bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Pemberian Dehydroepiandrosterone (DHEA) terhadap Aktivitas Peroksidasi Lipid dan Kadar Nitrit Oksida pada Aorta Tikus Wistar Jantan yang Terpapar Asap Rokok
Authors: Ridhayani Nila , Arsyad Aryadi , Djabir Yulia Yusrin
Published: October 2022

The research aimed to investigate the effect of dehydroepiandrosterone (DHEA) on lipid peroxidation activity and nitric oxide concentration in the aorta of male Wistar rats due to exposure to cigarette smoke. This study was a laboratory experiment using 21 rats that were divided into 3 groups: KN-14 (negative control that is given smoke exposure within 14 days), DHEA-1 (15 mg/kgBW + smoke exposure for 14 days), and DHEA-2 (30 mg/kgBW + smoke exposure for 14 days). The exposure was carried out in 25-45 minutes with 4 cigarettes/day. Surgery was performed in the 15th days to remove the aorta to examine malondialdehyde (MDA) as a biomarker of lipid peroxidation and nitric oxide (NO) concentration using Enzyme-linked Immunosorbent Assay (ELISA). The results showed that a significant increase in MDA concentrations was found in the group administered with DHEA, particularly at a dose of 30 mg/kgBW (p<0.05). Meanwhile, the administration of DHEA at doses of 15 and 30 mg/kgBW did not have a significant effect (p>0,05) on aortic NO concentrations compared to the group KN-14. Therefore, it can be concluded that the administration of DHEA could increase lipid peroxidation activity, as indicated by a significant increase in aortic MDA concentrations, without affecting aortic NO concentrations in rats exposed to cigarette smoke for 14 days.Keyword: Dehydroepiandrosterone (DHEA), MDA, NO, lipid peroxidation, exposure of cigarettes smoke
Pengaruh Pemberian Dehydrepiandrosterone (DHEA) Terhadap Paru-Paru Tikus Wistar Jantan Yang Terpapar Asap Rokok
Authors: Asad Sri Hardiyanti , Arsyad Aryadi , Djabir Yulia Yusrini
Published: October 2022

This study investigated the effect of dehydroepiandrosterone on the lungs of male Wistar rats exposed to cigarette smoke. A total of 21 male Wistar rats were divided into 3 groups, namely the first group (negative control exposed to cigarette smoke for 14 days), the second group (given DHEA at a dose of 15 mg/kgBW and exposure to cigarette smoke), the third group (given DHEA at a dose of 30 mg/kgBW and exposure to cigarette smoke). DHEA was administered orally through a cannula 30 minutes before exposure to cigarette smoke with an exposure duration of about 25-40 minutes, equivalent to 4 cigarettes/day. After 14 days of experimentation, the rats were euthanized. Right lungs were harvested for malondialdehyde level measurement using the thiobarbituric acid reactive substance method measured by UV-Visible spectrophotometer at a wavelength of 532 nm. The left lungs were also harvested for histopathological examination. This study showed that pulmonary malondialdehyde levels in second and third group tended to be higher than in first group. Meanwhile, histopathological examination showed that the percentage of lung tissue damage in second and third group tended to be lower than in first group. Thus, it can be concluded that DHEA at doses of 15 and 30 mg/kgBW tended to act as a pro-oxidant but might potentially improve lung histology due to cigarette smoke exposure.
Analisis Fitokimia dan Pengaruh Ekstrak Etanol Daun Kayu Jawa (Lannea coromandelica) terhadap Pertumbuhan Bakteri Staphylococcus aureus
Authors: Pagarra Halifah , Sahribulan Sahribulan
Published: October 2022

Tanaman di Indonesia sangat beragam dan banyak diantaranya yang merupakan tanaman yang dapat digunakan oleh masyarakat sebagai tanaman obat. Salah satunya adalah tanaman kayu jawa. Tumbuhan kayu jawa dilaporkan mengandung senyawa metabolit sekunder seperti alkohol, steroid, triterpenoid, fenolik, flavonoid, tanin, dan saponin (Kumar,2015). Senyawa-senyawa ini termasuk golongan senyawa bioaktif yang menunjukkan aktivitas antioksidan, antiinflamasi, antibakteri, antikanker dan imunomodulator. Penelitian ini dilakukan pengujian fitokimia kandungan senyawa dari ekstrak etanol daun kayu jawa kemudian melihat pengaruhnya terhadap pertumbuhan bakteri Staphylococcus aureus. Metode yang dilakukan yaitu melakukan uji kandungan senyawa alkaloid, tanin, flavonoid dan saponin. Pengamatan pengaruh ekstrak etanol daun kayu jawa terhadap pertumbuhan bakteri Staphylococcus aureus dilakukan pengujian aktivitas antibakteri dari ekstrak etanol 96% daun kayu jawa dengan pemberian konsentrasi masing-masing 5%, 10%, 15%, 20%, 40%, 60%, dan 80%. Hasil penelitian menunjukkan bahwa ekstrak etanol 96% daun kayu jawa mengandung alkaloid dengan pereaksi Dragendorff, tanin, flavonoid, dan saponin. Pada pengujian aktivitas antibakteri dari ekstrak etanol 96% daun kayu jawa pada konsentrasi yang ditentukan menunjukkan adanya zona hambat yang terbentuk yaitu pada konsentrasi 5% sebesar 9.4 mm, 10% sebesar 11.6, 15% sebesar 14.4 mm, 20% sebesar 11.5 mm, 40% sebesar 15.6, 60% sebesar 18.7 mm, dan 80% sebesar 18.8 mm.
Pengaruh Formulasi Pakan Terhadap Kandungan Nutrisi Larva Black Solder Fly (BSF) Hermetia illucens.
Authors: Hartati Hartati , Chamila Ainun , Syamsiah Syamsiah , Jumadi Oslan , Kurnia Nani , Junda Muhammad , Sahribulan Sahribulan , Djawad Yasser Abd. , Harianto Fajar
Published: October 2022

