bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Safety through Leveraging Machine Learning and Safety-Related Data in the Construction Industry
Authors: Pilskog Orvik Casper 
Published: September 2024

Abstract
This study presents a conceptual framework for integrating safety-related data with machine learning to improve its understanding of safety performance and construction safety management. Machine Learning techniques could discover latent hazards and risks by utilizing project-specific safety-related data and improve safety and decision-making processes. Findings suggest that machine learning can significantly improve safety performance by proactively identifying risks and measures from safety-related data rather than relying solely on historical safety outcomes and data. This could also provide a better understanding of the forthcoming construction projects’ complex challenges and the impact of increasingly technical and organizational complexities on safety. However, challenges such as data compatibility, lack of standardization, misinformation risks, and ethical concerns (transparency, privacy, and fairness) necessitate a cautious approach to the use of machine learning. This proactive approach could lead to safer construction environments and continuous improvements in safety management. Future work will refine data collection and develop predictive models, with the current research in the ‘DiSCo’ project aiming for sustainable safety improvements in the construction industry.
Innovation meets institutions: AI and the Finnish construction ecosystem
Authors: Ainamo A , Peltokorpi A 
Published: September 2024

Abstract
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are technologies that have recently transformed many industries. The construction industry has traditionally been a laggard industry in terms of digital-technology adoption. When leading firms in this industry have experimented with these technologies, many of these experiments have met resistance. In this paper we take an institutional lens to study why and particular social structures appears to have contributed to the resistance and paucity of success stories. Within institutional research, we focus on research with traces to cognitive science and psychology. We have carried out a qualitative embedded multiple-case study on resistance to new technologies and how to overcome such resistance. The study involves four use cases in the Finnish construction industry: (1) automation of a material-product subcontractor’s production planning; (2) business-model innovation by contractor on how to best work across multiple construction sites at once; (3) machine learning and automation of documentation by a software firm; and (4) promotion of a vision of information sharing across organizations by the above software firm. Based on within and cross-case analyses, preliminary empirical findings are that AI, ML and DL have in the Finnish construction industry challenged institutionalized forms of organizing and workflow established long since in the industry and, until about the time of this piece of research, taken for granted. Resistance was nonetheless beginning to be overcome at the time of writing this piece of research with small-group interaction across firms – such as those in this study - - in the industry ecosystem. Human-human mediation and face-to-face encounters were building trust in and across the organizations. The implication for practice and policy is that business transformation will not quickly and autonomously transform into “impersonal” or machine-machine exchange but, before that, requires human-human mediation. “ In the long-term, AI and analytics have boundless potential use cases in E&C [i.e. engineering and construction]. Machine learning is gaining some momentum as an overarching use case (that is, one applicable to the entire construction life cycle, from preconstruction through O&M 8i.e. operations and management), particularly in reality capture (for example, in conjunction with computer vision) as well as for comparison of in situ field conditions with plans (for example, supporting twin models). Indeed, by applying machine learning to an ongoing project, schedules could be optimized to sequence tasks and hit target deadlines, and divergences from blueprints could be caught closer to real time and corrected using a variety of predetermined potential scenarios.” [1]
Opportunism in the management of opportunities: client’s perspective
Authors: Marsov A , Olsson N O E 
Published: September 2024

AbstractOpportunity management offers the possibility of increasing the chances of achieving the project objectives. In construction projects, some opportunities may be associated with quality improvement, whereas others may lead to time and cost savings. Opportunities leading to savings may be beneficial for one contracting party and disadvantageous for other project stakeholders. An unbalanced distribution of benefits from opportunities, sometimes coupled with negative effects, can arise from opportunistic behaviours. Much is known about opportunism in the context of project management. Although the link between opportunism in projects and opportunity management is evident, extant literature does not explicitly bridge these two aspects of project management practice. This paper is conceptual, and its purpose is to broaden the view on opportunity management in construction projects by emphasising that the implementation of some opportunities may be associated with opportunism. This paper summarises realistic examples of certain opportunities arising from opportunism. These examples show that some opportunities favouring one party can trigger negative effects that compromise the needs of various project stakeholders such as clients, end users, and project owners of current and future expansion projects. The perspective on opportunity management from the lens of opportunism helps clients recognise possible hidden agendas of contractors striving to optimise design solutions and ease project execution. Understanding the opportunistic nature of certain opportunities proposed by internal project team members can prevent clients from exploiting such opportunities. Since it is impossible to eradicate opportunism in construction projects, clients need to be prepared to deal with opportunism before it becomes a matter of concern. Thus, the early involvement of knowledgeable project members capable of flagging the presence of antecedents of opportunism is beneficial for clients.
Exploring the molecular biology of zinc-doped hydroxyapatite nanocomposites as fillers for dental materials: a self-defensive approach targeting bacterial DNA
Authors: Hamza Muhammad , Kanwal Qudsia , Ali Zahid , Zargar Seema , Alshammari Atekah Hazzaa , Wani Tanveer A , Rizwan Muhammad , Rohm Kanwal , Mushtaq Muhammad Asim 
Published: September 2024

