberapa aspek yaitu bau, limbah (fases) dan kebersihan kandang ternak ayam. Aspek Bau memiliki rata – rata persentase 66,4% dengan kategori Baik, Aspek limbah (fases) memiliki rata – rata persentase 57,6% dengan kategori Kurang Baik, dan aspek Kebersihan Kandang memiliki rata – rata persentase 41,6% dengan kategori Kurang Baik. Dampak sosial masyarakat akibat adanya usaha peternakan ayam petelur di Kelurahan Benteng Kecamatan Baranti ada dampak negatif yaitu adanya bau yang tidak sedap ketika musim penghujan sehingga mengganggu aktivitas kehidupan sehari-hari masyarakat yang tinggal di dekat usaha peternakan ayam petelur. Sedangkan dampak positifnya yaitu warga mendapatkan kontribusi dari pemilik usaha peternakan ayam petelur, memperbaiki ekonomi keluarga yang tinggal di dekat usaha peternakan ayam petelur karena dibutuhkan tenaganya, dan juga warga dengan mudah mendapatkan telur dan daging ayam dengan harga yang murah.
ANALISIS SALURAN PEMASARAN KOMODITAS KENTANG
Authors: Idris Idris , Balla P. Tandi , Mustaman Wahyuni , Artasastah Ezra , Pratama Andhyka
Published: January 2024

Produksi kentang belum mampu memenuhi kebutuhan dalam negeri Indonesia yang tingkat konsumsi kentang dari sektor rumah tangga mencapai 771.46 ribu ton atau sebesar 28.42%. Desa Kanreapia, Kecamatan Tombolopao, cukup potensial untuk pengembangan tanaman kentang karena didukung oleh cuaca/iklim, dan produktivitasnya 208.80 kuintal/hektar. Sebagai penghasil terbesar tanaman kentang, petani masih sulit mendapatkan harga yang layak, penyebabnya yaitu kurangnya informasi pasar dan petani belum melakukan perhitungan dalam berusahatani. Tujuan penelitian ini yaitu: 1) mengetahui sistem saluran pemasaran kentang di dan 2) menganalisis margin pemasaran, farmer’s shared, komoditas kentang. Responden  ditunjuk langsung (purposive) yaitu petani pembudidaya dan pedagang kentang. Metode analisis data, yaitu metode analisis kualitatif dan kuantitatif. Analisis data kualitatif dilakukan untuk menganalisis saluran pemasaran. Formulanya yaitu, MP = KP + BP, dan untuk mengetahui bagian keuntungan yang diterima petani (farmer’s share) menggunakan rumus: Fs = Pf/Pr x 100%. Hasil penelitian menunjukkan bahwa pemasaran kentang di Desa Kanreapia melalui satu pola saluran pemasaran yaitu: saluran pemasaran dari petani lalu dijual ke pedagang pengumpul dan terakhir ke konsumen. Nilai margin, biaya, keuntungan pemasaran, dan farmer’s share pada saluran pertama masing-masing Rp 2.500/kg, Rp 12.500/kg, Rp 1.000/kg, dan 80%.
RESPONS PETANI PADA PEMANFAATAN PESTISIDA DAUN KETEPENG CINA (Cassia alata L.) DALAM PENGENDALIAN HAMA TANAMAN CABAI (Capsicum frutescens L.)
Authors: Badaruddin Munawir , Hamzah Faisal , Sukarsih Sri Endang , Yani Reski
Published: January 2024

