bluuebunny/crossref_metadata_2025_split huggingface dataset:
Why is nutrient cycling in food systems so limited? A case study from the North-Netherlands region
Authors: Tamsma Durk W. , van Middelaar Corina E. , de Boer Imke J. M. , Kros Johannes , van Ittersum Martin K. , Schut Antonius G. T. 
Published: April 2024

AbstractIdentifying pathways to circular agriculture requires a profound understanding of nutrient flows and losses throughout the food system, and of interactions between biophysical conditions, land use, food production and food consumption. We quantified nitrogen (N) and phosphorus (P) flows of the food system of the North-Netherlands (NN) region and of its 30 subregions varying in biophysical and socio-economic conditions. The food system included agriculture, food processing, consumption, and waste processing. Nitrogen use efficiency (NUE), phosphorus use efficiency (PUE) and the nutrient cycling counts were calculated. Results show a low NUE (25%) and PUE (59%) of the food system. External inputs were used to maintain high yields and production. Nutrient cycling was very limited with losses from agriculture ranging from 143 to 465 kg N ha−1 y−1 and 4 to 11 kg P ha−1 y−1. Food system losses ranged from 181 to 480 kg N ha−1 y−1 and from 7 to 31 kg P ha−1 y−1 and varied with biophysical conditions, population density and farming systems. Large losses were associated with livestock farming and farming on drained peat soils. Food system efficiency was strongly associated with the utilization of produce. We conclude that increasing circularity requires tailoring of agriculture to local biophysical conditions and food system redesign to facilitate nutrient recycling. Steps towards circularity in NN include: matching livestock production to feed supply from residual flows and lands unsuitable for food crops, diversifying crop production to better match local demand and increasing waste recovery.
Dual effects of drying and urban pollution on leaf litter decomposition and shredder consumption in permanent and intermittent streams
Authors: Martinez-Sanz Itxaso , Muñoz Isabel , Viza Aida , Oliva Francesc , Menéndez Margarita 
Published: April 2024

AbstractIn the present study we investigated the combined effect of flow intermittence and wastewater treatment plant (WWTP) effluent on leaf litter decomposition in two Mediterranean streams with permanent and intermittent flow regimes. The litter decomposition experiment was performed using black poplar litter bags where we measured litter mass loss, fungal biomass, aquatic hyphomycete sporulation and species richness and C:N ratio. Afterwards, the remaining litter material from the different treatments was offered to the shredder Potamophylax latipennis in a microcosm laboratory experiment, and its growth and consumption rates were studied. We hypothesised that drying would decrease leaf litter decomposition in the permanent stream more than in the intermittent stream and that WWTP effluent would compensate for drying effects when both stressors interacted. The results indicated that drying has the greatest impact on the permanent stream, decreasing decomposition, aquatic hyphomycete species richness, and leaf litter quality. In the intermittent stream, WWTP effluent decreases aquatic hyphomycete species richness, but drying increases it, showing an opposing effect and a synergistic interaction. In the permanent stream drying provoked an increase in the shredder consumption rate. Thus, the expected more frequent intermittence in rivers under climate change may threaten the processing of organic matter.
Knowledge-based, computerized, patient clinical decision support system for perioperative pain, nausea and constipation management: a clinical feasibility study
Authors: Noll Eric , Noll-Burgin Melanie , Bonnomet François , Reiter-Schatz Aurelie , Gourieux Benedicte , Bennett-Guerrero Elliott , Goetsch Thibaut , Meyer Nicolas , Pottecher Julien 
Published: April 2024

