bluuebunny/crossref_metadata_2025_split huggingface dataset:
A qualitative study of infection prevention and control practices in the maternal units of two Ghanaian hospitals
Authors: Sunkwa-Mills Gifty , Senah Kodjo , Breinholdt Mette , Aberese-Ako Matilda , Tersbøl Britt Pinkowski 
Published: August 2023

Abstract
Healthcare-associated infections (HAIs) remain a common challenge in healthcare delivery, with a significant burden in low- and middle-income countries. Preventing HAIs has gained enormous attention from policy makers and providers of healthcare services in resource-limited settings including Ghana. Despite policies to position health facilities to enforce infection prevention and control (IPC) measures as a means for improving patient experience of care and preventing HAIs, evidence points to poor IPC practices in hospitals in Ghana. In this study we explore the experiences of healthcare providers and post-natal women, and assess facilitators and barriers to IPC practice in two hospitals in Ghana. Methods: The study used a qualitative approach involving semi-structured interviews, focus group discussions, and observations from January 2019 to June 2019. Interviews were recorded and transcribed verbatim for thematic analysis. The data sets were uploaded into the qualitative software NVivo 12 to facilitate coding and analysis. Observation notes were incorporated into the data analysis. Findings: Healthcare providers’ responsibility to care for patients and establish professional identity, influenced their approach to care. Hygiene behaviour and IPC compliance were driven by the desire to care for and protect oneself from infections. Other IPC facilitators include leadership commitment and support, training and education for IPC. Postnatal women were informed about IPC during antenatal care visits, and their practices were also shaped by their background and their communities. IPC barriers include the poor documentation or ‘invisibility’ of HAIs, low prioritization of IPC tasks, lack of clear IPC goals, inadequate deference to guidelines, and communication-related challenges. The results of this study demonstrate the need for a comprehensive understanding of the individual and collective perceptions of HAIs, and what constitutes ‘care’, to drive hygiene behaviour to improve IPC in hospital wards. Hospital managers need to position themselves as key drivers of IPC and develop clear goals and define roles to stimulate IPC while engaging postnatal mothers and communities.
Semantic Similarity Loss for Neural Source CodeSummarization
Authors: Su Chia-Yi , McMillan Collin 
Published: August 2023

Abstract
This paper presents an improved loss function for neural source code summariza-tion. Code summarization is the task of writing natural language descriptions ofsource code. Neural code summarization refers to automated techniques for gen-erating these descriptions using neural networks. Almost all current approachesinvolve neural networks as either standalone models or as part of a pretrainedlarge language models e.g., GPT, Codex, LLaMA. Yet almost all also use acategorical cross-entropy (CCE) loss function for network optimization. Twoproblems with CCE are that 1) it computes loss over each word prediction one-at-a-time, rather than evaluating a whole sentence, and 2) it requires a perfectprediction, leaving no room for partial credit for synonyms. We propose and eval-uate a loss function to alleviate this problem. In essence, we propose to use asemantic similarity metric to calculate loss over the whole output sentence pre-diction per training batch, rather than just loss for each word. We also proposeto combine our loss with traditional CCE for each word, which streamlines thetraining process compared to baselines. We evaluate our approach over severalbaselines and report an improvement in the vast majority of conditions.
Identifying Critical Success Factors to Adopt Circular Economy in Healthcare Sector for Sustainable Waste Management
Authors: Dixit Anuj , Dutta Pankaj 
Published: August 2023

