bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence and Large Language Models in Advancing Cyber Threat Intelligence: A Systematic Literature Review
Authors: Alturkistani Hilalah , Chuprat Suriayati 
Published: November 2024

Abstract

The evolution of Cyber Threat Intelligence (CTI) is pivotal in addressing the increasingly sophisticated landscape of cyber threats. Traditional CTI methods, while foundational, are rapidly obsoleting by the complexity and dynamism of modern cyber threats. This demands a shift towards more advanced, adaptive strategies integrating Artificial Intelligence (AI) and Large Language Models (LLMs). This literature survey examines recent developments in CTI, focusing on traditional methods, the incorporation of AI for enhanced forecasting and detection, and the pioneering application of LLMs for automating intelligence report generation, enhancing threat detection, recognition, and mitigation accuracy, and facilitating the construction of comprehensive threat prevention framework. Key findings indicate that while traditional CTI methods provide a critical baseline for threat intelligence, their static nature and reliance on outdated indicators of compromise limit their effectiveness against novel, sophisticated cyber threats. Integrating AI techniques, particularly machine learning and deep learning, marks a significant advancement in CTI, offering improved predictive capabilities, dynamic threat detection, and more nuanced analysis of cyber threats. Further, the advent of LLMs in CTI represents a transformative shift, enabling the automated generation of intelligence reports, processing of unstructured CTI data, threat detection, recognition, and actionable step generation to mitigate potential cyber threats. However, challenges persist, including data privacy concerns, the dynamic nature of cyber threats, and the integration of LLMs into existing cybersecurity frameworks. The reviews provide limitations for each study and future research directions with particular emphasis on developing adaptive, intelligent CTI systems capable of proactively addressing the evolving cyber threat landscape.
Evaluation of the drinking water parameters in primary and secondary schools located in the Gaziantep Province Center
Authors: Petek Osman , Aydın Neriman 
Published: November 2024

ABSTRACT
This cross-sectional study aimed to assess the drinking water parameters in primary and secondary schools. A questionnaire encompassing the schools’ general characteristics and information about drinking water was administered to school administrators. Drinking water samples were taken from 60 schools to evaluate drinking water parameters. The data were analyzed using Chi-Square, Mann–Whitney U, and Kruskall–Wallis tests, T-test, and SPSS 22.0 software. Non-compliance with national legislation was observed in 16.7% of schools during drinking water analysis. The microbiological parameters exceeded the threshold values in 90% of the schools where the analysis results of the drinking water were deemed inadequate. The analysis yielded no discernible variations based on school district or type. Mains water was the primary drinking water source in 91.7% of schools. The rate was lower in rural schools compared to urban schools, with a difference of 85 and 95%, respectively. It was found that 41.7% of schools lacked canteens or the sale of packaged water. Consequently, the availability of tap water in schools falls short of the desired standard. It is imperative to investigate the factors leading to microbiological contamination in school drinking water and formulate short-, medium-, and long-term strategies to enhance its safety.
Short-term water demand prediction using stacking regression–based machine learning
Authors: Mohamed Hussain K. , Sivakumaran N. , Radhakrishnan T. K. , Swaminathan G. , Sankaranarayanan S. 
Published: November 2024

ABSTRACT

Forecasting of water demand and equitable allocation of local water resources are used to reduce and eliminate water shortages and waste. The key emphasis of this research article is to estimate water demand using the prediction model for the Peroorkada urban water distribution network. The characteristics, such as head, pressure, and base demand, related to the water demand were the features of the prediction model. The prediction model has been developed using python. The water distribution network consists of 99 nodes. The demand graph for a time interval of 6 h has been plotted and predicted for all the nodes, and 24-h interval demand has been plotted for vulnerable nodes, which were determined by the sensor placement toolkit. This study included 13 machine learning algorithms, including three hybrid/stacked regression techniques. The least absolute shrinkage and selection operator-based stacking regressor model performs the best at demand prediction. Single prediction models were outperformed by stacking regressor models.
Modelling and set-point definition for the development of a joint control system of two interconnected wastewater treatment plants and its application in practice
Authors: Schütz Jan Tobias , Kleyböcker Anne , Larsen Sille Bendix , Kristensen Malene , Remy Christian , Miehe Ulf 
Published: November 2024