Abstract
Infections during or after the denture filling pose a serious threat to recovery later, and antibiotics are scanty for quick healing. The antibacterial potential of Zn has convinced us to fabricate its composites with hydroxyapatite (HA), which has distinct biological properties. The main aim of the study is to evaluate the antibacterial potential of Zn-doped HA and investigate its molecular interaction with bacterial strains to enhance its defensive ability against bacterial attack. This study investigates the synthesis of zinc-doped hydroxyapatite nano-powder (Zn-HA) as coating and filling materials for dental applications. The co-precipitation technique was used in this regard to obtain the purest form of nanoparticles. The as-prepared Zn-HA samples were characterized by FTIR spectroscopy to assess the interaction of functional groups between components, x-ray diffraction (XRD) to determine percentage crystallinity, SEM to explore the surface morphology of composites, and EDX to confirm the incorporation of Zn in the apatite structure. The crystalline size of Zn-HA decreases from 99 nm to 40 nm with an increase in the doping of Zncl2 from 0 to 2.5 g. With increasing the concentration of Zn doping, the effectiveness of antibacterial potential was increased. Due to an increased in the concentration of doped Zn, the Zn ions effectively rupture the bacterial membrane and destroy its DNA. Meanwhile, it shows the highest antibacterial activity against Pseudomonas (20.2 ± 0.02), Klebsiella (25.8 ± 0.05), Bacillus (18.3 ± 0.09), S. aureus (24 ± 0.03), and E. coli (19.3 ± 0.09) with Zn/HA5. It showed that by increasing the concentration of Zinc ions in hydroxyapatite, the antibacterial potential increased. This increased concentration of zinc ions in HA enhances its-defensive ability against the attack of various bacterial strains, this makes it a potential material and enhances its efficacy in dental applications like coating, filling material, or dental restoratives, in inhibiting the growth of bacterial colonies and biofilm formation in the oral cavity. In this way, Zn/HA as an efficient dental composite as a restorative material with enhanced antibacterial potential may help to mitigate the risk of dental infections, promote dental as well as oral health, and overall improve the life of dentine.
Zinc oxide behavior in CO detection as a function of thermal treatment time
Authors: Munguía-Martín M P , Sánchez-Campos D , Mendoza-Anaya D , Karthik T V K , Villaseñor-Cerón L S , Reyes-Valderrama M I , Rodríguez-Lugo V 
Published: September 2024

Abstract
Gas sensors are crucial for safety and well-being in various environments. Zinc oxide (ZnO) gas sensors are notable for their broad gas detection capabilities. In this study, ZnO structures were synthesized by optimized chemical precipitation method with urea, followed by a thermal treatment at 500 °C for 5, 10, 13, and 15 h. The microstructural, morphological, and CO sensing properties were examined. X-ray Diffraction analysis confirmed the hexagonal wurtzite phase. Crystallite size increased from 17.28 to 18.95 nm with longer thermal treatment times. Scanning Electron Microscopy revealed spherical and semi-spherical agglomerates with middle distribution of particle sizes ranging from 140 to 445 nm. The synthesized ZnO structures were evaluated as gas sensors for CO detection. Response time, recovery time, and sensor response were analyzed in a CO atmosphere at 100, 200, and 300 °C. The sample with thermal treatment for 13 h exhibited the lowest Tr of 2.43 s at a concentration of 166 parts per million and 300 °C. The Tr reduction correlated with a ZnO decrease particle size observed with longer thermal treatment times, highlighting the influence of particle size on sensor performance.
Determinants of non-physical intimate partner violence: A cross-sectional study with nationally representative data from Canada
Authors: Shields Margot , Tonmyr Lil , Pollock Nathaniel , Gonzalez Andrea , Hovdestad Wendy , Tanaka Masako , MacMillan Harriet 
Published: September 2024