Abstract
The rapid growth of healthcare sector especially after Covid-19 pandemic has augmented the emission of healthcare waste (HW) globally. It is a major concern for developing countries like India rather developed. Thus, this study has recognized, estimated the importance, prioritized the criticality of critical success factors (CSFs) for the adoption of circular economy (CE) in HW management (called as healthcare circular economy i.e., ‘HCE’) to minimise the HW generation in Indian perspective. Initially, descriptive statistics followed by questionnaire surveys is accomplished for data analysis based on the organization’s size and domain of expertise. Then, an analysis of variance (ANOVA) is executed to identify the substantial differences in the adoption of HCE having different sizes or domains of expertise. Finally, the interpretive structural modelling (ISM) embedded fuzzy-decision making trial and evaluation laboratory (F-DEMATEL) method is used to identify the most significant CSFs for adoption of HCE. Among 17 finalised CSFs, 12 CSFs are identified in a causal group, while rest 5 is in effect group. The government responsibility and stakeholders’ participations are having highest driving potential, whereas the segregation and collection of HW is highly dependent on other causal-CSFs. This is an original and unique study of its kind to recognize and evaluate the CSFs for the adoption of HCE in general and India in specific. This study also shows the suitable extents for assigning the resources and efforts to enhance the adoption of HCE in Indian perspective. However, the results obtained are India specific, but this can be a platform to understand and analyse CSFs for adoption of HCE for other developing economies. This study will also help to Indian policymakers to develop the strategies for adoption of HCE practices.
RETRACTED: Comparative analysis of economic and socio-cultural factors influencing reproductive behaviour in the Northern and Southern regions of Kazakhstan
Authors: Bodaukhan Kairat , Zhanerke Kochyigit , Darima Zhenskhan , Amerkhanova Indira , Nurmukhametov Nurbakhyt 
Published: August 2023

Abstract

The authors have requested that this preprint be removed from Research Square.
Sound analysis and extraction of data from Smart Contract
Authors: Ayub Maha , Khan Waiz , Janjua Muhammad Umar 
Published: August 2023

Abstract
With the addition of multiple blockchain platforms in the ecosystem, the Dapp owners need to migrate their smart contracts from one platform to another to remain competitive , cost-effective, and secure. A smart contract is a piece of code that contain logic and data. To migrate a smart contract, whether it’s on the same blockchain platform or a different one, we need both its source code that represents the logic and data which indicate the state of the contract. The source code can be easily set up, but to complete the migration, we have to extract the current state of the contract. In this paper, we have developed an advanced state extraction technique that uses static analysis to analyze the smart contract’s call graph and events, and extracts the entire storage state from the storage trie, along with the proper associations across function calls, enabling users to visualize, manage, and transform the state as desired for migration. The soundness of the extracted state was confirmed using the method of abstract interpretation. Further, the migration adapter is designed that transform extracted state into slot-value pair and migrated it to the target blockchain. Our new approach has allowed us to analyze 14% more smart contracts with the extraction of 15% more data from 67,993 contracts, and migrate some of them to the Polygon test-net.
Fast defect detection of image sensor based on lightweight network
Authors: Zhang Hui , Yu Mingqi , Yue Hongwei , Kou Kit Ian , Lin Qinyong , Chen Huazhou , Chen Chuangquan , Cai Ken 
Published: August 2023

Abstract
Image sensor is the main device in camera production and its quality influences the imaging quality directly. Existing detection methods can not accurately detect the defects of image sensors. To address these problems, a two-branch network based on transfer learning is proposed to solve the problem of image sensor defect detection. Firstly, model parameters and detection speed were improved by replacing the backbone feature extraction network of YOLOV with a lightweight network. Secondly, anchor box optimization and focal loss were used to solve the problem of accurate positioning and unbalanced sample classification. Finally, with the cosine annealing scheduler and fixed learning rate, the network's convergence rate and overall performance are improved. The proposed lightweight network algorithm achieves a better effect than the original algorithm. The proposed algorithm has higher detection speed and smaller parameter size than other detection methods under the same size.
Smoking cessation interventions and implementations across multiple settings in Japan: A scoping review and supplemental survey
Authors: Nagasawa Tomomi , Saito Junko , Odawara Miyuki , kaji Yuki , Yuwaki Keiichi , Imamura Haruhiko , Nogi Kazuya , Nakamura Masakazu , Shimazu Taichi 
Published: August 2023

