bluuebunny/crossref_metadata_2025_split huggingface dataset:
Advancing Autonomous Nanomedicine: Bridging the Gap from Concept to Potential Clinical Studies
Authors: Chohan Diya Pratish , Dey Bipasa , Tarkunde Arshia , Vyas Vaishnavi , De Sarkar Srijita , Sundara Babitha Kampa 
Published: September 2024

AbstractAutonomous nanomedicine, a burgeoning field within nanotechnology and biomedical sciences, is poised to revolutionize healthcare by eliminating the need for external intervention in targeted applications within the body. This article elucidates the promise and challenges of autonomous nanomedicine, emphasizing its ability to overcome the limitations of traditional methods such as chemotherapy and radiotherapy. Central to its efficacy are nano-sized carriers, which autonomously navigate the body to deliver therapeutic agents with precision and control. By integrating automated nanoscale tools into disease detection processes, this technology offers swift and personalized assessments, reshaping disease management paradigms. To advance the clinical translation of autonomous nanomedicine, rigorous preclinical studies are imperative. However, challenges persist in ensuring reproducibility and safety, hindering progress in clinical trials. This article examines current studies with potential clinical translation, shedding light on the regulatory and ethical considerations crucial for its safe implementation. As the field progresses, maintaining a balance between innovation and safety remains paramount for harnessing the full potential of autonomous nanomedicine while safeguarding patient well-being.
Graphical Abstract
Novel efficient reservoir computing methodologies for regular and irregular time series classification
Authors: Li Zonglun , Andreev Andrey , Hramov Alexander , Blyuss Oleg , Zaikin Alexey 
Published: September 2024

Abstract
Time series is a data structure prevalent in a wide range of fields such as healthcare, finance and meteorology. It goes without saying that analyzing time series data holds the key to gaining insight into our day-to-day observations. Among the vast spectrum of time series analysis, time series classification offers the unique opportunity to classify the sequences into their respective categories for the sake of automated detection. To this end, two types of mainstream approaches, recurrent neural networks and distance-based methods, have been commonly employed to address this specific problem. Despite their enormous success, methods like Long Short-Term Memory networks typically require high computational resources. It is largely as a consequence of the nature of backpropagation, driving the search for some backpropagation-free alternatives. Reservoir computing is an instance of recurrent neural networks that is known for its efficiency in processing time series sequences. Therefore, in this article, we will develop two reservoir computing based methods that can effectively deal with regular and irregular time series with minimal computational cost, both while achieving a desirable level of classification accuracy.
Ethical conflicts in patient care situations of community pharmacists: a cross-sectional online survey
Authors: Wernecke Kathrin , Nadolny Stephan , Schildmann Jan , Schiek Susanne , Bertsche Thilo 
Published: September 2024

Abstract
Background
Community pharmacy practice is rarely considered in ethical research, although various ethical conflicts are known for this setting. Data on the actual frequency and perceived burden of ethical conflicts occurring in the community pharmacy setting are required.

Aim
The survey aimed at investigating the frequency and perceived burden of ethical conflicts, reasons for the perceived burden and influences on decision-making in ethical conflicts in German community pharmacists.

Method
An online survey was conducted among community pharmacists. It contained 15 ethical conflicts in which the ethically required action conflicts with another principle (e.g. law). Basing on these conflicting principles, 12 considerations relevant for decision-making were defined (e.g. solidarity principle). Participants were asked to rate the ethical conflicts in terms of frequency and perceived burden and to rate the influence on decision-making for the considerations. Results were analysed descriptively.

Results
Five hundred and thirty-five questionnaires were evaluated. The participant’s median age was 39 (min–max: 20–78) years, 378 (71%) were female. Seven of the 15 predefined ethical conflicts were rated as occurring predominantly at least once a week. “Generic drug is not most suitable” was rated as the most frequent. Three ethical conflicts were rated mainly with a (very) strong burden. “Concerns for an unborn child” was rated as the most burdensome. Three of the 12 predefined decision-making considerations: pharmaceutical knowledge, legal requirements and personal values were rated primarily as having a very strong influence on decision-making.

