bluuebunny/crossref_metadata_2025 huggingface dataset:
Potential Uses for Precision Tinted Lenses in a Neurology Clinic
Authors: Wilkins Arnold J ,   Department of Psychology, University of Essex, Colchester, UK, Evans Bruce JW ,   Institute of Optometry, London, UK,   City, University of London, London, UK, Plant Gordon T ,   University College London, London, UK
Published: December 2022

Photophobia is a complex disorder that can involve aversion not simply to bright light but to spatial patterns, colour and flicker. The use of tinted lenses to reduce photophobia in neurological diseases has a long history. Rarely has the possibility that different individuals might require lenses individually tailored to their needs been considered. Here we explore this possibility using the Intuitive Colorimeter system. The colorimeter illuminates text with coloured light and permits the colour, strength of colour and brightness to be manipulated separately. Many individuals find that a colour other than white is comfortable for viewing text. This colour is optimized in the colorimeter by a search strategy that allows for colour adaptation. Spectacle lenses are then individually tinted so that the chosen colour is experienced under conventional lighting. In this article, we review the use of the Intuitive Colorimeter in photosensitive epilepsy, autism, migraine, cluster headache, visual snow, stroke, multiple sclerosis and concussion; these are disorders in which individually coloured tints have featured in scientific and clinical reports. The evidence is preliminary rather than conclusive, but it suggests that precision tints can reduce photophobia. In patients with autism, the perception of emotion in facial expression is improved. Occasionally, the clinical benefit extends to the reduction of seizures and headaches and to an increase in reading speed.
On the Death of Diversity Jurisdiction: An Empirical Study Establishing That Diversity Jurisdiction Is No Longer Justified
Authors: DeVito Scott
Published: December 2022

American federal diversity jurisdiction was created in response to the concern that out-of-state litigants would suffer bias in state court due to their out-of-state status (“geographic bias”). As attested in the record from the state ratification conventions, in the legislative history of diversity jurisdiction, and in seventeen U.S. Supreme Court opinions (the most recent in 2021), the creation of an impartial tribunal to mitigate geographic bias was and is the central rationale for federal diversity jurisdiction. Even though geographic bias is the rationale for diversity jurisdiction, no (prior) empirical studies have established whether geographic bias remains a problem in the American civil justice system. This Article provides the results of an empirical study of objective data, representing over one million cases across thirty years, demonstrating that geographic bias is no longer an issue in the civil justice system. Given that this result eliminates the very reason for the existence of federal diversity jurisdiction, the outcome provides a strong basis for Congressto either modify or abolish diversity jurisdiction.
Relationships Between COVID-19 Infection Rates, Healthcare Access, Socioeconomic Status, and Cultural Diversity
Authors: Barnes MarGhece P.J.
Published: December 2022

The COVID-19 pandemic has had a disproportionate impact on racial and ethnic minority groups, with high infection rates throughout those communities. There are a complex set of factors that account for COVID-19 disparities. Focusing on infection and death rates alone without also examining health equity, underestimates the true impact of the pandemic. To gain a more clear understanding of COVID-19’s impact in these communities, we analyzed the relationship between state COVID-19 infection rates with social determinants of health: cultural diversity, health care access, and socioeconomic status. Our approach to identifying this relationship was to estimate infection rates by fitting John Hopkins COVID-19 data to an SIR compartmental model commonly used in epidemiology to model infectious disease. These infection rates were then analyzed as a function of state indices with regard to healthcare access, and socioeconomic status, as well as measures of each states cultural diversity.

Nationally we do not see a relationship between COVID-19 infection and removal rates to cultural diversity, healthcare access, and socioeconomic status during the time period. However an analysis of states with the highest and lowest infection rates show that more culturally diverse states had higher infection rates during this time period. In addition, states that ranked low in healthcare access had infections an order of magnitude larger than states with good healthcare access. Alternatively, states grouped by low and high socioeconomic status had similar infection rates.
Going Viral
Authors: Evancho Schumacher Corinn
Published: December 2022

In the recent years of the rise of Web 2.0, health information has become more accessible. With this shift has come a reliance on social media to inform and educate the masses on vaccines. With the introduction of the Covid-19 vaccine, the conversation surrounding vaccines moved to the forefront of the United States’ zeitgeist from late 2021 to early 2022. In this research, I conducted a case study on the communication surrounding vaccines on social media while examining specific Facebook groups that advance the misinformation surrounding vaccines. I examined over 300 posts from four public Facebook groups in order to determine how the Covid-19 vaccines and vaccines in general were being discussed. Through qualitative content analysis, multiple strategies were revealed that illuminated how the masses on Facebook utilize social media to participate in anti-vaccination culture.
Open-Source Workflows for Reproducible Molecular Simulation
Authors: Fothergill Jenny W.
Published: December 2022

We apply molecular simulation to predict the equilibrium structure of organic molecular aggregates and how these structures determine material properties, with a focus on software engineering practices for ensuring correctness. Because simulations are implemented in software, there is potential for authentic scientific reproducibility in such work: An entire experimental apparatus (codebase) can be given to another investigator who should be able to use the same processes to find the same answers. Yet in practice, there are many barriers which stand in the way of reproducible molecular simulations that we address through automation, generalization, and software packaging. Collaboration on and application of the Molecular Simulation and Design Framework (MoSDeF) features prominently.