Abstract
Background: Smoking is the leading risk factor for death worldwide. In Japan, although several evidence-based interventions (EBIs) for smoking cessation have been disseminated or adopted, there is a gap between scientific evidence and the actual implementation. This scoping review aimed to describe the knowledge gaps in local-level smoking cessation interventions in Japan, their implementation outcomes, implementation barriers and facilitators, and the use of implementation strategies.Methods: This study comprised two approaches: (1) a comprehensive scoping review of primary and grey literature, and (2) a supplemental survey of organizations in the grey literature. For the scoping review, we included original studies or reports on smoking cessation interventions targeting adults aged 18 years and older, or providers of cessation support at various settings (community, workplace, school, and clinical settings) in Japan. The extracted data included basic characteristics, intervention categories, implementation outcomes, factors influencing implementation, and implementation strategies for each intervention. Responses to the supplemental survey were extracted same used for the scoping review. To gain a deeper understanding, semi-structured interviews were conducted with some of the organizations in the survey.Results: A total of 600 interventions with 691 intervention components, based on EBIs in the 2020 U.S. Surgeon General Report, from 498 articles were included in the data extraction; 32 of the 88 organizations responded to the survey. Regarding the overall knowledge about smoking cessation intervention components, behavioral counseling and cessation medication in clinical settings were mostly reported (34.7%). Implementation outcomes were measured in 18 articles (3.0%) and penetration was mostly reported. Regarding influential factors, “available resources,” and “knowledge and beliefs about the intervention” for barriers, and “relative priority” for facilitators were mostly reported. Implementation strategies were measured in 29 articles (4.8%), and “Train and educate stakeholders” was mostly reported.Conclusions: Most EBIs reported in the Japanese literature included smoking cessation treatments in clinical settings. While a few articles focused on the implementation indicators in Japan, significant knowledge and experience were extracted from the grey literature, especially in the workplace and community settings. Future research should focus more on implementation to reduce the knowledge gap regarding smoking cessation interventions.
A Hybrid Recurrent Neural Network Approach for Detecting Abnormal User Behavior in Social Networks
Authors: Ebrahimzadeh Fereshteh , Nazari Amin , Feizi-derakhshi Mohammad Reza , Mansoorizadeh Muharram 
Published: August 2023

Abstract

With the recent advances in Internet technology, various aspects of the human lifestyle are influenced by the digital world. Users join social networks to communicate and perform their social activities. In addition to the vast benefits, social networks may also negatively affect different dimensions of user activities. One of the major challenges in social networks is the security issues of the individual user activities since usually there is no mechanism to detect abnormal behavior of the users. On the other hand, due to the continuous expansion and dynamic nature of social networks, identifying anomalies and abnormal behaviors of users in such networks is of particular importance. The literature offers several methods to manage the challenge of the security breach of user activities in social networks. A thorough inspection of these methods shows that most of them focus on two important factors: selecting the appropriate feature and increasing the accuracy of detecting abnormal behaviors. The main innovation of this paper is the proposal of a new hybrid method based on recurrent neural networks to analyze the abnormal behavior of users and manage the two challenges mentioned above. Our motivation for using recurrent neural networks as the basis of the proposed method is the temporal nature of user activities in social networks and the reasonable performance of these methods in identifying abnormal behaviors, processing time-series data, and the ability to analyze relationships between them. In the proposed method, statistical methods are used for the initial labeling of data. In the next step, the CNN-BiLSTM hybrid method is applied to the data in order to re-label them, establish order between the features and thus reduce the error. Then the abnormal batches are identified using the K-means clustering algorithm. The experiments show that the proposed method provides more accurate results compared to other statistical and learning-based methods. The efficiency of the proposed method is also reported on the VAST Challenge 2008 and Enron-Email datasets for the F-score benchmark, 0.82 and 0.88, respectively.
Cancer treatment closer to the patient, reduces travel burden, time toxicity and improve patients satisfaction. Results on 546 consecutive patients in a North Italy district
Authors: Cavanna Luigi , Citterio Chiara , Mordenti Patrizia , Proietto Manuela , Bosi Costanza , Vecchia Stefano 
Published: August 2023

