bluuebunny/crossref_metadata_2025 huggingface dataset:
Does Humor Matter? An Analysis of How Hard News versus Comedy News Impact the Agenda-Setting Effects
Authors: Kowalewski Jennifer
Published: May 2023

Although hundreds of studies have investigated public opinion formation through agenda-setting research, many scholars have not examined how atypical news programs, such as comedy news, might impact the transfer of issue salience from the media’s agenda to the public’s agenda. With the increasingly popularity of these programs, scholars need to examine if comedy news, such as The Daily Show with Jon Stewart and The Colbert Report with Stephen Colbert, impact agenda setting effects, and how that might compare to typical hard news programs, such as those seen on CNN, MSNBC, or Fox News. In an experiment, this study tested how individuals who received the same information only presented differently, namely either hard news or comedy news, reacted to the different presentation styles; and how those different presentation styles impacted individuals’ acceptance of the media’s agenda, taking into account their existing attitudes. Results indicated that when individuals agreed with the information presented in the news story, the hard news was more successful in the transfer of issue salience than the comedy news; however, when individuals disagreed with the information, the comedy news was more successful in the transfer of issue salience than the hard news. Overall, the results indicate typical hard news does not have a monopoly on the agenda-setting process because comedy news can set the agenda of audience members under certain conditions.
Are You Talking To Me? The Social-Political Visual Rhetoric of the Syrian Presidency’s Instagram Account
Authors: Holiday Steven , Lewis Matthew J , LaBaugh Jack L.
Published: May 2023

In 2013, the Syrian presidency used Instagram to frame itself in a mediated way to two distinct audiences—one English language, and the other Arabic language. This study used Grounded Theory to examine the recurring themes of the visual rhetoric and quantitative content analysis to identify the thematic differences that were visually communicated to each audience. Results indicated statistically significant differences in the visual presentation of the rhetorical themes of patriotism and nationalism, more prevalently communicated to the English language audience, and empathy, more prevalently communicated to the Arabic language audience. The study discussed implications in media gatekeeping, socio-political communication, and reputation management.
The Miracle Question: The Islamic ideal through the prism of Senegalise Sufi Brotherhoods
Authors: Wotkyns III R.S. Kip
Published: May 2023

The biggest news story in journalism for the past two decades has been chronicling the rise of radical Islamic terrorism. But, what do Muslims want? What would the world be like if they achieved their goals? If Muslims went to sleep one magical night and a miracle occurred, what would their ideal world be like upon awakening? What if all the problems were gone? I posed the “miracle question” to Sufi Islamic scholars, marabouts, disciples of Sufi Brotherhood orders, the Tijaniyyah, the Mourides and the Layene Brotherhoods in Senegal, West Africa in June 2015. I asked them to envision how the future would be different if the Sufi Islamic ideal was achieved. This research uses narrative inquiry to examine the endgame of Islam.
Cigars, Castro and Communism: Country Concept as a factor in Cuban tourism advertising effectiveness
Authors: Fullerton Jami , Kendrick Alice , Broyles Sheri
Published: May 2023

A study conducted prior to the historic announcement about resumption of diplomatic relations between Cuba and the United States measured US adults’ attitudes toward Cuba as well as their interest in visiting the island nation. Within the framework of the Model of Country Concept, this paper presents U.S. respondent attitudes toward Cuba as well as whether those attitudes moderate the effectiveness of a Cuban tourism television commercial. The study is the first to couple a scale of Country Reputation and a measure of Country Image in an effort to arrive at Country Concept and its potential to moderate advertising effects.
Mobile Communication and Food Trucks in an Urban Environment: A Case Study
Authors: Edwards Jennifer , Maben Sarah K.
Published: May 2023

The purpose of this study is to discover the interactions between mobile food truck operators and their external/internal audiences via Twitter. Researchers examined the tweets (n=483) that emerged from patrons and owners of seven food trucks within a metropolitan food park within a onemonth period. A content analysis of Twitter feeds for multiple trucks provided a glimpse into one social media channel and that communication among mobile food truck operators and their external groups (loyal customers and potential customers). Results indicate tweets fell into three categories of basic Twitter-based communication, truck presence, and marketing and public relations. Tweets created social presence by linking the digital food truck to the physical truck. Messages were one-way in nature, and geared more toward marketing and less toward engaging customers and creating a community.
Socially Mediated Stranger Things: Audience Cultures and Full-Season Releases
Authors: Pouls Samantha , Gilpin Dawn
Published: May 2023

Research on social television has primarily emerged based upon regularly scheduled broadcasts that fans could discuss more or less simultaneously in real time over the course of emergent narratives. The advent of full-season releases on streaming services has disrupted those patterns and prompts a re-examination of audience behaviors and the theoretical models that seek to describe and explain them. The present article proposes an “atomized engagement model” as an alternative to the linear model of social television for today’s nonlinear television environment.
A Importância e Centralidade dos Aeroportos Brasileiros no Tráfego Regular de Passageiros
Authors: Brito Eliane , Silva Jorge Miguel , Baltazar Maria Emília , Martins Bárbara
Published: May 2023

