bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consumerist Environmentalism in “The Sims 4: Eco Lifestyle”
Authors: Imbierowicz Eleonora 
Published: January 2024

In 2021, the United States was the second largest emitter of greenhouse gases globally (after China, whose population was then over four times larger). The scale and urgency of the problem has been broadcasted for years, but the necessity and methods of dealing with it on a national and individual level seem to be quite obscure. Recycling is not the norm, oil and meat consumption is high, and excess spending is common. Consumption is still seen and presented as the means of satisfying most of one’s needs as well as a necessary condition for achieving a high social status. Dealing with climate change, as much as materially possible, involves dealing with the specificity of American geopolitics and culture: among other factors, its post-Protestant views on money and poverty, imperial position, and extreme individualism.
The expansion pack The Sims 4: Eco Lifestyle, produced by California-based Electronic Arts, fits into the consumerist approach to climate change popular in the United States. Eco Lifestyle introduces air pollution, recycling, living off -grid, and local politics, as well as the styles and activities related to pro-environmental attitudes. However, these activities are stripped of their material sense, as the actions depicted in the game are not what limits emissions and carbon footprint in the real world. This seems to be more a strategy than an error; and yet, it is hard not to regret a missed opportunity of creating a playable and ecologically sound Sims expansion for the lovers of the gameworld—and the world.
Laboratory Diagnostic Pathways Using Machine Learning
Authors: Arif Muhammad , Aslam Muhammad Ahsan , Rehman Haseeb Ur , Abbas Mohsin , Bukhari Sarrah 
Published: January 2024

The healthcare is one of the serious problems for the society. It is necessary for patients and to detect disease quickly in order to start appropriate cares. The most difficult problem is to detect disease, some other fields such as computer science and statistics support for searches [1]. In a formal way to detect human disease i.e. through medical tests are too expensive that poor patients don’t afford. It is need of need of society to propose the alternative path to detect human disease. Modern machine learning algorithms are used to uncover intriguing patterns and provide non-trivial predictions that are valuable in decision making. In this research blood donation and HEP-C advancement are predicted hybrid by using simple tests reports values that are simple, easy, non invasive and cheapest way especially for periodically repeated HEP_C diagnosing patients. The accuracies of 5 algorithms is compared to each other by using recall, precision,f1 score and specificity metrics [2]. In this study, five different methods for predicting HEP-C progression are examined. The logistic regression model was found to be the best classifier for HEP-C progression prediction in the comparison research.
Cross-Platform Mobile Development Approaches and Frameworks
Authors: Farooq Muhammad Shoaib , Riaz Shamyla , Alvi Atif , Ali Asghar , Rehman Ibtesam U 
Published: January 2024

The use of mobile applications is rapidly increasing, due to rapid development of smartphones. Mobile apps, on the other hand, are platform-specific, making development more difficult and expensive. Cross platform or multiplatform application development is a fairly new idea in which developers apply a single code to create apps for several platforms like Android, BlackBerry, Windows Mobile, iOS etc. With the rising usage of these frameworks, it is important to comprehend both contributions and limitations in this emerging field. This paper presents a systematic literature review (SLR) of the research studies in the field of cross platform mobile app Development including approaches and tools. The SLR has been compiled by reviewing the research studies published between 2012-2022 in reputed venues. A Total of 22 studies has been selected and classified by using systematic process. The review has been presented on cross platform approaches, tools and challenges. Further an approach has been proposed for cross platform mobile application development. Finally, open issues and challenges in the field of cross platform have been presented to provide future direction to the researchers.
Analysis of Covid-19 Data using Machine Learning Algorithms
Authors: Irfan Muhammad , Bukhari Sarah , Arif Muhammad 
Published: January 2024

Covid-19 cases reported on Jan, 30, 2020 and the numeral of cases stated increased deeply from March, 1, 2020. The researcher analysis the affected state of India (checking to only those state with sufficient data for forecast) and analysis those state which is most effected by covid-19 using machine learning algorithms. Machine learning algorithms are utilized to identify collective behavior together with the forecast of the expected spread of covid-19. Researcher aim to address each state separately on the base of covid-19. It enable the government (to apply the smart lockdown) to utilize the limited available resources and researcher analysis the states which is most affected by the Covid-19. A relative evaluation also performed to know that model fits for our data. Collect the data from Kaggle website. Variable such as Active Cases, Cured/Discharge, Deaths and Total Confirmed Cases etc. Analysis of data is represented using R and Visualization of Data procedures. The results show that Maharashtra is most affected by covid-19 and multiple regression Model is outperforming from other model.
A Systematic literature Review- “Effects of Hybrid-Agile approaches and methods on the quality of software.”
Authors: Farooq Muhammad Shoaib , TAHIR ESHA FATIMA 
Published: January 2024

