bluuebunny/crossref_metadata_2025_split huggingface dataset:
Orientational Knowledge in the Adoption and Use of Robots in Care Services
Authors: Melkas Helinä , Pekkarinen Satu , Hennala Lea 
Published: January 2024

AbstractHealth and welfare technology are expected to help people live independent and healthy lives, including contributing to elderly care. Robots have gained more cognitive functions and improved safety, which makes it possible to use them to provide new types of services. Although care robots have great potential for health and welfare, their application proves challenging. A lack of knowledge is a major barrier to the use of robots in care. This chapter’s authors examine knowledge and knowledge building as a central perspective when introducing, using, and assessing care robots. In their research synopsis, they focus on the micro, meso, and macro levels related to care robot use while unveiling a more systemic view of the issue of its related orientation—a tangible example of the multi-faceted encounter of knowledge and technology. They propose focusing on orientation to care robot use as the continuous co-creative process of introduction to technology use and its familiarization, including the learning of multi-faceted knowledge and skills for its effective use. With this perspective, the authors emphasize the processes and actions that take place among orientation givers and receivers at different levels. The societal-level actors—such as business and industry, public administration, and the non-profit sector, the media, and other stakeholders in the related innovation ecosystem—should not be overlooked.
On the Need to Understand Human Behavior to Do Analytics of Behavior
Authors: Meyer Joachim 
Published: January 2024

AbstractArtificial Intelligence and data science are rapidly gaining importance as parts of decision support systems. As these systems improve, it becomes necessary to clarify humans’ roles in the decision-making processes. Humans may not be able to improve on the choices a good algorithm makes, they may not be able to adjust the parameters of the algorithm correctly, and their role in processes that use good algorithms may be limited. However, this does not mean human involvement in data-supported decision processes is unnecessary. A closer look at the analytical process reveals that each step entails human decisions, beginning with the data preparation through the choice of algorithms, the iterative analyses, and the display and interpretation of results. These decisions may affect the following steps in the process and may alter the resulting conclusions. Furthermore, the data for the analyses often result from recordings of human actions that do not necessarily reflect the actual recorded events. Data for certain events may often not be recorded, requiring a “big-data analysis of non-existing data.” Thus, adequate use of data-based decisions requires modeling relevant human behavior to understand the decision domains and available data to prevent possible systematic biases in the resulting decisions.
Boosting Consumers: Algorithm-Supported Decision-Making under Uncertainty to (Learn to) Navigate Algorithm-Based Decision Environments
Authors: Rebitschek Felix G. 
Published: January 2024

AbstractFinding information that is quality assured, objectively required, and subjectively needed is essential for consumers navigating uncertain and complex decision environments (e.g., retail or news platforms) and making informed decisions. This task is particularly challenging when algorithms shape environments and choice sets in the providers’ interest. On the other side, algorithms can support consumers’ decision-making under uncertainty when they are transparent and educate their users (boosting). Exemplary, fast-and-frugal decision trees as interpretable models can provide robust classification performance akin to expert advice and be effective when integrated in consumer decision-making. This study’s author provides an overview of expert-driven decision-tree developments from a consumer research project. The developed tools boost consumers making decisions under uncertainty across different domains. Informed decision making in highly uncertain, non-transparent algorithm-controlled decision environments pose a need for applicable and educative tools, which calls for public engagement in their development within the field of consumer education.
The Datafication of Knowledge Production and Consequences for the Pursuit of Social Justice
Authors: Ettlinger Nancy 
Published: January 2024

AbstractEducational institutions shape knowledges that students learn upstream and apply downstream in everyday life, notably in workplaces. Based on a critical synthesis of interdisciplinary literatures, the paper argues that current pedagogy datafies knowledges through the edtech industry, prioritizing skills and circumventing contextual and conceptual knowledges. The pedagogical orientation inculcates technocratic, non-relational thinking, obscures the effects of applications to which workers bring their learned skills, and prefigures deepening social and data injustice in a world beset with intensifying societal tensions and deep inequalities. Although scholarship on current educational trajectories presumes that the new, purportedly ‘disruptive’ digitalized technology has prompted a new pedagogy, I show that the so-called ‘new’ pedagogy has a history that would have predicted the wide-ranging problems evident in non-relational thinking and lack of critique in the data sciences and among its users.
Dynamic Changes of Soil Microbial Communities During the Afforestation of Pinus Armandii in a Karst Region of Southwest China
Authors: He Bin , Li Qing , Zou Shun , Bai Xiaolong , Li Wangjun , Chen Yang 
Published: January 2024

