bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparing and assessing physical activity and sedentary behaviour guidelines for different populations with and without chronic conditions and/or disabilities: a systematic review protocol
Authors: Arumugam Ashokan , Shalash Reime Jamal , Qadah Raneen Mohammad , Alsaafin Nour , Mohammad Zadeh Shima A , Chaput Jean-Philippe , Pesola Arto J , Kumaran D Senthil , Moss Sarah J , Veldsman Tamrin , Ramakrishnan Sivapriya , Regan Raja , Arora Esha , A Sulfikar Ali , Raghunathan Monica Raja Kumari , Al Tunaiji Hashel , Baltatu Ovidiu Constantin , Nassis George P , McDonough Suzanne , Dempsey Paddy C , Häger Charlotte
Published: December 2024

Physical activity guidelines targeting different populations with and without chronic diseases or disabilities are required to meet the diverse functional and physiological needs experienced by different subgroups of people to achieve optimal health benefits. As the importance of physical activity guidelines in promoting optimal health and well-being becomes increasingly recognised, there is a critical need for their systematic evaluation to ensure they remain effective, applicable and aligned with evolving health needs and scientific insights. This study aims to systematically review, critically evaluate, and compare global physical activity and sedentary behaviour guidelines on frequency, intensity, time, and type of exercise for adults, pregnant and postpartum women, and people living with chronic conditions and/or disabilities. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols checklist. We will search the Allied and Complementary Medicine Database, APA PsycInfo, Cumulative Index of Nursing and Allied Health Literature, Cochrane Library, Education Resources Information Center, Google Scholar, MEDLINE, PubMed, Scopus, SPORTDiscus, Web of Science and grey literature databases from 2010 to October 2024. Two reviewers will independently select guidelines, extract data and assess methodological quality using the Appraisal of Guidelines for Research and Evaluation II Instrument . Key recommendations will be summarised and classified as ‘strong’ and ‘conditional’ based on established criteria. A comprehensive evaluation of current guidelines will identify their differences and similarities and reveal their relevance in practical settings. The findings will guide healthcare professionals, researchers and policymakers in implementing evidence-based recommendations for managing physical activity and sedentary behaviour in targeted populations. Additionally, we will highlight current knowledge gaps and potential shortcomings in existing guidelines. PROSPERO registration number: CRD42023491339.
Combined collaterals and hemodynamic features to predict the prognosis in acute ischemic stroke patients undergoing mechanical thrombectomy
Authors: Song Zhiruo , Fang Xiang , Jia Xuerong , Liu Rui , Yang Dong , Liao Anyu , Kasaer Feiluola , Xu Yan , Zhang Zhiguo , Zhu Wusheng , Huang Kangmo , Liu Xinfeng
Published: December 2024

Background

Despite successful recanalization via mechanical thrombectomy (MT), only half of acute ischemic stroke (AIS) patients achieve functional independence. Post-MT hemodynamic features are insufficiently investigated, and the lack of bilateral comparisons limits individual assessment. Therefore, we aimed to explore how individualized hemodynamic features affect functional outcomes using quantitative digital subtraction angiography (Q-DSA).

Methods

We screened three prospective databases for anterior circulation AIS participants with successful recanalization via MT. Hemodynamic features, including time to peak (TTP), were measured for four regions of interest (ROIs) on each side with Q-DSA. ROIC2served as a reference for correcting TTP values. We defined TTP variation (TTPV), as the degree of difference in TTP between the symptomatic and the healthy sides. A composite indicator, integrating TTPV and collateral status, was developed to reflect comprehensive perfusion capacity. Multivariable logistic regression was employed to investigate the association between TTPV, or the composite indicator, and functional outcomes (90-day modified Rankin Scale <2).

Result

Finally, 201 participants (median (interquartile range (IQR) age 68 (57, 75] years, 64.2% males) were enrolled, and 97 (48.3%) patients achieved favourable functional outcomes. TTPVC2-M1was significantly related to favorable functional outcome (adjusted OR (aOR) = 0.627; 95% confidence interval (0.407, 0.966); p=0.034). Additionally, the composite indicator of good collaterals and low TTPVC2-M1was a powerful predictor for the favorable outcome (aOR=6.693; 95% confidence interval (2.652, 16.896); P<0.001).

Conclusion

TTPVC2-M1and the composite perfusion indicator showed significant predictive value in prognosis and potentially guided intra-procedure interventions and subsequent management among AIS patients.
Precarious employment, strenuous working conditions and the long-term risk of diagnosed chronic musculoskeletal disorders
Authors: Badarin Kathryn , Mangot-Sala Lluís , Matilla Santander Nuria , Kreshpaj Bertina , Hernando Rodríguez Julio César , Aronsson Amanda Emma , Kvart Signild , Thern Emelie , Gunn Virginia , Ostergren Per-Olof , Julia Mireia , Baron Sherry , Muntaner Carles , Wegman David H , Bodin Theo
Published: December 2024

Objectives

To investigate the effect of precarious employment (PE) on the risk of diagnosed chronic musculoskeletal disorders (MSDs) among Swedish workers in occupations with strenuous working conditions.