Penelitian bertujuan untuk mengetahui pengaruh pemberian pestisida daun ketepeng cina dalam pengendalian hama tanaman cabai. Selain itu juga untuk mengetahui respons petani dalam perubahan pengetahuan, sikap dan keterampilan pada pemanfaatan pestisida daun ketepeng cina dalam pengendalian hama tanaman cabai. Penelitian dilaksanakan selama 3 bulan dari Bulan April sampai Juni 2022 di Desa Sokkolia, Kecamatan Bontomarannu, Kabupaten Gowa. Penelitian ini dilakukan dengan menggunakan analisis rancangan acak kelompok (RAK). Setelah melakukan penelitian Selanjutnya dilaksanakan penyuluhan I dan II pada kelompok tani Bintang Mandiri. Hasilnya, diketahui pengaruh pemberian pestisida daun ketepeng cina dalam pengendalian hama tanaman cabai dan diketahui respons petani dalam hal perubahan pengetahuan meningkat sebesar 34.2%, aspek sikap meningkat sebesar 32.2% dan aspek keterampilan meningkat sebesar 29.6%. Adapun hasil evaluasi penyuluhannya yaitu sebesar 74.7% dengan kategori Efektif.
Pengaruh Molasses Multi Nutrient Soft (Mms) Terhadap Upaya Mengatasi Silent Heat Pada Sapi Limousin
Authors: Asser Andi , Rezkyanti Andi , Ningsih Dewi Wantika , Syarifuddin Syarifuddin , Firmiaty Sri
Published: January 2024

Sapi Limosin, suatu varietas sapi potong yang sedang dalam tahap pengembangan di Indonesia, asalnya berasal dari benua Eropa dan banyak ditemukan di Prancis. Penelitian ini bertujuan untuk menguji pengaruh Molasses Multi Nutrient Soft (MMS) dalam mengatasi Silent Heat pada sapi limousin. Penelitian ini dilaksanakan di Dusun Paramputan Desa Biangkeke, Kecamatan Pajukukan, Kabupaten Bantaeng, pada periode Maret-Mei 2023. Dalam penelitian ini, digunakan metode kuantitatif dengan menganalisis data menggunakan uji T melalui perbandingan sebelum dan sesudah perlakuan, dengan bantuan SPSS. Hasil dari pengujian T menunjukkan bahwa pemberian pakan MMS tidak menunjukkan pengaruh yang signifikan terhadap BCS (P > 0,5). Meskipun analisis statistik tidak menemukan perbedaan yang berarti, namun setelah 30 hari perlakuan, terdapat peningkatan yang dapat diamati. Dalam penelitian ini, Molasses Multinutrient Soft (MMS) terbukti menjadi pakan padat gizi dengan kandungan nutrisi tinggi, namun belum dapat mengatasi silent heat pada sapi limousin keturunan.
Performa Reproduksi Sapi Peranakan Limousin Betina Pada Paritas Berbeda Di Kecamatan Awangpone Kabupaten Bone
Authors: Asfar A.M.Fajri , S Syarifuddin , Firmiaty Sri
Published: January 2024

Penurunan rataan produksi daging pada tahun 2021 adalah 70,37 juta ton dan 70,6 juta ton dari tahun 2018 hingga 2020. Hal ini juga tercermin dari data konsumsi yang turun sebesar 230.000 ton. Konsumsi rataan dari tahun 2018 hingga 2020 adalah 70,3 juta ton dan 70,1 juta ton pada tahun 2021. Upaya pemerintah dalam meningkatkan populasi sapi dan penyediaan daging secara nasional telah dilakukan secara terus menerus, salah satunya melalui pelaksanaan program Upaya Khusus Sapi Indukan Wajib Bunting (UPSUS SIWAB) yang dimulai sejak 2017 melalui  program Inseminasi Buatan (IB). Faktor yang mempengaruhi program Inseminasi Buatan keterampilan petugas inseminator. Petugas inseminator yang berpengalaman menginseminasi cukup lama, memiliki sertifikat inseminasi dan surat izin melakukan Inseminasi Buatan (SIMI) dan handling semen. Hasil penelitian menujukkan bahwa Paritas 1, Paritas 2 dan Paritas 3 masing masing: Nilai EPP 101,00 hari; 85,00 hari dan 78,87 hari. Untuk nilai (S/C) 1,60; 1,56 dan 1,46. Untuk nilai CR 43,33%; 60,00% dan 60,00%. Untuk nilai DO 129,40 hari, 106,63 hari dan 100,47 hari. Nilai CI 405,07 hari, 386,40 hari dan 376,73 hari. Pada penelitian ini menujukkan peranakan Limousin normal. Kesimpulan bahwa performa reproduksi sapi peranakan Limousin berbeda setiap paritas dan performa terbaik pada Paritas 3.
Analisis Korelasi Terhadap Produksi Kelapa Sawit (Elaeis guineensis Jacq.) Dan Pemupukan NPK
Authors: Rossy Mirasari  , Puspita Puspita , Ramli Ramli
Published: January 2024