AbstractClarifying the response of soil microbial communities to vegetation restoration is essential to comprehend biogeochemical processes and ensure the long-term viability of forest development. To assess the variations in soil microbial communities throughout the growth of Pinus armandii plantations in the karst region, we utilized the “space instead of time” approach and selected four P. armandii stands with ages ranging from 10 to 47 years, along with a grassland control. The microbial community structure was determined by conducting Illumina sequencing of the 16 S rRNA gene and the ITS gene, respectively. The results demonstrated that afforestation with P. armandii significantly influenced soil microbial communities, as indicated by notable differences in bacterial and fungal composition and diversity between the plantations and the control. However, soil microbe diversity did not display significant variation across stand ages. Moreover, the bacterial community exhibited higher responsiveness to age gradients compared to the fungal community. Soil physicochemical factors play a critical role in elucidating microbial diversity and community composition variations during restoration processes. TN, AN, TP, AP, SOC, AK, and pH were the most significant influencing factors for the composition of bacterial community, while TC, SOC, pH, and TCa were the most significant influencing factors for the composition of fungal community. Our findings indicate substantial changes in soil bacterial and fungal communities across successive stages of development. Additionally, the changes in dominant bacteria and fungi characteristics across the age gradient were primarily attributed to variations in the prevailing soil conditions and chemical factors.
Confocal laser endomicroscopy in glial tumors—a histomorphological analysis
Authors: Radtke Karen , Schulz-Schaeffer Walter J. , Oertel Joachim 
Published: January 2024

Abstract
Objective
The extent of resection and neurological outcome are important prognostic markers for overall survival in glioma patients. Confocal laser endomicroscopy is a tool to examine tissue without the need for fixation or staining. This study aims to analyze gliomas in confocal laser endomicroscopy and identify reliable diagnostic criteria for glial matter and glial tumors.

Material and methods
One-hundred-and-five glioma specimens were analyzed using a 670-nm confocal laser endomicroscope and then processed into hematoxylin-eosin-stained frozen sections. All confocal images and frozen sections were evaluated for the following criteria: presence of tumor, cellularity, nuclear pleomorphism, changes of the extracellular glial matrix, microvascular proliferation, necrosis, and mitotic activity. Recurring characteristics were identified. Accuracy, sensitivity, specificity, and positive and negative predictive values were assessed for each feature.

Results
All 125 specimens could be processed and successfully analyzed via confocal laser endomicroscopy. We found diagnostic criteria to identify white and grey matter and analyze cellularity, nuclear pleomorphism, changes in the glial matrix, vascularization, and necrosis in glial tumors. An accuracy of > 90.0 % was reached for grey matter, cellularity, and necrosis, > 80.0 % for white matter and nuclear pleomorphism, and > 70.0 % for microvascular proliferation and changes of the glial matrix. Mitotic activity could not be identified. Astroglial tumors showed significantly less nuclear pleomorphism in confocal laser endomicroscopy than oligodendroglial tumors (p < 0.001). Visualization of necrosis aids in the differentiation of low grade gliomas and high grade gliomas (p < 0.002).

Conclusion
Autofluorescence-based confocal laser endomicroscopy proved not only useful in differentiation between tumor and brain tissue but also revealed useful clues to further characterize tissue without processing in a lab. Possible applications include the improvement of extent of resection and the safe harvest of representative tissue for histopathological and molecular genetic diagnostics.
Boosting the hospital by integrating mobile robotic assistance systems: a comprehensive classification of the risks to be addressed
Authors: Bernhard Lukas , Schwingenschlögl Patrik , Hofmann Jörg , Wilhelm Dirk , Knoll Alois 
Published: January 2024

AbstractMobile service robots are a promising technology for supporting workflows throughout the hospital. Combined with an understanding of the environment and the current situation, such systems have the potential to become invaluable tools for overcoming personal shortages and streamlining healthcare workflows. However, few robotic systems have actually been translated to practical application so far, which is due to many challenges centered around the strict and unique requirements imposed by the different hospital environments, which have not yet been collected and analyzed in a structured manner. To address this need, we now present a comprehensive classification of different dimensions of risk to be considered when designing mobile service robots for the hospital. Our classification consists of six risk categories – environmental complexity, hygienic requirements, interaction with persons and objects, workflow flexibility and autonomy – for each of which a scale with distinct risk levels is provided. This concept, for the first time allows for a precise classification of mobile service robots for the hospital, which can prove useful for certification and admission procedures as well as for defining architectural and safety requirements throughout the design process of such robots.
Test suite assessment of safety-critical systems using safety tactics and fault-based mutation testing
Authors: Gurbuz Havva Gulay , Tekinerdogan Bedir , Catal Cagatay , Er Nagehan Pala 
Published: January 2024