We present structural investigations of organic molecule aggregates and the development of infrastructure and workflows that help manage, initialize, and analyze molecular simulation results through the following scientific applications (1) A screening study wherein we validate self-assembled poly-3-hexylthiophene (P3HT) morphologies show the same state dependency as in prior work, and (2) A multi-university collaborative reproducibility study where we examine modeling choices that give rise to differences between simulation engines. In aggregate, we reinforce the need for pipelines and practices emphasizing transferability, reproducibility, useability, and extensibility in molecular simulation.
Towards Making Transformer-Based Language Models Learn How Children Learn
Authors: Mahdy Yousra
Published: December 2022

Transformer-based Language Models (LMs), learn contextual meanings for words using a huge amount of unlabeled text data. These models show outstanding performance on various Natural Language Processing (NLP) tasks. However, what the LMs learn is far from what the meaning is for humans, partly due to the fact that humans can differentiate between concrete and abstract words, but language models make no distinction. Concrete words are words that have a physical representation in the world such as “chair”, while abstract words are ideas such as “democracy”. The process of learning word meanings starts from early childhood when children acquire their first language. Children learn their first language through interacting with the physical world by using simple referring expressions. They do not need many examples to learn from, and they learn concrete words first from interacting with their physical world and abstract words later, yet language models are not capable of referring to objects or learning concrete aspects of words.

In this thesis, I derived motivation from the way children acquire language and combined a concrete representation of certain words into LMs while leveraging its existing training regime. My methodology involves using referring expressions to visual objects as a way of linking the visual world representations (images) with text. This takes place by extracting word-level visual embeddings for concrete words from images, while extracting word-level contextual embeddings for abstract words from text and then using them to train language models. In order to enable the model to differentiate between concrete and abstract words, I use a dataset that gives an indication of the level of concreteness for words to determine how information about each word was applied during training.

The work presented in this thesis is evaluated using a standard language understanding benchmark by analyzing the effect of using the proposed training regime on the language model and comparing its performance with traditional language models trained on large corpus data. In the final analysis, the results demonstrate that using referring expressions as the input text to train language models yields better performance on some language understanding tasks than using traditional, corpus-based text. However, the proposed approach cannot affirm that adding visual knowledge and/or concreteness distinction knowledge enriches LMs.
The Relationship Between Motivation, Academic Achievement, and Engagement in Mathematics Using Digital Game-Based Learning
Authors: Morgan Amy Rose
Published: December 2022

Digital game-based learning (DGBL) has unique factors that can engage students in the learning process. It has been shown that incorporating DGBL into mathematics can help bridge the learning gap, differentiate instruction, and engage students (Yang et al., 2018; Hulse et al., 2019; Chen et al., 2012; Naik, 2017). This study examined how students’ prior engagements are related to their academic achievement as well as investigated students’ motivation while utilizing DGBL in mathematics. An explanatory sequential mixed methods design was utilized to collect the quantitative data followed by the qualitative data. There were eighteen middle school participants in grades six through eight who all attended the same school within the Northeastern United States. The self-determination theory (SDT) served as the theoretical framework for examining the results.

Data was collected through a pretest, posttest, an open-ended survey, and a closed-ended survey. The results of this study indicated that DGBL can improve academic achievement in mathematics. However, it was determined that students’ prior engagement was not related to their academic achievement. Additional research should be conducted on the motivational aspect of relatedness and DGBL since it was shown that there was a strong correlation between relatedness and the engagement themes of learning with peers and experiences with faculty.
Ontology-Based Formal Approach for Safety and Security Verification of Industrial Control Systems
Authors: Neupane Ramesh
Published: December 2022

Control logics, as part of the Industrial Control Systems (ICS), are used to control the physical processes of the critical infrastructures such as power plants, water, and gas distribution, etc. Most commonly, the Programmable Logic Controller (PLC) manages these processes through actuators based on information received from sensor readings. Any safety issues or cyberattacks on these systems may have catastrophic consequences on human lives and the environment. In an effort to improve the resilience and security of control logics, this thesis provides algorithms and tools to formally define the safety and security requirements w.r.t. the physical processes, and the industrial domain. Web Ontology Language (OWL) is utilized to create semantic relationships between the elements of industrial processes and knowledge mapping of the input and output of the control logic. Description Logic (DL) knowledge bases derived from OWL allow us to reason about the semantic security and safety concepts to ensure their consistency. Next, these formal specifications are translated to Timed Computational Tree Logic (TCTL) queries for the verification of the control logic modeled in UPPAAL as a network of timed automata (TA). In the second part of the thesis, boundary conditions are checked to perform a model verification. Boundary checking is essential in ICS because the sensor’s readings and actuator’s values need to be within the safe range to ensure secure ICS operation.

