bluuebunny/crossref_metadata_2025_split huggingface dataset:
How does robotic process automation create value for firms?
Authors: Durão Daniel , dos Reis António Palma 
Published: August 2024

AbstractRPA’s potential for efficiency improvement highly recommends the analysis of its applicability to the organizations’ processes. This paper analyses the value of RPA as its impact on the main activity groups in the value chain: Procurement, Internal Operations and Marketing and Sales. The analysis of RPA’s contribution for each of the activity groups addresses a research question through an exploratory approach: “How does RPA create value for organizations and which RPA features are critical for such value creation?”. The data was collected through interviews, analyzed using content analysis, and shows that RPA has impacts on both downstream and internal dimensions. Regarding upstream dimension further research is required to ascertain RPA’s potential.
Corporate Weakness of Will
Authors: Silver Kenneth 
Published: August 2024

AbstractProponents of corporate moral responsibility take certain corporations to be capable of being responsible in ways that do not reduce to the responsibility of their members. If correct, one follow-up question concerns what leads corporations to fail to meet their obligations. We often fail morally when we know what we should do and yet fail to do it, perhaps out of incontinence, akrasia, or weakness of will. However, this kind of failure is much less discussed in the corporate case. And, where it is discussed, the view is that corporations are less prone to weakness. Here, I argue that proponents of corporate responsibility should say that corporations can and often do instantiate weakness of the will, and that this is important to recognize. Weakness of the will requires certain capacities that these proponents typically take corporations to have. And once this is appreciated, we can assess how corporate weakness might proceed differently than how it does for individuals. We can also begin a conversation about how best to meet the distinctive challenges for recognizing and correcting corporate weakness, using a number of resources from management scholarship.
Land use land cover change in the African Great Lakes Region: a spatial–temporal analysis and future predictions
Authors: Kayitesi Naomie M. , Guzha Alphonce C. , Tonini Marj , Mariethoz Gregoire 
Published: August 2024

AbstractThe African Great Lakes Region has experienced substantial land use land cover change (LULCC) over the last decades, driven by a complex interplay of various factors. However, a comprehensive analysis exploring the relationships between LULCC, and its explanatory variables remains unexplored. This study focused on the Lake Kivu catchment in Rwanda, analysing LULCC from 1990 to 2020, identifying major variables, and predicting future LULC scenarios under different development trajectories. Image classification was conducted in Google Earth Engine using random forest classifier, by incorporating seasonal composites Landsat images, spectral indices, and topographic features, to enhance discrimination and capture seasonal variations. The results demonstrated an overall accuracy exceeding 83%. Historical analysis revealed significant changes, including forest loss (26.6 to 18.7%) and agricultural land expansion (27.7 to 43%) in the 1990–2000 decade, attributed to political conflicts and population movements. Forest recovery (24.8% by 2020) was observed in subsequent decades, driven by Rwanda’s sustainable development initiatives. A Multi-Layer Perceptron neural network from Land Change Modeler predicted distinct 2030 and 2050 LULC scenarios based on natural, socio-economic variables, and historical transitions. Analysis of explanatory variables highlighted the significant role of proximity to urban centers, population density, and terrain in LULCC. Predictions indicate distinct trajectories influenced by demographic and socio-economic trends. The study recommends adopting the Green Growth Economy scenario aligned with ongoing conservation measures. The findings contribute to identifying opportunities for land restoration and conservation efforts, promoting the preservation of Lake Kivu catchment’s ecological integrity, in alignment with national and global goals.
Graphical Abstract
Connexive Exclusion
Authors: Shramko Yaroslav , Wansing Heinrich 
Published: August 2024