ABSTRACT Software developers have a reputation of embracing new advancements and practices based on their novelties or empirical indications of their potential. We are conducting an SLR, this paper describes the findings of SLR aimed at determining what combinations of agile practices, and methodologies are utilized in hybrid approaches, as well as how the usage of hybrid and agile approaches affects quality of software. In the last 5 years there are more than 3800 original scientific published were considered in the review, from which 14 papers are yielded on the topic. There are six distinct agile methodologies that have been observed effecting the quality of software, as well as Twenty-one agile practices that have an effect on twenty-five different quality attributes of software.
Patient Acceptability of Telehealth Medication Abortion Care in the United States, 2021‒2022: A Cohort Study
Authors: Koenig Leah R. , Ko Jennifer , Valladares Ena Suseth , Coeytaux Francine M. , Wells Elisa , Lyles Courtney R. , Upadhyay Ushma D. 
Published: January 2024

Objectives. Despite the recent expansion of direct-to-patient telehealth abortion care in the United States, patient experiences with the service are not well understood. Methods. We described care experiences of 1600 telehealth abortion patients in 2021 to 2022 and used logistic regression to explore differences by race or ethnicity and between synchronous (phone or video) and asynchronous (secure messaging) telehealth abortion care. Results. Most patients trusted the provider (98%), felt telehealth was the right decision (96%), felt cared for (92%), and were very satisfied (89%). Patients most commonly cited privacy (76%), timeliness (74%), and staying at home (71%) as benefits. The most commonly reported drawback was initial uncertainty about whether the service was legitimate (38%). Asian patients were less likely to be very satisfied than White patients (79% vs 90%; P = .008). Acceptability was high for both synchronous and asynchronous care. Conclusions. Telehealth abortion care is highly acceptable, and benefits include privacy and expediency. Public Health Implications. Telehealth abortion can expand abortion access in an increasingly restricted landscape while maintaining patient-centered care. ( Am J Public Health. 2024;114(2):241–250. https://doi.org/10.2105/AJPH.2023.307437 )
PERFORMANSI ALAT PENGASAPAN IKAN DENGAN SISTEM ROTARI HORIZONTAL
Authors: Perdana Mastariyanto , Hafni Hafni , Yulianti Lusi , Romi Romi , Alfith Alfith , Pratiwi Putri , Nurzal Nurzal 
Published: January 2024

Salah satu teknik pengawetan ikan yang sangat banyak dimanfaatkan pada saat ini adalah teknik pengasapan. Pada umumnya proses pengasapan ikan masih secara tradisional dan menggunakan oven konvensional. Metode tersebut merupakan cara yang mudah untuk melakukan pengasapan ikan. Tujuan penelitian ini adalah untuk mengetahui efisiensi alat pengasapan ikan dengan menggunakan sistem rotari horizontal. Alat pengasapan ikan ini terdiri dari ruang pengasapan, tungku pembakaran, dudukan lele sistem rotari, motor DC, roda gigi, dan rantai. Kapasitas alat pengasapan ikan system rotari ini adalah 9 kg ikan basah. Ikan yang digunakan pada penelitian ini adalah ikan lele dan ikan cakalang. Pengasapan ini menggunakan hasil dari pembakaran kayu kulit manis. Kayu kulit manis akan menghasilkan asap yang berguna untuk pengasapan ikan. Berat ikan basah dalam satu kali pengasapan pada penelitian ini adalah 8 Kg. Metode pengasapan mempunyai standar perbandingan berat ikan basah dan berat ikan kering sebesar 4:1. Untuk pengasapan ikan lele, setelah dilakukan pengasapan selama 2 jam dengan temperatur 80 oC – 90 oC, berat ikan lele menjadi 2 kg. Untuk pengasapan ikan cakalang, setelah dilakukan pengasapan selama 2,5 jam dengan temperatur 80 oC – 90 oC, berat ikan cakalang menjadi 2 kg. Dari hasil pengujian, dapat disimpulkan bahwa alat pengasapan ikan dengan sistem rotari sangat bagus untuk digunakan sebagai alat pengasapan ikan karena memiliki efisiensi yang cukup tinggi dibandingkan dengan alat tradisional seperti oven dimana metode pengasapan tradisional membutuhkan waktu selama 36 jam dan metode pengasapan sistem oven membutuhkan waktu selama 12 jam.
Impulsionadores e Restritores à Participação na Coleta Seletiva Residencial de Resíduos Sólidos Urbanos
Authors: Silva Leandro Moura da , Pinto Francisco Roberto , Gerhard Felipe , Roberto Felipe 
Published: January 2024