Ketersediaan unsur hara dapat mempengaruhi pertumbuhan, perkembangan, dan produksi tanaman. Pupuk NPK merupakan salah satu jenis pupuk yang dibutuhkan oleh tanaman kelapa sawit. Penelitian ini bertujuan untuk menganalisis tingkat korelasi linier pada produksi kelapa sawit dalam bentuk tandan buah segar (TBS) dengan pemupukan NPK. Metode penelitian yang dilakukan yaitu deskriptif kuantitatif, dengan mengumpulkan data sekunder produksi tandan buah segar dan jumlah pemupukan NPK dari perusahaan perkebunan sawit, dan melakukan analisis korelasi Pearson antara produksi tandan buah segar dengan pemupukan NPK. Dari hasil perhitungan korelasi Pearson antara produksi TBS dengan pemupukan NPK didapatkan nilai 0,938 yang berarti terdapat korelasi yang signifikan dengan tingkat koefisien korelasi sangat kuat dan bersifat positif. Hal ini menunjukkan bahwa semakin tinggi pemberian pupuk NPK maka produksi TBS juga semakin meningkat.
Hepatorenal Protective role of Red and White Onion in Albino Wistar Rats
Authors: Nayak Shyamala , Arun Kumar Nayanatara , Hegde Anupama , D Kini Rekha , Kumarchandra Reshma , Blossom Vandana , Pai Shivanand
Published: January 2024

Allium cepa Linn (Onion) is a widely used organosulfur dietary compound having profound medicinal benefits. Eighteen Albino Wistar rats weighing 150-200g were divided into three different groups (n=6; n= number of animals in each group). Normal Control (Group I) was administered with 1 ml distilled water. Group II rats were treated with red onion extract (500mg/kg body weight) and the rats belonging to Group III, received white onion extract (500mg/kg body weight). The treatment was given orally once in a day for 30 days. At the end of the 30th day, the animals were anaesthetized and blood samples were collected through cardiac puncture and were analyzed for liver function test and kidney function test. The histological changes in the liver and the kidneys were also observed. Total cholesterol (P<0.05), TG(P<0.001) and LDL(P<0.001) level significantly decreased (P<0.001) in the red onion group compared to the white onion group. Red onion treated groups showed significant reduction (P<0.001) in the level of urea, creatinine and uric acid when compared to white onion treated group. Alkaline phosphate significantly reduced (P<0.001) in red onion treated when compared to white onion treated group. All the findings were well supported by the histological analysis. In conclusion, our study evaluated the protective effect of red onion and white onion. However, in comparison, red onion treated group showed better protective role when compared to white onion.
Analysis of Chlorpheniramine Maleate in Microcapsules Formulation with Eudragit E PO Polymer using Spray Drying Method
Authors: Andayani Regina , Hasbi Hasbi , Febriyenti Febriyenti
Published: January 2024

