bluuebunny/crossref_metadata_2025_split huggingface dataset:
Luminance and chromaticity characteristics of different phosphor types in laser lighting
Authors: Jensen Ole Bjarlin , Chen Xinrong , Xu Jian , Corell Dennis Dan , Dam-Hansen Carsten 
Published: January 2024

Abstract
Laser lighting is emerging as a viable replacement for other light sources in applications requiring high luminance not achievable with LEDs. Phosphor materials for laser lighting are often optimized in terms of luminous efficiency and/or colorimetric properties, while the light homogeneity is often neglected. We present a thorough investigation of the homogeneity of the chromaticity and the luminance profile for the most common types of phosphors used in laser lighting. We find that the achievable luminance and homogeneity of the light spot depends significantly on the phosphor used to convert the blue laser light to white light. The findings of these investigations will present guidelines for optimal phosphor material parameters to achieve high luminance combined with homogeneous chromaticity.
Combining simulation models and machine learning in healthcare management: strategies and applications
Authors: Ponsiglione Alfonso Maria , Zaffino Paolo , Ricciardi Carlo , Di Laura Danilo , Spadea Maria Francesca , De Tommasi Gianmaria , Improta Giovanni , Romano Maria , Amato Francesco 
Published: January 2024

Abstract
Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.
Holistic evaluation of involute gear surfaces using 3D point cloud inversion
Authors: Przyklenk Anita , Reavie Tom , Stein Martin , Frazer Robert 
Published: January 2024

Abstract
Gear users desire increased performance from their products and are interested in predicting the behavior (vibration, loss, service life, etc), and gear manufacturers are interested in adjusting machining parameters to manufacture even better gears. Both require proficient metrology. The evaluation standards were originally written to classify the manufacturing capability of machine tools and as tolerances tighten they may not provide enough insight into the functional performance of the manufactured parts. Furthermore, the advantage of modern coordinate and gear measuring machines is not fully taken into account when applying standardized measurement and evaluation strategies. Improved analysis tools can increase the relevant information gained from current measurement capabilities and enable more useful functional characteristics to be specified by designers and manufacturers. A key improvement is to move from profile and helix line traces to flank surfaces and corresponding fitted form elements. This is achieved by the introduced method. We present an inversion algorithm that fits dimension, form, and pose, and is applicable with current methods through freezing of specific fitting parameters. The method fits a synthetic point cloud with output results that are accurate to the 7th digit, or in practical terms are numerically equal. The inversion method can increase the understanding of error contributions and the functional performance of gears.
Innovative triboelectric nanogenerator (TENG) design utilizing a stress ball for energy harvesting, wellness, and safety applications
Authors: Lakshakoti B , Sankar P Ravi , Supraja P , Navaneeth M , Mahesh V , Kumar K Uday , Prakash K , Kumar R Rakesh 
Published: January 2024

Abstract
The present manuscript introduces a cost-effective and simple method for creating a Triboelectric nanogenerator (TENG) that utilizes a stress ball as a base and FEP, Aluminum as triboelectric pairs. The TENG design demonstrates an innovative approach that can be used for energy harvesting, stress relief, and human safety simultaneously. The unique aspect of this TENG design is using a Polyurethane foam-based ball (Stress-Ball) with excellent elasticity as a spacer, eliminating the need for additional spacers. The TENG prototype produced a power density of 625 mW m−2 and 1.75 W m−2 in squeezing and tapping mode, respectively. Furthermore, four TENG devices integrated in the prototype tiles enable electrical energy generation to power 720 LEDs with every casual footstep. The TENG converts biomechanical energy produced from hand-squeezing exercises into electrical energy that can power small, wearable, and portable electronic devices such as calculators, thermometers, and watches. Wearable LEDs powered by squeezing the ball while walking on dark roads provided safety applications. Overall, the presented TENG design showcases an efficient and affordable way of generating electrical energy while also promoting human wellness and safety.
Tribological and impact- toughness performance of friction stir processed AA7075
Authors: Kumar Amit , Kumar Vineet 
Published: January 2024

Abstract
This technical investigation used friction stir processing to modify the AA7075- T6 alloy and aimed to analyze the wear and impact toughness properties of the modified surfaces. Effects of the input parameters of the friction stir process (FSP) were also studied. Initially, the as-received AA-7075 alloy was processed using FSP at the selected parameters to get defect-free processed specimens. Next, the microstructures of the base alloy and processed regions were examined through an optical microscope. Then, the impact toughness was measured using the Charpy test, and sliding wear behavior was evaluated on a pin-on-disc tribo-tester. The processed samples’ average impact energy was better than the base material’s. Also, their wear property, i.e., coefficient of friction and weight loss, were more promising than the parent metal. Finally, fractography of the fractured Charpy test sampleswas done to study the fracture behavior; andSEM micrographs and 2D profiles of wear tracks were analysed to examine the wear behaviour properly.
Nanoscale dielectric properties of TiO2 in SiO2 nanocomposite deposited by hybrid PECVD method
Authors: Villeneuve-Faure C , Mitronika M , Dan A P , Boudou L , Ravisy W , Besland M P , Richard-Plouet M , Goullet A 
Published: January 2024