Penelitian ini bertujuan untuk mengetahui pengaruh variasi formulasi pakan Black soldier fly (BSF) Hermetia illucens yang terdiri dari limbah ikan dan sayuran terhadap kandungan nutrisi larva BSF. Jenis penelitian ini adalah eksperimen dengan menggunakan 4.000 larva BSF yang telah memasuki fase instar dua. Pemeliharaan larva BSF dilakukan selama 15 hari. Pemberian pakan dilakukan sekali dalam 2-3 hari. Penelitian ini menggunakan Rancangan Acak Kelompok (RAK) dengan 3 perlakuan dan 3 kali ulangan. Perlakuan A yaitu 10% limbah ikan : 90% limbah sayuran, B yaitu 20% limbah ikan : 80% limbah sayuran, C yaitu 30% limbah ikan : 70% limbah sayuran. Parameter yang diamati yaitu Kandungan nutrisi dalam tubuh larva BSF dianalisis menggunakan analisis proksimat. Data hasil analisis proksimat dianalisis menggunakan analysis of variance (ANOVA) dan Uji lanjut Duncan dengan taraf kepercayaan 95%, Hasil penelitian menunjukkan perlakuan C dengan 30% limbah ikan dan 70% limbah sayur memberikan hasil yang terbaik dengan kandungan protein tertinggi dibandingkan perlakuan A dan B. Maka dapat disimpulkan bahwa variasi formulasi pakan berpengaruh signifikan terhadap kandungan nutrisi larva BSF Hermetia illucens. Kata kunci: Formulasi pakan, Nutrisi, Hermetia illucens.This study aims to determine the effect of variations in the feed formulation Black soldier fly (BSF) Hermetia illucens consisting of fish and vegetable waste on the nutritional content of BSF larvae. This type of study is an experiment using 4,000 BSF larvae that have entered the instar phase two. The maintenance of BSF larvae is carried out for 15 days. Feeding is carried out once in 2-3 days. This study used a Randomized Group Design (RAK) with 3 treatments and 3 repeats. Treatment A is 10% fish waste: 90% vegetable waste, B is 20% fish waste: 80% vegetable waste, C is 30% fish waste: 70% vegetable waste. The observed parameters, namely the content of nutrients in the body of BSF larvae was analyzed using proximate analysis. The data from the proximate analysis were analyzed using analysis of variance (ANOVA) and Duncan's advanced test with a confidence level of 95%, the results of the study showed that the behavior ofC with 30% of fish waste and 70% of vegetable waste gave the best results with the highest protein content compared totreatments A and B.   to the nutritional content of the larvae of BSF Hermetia illucens.Keywords: Feed formulation, Nutrition, Hermetia illucens.
Implementasi Model Pembelajaran Kooperatif Tipe NHT (Numbered-Head-Together) untuk Meningkatkan Hasil Belajar IPA Siswa Kelas IX B SMP Negeri 4 Alla Enrekang
Authors: Murwanto Sri
Published: October 2022

Hasil observasi di SMP Negeri 4 Alla Kabupaten Enrekang diperoleh fakta yang menunjukkan bahwa dalam proses pembelajaran masih banyak permasalahan di dalamnya yang dapat dilihat dari hasil belajarnya. Permasalahan tersebut diantaranya: guru masih mengajar menggunakan metode konvensional dan hasil belajar siswa yang masih rendah.  Sedangkan dari hasil tanya jawab dengan guru mata pelajaran IPA  rata-rata hasil belajar IPA siswa kelas IX B SMP Negeri 4 Alla Enrekang pada tahun pelajaran 2018/2019 awal semester ganjil masih sangat rendah. Masalah tersebut timbul akibat model mengajar guru yang masih bersifat monoton yaitu, guru lebih aktif dibandingkan siswanya. Hal ini tentu saja akan berdampak pada kebermaknaan belajar yang berujung pada keberhasilan belajar. Penelitian ini merupakan penelitian tindakan kelas (PTK) yang terdiri atas tahapan perencanaan, pelaksanaan tindakan, observasi, dan  refleksi. Subjek penelitian adalah siswa kelas IX B SMP Negeri 4 Alla Enrekang tahun pelajaran 2018/2019. Hasil belajar siswa diukur dengan menggunakan tes esay setiap akhir siklus. Hasil penelitian menunjukkan bahwa implementasi model pembelajaran kooperatif  type NHT (Numbered-Head-Together) dapat meningkatkan hasil belajar IPA Siswa Kelas  IX B SMP Negeri 4 Alla Enrekang.Kata kunci: Model pembelajaran kooperatif, Number-Head-Together, Hasil belajar IPAObservation results at SMP Negeri 4 Alla Enrekang Regency obtained facts that refer to the learning process there are still many problems that can be seen from the results of learning. This issue is issued: the teacher still uses conventional methods and student learning outcomes are still low. While the results of questions and answers with natural science subject teachers the average learning outcomes of students of class IX B of SMP Negeri 4 Alla in the 2018/2019 academic year at the start of the odd semester were still very low. The problem arises because the teacher's teaching model is still monotonous, that is, teachers are more active than their students. This of course will have an impact on meaningful learning that leads to successful learning. This research is a classroom action research (CAR) consisting of planning, implementing, observing, and reflecting. The research subjects were students of class IX B SMP Negeri 4 Alla  Enrekang in the 2018/2019 academic year. Student learning outcomes use essay tests at the end of each cycle. The results showed that the implementation of the NHT (Numbered-Head-Together) type of cooperative learning model could improve the learning outcomes of Science IX Class B students of SMP Negeri 4 Alla Enrekang.Keywords: Cooperative learning model, Number-Head-Together, Science learning outcomes.
Model SEIR Kecanduan Game Online pada Siswa di SMP Negeri 3 Makassar
Authors: Side Syafruddin , Muzakir Nurul Azizah , Pebriani Dian , Utari Syana Nurul
Published: October 2022