Contextualização: O número de municípios que disponibilizam programas de coleta seletiva está aumentando no Brasil. No entanto, observa-se que uma grande quantidade de materiais recicláveis é destinada indevidamente a aterros sanitários, evidenciando a baixa efetividade dos programas de coleta seletiva. Este fato é ocasionado pela baixa adesão popular aos programas de coleta seletiva.
Objetivo: com base nessa problemática, a presente pesquisa teve por objetivo identificar os fatores que impulsionam e que restringem a participação popular em programas de coleta seletiva.
Enquadramento Teórico: foi proposto um modelo teórico para identificar quais fatores influenciam a participação popular em programas de coleta seletiva.
Metodologia: O estudo de campo consiste em uma pesquisa de natureza quantitativa, realizada com residentes de uma grande capital brasileira, com a aplicação de questionário de forma presencial e on-line. Os dados coletados foram submetidos à análise descritiva, análise fatorial confirmatória e Modelagem de Equações Estruturais.
Resultados: As hipóteses levantadas foram aceitas e seus resultados confrontados com a teoria expressa. Os resultados permitiram identificar a existência de fatores que impulsionam e que restringem a participação popular em programas de coleta seletiva. Apesar de algumas limitações, vale ressaltar que os objetivos foram alcançados.
Originalidade: Esta pesquisa inova por empregar uma metodologia, de natureza quantitativa, com técnicas robustas de análise multivariada e Modelagem de Equações Estruturais para apoiar os aspectos teóricos originais.
Contribuições teóricas e práticas: Os achados dessa pesquisa podem mudar a forma como são definidas as estratégias de divulgação e operacionalização dos programas de coleta seletiva. Outro ponto relevante refere-se à educação formal e social, já que o estudo apontou que esses motivos também não foram capazes de influenciar a participação popular em programas de coleta seletiva.
Effects of Changes in Internet Information Service Use Rates on Life Satisfaction of Older Adults during the COVID-19 Pandemic
Authors: Park Eun-Young , Kim Jung-Hee 
Published: January 2024

Abstract
Backgroud : It is helpful to identify factors, such as optimistic attitudes or feelings of control, that protect against the negative mental health burdens of a pandemic. The COVID-19 pandemic crisis has motivated some older people to adopt new technologies to simplify their chores and to provide a powerful defense against loneliness and social isolation. The purpose of this study was to assess the effects of changes in Internet information service use rates on the life satisfaction of older adults after the COVID-19 pandemic. Methods : This study analyzed data from the digital divide information survey by the National Information Society Agency (NIS) in 2020. The age criterion was set at 65 years or older, resulting in 1150 data points that were analyzed. A multivariate linear regression was conducted to investigate the associations of age, education level, employment, and income with changes in the seven categories of the Internet information service use rate. Resuts : Income (β = 0.146, p < 0.001) significantly affected life satisfaction of older adults. A high income was associated with high life satisfaction. Among changes in Internet information service use rate, increased SNS (β = 0.127, p < 0.001) and digital consumption (β = 0.074, p = 0.048) were significantly associated with greater life satisfaction. Conclusion : It is important to encourage older adults to use digital device to stay in touch with loved ones and friends and to take part in person-centered activity programs that are both safe and accessible. To this end, policymakers should offer public assistance to older adults to use Internet and mobile applications technologies.
Workforce outcomes among substance use peer supports and their contextual determinants: A scoping review protocol
Authors: Bell Justin S. , Griffin Tina , de Martell Sierra Castedo , Kay Emma Sophia , Hawk Mary , Ray Bradley , Watson Dennis 
Published: January 2024

Abstract
Background
Peer recovery support services are a promising approach for improving harm reduction, treatment, and recovery-related outcomes for people who have substance use disorders. However, unique difficulties associated with the role may put peer recovery support staff (i.e., peers) at high risk for negative workforce outcomes, including burnout, vicarious trauma, and compassion fatigue, which impact one’s personal recovery journey. Little is known about the extent to which peers experience such negative outcomes or the influence the service setting context has upon them. This scoping review aims to describe the nature and extent of research evidence on peers’ workforce outcomes and how these outcomes might differ across service settings.
Methods
A scoping review will be conducted with literature searches conducted in PsycINFO®, (EBSCO), Embase® (EBSCO), CINAHL® (EBSCO), Web of Science™ (Clarivate), and Google Scholar databases for relevant articles discussing US-based research and published in English from 1 January 1999 to 1 August 2023. The study will include peer-reviewed and grey-literature published materials describing the experiences of peers participating in recovery support services and harm reduction efforts across a variety of service settings. Two evaluators will independently review the abstracts and full-text articles. We will perform a narrative synthesis, summarizing and comparing the results across service settings.
Conclusions
This review will assess the state of the literature on peer workforce-related outcomes and how outcomes might vary by service setting context. Exploration will include individual characteristics of peers that moderate workforce outcomes, and workforce outcomes that mediate personal recovery outcomes. Results will inform the field regarding future directions for research in this area.
Systematic review registration
Submitted to Open Science Framework, August 22nd, 2023.
Locating Scheme Planning Algorithm based on Positioning Normal Line and Analytical Hierarchy Process
Authors: Zhang Duo , Wang Youli , Wang Xiaohui , Guo Jiacheng 
Published: January 2024