ABSTRACT
The use of activated sludge models (ASMs) is a common way in the field of wastewater engineering in terms of plant design, development, optimization, and testing of stand-alone treatment plants. The focus of this study was the development of a joint control system (JCS) for a municipal wastewater treatment plant (mWWTP) and an upstream industrial wastewater treatment plant (iWWTP) to create synergies for saving aeration energy. Therefore, an ASM3 + BioP model of the mWWTP was developed to test different scenarios and to find the best set-points for the novel JCS. A predictive equation for the total nitrogen load (TN) coming from the iWWTP was developed based on real-time data. The predictive TN equation together with an optimized aeration strategy, based on the modelling results, was implemented as JCS. First results of the implementation of the JCS in the real environment showed an increase in energy efficiency for TN removal.
Overexpression of HIF2α Enhances the Angiogenesis-Promoting Effect of hUC-MSC-Derived Extracellular Vesicles by Stimulating miR-146a
Authors: Chen Yihui , Hong Shichai , Wang Zhefeng , Hong Xiang , Chen Gang , Huang Yulong , Lin Yue , Xie Xinsheng , Lin Chenwei , Lu Weifeng 
Published: November 2024

Objective:
This study aimed to explore whether excessive HIF2α can amplify the impact
of human Umbilical Cord Mesenchymal Stem Cell-derived Extracellular Vesicles (hUC-MSC-
EVs) on endothelial cells.

Methods:
In this study, we created HIF2α-overexpressing hUC-MSC-EVs and compared their
pro-angiogenic effects with control EVs on Human Umbilical Vein Endothelial Cells (HUVECs).
MTT assay and Edu staining were used to detect the viability and proliferation ability of HUVECs,
and Transwell and tube formation assays were used to detect cell migration and tube formation
ability. qPCR assay was used to detect the expression of cellular angiogenic markers. Subsequently,
miRNAs that might be regulated by HIF2α were predicted by bioinformatics analysis,
and qPCR was used to detect the relative expression of miRNAs in HUVECs treated with hUC-MSC-
EV, which over-expresses HIF2α. Subsequently, miR-146a inhibitors were used to investigate
the role of miR-146a in mediating the pro-angiogenic effect of HIF2α on HUVECs by detecting
cell viability, proliferation, migration, tube-forming ability, and expression of angiogenic markers.
Finally, AKT/ERK phosphorylation and Spred1 expression were detected using Western blotting.

Results:
Our findings have indicated that overexpression of HIF2α significantly enhances the ability
of hUC-MSC-EVs to stimulate proliferation, migration, and tube formation in HUVECs, as demonstrated
by MTT/Edu staining, Transwell assay, and tube formation assay results, respectively.
Mechanistically, excessive HIF2α has been found to induce the expression of miR-146a in HUVECs
and the overexpression of a miR-146a inhibitor to negate the influence of excessive HIF2α
on hUC-MSC-EV-induced activity in HUVECs.

Conclusion:
The overexpression of HIF2α is an effective strategy for enhancing the pro-angiogenic
function of hUC-MSC-EVs.
Thrombectomy Failure and Associated Factors for Large-Vessel
Occlusion Stroke
Authors: Rilianto Beny , Kurniawan Ricky Gusanto , Prasetyo Bambang Tri , Rajab Nurfadilah M. , Arham Abrar 
Published: November 2024

Background:
Ischemic stroke due to Large Vessel Occlusion (LVO) represents a critical
and time-sensitive neurological emergency. Advancements in imaging technology and endovascular
therapies have transformed the management of LVO. Nonetheless, thrombectomy failure
diminishes the chances of patients achieving a favorable clinical outcome.