Conclusion
Pharmacists in community pharmacies are frequently affected by burdensome ethical conflicts in patient care situations.
On representations and topological aspects of positive maps on non-unital quasi *- algebras
Authors: Bellomonte Giorgia , Djordjević Bogdan , Ivković Stefan 
Published: September 2024

AbstractIn this paper we provide a representation of a certain class of C*-valued positive sesquilinear and linear maps on non-unital quasi *-algebras, thus extending the results from Bellomonte (GNS-construction for positive $$C^*-$$

C
∗

-

valued sesquilinear maps on a quasi $$*-$$

∗
-

algebra, Mediterr. J. Math., 21 166 (22 pp) (2024)) to the case of non-unital quasi *-algebras. Also, we illustrate our results on the concrete examples of non-unital Banach quasi *-algebras, such as the standard Hilbert module over a commutative C*-algebra, Schatten p-ideals and noncommutative $$L^2$$

L
2

-spaces induced by a semifinite, nonfinite trace. As a consequence of our results, we obtain a representation of all bounded positive linear C*-valued maps on non-unital C*-algebras. We also deduce some norm inequalities for these maps. Finally, we consider a noncommutative $$L^2$$

L
2

-space equipped with the topology generated by a positive sesquilinear form and we construct a topologically transitive operator on this space.
Temporal trends and presidential traits in the Italian end-of-year addresses: comparing and contrasting KBS and STM results
Authors: Sciandra Andrea , Trevisani Matilde , Tuzzi Arjuna 
Published: September 2024

AbstractThis study compares and contrasts the results of two lexical-based methods aimed at identifying content temporal trends in diachronic text corpora. A corpus of end-of-year addresses of the presidents of the Italian Republic constitutes a relevant case of political speech useful to understand how the temporal evolution of topics can be represented and whether a downward (ex post) or an upward (ex ante) extraction of topics is more effective for the identification of presidents’ distinctive traits and trends. The first method is a knowledge-based system (KBS), which identifies clusters of words sharing a similar temporal pattern through a three-step statistical learning procedure. The second is a structural topic model (STM), which identifies main topics by probing the possible effect of the year and president factors on the speech-topic and the topic-word distributions. In KBS clusters, the individual trait of the president stands out as one of the most relevant elements and determines the contents of speeches; moreover, topic trends can also be discerned ex post while interpreting the results. On the other hand, STM directly achieves the whole topic structure but seems not as powerful as expected in portraying the life cycle of words and detecting groups of words that distinguish the speeches of a specific president. As most presidential speeches are rich and cover a wide range of topics, the results suggest that, in this case, the interpretative tool offered by STM brings out more challenges than strengths. Conversely, direct observation of the temporal trajectory of individual words allows for more detailed analyses and meaningful results, thanks to the flexible and adaptive KBS approach.
Bisphenols in daily clothes from conventional and recycled material: evaluation of dermal exposure to potentially toxic substances
Authors: Jurikova Martina , Dvorakova Darina , Bechynska Kamila , Pulkrabova Jana 
Published: September 2024