A rede aeroportuária brasileira passou por alterações significativas a partir de 2000. Houve mudanças na estrutura de propriedade, gestão e operação, destacando-se o processo de concessões aeroportuárias. A rede possui uma estrutura flexível e sujeita à interferência de fatores externos. A importância e centralidade dos aeroportos sofreram variações no período 2000-2020. O estudo aplica o indicador de confiabilidade, analisando os impactos da pandemia Covid-19. O indicador tem como base a centralidade dos aeroportos e permite classificar os mesmos em relação à continuidade operacional (contextos crítico, preocupante e adequado). Os resultados confirmam o indicador como ferramenta que permite uma análise preliminar de redes aeroportuárias, no segmento doméstico regular.
A participação pública nos Planos Diretores Municipais de 3ª geração em Portugal: entre a oportunidade e a formalidade
Authors: Valente Rafael , Dias Ricardo Cunha , Guedes Vidal Diogo , Seixas Paulo Castro
Published: May 2023

Esta pesquisa analisa as estratégias de promoção e transposição da participação pública adotadas pelos municípios portugueses no quadro da recente (2014-2020) reformulação dos Planos Diretores Municipais (PDMs). Metodologicamente, a pesquisa centrou-se no estudo de caso dos 18 municípios da Área Metropolitana de Lisboa (AML), através de uma análise de conteúdo aos documentos revistos dos PDMs disponibilizados nos sítios online desses municípios. Os resultados demonstraram uma ausência de preocupação dos municípios da AML com a participação pública nos PDMs, sendo que nos casos em que a mesma existiu, foram utilizadas estratégias maioritariamente de apaziguamento. O contributo deste texto foi o de identificar tais práticas/estratégias de promoção e transposição da participação cidadã num quadro aplicado, ao mesmo tempo que se evidenciaram algumas das lacunas que persistem nesses processos.
Outlier detection based on the functional coefficient of variation
Authors:   Econometrics Dept., Dokuz Eylul
            University, Izmir, Turkey, Deveci Kocakoç Ipek , Köymen Keser Istem ,   Econometrics Dept., Dokuz
            Eylul University, Izmir, Turkey
Published: May 2023

The coefficient of the variation function is a useful descriptive statistic,
especially when comparing the variability of more than two curve groups, even when they
have significantly different mean curves. Since the coefficient of variation function is
the ratio of the mean and standard deviation functions, its particular property is that
it shows the acceleration more explicitly than the standard deviation function. The aim
of the study is twofold: to show that the functional coefficient of variation is more
sensitive to abrupt changes than the functional standard deviation and to propose the
utilisation of the functional coefficient of variation as an outlier detection tool.
Several simulation trials have shown that the coefficient of the variation function
allows the effects of outliers to be seen explicitly.
Modelling the volatility of African capital markets in the presence of the Covid-19
pandemic: evidence from five emerging economies in Africa
Authors:   Deartment of Statistics, Osun
            State University, Osogbo, Nigeria, Olawale Adeboye Nureni , Oluwabukonla Folorunso Sakinat ,   Department of Mathematical
            Sciences, Olabisi Onabanjo University, Ago Iwoye, Nigeria, Abimbola Olawale Victor ,   Data Science Nigeria,
            Nigeria, Yinka Akinbo Rasaki ,   Department of Mathematical
            Sciences Federal Polytechnic Ilaro, Nigeria
Published: May 2023

The growing concern over the global effects of the COVID-19 pandemic on every
aspect of human endeavour has necessitated a continuous modelling of its impact on
socio-economic phenomena, allowing the formulation of policies aimed at sustaining
future economic growth and mitigating the looming recession. The study employed
Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH)
procedures to develop stock volatility models for the pre- and COVID-19 era. The
Fixed-Effects Two Stage Least Square (TSLS) technique was utilised to establish an
empirical relationship between capital market volatility and the COVID-19 occurrence
based on equity market indices and COVID-19 reported cases of five emerging African
economies: Nigeria, Egypt, South Africa, Gabon and Tanzania. The stock series was made
stationary at the first order differencing and the model results indicated that the
stock volatility of all the countries responded sharply to the outbreak of COVID-19 with
the average stock returns of Nigeria and Gabon suffering the most shocks. The forecast
values indicated a constant trend of volatility shocks for all the countries in the
continuous presence of the COVID-19 pandemic. Additionally, the confirmed and death
cases of COVID-19 were found to increase stock prices while recovered cases bring about
a reduction in the stock prices in the studied periods.
Bayesian estimation of fertility rates under imperfect age reporting
Authors:   Department of Statistics, Assam
            University, Silchar, Assam, India, Verma Vivek , Nath Dilip C. ,   School of Applied and Pure
            Sciences, Royal Global University, Guwahati, Assam, India, Dwivedi S. N. ,   All India Institute of
            Medical Sciences, New Delhi, India
Published: May 2023