Methods

This nationwide register-based cohort study included workers registered as living in Sweden in 2005, aged 21–60 at the 2010 baseline. Three samples were included: workers with high biomechanical workload (n=680 841), repetitive work (n=659 422) or low job control (n=703 645). PE was evaluated using the SWE-ROPE (2.0) construct, which includes: contractual insecurity, temporariness, multiple jobs, income and collective bargaining agreement from 2010. Three exposure groups were created: PE, substandard and standard employment (SE). MSD data were obtained from outpatient registers (2011–2020). Cox proportional-hazards models estimated crude and adjusted sex-specific HRs with 95% CIs. Various outcomes were investigated for the different samples.

Results

Among workers with heavy biomechanical workload, results suggest increased risks of back MSDs in PE compared with those in SE. No association was found between PE and tendonitis in repetitive work, but PE was associated with an increased Carpal Tunnel Syndrome risk among men. Among workers with low job control, PE was associated with increased risks of soft tissue disorders among men and fibromyalgia among women.

Conclusions

PE was associated with an increased risk of MSDs among workers with strenuous working conditions, with variations depending on disorder and sex. The findings suggest a differential exposure to biomechanical workload within occupations. Targeted interventions and strengthened workplace safety regulations are needed to protect the musculoskeletal health of workers in PE.
Anaemia, blood transfusions and survival in high-grade endometrial cancer: retrospective study
Authors: Foggin Hannah H. , Lambert Pascal , Tsang Lung Fung , Nachtigal Mark W. , Ibrahim Nourah , Robinson Christine , Roberts Lesley F. , Altman Alon D.
Published: December 2024

Objective

To determine if anaemia and blood transfusions in the perioperative, chemotherapy and radiation treatment periods are associated with overall survival (OS) and recurrence-free survival (RFS) in high-grade endometrial cancer.

Methods

This retrospective cohort study examined patients at a single centre treated for high-grade endometrial cancer (2010–2023). This included International Federation of Gynecology and Obstetrics (FIGO) grade 3 endometrioid, serous, carcinosarcoma, mixed, clear cell, mucinous, dedifferentiated and undifferentiated histology. Primary outcomes were OS and RFS. Predictor variables were nadir haemoglobin and transfusion status. Multivariable Cox regression models for OS and RFS analysed the associations of treatment period-specific anaemia, overall transfusion status and confounder variables.

Results

Two hundred twenty-seven cases were included; 64–86% of patients were anaemic during any treatment, with 0–10% having severe anaemia. Twenty-two patients (9.7%) had at least one blood transfusion. Transfusion in the perioperative and chemotherapy periods was associated with poorer survival, significant only for shorter RFS in the chemotherapy cohort (HR 3.22, p=0.04). There was no association between anaemia and survival.

Conclusion

This study is among the first to assess anaemia in treated patients with high-grade endometrial cancer and the associations of anaemia and blood transfusion with survival outcomes. Further larger studies are needed to strengthen evidence and guide transfusion policies.
Pre-post implementation policy support for warnings on cigarette filters in Canada among adults who smoke cigarettes
Authors: Hackworth Emily E , Petillo Samantha L , Sun Yanwen , Vidaña-Pérez Dèsirée , Moodie Crawford , Niederdeppe Jeff , Hammond David , Thrasher James F
Published: December 2024

Background

Canada has become the first country in the world to require warnings to be displayed on cigarette sticks. Tobacco companies were given a transition period, from 30 April to 31 July 2024 to ensure that all king size cigarettes sold in the country included warnings on the filter of each cigarette stick. Warnings on the filter of regular size cigarettes will be implemented from April 2025.

Methods

We used an online panel provider to recruit an open cohort of adults in Canada who smoked factory-made or roll-your-own cigarettes. Seven waves of data were collected at 3-month intervals between February 2023 to August 2024, with 10 521 observations across 4310 participants. Participants were asked about support for the inclusion of warnings on cigarette filters, accompanied by an image of one of the six prescribed warnings (eg, ‘Cigarettes damage your organs’). Smoking behaviours and sociodemographic characteristics were also queried.

Results

Generalised estimated equations were used to estimate support at each wave. At W1 (February 2023, prior to the policy announcement), 25% of participants reported supporting printed health messages on cigarette sticks, significantly increasing to 27% by W7 (August 2024, at the end of the implementation transition period). Participants were more likely to support the policy if they used other nicotine/tobacco products, roll-your-own cigarettes, had a recent quit attempt, intended to quit, or had higher risk perception of smoking harms, and less likely if they were older, female, white or reported greater reactance to warnings on cigarette packs.