Research on the analysis of chlorpheniramine maleate (CTM) in microcapsules formulation with eudragit E PO polymer using the spray drying method had been carried out. The study aims to cover the bitter taste of the drug and know the encapsulation efficiency of several formulas by calculating the drug content in them. The formulations were made with the ratio of CTM and eudragit E PO, namely 1:1, 1:2, and 1:3. The evaluation of the microcapsules included the shape and morphology of the particles using a Scanning Electron Microscope (SEM), particle size distribution, infrared spectroscopy, encapsulation efficiency, and leak tests. The results showed that the microcapsules obtained had irregular shapes and morphology in the three formulas. The average particle size distribution of microcapsules ranged from 60-200μm. The results of infrared spectroscopy analysis showed that there was no interaction between the active substance, polymer, and additives, with no new peak formation. The result of the greatest encapsulation efficiency was found in formula 3 (1:3), which was 95%. The results of the leak test on formula 1, formula 2, and formula 3 were 91.57%, 74%, and 67.72%, respectively. The results of the leak test were expected to be negative, but there were leaks in the three formulas. It can be concluded that the CTM-eudragit E PO microcapsules using the spray drying method using an inlet temperature of 80ºC, an outlet temperature of 60ºC, a spraying speed of 3 mL/min, and a nozzle of 30 μm were not very effective.
Anomalous Origin of the Right Coronary Artery from the Left Anterior Descending Artery in adult with Single Left Coronary Artery and ST-Elevation Myocardial Infarction
Authors: Diajeng Larasati Nindyasari , Amrilla Fagi Rosi , Her Oktaviono Yudi , Aditya Muhammad , Adrian Nugraha Ricardo , Ramadhan Muhammad
Published: January 2024

Single coronary artery (SCA) is a rare anomaly, with a reported prevalence of only 0.024% - 0.066% among patients undergoing routine coronary angiography. Most SCA is found only incidentally in conventional coronary angiography or coronary computed tomography angiography. Some subgroups of SCA can lead to angina pectoris, acute myocardial infarction, or even sudden cardiac death. We present the extremely unusual case of a patient with anteroseptal ST elevation myocardial infarction (STEMI) who was also diagnosed with SCA upon further evaluation. A 52-year-old man with a history of smoking and hypertension was referred to our hospital with an anterior ST-elevation myocardial infarction. Coronary angiography was performed immediately and demonstrated a single coronary artery arising from the left coronary sinus with an anomalous right coronary artery originating as a separate branch from the left anterior descending artery (LAD). The culprit lesion was subtotal occlusion of the proximal segment of LAD, which was recanalized and treated with drug-eluting stent angioplasty. A coronary computed tomography angiography (CCTA) showed an anomalous right coronary artery (RCA) that arose from the proximal LAD, which coursed anteriorly to the pulmonary artery to reach the territory normally served by the RCA. The distal left circumflex (LCX) coronary artery gave rise to the posterior descending and posterolateral branches. In conclussion, the RCA originating from the (LAD) is an extremely rare coronary anomaly; in such cases, the RCA arises from the proximal or mid-portion of the LAD artery. So far, only 40 cases have been reported. Enhanced awareness of congenital cardiac anomalies may help guide management.
Impact of In-Hospital Hyperglycemia on the Outcome of Patients with COVID-19
Authors:   Resident in the Multiprofessional Integrated Residency Program in Urgency and Emergency, Federal University of Pampa, Uruguaiana, Brazil, Ritter Julianna do Amaral
Published: January 2024

To investigate the relationship between in-hospital hyperglycemia and clinical outcomes in hospitalized patients affected by Coronavirus Disease 2019 (COVID-19).
Nutritional Assessment in Stroke Patients
Authors:   IRCCS Humanitas Research Hospital via Manzoni, 56 20089 Rozzano, Milano, Italy., RN Stefano Mancin
Published: January 2024

Stroke is a common acute neurological disorder that leads to significant morbidity and mortality worldwide. Malnutrition is a frequent problem among stroke patients, with a prevalence of approximately 20% at the time of hospital admission.
Realization of Small Scale Models of FCATS Devices and its Optimization in Power Systems using Stability Indices and Particle Swarm Optimization
Authors: Yarlagadda V. Et al.
Published: January 2024

This article focuses on the physical realization of SVC, TCSC, combined SVC and TCSC scale down models, which have been realized in the laboratory, developed and tested and presented the experimental results. These practical test results have been proven the effectiveness of these devices with closed loop control systems developed with microcontrollers. The voltage stability can be assessed accurately using stability indices. These indices can either reveal the critical bus or line of a power system. The said indices plays vital role in identifying the most critical n-1 and n-2 contingencies. This article further focuses on optimization of these devices in IEEE 9 bus, 14 bus and 30 bus test systems using stability indices and Particle Swarm Optimization (PSO). The simulation results for all these systems have been presented and which proves the tremendous improvement in voltage stability of power systems.
Phase modulation of carriers for cascaded multilevel three-phase inverters
Authors: Nguyen V.-Q. Et al.
Published: January 2024

