bluuebunny/crossref_metadata_2025_split huggingface dataset:
Digital supply chains for ecosystem resilience: a framework for the Italian case
Authors: Fornasiero Rosanna , Tolio Tullio A. M. 
Published: October 2024

AbstractContingency where exogenous and dramatic factors (i.e. Covid) impact not only on political and social life but also on economy is changing the way business is managed. Grounded on recent works studying the relationship between digitalisation and resilience, this work aims to systematize the links between the two dimensions at Supply Chains (SC) and at ecosystem level. A conceptual framework for manufacturing companies and policy makers is proposed to cope with disruptions thanks to digital technology implementation. The work is based on the results of an explorative analysis held with the support of practitioners from the manufacturing sector, IT providers and policy makers in Italy to systematise results and to demonstrate that public–private partnership can help to face disruptions. This paper contributes to the theory of ecosystems to establish a systemic framework to go beyond the border of each SC proposing a cross-collaboration model.
Remote-Controlled Gene Delivery in Coaxial 3D-Bioprinted Constructs using Ultrasound-Responsive Bioinks
Authors: Lowrey Mary K. , Day Holly , Schilling Kevin J. , Huynh Katherine T. , Franca Cristiane M. , Schutt Carolyn E. 
Published: October 2024

Abstract
Introduction
Coaxial 3D bioprinting has advanced the formation of tissue constructs that recapitulate key architectures and biophysical parameters for in-vitro disease modeling and tissue-engineered therapies. Controlling gene expression within these structures is critical for modulating cell signaling and probing cell behavior. However, current transfection strategies are limited in spatiotemporal control because dense 3D scaffolds hinder diffusion of traditional vectors. To address this, we developed a coaxial extrusion 3D bioprinting technique using ultrasound-responsive gene delivery bioinks. These bioink materials incorporate echogenic microbubble gene delivery particles that upon ultrasound exposure can sonoporate cells within the construct, facilitating controllable transfection.

Methods
Phospholipid-coated gas-core microbubbles were electrostatically coupled to reporter transgene plasmid payloads and incorporated into cell-laden alginate bioinks at varying particle concentrations. These bioinks were loaded into the coaxial nozzle core for extrusion bioprinting with CaCl2 crosslinker in the outer sheath. Resulting bioprints were exposed to 2.25 MHz focused ultrasound and evaluated for microbubble activation and subsequent DNA delivery and transgene expression.

Results
Coaxial printing parameters were established that preserved the stability of ultrasound-responsive gene delivery particles for at least 48 h in bioprinted alginate filaments while maintaining high cell viability. Successful sonoporation of embedded cells resulted in DNA delivery and robust ultrasound-controlled transgene expression. The number of transfected cells was modulated by varying the number of focused ultrasound pulses applied. The size region over which DNA was delivered was modulated by varying the concentration of microbubbles in the printed filaments.

Conclusions
Our results present a successful coaxial 3D bioprinting technique designed to facilitate ultrasound-controlled gene delivery. This platform enables remote, spatiotemporally-defined genetic manipulation in coaxially bioprinted tissue constructs with important applications for disease modeling and regenerative medicine.
Historical Quarry Landscapes of Three Finnish Granites
Authors: Härmä Paavo , Selonen Olavi 
Published: October 2024

AbstractThe quarry landscapes of Virolahti, Uusikaupunki, and Vehmaa are results of quarry operations during the most important periods of natural stone quarrying in Finland. The quarry landscape in Virolahti was formed in connection to the quarrying of red Virolahti pyterlite for the construction of St. Petersburg in Russia from the middle of 1700s to the beginning of the 1900s, while in Uusikaupunki, the quarry landscape is connected to the start of the modern Finnish stone industry and the National Romantic architecture at the beginning of the 1900s. The quarry landscape in Vehmaa is related to the start of the modern Finnish stone industry and export of Finnish granite at the beginning of the 1900s. In Virolahti, several small quarries can be seen, while in Vehmaa, a tendency towards fewer, but larger quarries start to emerge. In each area, human settlement was formed around the quarries. The geological basis for all areas is a rock type suitable for natural stone quarrying. The geotechnical features of each rock type indicate high resistance to wear and the products from these areas are early examples of durable construction. The quarry landscapes represent well the typical quarrying technology of the time and its development from channelling by chisels through hand drilling to mechanical drilling and blasting. The quarry areas form unique industrial and cultural heritage landscapes in Finland.
A Multi-level Reference Model and a Dedicated Method for Cyber-Security by Design
Authors: de Kinderen Sybren , Kaczmarek-Heß Monika , Hacks Simon 
Published: October 2024