Penelitian ini membahas model matematika SEIR kecanduan game online. Data yang digunakan adalah data primer berupa jumlah siswa SMP Negeri 3 Makassar yang kecanduan game online yang diperoleh dengan cara membagikan angket kepada siswa. Penelitian ini dimulai dari membangun model SEIR kecanduan game online, menentukan titik keseimbangan, menganalisis kestabilan titik keseimbangan, menentukan nilai bilangan reproduksi dasar ( 𝑅0) , melakukan simulasi model menggunakan software Maple, dan menginterpretasikan hasil simulasi. Dalam artikel ini diperoleh model matematika SEIR kecanduan game online; dua titik keseimbangan, yaitu titik keseimbangan bebas kecanduan dan titik keseimbangan kecanduan; kestabilan titik keseimbangan bebas kecanduan dan kecanduan; serta bilangan reproduksi   dasar  𝑅0 = 0,089  yang   menunjukkan   bahwa   tidak   terjadi   penularan kecanduan dari satu individu ke individu lain.Kata kunci: Model Matematika, Kecanduan Game Online, Model SEIR. This research discuss a SEIR mathematical model of online game addiction. The data used is primary data of the number of students in Junior High School 3 Makassar who are addicted to online game which obtained by share the questionnaires to  students. This research starts from constructing a SEIR model of online game addiction, determining the equilibrium point, analysing the stability of equilibrium point, determining  the  basic  reproduction  number  (𝑅0),  doing  a  simulation  of  model  using Maple, and interpreting the result of the simulation. In this paper we obtained a SEIR mathematical model of online game addiction; two equilibrium points which are addiction free and addiction equilibrium point; the stability of addiction free and addiction equilibrium; and the basic reproduction value𝑅0 = 0,089 indicates that there is no transmission of addiction from one individual to another.Keywords: Mathematical Model, Game Online Addiction, SEIR Model
Geoinformational assessment of the interest of internet users in the border region to cross-border mobility
Authors: Mikhaylova Anna , Hvaley Dmitriy , Mikhaylov Andrey
Published: October 2022

Features of the economic-geographical position of the border regions are manifested, on the one hand, in their territorial proximity to foreign markets, international resource flows and infrastructure, and on the other hand, in their location on the periphery of the national spaces of neighboring states. In this regard, the economy of the borderland, in comparison with the inland regions, is more closely integrated into the international agenda and is more dependent on the permeability of the state border. An important component of the competitive potential of border areas is the development of the transport and logistics system, including its ability to ensure the efficiency and sustainability of international passenger and cargo transportation. The global course towards the digitalization of all social relations has also influenced such a traditional area of economic activity as transport. The growth of digital literacy and accessibility of the Internet, the expansion of the variety of information and communication channels and specialized digital services have created new opportunities for the border regions of neighboring countries to develop cooperation in relation to cross-border mobility of the population and joint management of this process. The digital environment is becoming an increasingly important component in maintaining the transport and logistics connectivity of border areas, and its development is accompanied by the generation and accumulation of an increasing amount of digital data about users. This article proposes an approach to assessing the geography of the interest of internet users of the border region in cross-border movement based on the analysis of big digital data. On the example of the Kaliningrad region (exclave territory of the Russian Federation), the level of attractiveness for Internet users of transport routes, covering the settlements of neighboring countries—Poland and Lithuania, located no further than 100 km from the border, was analyzed in the period from midJanuary to mid-March 2022. The data source is the Yandex Wordstat service. Based on the results of a study of search queries in relation to 194 cities of the Polish and Lithuanian adjacent border areas, two main types were identified—the final points of the route and transport hubs with outgoing mobility to other cities and countries. In total, the interest of users from the Kaliningrad region covered 130 international destinations. The conclusion is made about the transport connectivity of the border regions of Poland, Lithuania and the Kaliningrad region of Russia, however, the digitalization of this process remains off-system in nature.
Atlas information system “Regional Capitals of Southern European Russia”
Authors: Sopnev Nikolai , Belozerov Vitaly , Panin Aleksandr
Published: October 2022