Abstract
Based on a nationally representative survey of the Canadian population conducted in 2019/2020, this study examined the prevalence and determinants of non-physical Intimate partner violence (NP-IPV). NP-IPV was defined as experiences of emotional abuse, controlling behaviours, or economic abuse during the past five years. Women (17.3%) and men (16.9%) were equally likely to report NP-IPV, often without co-occurring physical/sexual IPV. For both genders, the risk of NP-IPV decreased with age, and increased with financial strain and having a disability. Childhood maltreatment (sexual abuse, emotional abuse, and exposure to emotional IPV for women, and sexual abuse and emotional abuse for men) was associated with an increased likelihood of reporting NP-IPV in adulthood. Other risk factors included heavy episodic drinking (self and/or spouse/partner) for women and living in a neighbourhood with high levels of social disorder for men. Having confidence in the police was a protective factor for both genders. These associations generally persisted in regression analyses controlling for co-occurring physical/sexual IPV. Future research should focus on methods of preventing NP-IPV and the development of gender-specific interventions to reach and support those who experience NP-IPV. Furthermore, there is a need to improve and standardize measures of NP-IPV.
High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data
Authors: Wang Tiansheng , Pate Virginia , Wyss Richard , Buse John B , Kosorok Michael R , Stürmer Til 
Published: September 2024

Abstract
We recently developed a machine-learning subgrouping algorithm, iterative causal forest (iCF), to identify subgroups with heterogeneous treatment effects (HTEs) using predefined covariates. However, such predefined covariates may miss or poorly define important features leading to inaccurate subgrouping. To address such limitations, we developed a new semi-automatic subgrouping algorithm, hdiCF, which adapts methodology from high-dimensional propensity score for feature recognition in claims data. The hdiCF algorithm has 3 steps: 1) high-dimensional feature identification by International Classification of Diseases, Current Procedural Terminology, and Anatomical Therapeutic Chemical codes (in/outpatient diagnoses, procedures, prescriptions) and creation of ordinal variables by frequency of occurrence; 2) propensity score trimming and high-dimensional feature preparation; 3) iCF implementation to identify subgroups. We applied hdiCF in a 20% random sample of fee-for-service Medicare beneficiaries who initiated sodium-glucose cotransporter-2 inhibitors (SGLT2i) or glucagon-like peptide-1 receptor agonists to identify subgroups with HTEs for incidence of hospitalized heart failure. HdiCF findings were consistent with studies suggesting SGLT2i to be more beneficial for patients with pre-existing heart failure or chronic kidney disease. HdiCF is not dependent on prior hypotheses about HTEs and identifies subgroups with markers for potential HTEs in real-world evidence studies where active-comparator, new-user study designs limit the potential for unmeasured confounding.
Compensating for the corolla? Pollen exposure is not associated with pollen-collecting hair length
Authors: Makowski Hanna , Scott Emily , Lamb Keric , Galloway Laura F 
Published: September 2024

Abstract

Background and Aims
Secondary pollen presentation, the relocation of pollen from the anthers to elsewhere on the flower, has evolved multiple times across many plant families. While hypotheses suggest it evolved to promote outcrossing, a by-product of relocation may be protection of pollen from loss due to abiotic factors. In Campanulaceae pollen is presented on pollen-collecting hairs along the style and the hairs retract over time and release pollen for transfer. Campanulaceae taxa vary in the degree to which pollen is exposed to environmental factors due to variation in the corolla shape and size. We tested the protective function of pollen-collecting hairs by assessing whether there was a trade-off between the protection provided by the corolla and the pollen-collecting hairs.

Methods
We used phylogenetic comparative methods to test for associations between pollen-collecting hair length, floral shape and size metrics, and pollen exposure traits across 39 species.

Results
We anticipated longer pollen-collecting hairs in taxa with more exposed pollen presentation but found there was no relationship between estimates of pollen exposure and pollen-collecting hair length. However, pollen-collecting hair length scaled allometrically with floral size, and variation in pollen-collecting hairs, as well as most floral traits, was phylogenetically structured.

Conclusions
These results indicate that variation in pollen exposure across species does not structure variation in the pollen-collecting hairs, rather hair length scales allometrically and is phylogenetically constrained; therefore pollen-collecting hairs are unlikely to facilitate protection from environmental pressures.
HMM for discovering decision-making dynamics using reinforcement learning experiments
Authors: Guo Xingche , Zeng Donglin , Wang Yuanjia 
Published: September 2024

Abstract
Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.
Liquid nitrogen-based cryoablation: complication rates for lung, bone, and soft tissue tumors cryoablation
Authors: Orsi Franco , Hamiddin Aida Shazlin , Sattin Caterina , Pizzi Caterina , Varano Gianluca Maria , Della Vigna Paolo , Mauri Giovanni , Maiettini Daniele , Bonomo Guido 
Published: September 2024

Abstract

Objective
This study aimed to assess the complication rate during and 24 hours after cryoablation in lung, bone, and soft tissue tumors.