AbstractThe increased reliance of organizations on information technology inherently increases their vulnerability to cyber-security attacks. As a response, a host of cyber-security approaches exists. While useful, these approaches exhibit shortcomings such as an inclination to be fragmented, not accounting for up-to-date organizational data, focusing on singular vulnerabilities only, and being reactive, i.e., focusing on patching up vulnerabilities in current systems. The paper presents and evaluates a modeling method aiming to address those shortcomings and to support security by design with a focus on the electricity sector. The proposed modeling method encompasses a multi-level reference model reconstructing and integrating existing initiatives and supporting top-down and bottom-up analyses. Compared to earlier work, the paper contributes (1) a process model for cyber-security by design, which proactively considers security as a first-class citizen during the design process, (2) a complete coverage of the multi-level model, in terms of three views complementing the introduced process model, (3) an elaborated evaluation, in terms of reporting on an additional design science cycle.
Transfer learning for probabilistic localization of hidden cracks in concrete structures
Authors: Miele S. , Karve P. , Mahadevan S. 
Published: October 2024

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.
Significant Association Between Arctic Oscillation and Winter Wildfires in Southern China
Authors: Meng Meng , Gong Daoyi , Lan Yunfei , Yao Qichao , Shi Lamei , Wang Zhou 
Published: October 2024

AbstractThe recent increase of regional wildfire occurrences has been associated with climate change. In this study, we investigated the association between the February to March wildfire points and burned area in the southern region of China (20°N–30°N and 105°E–115°E) and the simultaneous Arctic Oscillation (AO) index during 2001–2022 and 2001–2020, respectively. After removing the El Niño-Southern Oscillation and Indian Ocean Dipole signals, time series of the regional mean fire points and burned area over the study area is significantly correlated with the AO index at − 0.37 and − 0.47, significant at the 0.1 level. Precipitation significantly affects wildfire variations. The positive AO could trigger a southeastward Rossby wave train and induce anomalous cyclone activity approximately located in the area encompassed by 15°N–27°N and 85°E–100°E. This outcome could help to enhance the southern branch trough and results in positive precipitation anomalies in southern China. This increasing moisture is conductive to reducing wildfire risks, vice versa. Our results are potentially useful for strengthening the understanding of the mechanisms of wildfire occurrences in southern China.
Characterizing CRP dynamics during acute infections
Authors: Cherny Stacey S. , Brzezinski Rafael Y. , Wasserman Asaf , Adler Amos , Berliner Shlomo , Nevo Daniel , Rosset Saharon , Obolski Uri 
Published: October 2024

Abstract
Purpose
C-reactive protein (CRP) is a common proxy of inflammation, but accurate characterizations of its dynamics during acute infections are scant. The goal of this study was to examine C-reactive protein (CRP) trajectories in hospitalized patients with viral infections, confirmed bacteremia (stratified by Gram-negative or Gram-positive bacteria), and non-bacteremic infections/inflammations, considering antibiotic treatment.

Methods
Electronic medical records from Tel Aviv Sourasky Medical Center (July 2007-May 2023) were analyzed. Patients with blood cultures or positive viral tests were included. CRP levels were modeled using generalized additive mixed-effects models (GAMMs) and observed up to 150 h after initial infection diagnosis. Patients with initial CRP levels > 31.9 were excluded, to remove individuals already in a highly active inflammatory process. The shapes of the CRP curves were characterized and peak CRP as well as area under the CRP curve were the primary variables of interest.

Results
Viral infections had the lowest and flattest CRP curves. Non-bacteremic infections showed intermediate levels, while bacteremia (especially Gram-negative under antibiotic treatment) had the highest CRP peaks. For instance, peak CRP ranged from 15.4 mg/L in viral infections without antibiotics to 140.9 mg/L in Gram-negative bacteremia with antibiotics.