For the proof of concept, we have studied a part of an industrial chemical process to implement our proposed approach. Experimental results in this work proved that the proposed method detects the inconsistencies in safety and security requirements and ensures that the input and output variables of the control logic are within a safe and secure range. The performance study of our implementations shows that the time grows linearly with the number of axioms in the ontology and the number of iterations in TA model simulations. Hence, the approach is scalable to have a practical implementation to help the technicians and engineers to create a safer and more secure control logic for ICS processes.
Improving the Ribozyme Toolbox
Authors: Roberts Jessica Michelle
Published: December 2022

Self-cleaving ribozymes are a naturally occurring class of catalytically active RNA molecules which cleave their own phosphate backbone. In nature, self-cleaving ribozymes are best known for their role in processing concatamers of viral genomes into monomers during viral replication in some RNA viruses, but to a lesser degree have also been implicated in mRNA regulation and processing in bacteria and eukaryotes. In addition to their biological relevance, these RNA enzymes have been harnessed as important biomolecular tools with a variety of applications in fields such as bioengineering. Self-cleaving ribozymes are relatively small and easy to generate in the lab using common molecular biology approaches, and have therefore been accessible and well exploited model systems used to interrogate RNA sequence-structure-function relationships. Furthermore, self-cleaving ribozymes are also being implemented as parts in the development of various biomolecular tools such as biosensors and gene regulatory elements. While much progress has been made in these areas, there are still challenges associated with the performance and implementation of such tools.

The work contained in this dissertation aims to address several of these challenges and improve the ribozyme toolbox in several diverse areas. Chapter one provides an introduction to pertinent background information for this dissertation. Chapter two aims to improve the ribozyme toolbox by providing and analyzing new high-throughput sequence-structure-function data sets on five different self-cleaving ribozymes, and identifying how trends in epistasis relate to distinct structural elements. Chapter three uses such high-throughput data to train machine learning models that accurately predict the historically difficult to predict functional effects of higher order mutations in functional RNA’s. Finally, in chapter four, I developed a biologically relevant platform to study the real time performance and kinetics of self-cleaving ribozyme-based gene regulatory elements directly at the site of transcription in mammalian cells.
From River Channel to Cloud Tops
Authors: Rudisill William J.
Published: December 2022

Mountain ranges are vital ”water towers” of the world and are uniquely threatened by anthropogenic climate change. At the same time, the paucity of observing networks limits our understanding of hydrometeorological processes in water-resource critical regions, including the Western United States. In the past decade, non-hydrostatic, convection permitting ( 1-4km horizontal resolution) regional climate models (RCMs) have emerged as a promising tool for both reconstructing regional scale mountain hydroclimates, and for forecasting the impacts of climate perturbations on watersheds and water-resources. Still, challenges remain. To-date, computational and data storage limitations have generally precluded many RCM studies to a handful of individual years, limiting the characterization of model uncertainties/biases and thus the interpretation of model outputs. Moreover, validating spatial precipitation fields from RCMs remains a challenge, as gridded precipitation datasets are highly uncertain in locations far away from observing stations. Consequently, further validations of regional climate models require leveraging diverse or indirect sources of hydrologic information. I develop three studies to meet these challenges in this dissertation. In the first, I examine the fidelity of coupled hydrologic-model/RCM for simulating streamflow in four water resource significant, snow-dominated basins in the Boise River basin. In the second, I develop a long-term RCM simulation (1987- 2020) in the Upper Colorado River basin and evaluate precipitation fields using a novel precipitation-from-streamflow bayesian inference strategy. The third chapter of the dissertation examines orographic precipitation sensitivities to cloud-microphysical parameterizations schemes, and leverages Airborne LIDAR snow-depth datasets to evaluate both spatial patterns of precipitation enhancement and watershed-total precipitation delivery. Together, the results from this dissertation demonstrate the utility of multi-decadal regional climate modeling for interrogating mountain hydro-climates, and demonstrates the opportunities and challenges for leveraging diverse hydrologic data sources (streamflow, airborne LIDAR) and methods (bayesian inference) for evaluating RCMs.
Cognitive Demand of Teacher-Created Mathematics Assessments
Authors: Schmidt Megan Marie
Published: December 2022