Cascaded multilevel three-phase inverters are commonly used in industrial applications and the quality of these inverters is highly dependent on the modulation techniques. The common mode voltage is one of the factors affecting the quality of the three-phase inverter output voltage. This paper proposes a carrier phase modulation method for cascade multilevel inverters to reduce the magnitude of common-mode voltage magnitude and harmonics. In the proposed method, the carrier phase modulator is considered as an adaptive controller and the phase of carrier is completely controlled by the magnitude of control signal. The phase modulation has a high immunity to the noises compared with the modulations of frequency and amplitude because it is difficult for noises to intrude into the phase of carrier. In addition, the algorithm of carrier phase modulation is simple and requires little memory. Moreover, the number of switching commutations of the proposed method also reduces significantly. Simulation and experiment results based on a cascaded five-level three-phase inverter system have confirmed the effectiveness of the proposed method compared with that of the popular methods using unmodulated carriers such as phase disposition (PD) and phase opposite disposition (POD).
Construction And Performance Evaluation of Big Data Prediction Model Based on Fuzzy Clustering Algorithm in Cloud Computing Environment
Authors: Yanhua Hu Et al.
Published: January 2024

In the evolving landscape of biomedical biometrics, where multimodal approaches are increasingly crucial for reliable user authentication, this research presents a comprehensive study. The primary focus is on the construction and performance evaluation of a robust big data prediction model within a cloud computing environment. The advent of big data and cloud computing has revolutionized the field of biomedical biometrics, offering immense potential for advanced data analysis and prediction. This research presents the development and evaluation of a robust prediction model for multimodal biometric data in biomedical applications. The proposed model incorporation of Reliable Discrete Variable Topology (RDVT) into the prediction model. RDVT introduces a topological data structure that enhances data reliability and ensures the integrity of multimodal biometric information. The construction and training of the prediction model are meticulously detailed, encompassing data preprocessing, feature extraction, clustering, classification, and model evaluation. Additionally, the integration of a fuzzy clustering algorithm enhances the model's ability to handle uncertainty and imprecision in biometric data. The advancement of multimodal biometrics in the biomedical field by introducing the Reliable Discrete Variable Topology (RDVT) and a big data prediction model based on a fuzzy clustering algorithm in a cloud computing environment. The model's performance is rigorously assessed through extensive experimentation, including accuracy, precision, recall, and F1-score measurements.
Innovative Elemental Packaging Design Based on Machine Vision Cognition
Authors: Linlin Zhang Linlin Zhang
Published: January 2024

Elemental packing plays a fundamental role in understanding the behaviour of materials at the atomic level, influencing their mechanical, thermal, and electrical properties.  Optimization plays a crucial role in elemental packing, particularly in the field of materials science and crystallography. The goal of optimization is to find the most energetically favourable arrangement of atoms or molecules within a given structure, aiming to minimize energy and achieve stability. In the context of elemental packing, optimization algorithms are employed to determine the most efficient and stable configurations of atoms in a crystalline lattice. Hence, this paper constructed the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO) to amplify the capabilities of Machine Cognition systems. At the forefront of this study is the reimagination of elemental packaging design, characterized by aesthetics, ergonomics, and functionality. The infusion of machine vision cognition into these designs not only enhances user experience but also prompts contemplation of ethical considerations surrounding privacy, accessibility, and informed consent. Ethical and social implications are scrutinized comprehensively, acknowledging the profound impact of Machine cognition on individual rights, security, and privacy. The research probes into equitable access to Machine Cognition technologies, ethical data utilization in elemental packing design, identity verification, and surveillance contexts. Central to this multidisciplinary inquiry is the integration of Hidden Markov Probabilistic Swarm Optimization (HMPSO). With swarm intelligence and probabilistic modelling, HMPSO enhances the efficiency, accuracy, and reliability of elemental packing Machine Cognition systems. It addresses the critical challenge of reducing false positives and false negatives in Machine Cognition authentication. The research methodology comprises performance evaluations, ethical analyses, and sociocultural investigations, offering a comprehensive view of the interplay between design innovation, machine vision cognition, ethical awareness, and the application of HMPSO in the elemental packing Machine Cognitions.
Evaluation Method of English-Speaking Self-Learning System Based on Natural Language Processing Technology
Authors: Xiaolan Guo Xiaolan Guo
Published: January 2024