This article outlines the application of the Bayesian method of parameter
estimation to situations where the probability of age misreporting is high, leading to
transfers of an individual from one age group to another. An essential requirement for
Bayesian estimation is prior distribution, derived for both perfect and imperfect age
reporting. As an alternative to the Bayesian methodology, a classical estimator based on
the maximum likelihood principle has also been discussed. Here, the age misreporting
probability matrix has been constructed using a performance indicator, which
incorporates the relative performance of estimators based on age when reported correctly
instead of misreporting. The initial guess of performance indicators can either be
empirically or theoretically derived. The method has been illustrated by using data on
Empowered Action Group (EAG) states of India from National Family Health Survey-3
(2005–2006) to estimate the total marital fertility rates. The present study reveals
through both a simulation and real-life set-up that the Bayesian estimation method has
been more promising and reliable in estimating fertility rates, even in situations where
age misreporting is higher than in case of classical maximum likelihood
estimates.
New generators for minimal circular generalised neighbour designs in blocks of two
different sizes
Authors:   Department of Statistics, The
            Islamia University of Bahawalpur, Pakistan, Nadeem Muhammad , Noreen Khadija ,   Department of Statistics,
            The Islamia University of Bahawalpur, Pakistan, Kashif Rasheed H. M. ,   Department of Statistics,
            The Islamia University of Bahawalpur, Pakistan, Ahmed Rashid ,   Department of Statistics,
            The Islamia University of Bahawalpur, Pakistan, Ul Hassan Mahmood ,   Department of Statistics,
            Stockholm University, Stockholm, Sweden
Published: May 2023

Minimal neighbour designs (NDs) are used when a response of a treatment (direct
effect) is affected by the treatment(s) applied in the neighbouring units. Minimal
generalised NDs are preferred when minimal NDs cannot be constructed. Through the method
of cyclic shifts (Rule I), the conditions for the existence of minimal circular
generalised NDs are discussed, in which v/2 unordered pairs do not appear as neighbours.
Certain generators are also developed to obtain minimal circular generalised NDs in
blocks of two different sizes, where k2 = 3, 4 and 5. All these designs are constructed
using i sets of shifts for k1 and two for k2.
Dynamics of survey responses before and during the pandemic: entropy and
dissimilarity measures app
Authors:   SGH Warsaw School of Economics,
            Poland, Tomczyk Emilia
Published: May 2023

This article is set within the framework of studies focusing on the impact of the
SARS-CoV-2 virus on the dynamics of economic activity. For the purposes of the analysis
of the expectations expressed in business tendency surveys, the paper aims to verify
whether the pandemic of 2020-2022 can be seen as just another contraction phase. Entropy
and dissimilarity measures are employed to study the characteristics of the expectations
and assessments expressed in the business tendency survey of Polish manufacturing
companies. The empirical results show that the dynamics of the manufacturing sector
data, particularly as far as general economic conditions are concerned, set the pandemic
period apart. The economic consequences of the COVID-19 pandemic expressed in business
tendency surveys tend to be unfavourable, but the statistical properties or the degree
of the concentration of respondents’ answers do not correspond closely either to the
expansion or contraction phases of the business cycle.
Ability to Identify Occupational Health and Safety (OHS) Hazards in Small Sized Enterprises Workers in Cimanggis District, Depok City, West Java
Authors: Tarigan Samuel Peratenta , Ramdhan Doni Hikmat , Nuridzin Dion Zein , Nugraha Fajar , Susetyo Heru
Published: May 2023

Background: The high number of SMEs players in Depok has an impact on high employment. A large number of workers in SMEs that have not been maximized in applying safety and health aspects in the workplace has the risk of causing several problems such as minor injuries, ergonomic problems, old and insecure equipment, lack of workers' knowledge and poor work environment conditions. Objective: This study aims to assess the ability of workers to recognize OHS hazards in SMEs assisted by Cimanggis District Health Center, Depok City, West Java. Methods: This study used a cross-sectional design carried out on 36 SMEs assisted by Cimanggis Health Center, Depok City, West Java. In each of the selected SMEs, one worker was then interviewed using the ODK Collect application to assess characteristics, knowledge, attitudes, behavior, and assessment of the ability to recognize OHS hazards in SMEs. Results: The results of the study showed that only 41.7% of SMEs were able to recognize OHS hazards properly. In addition, the results show that there are more who have good knowledge (53%), poor attitudes (64%) and bad behavior (61%). Female workers, workers who have working hours of more than 8 hours per day, and SMEs workers with low occupational risks are found to be better in their ability to recognize OHS hazards. Conclusion: The ability to recognize OHS hazards for SMEs workers assisted by Cimanggis District, Depok, West Java is still very slight (under 50%). For this reason, it is necessary to intervene in OHS aspects in SMEs so that these hazards can be minimized
Factors Affecting Safety Action in JOB Pertamina-Medco E&P Tomori Sulawesi
Authors: Harjanto Meddy , Djunaidi Zulkifli
Published: May 2023