Abstract
Planning methods tailored for individual stages of locating schemes often fall short of achieving overall optimization. Therefore, by employing the positioning normal line as a medium and based on the geometric theorem of workpiece locating concerning the positioning normal line, the relationship between workpiece degrees of freedom and the positioning normal line can be deduced. This enables the acquisition of positioning normal line information for the freedom constraint model, consequently deriving an initial layout for the locating points. This initial layout sieves through all surfaces of the workpiece, establishing a set of candidate locating references. The intrinsic conditions of the workpiece surfaces, alongside the corresponding clamping surface quality, are utilized as measures to gauge their locating capabilities. Aiming for optimal locating capabilities, the Analytic Hierarchy Process (AHP) is employed to ascertain a comprehensive, optimal vector encompassing the combined locating capabilities of various workpiece surfaces, aiding in the selection of locating references. The feasibility of this method is validated through practical examples. Results demonstrate that the layout of locating points planned using this method, in conjunction with the positioning normal information provided by different characteristic types as summarized in this paper, effectively guides machining personnel in formulating locating schemes.
Classification and counting of cells in brightfield microscopy images: an application of Convolutional Neural Networks
Authors: Ferreira E. K. G. D. , Silveira Guilherme 
Published: January 2024

Abstract

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification—something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images
The Treatment of diarrheal mice with Tenebrio Molitor meal
Authors: Liu Tingting , Wang Qiaoli , Wang Zhengli , Yan Jiaxu , zhu jian jun , wang Jungang 
Published: January 2024

Abstract

Feeding Tenebrio Molitor meal has an important effect on promoting the growth, absorption, reproduction, and disease resistance of animals. In this study, 3×108cfu/ml Escherichia coli was used to establish a mouse diarrhea model. Different doses (8%, 5%, 2.5%) of tenebrio molitor meal were added to the basic diet, respectively. The feed intake, water intake, body weight, loose stool rate, diarrhea rate, intestinal flora number, immune organ index, intestinal enzyme, and serum enzyme activities of the diarrhea mice were detected. The results showed that compared with the model group, the feed intake, water intake, and body weight of mice with diarrhea were improved by adding tenebrio molitor meal, and the dosage was proportional to that of tenebrio molitor meal. The rate of loose stool and diarrhea decreased with the increase of tenebrio molitor meal. The total bacterial count and Escherichia coli count in the intestinal tract of mice with diarrhea were negatively correlated with the dosage of tenebrio molitor meal. The immune organ index of the diarrhea mice in the three tenebrio molitor meal supplementation groups was higher than that in the model group and was proportional to the dosage. The liver index of the 8% tenebrio molitor meal supplementation group was 11.79mg/g higher than that in the blank group. Compared with the blank group, diarrhea significantly decreased the activities of various enzymes in the intestinal tract and serum of mice (P < 0.05). Compared with the model group, the activities of intestinal and serum protective enzymes (superoxide dismutase, peroxidase, catalase), detoxification enzymes (glutathione-S transferase, acetylcholinesterase, acid phosphatase) and digestive enzymes (serum amylase, serum lipase, lactate dehydrogenase) in diarrhea mice were increased by adding tenebrio molitor meal (P < 0.05). The results showed that tenebrio molitor meal had a positive effect on the treatment of diarrhea in mice.
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design
Authors: Dobberstein Niklas David , Maass Astrid , Hamaekers Jan 
Published: January 2024

Abstract
Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present LLamol, a single novel generative transformer model based on the LLama 2 architecture, which was trained on a 13M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce Stochastic Context Learning as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making LLamol a potent tool for de novo molecule design, easily expandable with new properties.
Scientific Contribution: We developed a novel generative transformer model, LLamol, based on the LLama 2 architecture that was trained on a diverse set of 13M organic compounds. It introduces Stochastic Context Learning (SCL) as a new training procedure, allowing for flexible and robust generation of valid organic molecules with up to multiple conditions that can be combined in various ways, making it a potent tool for de novo molecular design.