bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluación de parámetros microbiológicos en el arroyo Santa María, Jalpa de Méndez, Tabasco, México
Authors: Cárdenas-Córdova María Elena , Solís-Silván Rudy , Fraire-Vázquez Azucena del Rosario , López-Ocaña Gaspar , Díaz-Palacios Ricardo 
Published: December 2024

La contaminación fecal en cuerpos de aguas superficiales causa impactos negativos en la salud y el medio ambiente a nivel mundial en países subdesarrollados. El objetivo del siguiente estudio fue evaluar la calidad microbiológica del agua del arroyo Santa María de Jalpa de Méndez, Tabasco, México. A lo largo del arroyo se establecieron tres estaciones de muestreos a diferentes profundidades (30 y 60 cm). La estación uno se ubicó en la dirección contraria a la desembocadura del arroyo en un río conocido como los Caballos, el segundo a mitad del arroyo y el tercero donde el arroyo desembocaba en el río. Los parámetros microbiológicos evaluados fueron coliformes fecales (CF) y coliformes totales (CT). Para el muestreo y análisis de los CT y CF se emplearon las recomendaciones y método del número más probable en tubos múltiples de acuerdo NMX-AA-042-SCFI-2015 respectivamente. Se llevó a cabo un análisis de varianza factorial de 3 x 2 para encontrar diferencias estadísticas significativas entre las diferentes estaciones de muestreo y profundidades. Las tres estaciones mostraron valores promedios mínimos y máximos de CF y CT (650 a 1131 y 712 a 1225 NMP/100 ml respectivamente) superiores a los límites establecidos por criterios ecológicos CE-CCA-001/89 (> 200 NMP/100 ml). Así mismo no cumple con los límites máximos permisibles de la Norma Oficial Mexicana de consumo humano NOM-127-SSA1-1994 (2 NMP/100 ml). El arroyo Santa María presenta altas concentraciones de contaminación fecal que impacta directamente a la salud de la gente cercana al arroyo y el medio ambiente.
Minimally Invasive Hemilaminectomy in Baastrup Disease
Authors: Schwender Eric A. , Hohertz Joshua J. , Padhye Kedar , Kim Daniel C. , Dawson John M. , Schwender James D. 
Published: December 2024

Case:
A patient presented with midline back pain radiating into the left lower extremity. Magnetic Resonance images revealed an abnormal midline dorsal structure described as a facet cyst. Rupture was attempted by spinal needle under fluoroscopic guidance. His pain returned in less than 2 weeks. Further study of the images showed the structure was more medial and cephalad than typical facet cysts, leading to a revised diagnosis of Baastrup disease. The cyst was surgically removed by minimally invasive techniques; an uninstrumented spinal arthrodesis was performed.

Conclusion:
Baastrup disease is commonly misdiagnosed. Despite the delay in treatment, surgical intervention was effective.
A multilingual telephone service for crisis communication with migrant groups: Swedish experiences of responding to the COVID-19 pandemic
Authors: Bäärnhielm Sofie , Al-Ammari Baidar , Cetrez Önver , Puthoopparambil Soorej Jose , Strand Mattias 
Published: December 2024

Abstract

Migrants living in socioeconomically disadvantaged neighborhoods in Sweden were overrepresented among the infected and deceased in COVID-19 and vaccination coverage was substantially lower, despite being free of charge. The overarching aim of this study was to learn from the experiences of operating a multilingual telephone service for public health crisis communication targeting migrant communities in Sweden during COVID-19. An additional aim was to achieve an in-depth understanding of opportunities and challenges in communicating up-to-date information about COVID-19 and vaccination to lay persons. A mixed methods approach was used, with a quantitative component analyzing descriptive data from the telephone service and a qualitative component consisting of interviews with telephone service staff. The quantitative component revealed that relatively few callers requested basic information about the virus or asked about topics such as where to turn in case of illness. The most common topic was testing for current infection. For the qualitative part, interviews were conducted with 12 health communicators staffing the telephone line. The thematic analysis identified seven major themes: The need for a multilingual telephone service; the interaction between language, culture, and profession for building trust; successful knowledge transfer; the importance of cooperation with other actors and organizations; managing existential concerns, emotions, and mental distress; replying to false information and myths; and lessons for a telephone service in the event of a new health crisis. To conclude, for dissemination of information about COVID-19 and vaccination to migrants during the pandemic, experiences from the multilingual telephone service point to the value of communication that includes the possibility of dialogue with health professionals in a culturally safe mode using one's native language. For future health crises, our findings emphasize the importance of having a communication strategy targeting vulnerable groups in place as a part of a comprehensive pandemic plan when the need emerges.
Data-driven Flood Prediction for Bangladesh at a 2-km Spatial Resolution Using ConvLSTM
Authors: Khandekar Varun , Liu Che , Quilodrán-Casas César 
Published: December 2024