Abstract
In this paper, nanocomposites (TiO2 in SiO2) are produced by an advanced hybrid aerosol-PECVD method based on direct liquid injection of a non-commercial colloidal solution in an O2 / hexamethyldisiloxane (HMDSO) low-pressure plasma. Dielectric properties are investigated at nanoscale using techniques derived from Atomic Force Microcopy in terms of relative dielectric permittivity, charge injection and transport. Results show that a concentration in TiO2 up to 14% by volume makes it possible to increase the relative dielectric permittivity up to 4.8 while maintaining the insulating properties of the silica matrix. For a TiO2 concentration in the range 15%–37% by volume, the relative dielectric permittivity increases (up to 11 for 37% TiO2 by volume) and only few agglomerated nanoparticles lowering the insulating properties are observed. For TiO2 concentration above 40% by volume, the relative dielectric permittivity still increases but the quantity of agglomerated nanoparticles is very high, which greatly increases the charge transport dynamic and degrades the insulating properties. Finally, 37% of TiO2 by volume in the SiO2 matrix appears to be the best compromise, between high dielectric permittivity and low leakage current for the MIM applications aimed.
Drivers of heterogeneity in tundra vegetation productivity on the Yamal Peninsula, Siberia, Russia
Authors: Tassone Morgan S , Epstein Howard E , Armstrong Amanda H , Bhatt Uma S , Frost Gerald V , Heim Birgit , Raynolds Martha K , Walker Donald A 
Published: January 2024

Abstract
The direction and magnitude of tundra vegetation productivity trends inferred from the normalized difference vegetation index (NDVI) have exhibited spatiotemporal heterogeneity over recent decades. This study examined the spatial and temporal drivers of Moderate Resolution Imaging Spectroradiometer Max NDVI (a proxy for peak growing season aboveground biomass) and time-integrated (TI)-NDVI (a proxy for total growing season productivity) on the Yamal Peninsula, Siberia, Russia between 2001 and 2018. A suite of remotely-sensed environmental drivers and machine learning methods were employed to analyze this region with varying climatological conditions, landscapes, and vegetation communities to provide insight into the heterogeneity observed across the Arctic. Summer warmth index, the timing of snowmelt, and physiognomic vegetation unit best explained the spatial distribution of Max and TI-NDVI on the Yamal Peninsula, with the highest mean Max and TI-NDVI occurring where summer temperatures were higher, snowmelt occurred earlier, and erect shrub and wetland vegetation communities were dominant. Max and TI-NDVI temporal trends were positive across the majority of the Peninsula (57.4% [5.0% significant] and 97.6% [13.9% significant], respectively) between 2001 and 2018. Max and TI-NDVI trends had variable relationships with environmental drivers and were primarily influenced by coastal-inland gradients in summer warmth and soil moisture. Both Max and TI-NDVI were negatively impacted by human modification, highlighting how human disturbances are becoming an increasingly important driver of Arctic vegetation dynamics. These findings provide insight into the potential future of Arctic regions experiencing warming, moisture regime shifts, and human modification, and demonstrate the usefulness of considering multiple NDVI metrics to disentangle the effects of individual drivers across heterogeneous landscapes. Further, the spatial heterogeneity in the direction and magnitude of interannual covariation between Max NDVI, TI-NDVI, and climatic drivers highlights the difficulty in generalizing the effects of individual drivers on Arctic vegetation productivity across large regions.
Effects of engagement, persistence and adherence on cognitive training outcomes in older adults with and without cognitive impairment: a systematic review and meta-analysis of randomised controlled trials
Authors: Li Zhen , He Hao , Chen Yiqi , Guan Qing 
Published: January 2024

Abstract

Background
Limited understanding exists regarding the influences of engagement, persistence and adherence on the efficacy of cognitive training for age-related cognitive decline and neurodegenerative cognitive impairment.

Methods
This study conducted a meta-analysis of randomised controlled trials (RCTs). We systematically searched MEDLINE, PubMed, Web of Science, Embase and CINAHL databases from 1 January 2012 to 13 June 2023, and included RCTs assessing the effects of cognitive training in older adults, both with and without cognitive impairment. Hedges’ g with a 95% confidence interval (CI) was used to synthesise cognitive training effect sizes on various neuropsychological tests. Subgroup analyses were conducted based on variables including engagement, persistence, adherence and cognitive conditions of normal cognition, mild cognitive impairment (MCI) or neurodegenerative dementia.