This study analyzed assessments created by middle school mathematics teachers participating in a large scale research project in the Northwestern
United States. Assessments were coded using the frameworks of Webb’s Depth of Knowledge (DOK) for mathematics content (2002), and Smith and Stein’s
Levels of Demand (LOD) (1998). Teachers in the study were instructed to create an assessment using 5 common-items provided by the research team and 5
of their own sourced items. Assessment items were coded using each framework and data was collected based on the DOK framework, LOD framework, and grade
level. Findings indicate on average, teachers assessments were relatively balanced between procedures and conceptual understanding and balanced
between recollection, application of a skill/concept, and explanation of thinking. When looking at
data based on grade level, assessments tended to address higher-level thinking as grade levels progressed. However when common-items were not included in the analysis, assessments tended to address the items using procedural thinking, and less frequently required explanation of thinking for solutions.
Data-Driven Passivity-Based Control of Underactuated Robotic Systems
Authors: Sirichotiyakul Wankun
Published: December 2022

Classical control strategies for robotic systems are based on the idea that feedback control can be used to override the natural dynamics of the
machines. Passivity-based control (Pbc) is a branch of nonlinear control theory that follows a similar approach, where the natural dynamics is
modified based on the overall energy of the system. This method involves transforming a nonlinear control system, through a suitable control input,
into another fictitious system that has desirable stability characteristics. The majority of Pbc techniques require the discovery of a
reasonable storage function, which acts as a Lyapunov function candidate that can be used to certify stability.

There are several challenges in the design of a suitable storage function, including: 1) what a reasonable choice for the function is for a given control system, and 2) the control synthesis requires a closed-form solution to a set of nonlinear partial differential equations. The latter is in general difficult to overcome, especially for systems with high degrees of freedom, limiting the applicability of Pbc techniques.

A machine learning framework that automatically determines the storage function for underactuated robotic systems is introduced in this dissertation. This framework combines the expressive power of neural networks with the systematic methods of the Pbc paradigm, bridging the gap between controllers derived from learning algorithms and nonlinear control theory. A series of experiments demonstrates the efficacy and applicability of this framework for a family of underactuated robots.
Machine-Learning Reveals Aftershock Locations for Three Idaho Earthquake Sequences
Authors: Wilbur Spencer F.
Published: December 2022

I explore spatial and temporal aftershock patterns related to three instrumentally recorded earthquakes in Idaho -- the Sulphur Peak,
the Challis, and the Stanley earthquakes. These three M > 5 earthquakes border the eastern Snake River Plain and lie within the Intermountain Seismic Belt and Centennial Tectonic Belt. Using machine learning for event detection and phase picking from local and regional seismic networks, I generate new aftershock catalogs. I locate more aftershocks than in the USGS catalog due to lower signal-to-noise detections. Using my phase picks, I locate aftershocks using a range of velocity models and select a catalog that represents the smallest residuals in hypocenter locations. I compare my results with handpicked phases and previously published velocity models. My 2014-2017 Challis catalog is consistent with the work of Pang et al. (2018), with more high-quality events with similar average vertical error. My one-month aftershock catalog for the 2017 Sulphur Peak earthquake is spatially consistent with the results of Koper et al. (2018); however, I show that my machine-learning approach produced relatively few aftershocks because afterslip events were not matched using a coseismic training dataset. Finally, I locate a factor of five more aftershocks from the 2020 Stanley earthquake when compared to the USGS catalog. I relocate the mainshock using biases computed by differencing my aftershock epicenters with the same aftershocks in the USGS catalog. The revised mainshock location now lies within a large and pronounced aftershock zone. My catalog suggests no motion along the active Sawtooth Fault, but instead I map a new N10W trending fault that accommodated the mainshock and much of the aftershock slip. I conclude that aftershock catalogs derived from a machine-learning approach can enhance seismic detection and aid in determining the driving mechanisms responsible for a coseismically driven earthquakes.
Characterizing Wildfire in the Frank Church Wilderness, Idaho, Between 1972-2012
Authors: Llewellyn Shoshanna
Published: December 2022

Our increasingly information driven world is growing the demand for new storage technologies. Current estimates place the total storage demands
exceeding the supply of usable silicon by 2040 [1]. DNA is an attractive technology due to its incredible density, almost negligible energy requirements, and data retention measured in centuries [1]. DNA does, however, come with new challenges. It is an organic compound with complex internal interactions which complicate the design and synthesis of DNA sequences for the purpose of data storage. In this work we demonstrate a new encoding-decoding process that accounts for some of the challenges in encoding and decoding, including issues arising from the secondary structure of the sequence, repeated nucleotides, unwanted subsequences, as well as GC content, vital for ensuring stable sequences. This is accomplished by using a graph representation of the possible encoding space that captures the relevant constraints, combined with a search algorithm that identifies the optimal encoding for the given input data accounting for these constraints. A benefit of our approach is that by leveraging the constraints on the encoding process, the decoding algorithm is able to correct single point errors without the aid of error correction codes; this is something no current competing solution can accomplish.
Estimation of Multicomponent Stress-strength Reliability under Inverse Topp-Leone Distribution
Authors: Pasha-Zanoosi Hossein
Published: December 2022