AbstractOpioid administration is particularly challenging in the perioperative period. Computerized-based Clinical Decision Support Systems (CDSS) are a promising innovation that might improve perioperative pain control. We report the development and feasibility validation of a knowledge-based CDSS aiming at optimizing the management of perioperative pain, postoperative nausea and vomiting (PONV), and laxative medications. This novel CDSS uses patient adaptive testing through a smartphone display, literature-based rules, and individual medical prescriptions to produce direct medical advice for the patient user. Our objective was to test the feasibility of the clinical use of our CDSS in the perioperative setting. This was a prospective single arm, single center, cohort study conducted in Strasbourg University Hospital. The primary outcome was the agreement between the recommendation provided by the experimental device and the recommendation provided by study personnel who interpreted the same care algorithm (control). Thirty-seven patients were included in the study of which 30 (81%) used the experimental device. Agreement between these two care recommendations (computer driven vs. clinician driven) was observed in 51 out 54 uses of the device (94.2% [95% CI 85.9–98.4%]). The agreement level had a probability of 86.6% to exceed the 90% clinically relevant agreement threshold. The knowledge-based, patient CDSS we developed was feasible at providing recommendations for the treatment of pain, PONV and constipation in a perioperative clinical setting.Trial registration number & date The study protocol was registered in ClinicalTrial.gov before enrollment began (NCT05707247 on January 26th, 2023).
Unlocking the potential of RNN and CNN models for accurate rehabilitation exercise classification on multi-datasets
Authors: Zaher Moamen , Ghoneim Amr S. , Abdelhamid Laila , Atia Ayman 
Published: April 2024

Abstract
Physical rehabilitation is crucial in healthcare, facilitating recovery from injuries or illnesses and improving overall health. However, a notable global challenge stems from the shortage of professional physiotherapists, particularly acute in some developing countries, where the ratio can be as low as one physiotherapist per 100,000 individuals. To address these challenges and elevate patient care, the field of physical rehabilitation is progressively integrating Computer Vision and Human Activity Recognition (HAR) techniques. Numerous research efforts aim to explore methodologies that assist in rehabilitation exercises and evaluate patient movements, which is crucial as incorrect exercises can potentially worsen conditions. This study investigates applying various deep-learning models for classifying exercises using the benchmark KIMORE and UI-PRMD datasets. Employing Bi-LSTM, LSTM, CNN, and CNN-LSTM, alongside a Random Search for architectural design and Hyper-parameter tuning, our investigation reveals the (CNN) model as the top performer. After applying cross-validation, the technique achieves remarkable mean testing accuracy rates of 93.08% on the KIMORE dataset and 99.7% on the UI-PRMD dataset. This marks a slight improvement of 0.75% and 0.1%, respectively, compared to previous techniques. In addition, expanding beyond exercise classification, this study explores the KIMORE dataset’s utility for disease identification, where the (CNN) model consistently demonstrates an outstanding accuracy of 89.87%, indicating its promising role in both exercises and disease identification within the context of physical rehabilitation.
Design of miniaturized wide band-pass plasmonic filters in MIM waveguides with tailored spectral filtering
Authors: Ebadi Seyed Morteza , Khani Shiva , Örtegren Jonas 
Published: April 2024

AbstractThis paper reports the design and numerical results of three new extremely compact and efficient flat-top band-pass plasmonic filters operating in the near-infrared region. The proposed structures are realized in metal–insulator-metal plasmonic waveguides based on stub, tilted T-junction and right-angle trapezoid configurations. A built-in parameterized genetic algorithm is applied to maximize the transmission efficiency, while at the same time contributing to shrinking down the size of the device structures. It is shown that the tunability of the optical filters can be realized by modulating their structural parameters to gain control over the band-pass filtering wavelengths. Numerical calculations are conducted based on the finite element method of CST Microwave Studio and demonstrate that the suggested ultra-compact plasmonic waveguide filters offer wide bandwidths of more than 270 nm, 424 nm, and 289 nm, with transmission efficiencies of higher than 80%, 74.2%, and 74.3%, respectively. The sizes of the proposed wavelength filters are 490 nm × 575 nm, 350 nm × 180 nm, and 420 nm × 150 nm, respectively, which make them attractive candidates for applications in high density photonic integrated circuits (PICs). As a result, because of the promising characteristics of the proposed topologies such as their high efficiency, compact size, tunability, and simple structure they may find applications in on-chip integration, laser technology, and multi-photon fluorescence.
Tourism marketing, ileity and the construction of a real ideality
Authors: de Carvalho Adalberto Dias , Oliveira Cristiana 
Published: April 2024