Conclusion

Just as increased support has been found for a range of tobacco control policies post-implementation, we found increased support for warnings on cigarettes during the transition period. However, support for this policy among people who smoke remains low. Other countries considering implementing this policy may consider these findings.
Insights into structural stability and thermal energy conversion into electrical properties of Mg3X2 (X = P, As, and Sb) compounds: A first-principles study
Authors: Rolania Mangal Chand , Kamlesh Peeyush Kumar , Kumar Pawan , Sharma Ghanshyam , Verma Ajay Singh
Published: December 2024

Thermoelectric (TE) materials that convert heat directly into electricity are crucial for waste heat recovery and renewable energy applications. This study focuses on Mg3X2 ([Formula: see text], As, and Sb) compounds, which have emerged as promising candidates for TE applications due to their abundance, non-toxicity, and potential for high efficiency across a broad temperature range. We employed density functional theory and the VASP simulation code to compute the structural, electronic, and TE properties of Mg3X2 compounds. Structural analysis indicates that Mg3P2 and Mg3As2 crystalize in cubic form and Mg3Sb2 acquire a trigonal structure. Mg3P2 and Mg3As2 have direct bandgaps of 1.60 and 1.42[Formula: see text]eV, while Mg3Sb2 has an indirect bandgap of 0.24[Formula: see text]eV. Mg3P2 and Mg3As2, with their lower thermal conductivities, show high figures of merit (ZT), particularly at low temperatures. Mg3Sb2, though exhibiting a higher thermal conductivity, demonstrates superior power factors at elevated temperatures. The mechanical stability and phonon dispersion curves analysis confirms that all compounds meet the criteria for structural stability, suggesting their suitability for practical applications. This work highlights the potential of Mg3X2 compounds as viable TE materials and provides insights for future experimental and theoretical studies aimed at improving their performance.
IOT-DRIVEN HEART DISEASE PREDICTION WITH INTELLIGENT CLASSIFIER AND SQUIRREL SEARCH FEATURE SELECTION
Authors: DAREM ABDULBASIT A. , ALOHALI MANAL ABDULLAH , HASSINE SIWAR BEN HAJ , ZAQAIBEH BELAL , ABOROKBAH MAJED , SALAMA AHMED S.
Published: December 2024

Cardiovascular disease (CVD) is the leading cause of global mortality in the modern world. This situation is difficult to predict and requires a combination of advanced techniques and specialist knowledge. Healthcare systems have recently adopted the Internet of Things (IoT) to collect critical sensor data to diagnose and predict CVD. Predictive models can be made more accurate and effective through such integration, which could radically change how we manage cardiovascular health. This study presents an improved squirrel search optimization algorithm for searching vital indications of CVD. To address the issue of low-cardiac diagnostic accuracy, the proposed IoT system uses enhanced squirrel search optimization with deep convolutional neural networks (SSO-DCNN). This new approach uses data from smartwatches and cardiac devices, which monitor patients’ electrocardiogram (ECG) and blood pressure readings. The proposed SSO-DCNN performs well compared to well-known deep learning networks such as logistic regression. The findings show an accuracy of 99.1% over current classifiers, suggesting effectiveness in the CVD prediction.
AUTOMATING METER CLASSIFICATION OF ARABIC POEMS: A HARRIS HAWKS OPTIMIZATION WITH DEEP LEARNING PERSPECTIVE
Authors: AL-ONAZI BADRIYYA B. , ELTAHIR MAJDY M. , ALZAIDI MUHAMMAD SWAILEH A. , EBAD SHOUKI A. , ALOTAIBI SHOAYEE DLAIM , SAYED AHMED
Published: December 2024

Meter classification in Arabic poetry is a crucial factor that describes the rhythmic structure of poems. Classical Arabic poetry relies on explicit meters, referred to as “Arud”, to create a structured and harmonious flow. Arabic meter is based on the pattern of short and long syllables, and each meter has a particular combination of feet (taf’ilah) that defines its unique rhythmic structure. Poets use diverse Arabic meters to evoke aesthetic or emotional qualities in their poetry. The mastery of meter is considered a sophisticated and skillful aspect of traditional Arabic poetry, which reflects the rich heritage of Arabic literature. The meter provides poets with unique opportunities and constraints, influencing the style and tone of their verses. Using deep learning (DL) for the meter classification of Arabic poems includes leveraging a neural network to automatically learn the features and patterns that discriminate between various meters. This paper presents a Fractal Harris Hawks Optimization with DL-based Meter Classification of Arabic Poems (HHODL-MCAP) technique. The HHODL-MCAP technique exploits the optimal DL model for the identification of distinct classes of meters of Arabic poems. The HHODL-MCAP technique involves a three-layered process. Primarily, the HHODL-MCAP technique performs data preprocessing to transform the data into a beneficial format. Second, the HHODL-MCAP technique applies long short-term memory (LSTM) with a Bidirectional Temporal Convolutional Networks (BiTCNs) model for the automated identification of various Arabic meter classes. At last, the HHO algorithm can be exploited to choose the hyperparameter values of the LSTM-BiTCN model optimally. A series of experiments were conducted to ensure the improved detection outcomes of the HHODL-MCAP technique. The extensive simulation results underline the supremacy of the HHODL-MCAP technique in the meter classification process.
COMPUTATIONAL INSIGHTS INTO ARABIC PROPAGANDA: AN INTEGRATION OF CORPUS LINGUISTICS WITH DEEP LEARNING APPROACH
Authors: ALZAIDI MUHAMMAD SWAILEH A. , ALRSLANI FAHEED A. F. , ALSHAMMARI ALYA , ELTAHIR MAJDY M. , AL SULTAN HANAN , SALAMA AHMED S.
Published: December 2024