In the education, the use of advanced natural language processing techniques has gained prominence for their potential to revolutionize the oral learning experience. Self-learning in English oral learning has become increasingly popular due to its flexibility and accessibility. With the advent of digital resources and language learning apps, students can now engage in language acquisition on their own terms. This paper presented a novel framework that combines the power of the Bidirectional Encoder Representations from the Transformers (BERT) model with the Hidden Condition Random Model (HCRM) for the enhancement of English oral learning. The primary goal is to provide educators and institutions with a robust tool for evaluating the relevance and quality of oral learning materials. The HCRM architecture incorporates sentiment analysis, feature extraction, and classification, making it a comprehensive solution for assessing the suitability of documents in the context of English oral learning. The model takes into account the opinions of both students and teachers, ensuring a holistic perspective on the oral learning materials' effectiveness. By effectively analyzing sentiments and extracting pertinent features, the HCRM facilitates a nuanced understanding of the potential impact of educational content. This paper's findings suggest that the integration of BERT with HCRM has the potential to greatly enhance English oral learning by providing a more accurate, holistic, and data-driven approach to material assessment. The innovative framework presented in this research holds promise for improving the quality and relevance of oral learning materials in the field of English instruction.
English Speaking Teaching Strategies Based on Natural Language Processing
Authors: Nan Sun Nan Sun
Published: January 2024

The study of cognitive processes in English teaching methods from the perspective of HPM (History and Pedagogy of Mathematics) provides valuable insights into how students learn and understand the English language. Cognitive research in language learning focuses on the mental processes involved in acquiring, processing, and using language skills. By incorporating the principles of HPM, which examines the historical development and pedagogical approaches in mathematics education, researchers can draw parallels and apply similar methodologies to English language instruction. This paper constructed Bloom’s taxonomy Fuzzy Decision Support System (BTF-DSS). Bloom’s taxonomy provides information about the teacher’s understanding towards the teaching. Through BTF-DSS model offers teachers provides the knowledge and expertise for effective teaching with the fuzzy model. The BTF-DSS model uses the fuzzy logic decision support system for the appropriate significant teaching and learning experience. The simulation analysis of the BTF-DSS model expressed that the fuzzy model exhibits significant performance for the English teaching method in China through the HPM process.
Network Attack Chain Security Model Construction Based On Attack Framework
Authors: Jian Hu Et al.
Published: January 2024

All facets of society's governance, economics, and culture have been impacted by networking. Network attacks have become more common as a result of the digital revolution, which has also facilitated major changes in worldwide communication and accelerated the development of human society. An unauthorized attempt to access a network with the goal of committing theft or other forms of damage is what we call a network attack. This article focuses on building a chain security model utilising the block chain concept in order to address the issue of inaccurate assessments of forged malicious behavioural methods for identification. It uses the NIK-256 hashing algorithm to identify valid users utilising time-dependent verification. Passwords and recorded times are maintained in a combined chain database, which combines blockchain technology and the Trusted Platform Module (TPM), improving data security and privacy. After that, we schedule users using the Whale optimisation algorithm (WOA), which decreases difficulty, and then a smart contract is developed that grants authorised users access control based on their level of trust and permission. The proposed approach, known as Hyb_chain_TPM, implements countermeasures in accordance with the attack risk level and stores the attack graph in a combined chain database for future attack forecasting. Utilising diverse attack datasets, extensive tests are run to validate this system. Additionally, the outcomes of privacy protection and AI processes are assessed independently and contrasted using a variety of current techniques.
Fuzzy Evolutionary Algorithm in Construction Project for Valuation
Authors: Huijing Li Huijing Li
Published: January 2024