AbstractA safety-critical system is a system in which the software malfunctioning could result in death, injury, or damage to the environment. Addressing safety concerns early on at the architecture design level is critical to guide the subsequent life cycle activities to ensure that the eventual system is reliable. A fundamental approach to address safety at the design level is the adoption of architectural tactics. It is crucial for safety-critical systems to correctly implement the constraints as defined by the selected safety tactics. Given the limitations of traditional safety measures, particularly in conducting thorough analyses of safety tactics at the architectural design level, our motivation is to close this gap by integrating safety tactics and fault-based mutation testing. This article proposes a systematic approach for assessing the adequacy of test suites of safety-critical systems based on these architectural safety tactics. Our proposed approach involves the integration of safety tactics and fault-based mutation testing to comprehensively assess the adequacy of test suites in safety-critical systems. At the core of our methodology is the adoption of architectural safety tactics, addressing potential issues at the design level. We leverage fault-based testing, utilizing a dedicated metamodel and domain-specific language (DSL) to model safety views and their relation to the code. We use a case study to evaluate the effectiveness of our approach using fault-injection techniques. Our study shows that this systematic approach is feasible and effective for test suite assessment of safety-critical systems.
Building a Community Partnership for the Development of Health Ministries Within the African American Community: The Triad Pastors Network
Authors: Gwathmey TanYa M. , Williams K. Lamonte , Caban-Holt Allison , Starks Takiyah D. , Foy Capri G. , Mathews Allison , Byrd Goldie S. 
Published: January 2024

AbstractAfrican Americans continue to have worse health outcomes despite attempts to reduce health disparities. This is due, in part, to inadequate access to healthcare, but also to the health care and medical mistrust experienced by communities of color. Churches and worship centers have historically served as cultural centers of trusted resources for educational, financial, and health information within African American communities and a growing number of collaborations have developed between academic institutions and community/faith entities. Herein, we describe the infrastructure of a true and sustainable partnership developed with > 100 prominent faith leaders within the Piedmont Triad region of North Carolina for the purpose of developing or expanding existing health ministries within houses of worship, to improve health literacy and overall health long-term. The Triad Pastors Network is an asset-based partnership between the Maya Angelou Center for Health Equity at Wake Forest University School of Medicine and faith leaders in the Piedmont Triad region of North Carolina that was created under the guiding principles of community engagement to improve health equity and decrease health disparities experienced by African American communities. A partnership in which co-equality and shared governance are the core of the framework provides an effective means of achieving health-related goals in a productive and efficient manner. Faith-based partnerships are reliable approaches for improving the health literacy needed to address health disparities and inequities in communities of color.
Statistical Analysis and Automation Through Machine Learning of Resonant Ultrasound Spectroscopy Data from Tests Performed on Complex Additively Manufactured Parts
Authors: Obaton Anne-Françoise , Fallahi Nasim , Tanich Anis , Lafon Louis-Ferdinand , Weaver Gregory 
Published: January 2024

AbstractAdditive manufacturing brings inspection issues for quality assurance of final parts because non-destructive testing methods are faced with shape complexity, size, and high surface roughness. Thus, to drive additive manufacturing forward, advanced non-destructive testing methods are required. Methods based on resonant ultrasound spectroscopy (RUS) can take on all the challenges that come with additive manufacturing. Indeed, these full body inspection methods are adapted to shape complexity, to nearly any size, and to high degrees of surface roughness. Furthermore, they are easy to implement, fast and low cost. In this paper, we present the benefit of a resonant ultrasound spectroscopy method, combined with a statistical analysis through Z score implementation, to classify supposedly identical parts, from a batch comprised of several individual builds. We also demonstrate that the inspection can be further accelerated and automated, to make the analysis operator independent, whether the analysis of the resonant ultrasound spectroscopy data is performed supervised or unsupervised with machine learning algorithms.
Hybrid approaches to optimization and machine learning methods: a systematic literature review
Authors: Azevedo Beatriz Flamia , Rocha Ana Maria A. C. , Pereira Ana I. 
Published: January 2024