The Arab nation is seriously affected by computational propaganda. The detection of Arab computational propaganda has become a hot research topic in social networking platforms. Propaganda campaigns endeavor to influence people’s mindsets to improve a particular agenda. They automatically employ the anonymity of the Internet, the micro-profiling capability of social network platforms, and the ease of managing and creating coordinated networks to reach masses of social network users with persuasive messages, mainly aimed at topics each user is sensitive to, and ultimately affecting the outcomes on the targeted problem. Using computation techniques and methods, analysts and researchers can better understand the scope, scale, and impact of propaganda efforts in Arabic-speaking communities and develop strategies to counter them. In recent times, deep learning (DL) approaches targeted explicitly at analyzing, detecting, or countering propaganda within online platforms or Arabic-speaking communities. DL is a subset of machine learning (ML), which includes training artificial neural networks (ANNs) with multiple layers for learning data representation. This paper designs an improved fractal walrus optimization algorithm with DL-based Arab computation propaganda detection (IWOADL-ACPD) technique. The IWOADL-ACPD method mainly focuses on the recognition and classification of propaganda in the Arabic language. The IWOADL-ACPD method begins with a preprocessing step to standardize and clean raw Arabic text data. Consequently, BERT word embedding encodes meaningful data, capturing contextual nuances vital for accurately detecting propaganda. In addition, the stacked sparse autoencoder (SSAE) detection technique is employed to discern subtle patterns indicative of propaganda content. To improve the performance of the SSAE method, the IWOADL-ACPD method uses IWOA to fine-tune the hyperparameter effectively. The proposed IWOADL-ACPD method contributes to Arabic computation propaganda detection by providing an adaptive and comprehensive technique for the complexity of cultural, digital, and linguistic landscapes specific to the Arabic-speaking context. The robustness and efficacy of the IWOADL-ACPD technique are demonstrated through stimulation analysis on the Arabic dataset, which showcases its capability to perform better than other existing methods. The IWOADL-ACPD technique exhibited a superior accuracy value of 95.25% over existing methods.
Mixing Flipped Classroom with Mental Mind Mapping for Deeper Learning: A Case Study within the Tunisian Higher Education Sector
Authors: Kamoun-Chouk Souad
Published: December 2024

To meet the needs of our knowledge-based society, students must have problem-solving, critical thinking, teamwork, learning-to-learn, and communication skills. Transferring knowledge is important to using these skills in non-school environments, communicating effectively, and applying content in new situations and future work. Flipped Learning has the potential to promote Deep Learning (DL). This paper describes a case study involving Flipped learning, to engage students in DL in an Economic intelligence course. We use the lens of Cultural-Historical Activity Theory (CHAT) to understand how the relationship between the human mind (what people think and feel) and activity (what people do) operates in mixing Flipped classrooms with mind-mapping techniques to promote DL. The evaluation of the results shows that students have a deeper understanding of the subject, but it also confirms a strong need for time and generalisation. The new pedagogy has to be introduced earlier in the school and integrated within the curricula to reinforce the students’ capacity for transfer and application. Isolated experimentation by teachers of Flipped Classrooms and Mind Mapping is insufficient to transfer knowledge in problem-solving situations. This limitation could negatively impact future entrepreneurs’ cognitive skills and task identity. Entrepreneurs as the main drivers of economic development need competencies in information and knowledge searching to reduce environmental uncertainty. They also have to make sense of early warning information to convert it into knowledge supporting the decision-making process.
LOGISTIC FORECASTING OF GDP COMPETITIVENESS
Authors: RAY ARNAB K.
Published: December 2024

The growth of the nominal Gross Domestic Product (GDP) of national economies is modeled by the logistic function. Applying it on the GDP data of the World Bank till the year 2020, we forecast the outcome of the competitive GDP growth of Japan, Germany, the UK and India, all of whose current GDPs are very close to one another. Fulfilling one of the predictions, the GDP of India overtook the GDP of the UK in 2022. We further forecast that in 2047 the GDP of India will exceed the GDPs of both Japan and Germany. When trade saturates, large and populous countries (like India) have the benefit of high domestic consumption to propel their GDP growth.
Making Water Pivotal in the Design of Food Systems
Authors: Hellegers Petra , Schmitter Petra , Talbi Amal , van Iwaarden Coen
Published: December 2024

Water plays a crucial role in our food systems and food security. However, the essential role of water for a functioning food system and the impacts of food systems on water availability and quality have not yet been adequately recognized. Due to a lack of coordination among water and food systems actors, there are siloed water, food security, and nutrition strategies. This paper presents the case to make water pivotal in designing food systems, laying out action perspectives for different actors to move toward what we call “water-responsible food systems”. This paper is based on input from many participants during workshops and existing literature. A food systems approach provides an excellent entry point to link food with water considering climate change and energy. Moreover, collective and cross-cutting actions between actors in food systems are essential to make decisive progress, as well as a common language and insight into the trade-offs of the multiple values of water for a clear prioritization of water use and allocation.
Efficiency of Self-Adjusting Heaps
Authors: Sinnamon Corwin , Tarjan Robert E.
Published: December 2024

Since the invention of the pairing heap by Fredman, Sedgewick, Sleator, and Tarjan [8], it has been an open question whether this or any other simple “self-adjusting” heap supports decrease-key operations in