Increasing oil and gas production is one of the main concerns for companies engaged in oil and gas mining Human behavior related to safety is an approach to analyze what is needed to make the safe action more possible and reduce risky behavior. Therefore, research is conducted on the factors that influence safe behavior so that these factors can be more optimized. This research is a quantitative study with a cross-sectional design. The population in this study amounted to 291 people. Data retrieval is done randomly with a sample of 130 respondents conducted using the simple random sampling method. Bivariate analysis was carried out by the chi-square test. Based on the results of the study, it was found that 63.8% of workers behaved safely, and 36.2% of workers behaved unsafely. Factors that do not affect safe behavior are knowledge, attitude, perception, motivation, age, length of work, availability of PPE, safety regulations, safety promotion, and training. Whereas, the factors that are proven to influence safe behavior are the supervisory role and the role of co-workers. Therefore, the researcher suggested that supervisors play an active role and be monitored regularly and consistently. In addition, care for co-worker needs to be improved through the Safety Observation program.
Dimensi Iklim Keselamatan dan Perbandingan Variabel di PT. XYZ Tahun 2021
Authors: Novriza Venti , Lestari Fatma
Published: May 2023

Pemerintah Indonesia saat ini memprioritaskan pembangunan infrastruktur yang berdampak pada meningkatnya permintaan semen dan beton siap pakai. PT. XYZ sebagai salah satu produsen beton siap pakai terbesar di Indonesia menekankan pada upaya untuk menciptakan lingkungan kerja yang baik untuk mencapai zero harm. Penelitian ini bertujuan untuk mengeksplorasi profil iklim keselamatan dan membandingkan variabelnya antara batch plant dan posisi. Ini adalah studi cross-sectional menggunakan kuesioner online yang diadopsi dari studi Safety Climate Survey sebelumnya. Data ditabulasi dan dianalisis menggunakan metode univariat dan hasilnya disajikan dalam grafik, variabel, dan skoring. Secara umum, iklim keselamatan di perusahaan PT XYZ rata-rata 4,10 dalam skala 1 – 6, cukup memuaskan. Hasil penilaian tingkat manajemen puncak lebih rendah dari hasil penilaian manajemen bawah, yang menarik.
Hubungan Antara Iklim Keselamatan dengan Perilaku Keselamatan pada Pekerja Proyek PLN PUSMAPRO PST JATENG I
Authors: Kumala Charisha Mahda , Ramdhan Doni Hikmat
Published: May 2023

Latar Belakang: Sistem manajemen keselamatan sangat diperlukan untuk penerapan di industri konstruksi dalam mengelola keselamatan di site project. Keberlangsungan sistem yang berjalan dapat dinilai dengan menganalisis hubungan iklim keselamatan dengan perilaku keselamatan tanpa harus menunggu terjadinya kecelakaan. Tujuan: Menganalisis iklim keselamatan dengan perilaku keselamatan pada pekerja di PLN PUSMANPRO di project pembangunan kelistrikan PST JATENG I. Metode: Penelitian cross-sectional ini melibatkan 120 orang pekerja pada Project PLN PUSMANPRO PST JATENG I.  Data primer diperoleh menggunakan kuesioner skala likert yang mengacu pada kuesioner dari beberapa penelitian sebelumnya pada variabel yang telah diuji validitas dan reliabilitas. Hasil: Pada dimensi iklim keselamatan hasil analisis univariat mayoritas berkategori rendah dan analisis bivariat dengan P value = 0,000 dan nilai correlation 0,402 yang merupakan hubungan positif dan cukup kuat antara iklim keselamatan dan perilaku keselamatan. Kesimpulan: Terdapat hubungan yang cukup kuat antara iklim keselamatan dengan perilaku keselamatan sehingga penerapan keselamatan kerja pada PLN PUSMANPRO PST JATENG I untuk membentuk perilaku keselamatan pada pekerja dapat melalui peningkatan iklim keselamatan serta fasilitas yang mendukung program keselamatan yang dapat mengurangi terjadinya kecelakaan pada proyek.
Kajian Hubungan Antara Iklim Keselamatan Psikososial (Psychosocial Safety Climate) dengan Perundungan di Tempat Kerja (Workplace Bullying) di PT.WID
Authors: Wahyuningtias Ika Agustina , Erwandi Dadan , Nasri Sjahrul Meizar , Kadir Abdul
Published: May 2023