Regional capitals are an important category of cities in the South of European Russia, where more than a quarter (25.6 %) of the population of the macroregion lives and a significant demographic potential of the region is concentrated. The south of European Russia includes regions with natural population growth which are rare for modern Russia, regions and capitals with high rates of migration growth, as well as territories with an unfavorable demographic situation. To study territories with such a differentiated multidimensional demographic situation, geoinformation technologies were used to consolidate statistical data from various sources, conduct spatial analysis, model and visualize the processes under study. The realization of this request is possible with the use of atlas information systems (AIS), which are actively used in various subject areas, including the study of socio-economic and demographic processes. As part of the research, we have developed an atlas information system “Regional Capitals of the South of European Russia”, consisting of 2 main blocks: a structural block includes a basic GIS platform, a cartographic framework, a geodata database and a modeling and visualization unit responsible for the preparation of cartographic and visual models. AIS contains 6 thematic sections: ethnodemographic and migration processes (“Population size”, “Demographic indicators”, “Migration indicators”, “Ethnic structure of the population”), “Covid-19” (contains indicators for the sick, deceased, cured of coronavirus infection, natural population growth), “Big Data (Big Data)” consolidating indicators on the real estate market and the urban environment quality index as important factors in the formation of the demographic and migration situation. This makes it possible to conduct modeling, thematic mapping, creation of various visual plots for comprehensive complex spatial analysis. It allows to increase the level of analytics in the process of making economically determined management decisions in the regional capitals of Southern European Russia. Additionally, the possibility of integrating AIS with Web services is described.
Mapping of transport mobility of the population of the Republic of Sakha (Yakutia)
Authors: Savvinova Antonina , Zakharov Moisey
Published: October 2022

The issues of the transport system development and the population mobility are becoming most relevant in the regions of Arctic and Far East Russia. The article is devoted to the study and mapping of the population transport mobility of the Republic of Sakha (Yakutia). Thematic maps were compiled: “Automobile and air transport network of the Republic of Sakha (Yakutia)”, “Intra-municipal aviation mobility of the Republic of Sakha (Yakutia) in 2020”, “Intra-municipal transport mobility by road of the Republic of Sakha (Yakutia) in 2020” presenting the regional transport system spatial differentiation of the Republic of Sakha (Yakutia) according to these indicators. We distinguished the territorial unevenness of the transport and logistics infrastructure due to the lack of year-round communications in many areas. As a result, there is a spatial contrast in the distribution of population mobility indicators. As a result of the analysis of indicators of intra-municipal aviation and transport mobility by road transport of the population in 2020, a grouping of regions of the Yakutia was made. The highest value indicators of transport mobility by road are charactered in Yakutsk, Aldan, Neryungri and Ust-Aldan municipals; in many Arctic regions, land transport mobility is at a low level. At the same time, the mobility indicator is affected by the motorization of the population and the development of transport services carried out by individual entrepreneurs and private individuals. A relatively high level of aviation mobility is inherent in the Arctic regions (Oleneksky, Bulunsky and Srednekolymsky), this is mainly due to the presence of subsidized flights on socially significant air routes. In comparison with the average indicators for Russia, there is a low aviation mobility, the level of which on local routes is 3 times lower than in Russia. in transport mobility of the inhabitants of Yakutia. The low level of development of local air and road passenger transportation is a consequence of the high cost of transportation in comparison with the income level of the population, which hinders the increase in transport mobility.
Cartographic support for the study of intra-agglomeration features of the demographic development of the Ivanovo urban agglomeration
Authors: Chikhichin Vasily , Soloviev Ivan , Ovsyannikov Evgeny , Zolnikova Julia
Published: October 2022

The article presents the results of a study of intra-agglomeration features of demographic and migration processes in the territory of the Ivanovo urban agglomeration. The purpose of the work is to visualize the spatial and temporal features of the demographic and migration situation in this territory using GIS technologies. Thematic maps have been constructed, on the basis of which the dynamics of the population of the Ivanovo urban agglomeration from 1989 to 2020 have been analyzed, the main trends in fertility, mortality and natural population growth have been identified, trends in migration processes have been identified. Official statistics data became the information basis for mapping. From 1989 to 2020, the population of the core of the Ivanovo agglomeration decreased by 1.2 times. The largest decrease was observed in rural areas (1.38 times). The main trend of the last three decades is a gradual increase in the area of depopulating agglomeration territories. Currently, the Ivanovo urban agglomeration is experiencing one of the deepest demographic crises in Russia. The stability of the demographic development of the Ivanovo urban agglomeration in conditions of deep depopulation of the population largely depends on migration processes. However, its migration attractiveness is very low. Demographic development has territorial differences within the agglomeration. The most favorable situation is developing in the satellite city of Kohme and the suburban Ivanovo district. During the pandemic, the natural decline of the population has significantly increased and there has been a tendency to change migration trends. Many territories with a steady outflow of population have experienced a migration increase due to the return of migrants to their homeland, which somewhat mitigates depopulation processes.
Pengabdian Kepada Masyarakat “Moderasi Beragama Untuk Penguatan Karakter Bangsa Di Tingkat Remaja Pada SMAN 2 Martapura Kalimantan Selatan”
Authors: Arief Muhammad Ihsanul , Maisarah Maisarah , Husin Gt. Muhammad Irhamna , Mailita Mailita , Ainah Noor , Yusuf Muchamad , Ramadhan Hisyam
Published: October 2022