Conclusions
CRP trajectories significantly differ based on infection type and antibiotic treatment. Frequent CRP measurement could be a valuable diagnostic and risk stratification tool in hospitalized patients.
Development of automation and monitoring methods for narrow-gap hot-wire laser welding using camera images
Authors: Marumoto K. , Sato Y. , Fujinaga A. , Takahashi T. , Yamamoto H. , Yamamoto M. 
Published: October 2024

AbstractIn this study, an image-based method was developed for hot-wire laser narrow gap welding. The welding process was monitored based on image information processed using semantic segmentation, a method of classifying images by pixel. To control the welding position, an experimental system was configured that automatically follows the welding position by recognizing the position of the welding groove from the image during welding. In monitoring weld defects, a method was developed to predict the lack of fusion occurring on the wall surface using brightness information near the wall surface. For the lack of fusion occurring at the bottom of the groove, a defect detection method was developed by monitoring the molten pool shape using semantic segmentation. Defects were generated by intentionally reducing the laser power, and the defects were monitored from images taken during processing. In the unstable state where the laser power was reduced, the shape in front of the molten pool became unstable, and the occurrence of defects was monitored by capturing the shape change. In conclusion, this research made it possible to control and monitor the welding process with a single camera.
Abstract Versus Concrete Risk Identification in Clinical Research in Japan: Randomized and Prospective Pilot Research on the Effect of Risk Reduction Activities in a Risk-Based Approach
Authors: Kondo Hidenobu , Chiu Shih-Wei , Hayashi Yukikazu , Takahashi Naoto , Yamaguchi Takuhiro 
Published: October 2024

Abstract
Background
The risk-based approach (RBA) of clinical trial was first introduced in 2011–2012. RBA necessitates implementing risk reduction activities that are proportionate to risk in order to reduce avoidable quality issues. However, there is no consistent methodology or research for identifying and evaluating risks and planning risk reduction activities. We aimed to evaluate risk reduction activities and their effects by using two risk identification and evaluation methods.

Methods
Among the risk identification and evaluation methods, we selected one method with the lowest number of categories for identifying risks [risk assessment form (RAF)] and one with the highest number [risk assessment tool (RAT)]. Each method was used to identify and evaluate risks in and plan risk reduction activities for the research on ponatinib blood concentration and treatment outcome in patients with chronic phase chronic myelogenous leukemia. RAF and RAT can identify risk using abstract questions and a list of concrete risks, respectively. The sites were randomized into two groups to implement planned risk reduction activities using RAF and RAT and to compare the mean of errors and protocol deviation per subject visit between the two groups.

Results
The mean of errors per subject visit and the mean of protocol deviation per subject visit were lower in the RAF group than in the RAT group.

Conclusions
Our study indicates that risk reductions can be successfully implemented by using a method to identify and evaluate risks in a small number of abstract categories that are critical to quality of clinical research.
Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth
Authors: Saruk B. S. , Rayalu G. Mokesh 
Published: October 2024

AbstractAgriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. Agriculture is the main source of income for most developing nations. The purpose of the study is to investigate the significant role that agriculture plays in boosting India's economic growth. Additionally, the research considers the challenges posed by a growing population and a changing environment in terms of agricultural production and food security. The research focuses on analysing the complex characteristics of the agricultural industry, with a particular emphasis on the nutritional importance of tropical fruits, notably bananas and arecanut. These fruits are well-known for their vital nutrients and their role in ensuring world food security. This study acknowledges the importance of sustainable agriculture practices and incorporates sophisticated machine learning algorithms as dynamic tools to forecast crop yields and enhance decision-making processes throughout the crop development cycle. The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. The validation method utilises important metrics like R square (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). This present research adds to the continuing discussion on using creative methods to promote sustainable agricultural growth and ensure food security.
Learning to Live-with Climate Change through Film: The Arche-Cinema of Gummo as Climating and Becoming-Climate
Authors: Ferguson Joseph Paul 
Published: October 2024

AbstractI am a cinematic being of the Anthropocene. As a concerned citizen and environmental educator, I immerse myself in film. Gummo is a 1997 film by Harmony Korine that deeply resonates with me as a testament to the capacity and desire for humanity to realise the potential to rise from the epochal fall of the Anthropocene. I propose that my relationship with Gummo as arche-cinema is not just a process of watching and interpreting Korine’s cinematic world, but also (re)projecting my dreams of a new reality for the whole-Earth ecosystem onto the world-out-there. I suggest that my entanglement with Gummo exemplifies my climating and becoming-climate as film in our current human-induced climate crises, and in this way, I argue that I am learning to live-with climate change through film.
Geometrical analysis of naturally grown timber for the design of load-bearing structures
Authors: Moreno Gata Kevin , Spahn Florian , Klinkel Sven , Trautz Martin 
Published: October 2024