AbstractGiven the increasing concern about chemical exposure from textiles, our study examines the risks of dermal exposure to bisphenol A (BPA), bisphenol S (BPS), bisphenol B (BPB) and bisphenol F (BPF) from conventional and recycled textiles for adults, aiming to obtain new data, assess exposure, and evaluate the impact of washing on bisphenol levels. A total of 57 textile samples (33 from recycled and 24 from conventional material) were subjected to ultrasound-assisted extraction (UAE) followed by ultra-high performance liquid chromatography with tandem mass spectrometry analysis (UHPLC-MS/MS). The BPA and BPS concentrations varied widely (BPA: < 0.050 to 625 ng/g, BPS: 0.277–2,474 ng/g). The median BPA content in recycled textiles (13.5 ng/g) was almost twice as high as that of 7.66 ng/g in conventional textiles. BPS showed a median of 1.85 ng/g in recycled textiles and 3.42 ng/g in conventional textiles, indicating a shift from BPA to BPS in manufacturing practices. Simulated laundry experiments showed an overall reduction in bisphenols concentrations after washing. The study also assessed potential health implications via dermal exposure to dry and sweat-wet textiles compared to a tolerable daily intake (TDI) of 0.2 ng/kg bw/day for BPA set by the European Food Safety Authority (EFSA). Exposure from dry textiles remained below this threshold, while exposure from wet textiles often exceeded it, indicating an increased risk under conditions that simulate sweating or humidity. By finding the widespread presence of bisphenols in textiles, our study emphasises the importance of being aware of the potential risks associated with recycling materials as well as the benefits.
Microstructure and Mechanical Properties of Low Stacking-Fault Energy Cu-Based Alloy Wires
Authors: Semboshi Satoshi , Arauchi Ryusei , Kaneno Yasuyuki , Lim Sung Hwan , Choi Eun-Ae , Han Seung Zeon 
Published: September 2024

AbstractInnovations in electronic devices and their capabilities have driven the demand for improved conductive materials relevant to device fabrication. To gain insights on developing solid solution-type Cu alloy thin wires with a superior balance of strength and conductivity, this study investigated variations in the microstructures and properties of pure Cu wires and Cu–5 at. pct Zn, Cu–5 at. pct Al, and Cu–5 at. pct In alloy wires during intense drawing and analyzed the effects of stacking-fault energy (SFE) of Cu alloys on their microstructural evolution. During the initial drawing stages, lower SFE Cu–5 at. pct Al and Cu–5 at. pct In alloys yielded more high-density deformation twins than pure Cu and Cu–5 at. pct Zn. Deformation twins promoted grain refinement during drawing. Effective grain refinement and dislocation accumulation during drawing in low-SFE Cu alloys substantially strengthened them without adversely impacting electrical conductivity. During intense drawing in the Cu–5 at. pct In alloy wires, ultrafine fibrous grains (diameter ~ 80 nm) and a high-dislocation density yielded excellent tensile strength and conductivity. These results indicate that adjusting the solute element content in Cu matrix to reduce SFE and optimizing deformation strain via wire drawing significantly improve alloy wire performance.
An integral RB operator
Authors: Jahn Marvin , Massopust Peter 
Published: September 2024

AbstractWe introduce the novel concept of integral Read–Bajraktarević (iRB) operator and discuss some of its properties. We show that this iRB operator generalizes the known Read–Bajraktarević (RB) operator and we derive conditions for the fixed point of the iRB operator to belong to certain function spaces.
Algebras with superautomorphism: simple algebras and codimension growth
Authors: Ioppolo Antonio , La Mattina Daniela 
Published: September 2024

Abstract
Let A be an associative algebra endowed with a superautomorphism φ. In this paper we completely classify the finite-dimensional simple algebras with superautomorphism of order ≤ 2. Moreover, after generalizing the Wedderburn–Malcev Theorem in this setting, we prove that the sequence of φ-codimensions of A is polynomially bounded if and only if the variety generated by A does not contain the group algebra of ℤ2 and the algebra of 2 × 2 upper triangular matrices with suitable superautomorphisms.
First Impressions Matter: Feeling Welcome and Onward Migration Intentions of Highly Skilled Migrants
Authors: Reinold Julia , Siegel Melissa 
Published: September 2024

AbstractIn the context of the global competition for highly skilled migrants, policy makers follow different approaches to attract and retain this highly sought-after group. One common assumption is that the establishment of a welcoming culture can facilitate the retention of highly skilled migrants. Relatively little is known, however, about the impact such policies can have on highly skilled migrants’ decisions to remain in the destination country. We address this gap by analysing the association between feeling welcome and highly skilled migrants’ spatial intentions. We use a mixed methods approach that combines a survey conducted among highly skilled migrants in the Euregio Meuse-Rhine (n = 410) and two rounds of semi-structured interviews (wave 1: n = 67; wave 2: n = 49). Our results suggest that respondents who feel welcome are more likely to have the intention to stay in the region permanently. This association is stronger for migrants with relatively more agency. The paper underlines the importance of early experiences in the host country and the role of subjective and intangible factors like feeling welcome in migration decision-making.
Estimation of the lateral variation of photon beam energy spectra using the percentage depth dose reconstruction method
Authors: Chakraborty Puspen , Saitoh Hidetoshi , Miyake Yuta , Suzuki Tenyoh , Chang Weishan 
Published: September 2024