Indonesia adalah negara yang multiagama dan multietnis. Keragaman negara ini memberi gambaran bahwa negara kepulauan ini memiliki kekayaan yang tidak dimiliki negara lain. Hubungan harmoni antar masyarakat harus kita jaga sebaik-baiknya agar jati diri bangsa ini memiliki marwah di mata dunia Internasional. Potensi ancaman kelompok ekstrim yang mengarah pada sikap radikal harus diantisipasi sejak dini. Para remaja harus dipersiapkan agar menjadi bagian yang mampu membentengi paham radikal yang tidak sejalan dengan ajaran agama yang moderat. Kegiatan pengabdian kepada masyarakat yang dilaksanakan oleh tim dosen merupakan bentuk sosialisasi dengan tema “Moderasi Beragama Untuk Penguatan Karakter Bangsa Di Tingkat Remaja Pada SMA Negeri 2 Martapura, Kalimantan Selatan”, yang memberikan kontribusi pemahaman terhadap siswa/siswi tingkat remaja. Pemahaman moderasi beragama bukan hanya sekeder konsep, tetapi.harus mengarah pada pengamalan dalam hidup sehari-hari. Ukuran keberhasilan dari kegiatan pengabdian ini dapat terukur dari hasil respon siswa dan siswi terhadap kemudahan dalam memahami materi dibuktikan dengan 32.8% menjawab sepakat atau bisa mereka pahami dengan baik. Dan sebanyak 25.4% menjawab sangat sepakat atau sangat mudah mereka pahami. Berikutnya respon terkait seberapa besar potensi materi yang telah disampaikan dapat diterapkan  melalui hasil respon jawaban “ya” sebanyak 77.7%. Terkait relevansi materi siswa dan siswi menjawab setuju sebanyak 33.4%, dan partisipan yang menjawab sangat setuju sebanyak 34.1%. Terakhir, respon siswa dan siswa terkait manfaat kegiatan, katagori sangat setuju yaitu sebanyak 64.1% . Siswa dan siswi yang menjawab setuju 23.3%. Semua respon jawaban memberikan kesimpulan materi moderasi beragama dapat dipahami dan diamalkan siswa dan siswi kalangan remaja. Hal demikian memberikan energi positif untuk selalu dilaksanakan kegiatan ini. Selain itu mereka pada hakikatnya penerus bangsa ini pada generasi selanjutnya. Jika tidak sekarang mereka dipupuk dengan paham cinta tanah air dan berbangsa yang setia, maka sulit kita banyangkan bagaimana Indonesia di masa depan.
Characteristics of Health Protocols and Incidence of COVID-19 in Young Doctors Complete Vaccinated
Authors: Kristianti Asti , Indriyana Sri Q , Fiddiyanti Ilma
Published: October 2022

COVID-19 is a respiratory disease that has currently become a pandemic in the world. West
Java is one of the provinces with the most cases of COVID-19 in Indonesia. Health workers

including young doctors are a group that has a very high risk of getting infected with COVID-
19. The purpose of this study was to determine the characteristics of health protocols and

COVID-19 cases in young doctors at Faculty of Medicine, General Achmad Yani University
(FK UNJANI) who were fully vaccinated against COVID-19. This study was designed as a
descriptive study with a cross-sectional approach. The characteristics assesses in this study
were: age, gender, and a confirmed history of COVID-19. Data was collected in June 2021-
September 2022 using google form to fill out a questionnaire. The questionnaire was filled out
by 105 eligible respondents. Respondents have received at least 2 doses of the COVID-19
vaccine and are aged between 22-23 years. The characteristics of respondents were 71 females
(68.57%) and 33 males (31.43%). A total of 78.1% had confirmed COVID-19 and 21.9% had
never been infected. A total of 52,4% respondents had good health protocols and 47.6% had
poor health protocols.
Cardiac damage in an adolescent patient with COVID-19: a case report
Authors: Pujowaskito Prihati , Tartila Tamia S , Tafriend Novaro A , Jannah Fatimah D K , Mayasari Elsy
Published: October 2022