Abstract

Flood prediction is a critical component of disaster preparedness, especially given the substantial economic and social consequences of flooding. As climate change intensifies the frequency and severity of extreme weather events, the need for accurate flood forecasting becomes even more urgent. This study focuses on Bangladesh, applying Convolutional Long-Short Term Memory (ConvLSTM) architectures to predict flooding at a spatial resolution of approximately 2 km, using a straightforward set of publicly available satellite images. The objective is to explore whether machine learning can simplify flood prediction while expanding the coverage of existing models. The model produces pixel-level flood probability maps, showing promising results in predicting large-scale flooding with Area Under the Curve (AUC) values above 0.9 and Structural Similarity Index (SSIM) scores exceeding 0.8. These outputs are both plausible and readily integrable into existing or new disaster response frameworks. As many regions remain underserved by flood prediction systems, this work lays the foundation for scalable machine learning applications that can help mitigate inequalities exacerbated by the increasing risk of flooding.
Distribution Characteristics of Microplastics in Domestic Sewage Waters: A Case Study in Guilin City, China
Authors: Lu Meiyuan , Shan Huimei , Zhan Hongbin , Shi Yuxin , Liu Yunquan , Peng Sanxi 
Published: December 2024

Abstract

Microplastics (MPs) resulting from plastic fragmentation with a size less than 5 mm have become one of the main pollutants endangering the water environment. Therefore, it is necessary to know about the abundance and size distribution in sewage waters as well as the influences of water quality on MPs. In this study, water samples are collected from 20 sewage outlets in Guilin, China, to analyze the abundance and morphology of the MPs and their hydrochemical characteristics. Multivariate statistical analyses are conducted to identify the major factors related to the MPs’ distribution in sewage waters. Results show that MPs in sewage water are mainly composed of fiber and film, and about 67.8% is in the size of <0.3 mm. The abundance is in the range of 6 (±1)–47 (±3) items/L. The correlation analysis presents that the abundance of MPs is weakly correlated with hydrochemical parameters and metal ions due to the complexity of the abundance data. The redundancy analysis indicates that the MPs’ morphology distribution is significantly affected by NO3-N, Zn, Ca, and Cu contents, and the MPs’ size distribution is mainly related to Zn, Ca, and Cu contents. The study highlights the occurrence characteristics and environmental influencing factors of the MPs in sewage water, which may be significant for future studies on the pollution control of MPs.
A study evaluation framework for measuring cognition. Lessons learned in cross-national contexts from four English-speaking aging cohorts.
Authors: Hayat Shabina , Assaad Sarah , Brayne Carol , Ahmed Nasrin , Steptoe Andrew 
Published: December 2024