AbstractIn photon-collapsed cone convolution (pCCC) algorithm of the Monaco treatment planning system (TPS), the central-axis energy spectrum is assumed constant throughout the entire irradiation area. To consider lateral variations, an off-axis softening factor is applied to attenuation coefficients during the total energy released per unit mass calculation. We evaluated this method through comparison studies of percentage depth doses (PDDs) and off-axis ratios (OARs) calculated by Monaco and measured for a 6 MV photon beam at various off-axis angles and depths. Significant differences were observed, with relative differences exceeding ± 1%. Therefore, this method may not accurately represent lateral variations of energy spectra. We propose directly implementing energy spectra on both central-axis and off-axis to improve dose calculation accuracy for large field. To this end, we introduce reconstruction of PDDs from monoenergetic depth doses (MDDs) along off-axis angles, thereby estimating energy spectra as functions of radial distance. This method derives energy spectra quickly without significantly increasing the beam modeling time. MDDs were computed through Monte Carlo simulations (DOSRZnrc). The variances between reconstructed and measured PDDs were minimized using the generalized-reduced-gradient method to optimize energy spectra. Reconstructed PDDs along off-axis angles of 0°, 1.15°, 2.29°, 3.43°, 4.57°, 5.71°, 6.84°, 7.97°, 9.09°, 10.2° to estimate energy spectra at radial distances of 0–18 cm in 2 cm increments and OARs calculated using estimated energy spectra at 5, 10, and 20 cm depths, well agreed with measurement (relative differences within ± 0.5%). In conclusion, our proposed method accurately estimates lateral energy spectrum variation, thereby improving dose calculation accuracy of pCCC algorithm.
Joint segmentation of sternocleidomastoid and skeletal muscles in computed tomography images using a multiclass learning approach
Authors: Ashino Kosuke , Kamiya Naoki , Zhou Xiangrong , Kato Hiroki , Hara Takeshi , Fujita Hiroshi 
Published: September 2024

AbstractDeep-learning-based methods can improve robustness against individual variations in computed tomography (CT) images of the sternocleidomastoid muscle, which is a challenge when using conventional methods based on probabilistic atlases are used for automatic segmentation. Thus, this study proposes a novel multiclass learning approach for the joint segmentation of the sternocleidomastoid and skeletal muscles in CT images, and it employs a two-dimensional U-Net architecture. The proposed method concurrently learns and segmented segments the sternocleidomastoid muscle and the entire skeletal musculature. Consequently, three-dimensional segmentation results are generated for both muscle groups. Experiments conducted on a dataset of 30 body CT images demonstrated segmentation accuracies of 82.94% and 92.73% for the sternocleidomastoid muscle and entire skeletal muscle compartment, respectively. These results outperformed those of conventional methods, such as the single-region learning of a target muscle and multiclass learning of specific muscle pairs. Moreover, the multiclass learning paradigm facilitated a robust segmentation performance regardless of the input image range. This highlights the method’s potential for cases that present muscle atrophy or reduced muscle strength. The proposed method exhibits promising capabilities for the high-accuracy joint segmentation of the sternocleidomastoid and skeletal muscles and is effective in recognizing skeletal muscles, thus, it holds promise for integration into computer-aided diagnostic systems for comprehensive musculoskeletal analysis. These findings are expected to enhance medical image analysis techniques and their applications in clinical decision support systems.
Straightforward identification of flow curve and yield locus parameters from three-point bending experiments
Authors: Hartmann Christoph , Maier Lorenz , Liu Tianyou , Norz Roman , Volk Wolfram 
Published: September 2024