Objective:
We aimed to determine the factors influencing recanalization failure in order to optimize
thrombectomy therapy along with enhancing patient outcomes.

Methods:
A retrospective analysis was performed employing consecutive LVO patients who underwent
Endovascular Thrombectomy (EVT) in a tertiary comprehensive stroke center between
January 2020 and June 2024. Recanalization failure (mTICI 0-2a) following thrombectomy was assessed
using the Kolmogorov-Smirnov test, χ2 test, Fisher’s exact test, and multivariable logistic
regression to identify the related factors.

Results:
A total of 82 EVT patients were analyzed. The mean age was 58.20 years and 70.73% of
the patients were male. The rate of recanalization failure was 61%. Multivariable logistic regression
analysis with age-sex adjusted factors has revealed hypertension [aOR: 5.31 (95% CI:
1.23-22.77); p =0.025] and no IVT [aOR: 2.75 (95% CI: 1.06-7.14); p =0.037] to be independent
predictors of recanalization failure in this study.

Conclusion:
Hypertension and the absence of prior intravenous thrombolysis have been found to
be significant contributing factors to the high rate of thrombectomy failure in large-vessel occlusion.
Transforming Alzheimer’s Digital Caregiving through Large Language Models
Authors: Kim Sujin , Han Dong Y. , Bae Jihye 
Published: November 2024

Introduction/Objective:
Alzheimer's Disease and Related Dementias (AD/ADRD) present
significant caregiving challenges, with increasing burdens on informal caregivers. This study
examines the potential of AI-driven Large Language Models (LLMs) in developing digital caregiving
strategies for AD/ADRD. The objectives include analyzing existing caregiving education materials
(CEMs) and mobile application descriptions (MADs) and aligning key caregiving tasks with
digital functions across different stages of disease progression.

Methods:
We analyzed 38 CEMs from the National Library of Medicine’s MedlinePlus, along
with associated hyperlinked web resources, and 57 MADs focused on AD digital caregiving. Using
ChatGPT 3.5, essential caregiving tasks were extracted and matched with digital functionalities
suitable for each stage of AD progression, while also highlighting digital literacy requirements
for caregivers.

Results:
The analysis categorizes AD caregiving into 4 stages-Pre-Clinical, Mild, Moderate, and
Severe-identifying key tasks, such as behavior monitoring, daily assistance, direct supervision,
and ensuring a safe environment. These tasks were supported by digital aids, including memory-
enhancing apps, Global Positioning System (GPS) tracking, voice-controlled devices, and advanced
GPS tracking for comprehensive care. Additionally, 6 essential digital literacy skills for
AD/ADRD caregiving were identified: basic digital skills, communication, information management,
safety and privacy, healthcare knowledge, and caregiver coordination, highlighting the need
for tailored training.

Conclusion:
The findings advocate for an LLM-driven strategy in designing digital caregiving interventions,
particularly emphasizing a novel paradigm in AD/ADRD support, offering adaptive
assistance that evolves with caregivers' needs, thereby enhancing their shared decision-making
and patient care capabilities.
Impact of the COVID-19 Pandemic on People with Dementia and their Caregivers: A Multiphase Observational Study from India
Authors: Arshad Faheem , Hurzuk Saadiya , Tiwari Megha , Varghese Feba , Hoskeri Rakshith Maneshwar , Paplikar Avanthi , Goyal Sheetal , Dhiren Shah Rutul , Ganeshbhai Patel Vishal , Ansari Mohammed Farhan , Komaravolu Shashidhar , Kammammettu Chandrashekar , Thomas Priya Treesa , Rao Girish Nagaraja , Alladi Suvarna 
Published: November 2024

Introduction:
The COVID-19 pandemic has had multifaceted and enduring impacts on
people with dementia and their caregivers; however, our understanding of the long-term outcomes
remains limited. We aimed to explore the long-term effects of the COVID-19 pandemic on cognitive
symptoms and vaccination rates in people living with dementia.