Abstract

The Harmonized Cognitive Assessment Protocol (HCAP) is a detailed battery assessing cognition among older people used by studies across the world. Data harmonization is a key priority for HCAP studies. We used a mixed-methods approach using established theories from the existing literature detailing the methodologies of longitudinal studies and from the implementation of HCAP in four English-speaking studies adopting the same protocol. Through a detailed investigation involving the English Longitudinal Study of Ageing (ELSA), the Health and Retirement Study (HRS), The Irish Longitudinal Study on Ageing (TILDA), and the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA), we identified 60 factors contributing to the development of a conceptual framework for the evaluation and implementation of HCAP. We present this framework and a prototype checklist as a tool for providing a transparent and structured approach to improve data quality, cross-country comparability and for identifying, mitigating, and monitoring sources of bias. The framework consisting of four broad headings: (1) Organisation and design, (2) Competency of personnel and systems, (3) Implementation and outputs, and (4) Feedback and communication. Studies seeking to harmonize results in cross-national contexts should give operational aspects of fieldwork careful consideration as part of the harmonization process.
Machine Learning-Based Network Intrusion Detection for IOT and Smart Detection Using Recursive Feature Elimination, Binning Technique and Grid Search CV
Authors: Akinola Damilola , Ajinaja Micheal Olalekan , Adewuyi Joel Adeyanju 
Published: December 2024

Abstract

The exponential growth of Internet of Things (IoT) devices and smart technologies has escalated the risk of network intrusions, necessitating advanced Intrusion Detection Systems (IDS) to ensure robust cybersecurity. This study presents a machine learning-based IDS framework tailored for IoT networks, employing Recursive Feature Elimination (RFE), binning techniques, and GridSearchCV for comprehensive feature selection and hyperparameter tuning. The CICIDS2017 dataset, a benchmark dataset for intrusion detection, is utilized to train and validate the models. The proposed pipeline begins with data pre-processing, including attribute verification, duplicate removal, and label encoding, followed by a detailed feature selection process. Recursive Feature Elimination (RFE) was utilized to identify and retain the most significant features, feature engineering incorporated domain knowledge to create new attributes and employed binning techniques to effectively manage continuous features and GridSearchCV was applied to identify the best parameter combinations. Multiple machines learning models, including LR, DT, RF, NB and SVM were analyzed and optimized through this comprehensive pipeline. Visualization techniques further enhanced understanding of feature importance and model behavior. The performance metrics reveal the effectiveness of the approach, with Random Forest achieving a remarkable accuracy of 99.78%, closely followed by Decision Tree at 99.50%. This underscores the efficacy of the methodology in addressing network intrusion detection challenges within IoT ecosystems. The framework provides a robust, scalable solution for securing interconnected systems against evolving cyber threats.
The use of 360-videos to bridge the gap between pre-clinical and clinical dental teaching
Authors: Caratela Nabeela , Shykhon Menna , Milward Michael , Yonel Zehra 
Published: December 2024

Abstract

Background
The transition from pre-clinical to clinical teaching is often a time of heightened anxiety for students. With the shift to bi-modal teaching during the pandemic there was an opportunity to explore the use of 360-degree videos and virtual reality (VR) simulation teaching to enhance educational experience and smooth the transition from pre-clinical to clinical teaching.
Methods
Two groups of students were recruited all of whom were about to have their clinical introduction to the periodontology department. All students received current standard induction programme. One group in addition to standard teaching methods also received access to 360-degree video and VR headset prior to standard teaching. Focus groups were then conducted with the students. A topic guide was developed and piloted. Focus groups were conducted online, audio was recorded and transcribed verbatim. Transcripts were analysed codes and themes were developed using thematic analysis as a framework for analysing the focus groups.
Results
This study demonstrates how 360-videos and VR technology may enhance dental education, provided it is implemented appropriately and at the correct time in training. Overall, students had a positive attitude towards using 360-videos and acknowledged its value in meeting a range of learning objectives, including infection control, IT training, and clinic orientation.
Harnessing Artificial Intelligence and Machine Learning to Transform Cloud Computing with Enhanced Efficiency and Personalization
Authors: Sankaradass Veeramalai , Devasenan Ramsriprasaath 
Published: December 2024