Construction project valuation refers to the process of determining the economic value or worth of a construction project. This assessment is crucial for various stakeholders involved in the construction industry, including investors, lenders, developers, and contractors. Clustering algorithms has emerged as an important data mining technique for pattern recognition, data analysis and dimensionality reduction. Clustering is usually used in all fields to merge the same feature objects into a single group. The clustering method is incorporated with search algorithms to search the dataset from the databases. For large databases, there is a need of good clustering algorithm with high accuracy. Despite its high performance, the existing methods show some limitations. This paper focused on optimize the clustering method with a search structure for large multidimensional databases with dynamic indexes for the effective validation of construction projects. In the construction project validation to improve the clustering methods using Evolutionary Constrained Differential Optimization (ECDO) and Distributed Weighted Fuzzy C-Means algorithm to improve clustering performance than the centralized clustering approaches. The proposed methodology includes ECDO which presents new cluster tree indexing approach with cluster speed improvement in the validation of the construction projects. Distributed Fuzzy Possibilistic C-Means algorithm is proposed in improvement of cluster centre to overcome coincidence cluster problems. The comparative analysis stated that proposed ECDO model exhibits ~6% reduced memory utilization ~11% reduced scalability and ~8% minimized selectivity than the conventional WPCM model.
Multimedia Identification and Analysis Algorithm of Piano Performance Music Based on Deep Learning
Authors: Jing Yang Et al.
Published: January 2024

A Multimedia Identification and Analysis Algorithm is a computational method designed to recognize and analyze various forms of multimedia content, such as images, videos, and audio. In Piano Music, the study presents a pioneering research endeavor focused on the development of a multimedia identification and analysis algorithm tailored for piano performance music. With the power of deep learning techniques, this algorithm has been designed to address the intricate challenges posed by the convergence of musical data analysis and Piano Musics. The core objectives encompass the extraction, recognition, and interpretation of Piano Music information from piano performances, exploring the intricate patterns, nuances, and individualistic characteristics inherent to musicians. The study focuses on the development of advanced Piano Music authentication and identification systems capable of capturing and analyzing a user's unique behavioral patterns across diverse modalities. These Piano Music modalities offer the potential for highly secure and non-intrusive user identification. Hence, this paper developed an architecture of Marker Controlled Point (MCP) Estimation for the computation of the gesture in Piano Music-based applications. This architecture utilizes markers or reference points to precisely track and analyze user gestures, resulting in accurate and reliable Piano Music data. The research details the architecture's implementation, integrating advanced deep-learning techniques for feature extraction, pattern recognition, and authentication. This system finds versatile applications in various domains, from piano music and cybersecurity to finance, where secure and user-friendly authentication is paramount. Experimental results underscore the system's effectiveness and robustness, demonstrating its potential for enhancing Piano Music authentication. The proposed model represents a significant stride in Piano Music technology, offering secure, non-intrusive user identification through the synergy of behavior Piano Music and emerging modalities.
Consumer Preference Measurement of Folk Culture Based on Confidence Rule Base Model
Authors: Yuan Xie Yuan Xie
Published: January 2024

Deep learning algorithms can help uncover patterns, make predictions, or generate new content related to folk culture, thus bridging the gap between heritage and advanced technology. The confidence model in a cloud context refers to a system or approach used to assess the reliability, security, or performance of cloud services. It might involve factors such as service uptime, data security, scalability, and compliance with industry standards. In this paper focused on the dynamic landscape of consumer preferences within folk music through cloud-based technologies integrated with deep learning. Folk music, with its rich cultural diversity and historical significance, presents a unique context for investigating the intricacies of consumer taste. The proposed model uses the "Ranking" based deep learning within cloud-based resources to predict and classify consumer preferences effectively. With the integration of the cloud confidence model ranking is implemented for the estimation of tracks in folk music. The estimated tracks are evaluated and stored in the cloud environment based on the preferences of the customers. The classification of the tracks and consumer preferences are ranked with the cloud model features. The simulation results demonstrated that the ranking of tracks effectively improves consumer preferences with the cloud confidence model in folk music. The results enhancing personalized experiences and facilitating informed decision-making for businesses and cultural institutions operating in the rich and diverse landscape of folk culture.
Explainable AI-Powered IoT Systems for Predictive and Preventive Healthcare - A Framework for Personalized Health Management and Wellness Optimization
Authors: Uddhav T. Kumbhar Et al.
Published: January 2024