AbstractWe present a logic which deals with connexive exclusion. Exclusion (also called “co-implication”) is considered to be a propositional connective dual to the connective of implication. Similarly to implication, exclusion turns out to be non-connexive in both classical and intuitionistic logics, in the sense that it does not satisfy certain principles that express such connexivity. We formulate these principles for connexive exclusion, which are in some sense dual to the well-known Aristotle’s and Boethius’ theses for connexive implication. A logical system in a language containing exclusion and negation can be called a logic of connexive exclusion if and only if it obeys these principles, and, in addition, the connective of exclusion in it is asymmetric, thus being different from a simple mutual incompatibility of propositions. We will develop a certain approach to such a logic of connexive exclusion based on a semantic justification of the connective in question. Our paradigm logic of connexive implication will be the connexive logic $${\textsf{C}}$$
C
, and exactly like this logic the logic of connexive exclusion turns out to be contradictory though not trivial.
Adaptive acquisition planning for visual inspection in remanufacturing using reinforcement learning
Authors: Kaiser Jan-Philipp , Gäbele Jonas , Koch Dominik , Schmid Jonas , Stamer Florian , Lanza Gisela 
Published: August 2024

AbstractIn remanufacturing, humans perform visual inspection tasks manually. In doing so, human inspectors implicitly solve variants of visual acquisition planning problems. Nowadays, solutions to these problems are computed based on the object geometry of the object to be inspected. In remanufacturing, however, there are often many product variants, and the existence of geometric object models cannot be assumed. This makes it difficult to plan and solve visual acquisition planning problems for the automated execution of visual inspection tasks. Reinforcement learning offers the possibility of learning and reproducing human inspection behavior and solving the visual inspection problem, even for problems in which no object geometry is available. To investigate reinforcement learning as a solution, a simple simulation environment is developed, allowing the execution of reproducible and controllable experiments. Different reinforcement learning agent modeling alternatives are developed and compared for solving the derived visual planning problems. The results of this work show that reinforcement learning agents can solve the derived visual planning problems in use cases without available object geometry by using domain-specific prior knowledge. Our proposed framework is available open source under the following link: https://github.com/Jarrypho/View-Planning-Simulation.
Psychometric Validation of the Connectedness to the LGBT Community Scale among Black Sexual Minority Men Living with HIV
Authors: Turpin Rodman , Dangerfield II Derek T. , Oke Temitope , Thorpe Roland J. , Hickson DeMarc A. 
Published: August 2024

Abstract
Purpose: LGBTQ + community connectedness is generally a protective health factor for sexual and gender minorities. However, existing scales have not been validated among Black sexual minority men living with HIV (SMMLWH), who face unique marginalized experiences that disproportionately impact several health outcomes compared to the general LGBT + community. We validated the Connectedness to the LGBT Community Scale among Black SMMLWH. Methods: We validated the 9-item Connectedness to the LGBT Community Scale from Frost and Meyer using preliminary data from a cohort of Mid-Atlantic Black SMMLWH (n = 650). Factor analysis and intercorrelations were conducted to assess unidimensionality, and Cronbach’s alpha was measured for reliability. Correlations and cumulative ordinal regression models were generated using internalized homophobia, hopelessness, depression, HIV stigma, social support, and resilience as criterion constructs. Models were adjusted for sociodemographic and behavioral characteristics. Results: The Connectedness to the LGBT Community Scale demonstrated high internal consistency (alpha = 0.948) and strong item intercorrelation with a single factor structure. The scale was associated with all criterion measures before and after adjustment, including lower internalized homophobia (aCOR = 0.19, 95% CI 0.15–0.25), lower hopelessness (aCOR = 0.53, 95% CI 0.41–0.68), lower HIV stigma (aCOR = 0.58, 95% CI 0.47–0.72), and lower depression (aCOR = 0.61, 95% CI 0.50–0.75). The scale was also associated with greater social support (aCOR = 2.38, 95% CI 1.91–2.97) and resilience (aCOR = 2.53, 95% CI 2.03–3.15). Conclusion: The Connectedness to the LGBT Community Scale is a valid measure for use among Black SMMLWH. Future studies should explore relationships between community connectedness and HIV care outcomes and quality of life among Black SMMLWH.
ATENA: Adaptive TEchniques for Network Area Coverage and Routing in IoT-Based Edge Computing
Authors: Jagho Mdemaya Garrik Brel , Kengne Tchendji Vianney , Velempini Mthulisi , Atchaze Ariege 
Published: August 2024