Penelitian ini bertujuan untuk mengetahui dan mengkaji hubungan serta pengaruh Iklim Keselamatan Psikososial terhadap kejadian perundungan di tempat kerja di PT WID (perusahaan pembangkit tenaga listrik) yang melibatkan enam area kerja pembangkit listrik di seluruh Indonesia. Metode: Penelitian ini merupakan studi potong lintang (cross-sectional study) dengan melibatkan 100 orang tenaga kerja di lini bisnis Operational & Maintenance yang memiliki usia di atas 17 tahun dan pengalaman kerja minimal 6 bulan, untuk ikut berpartisipasi dalam pengisian kuesioner Psychosocial Safety Climate (PSC 12) untuk mengukur tingkat iklim keselamatan psikososial dan Negative Acts Qustionnaire-Revised (NAQ-R) untuk mengatahui tingkat kejadian perundungan di perusahaan. Hasil: Hasil analisa menunjukkan bahwa Iklim Keselamatan Psikososial berpengaruh secara signifikan terhadap tingkat kejadian perundungan di tempat kerja di perusahaan, dengan arah hubungan negatif dan berkekuatan sedang (p-sig=0,003; r=0,292; ß=-0,257). Selain itu hasil uji regresi linear berganda menunjukkan bahwa Komitmen Manajemen memberikan pengaruh yang signifikan (p-sig= 0,013) terhadap penurunan tingkat perundungan di tempat kerja, dibandingkan Prioritas Manajemen, Partisipasi Organisasi dan Komunikasi Organisasi. Simpulan: Hal ini menunjukkan jika terjadi kenaikan satu tingkat Iklim Keselamatan Psikososial maka terjadi penurunan Tindakan perundungan sebesar 0,257. Dimana Komitmen manajemen menjadi variable yang memiliki pengaruh paling dominan terhadap penurunan tingkat perundungan di tempat kerja.
Systematic Literature Review: Physical Work Environment Factors Associated with Work Fatigue in Hospital Nurses
Authors: Adventina Twinda , Widanarko Baiduri
Published: May 2023

Fatigue in hospital nurses can endanger the safety of both the nurse and the patient. One of the factors that can cause fatigue in hospital nurses is the physical environmental, such as lighting, noise, and work climate. The purpose of this study is to analyze the physical work environment factors (lighting, noise, and work climate) associated with work fatigue in hospital nurses. This is a systematic literature review on studies that discuss physical work environment factors related to work fatigue in hospital nurses published in international journals and local journals that can be accessed through the UI Library, especially those that are Full Open Access. Fourteen articles that discussed the relationship between physical environmental factors and work fatigue in hospital nurses were identified, consisting of 11 articles on the effect of lighting on work fatigue in hospital nurses. 6 articles discussed how noise influenced work fatigue in hospital nurses, and 3 articles discussed the effect of hot work climate on work fatigue in hospital nurses. Based on the review, lighting, noise, and work climate significantly link to work fatigue in hospital nurses. Keywords: hospital, lighting, noise, nurses, work climate, work fatigue
Hubungan Postur Kerja dengan Kejadian Nyeri Leher pada Pembatik di Kampung Batik Laweyan Surakarta
Authors: Firnadi Jossefa Almanita Haepie , Handayani Selfi , Munawaroh Siti , Wiyono Nanang
Published: May 2023

Nyeri leher merupakan salah satu keluhan muskuloskeletal yang sering dikeluhkan masyarakat. Postur kerja tidak ergonomis merupakan salah satu faktor risiko yang dapat menyebabkan nyeri leher. Pada pembatik, postur kerja tidak alamiah dilakukan dalam waktu lama beserta gerakan berulang. Penelitian ini bertujuan untuk mengetahui hubungan postur kerja terhadap kejadian nyeri leher pada pembatik. Penelitian ini merupakan penelitian observasional analitik dengan desain cross sectional. Teknik sampling yang digunakan adalah total sampling. Subjek penelitian merupakan 41 pembatik yang memenuhi kriteria inklusi dan eksklusi. Postur kerja pembatik diukur dengan Rapid Upper Limb Assessment (RULA). Pengukuran nyeri leher menggunakan kuesioner Neck Disability Index (NDI). Data dianalisis menggunakan uji korelasi Kendall's tau. Hasil penelitian postur pada pembatik paling banyak berisiko tinggi yaitu pada 25 orang. Untuk nyeri leher yang paling banyak dikeluhkan pembatik berada pada kategori nyeri ringan pada 26 orang. Berdasarkan uji analisis bivariat menggunakan uji korelasi Kendall's tau, didapatkan hasil signifikan (p=0,014; r=0,358). Terdapat hubungan yang signifikan dengan tingkat korelasi yang lemah antara postur kerja dengan kejadian nyeri leher pada pembatik di Kampung Batik Laweyan Surakarta. Kata Kunci:Postur kerja, nyeri leher, dan pembatik.
Analisis Insiden Alat Angkut Material pada Area Hauling dengan Menggunakan Metode Bowtie di PT. XYZ Periode 2018-2020
Authors: Deliani Febby Fauzia , Wirawan Mufti
Published: May 2023