In this article, the reliability inference for a multicomponent stress-strength (MSS) model, when both stress and strength random variables follow inverse Topp-Leone distributions, was studied. The maximum likelihood and uniformly minimum variance unbiased estimates for the reliability of MSS model were obtained explicitly. The exact Bayes estimate of MSS reliability was derived the under squared error loss function. Also, the Bayes estimate was obtained using the Monte Carlo Markov Chain method for comparison with the aforementioned exact estimate. The asymptotic confidence interval was determined under the expected Fisher information matrix. Furthermore, the highest probability density credible interval was established through using Gibbs sampling method. Monte Carlo simulations were implemented to compare the different proposed methods. Finally, a real life example was presented in support of the suggested procedures.
The Type II Quasi Lambert Family
Authors: Hamedani G.G. , Korkmaz Mustafa C , Butt Nadeem Shafique , Yousof Haitham M.
Published: December 2022

Probability distributions and their families play an effective role in statistical modeling and statistical analysis. Recently, researchers have been increasingly interested in generating new families with high flexibility and low number of milestones. We propose and study a new family of continuous distributions. Relevant properties are presented. Many bivariate versions of the new family are derived under the Farlie-Gumbel-Morgenstern copula, modified Farlie-Gumbel-Morgenstern copula, Clayton copula, entropy copula and Ali-Mikhail-Haq copula. We present two characterizations of the new family. Different estimation methods such as the maximum likelihood estimation, maximum product spacing estimation, least squares estimation, weighted least squares estimation, Anderson-Darling estimation and the Cramer-von Mises estimation methods are considered. Simulation studies for comparing estimation methods are performed based on the baseline Lindley model. Two real data sets are analyzed for comparing the competitive models.
Modified Regression Estimators for Improving Mean Estimation -Poisson Regression Approach
Authors: Jana Zakir Hussain Wani , Rizvi S.E.H. , Sharma Manish , Bhat M. Iqbal Jeelani , Mushtaq Saqib
Published: December 2022

In this article, a class of Poisson-regression based estimators has been proposed for estimating the finite population mean in simple random sampling without replacement (SRSWOR). The Poisson-regression model is the most common method used to model count responses in many studies. The expression for bias and mean square error (MSE) of proposed class of estimators are obtained up to first order of approximation. The proposed estimators have been compared theoretically with the existing estimators, and the condition under which the proposed class of estimators perform better than existing estimators have been obtained. Two real data sets are considered to assess the performance of the proposed estimators. Numerical findings confirms that the proposed estimators dominate over the existing estimators such as Koc (2021) and Usman et al. (2021) in terms of mean squared error.
Characterizations of Fourteen (2021-2022) Proposed Discrete Distributions
Authors: Hamedani G.G. , Roshani Amin
Published: December 2022

As we mentioned in our previous works, sometimes in real life cases, it is very difficult to obtain samples from a continuous distribution. The observed values are generally discrete due to the fact that they are not measured in continuum. In some cases, it may be possible to measure the observations via a continuous scale, however, they may be recorded in a manner in which a discrete model seems more suitable. Consequently, the discrete models are appearing quite frequently in applied fields and have attracted the attention of many researchers.

Characterizations of distributions are important to many researchers in the applied fields. An investigator will be vitally interested to know if their model fits the requirements of a particular distribution. To this end, one will depend on the characterizations of this distribution which provide conditions under which the underlying distribution is indeed that particular distribution. Here, we present certain characterizations of 14 recently introduced discrete distributions.
Integration of 4253HT Smoother with Intuitionistic Fuzzy Time Series Forecasting Model
Authors: Alam NIK Muhammad Farhan , Ramli Nazirah , Mohamed Adie Safian Ton , Adnan Noor Izyan Mohamad
Published: December 2022

Fuzzy time series is widely used in forecasting time series data in linguistic forms. Implementing the intuitionistic fuzzy sets (IFS) in fuzzy time series can better handle uncertainties and vagueness in the time series data. However, the time series data always fluctuate randomly and cause drastic changes. In this study, the 4253HT smoother is integrated with the intuitionistic fuzzy time series forecasting model to improve the forecasting accuracy. The proposed model is implemented in predicting the Malaysian crude palm oil prices. The data are firstly smoothed, and followed with the fuzzification process. Next are the transformation of fuzzy sets into IFS and the de-i-fuzzification via equal distribution of hesitancy. The forecasted data are calculated based on the defuzzified values considering the new membership degrees of the IFS after de-i-fuzzification. The results show that the integrated model produces a better forecasting performance compared to the common intuitionistic fuzzy time series forecasting model. In the future, the integration of the data smoothing should be considered before the forecasting of data using fuzzy time series could be performed.
Cross sectional analyses on the prevalence and usage of braille among blind children
Authors: Kumaresan Vinisha , Prem Astha , Anuradha P.
Published: December 2022