COVID-19 has been found to affect the cardiovascular system leading to myocardial damage.
A study of 41 patients in Wuhan, China, found that 12% of COVID-19 patients experienced
virus-related acute cardiac damage.Subsequent bigger Chinese studies also found acute cardiac
damage in 7.2% to 27.8% of hospitalized patients. As a chronicsequela, this condition may
result in cardiomyopathy. We report acase of an adolescent COVID-19 survivor with dilated
cardiomyopathy with no underlying heart disease. A male patient aged 16 years old was
admitted to our outpatient clinic with the primary symptom of exhaustion and had recovered
frommild to moderate COVID-19 one month prior to the visit. No previous history of heart
disease was documented. Physical examination showed no abnormalities. Laboratory results
revealed substantially elevated NT-proBNP (7705 pg/mL) and D-dimer (1850 ng/mL). ECG
presented normal sinus rhythm with poorR wave progression. Echocardiography revealed all
chamber dilatation, eccentric left ventricular hypertrophy, globalhypokinetic, moderate mitral
regurgitation, and reduced ejection fraction (22%). We diagnosed the patient with new-onset
dilated cardiomyopathy and began treatment with candesartan, bisoprolol, furosemide,
spironolactone, rivaroxaban, and trimetazidine. The recovery was steady at three-month
follow-up visit. The emergence of new-onset cardiomyopathy in this previously healthy
adolescent raisesthepossibility of COVID-19 acting asthe sole cause of myocardial injuryin the
absence of underlying heart disease. To avoid further complications, comprehensive evaluation
and effective therapy should be implemented during hospitalization and post-discharge.
Additional tests such as cardiac magnetic resonance imaging and endomyocardial biopsies
shouldbe performed to support final proof.
Description of injury to the victim of motorcycle traffic accident
Authors: Rusman Andri A , Aflah Livaldi N , Kusuma Andri A
Published: October 2022

Road accidents are events that can lead to injury or death. The number of fatalities from traffic
accidents is the eighth highest in the world. In Southeast Asia, up to 316,000 people die each
year in road accidents. Motorcycles are the type of vehicle with the highest number of accidents
compared to other types of vehicles. The increase in motorcycle purchases in Indonesia is
associated with an increase in road accidents. Road accident injuries can occur on multiple parts
of the body and can cause different types of injuries. The purpose of this study is to determine
the description of the victims of motorcycle traffic accidents in Southeast Asia. This research
is a descriptive study using literature study method by searching various literature that uses
electronic databases such as Google Scholar, ResearchGate, PubMed, NCBI, WHO, Ministry
of Health, ScienceDirect, and Springer to search related literature. Keywords used when
searching for items are injuries, car accidents, motorcycles, and Southeast Asian descriptions.
A total of seven articles were selected for analysis using synthetic matrices, and the results
showed that working-age and male gender were most frequently involved in motorcycle road
accidents. Extremities injuries are the most common site of injury and the majority of injuries
obtained are soft tissue injuries. In order to add data on the most common types of injuries to
motorcycle traffic accident victims in Indonesia, this study needs to be further conducted in
Indonesia on the most common types of injuries to motorcycle traffic accident victims.
The contributions and correlations of two theoretical models for the concept of social and urban sustainability: Rio (2015) and Rio and Amorim (2017)
Authors:   Fundação Gaúcha do Trabalho e Ação Social, Borges de Amorim Diego Felipe , Rojas del Rio Javiera Andrea ,   Fundação Gaúcha do Trabalho e Ação Social
Published: October 2022

The dialogical emergence of mesoanalytic rules as a potential guide to the interpretation of social phenomena, dynamic and complex in nature, is something new in the search for clues that help to polish the concept of sustainability, especially the Social and Urban Sustainability-SSU. In this sense, the present research sought to correlate two theoretical models of SSU, Rio (2015) and Rioand Amorim (2017), in order to identify the convergences and the contributions of each one to the construction of the SSU concept. In order to achieve this objective, a qualitative, exploratory and comparative study was proposed, in line with the Multivariable Analysis Theory and with the method known as Quantitative Propositional Analysis (APQ) for the transformation of qualitative and quantitative data. The results pointed to the convergence of the understanding of both on the structural bases of the SSU model through the use of mesoanalytic rules for the interpretation of complex social variables; Complexity Theory and Multilevel Analysis, the concepts of hospitality and hostility, and Learning Theories as fundamental tools for the search for a broader and deeper interpretive route of the complex social dynamics from which the SSU emerges. The conclusion indicated the reinforcement of the unique, complex, dynamic and cyclical character of the actions and norms of each community that one wishes to study, so that the conception of a theoretical model of SSU must be constantly tested, revised and developed, in line with reality of each community, in order to improve the emerging SSU concept.
New Data Processing Method for Acoustic Emission Signals for Metalworking Fluid Classification
Authors: Wei Xiao , Soeffker Dirk
Published: October 2022

Metalworking fluid (MWF) differentiating is of important significance as their additives affect metalworking process extremely so the evaluation of MWF should be suitable conducted. Acoustic Emission (AE) measurements can be easily taken as process-close measures for evaluating cutting and forming processes as well as MWFs performance. In thread forming process, AE measurements from different kinds of MWFs could be – related to the position of the tool - divided into different process phases: air, forward, and reverse phases. From physical view, forward part contains most useful AE data. Therefore, extracting forward part data from the overall signal is significant for MWFs classification. Because boundaries among different parts in time domain of AE signals are not clear, in this contribution, a new data processing method is proposed to abstract the forward part data from non-related parts of the whole measurement signal. For the first time, scalogram is applied to find the boundaries in time domain as an aid. Firstly, the intact measurement signal is transformed from time domain to time-frequency domain by continuous wavelet transform (CWT) and scalogram is acquired. As boundaries among different phases are obvious in scalogram, by reverse calculation, boundaries’ location in time domain could be defined and forward part data of each measurement are picked out. Afterwards, data in forward phase are divided into different samples and each sample contains data of one round. Finally, samples’ features are extracted and classified by convolutional neural network (CNN). By adjusting CNN structure and hyperparameters with cross validation method, features in time domain could be distinguished well. For five kinds of testing MWF, the classification accuracy is as high as 98.11 %. For reference, oil-based, and water-based MWF classification, the results can reach to 98.94 %. Accuracy for water-based MWF distinction is 97.55 % while for oil-based MWF distinction is 98.29 %. Comparing with results using the whole AE measurements, these results improve significantly. Good results show that the proposed data processing method could extract most useful information from the whole AE measurements in time domain for MWF distinction. Besides, the proposed method also provides an effective way for data analysis in the future.
Towards Predictive Maintenance of a Heavy-Duty Gas Turbine A New Hybrid Intelligent Methodology for Performance Simulation
Authors: Liu Yiyang , Jiang Xiaomo
Published: October 2022