Abstract
Purpose
The distance to cancer facilities may cause disparities by creating barriers to oncologic diagnosis and treatment, and travel burden may cause time and financial toxicity.
Methods
To relieve travel burden, a program to deliver oncologic treatment closer to the patient was initiated in our district some years ago. The oncologic activities are performed by oncologists and by nurses who travel from the oncologic ward of the city hospital, to territorial centers for delivering cancer patients management. This model is called Territorial Oncology Care (TOC): patients are managed near their home, in three territorial hospitals and in a health center, named “Casa della Salute” (CDS). We performed a retrospective study and we analyzed electronic records of patients with cancer managed in the TOC program. The primary endpoints are the kms and time saved, the secondary endpoints: reduction of caregiver need for transport and patient’s satisfaction.
Results
546 cancer patients managed in the TOC program from January 2, 2021 to June 30, 2022 were included in this study. Primary endpoints: median kms to reach the city hospital: 26(range 11-79 kms) median time: 44 minutes ( range 32-116); median kms in the TOC program: 7(range 1-35 kms), median time: 16 minutes (range 6-54), p<0,001. Secondary endpoints: 64.8% of patients who needed a caregiver for the city hospital could travel alone in the TOC program and 99,63% of patients were satisfied.
Conclusions
The results of our retrospective study highlight the possibility of treating cancer patients near their residence, reducing travel burden and time saving.
A feasibility study of systematic symptom assessment in palliative care using the PERS2ON score
Authors: Tscherny Katharina , Grafeneder Jürgen , Wandl Bettina , Niederer Maximilian , Haider Martina , Masel Eva Katharina , Roth Dominik , Egger Alexander 
Published: August 2023

Abstract
PURPOSE
A comprehensive and standardized assessment of symptoms is fundamental for individualized palliative care (PC). Numerous scoring systems are available, but they are often cumbersome and hence unsuitable for routine use. The PERS2ON score has been developed to provide a short, and feasible score to evaluate symptom burden. We performed an external evaluation of the feasibility of this score in a rural hospital.
METHODS
Patients admitted to the palliative care unit (PCU) of a rural hospital were rated according to the seven criteria of the PERS2ON score, pain, eating, rehabilitation, social situation, O2 and nausea/emesis, on a scale ranging from 0 to 10, with higher scores indicating greater symptom load. The assessments were performed on admission, seven days after admission, and on the day of discharge. We calculated symptom intensity scores and evaluated the change over time for each patient.
RESULTS
40 patients met inclusion criteria, nine died, a re-assessment after seven days was therefore possible for 35 patients, and an assessment at discharge for 31 patients. The mean PERS2ON Score declined from 28 (SD 12) on admission to 21 (SD 11) after seven days (absolute difference 7, 95% CI: 3–11, p = 0.002) and further to 17 (SD 10) at discharge (absolute difference 11, 95% CI: 6–15, p < 0.001).
CONCLUSION
Assessment of the PERS2ON score was feasible in all patients during their stay. The implementation of the PERS2ON score might be useful to direct clinical practice and targeted symptom management even at smaller PCUs.
Performance interference of co-allocated applications: a systematic literature review
Authors: Urzua-Barrientos Diego Salvador , Duran-Limon Hector Alejandro , Mora Manuel 
Published: August 2023

Abstract
Several efforts have been made to study the nature of performance interference in co-allocated applications running in different environments. Such performance interference degrades the performance of co-allocated applications. Many researchers propose techniques, methods and approaches that use mathematical equations, machine learning algorithms or other kind of techniques to predict/analyze performance degradation. Hardware counters, virtual machine monitor metrics and third-party tools are commonly used to provide the input data for prediction techniques. Performance prediction/analysis is useful for understanding, preventing, and minimizing performance degradation. This paper presents a systematic literature review of methods for the prediction/analysis of performance degradation due to performance interference of co-allocated applications, which were published in the open literature during the period 2006-2023. We classified and categorized each analyzed article depending on the method used and the environment employed, among others features. In addition, we identify some directions and trends in performance interference, as well as some unresolved issues.
Optimization of trans-4-hydroxyproline synthesis pathway by rearrangement center carbon metabolism in Escherichia coli
Authors: gong yu , wang ruiqi , Ma Ling , wang shuo , li changgeng , xu qingyang 
Published: August 2023