Methods:
This study was conducted as a part of a longitudinal study design in two specialized hospitals
in South India. In this study, patients with dementia and their caregivers assessed in earlier
phases (‘period of lockdown with phased relaxations - phase-I’ and ‘cluster of cases transmission
phase - phase-II’) were telephonically interviewed. We adopted a quantitative approach to understand
disease progression during the three-year course of the pandemic. Changes in cognition and
disease severity were measured using the Clinical Dementia Rating (CDR) scale. In brief, semistructured
interviews were carried out with caregivers of people with dementia to gain insights into
vaccination rates. Data obtained from the current study (phase III) were compared against phase
I data, which served as the baseline. Among the 72 participants contacted in the current phase, 59
(81·9%) could be reevaluated for dementia severity and vaccination status, whereas 13 (18·0%)
had died. Among the 59 participants, 33 (55·9%) had severe dementia (CDR 3). This is in contrast
to phases I and II, when 17·6% and 19·2% of the participants, respectively, were classified as
CDR 3.

Results:
A significant difference in dementia severity between the two phases (phases I and III)
was observed. In addition, we observed vaccination hesitancy among caregivers of patients with
dementia. This study would provide valuable insights into the long-term impact of the COVID-19
pandemic on the cognitive outcomes and vaccination status of patients with dementia.

Conclusion:
This overall longitudinal study has compared dementia severity between different
phases throughout the pandemic, with implications for future studies to tailor home-based support
and healthcare interventions in order to meet these evolving needs.
Digital Teaching and Learning Media for Nursing and Health Care Courses in Germany: Protocol for a Scoping Review
Authors: Vogel Jann Niklas , Letzin Jaqueline , Schmidt Stefan 
Published: November 2024

Background
In Germany, digital transformation and legal regulations are leading to the need to integrate digital technologies into the nursing profession. In addition, to nursing practice, they are also being incorporated into nursing training. Despite comprehensive regulations regarding the use of digital teaching and learning media in nursing education, their specific applicability and implementation vary.

Objective
This study aims to map evidence and identify the main concepts, theories, sources, and knowledge gaps in the use of digital teaching and learning formats in nursing and health care education in Germany.

Methods
The study is planned as a scoping review. The reporting of the study is based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 guidelines. The sources of information for the review include six bibliographic databases (MEDLINE via PubMed, Cochrane Library, Web of Science Core Collection, ERIC, PROSPERO, and APA PsycInfo). The search results will be presented in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The eligibility of studies is based on the population, concept, and context criteria: (1) learners of nursing and health care professions, (2) digital teaching and learning formats, and (3) forms of implementation in Germany since 2007.

Results
The literature search is planned for January 2025. The selection of titles, the coding of the data, and the data analysis are expected to be completed by March 2025.

Conclusions
In Germany, there is a growing interest in integrating digital teaching and learning formats into nursing and health care education. Our scoping review will map applications of digital teaching and learning media in the education of nursing and health care professions in Germany. In this way, the scoping review provides relevant impulses for fields of application and design aspects of digital teaching or learning media for nursing and health care education.

International Registered Report Identifier (IRRID)
PRR1-10.2196/60427
Improving Self-Efficacy, Quality of Life, and Glycemic Control in Adolescents With Type 1 Diabetes: Randomized Controlled Trial for the Evaluation of the Family-Centered Empowerment Model
Authors: Alzawahreh Salah , Ozturk Candan 
Published: November 2024

Background
Poor glycemic management in adolescents with type 1 diabetes mellitus (T1DM) increases complications. Enhanced control is associated with other factors, such as cultural, socioeconomic, and health care system disparities specific to the Middle East, which can greatly influence individuals’ ability to get and use health care services as well as their reaction to treatment approaches.

Objective
This study aims to evaluate the impact of the family-centered empowerment model on Jordanian adolescents with T1DM, focusing on their glycosylated hemoglobin levels, self-efficacy, and quality of life (QOL).