Skripsi ini menganalisis insiden alat angkut material yang terjadi pada area hauling PT. XYZ periode 2018-2020 dengan menggunakan metode bowtie. Insiden kemudian dianalisis dengan bowtie untuk mengidentifikasi faktor risiko atau ancaman, pengendalian, konsekuensi, serta faktor dan kontrol eskalasinya. Tujuan dari penelitian ini adalah untuk menganalisis insiden yang terjadi dengan metode bowtie agar dapat mengidentifikasi lebih lanjut kegagalan barrier yang menyebabkan insiden. Penelitian ini berbentuk kualitatif dengan desain case series dan data investigasi insiden sebagai unit analisis. Hasil dari penelitian menyarankan perusahaan untuk melakukan pengkajian ulang prosedur, standar, serta metode pengendalian pencegahan dan dokumen HIRA, melakukan workload analysis terhadap pengawas, meningkatkan pengawasan terhadap faktor manusia serta peningkatan performa teknologi pengendalian
Kajian Tingkat Keparahan Postur Janggal yang Berkontribusi kepada Gangguan Sistem Muskuloskeletal (Studi Pustaka Naratif)
Authors: Safhira Ira , Satrya Chandra
Published: May 2023

Latar Belakang: Tidak semua postur janggal yang ada di industri harus dihilangkan. Karena faktanya, postur janggal tidak dapat dipisahkan dari aktivitas kerja sehari-hari. Setiap ada pergerakan yang membuat sudut tubuh menjauhi posisi normal, dapat dikatakan sebagai postur janggal. Kesalahpahaman terkait konsep dari postur janggal terlebih diikuti dengan fakta bahwa konsep penilaian metode observasi yang sering digunakan untuk menilai postur janggal (REBA, RULA, dan OWAS) kurang sesuai dengan teori yang seharusnya, dapat menurunkan keefektifan pencegahan dan pengendalian gangguan sistem muskuloskeletal. Untuk mengatasi hal itu, maka diperlukan kajian literatur yang menjelaskan variabel apa saja yang dapat mempengaruhi tingkat keparahan postur janggal. Metode: Penelitian ini menggunakan metode studi pustaka naratif dengan tujuan untuk menggambarkan bagaimana hubungan antara durasi, frekuensi, dan sudut kemiringan postur dengan tingkat keparahan postur janggal. Hasil: Didapatkan 15 literatur yang berasal dari pangkalan data online Science Direct, ProQuest, dan Google Cendekia, dengan rincian 9 literatur menggunakan metode semi-eksperimental, 5 literatur menggunakan metode cross sectional, dan 1 literatur menggunakan metode studi pustaka sistematik. Kesimpulan: Berdasarkan hasil literatur tersebut, didapatkan bukti adanya hubungan yang linear antara durasi, frekuensi, dan sudut kemiringan postur dengan tingkat keparahan postur janggal.
Analisis Faktor Risiko Ergonomi Terhadap Keluhan Gangguan Muskuloskeletal pada Pekerja UMKM Pengrajin Alas Kaki di Kecamatan Ciomas
Authors: Mayasari Dita , Susilowati Indri Hapsari
Published: May 2023

Aktivitas pekerjaan pada Usaha Mikro Kecil dan Menengah (UMKM) pengrajin alas kaki antara lain melakukan pekerjaan dengan posisi duduk membungkuk, leher menekuk, serta dalam waktu kerja yang lama dan tidak menentu dapat menimbilkan nyeri yang mengarah pada kondisi keluhan gangguan muskuloskeletal. Penelitian ini bertujuan untuk menganalisis faktor risiko individu, faktor risiko pekerjaan, faktor risiko lingkungan kerja, dan faktor risiko peralatan kerja terhadap keluhan gangguan muskuloskeletal pada pekerja UMKM pengrajin alas kaki di Kecamatan Ciomas. Penelitian menggunakan desain studi cross sectional. Hasil penelitian menunjukkan bahwa sebanyak 84,7% responden mengalami keluhan gangguan muskuloskeletal. Pada beberapa faktor risiko yang diteliti, secara statistik hanya terdapat hubungan antara faktor risiko individu perilaku merokok terhadap keluhan gangguan muskuloskeletal pada bahu serta terdapat hubungan antara faktor risiko pekerjaan tingkat pajanan risiko punggung terhadap keluhan gangguan gangguan muskuloskeletal pada leher. Pada pengukuran peralatan kerja, yaitu workstation, hanya terdapat beberapa workstation yang sesuai dengan standar antropometri yaitu meja open pada UMKM 4, 5, dan 8, mesin jahit, dan meja finishing pada UMKM 8.
FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow
Authors: Mocanu Sergiu , Moody Alan R. , Khademi April
Published: May 2023