With the growing integration of Internet of Things (IoT) technologies and Artificial Intelligence (AI) in healthcare, it is crucial to prioritize transparency and interpretability in the decision-making process. This paper presents a novel framework that utilizes Explainable AI (XAI) to improve the interpretability of predictive healthcare models. The proposed system integrates feature importance-based methodologies with the Local Interpretable Model-agnostic Explanations (LIME) technique to offer a comprehensive comprehension of the predictive and preventive healthcare recommendations. The framework commences by conducting an in-depth examination of the present condition of Internet of Things (IoT) in the healthcare sector, as well as the importance of predictive and preventive healthcare. The literature review examines the difficulties related to the comprehensibility of artificial intelligence (AI) in the healthcare field and presents feature importance-based approaches and LIME as potential remedies. The focus is on the hybrid approach that combines these techniques, as it has the potential to offer precise predictions while also ensuring a strong level of interpretability. The methodology section delineates the procedure for gathering healthcare data and IoT sensor data, subsequently followed by preprocessing measures such as data cleansing and feature engineering. The predictive models undergo a process of selection, training, and evaluation, with the primary objective of attaining a notable accuracy level of 0.961. This text provides a detailed explanation of how the combination of feature importance-based approaches and LIME improves the transparency and interpretability of the model. An extensive case study is provided to illustrate the implementation of the suggested framework in an actual situation. The results and evaluation section showcases the exceptional precision of 0.961, as well as enhanced interpretability scores and decreased computational time in comparison to the baseline XAI models. The discussion section juxtaposes the suggested hybrid approach with conventional models, examines ethical considerations, and investigates the scalability and generalizability of the framework. To conclude, the paper provides a concise overview of the findings and implications of the Explainable AI-Powered IoT Systems for Predictive and Preventive Healthcare framework. This hybrid approach demonstrates high accuracy, improved interpretability, and efficient computational performance, making it a promising advancement in personalized health management and wellness optimization. This research adds to the expanding collection of literature on Explainable Artificial Intelligence (XAI) in the healthcare sector, thus opening up possibilities for future research avenues and practical applications in this domain.
Optimizing Resource Allocation in Cloud for Large-Scale Deep Learning Models in Natural Language Processing
Authors: Gauri Dhopavkar Et al.
Published: January 2024

The need for big deep learning models in Natural Language Processing (NLP) keeps rising, it's important to find the best way to divide up cloud resources so that they can be used efficiently and at high speeds. This solves the problems that come with setting up and handling large NLP models by suggesting a complete strategy for making the best use of cloud-based platforms' resources. Combining model parallelism, data parallelism, and dynamic scaling methods, the suggested approach spreads the computing load across multiple cloud instances in better way. The framework constantly changes how resources are allocated to handle changes in workload by taking into account the specifics of NLP tasks, such as the need for different model designs and data processing needs. To improve scale and cut down on inference delay, a new auto-scaling method is introduced that lets computing resources be changed automatically based on demand in real time. The framework uses machine learning-based prediction models to figure out what resources will be needed in the future. This lets you make proactive decisions about scaling and keeps you from underusing or overprovisioning resources. It also solves the problem of communication overhead in distributed environments by improving data exchange protocols and using advanced inter-process communication techniques. The results of the experiments show that the proposed framework works well at improving both cost-effectiveness and prediction performance for large-scale NLP models by making the best use of resources. The framework is flexible enough to work with a wide range of natural language processing (NLP) tasks. It makes a useful addition to the efficient use of deep learning models in cloud settings.
Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy
Authors: Pawan Kumar Mall Et al.
Published: January 2024

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.This study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%.