Methods
A randomized controlled trial involved 68 adolescents with T1DM visiting Jordanian Royal Medical Services’ clinics. Two sets of participant groups were created: control (n=34) and intervention (n=34). Participants were randomly assigned to either the intervention group, receiving the family-centered empowerment model intervention, or the control group, receiving standard care. Data were collected through face-to-face interviews and medical records.

Results
From April to October 2023, a total of 68 adolescents with T1DM participated in the study at the Jordanian Royal Medical Services. QOL had significant improvement among 13 (38%) of the 34 participants in the intervention group, and the program significantly improved moderate self-efficacy levels in 12 (35%) patients (P<.001). In addition, the average glycosylated hemoglobin levels dropped from 11.25% to 10.23% (P<.001). Additionally, improvements were seen in stress management, communication, and treatment adherence, with a substantial decrease in treatment obstacles. The intervention was successful in improving both clinical and psychosocial outcomes, as evidenced by the fact that the control group showed no noticeable improvements in these parameters.

Conclusions
The study suggests that patients with T1DM should receive continuous care education sessions, including self-care training, to improve their health. Nurses should also incorporate this training into treatment plans and educational programs for adolescents to enhance their QOL.

Trial Registration
ClinicalTrials.gov NCT06694467; https://clinicaltrials.gov/study/NCT06694467
Continuous Glucose Monitoring Among Patients With Type 1 Diabetes in Rwanda (CAPT1D) Phase I: Prospective Observational Feasibility Study
Authors: Baker Jason , Cappon Giacomo , Habineza Jean Claude , Basch Corey H , Mey Steven , Malkin-Washeim Diana L , Schuetz Christian , Simon Pierre Niyonsenga , Uwingabire Etienne , Mukamazimpaka Alvera , Mbonyi Paul , Narayanan Sandhya 
Published: November 2024

Abstract

Background
The development of minimally invasive continuous glucose monitoring systems (CGMs) has transformed diabetes management. CGMs have shown clinical significance by improving time in the euglycemic range, decreasing rates of hypoglycemia, and improving hemoglobin A1c (HbA1c). In Rwanda, CGMs are currently not routinely used, and no clinical studies of CGM use were identified in the literature.

Objective
This study aims to determine the impact and feasibility of real-time CGM use among people living with type 1 diabetes (T1D) in Rwanda through assessment of sensor use, time in range, rates of hypoglycemia and hyperglycemia, HbA1c, and rates of diabetes-related hospitalizations over time.

Methods
The Continuous Glucose Monitoring Among Patients with Type 1 Diabetes in Rwanda (CAPT1D) study is a single-arm, prospective observational study conducted at the Rwandan Diabetes Association clinic in Kigali, Rwanda, aiming to assess the impact and feasibility of CGM use in Rwanda. A cohort of 50 participants diagnosed with T1D were enrolled. Participants were at least 21 years old, undergoing multiple daily insulin therapy, and not currently pregnant. Phase I of the study was conducted over 12 months, using the Dexcom G6 CGM. Phase II and Phase III extended CGM use for an additional 6 months respectively, using the next-generation Dexcom G7 CGM. Here, we report the quantitative results of the Phase I study.

Results
Participants used the sensor for >80% of the time throughout the study period. A significant increase in time in range was observed within 3 months and sustained over 12 months. HbA1c decreased significantly in 3 months and stayed lower throughout the 12-month period. Mean HbA1c levels decreased by 2.8% at 6 months (P<.001) and 3.2% at 12 months (P<.001). A total of 12 diabetes-related hospitalizations were reported during the study period. No cases of diabetic ketoacidosis or episodes of severe hypoglycemia occurred.

Conclusions
Significant and meaningful improvements in key glycemic indices indicate the potential feasibility and impact of a CGM among people living with T1D in Rwanda. Future studies could be designed to include pre- and postintervention analysis to determine the effectiveness in terms of complications and costs.