Methods
We reviewed complications in a total of 85 consecutive patients who underwent cryoablation using a liquid nitrogen-based system in various lesions between April 2017 and October 2022. There were no liver and renal lesions. Complications were categorized using the Society of Interventional Radiology classification.

Results
Eighty-five patients were treated for 96 lesions in the bone (36.4%; 35 of 96), lung (18.8%; 18 of 96), and soft tissue (44.8%; 43 of 96). The primary technical success rate was 97.7% (83 of 85). The total grade 2 and 1 complication rates were 5.2% (5/96) and 20.8% (20/96), respectively. Two patients had asymptomatic pulmonary embolisms incidentally noted at the 24-hour follow-up computed tomography (grade 2). The most frequent complications were simple and hemorrhagic pleural effusions (18.7%; 18 of 96). Lung procedures had the highest complication rate, where 13 patients (72.2%; 13 of 18) reported complications, including 2 cases of symptomatic hydropneumothorax requiring drainage (grade 2) and an additional 2 days of hospital stay. Eight patients (24.2%; 8 of 33) with bone lesions and 4 (9.3%; 4 of 43) with soft tissue lesions experienced complications.

Conclusion
Cryoablation using a liquid nitrogen-based system is safe, with only minor complications observed.

Advances in knowledge
This study provides data on the safety of liquid nitrogen-based percutaneous cryoablation in tumors located in lung, in bones and in soft tissues. Despite using larger diameter cryoprobes than those typically reported with argon-based system, our experience shows that complications are mostly low and comparable in frequency and severity.
Cefepime–taniborbactam activity against antimicrobial-resistant clinical isolates of Enterobacterales and Pseudomonas aeruginosa: GEARS global surveillance programme 2018–22
Authors: Karlowsky James A , Wise Mark G , Hackel Meredith A , Six David A , Uehara Tsuyoshi , Daigle Denis M , Pevear Daniel C , Moeck Greg , Sahm Daniel F 
Published: September 2024

Abstract

Objectives
Taniborbactam is a boronate-based β-lactamase inhibitor in clinical development in combination with cefepime.

Methods
Cefepime–taniborbactam and comparator broth microdilution MICs were determined for patient isolates of Enterobacterales (n = 20 725) and Pseudomonas aeruginosa (n = 7919) collected in 59 countries from 2018 to 2022. Taniborbactam was tested at a fixed concentration of 4 mg/L. Isolates with cefepime–taniborbactam MICs ≥ 16 mg/L underwent WGS. β-Lactamase genes were identified in additional meropenem-resistant isolates by PCR/Sanger sequencing.

Results
Taniborbactam reduced the cefepime MIC90 value for all Enterobacterales from &gt;16 to 0.25 mg/L (&gt;64-fold). At ≤16 mg/L, cefepime–taniborbactam inhibited 99.5% of all Enterobacterales isolates; &gt;95% of isolates with MDR and ceftolozane–tazobactam-resistant phenotypes;  ≥ 89% of isolates with meropenem-resistant and difficult-to-treat-resistant (DTR) phenotypes; &gt;80% of isolates with meropenem–vaborbactam-resistant and ceftazidime–avibactam-resistant phenotypes; 100% of KPC-positive, 99% of OXA-48-like-positive, 99% of ESBL-positive, 97% of acquired AmpC-positive, 95% of VIM-positive and 76% of NDM-positive isolates. Against P. aeruginosa, taniborbactam reduced the cefepime MIC90 value from 32 to 8 mg/L (4-fold). At ≤16 mg/L, cefepime–taniborbactam inhibited 96.5% of all P. aeruginosa isolates; 85% of meropenem-resistant phenotype isolates; 80% of isolates with MDR and meropenem–vaborbactam-resistant phenotypes; &gt;70% of isolates with DTR, ceftazidime–avibactam-resistant and ceftolozane–tazobactam-resistant phenotypes; and 82% of VIM-positive isolates. Multiple potential mechanisms of resistance, including carriage of IMP, or alterations in PBP3 (ftsI), porins (decreased permeability) and efflux (up-regulation) were present in most isolates with cefepime–taniborbactam MICs ≥ 16 mg/L.

Conclusions
Cefepime–taniborbactam exhibited potent in vitro activity against Enterobacterales and P. aeruginosa, and inhibited most carbapenem-resistant isolates, including those carrying serine carbapenemases or NDM/VIM MBLs.