Abstract
Combining tradition and innovation, timber plays essential roles in building structures for architecture and engineering. Tree branching geometries and timber in its natural state often serve as sources of inspiration. However, the mechanical properties of naturally grown timber, inherently inconsistent and geometrically varied, remain insufficiently studied, particularly for construction and simulations. This knowledge gap perpetuates the prevalent use of straight, uniformly harvested timber while neglecting curved and bifurcated elements with smaller cross-sections.
This research investigates the potential of naturally grown timber in structural design, emphasizing the importance of understanding the natural characteristics and growth patterns of trees to optimize timber use. The developed methodology leverages noninvasive technologies, such as computerized tomography (CT), to precisely capture the geometrical and material properties of wood. These data sources are then integrated to visualize cross-sectional geometries and material properties, forming the basis for our analytical approach. Utilizing generalized scaled boundary isogeometric analysis, the methodology enhances the accuracy and efficiency of simulations, aligning structural design with natural growth principles. This approach not only fosters sustainable resource practices by promoting the use of major tree parts but also transforms discarded materials into valuable resources. The paper concludes with a demonstration of this methodology applied in a practical construction scenario.
The Liberal Case for Community Land Trusts
Authors: Goëtzmann Marc 
Published: October 2024

AbstractThis paper argues that community land trusts (CLTs) can be part of a liberal housing policy from both an economic and a legal point of view. In this, I depart from the defense of community land trusts based on the so-called ‘decommodification’ of housing. First, from an economic point of view, CLTs are a comparatively better option than other traditional policies, such as rent control, once the political complexity of housing policies is considered. Second, fostering autonomy and the capacity for self-authorship requires that individuals be able to choose from a diverse set of valuable institutional designs; CLTs correspond to that ideal and bring a unique nuance to the existing options within common-interest communities. Additionally, I look at the somewhat recent legal innovations that brought CLTs to Canada and continental Europe to show that the institutional flexibility of CLTs allows them to support different visions of self-realization through ownership.
Predicting food insecurity in a pediatric population using the electronic health record
Authors: Rigdon Joseph , Montez Kimberly , Palakshappa Deepak , Brown Callie , Downs Stephen M. , Albertini Laurie W. , Taxter Alysha 
Published: October 2024

Abstract

Introduction:
More than 5 million children in the United States experience food insecurity (FI), yet little guidance exists regarding screening for FI. A prediction model of FI could be useful for healthcare systems and practices working to identify and address children with FI. Our objective was to predict FI using demographic, geographic, medical, and historic unmet health-related social needs data available within most electronic health records.

Methods:
This was a retrospective longitudinal cohort study of children evaluated in an academic pediatric primary care clinic and screened at least once for FI between January 2017 and August 2021. American Community Survey Data provided additional insight into neighborhood-level information such as home ownership and poverty level. Household FI was screened using two validated questions. Various combinations of predictor variables and modeling approaches, including logistic regression, random forest, and gradient-boosted machine, were used to build and validate prediction models.

Results:
A total of 25,214 encounters from 8521 unique patients were included, with FI present in 3820 (15%) encounters. Logistic regression with a 12-month look-back using census block group neighborhood variables showed the best performance in the test set (C-statistic 0.70, positive predictive value 0.92), had superior C-statistics to both random forest (0.65, p < 0.01) and gradient boosted machine (0.68, p = 0.01), and showed the best calibration. Results were nearly unchanged when coding missing data as a category.

Conclusions:
Although our models could predict FI, further work is needed to develop a more robust prediction model for pediatric FI.
Pager Explosion in Beirut: An Unprecedented Event
Authors: Helou Mariana , Weinstein Eric S. , Kalaji Joelle , Chaaban Toufic , Yammine Kaissar 
Published: October 2024

Abstract
On September 17, 2024, at 15:30 local time, thousands of pagers used by members of a specific party group detonated across Lebanon. As a result of the explosions, 2800 were wounded and 12 lost their lives. Almost two-thirds of the injuries were in the face, eyes, or hands. The Lebanese American University Medical Center received 38 injured and admitted 36 patients, 13 of them to the Intensive care unit. A total of 33 patients needed surgeries. All medical and nursing staff were deployed. The health care workers faced major challenges that night: the severity of the injuries and the unprecedented types of injuries with the same pattern, and the urgent need for ophthalmology and orthopedics within the hospital and across the country. Learning from the Pager Explosion, each hospital should perform assessments of their disaster response plan, develop trainings, and conduct regular exercises in preparation for future disasters.