As the increasing demands of global clean power for the main purpose of lowering environmental pollution, heavy-duty gas turbines are playing an increasingly important role in energy fields because of their low emission, high thermal efficiency and flexible start-up capacity.
Accurate modeling and simulation of the turbine performance is extremely needed to precisely design a large-watt industrial heavy-duty gas turbine, which is still very challenging due to the high nonlinearity, system complexity, varying conditions, and strong coupling interaction of high-dimension parameters under the harsh operation environment of the turbine.
In order to improve the accuracy and efficiency of the performance simulation model for a heavy-duty gas turbine at various operating conditions, this paper presents a new hybrid intelligent methodology to adeptly integrate system thermodynamic balance mechanism with multivariate data by seamlessly combing advanced signal processing, machine learning and artificial neural network modeling techniques. The thermodynamic model of a complicated single-shaft gas turbine is first created based on the balances of both flow and power in various subsystems including inlet, compressor, turbine, combustor and exhaust. The characteristic curves of compressor and turbine are utilized to accurately represent the physical mechanism and effectively simulate the high nonlinear behaviors of subsystems. Wavelet signal processing and machine learning based feature extraction are employed to preprocess the multivariate raw data of the turbine. Multilayer artificial neural network models are explored to efficiently simulate the start-up transient process of the turbine, thus improve the simulation efficiency and accuracy of the complicated system. Multivariate data collected from a real-world industrial heavy-duty gas turbine is employed to illustrate the effectiveness and feasibility of the proposed methodology.
Discussion on the Differences Between the Cyclic Spectral Correlation and Coherence for Cyclostationarity-Based Condition Monitoring
Authors: Kestel Kayacan , Peeters Cédric , Antoni Jérôme , Léclere Quentin , Girardin Franc ̧ois , Brijder Robert , Helsen Jan
Published: October 2022

Vibration signals measured on rotating machinery typically exhibit cyclostationarity due to the inherent nature of real-world rotating vibration sources. Hence, the development of signal processing tools devoted to investigating or exploiting this cyclostationarity for condition monitoring purposes of gears and bearings has seen a significant increase in research interest. One of the main approaches to analyze a vibration signal’s cyclostationary behavior is the cyclic spectral correlation and its normalized derivative the cyclic spectral coherence. Even though these two methods are closely related, they do offer different statistical insights which may influence the fault detection and trending capabilities of these tools. The aim of this work is to investigate the performance of these two methods with regard to the accuracy of tracking mechanical degradation over time. The normalization of the spectral coherence which makes it independent of the signal power spectrum improves the interpretability of the resulting coherence spectrum but it may lead to suppress or equalize fault-related frequency bands relative to other frequency bands and it may skew the coherence spectrum amplitudes of fault harmonics in different operating regimes for complex machinery. Tracking the evolution of a second-order cyclostationary component over time might thus be hindered by this normalization which can lead to issues when combining such a tool with a data-driven machine learning technique that employs the operating conditions for making the cyclostationary indicators operating condition independent. Instead, using the cyclic spectral correlation which is not normalized, may provide a more accurate depiction of the degradation process. This paper investigates whether there is any significant benefit to using the cyclic spectral correlation over the cyclic spectral coherence for monitoring complex rotating machinery and if so, when does it make sense to prefer one over the other.  To answer these questions, both simulated and experimental vibration data is examined in order to highlight the differences between the two concepts.
Voltage-Based Physical Layer Fault Diagnosis for Controller Area Network
Authors: Bani Milhim Alaeddin , Ramadhan Hadyan , Kazemi Hamed , Du Xinyu , Jiang Shengbing , Sadjadi Hossein
Published: October 2022

Controller Area Network (CAN) is the most prevalent communication protocol used in the automotive industry. This in-vehicle network provides a means communication between Electronic Control Units (ECUs) and components within the vehicle. The recent rapid development of connected, electric, and autonomous vehicles expands the complexity and information exchange within CAN and demands an increase in the reliability of the network. Efficient system-level diagnosis functions need to be integrated over the network to ensure for reliability and enhance the ease of troubleshooting.

This paper presents a method to identify physical CAN faults such as loss of electrical connections and shorted wires. Fault signatures of predefined physical CAN faults are used to detect and identify the failure modes. The method can identify both permanent and intermittent faults caused by, for instance, damaged connectors and vibrations, respectively.