Background: Childhood blindness is a serious issue which has to be dealt with importance because the disability adjusted life years (DALY) loss in a blind child is much more than that of adults with blindness. The ability to read and write in any one language improves their confidence and makes them more independent. It gives them hope not just to live but also to make a living. This study was done to emphasize on the importance of braille. This study analyses the prevalence and utilization of braille among blind children and its impact on education and overall quality of life.Methods: The data required was collected from the ophthalmology outpatient department (OPD), Saveetha Medical College and Hospital, Thandalam. The children were asked to answer questions based on a questionnaire after taking informed consent from them and their parents.Results: According to the data collected from 50 blind children over 6 months, only 12% used only braille for their educational purposes, 58% used only other assistive technologies, and 30% used both braille and other assistive technologies.Conclusions: This study shows that there is more inclination towards using assistive technology than Braille for education and practical purpose as it is easy to learn. But it is important that the government encourages schools to teach Braille to all students and also incorporate Braille in the public forum as its advantages are many which cannot be replaced by other assistive technologies.
Efficacy of early screening for prostate cancer in an elderly patient with absence of urinary symptoms but a manifestation of a rare symptom: a case report
Authors: Salman M. R. J. , Jalal A. H.
Published: December 2022

This is a case report of an unusual symptom of prostate cancer, constipation seen in an elderly 81-year-old patient. The patient was initially referred for physiotherapy for concerns involving his lumbosacral spines. Following which, he attended the clinic again with the same symptoms of back pain and constipation and was immediately referred for prostate cancer screening. The patient did not have urinary symptoms. Despite a number of factors that point towards aggressive prostate cancer, the patient was screened early and further investigation led to identifying his cancer. This allowed for the patient to undergo early prostate cancer treatment and is presently cancer free. This is a report to underline the importance of early prostate cancer screening in elderly men who present without the key concern of urinary symptoms.
Prevalence of hepatitis B and hepatitis C virus in haemodialysis patients
Authors: Alshebani Turki Badr , Alruwaili Yousef Musharraf , Alruwaili Mohammed Haddaj , Alotaibi Abdulaziz Najem , Alshaybah Fayez Abdulrahman , Alomran Abdulrahim Yousef , Al-Shaban Wafa Radhi , Alsaadi Nadiah Rabee , Alhawsawi Mohammad Ahmad , Alanazi Deema Faleh , Alhassan Shikih Hussain
Published: December 2022

Hepatitis B (HBV) and hepatitis C virus (HCV) infections continue to remain a significant global health challenge due to its morbidity and mortality profile. Almost 600,000 annual deaths are associated with the hepatitis B and hepatitis C virus. Patients on haemodialysis are at increased chance of acquiring infections. Infection among haemodialysis patients can lengthen the hospital stay and contribute to further complications. Hepatitis B and hepatitis C virus infection is common among the haemodialysis patients. Cross-contamination to patients through environmental surfaces, supplies, equipment, multiple-dose drug bottles, and staff members are among the modes and causes of transmission of hepatis B and C virus infections in haemodialysis facilities. The purpose of this research is to review the available information about the prevalence of hepatitis B and hepatitis C virus in haemodialysis patients. Global prevalence for hepatitis C virus among haemodialysis units reported in studies ranged from 2.6% to 22.9% and 13.5% on average while for hepatitis B virus the prevalence is less than 10% while some studies report the prevalence ranging from 2-20%. Literature has variable prevalence for hepatitis B and C virus across the different geographical regions. The variation in prevalence is observed due to numerous risk factors contributing to the significant burden of the disease. Epidemiological data in recent times is lacking in literature and conduction of population-based surveys can be beneficial in generalizing the prevalence especially in gulf countries where limited studies are available.
Comparative analysis of serum and sebum concentration of super-bioavailable itraconazole 50 versus 65 mg in healthy adult volunteers
Authors: Dhoot Dhiraj S. , Mahadkar Namrata , Jain Gaurav , Kesharwani Prashant
Published: December 2022

Super-bioavailable itraconazole (SBITZ) is a new form of itraconazole which overcomes the pharmacokinetic issues of conventional itraconazole (CITZ). Currently two strengths are approved globally for SBITZ; 50 mg and 65 mg which exhibit greater bioavailability than CITZ and widely prescribed in systemic mycosis and dermatophytosis. Though both the strengths have proven to be bioequivalent with CITZ 100 mg but difference was reported in achieving therapeutic concentration of ITZ in patients. Secondly, skin kinetics study in terms of sebum concentration is lacking for both the strengths.
The Reorganization of Global Value Chains: What’s in it for Latin America and the Caribbean?
Authors: Blyde Juan S. , Volpe Martincus Christian , Dolabella Marcelo , Marra de Artiñano Ignacio
Published: December 2022