AbstractThis study delves into the realm of island marketing, challenging prevailing assumptions held by potential mainland and urban tourists. It adopts a critical approach aimed at unraveling the motivations and expectations of these tourists in relation to island destinations, exploring the conflictual connections between the implicit beliefs in insularity and islandness. The study refutes simplistic stereotypes that often dominate insular tourism marketing, which abstractly portrays tropical islands as paradises of happiness and tranquility. The research identifies several values and benefits that significantly impact the motivation of island tourists, while recognizing the uniqueness of islands as tourist destinations. These include the allure of the sea and beaches as sources of leisure and well-being, the distinctiveness of island communities marked by authenticity and ancestral isolation, the promise of adventure in unfamiliar spaces, emotional involvement through personal spontaneity and freedom, and the opportunity to connect with preserved nature. For many small islands, tourism serves as a crucial economic resource, and their promotion centers around the idea of paradise and escape from the everyday routine—a concept referred to as “island desire” within a utopian context. The study also discusses the evolving role of communication technologies, the equivocal notion of authenticity, the significance of interculturality, and the contrasting perceptions of the sea for mainlanders and islanders. It emphasizes the need for island marketing professionals to draw from anthropological and epistemological insights to inform their creative communication strategies.
The influence of geopolitics on research activity and international collaboration in science: the case of Russia
Authors: Zhang Lin , Cao Zhe , Sivertsen Gunnar , Kochetkov Dmitry 
Published: April 2024

AbstractWe study the possible influences of the Russia-Ukraine War on Russia’s research productivity and international collaboration in science. For this purpose, we introduce and apply two recently developed indicators of relative intensity and balance in international collaboration. To see whether longitudinal trends have changed recently, we combine a long-term perspective based on annual updates since the year 2000 with a short-term perspective based on monthly updates since the beginning of 2022. The clearest change is that the productivity of Russian science, as measured within Web of Science, has dramatically decreased after several years of growth. There is also a clear decline in the degree of international collaboration in fields of research that heavily rely on large multinational infrastructures established through state agreements. In other fields, however, the degree of international collaboration is more stable. The general decline in Russian science seems to be more driven by internal factors than by loss of partnerships abroad.
Approximate bilateral filters for real-time and low-energy imaging applications on FPGAs
Authors: Spagnolo Fanny , Corsonello Pasquale , Frustaci Fabio , Perri Stefania 
Published: April 2024

AbstractBilateral filtering is an image processing technique commonly adopted as intermediate step of several computer vision tasks. Opposite to the conventional image filtering, which is based on convolving the input pixels with a static kernel, the bilateral filtering computes its weights on the fly according to the current pixel values and some tuning parameters. Such additional elaborations involve nonlinear weighted averaging operations, which make difficult the deployment of bilateral filtering within existing vision technologies based on real-time and low-energy hardware architectures. This paper presents a new approximation strategy that aims to improve the energy efficiency of circuits implementing the bilateral filtering function, while preserving their real-time performances and elaboration accuracy. In contrast to the state-of-the-art, the proposed technique allows the filtering action to be on the fly adapted to both the current pixel values and to the tuning parameters, thus avoiding any architectural modification or tables update. When hardware implemented within the Xilinx Zynq XC7Z020 FPGA device, a 5 × 5 filter based on the proposed method processes 237.6 Mega pixels per second and consumes just 0.92 nJ per pixel, thus improving the energy efficiency by up to 2.8 times over the competitors. The impact of the proposed approximation on three different imaging applications has been also evaluated. Experiments demonstrate reasonable accuracy penalties over the accurate counterparts.
Ski-orienteering: a scientific perspective on a multi-dimensional challenge
Authors: Ståhl Jonatan , Larsson Roland , Holmberg Hans-Christer 
Published: April 2024