Results
This meta-analysis included 55 RCTs with 4,455 participants with cognitive conditions spanning normal cognition, MCI and neurodegenerative dementia. The mean age of participants was 73.9 (range: 65.7–84.5) years. Overall, cognitive training showed a significant cross-domain effect (Hedges’ g = 0.286, 95% CI: 0.224–0.348). Training effects are significant when engagement or persistence rates exceed 60% or when adherence rates exceed 80%. Higher levels of persistence are required to achieve significant training effects in memory, visuospatial ability and reasoning than in executive function and attention and language. Higher persistence is also required for older adults with normal cognition to achieve significant training gains compared to those with cognitive impairment.

Conclusions
This systematic review highlights the critical roles of engagement, persistence and adherence in augmenting the efficacy of cognitive training.
Evaluation of disposable protective garments against epoxy resin permeation and penetration from anti-corrosion coatings
Authors: Mellette Michael P , Bello Dhimiter , Bello Anila , Xue Yalong , Yost Michael , Woskie Susan 
Published: January 2024

Abstract

Introduction
Epoxy-based resin formulations are a frequent cause of allergic and irritant contact dermatitis in the construction and painting industries. Cases of epoxy resin contact dermatitis continue to persist across many sectors and are likely attributable to the growing use of epoxy products, including epoxy-based anti-corrosion coatings and inadequate skin protection. There are no published performance data against epoxy resins for garment materials and gloves to guide proper material selection in the workplace.

Objectives
The objective of this study was to evaluate the resistance of 5 protective garment materials against permeation and penetration by bisphenol A diglycidyl ether and its higher oligomers found commonly in epoxy-based anti-corrosion coatings.

Methods
Five disposable garment materials were evaluated for resistance to bisphenol A diglycidyl ether monomers and oligomers during contact with epoxy-based anti-corrosion coatings, including latex gloves, nitrile gloves, Tyvek coveralls, polypropylene/polyethylene (PP/PE) coveralls, and a cotton T-shirt. A permeation test cell system was used to evaluate each garment material against an epoxy-based zinc-rich primer and an epoxy-based intermediate coating using a realistic application method. Glass fiber filters were used to collect permeating and penetrating epoxy resin during a 120-min test period. Bisphenol A diglycidyl ether quantification was performed with high-performance liquid chromatography–electrospray ionization-tandem mass spectrometry. Paint loading, coating thickness, and homogeneity were assessed on polytetrafluoroethylene filters sprayed in series in permeation test cells.

Results
Latex gloves provided the least resistance to permeation by BADGE in coating formulations, with a maximum cumulative permeation over the 2-h test interval of 21.7 ng cm−2 with the primer and 513.8 ng cm−2 with the intermediate coating product. Nitrile gloves were not permeated by either coating formulation. The Tyvek coveralls provided greater protection as compared to the PP/PE coveralls. The cotton T-shirt was penetrated by bisphenol A diglycidyl ether more frequently than any of the tested garment materials and resulted in a maximum cumulative penetration of 128 ng cm−2 with the primer and 28.0 ng cm−2 with the intermediate coating.

Conclusion
Although all the garment materials evaluated during this study provided sufficient protection to prevent cumulative permeation in excess of the established acceptable permeation thresholds, the use of nitrile gloves and Tyvek coverall is highly recommended to minimize skin exposure to bisphenol A diglycidyl ether. We recommend cotton T-shirts to be used under Tyvek coveralls as a secondary layer of skin protection and for added comfort, but not as a primary protection layer.
Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles
Authors: Zhong Yezhao , Seoighe Cathal , Yang Haixuan 
Published: January 2024

AbstractMotivationPost-market unexpected Adverse Drug Reactions (ADRs) are associated with significant costs, in both financial burden and human health. Due to the high cost and time required to run clinical trials, there is significant interest in accurate computational methods that can aid in the prediction of ADRs for new drugs. As a machine learning task, ADR prediction is made more challenging due to a high degree of class imbalance and existing methods do not successfully balance the requirement to detect the minority cases (true positives for ADR), as measured by the Area Under the Precision-Recall (AUPR) curve with the ability to separate true positives from true negatives [as measured by the Area Under the Receiver Operating Characteristic (AUROC) curve]. Surprisingly, the performance of most existing methods is worse than a naïve method that attributes ADRs to drugs according to the frequency with which the ADR has been observed over all other drugs. The existing advanced methods applied do not lead to substantial gains in predictive performance.ResultsWe designed a rigorous evaluation to provide an unbiased estimate of the performance of ADR prediction methods: Nested Cross-Validation and a hold-out set were adopted. Among the existing methods, Kernel Regression (KR) performed best in AUPR but had a disadvantage in AUROC, relative to other methods, including the naïve method. We proposed a novel method that combines non-negative matrix factorization with kernel regression, called VKR. This novel approach matched or exceeded the performance of existing methods, overcoming the weakness of the existing methods.AvailabilityCode and data are available on https://github.com/YezhaoZhong/VKR.
Exact global alignment using A* with chaining seed heuristic and match pruning
Authors: Groot Koerkamp Ragnar , Ivanov Pesho 
Published: January 2024

Abstract

Motivation
Sequence alignment has been at the core of computational biology for half a century. Still, it is an open problem to design a practical algorithm for exact alignment of a pair of related sequences in linear-like time.