AbstractMaterial testing and modeling is one of the cornerstones of virtual analysis of sheet metal forming processes. However, it is also becoming more and more relevant for incoming goods inspection, especially in view of the increasing amount of recycled material or frequent changes of suppliers, e.g. to provide workers, processes and/or process models with relevant information about a new batch of material. Efficient material testing and straight-forward test evaluation is essential for this. The flow curve and yield locus are central to describe the forming behavior of sheet metal materials. However, the parameters of the associated models are currently determined in various tests on different systems and with special sample geometries. The present work presents a methodology that allows the determination of a set of flow curve and yield locus parameters from three three-point bending tests only. The evaluation routine does not require finite element simulation and processes only the force-displacement information of the bending tests, which also places low demands on the measurement technology. The results were compared with a conventionally determined parameter set using a validation test, and the results are of reasonable quality, especially considering the minimal effort involved.
Raising awareness and education of genetic testing and counseling through fotonovelas among Latina women at risk for hereditary breast and ovarian cancer
Authors: Barajas Rolando , Barajas Clara B. , Ramos Yaideliz M. Romero , Trillos Sara Gómez , Sawhney Sabrina , Campos Claudia , Hurtado-de-Mendoza Alejandra , Rotunno Melissa , Gillanders Elizabeth 
Published: September 2024

AbstractLatinas are less likely to receive genetic counseling and genetic testing (GCT) compared to non-Latina Whites because of systemic and patient-level barriers. We developed and tested fotonovelas to increase awareness of GCT among Latinas at-risk of hereditary breast and ovarian cancer (HBOC). Content for the fotonovelas was drawn from an existing culturally targeted narrative video focused on improving GCT use among Latinas at-risk of HBOC. Using mixed methods, we interviewed cancer patients (n = 10) and their relatives (n = 10) to assess the preliminary efficacy of the fotonovelas through pre-and post-fotonovela items assessing self-rated knowledge of GCT and willingness to discuss cancer with family. Health workers (n = 10) provided feedback on the fotonovela content. McNemar’s test was used to examine differences in the proportions of the outcomes pre- and post-fotonovelas. Interviews were transcribed and coded in Dedoose using a consensual qualitative research approach. Reading the fotonovelas increased self-rated knowledge of GCT by 22% (p = 0.16), from 50 to 60% in patients and from 63 to 100% among relatives. Analogously, reading the fotonovela increased willingness to talk about cancer with family by 33% (p = 0.02), from 70 to 100% in patients and from 38 to 75% in relatives. We identified six themes, some centered around the fotonovela’s message, feedback, and perceived barriers to GCT. Overall, participants liked the use of fotonovelas to increase GCT awareness and cancer conversations with family. Fotonovelas could potentially be used as educational tools to increase GCT awareness and cancer conversations among Latino families at-risk of HBOC.
Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities
Authors: Boldi Paolo , D’Ascenzo Davide , Furia Flavio , Vigna Sebastiano 
Published: September 2024

Abstract
Among the properties describing the behavior of centrality measures with respect to network modifications, score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target with respect to the remaining nodes. It is known (Boldi and Vigna Intern Math 10:222–262, 2014, Boldi et al. Netw Sci 5(4):529–550, 2017) that score and rank monotonicity hold in directed graphs for almost all the classical centrality measures. In undirected graphs one expects that the corresponding properties hold when adding a new edge—in this case, both endpoints of the new edge should enjoy the increase in score/rank. However, recent results (Boldi et al. in Netw Sci 11(3):1–23, 2023) have shown that this is not true: for many centrality measures, it is possible to find situations in which adding an edge reduces the rank of one of its two endpoints. In this paper we introduce a weaker property for undirected networks, semi-monotonicity, in which just one of the two endpoints of a new edge is required to enjoy score or rank monotonicity. We show that this property is satisfied by closeness centrality, by a large class of distance-based centralities, and (somehow surprisingly) by betweenness centrality. In the last two cases, we prove in fact a stronger property, basin dominance, which is of independent interest.