Abstract
Background
Trans-4-hydroxyproline (T-4-HYP) is a promising intermediate in the synthesis of antibiotic drugs. However, its industrial production remains challenging due to the low production efficiency of T-4-Hyp.This study focused on designing the key nodes of anabolic pathway to enhance carbon flux and minimize carbon loss, thereby maximizing the production potential of microbial cell factories.
Results
First, a basic strain, HYP-1, was developed by releasing feedback inhibitors and expressing heterologous genes for the production of trans-4-hydroxyproline. Subsequently, the biosynthetic pathway was strengthened while inhibitory pathways were disrupted, resulting in increased metabolic flow of α-ketoglutarate in the Tricarboxylic acid cycle. The introduction of the NOG pathway rearranged the central carbon metabolism, redirecting glucose towards acetyl-CoA. Furthermore, the supply of NADH-reducing power was enhanced to improve the acid production capacity of the strain. Finally, the fermentation process of T-4-HYP was optimized using a continuous feeding method. The rate of sugar supplementation controlled the dissolved oxygen concentrations during fermentation, and Fe2+ was continuously fed to supplement the reduced iron for hydroxylation. These modifications ensured an effective supply of proline hydroxylase cofactors (O2 and Fe2+), enabling efficient production of T-4-HYP in the microbial cell factory system. The strain HYP-10 produced 89.4 g/L of T-4-HYP e in a 5L fermenter, with a total yield of 0.34 g/g, which is the highest reported T-4-HYP production so far.
Conclusion
This study presents a strategy for establishing a microbial cell factory capable of producing T-4-HYP at high levels, making it suitable for large-scale industrial production. Additionally, this study provides valuable insights into regulating Synthesis of other compounds with α-ketoglutaric acid as precursor.
Iron status and sarcopenia-related traits: A bi-directional Mendelian randomization study
Authors: Chen Honggu , Zhang Ziyi , Wang Yizhe , Ma Anpei , Li Lingbo , Zhao Guoyang 
Published: August 2023

Abstract

Background
Although there is a strong correlation between serum iron status and sarcopenia, there is insufficient comprehensive evidence to establish a definitive causal relationship between the two factors. Hence, the aim of this study is to employ Mendelian randomization techniques to elucidate the association between serum iron status and sarcopenia.
Methods
we conducted a bi-directional Mendelian randomization (MR) analysis. The analysis involved utilizing the inverse variance weighted (IVW), MR-Egger, and weighted median methods. Additionally, sensitivity analyses were performed to ensure the reliability of the obtained causal association results. Furthermore, an integrated proxy for iron status, derived from a combination of single nucleotide polymorphisms (SNPs), was employed in the MVMR analysis.
Results
The UVMR analyses demonstrated a significant causal effect of ferritin on appendicular lean mass (β= -0.051, 95%CI: -0.072~-0.031, p = 7.325×10− 07). Sensitivity analyses did not reveal any heterogeneity, pleiotropic effects, or fluctuations in the results due to outlying SNPs for the four iron status variables related to sarcopenia traits. Furthermore, the MVMR analyses indicated that ferritin (β= -0.068, 95%CI: -0.12~-0.017, p = 9.658×10− 03) plays a predominant role in the associations between iron status and appendicular lean mass.
Conclusion
Our study revealed a causal association between serum iron status and sarcopenia, with ferritin playing a key role in this relationship. These findings contribute to our understanding of the complex interplay between iron metabolism and muscle health.