\(\mathrm{O}(\log\log n)\)

time, where

\(n\)

is the number of heap items. Using powerful new techniques, we answer this question in the affirmative. We prove that both slim and smooth heaps, recently introduced self-adjusting heaps, support heap operations in the following amortized time bounds:

\(\mathrm{O}(\log n)\)

for delete-min and delete,

\(\mathrm{O}(\log\log n)\)

for decrease-key, and

\(\mathrm{O}(1)\)

for all other heap operations, including insert and meld, where

\(n\)

is the number of heap items that are eventually deleted: Items inserted but never deleted do not count in the bounds. We also analyze the multipass pairing heap, a variant of pairing heaps. For this heap implementation, we obtain the same bounds except for decrease-key, for which our bound is

\(\mathrm{O}(\log\log n\cdot\log\log\log n)\)

, where again items that are never deleted do not count in

\(n\)

. Our bounds significantly improve the best previously known bounds for all three data structures. For slim and smooth heaps our bounds are tight, since they match lower bounds of Iacono and Özkan [13].
Learning Semantic-aware Representation in Visual-Language Models for Multi-label Recognition with Partial Labels
Authors: Ruan Haoxian , Xu Zhihua , Yang Zhijing , Lu Yongyi , Qin Jinghui , Chen Tianshui
Published: December 2024

Multi-label recognition with partial labels (MLR-PL), in which only some labels are known while others are unknown for each image, is a practical task in computer vision, since collecting large-scale and complete multi-label datasets is difficult in real application scenarios. Recently, vision language models (e.g., CLIP) have demonstrated impressive transferability to downstream tasks in data limited or label limited settings. However, current CLIP-based methods suffer from semantic confusion in MLR task due to the lack of fine-grained information in the single global visual and textual representation for all categories. In this work, we address this problem by introducing a semantic decoupling module and a category-specific prompt optimization method in CLIP-based framework. Specifically, the semantic decoupling module following the visual encoder learns category-specific feature maps by utilizing the semantic-guided spatial attention mechanism. Moreover, the category-specific prompt optimization method is introduced to learn text representations aligned with category semantics. Therefore, the prediction of each category is independent, which alleviate the semantic confusion problem. Extensive experiments on Microsoft COCO 2014 and Pascal VOC 2007 datasets demonstrate that the proposed framework significantly outperforms current state-of-art methods with a simpler model structure. Additionally, visual analysis shows that our method effectively separates information from different categories and achieves better performance compared to CLIP-based baseline method.
SRLL: Improving Security and Reliability with User-Defined Constraint-Aware Logic Locking
Authors: Hashemi Mona , Mohammadi Siamak , Carlson Trevor E.
Published: December 2024

As chip fabrication costs rise, designers have shifted to a fabless and outsourced development model which opens up the possibility for IP piracy. To address these challenges, logic locking methods modify designs to limit functionality to authorized users that present a valid secret key. However, existing techniques often face limitations in resilience against advanced attacks and do not provide solutions to achieve user-defined constraints and goals. In this article, we propose SRLL, a user-defined constraint-aware logic locking technique that aims to improve the security and reliability of hardware designs. SRLL bridges the gap between exact and approximate attacks and allows the user to balance the resiliency against satisfiability-based, machine-learning-based, and constant propagation attacks while securing design constraints provided by the user. To enable this, we limit the locking functions to the non-critical path components and insert key gates at specific nodes, introducing a new set of critical parameters specifically designed to prevent target attacks. Finally, we obfuscate the netlist to hide inserted key gates and locking functions. Results show that SRLL maintains strong resiliency by exponentially increasing the required number of distinguishing input patterns, the complexity of finding these patterns, and adding sufficient structural complexity to the design. We evaluate SRLL using ISCAS ’85, MCNC ’91, and ITC ’99 benchmarks, demonstrating resiliency with low overhead against modern attacks, including SAT, AppSAT, OMLA, SAIL, and SCOPE.
Discovering Time-aware Hidden Dependencies with Personalized Graphical Structure in Electronic Health Records
Authors: Hadizadeh Moghaddam Arya , Nayebi Kerdabadi Mohsen , Liu Bin , Liu Mei , Yao Zijun
Published: December 2024