In this work we propose FlowReg, a deep learning-based framework that performs unsupervised image registration for neuroimaging applications. The system is composed of two architectures that are trained sequentially: FlowReg-A which affinely corrects for gross differences between moving and fixed volumes in 3D followed by FlowReg-O which performs pixel-wise deformations on a slice-by-slice basis for fine tuning in 2D. FlowReg-A warps the moving volume using gross global parameters to align rotation, scale, shear, and translation to the fixed volume. A correlation loss that encourages global alignment between the moving and the fixed volumes is employed to regress the affine parameters. The deformable network FlowReg-O operates on 2D image slices and is based on the optical flow CNN network that is adapted to neuroimaging with three loss components. The photometric loss minimizes pixel intensity differences, the smoothness loss encourages similar magnitudes between neighbouring vectors, and a correlation loss that is used to maintain the intensity similarity between fixed and moving image slices. The proposed method is compared to four open source registration techniques ANTs, Demons, SE, and Voxelmorph for FLAIR MRI applications. In total, 4643 FLAIR MR imaging volumes (approximately 255,000 image slices) are used from dementia and vascular disease cohorts, acquired from over 60 international centres with varying acquisition parameters. To quantitatively assess the performance of the registration tools, a battery of novel validation metrics are proposed that focus on the structural integrity of tissues, spatial alignment, and intensity similarity. Experimental results show FlowReg (FlowReg-A+O) performs better than iterative-based registration algorithms for intensity and spatial alignment metrics with a Pixelwise Agreement (PWA) of 0.65, correlation coefficient (R) of 0.80, and Mutual Information (MI) of 0.29. Among the deep learning frameworks evaluated, FlowReg-A or FlowReg-A+O provided the highest performance over all but one of the metrics. Results show that FlowReg is able to obtain high intensity and spatial similarity between the moving and the fixed volumes while maintaining the shape and structure of anatomy and pathology.
A Heteroscedastic Uncertainty Model for Decoupling Sources of MRI Image Quality
Authors: Shaw Richard , Sudre Carole H. , Ourselin Sebastien , Cardoso M. Jorge , Pemberton Hugh G.
Published: May 2023

Quality control (QC) of MR images is essential to ensure that downstream analyses such as segmentation can be performed successfully. Currently, QC is predominantly performed visually and subjectively, at significant time and operator cost. We aim to automate the process using a probabilistic network that estimates segmentation uncertainty through a heteroscedastic noise model, providing a measure of task-specific quality. By augmenting training images with k-space artefacts, we propose a novel CNN architecture to decouple sources of uncertainty related to the task and different k-space artefacts in a self-supervised manner. This enables the prediction of separate uncertainties for different types of data degradation. While the uncertainty predictions reflect the presence and severity of artefacts, the network provides more robust and generalisable segmentation predictions given the quality of the data. We show that models trained with artefact augmentation provide informative measures of uncertainty on both simulated artefacts and problematic real-world images identified by human-raters, both qualitatively and quantitatively in the form of error bars on volume measurements. Relating artefact uncertainty to segmentation Dice scores, we observe that our uncertainty predictions provide a better estimate of MRI quality from the point of view of the task (gray matter segmentation) compared to commonly used metrics of quality including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), hence providing a real-time quality metric indicative of segmentation quality.
Probabilistic dipole inversion for adaptive quantitative susceptibility mapping
Authors: Zhang Jinwei , Zhang Hang , Sabuncu Mert , Spincemaille Pascal , Nguyen Thanh , Wang Yi
Published: May 2023

A learning-based posterior distribution estimation method, Probabilistic Dipole Inversion (PDI), is proposed to solve the quantitative susceptibility mapping (QSM) inverse problem in MRI with uncertainty estimation. In PDI, a deep convolutional neural network (CNN) is used to represent the multivariate Gaussian distribution as the approximate posterior distribution of susceptibility given the input measured field. Such CNN is first trained on healthy subjects via posterior density estimation, where the training dataset contains samples from the true posterior distribution. Domain adaptations are then deployed on patient datasets with new pathologies not included in pre-training, where PDI updates the pre-trained CNN’s weights in an unsupervised fashion by minimizing the Kullback-Leibler divergence between the approximate posterior distribution represented by CNN and the true posterior distribution from the likelihood distribution of a known physical model and pre-defined prior distribution. Based on our experiments, PDI provides additional uncertainty estimation compared to the conventional MAP approach, while addressing the potential issue of the pre-trained CNN when test data deviates from training. Our code is available at https://github.com/Jinwei1209/Bayesian_QSM.
Adversarial Robust Training of Deep Learning MRI Reconstruction Models
Authors: Calivá Francesco , Cheng Kaiyang , Shah Rutwik , Pedoia Valentina
Published: May 2023

Deep Learning (DL) has shown potential in accelerating Magnetic Resonance Image acquisition and reconstruction. Nevertheless, there is a dearth of tailored methods to guarantee that the reconstruction of small features is achieved with high fidelity. In this work, we employ adversarial attacks to generate small synthetic perturbations, which are difficult to reconstruct for a trained DL reconstruction network. Then, we use robust training to increase the network’s sensitivity to these small features and encourage their reconstruction. Next, we investigate the generalization of said approach to real world features. For this, a musculoskeletal radiologist annotated a set of cartilage and meniscal lesions from the knee Fast-MRI dataset, and a classification network was devised to assess the reconstruction of the features. Experimental results show that by introducing robust training to a reconstruction network, the rate of false negative features (4.8%) in image reconstruction can be reduced. These results are encouraging, and highlight the necessity for attention to this problem by the image reconstruction community, as a milestone for the introduction of DL reconstruction in clinical practice. To support further research, we make our annotations and code publicly available at https://github.com/fcaliva/fastMRI_BB_abnormalities_annotation.
PathologyGAN: Learning deep representations of cancer tissue
Authors: Quiros Adalberto Claudio , Murray-Smith Roderick , Yuan Ke
Published: May 2023