AbstractThe Internet of Things (IoT) and Edge Computing (EC) are now pervasive. IoT networks are made up of several objects, deployed in an area of interest (AoI), that can communicate with each other and with a remote computing centre for decision-making. EC reduces latency and data congestion by bringing data processing closer to the source. In this paper, we address the problems of network coverage and data collection in IoT-based EC networks. Several solutions exist designed to solve these problems unfortunately, they are either not energy-efficient or do not consider connectivity and they do not cover AoI. The proposed routing mechanisms are often not suited for AoI coverage schemes and lead to poor data routing delay or high packet losses. To address these shortcomings, we propose ATENA, a periodic, lightweight and energy-efficient protocol that aims to improve network coverage based on the two new schemes used to define a few number of objects to be kept awake at each period it also uses an adaptive routing scheme to send the collected data to the computing centre. This protocol is designed to take into account the limited resources of objects and ensures a longer network lifetime. A comparison of ATENA’s simulation results with recent existing protocols shows that it significantly improves network coverage, network lifetime and end-to-end delay to the computing centre.
Long-Range First-Passage Percolation on the Torus
Authors: van der Hofstad Remco , Lodewijks Bas 
Published: August 2024

AbstractWe study a geometric version of first-passage percolation on the complete graph, known as long-range first-passage percolation. Here, the vertices of the complete graph$$\mathcal {K}_n$$Knare embedded in thed-dimensional torus$$\mathbb T_n^d$$Tnd, and each edgeeis assigned an independent transmission time$$T_e=\Vert e\Vert _{\mathbb T_n^d}^\alpha E_e$$Te=‖e‖TndαEe, where$$E_e$$Eeis a rate-one exponential random variable associated with the edgee,$$\Vert \cdot \Vert _{\mathbb T_n^d}$$‖·‖Tnddenotes the torus-norm, and$$\alpha \ge 0$$α≥0is a parameter. We are interested in the case$$\alpha \in [0,d)$$α∈[0,d), which corresponds to the instantaneous percolation regime for long-range first-passage percolation on$$\mathbb {Z}^d$$Zdstudied by Chatterjee and Dey [14], and which extends first-passage percolation on the complete graph (the$$\alpha =0$$α=0case) studied by Janson [24]. We consider the typical distance, flooding time, and diameter of the model. Our results show a 1, 2, 3-type result, akin to first-passage percolation on the complete graph as shown by Janson. The results also provide a quantitative perspective to the qualitative results observed by Chatterjee and Dey on$$\mathbb {Z}^d$$Zd.
Designing a Context-Driven Problem-Solving Method with Metacognitive Scaffolding Experience Intervention for Biology Instruction
Authors: Eticha Merga Dinssa , Hunde Adula Bekele , Ketema Tsige 
Published: August 2024

AbstractLearner-centered instructional practices, such as the metacognitive strategies scaffolding the problem-solving method for Biology instruction, have been shown to promote students’ autonomy and self-direction, significantly enhancing their understanding of scientific concepts. Thus, this study aimed to elucidate the importance and procedures of context analysis in the development of a context-driven problem-solving method with a metacognitive scaffolding instructional approach, which enhances students’ learning effectiveness in Biology. Therefore, the study was conducted in the Biology departments of secondary schools in Shambu Town, Oromia Region, Ethiopia. The study employed mixed-methods research to collect and analyze data, involving 12 teachers and 80 students. The data collection tools used were interviews, observations, and a questionnaire. The study revealed that conducting a context analysis that involves teachers, students, and learning contexts is essential in designing a context-driven problem-solving method with metacognitive scaffolding for Biology instruction, which provides authentic examples, instructional content, and engaging scenarios for teachers and students. As a result, the findings of this study provide a practical instructional strategy that can be applied to studies aimed at designing a context-driven problem-solving method with metacognitive scaffolding with the potential to influence instructional practices.
Deep learning-based automated assessment of canine hip dysplasia
Authors: Loureiro Cátia , Gonçalves Lio , Leite Pedro , Franco-Gonçalo Pedro , Pereira Ana Inês , Colaço Bruno , Alves-Pimenta Sofia , McEvoy Fintan , Ginja Mário , Filipe Vítor 
Published: August 2024

AbstractRadiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis.
Holo4Care: a MR framework for assisting in activities of daily living by context-aware action recognition
Authors: Benavent-Lledo Manuel , Mulero-Pérez David , Garcia-Rodriguez Jose , Martinez-Martin Ester , Vizcaya-Moreno Flores 
Published: August 2024

AbstractThe evolution of virtual and augmented reality devices in recent years has encouraged researchers to develop new systems for different fields. This paper introduces Holo4Care, a context-aware mixed reality framework designed for assisting in activities of daily living (ADL) using the HoloLens 2. By leveraging egocentric cameras embedded in these devices, which offer a close-to-wearer perspective, our framework establishes a congruent relationship, facilitating a deeper understanding of user actions and enabling effective assistance. In our approach, we extend a previously established action estimation architecture after conducting a thorough review of state-of-the-art methods. The proposed architecture utilizes YOLO for hand and object detection, enabling action estimation based on these identified elements. We have trained new models on well-known datasets for object detection, incorporating action recognition annotations. The achieved mean Average Precision (mAP) is 33.2% in the EpicKitchens dataset and 26.4% on the ADL dataset. Leveraging the capabilities of the HoloLens 2, including spatial mapping and 3D hologram display, our system seamlessly presents the output of the action recognition architecture to the user. Unlike previous systems that focus primarily on user evaluation, Holo4Care emphasizes assistance by providing a set of global actions based on the user’s field of view and hand positions that reflect their intentions. Experimental results demonstrate Holo4Care’s ability to assist users in activities of daily living and other domains.
From ashes to answers: decoding acoustically agglomerated soot particle signatures
Authors: Ko Yoon , Li Yuchuan , Mozaffari Hamed , McAlister Jamie , Cho Jae-Young , Henriques Kerri , Khalili Aria , Fellah Jahromi Arash , Jones Benjamin , Naboka Olga , McCarrick Brendan , Zhao Zelda 
Published: August 2024

AbstractThis study investigated the possibility of extending the soot morphology analyses to acoustically agglomerated soot deposited on the surface of smoke alarms and of applying the validity of soot analysis for unique chemical signatures in the field of fire investigations. Through collecting soot samples, including agglomerated soot acquired from smoke alarms, this research presents a pioneering stride in soot morphology data analyses conducted by leveraging advanced deep learning methodologies. Preliminary outcomes underline that the proposed convolutional neural network model has the potential to decode intricate soot characteristics and to distinguish soot particle images between diverse fuel types and burning conditions. In particular, for the acoustically agglomerated soot collected by smoke alarms, it was also found possible to decode their intricate morphology by applying the proposed data-driven approach.
Biological effective dose as a predictor of local tumor control in stereotactic radiosurgery treated parasellar meningioma patients
Authors: Shaaban Ahmed , Pham Duy , Tos Salem M. , Mantziaris Georgios , Schlesinger David , Sheehan Jason P. 
Published: August 2024

Abstract
Introduction
The radio-surgical literature increasingly uses biological effective dose (BED) as a replacement for absorbed dose to analyze outcome of stereotactic radiosurgery (SRS). There are as yet no studies which specifically investigate the association of BED to local tumor control in para-sellar meningioma.

Methods
we did a retrospective analysis of patients underwent stereotactic radiosurgery (SRS) for para-sellar meningioma during the period of 1995–2022. Demographic, clinical, SRS parameters, and outcome data were collected. The target margin BED with and without a model for sub-lethal repair was calculated, as well as a ratio of BED at the target margin to the absorbed dose at the target margin. Factors related to local control were further analyzed.