As Latin America and the Caribbean bounce back from a sanitary crisis of historic proportions, the search is on for policies that can accelerate recovery while boosting long-term growth. In a scenario of tight fiscal constraints, trade and integration (T&I) policies seem to fit this description. There are particularly high expectations in some policy circles that the benefits of T&I policies will be boosted by an impending reorganization of global value chains. Yet little is known about the relevance, shape, and impacts of this reorganization. Will this lead to reshoring, nearshoring, or some slightly modified version of the status quo? Will this benefit the region? This paper takes a stab at answering these questions. It begins with a critical review of the most frequently cited drivers of the reorganization. This is then followed by an analytical exercise that uses the 20182019 US import tariff hike as a quasi-natural experiment. The results seem more consistent with modest trade and investment gains for the region, associated with incremental rather than major adjustments to global value chains. It concludes by arguing that whatever the future brings, minimizing trade and investment costs is likely to remain the regions dominant strategy.
Mujeres líderes en el sector público de América Latina y el Caribe: brechas y oportunidades
Authors: Naranjo Bautista Sandra , Chudnovsky Mariana , Strazza Luciano , Mosqueira Edgardo , Castañeda Carmen
Published: December 2022

La presencia de mujeres en posiciones de liderazgo es fundamental, no solo por factores de igualdad, sino porque genera múltiples beneficios. Este inédito análisis llena un vacío de información al analizar, de forma comparable, los 4 niveles directivos más altos de las Administraciones públicas centrales en 12 sectores representativos para 15 países de la región. El análisis se complementa con variables normativas y organizacionales. El estudio evidencia que la presencia de mujeres aún es limitada en los niveles más altos de toma de decisiones de las Administraciones públicas centrales de América Latina y el Caribe y se concentra en sectores específicos. Existe una brecha entre las acciones formales y los resultados obtenidos. En la mayoría de países existen normativas, organizaciones y políticas públicas para transversalizar las políticas de género, aunque su efecto aún es incierto. La región requiere una agenda de género más ambiciosa, que pase de cumplir con la formalidad a un verdadero aprovechamiento del talento femenino de la región. El sector público podría ser el referente en cada país de lo que significa cerrar la brecha de género. Para ello es necesario acompañar metas aspiracionales con la construcción de capacidades estatales que permitan alcanzar los objetivos propuestos.
The Work Commitment of Construction Project Managers in Indonesia Using the Structural Equation Modelling Method
Authors: Sahadi  , Sriyono Edi , Putri Wika Harisma , Ika Siti Rochmah , Fudholi Ahmad
Published: December 2022

The aims of this research were to determine how much influence the independent variables have on the dependent variable and to identify the variable with the dominant influence on the dependent variable the work commitment of construction project managers. The independent variable is a variable that can affect the value of other variables. The dependent variable is influenced by the independent variable either directly or indirectly. After data collection, the research method used was data analysis using the Structural Equation Modeling (SEM) Amos program. The results showed that the following independent variables have a positive effect on the dependent variable work commitment: leadership, organizational climate, organizational culture, communication climate, trust, work motivation, work experience, salary, and job satisfaction. The variable with the dominant influence on the work commitment of construction project managers was found to be salary. The salary variable is the variable that has the highest influence on work commitment and is a motivator to achieve high performance. Good culture management will encourage the achievement of high productivity and the project can be completed according to plan. The contribution that can be made through this research is that salary is the main need that must receive attention from the company, and is a work motivator to achieve high productivity.
Material Flow Analysis for Assessing the Sustainability Solid Waste Management Strategy
Authors: Rimantho Dino , Hidayah Nur Yulianti , Saputra Agung , Chandra Averina , Rizkiya Ananda Nurul , Nashifah Gina , Fitriyani Pipit , Wesha Devi Milenia Putri
Published: December 2022

With increasing income levels and accelerating consumption, municipal solid waste management (MSW) has become important in developing countries. This study intends to use material flow analysis (MFA) to assess the waste management strategy in Bogor-Indonesia. Moreover, this study also determines the extent of the waste flow path and provides suggestions for improvement. Waste volume data is carried out directly for ten days, referring to Indonesian Standards (SNI 3242: 2008 and SNI 19-3964-1994) related to waste management in settlements in Indonesia. Furthermore, the waste generation data were analyzed by applying the MFA Method. Data processing using STAN (Substance Flow Analysis Version 3) software makes Material Flow Analysis (MFA) images. The data shows that the waste generated is 20 kg to 140 kg per day. The existing conditions indicate that the waste is burned or disposed of in municipal landfills. The proposed waste management strategy model reveals 30.66 tons/year of inorganic waste that can be recovered through recycling and about 20.13 tons/year. The strategy that can be done is to establish a waste bank that applies Maggot BSF cultivation to recycle the organic waste produced. Decision-makers need future studies of material flows to be able to plan for changes in waste flows.
Analyze of Sustainability of Ecotourism in Bukit Tiga Puluh National Park in Riau Province, Indonesia
Authors: Sadad Abdul , Thamrin  , Nofrizal  , Yoswaty Dessy
Published: December 2022