Histopathological images of tumours contain abundant information about how tumours grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows Generative Adversarial Networks (GANs) to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT, Germany) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI, Netherlands) and Vancouver General Hospital (VGH, Canada). Composed of 86 slide images and 576 tissue micro-arrays (TMAs) respectively. We show that our model generates high quality images, with a Frechet Inception Distance (FID) of 16.65 (breast cancer) and 32.05 (colorectal cancer). We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, generated images, and pretrained model are available at https://github.com/AdalbertoCq/Pathology-GAN
Nested Grassmannians for Dimensionality Reduction with Applications
Authors: Yang Chun-Hao , Vemuri Baba C.
Published: May 2023

In the recent past, nested structure of Riemannian manifolds has been studied in the context of dimensionality reduction as an alternative to the popular principal geodesic analysis (PGA) technique, for example, the principal nested spheres. In this paper, we propose a novel framework for constructing a nested sequence of homogeneous Riemannian manifolds. Common examples of homogeneous Riemannian manifolds include the spheres, the Stiefel manifolds, and the Grassmann manifolds. In particular, we focus on applying the proposed framework to the Grassmann manifolds, giving rise to the nested Grassmannians (NG). An important application in which Grassmann manifolds are encountered is planar shape analysis. Specifically, each planar (2D) shape can be represented as a point in the complex projective space which is a complex Grassmann manifold. Some salient features of our framework are: (i) it explicitly exploits the geometry of the homogeneous Riemannain manifolds and (ii) the nested lower-dimensional submanifolds need not be geodesic. With the proposed NG structure, we develop algorithms for the supervised and unsupervised dimensionality reduction problems respectively. The proposed algorithms are compared with PGA via simulation studies and real data experiments and are shown to achieve a higher ratio of expressed variance compared to PGA.
The code is available at https://github.com/cvgmi/NestedGrassmann
Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy
Authors: Arnavaz Kasra , Krause Oswin , Zepf Kilian , Bærentzen Jakob Andreas , Krivokapic Jelena M. , Heilmann Silja , Nyeng Pia , Feragen Aasa
Published: May 2023

Motivated by the challenging segmentation task of pancreatic tubular networks, this paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and expensive or difficult annotation. Our contributions are the following: a) We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth segmentations, applied to model selection and validation. b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data. In our method, we first use a semisupervised U-net architecture, applicable to generic segmentation tasks, which jointly trains an autoencoder and a segmentation network. We then use tracking of loops over time to further improve the predicted topology. This semi-supervised approach allows us to utilize unannotated data to learn feature representations that generalize to test data with high variability, in spite of our annotated training data having very limited variation. Our contributions are validated on a challenging segmentation task, locating tubular structures in the fetal pancreas from noisy live imaging confocal microscopy. We show that our semi-supervised model outperforms not only fully supervised and pre-trained models but also an approach which takes topological consistency into account during training. Further, our approach achieves a mean loop score of 0.808 for detecting loops in the fetal pancreas, compared to a U-net trained with clDice with mean loop score 0.762.
A Review of Causality for Learning Algorithms in Medical Image Analysis
Authors: Vlontzos Athanasios , Rueckert Daniel , Kainz Bernhard
Published: May 2023

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms.
We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.
Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
Authors: Yu Thomas , Hilbert Tom , Piredda Gian Franco , Joseph Arun , Bonanno Gabriele , Zenkhri Salim , Omoumi Patrick , Cuadra Meritxell Bach , Canales Rodriguez Erick , Kober Tobias , Thiran Jean-Philippe
Published: May 2023

Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as well as their generalizability, remain underexplored. In this paper, we investigate important aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability. Two self-supervised algorithms based on self-supervised denoising and the deep image prior were investigated. These methods are compared to a least squares fitting and a compressed sensing reconstruction using in-vivo and phantom data. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training. We show that prospective reconstructions can exhibit significant distortion relative to retrospective reconstructions/ground truth. Furthermore, pixel-wise quantitative metrics may not capture differences in perceptual quality accurately, in contrast to a perceptual metric. In addition, all methods showed potential for generalization; however, generalizability is more affected by changes in anatomy/contrast than other changes. We further showed that no-reference image metrics correspond well with human rating of image quality for studying generalizability. Finally, we showed that a well-tuned compressed sensing reconstruction and learned denoising perform similarly on all data. The datasets acquired for this paper will be made available online; see https://www.melba-journal.org/papers/2022:022.html for details.
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
Authors: Balelli Irene , Silva Santiago , Lorenzi Marco
Published: May 2023

We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Our approach allows to quantify the variability of data, views and centers, while guaranteeing high-quality data reconstruction as compared to the state-of-the-art autoencoding models and federated learning schemes.
The code is available at https://gitlab.inria.fr/epione/federated-multi-views-ppca