Over the past decade, significant advancements in mining electronic health records (EHRs) have enabled a broad range of decision-support applications and offered an unprecedented capacity for predicting critical events such as disease prognosis and mortality in healthcare. Despite the availability of comprehensive coding systems in EHRs (e.g., ICD-9), which are designed to record diverse information on diseases, procedures, and medications over time, the complex and dynamic dependencies among the recorded data are usually not captured. This limitation often hinders the contextual understanding of medical observations for effective EHR representation learning. Therefore, there is a compelling need to discover a hidden “EHR graph” that represents the medical relationship between the observed features according to a patient’s history. These hidden graphs consisting of the medical codes from the same visits can offer a comprehensive insight derived from disease-to-disease, disease-to-drug, and drug-to-drug dependencies. However, it is still unclear how to address the challenge that the dependencies may vary from patient to patient, and they can dynamically evolve from one visit to another. To this end, we propose
Time-aware Personalized Graph Transformer
(TPGT), a novel attention-based time-aware hidden graph model, that captures the personalized graphical structures among observed medical codes and summarizes the temporal code dependencies over time to improve patient representation for outcome prediction. Built upon an intra-visit and an inter-visit dual-attention mechanism to model patients’ EHR graphs, our model offers an interpretability of what diagnosis or medication in a patient’s history can interact, and how those interactions may change over time. We conduct extensive experiments on two real-world EHR datasets for different healthcare predictive tasks: acute kidney injury (AKI) prediction and ICU mortality prediction. The experimental results demonstrate a significant performance improvement of the proposed model over baselines through multi-aspect quantitative evaluation. Furthermore, we perform various qualitative studies to validate the interpretability of the model which highlights the application of the proposed method in the context of personalized medicine.
Exploring the Capabilities of LLMs for Code Change Related Tasks
Authors: Fan Lishui , Liu Jiakun , Liu Zhongxin , Lo David , Xia Xin , Li Shanping
Published: December 2024

Developers deal with code-change-related tasks daily, e.g., reviewing code. Pre-trained code and code-change-oriented models have been adapted to help developers with such tasks. Recently, large language models (LLMs) have shown their effectiveness in code-related tasks. However, existing LLMs for code focus on general code syntax and semantics rather than the differences between two code versions. Thus, it is an open question how LLMs perform on code-change-related tasks.

To answer this question, we conduct an empirical study using >1B parameters LLMs on three code-change-related tasks, i.e., code review generation, commit message generation, and just-in-time comment update, with in-context learning (ICL) and parameter-efficient fine-tuning (PEFT, including LoRA and prefix-tuning). We observe that the performance of LLMs is poor without examples and generally improves with examples, but more examples do not always lead to better performance. LLMs tuned with LoRA have comparable performance to the state-of-the-art small pre-trained models. Larger models are not always better, but
Llama
2 and
Code Llama
families are always the best. The best LLMs outperform small pre-trained models on the code changes that only modify comments and perform comparably on other code changes. We suggest future work should focus more on guiding LLMs to learn the knowledge specific to the changes related to code rather than comments for code-change-related tasks.
RuMono: Fuzz Driver Synthesis for Rust Generic APIs
Authors: Zhang Yehong , Wu Jun , Xu Hui
Published: December 2024

Fuzzing is a popular technique for detecting bugs, which can be extended to libraries by constructing executables that call library APIs, known as fuzz drivers. Automated fuzz driver synthesis has been an important research topic in recent years since it can facilitate the library fuzzing process. Nevertheless, existing approaches generally ignore generic APIs or simply treat them as non-generic APIs. As a result, they cannot generate effective fuzz drivers for generic APIs.

This paper explores the challenge of automating fuzz driver synthesis for Rust libraries with generic APIs. The problem is essential because Rust prioritizes security and generic APIs are widely employed in Rust libraries. We propose a novel approach and develop a prototype, RuMono, to tackle the problem. Our approach initially infers the API reachability from the generic API dependency graph, discovering the reachable and valid monomorphic APIs within the library. Further, we apply a similarity-based filter to eliminate redundant monomorphic APIs. Experimental results from 29 popular open-source libraries demonstrate that RuMono can achieve promising generic API coverage with a low rate of invalid fuzz drivers. Besides, we have identified 23 previously unknown bugs in these libraries, with 18 related to generic APIs.
Galvanic Corrosion between Mild Steel and Iron Sulfides in Acidic Solutions: Part I. Experimental Factors
Authors: Sharifi Abdar Payman , Brown Bruce , Nesic Srdjan
Published: December 2024

With the increase in production of sour oil and gas fields, mitigation of production-related failures due to H2S corrosion of mild steel is a key challenge. In H2S environments, most failures occur due to localized corrosion originating from the galvanic coupling between mild steel and conductive iron sulfide corrosion products. However, the mechanism of the galvanic coupling between mild steel and iron sulfides and the effect of influential parameters, have not been studied yet. Here, we provide a systematic experimental investigation on the galvanic corrosion between mild steel and iron sulfides by examining the effect of the critical factors: iron sulfide type, cathode to anode surface area ratio, and salt concentration. Specifically, we focus on pyrite and pyrrhotite as the main corrosion products found in localized corrosion of mild steel in H2S environments. Our results show that the cathodic current obtained on pyrrhotite was an order of magnitude higher than that obtained on pyrite, leading to a higher galvanic current for coupled mild steel-pyrrhotite compared to coupled mild steel-pyrite. Moreover, our study reveals that the increase of cathode to anode surface area ratio and, to some extent, the increase of salt concentration, enhance the galvanic current for the coupled materials.
Ni-Co-W-Zr(P) Quinary-based Medium Entropy Alloy Achieved by an Electrochemical Route and Its Properties
Authors: Unni Megha , Jothi Sudagar
Published: December 2024