Abstract

This work seeks to evaluate how ML and GAI could be integrated into the cloud computing model with an effort of optimizing the use of resources, minimizing energy consumption and providing value added services. Similar to other systems of its nature, which are large-scale distributed systems, cloud computing systems have several topics of concern including dynamic resource management, security issues, and the issues regarding with the user interface. To address these discrepancies, this work proposes a single D-PAL framework that uses the predictive ML model application and GAI for synthetic data generation. In the cloud environments of the framework, it employs workload prediction and scheduling for resource estimation through ML, and scheduling through Reinforcement Learning, and for data augmentation through GAN. From the experimental assessment one is able to observe implicit improvements in the performance of the cloud resources, energy consumption, and customised user services. In this regard, this paper advances theoretical and empirical understanding on personnel characteristics of AI on cloud systems and deploy new methods that improve cloud performance and maintain security and usability. Future work will be more focused on expanding of the proposed models to scale and integrating other AI techniques to increase the cloud control.
Effect of Macrocystis pyrifera scalding on water reusing from the pre treatment cycles
Authors: Sepúlveda Bernardo , Echeverría Patricia , Larrazabal José María , Sepúlveda Bruno , Tume Pedro , Maurelia René 
Published: December 2024

Abstract

In the scalding pretreatment, food is immersed in water or steam at specific temperatures and times, inactivating enzymes, reducing microorganisms, and softening tissues. This requires a large volume of water and energy, and it can generate contaminated water, limiting its reuse. Macrocystis pyrifera is a brown macroalga distributed along the Chilean coast and can be used as human food. The content of As, Hg, Cd, and Pb (µm/100 g) in the process water was evaluated after each scalding cycle of M. pyrifera. During scalding, there was transfer of As from the macroalga to the process water, while the others elements were not significant. From the first to the third scalding cycle, transferred arsenic reached 1.206 mg/L. According to Chilean regulations, the maximum environmental concentration is 0.01 mg/L, limiting water reuse from the first scalding cycle. Scalding water with arsenic should not be used for other food production processes, nor should it be discharged into the environment, because the arsenic content transforms the process water into a liquid industrial waste. It is suggested to evaluate techniques to reduce the concentration of dissolved contaminants to enable reuse, within the framework of the circular economy, making blanching more sustainable.
Mobilizing COVID-19 Vaccination Partnerships for Newcomer Refugees and Immigrants in the Calgary, Canada area, 2021-2022
Authors: Aghajafari Fariba , Wall Laurent , Weightman Amanda , Ness Alyssa , Lake Deidre , Anupindi Krishna , Moorthi Gayatri , Kuk Bryan , Santana Maria , Coakley Annalee 
Published: December 2024

Abstract

The COVID-19 public health emergency challenged the capacity for public health systems to vaccinate newcomer refugees and immigrants in the Calgary, Canada area. In response, community-health partnerships emerged in 2021-2022 to provide accessible, culturally responsive, and adapted services for community members. This research highlights community mobilization and partnerships’ role in COVID-19 vaccine delivery for newcomer refugees and immigrants, including challenges faced and lessons learned. Researchers conducted structured interviews with Government Assisted Refugees (GARs), and semi-structured interviews with Privately Sponsored Refugees (PSRs), private refugee sponsors, and diverse health and community informants involved in COVID-19 vaccine responses. Thematic analysis highlighted themes related to partnership development, strategies and actions of partnerships, alignment with World Health Organization recommendations, and partnership benefits and challenges. Multiple newcomer-specific vaccination models were developed through various community-based partnerships. The roles of community partners included information translation and transmission, outreach, and advocacy. Partnerships faced challenges, including existing infrastructure and inequitable policies for COVID-19 vaccine distribution. These existing elements required substantial advocacy to be more equitable in their outcomes. This research highlights how gaps in the public health system during early vaccination efforts catalyzed communities to mobilize and drove the creation of community-health partnerships in the Calgary area. These partnerships were effective at facilitating vaccine uptake through innovative, community-informed and culturally-responsive approaches. A key concern remains that, despite learnings and changes to COVID-19 vaccination systems, the health system has not sufficiently integrated changes to address equity in the long term.
Application of a Language Model Tool for COVID-19 Vaccine Adverse Event Monitoring Using Web and Social Media Content: Algorithm Development and Validation Study
Authors: Daluwatte Chathuri , Khromava Alena , Chen Yuning , Serradell Laurence , Chabanon Anne-Laure , Chan-Ou-Teung Anthony , Molony Cliona , Juhaeri Juhaeri 
Published: December 2024

Background
Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use. Our work aims to detect AEs from social media to augment those from spontaneous reporting systems.