AbstractSki-orienteering, which combines cross-country skiing with orienteering, dates back to the late 1800s, with the first World Championships in 1975. While researchers have explored the physiological and biomechanical determinants of success in cross-country skiing and orienteering separately in detail, scientific knowledge concerning ski-orienteering remains limited. Based on the information that is presently available together with interviews with elite ski-orienteers, we explore here for the first time the historical development, physiological, biomechanical, and psychological demands, certain training strategies, and future prospects and challenges associated with this sport, including its potential to become an Olympic event. A demanding endurance sport (with racing times of 12–120 min), ski-orienteering requires both considerable aerobic and anaerobic capacity, as well as well-trained upper and lower body muscles. In addition, ski-orienteering demands advanced skiing technique on various types of terrain, with frequent changes between sub-techniques, on both wide and narrow tracks and with numerous turns on downhill terrain. Moreover, success in this sport requires accurate and rapid orienteering—the ability to navigate a complex network of ski tracks with numerous intersections/crossings in a manner designed to pass the multiple control points in the order indicated on the map as rapidly as possible, i.e., advanced spatial cognition and highly developed navigational skills. Thus, ski-orienteering requires training designed to improve both relevant physiological characteristics and orienteering skills, which should become the focus of future interdisciplinary research on this complex sport.
Business intelligence and business analytics in tourism: insights through Gioia methodology
Authors: Jiménez-Partearroyo Montserrat , Medina-López Ana , Rana Sudhir 
Published: April 2024

AbstractAlthough Business Intelligence (BI) and Business Analytics (BA) have been widely adopted in the tourism sector, comparative research using BI and BA remains scarce. To fill this gap in the literature, the present study explores how BI and BA contribute to strategic innovation, address operational challenges, and enhance customer engagement. To this end, using a dual-method approach that incorporates both quantitative and qualitative methodologies, we first conduct a bibliometric analysis using SciMAT. This sets the stage for the subsequent application of the Gioia methodology. Specifically, we perform an in-depth qualitative examination of a total of 12 scholarly articles on the tourism sector, evenly split between BI and BA. Upon synthesizing the findings on the roles of BI and BA, we outline distinct pathways through which they influence tourism sector management solutions. Based on the obtained evidence, we argue that, while BI focuses on technological advancement and operational integration, BA is more aligned with predictive analytics and data-driven customer engagement. These insights provide managers with a better understanding of the roles of BI and BA, serving as a guide for their strategic applications, from improving service quality to innovating in customer engagement. The novelty of this approach lies in its use of the Gioia methodology, in a comparative analysis to evaluate the separate yet complementarily roles of BI and BA, and in enhancing tourism industry practices.
Economic interactions that are beyond simulation
Authors: Gallagher Shaun , Mastrogiorgio Antonio 
Published: April 2024

AbstractIn this paper, we argue that not all economic interactions can be simulated. Specific types of interactions, instantiated in and instantiating of institutional structures, are embodied in ways that do not admit entailing laws and cannot be expressed in a computational model. Our arguments have two implications: (i) zero intelligence is not merely a computational phenomenon but requires an “embodied” coupling with the environment (theoretical implication); and (ii) some interactions, on which collective phenomena are based, are unprestatable and generate emerging phenomena which cannot be entailed by computation (methodological implication).
Signalwirkung von Bildungskarrieren für die Einmündungschancen in eine duale Ausbildung: Ergebnisse einer Vignettenstudie
Authors: Schuchart Claudia , Schimke Benjamin 
Published: April 2024