Over the ages, the concept of alloying has evolved, and in the last fifteen years, the theory of high-entropy alloys has emerged, dramatically altering our understanding of alloy design. In this study, we report the novel synthesis of a quinary Ni-Co-W-Zr(P) medium-entropy alloy using an electroless deposition technique. This represents the first documented attempt to fabricate such a composition using this method, providing new insights into alloy development and synthesis processes. In this work, we examine how the microstructure and mechanical and electrochemical characteristics of the quinary medium-entropy alloy NiCoWZr(P) are interrelated. The morphology, phase composition, and elemental content of the samples were examined using SEM, XRD, and EDS, which revealed their microstructure. The mechanical properties of the materials were assessed through scratch testing. The coefficient of friction rose sharply from 0.23 to 3.25 due to the presence of additional elements in the Ni(P) matrix. To evaluate the corrosion characteristics of deposits in a non-deaerated 3.5% wt. NaCl solution, polarization analysis and EIS tests were conducted. The results show that the corrosion potential exhibited a positive shift, and the corrosion current density decreased to 62.803 μA/cm2 as the concentration of CoWZr components in the bath increased.
Primary cilia as antennas for oxygen
Authors: Schneider Pascal , Fandrey Joachim , Leu Tristan
Published: December 2024

Over the past few decades, the primary cilium, an inconspicuous cell organelle, has increasingly become the focus of current research. The primary cilium is a microtubule-based, nonmotile, antenna-like structure that is present in almost all mammalian cells. The ciliary membrane incorporates a large number of receptor molecules, which further characterize this cellular organelle. These include receptors of the Sonic hedgehog (Shh)-, Wnt-, or platelet-derived growth factor (PDGF) signaling pathways. For this reason, as well as due to the fact that extracellular signaling molecules can bind to the ciliary membrane, primary cilia have been named “the antenna of the cell.” In addition to their signaling function, the association of ciliary dysfunctions with a variety of diseases, so-called ciliopathies, underscores the importance of this functional cellular structure. Recent studies have also implicated primary cilia in the adaptation to low-oxygen conditions, which are characteristic of ischemia, such as in stroke or myocardial infarction, or tumor entities. The aim of this review is to provide an overview of these multiple facets and to take a closer look at the evolution of an inconspicuous cell organelle to a major player in hypoxia.
Antibiotic Resistance in Nitrogen‐Fixing Rhizobial Strains: Implications for Agriculture
Authors: Ajayi O. O. , Nwodo D. C. , Adedeji T. , Ogwugwa V. H. , Dianda M. , Fagade O. E.
Published: December 2024

Rhizobia are biological nitrogen‐fixing nonpathogenic microorganisms that make inert nitrogen available to legumes establishing symbiosis, living in nodules to promote growth. Livestock farmers use antibiotics without prescriptions to prevent losses, death and economic wastes, resulting in indiscriminate release of excess, unutilized antibiotics into the environment causing detrimental effects to soil microbes particularly rhizobia. This study evaluates the susceptibility pattern of selected rhizobia strains. The intrinsic antibiotic resistance (IAR) ability of common rhizobia strains including FA3, B574, RS15, USDA136, USDA9032, R25B, 532C, CC511, RANI22 and RAUG1 was investigated. The antibiotics used included ceftazidime, florfenicol, cefpodoxime, sulphamethoxazole, ciprofloxacin, gentamycin, kanamycin, ertapenem, tetracycline, meropenem and carbapenem. Strains showed highest resistance to carbapenem but were generally susceptible to ciprofloxacin, tetracycline, kanamycin and gentamycin. Rani 22 showed most resistant. Most strains showed no resistance to these antibiotics, but those with resistance abilities are of significance and can be used for recovering antibiotics contaminated sites, providing potential solutions for futuristic problems associated with AMR. This study suggests using a consortium of symbiotic rhizobia strains as a strategy to achieve optimal yields in antibiotic contaminated fields, since strains showed different susceptibility pattern. Also, the antibiotics produced active compounds/substances detrimental for rhizobia’s growth potentially hindering their activities as plant growth promoting bacteria, when applied as inoculant during agricultural practises. Antibiotics are therefore of major agricultural concern posing major threats by promoting poor plant growths, productivity and yields despite the use of rhizobia as plant growth enhancers.
High‐Throughput Shotgun Metagenomics of Microbial Footprints Uncovers a Cocktail of Noxious Antibiotic Resistance Genes in the Winam Gulf of Lake Victoria, Kenya
Authors: Khatiebi Sandra , Kiprotich Kelvin , Onyando Zedekiah , Mwaura John , Wekesa Clabe , Chi Celestine N. , Mulambalah Chrispinus , Okoth Patrick
Published: December 2024

Background: A diverse range of pollutants, including heavy metals, agrochemicals, pharmaceutical residues, illicit drugs, personal care products, and other anthropogenic contaminants, pose a significant threat to aquatic ecosystems. The Winam Gulf of Lake Victoria, heavily impacted by surrounding human activities, faces potential contamination from these pollutants. However, studies exploring the presence of antibiotic resistance genes (ARGs) in the lake remain limited. In the current study, a shotgun metagenomics approach was employed to identify ARGs and related pathways. Genomic DNA was extracted from water and sediment samples and sequenced using the high‐throughput Illumina NovaSeq platform. Additionally, phenotypic antibiotic resistance was assessed using the disk diffusion method with commonly used antibiotics.