Results
We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm. In order to efficiently align long sequences with high divergence, we extend the recently proposed seed heuristic with match chaining, gap costs, and inexact matches. We additionally integrate the novel match pruning technique and diagonal transition to improve the A* search. We prove the correctness of our algorithm, implement it in the A*PA aligner, and justify our extensions intuitively and empirically.

On random sequences of divergence d=4% and length n, the empirical runtime of A*PA scales near-linearly with length (best fit n1.06, n≤107 bp). A similar scaling remains up to d=12% (best fit n1.24, n≤107 bp). For n=107 bp and d=4%, A*PA reaches &gt;500× speedup compared to the leading exact aligners Edlib and BiWFA. The performance of A*PA is highly influenced by long gaps. On long (n&gt;500kb) ONT reads of a human sample it efficiently aligns sequences with d&lt;10%, leading to 3× median speedup compared to Edlib and BiWFA. When the sequences come from different human samples, A*PA performs 1.7× faster than Edlib and BiWFA.

Availability and implementation
github.com/RagnarGrootKoerkamp/astar-pairwise-aligner.
Surgeon views regarding the adoption of a novel surgical innovation into clinical practice: systematic review
Authors: Konda Nagarjun N , Lewis Thomas L , Furness Hugh N , Miller George W , Metcalfe Andrew J , Ellard David R 
Published: January 2024

Abstract

Background
The haphazard adoption of new surgical technologies into practice has the potential to cause patient harm and there are many misconceptions in the decision-making behind the adoption of new innovations. The aim of this study was to synthesize factors affecting a surgeon’s decision to adopt a novel surgical innovation into clinical practice.

Methods
A systematic literature search was performed to obtain all studies where surgeon views on the adoption of a novel surgical innovation into clinical practice have been collected. The databases screened were MEDLINE, Embase, Science Direct, Scopus, the Web of Science, and the Cochrane Library of Systematic Reviews (last accessed October 2022). Innovations covered multiple specialties, including cardiac, general, urology, and orthopaedics. The quality of the papers was assessed using a 10-question Critical Appraisal Skills Programme (CASP) tool for qualitative research.

Results
A total of 26 studies (including 1112 participants, of which 694 were surgeons) from nine countries satisfied the inclusion and exclusion criteria. Types of study included semi-structured interviews and focus groups, for example. Themes and sub-themes that emerged after a thematic synthesis were categorized using five causal factors (structural, organizational, patient-level, provider-level, and innovation-based). These themes were further split into facilitators and barriers. Key facilitators to adoption of an innovation include improved clinical outcomes, cost-effectiveness, and support from internal and external stakeholders. Barriers to adoption include lack of organizational support and views of senior surgeons.

Conclusion
There are multiple complex factors that dynamically interact, affecting the adoption of a novel surgical innovation into clinical practice. There is a need to further investigate surgeon and other stakeholder views regarding the strength of clinical evidence required to support the widespread adoption of a surgical innovation into clinical practice.
The use of CBCT in orthodontics with special focus on upper airway analysis in patients with sleep-disordered breathing
Authors: Savoldi Fabio , Dagassan-Berndt Dorothea , Patcas Raphael , Mak Wing-Sze , Kanavakis Georgios , Verna Carlalberta , Gu Min , Bornstein Michael M 
Published: January 2024

Abstract
Applications of cone-beam CT (CBCT) in orthodontics have been increasingly discussed and evaluated in science and practice over the last two decades. The present work provides a comprehensive summary of current consolidated practice guidelines, cutting-edge innovative applications, and future outlooks about potential use of CBCT in orthodontics with a special focus on upper airway analysis in patients with sleep-disordered breathing. The present scoping review reveals that clinical applications of CBCT in orthodontics are broadly supported by evidence for the diagnosis of dental anomalies, temporomandibular joint disorders, and craniofacial malformations. On the other hand, CBCT imaging for upper airway analysis—including soft tissue diagnosis and airway morphology—needs further validation in order to provide better understanding regarding which diagnostic questions it can be expected to answer. Internationally recognized guidelines for CBCT use in orthodontics are existent, and similar ones should be developed to provide clear indications about the appropriate use of CBCT for upper airway assessment, including a list of specific clinical questions justifying its prescription.