ZusammenfassungDas deutsche Bildungssystem erlaubt über allgemeinbildende und berufliche Schulen eine Vielfalt von Bildungswegen zu schulischen Abschlüssen. Im vorliegenden Beitrag wird mithilfe einer Vignettenstudie die Auswahl von Bewerberinnen und Bewerbern mit Haupt-, mittlerem Schulabschluss und Studienberechtigung von allgemeinbildenden und beruflichen Schulen für ein Bewerbungsgespräch für einen dualen Ausbildungsberuf experimentell untersucht. Auf der Grundlage eines Signalling-Ansatzes wurden fünf Hypothesen zur Signalwirkung von Schulabschluss, Schulart und des mit einem Schulabschluss erworbenen allgemeinbildenden vs. berufspraktischen Wissens entwickelt. Der Datensatz umfasst 884 Personalrekrutierende in Unternehmen mit anspruchsgeringen (mehr als die Hälfte der Auszubildenden verfügt höchstens über den mittleren Schulabschluss) und 1308 Rekrutierende in Unternehmen mit anspruchshohen Ausbildungsberufen (mehr als die Hälfte der Auszubildenden verfügt mindestens über einen mittleren Abschluss). Die Ergebnisse zeigen für beide Segmente, dass die deutlichste Signalwirkung auf die Einschlägigkeit des berufspraktischen Wissens entfällt: Die besten Chancen auf eine Einladung hatten Bewerber mit einem Schulabschluss von einer beruflichen Schule in einem für den Ausbildungsberuf einschlägigen Berufsfeld, während die geringsten Chancen Bewerber mit einem Schulabschluss von einer beruflichen Schule in einem für den Ausbildungsberuf distalen Berufsfeld hatten.
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Authors: Ji Wei , Li Jingjing , Bi Qi , Liu Tingwei , Li Wenbo , Cheng Li 
Published: April 2024

AbstractRecently, Meta AI Research approaches a general, promptable segment anything model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. In this study, we conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing and healthcare. We analyze and discuss the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks. By doing so, we aim to give a comprehensive understanding of SAM’s practical applications. This work is expected to provide insights that facilitate future research activities toward generic segmentation. Source code is publicly available at https://github.com/LiuTingWed/SAM-Not-Perfect.
View selection in multi-view stacking: choosing the meta-learner
Authors: van Loon Wouter , Fokkema Marjolein , Szabo Botond , de Rooij Mark 
Published: April 2024

AbstractMulti-view stacking is a framework for combining information from different views (i.e. different feature sets) describing the same set of objects. In this framework, abase-learneralgorithm is trained on each view separately, and their predictions are then combined by ameta-learneralgorithm. In a previous study, stacked penalized logistic regression, a special case of multi-view stacking, has been shown to be useful in identifying which views are most important for prediction. In this article we expand this research by considering seven different algorithms to use as the meta-learner, and evaluating their view selection and classification performance in simulations and two applications on real gene-expression data sets. Our results suggest that if both view selection and classification accuracy are important to the research at hand, then the nonnegative lasso, nonnegative adaptive lasso and nonnegative elastic net are suitable meta-learners. Exactly which among these three is to be preferred depends on the research context. The remaining four meta-learners, namely nonnegative ridge regression, nonnegative forward selection, stability selection and the interpolating predictor, show little advantages in order to be preferred over the other three.
Directional Solidification of Single-Crystal Blades in Industrial Conditions Using the Developed Gas Cooling Casting Method
Authors: Szeliga Dariusz , Piechowicz Łukasz , Lisiewicz Marcin , Wiechczyński Artur 
Published: April 2024

AbstractThe effect of gas cooling on microstructure refinement during the production of single crystal blades by the Developed Gas Cooling Casting (DGCC) method was investigated. Primary dendrite arm spacing (PDAS) reached the highest values in the airfoil and lowest in the blade platform. However, with the Bridgman method, the tendency of PDAS change along the blade was the opposite. When using the DGCC method, the PDAS decreased by about 100 µm in the platform compared to the conventional radiation cooling.