Results: The analysis of metagenomes sequences from the Gulf ecosystem and Comprehensive Antibiotic Resistance Database (CARD) revealed worrying levels of ARGs in the lake. The study reported nine ARGs from the 37 high‐risk resistant gene families previously documented by the World Health Organization (WHO). Proteobacteria had the highest relative abundance of antibiotic resistance (53%), Bacteriodes (4%), Verrucomicrobia (2%), Planctomycetes Chloroflexi, Firmicutes (2%), and other unclassified bacteria (39%). Genes that target protection, replacement, change, and antibiotic‐resistant efflux were listed in order of dominance. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed antibiotic resistance to beta‐lactamase and vancomycin. Phenotypic resistance to vancomycin, tetracycline, sulfamethoxazole, erythromycin, trimethoprim, tetracycline, and penicillin was reported through the zone of inhibition.

Conclusions: This study highlights that the Winam Gulf of Lake Victoria in Kenya harbors a diverse array of antibiotic‐resistant genes, including those conferring multidrug resistance. These findings suggest that the Gulf could be serving as a reservoir for more antibiotic‐resistant genes, posing potential risks to both human health and aquatic biodiversity. The insights gained from this research can guide policy development for managing antibiotic resistance in Kenya.
Enhancing Inflammatory Factors, Nitric Oxide, and Arterial Stiffness Through Aquatic Walking for Amelioration and Disease Prevention: Targeting in Obese Elderly Women
Authors: Son Woo-Hyeon , Jeong Woo-Min , Park In Young , Ha Min-Seong
Published: December 2024

In elderly women, hormonal changes lead to elevated body fat content, which results in elevated levels of vascular inflammatory factors, thereby increasing the risk of cardiovascular diseases (CVDs) associated with endothelial dysfunction. Regular physical exercises tend to keep these in check and are protective to the body. Aerobic exercise has been reported to improve CVD in obese elderly women; in this regard, aquatic exercises have been demonstrated to be more efficient in energy metabolism than land‐based exercise. This study aimed to examine the effect of aquatic walking exercises on the levels of inflammatory factors, nitric oxide (NO), and brachial–ankle pulse wave velocity (baPWV) in obese elderly women. We measured these in 26 obese elderly women who were randomly assigned to control (n = 12) and aquatic walking exercise (n = 14) groups. After subjecting them to aquatic walking exercises thrice a week for 12 weeks, we specifically found a significant reduction in IL‐6 levels and an increase in NO levels in these obese elderly women. This was paralleled with a reduction in the right baPWV (baPWV‐R). Together, these results indicate that aquatic walking exercises can help improve vascular inflammatory factors, NO levels, and arterial stiffness.
H2S Sensor Based on Cr2O3‐ɣFe2O3 Nanoparticles Synthesized via Photolysis Method
Authors: Hami Hawraa Kassem , Abdulah Hussain Ismail
Published: December 2024

A H2S gas sensor has been manufactured using Cr2O3‐ɣFe2O3 nanoparticles with ratios of (2:1, 1:1, and 1:2), using the photolysis method. The chemical composition and microstructure of the resulting samples were characterized using XRD, EDx, and SEM. The sensor’s gas‐sensing performance was examined at various operating temperatures ranging from 100°C to 250°C. The results demonstrated that the sensor had optimal response in detecting H2S gas at a ratio of 1:2. Furthermore, the discussion revolved around the sensor’s sensing mechanism specifically for H2S.
Red Meat, Poultry, and Fish Consumption and the Risk of Liver Cancer: A Prospective Cohort Study of 0.5 Million Chinese Adults
Authors: Wang Chun-Rui , Cai Dong , He Kun , Hu Jie-Jun , Dai Xin , Zhu Qian , Zhong Guo-Chao
Published: December 2024

Background:

Epidemiological evidence on meat consumption and liver cancer risk is limited and inconclusive; moreover, no prospective study has been conducted to investigate this association in China. Hence, we performed this study to examine the association of red meat, poultry, and fish consumption with the risk of liver cancer in a Chinese population.

Methods:

A total of 510,048 Chinese adults of ages 30 to 79 years were included and were followed up through December 31, 2016. Red meat, poultry, and fish consumption was evaluated using an interviewer-administered laptop-based questionnaire. HRs and 95% confidence intervals (CI) for liver cancer incidence were calculated using Cox regression.

Results:

Over a mean follow-up of 9.94 years, 1,906 liver cancer cases were observed. Each 50 g/day increase in red meat (HR 0.72; 95% CI, 0.49–1.05), poultry (HR 0.93; 95% CI, 0.83–1.03), and fish (HR 0.95; 95% CI, 0.85–1.05) consumption was not associated with the risk of liver cancer in the whole study population; however, subgroup analysis revealed an inverse association with poultry consumption in rural residents but not in urban residents (Pinteraction = 0.046). The initial associations did not change materially in a series of sensitivity analyses.

Conclusions:

Red meat and fish consumption is not associated with the risk of liver cancer in this Chinese population. The inverse association with poultry consumption in Chinese rural residents should be interpreted with caution.

Impact:

This is the first prospective study examining the association between meat consumption and the risk of liver cancer in the Chinese population.