Results
The study was comprised of 91 patients, 20 (22.0%) and 71 (78.0%) of whom were male and female, respectively. The median age was 55.0 (interquartile range Q1, Q3:47.5,65.5years). 34 (37%) patients had a resection of their meningioma prior to SRS. The median interval from SRS to last clinical follow up or progression was 89 months. 13 (14.3%) patients were found to have progression. 3-, 5- and 10-years local tumor control were 98%, 92% and 77%, respectively. In cox univariate analysis, the following factors were significant: Number of prior surgical resections (Hazard Ratio [HR] = 1.82, 95% CI = 1.08–3.05, p = 0.024), BED (HR = 0.96, 95% CI = 0.92-1.00, p = 0.03), and BED/margin (HR = 0.44, 95% CI = 0.21–0.92, p = 0.028). A BED threshold above 68 Gy was associated significantly with tumor control (P = 0.04).

Conclusion
BED and BED /margin absorbed dose ratio can be predictors of local control after SRS in parasellar meningioma. Optimizing the BED above 68Gy2.47 may afford better long-term tumor control.
Glioblastoma in the real-world setting: patterns of care and outcome in the Austrian population
Authors: Hainfellner Andreas , Borkovec Martin , Seebrecht Lukas , Neuhauser Magdalena , Roetzer-Pejrimovsky Thomas , Greutter Lisa , Surböck Birgit , Hager-Seifert Andrea , Gorka-vom Hof Doris , Urbanic-Purkart Tadeja , Stultschnig Martin , Cijan Clemens , Würtz Franz , Calabek-Wohinz Bernadette , Pichler Josef , Höllmüller Isolde , Leibetseder Annette , Weis Serge , Kleindienst Waltraud , Seiberl Michael , Bieler Lara , Hecker Constantin , Schwartz Christoph , Iglseder Sarah , Heugenhauser Johanna , Nowosielski Martha , Thomé Claudius , Moser Patrizia , Hoffermann Markus , Loibnegger Karin , Dieckmann Karin , Tomschik Matthias , Widhalm Georg , Rössler Karl , Marosi Christine , Wöhrer Adelheid , Hainfellner Johannes A. , Oberndorfer Stefan 
Published: August 2024

Abstract
Purpose
We present results of a retrospective population-based investigation of patterns of care and outcome of glioblastoma patients in Austria.

Patients and methods
In this nation-wide cooperative project, all Austrian glioblastoma patients newly diagnosed between 2014 and 2018 and registered in the ABTR-SANOnet database were included. Histological typing used criteria of the WHO classification of CNS tumors, 4th edition 2016. Patterns of care were assessed, and all patients were followed until the end of 2019.

Results
1,420 adult glioblastoma cases were identified. 813 (57.3%) patients were male and 607 (42.7%) female. Median age at diagnosis was 64 years (range: 18–88). Median overall survival (OS) was 11.6 months in the total cohort and 10.9 months in patients with proven IDH-wildtype. Median OS in the patient group ≤ 65 years receiving postoperative standard of care therapy was 16.1 months. In the patient group > 65 years with postoperative therapy, median OS was 11.2 months. Follow-up ≥ 5 years identified 13/264 (4.9%) long-term survivors. Brain tumor surgery frequently was assisted by 5-aminolevulinic acid (5-ALA) fluorescence (up to 55%). Postoperative treatment was initiated around one month after surgery (median: 31 days) following standardized protocols in 1,041/1,420 (73.3%) cases. In 830 patients (58.5%), concomitant radiochemotherapy was started according to the established standard of care. Treatment in case of progressive disease was considerably variable. 170/1,420 patients (12.0%) underwent a second surgical procedure, 467 (33.0%) received systemic treatment after progression, and 173 (12.2%) were re-irradiated.

Conclusion
Our data illustrate and confirm nation-wide translation of effective standard of care to Austrian glioblastoma patients in the recent past. In the case of progressive disease, highly variable therapeutic approaches were used, most frequently accompanied by anti-angiogenic therapy. Long-term survival was observed in a minor proportion of mostly younger patients who typically had gross total tumor resection, a favorable postoperative ECOG score, and standard of care therapy.