Diagnosis tasks are implemented on in-vehicle module by measuring and processing physical layer voltages of all CAN buses. A real-time data buffer of a predefined size is utilized to calculate health indicators from the physical layer CAN voltages. The health indicators are then compared to predefined thresholds to determine the presence and type of the fault. Compared to ground truth data, the results show that the presented method can identify with high accuracy physical CAN faults including open electrical connection and shorted wires.
Accelerometer-Based Bearing Condition Indicator Estimation Using Semi-Supervised Adaptive DSVDD
Authors: Meire Maarten , Brijder Robert , Dekkers Gert , Karsmakers Peter
Published: October 2022

To prevent unexpected and costly asset downtime, an accurate estimate of the assets condition is needed. In such prognostics setting typically a Condition Indicator (CI) score is calculated based on measurement data. When a future CI surpasses some predefined threshold an alarm can be automatically triggered. Recently, in the literature (data-driven) Deep Learning (DL) methods were proposed to estimate the CI. However, to the best of our knowledge such DL algorithms were never validated in terms of the quality of the CI they produce. In this work the CI estimation performance of Deep Support Vector Data Descriptor (DSVDD) was validated and compared to autoencoder and convolutional neural network alternatives using data from a rolling element bearing setup that is measured by an acceleration sensor. Furthermore, since there can be a distributional shift between data from the training and test runs, which might lead to a degradation of the model performance, both the effect and computational complexity of various SVDD model adaptation strategies on the model performance are studied. Only adapting the final layer of the model gives a performance comparable to that when the full model is adapted to the target domain while requiring less calculations. The paper also proposes a simple and easy to calculate center adaptation strategy. This procedure had a slightly reduced CI estimation performance compared to the previous two adaptation alternatives but does not require any DL training.
Development of an End State Vision to Implement Digital Monitoring in Nuclear Plants
Authors: Walker Cody , Agarwal Vivek , Ramuhalli Pradeep , Lybeck Nancy
Published: October 2022

Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, thereby lowering the costs associated with parts, labor, and costly planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from routine-based monitoring to online monitoring by taking advantage of advancements in sensors and wireless communication technologies. Advancements in data storage, mapping, management, and analytics would inform the transition from onsite- to cloud-based computing and storage services. Online monitoring would reduce the number of operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained all while scaling to both computational and storage demands. This paper summarizes an end state vision of how to shift from costly, labor-intensive preventative maintenance to cost-effective predictive maintenance.
Data Quality Scorecard for Assessing the Suitability of Asset Condition Data for Prognostics Modeling
Authors: Lukens Sarah , Rousis Damon , Baer Travis , Lujan Michael , Smith Marshall
Published: October 2022

High efficacy algorithm development for prognostics requires quality data from sensors and other contextual sources, such as maintenance, usage and inspection data. Data quality challenges, such as lack of sensor-based history (depth) across the entire fleet of components (breadth), can prohibit the ability to develop algorithms which are both cost-effective and useful. Therefore, the first step in prognostics modeling is determining the sufficiency of the data required to support the development of predictive algorithms. We present an assessment process for determining data suitability in the development of prognostic models based on available data that determines which modeling approaches are feasible, allowing for a first determination of decision-making for data adequacy. The assessment process follows a full data quality framework which also identifies where data eligibility and quality may be further enhanced using advanced technologies for data quality improvement approaches such as imputation, increasing the probability of obtaining the required data needed for the successful development of predictive algorithms. Use of this framework maximizes the quantity of quality data harvested from industrial data sources, increasing the probability of obtaining the required data needed for the successful development of predictive algorithms. Additionally, repeating this assessment as further data becomes available enables further expansion of the set of usable prognostic models as data availability grows.
An Ontology for Prognostic Health Management in Spacecraft Avionics
Authors: Halvorson Michael Cullen , Moyers Noah , Thomas I. Dale
Published: October 2022

Prognostic Health Management (PHM) is the disciplined application of measurement, monitoring, and support strategies to protect structural, electrical, or data entities precluding the failure of measured systems in all phases of operation. Model-Based Systems Engineering (MBSE) can be used to formalize system structure, operations, behavior, and requirements using an Architecture Framework (AF), Process Framework (PF), modeling language, and ontology; whereas the AF, PF, and modeling language may be specific to the program or mission employing MBSE, ontologies may be developed specific to a given domain. The PHM domain considers failure modes, effects, and criticality, and ontological system analysis in this domain can inform system structure, operations, behavior, or requirements. A reference ontology for the PHM domain in spacecraft avionics is presented here including aspects of existing ontologies such as the Basic Formal Ontology (BFO), a Top-Level Ontology (TLO) newly recognized by the International Organization for Standardization (ISO), the Information Artifact Ontology (IAO), and the Space Object Ontology (SOO). A distinction is made between a full PHM domain ontology, which would include many mechanical or electrical systems with myriad purposes, and a PHM domain ontology specific to spacecraft avionics. Present ontological development originated using the parlance and format of BFO and IAO in Stanford University’s Protégé software but diverged to include International Union of Pure and Applied Chemistry (IUPAC) terminology and classifications. When interacting with this ontology, engineers seeking to characterize system-specific failure modes, effects, and criticality can query the ontology with their hardware or software entities to obtain failure information specific to the operation of their system in a given operational environment. While this domain ontology is robust, the authors do not claim it to be complete or validated for all spacecraft avionics. It should be considered version one of a useful PHM tool with continual updates occurring after peer review and feedback.