This study aims to analyze the sustainability of ecotourism in Bukit Tiga Puluh National Park, Riau Province. This research uses the modified Rapfish - Rapid Appraisal for ecotourism (Rap-ecotourism) method which is based on the Multi-Dimensional Scaling (MDS) approach. The results showed that the sustainability status of ecotourism management in Bukit Tinggi National Park in a multidimensional manner was categorized as quite sustainable. The ecological dimension has a sustainable status with an index value of 91.68. The economic dimension is less sustainable with an index value of 49.88. the socio-cultural dimension has a fairly sustainable status with an index value of 67.38. The legal and institutional dimensions are quite sustainable with an index value of 55.06. The technology infrastructure dimension has a fairly sustainable status with an index value of 56.71. Therefore, the results of this analysis indicate that there is a need for intervention on the attributes that are the main levers so that ecotourism management can be carried out sustainably.
A Comprehensive Utilization Efficiency Evaluation of Iron Tailings based on the Differential — Benefit Evaluation
Authors: LI Yu-feng , BAO Jing-ling
Published: December 2022

At present, in the evaluation of iron tailings resources, the single evaluation of a certain aspect is limited to the qualitative evaluation, and there is no uniform standard in either theory or method. In this paper, based on the grey group analytic hierarchy process and fuzzy mathematics as the analysis tool, the benefit of the iron tailings resources is analyzed, From resource benefit, economic benefit, environmental benefit and social benefit several aspects to build the differential - efficiency index system and established the differential-benefit evaluation model and the influence of iron tailings resource evaluation of the weight of each index distribution, is obtained by correlation analysis and the likelihood of benefit index influencing the size of the order, to a certain extent, verified the rationality of the index weight, provides a theoretical guidance for iron tailings resource utilization.
Non-Darcy-Benard Double Diffusive Marangoni Convection with Dufour Effect in a Two-Layer System
Authors: Sumithra R. , Shivaraja J. M. , Arul Selvamary T.
Published: December 2022

Two - component / Double Diffusive Marangoni (DDM) convection with Dufour effects, in a two-layered system, has been studied analytically by using Darcy – Brinkmann Model. For fluid layer, the upper boundary is free with surface tension depending on both temperature and concentration, for porous layer the lower boundary is rigid and both the boundaries are insulating to both heat and mass. At the interface, the normal velocity, normal stress, shear stress, mass, mass flux, heat, heat flux is assumed to be continuous. The effect of different physical parameters on DDM convection is investigated in detail and results are presented graphically. The effect of Dufour parameter, which plays vital role in diffusion-thermal process, when the energy flux due to mass gradient appears, on DDM convection in a two- layer system, has been explored.
Effect of Al2O3 and CeO2 Nano-Additives on Performance and Emission Characteristics of Diesel Engine Fueled with Neem Oil-Biodiesel
Authors: R. Manjunatha  , H. V. Byregowda  , G. Panduranga Murthy
Published: December 2022

Due to the rapid depletion of petroleum reserves, many countries recommended the use of vegetable oils as diesel engine fuel. The best way to use edible and non-edible oil as a diesel fuel is to convert it into biodiesel. Biodiesel is a fuel which can be used instead of diesel fuel as an alternative for the existing system of CI engines. The usage of biodiesel causes a few disadvantages like high nitrogen oxides, utilization of high fuel and higher density. To conquer these problems some promising nano-additives are identified for having been used in the production of biodiesel. However, the addition of Nanoparticles will possibly enhance the performance and reduce the emissions. Therefore, the present investigation focuses on the influence of Al2O3 and CeO2 nano-additives on the performance and emissions characteristics of CI engine fueled with methyl esters of Neem. As nano-additives Al2O3 and CeO2 contain high oxygen content, it leads to complete combustion of fuel thereby increasing performance and reducing the oxide formation. In the experimentation, 50ppm, 100ppm of Al2O3 and CeO2 nano-additives were mixed in methyl esters of Neem. Subsequently, the fuel blends of Neem biodiesel (B20N) alone and Neem biodiesel with 50ppm, 100ppm of Al2O3 and CeO2 nano additives in CI engine were employed. The results revealed that, there is a increase in the break thermal efficiency and decrease in specific fuel consumption for B20N and B20N additive blends. Significant reductions in the parameters like CO, UBHC and NOx emissions are attained at B20N in conjunction with 50ppm, 100ppm of Al2O3 and CeO2 Nano-additives blends as compared with diesel. However, there is a slight increase in NOx emissions for B20N and and additive blends.