AbstractNotably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.
On the evaluation of the alternating multiple $$ t $$ value $$ t({\overline{1}},\ldots ,{\overline{1}}, 1, {\overline{1}},\ldots ,{\overline{1}}) $$
Authors: Charlton Steven 
Published: January 2024

AbstractWe prove an evaluation for the stuffle-regularised alternating multiple $$ t $$
t
value $$ t^{*,V}({\overline{1}},\ldots ,{\overline{1}}, 1, {\overline{1}},\ldots ,{\overline{1}}) $$

t

∗
,
V

(

1
¯

,
…
,

1
¯

,
1
,

1
¯

,
…
,

1
¯

)

in terms of $$ V $$
V
, the regularisation parameter, $$\log (2), \zeta (k) $$

log
(
2
)
,
ζ
(
k
)

and $$ \beta (k) $$

β
(
k
)

. This arises by evaluating the corresponding generating series using the Evans-Stanton/Ramanujan asymptotics of a zero-balanced hypergeometric function $$ {}_3F_2 $$

3

F
2

, and an evaluation established by Li in an alternative approach to Zagier’s evaluation of $$ \zeta (2,\ldots ,2, 3, 2,\ldots ,2) $$

ζ
(
2
,
…
,
2
,
3
,
2
,
…
,
2
)

. We end with some discussion and conjectures on possible motivic applications.
Lifespan and scientific leadership: a counterfactual analysis between presidents and fellows of the Royal Society
Authors: Chan Ho Fai , Mixon Franklin G. , Torgler Benno 
Published: January 2024

AbstractThis study investigates the potential relationship between scientific success and lifespan, with a particular focus on whether serving as President of the Royal Society is associated with a longer lifespan. Previous research has indicated that higher social status can confer health benefits, but it remains unclear whether holding leadership positions within esteemed academic organizations has a similar effect. To mitigate confounding factors, this study established a counterfactual by identifying Royal Society Fellows who are comparable to the Presidents. The analysis indicates that being elected as President of the Royal Society is indeed linked to a longevity advantage.
Willing, able, and engaged: roles of action-state orientation, intrinsic academic motivation, and time management on academic engagement
Authors: PEKER Mehmet 
Published: January 2024

AbstractThe current study investigates the longitudinal association between decision-related action orientation (AOD, the ability to initiate the intended actions decisively) and academic engagement. Moreover, this study examines the roles of time management and intrinsic academic motivation in this relationship. Specifically, it is proposed that time management mediates the relationship between AOD and academic engagement, and this mediated effect is moderated by intrinsic academic motivation. The analysis of the data collected in three waves from 222 undergraduates (180 women) with a mean age of 20.98 years (SD = 2.73) confirmed the predictions. Time management mediated the relationship between AOD and academic engagement for students who reported high intrinsic motivation. Study results suggest that AOD and intrinsic academic motivation work synergetically to shape academic engagement through efficient time management. The study findings provide valuable insights for practice, emphasizing the consideration of both motivational and volitional processes, and time management strategies to contribute to student engagement.
Animal Density Estimation for Large Unmarked Populations Using a Spatially Explicit Model
Authors: Herliansyah Riki , King Ruth , Rahman Dede Aulia , King Stuart 
Published: January 2024

AbstractObtaining abundance and density estimates is a particularly important aspect within wildlife conservation and management. To monitor wildlife populations, the use of motion-sensor camera traps is becoming increasing popular due to its non-invasive nature. However, animal identification is not always feasible in practice due to poor quality images and/or individuals not having uniquely identifiable physical characteristics. Spatially explicit models for unmarked individuals permit the estimation of animal density when individuals cannot be uniquely identified. Due to the structure of these models, a Bayesian super-population (data augmentation) approach is often used to fit the models to data, which involves specifying some reasonably large upper limit for the population. However, this approach presents substantial computational challenges for larger populations, as demonstrated by the motivating dataset relating to barking deer (Muntiacus muntjak) collected in Ujung Kulon National Park, Indonesia (with a population size in the low thousands). We develop a new and computationally efficient Bayesian algorithm for fitting the models to data that does not require specifying an upper population limit a priori. We apply the new algorithm to the large barking deer dataset, where the standard super-population approach is computationally expensive, and demonstrate a substantial improvement in computational efficiency.Supplementary material to this paper is provided online.