Objective
This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models.

Methods
We developed a language model–based web app to analyze social media, patient blogs, and forums (from 190 countries in 61 languages) around COVID-19 vaccine–related keywords. Following machine translation to English, lay language safety terms (ie, AEs) were observed using the PubmedBERT-based named-entity recognition model (precision=0.76 and recall=0.82) and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs (MedDRA terminology is an internationally used set of terms relating to medical conditions, medicines, and medical devices that are developed and registered under the auspices of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use). Weekly and cumulative aggregated AE counts, proportions, and ratios were displayed via visual analytics, such as word clouds.

Results
Most AEs were identified in 2021, with fewer in 2022. AEs observed using the web app were consistent with AEs communicated by health authorities shortly before or within the same period.

Conclusions
Monitoring the web and social media provides opportunities to observe AEs that may be related to the use of COVID-19 vaccines. The presented analysis demonstrates the ability to use web content and social media as a data source that could contribute to the early observation of AEs and enhance postmarketing surveillance. It could help to adjust signal detection strategies and communication with external stakeholders, contributing to increased confidence in vaccine safety monitoring.
Tysklands forsiktige nyorientering – våpenstøtte til Ukraina etter die Zeitenwende
Authors: Allers Robin 
Published: December 2024

Fra å nekte på prinsipielt grunnlag å sende våpen til konfliktområder, ble Tyskland etter Russlands fullskala invasjon i februar 2022 til en av de største leverandørene av krigsmateriell til Ukraina. Våpenstøtten ble et sentralt element i en omfattende nyorientering av tysk sikkerhetspolitikk, kalt Zeitenwende. Mens regjeringen, og spesielt kansler Olaf Scholz, fikk ros for sitt kursskifte, ble de også anklaget for å levere for lite, for å være for sent ute, og for å kommunisere beslutningene sine for dårlig. Denne studien ser nærmere på to spørsmål som har preget diskusjoner og analyser av Tysklands kursendring: Hva påvirker Tysklands politikk når det gjelder våpenstøtte, og i hvilken grad er Tysklands endrede holdning til våpenstøtte et uttrykk for en grunnleggende nyorientering? Fire faktorer trekkes fram: krigens utvikling, Tysklands anstrengte forhold til bruk av militærmakt, landets politiske system og kanslerens personlighet og kommunikasjonsevne. Disse faktorene vektlegges når Tysklands politikk undersøkes gjennom tre sentrale beslutninger: (1) beslutningen om å gå bort fra det prinsipielle nei til å sende våpen til et konfliktområde, (2) dragkampen om leveransen av tyskproduserte kampstridsvogner av typen Leopard 2, og (3) kontroversen om krysserraketten Taurus, der kansleren holdt fast ved sitt nei.
Abstract in English:Germany’s Cautious Policy Change – Arms Support to Ukraine in the Wake of the ZeitenwendeFollowing Russia’s full-scale invasion in February 2022, Germany went from refusing on principle to send weapons to conflict areas to becoming one of the largest suppliers of war material to Ukraine. Arms support became a key element of Germany’s policy change, widely known under the label Zeitenwende. While the government and especially Chancellor Olaf Scholz were praised for their change of course, they were also accused of delivering too little, of being too late, and of communicating their decisions too poorly. This study takes a closer look at two questions that have characterized political and academic discussions of Germany’s change of course: What influences Germany’s policy on arms support and to what extent is Germany’s changed attitude an expression of a fundamental reorientation? Four factors are highlighted: the development of the war, Germany’s complicated relationship with the use of military power, the country’s political system, and the chancellor’s personality and communication skills. These factors are emphasized in an analysis of three key policy decisions: 1) the departure from the principled no to sending weapons to a conflict area, 2) the tug-of-war over the delivery of German-made Leopard 2 main battle tanks, and 3) the controversy over the Taurus cruise missile.