bluuebunny/crossref_metadata_2025_split huggingface dataset:
Slim Tree-Cut Width
Authors: Ganian Robert , Korchemna Viktoriia 
Published: June 2024

AbstractTree-cut width is a parameter that has been introduced as an attempt to obtain an analogue of treewidth for edge cuts. Unfortunately, in spite of its desirable structural properties, it turned out that tree-cut width falls short as an edge-cut based alternative to treewidth in algorithmic aspects. This has led to the very recent introduction of a simple edge-based parameter called edge-cut width [WG 2022], which has precisely the algorithmic applications one would expect from an analogue of treewidth for edge cuts, but does not have the desired structural properties. In this paper, we study a variant of tree-cut width obtained by changing the threshold for so-called thin nodes in tree-cut decompositions from 2 to 1. We show that this “slim tree-cut width” satisfies all the requirements of an edge-cut based analogue of treewidth, both structural and algorithmic, while being less restrictive than edge-cut width. Our results also include an alternative characterization of slim tree-cut width via an easy-to-use spanning-tree decomposition akin to the one used for edge-cut width, a characterization of slim tree-cut width in terms of forbidden immersions as well as approximation algorithm for computing the parameter.
The relationship between lower urinary system symptoms and the level of independence and quality of life in children with Duchenne muscular dystrophy
Authors: Öztürk Demet , Karaduman Aynur Ayşe , Akbayrak Türkan 
Published: June 2024

Abstract
Background
The purpose was to investigate the frequency of lower urinary tract symptoms (LUTS) and lower urinary tract dysfunction (LUTD) in Duchenne muscular dystrophy (DMD) and the relationship between these symptoms and independence and quality of life (QoL).

Methods
The cross-sectional study included children aged 5–18 years and diagnosed with DMD and their families. Data were collected using the Dysfunctional Voiding and Incontinence Scoring System (DVISS), the Barthel Index, and the Pediatric Quality of Life™ 3.0 Neuromuscular Module (PedsQL-NMM).

Results
The study was completed with 45 children with DMD. LUTS was found in 86.66% and LUTD was found in 44.44%. The most common symptom was holding maneuvers (62.22%). Other common symptoms were urinary urgency (55.55%), daytime urinary incontinence (46.66%), and enuresis (31.11%). There was a significant correlation of the DVISS with the level of independence and QoL (p < 0.05). Moreover, higher LUTS score was associated with lower Barthel and PedsQL-NMM scores.

Conclusion
LUTS is a neglected condition, although it is frequently seen in children with DMD.
Clinical trial registration: NCT05464446

Graphical Abstract
The effect of a barrier membrane on the incorporation of deproteinized bovine bone mineral (DBBM) in experimental defects at the time of early implant placement. A preclinical study
Authors: Ellis Rebecca , Chen Stephen , Guzón Fernando Mª Muñoz , Darby Ivan 
Published: June 2024

Abstract
Objectives
This study aimed to assess membrane use with a bone substitute graft for guided bone regeneration (GBR) in experimental dehiscence defects.

Materials and methods
Maxillary second incisors (I2) in 9 dogs were extracted. Six weeks later, implants were inserted and experimental dehiscence defects (5 × 3 mm) created on the buccal aspect. The defects and surrounding bone were grafted with deproteinized bovine bone mineral. One side (test) was covered with a resorbable collagen membrane whereas the contralateral side (control) was not. After 6 weeks, histomorphometrical analysis was performed to evaluate: (a) first bone-to-implant contact (fBIC), (b) buccal bone thickness at 1 mm increments from implant shoulder, (c) regenerated area (RA), (d) area and percentages of new bone (B), bone substitute (BS) and mineralized tissue (MT).

Results
The histological appearance was similar between test and control sites. At central and lateral sections, there were no differences between groups for fBIC, buccal bone thickness, RA, BS, B, %B, MT and %MT. At central sections, membrane use favoured more %BS and %MT (p = 0.052). There was significantly more B, %B and MT at lateral compared to central sections.

Conclusions
Membrane use tended to retain more bone substitute, but had no effect on new bone ingrowth. Lateral sections showed significantly more bone ingrowth and mineralized tissue compared to central sections, confirming that new bone ingrowth takes place mainly from the lateral walls of the defect.

Clinical relevance
Preclinical research to clarify the dynamics of bone regeneration in GBR procedures is relevant in clinical practice.
Zwischen Vision und Wirklichkeit: Untersuchung zur Machbarkeit der Weiterleitung von weniger dringlichen Hilfesuchenden in die ambulante Versorgung
Authors: Oslislo Sarah , Witt Kalina , von Stillfried Dominik , Steiger Edgar , Thoß Reno , Thoß Sonja , Carnarius Sebastian , Bayeff-Filloff Michael 
Published: June 2024

Zusammenfassung
Hintergrund und Ziel der Arbeit
Untersucht wurden Machbarkeit und Akzeptanz einer Weiterleitung von Hilfesuchenden mit weniger dringlichem Behandlungsbedarf aus einer zentralen Notaufnahme (ZNA) in kooperierende Praxen.

Material und Methoden
Zu allgemeinen Praxisöffnungszeiten erhielten Hilfesuchende, die in der ZNA des RoMed Klinikums Rosenheim auf Basis des Manchester Triage System (MTS) als weniger dringlich (grün, blau) eingeschätzt wurden, eine erweiterte Einschätzung mittels Strukturierter medizinischer Ersteinschätzung (SmED). Patienten mit einer Empfehlung für die vertragsärztliche Versorgung wurden via IVENA eHealth an eine Kooperationspraxis (KP) vermittelt. Anwender in Klinik und Praxen wurden per Fragebogen zur Machbarkeit und Akzeptanz befragt. Es erfolgte eine deskriptive Datenauswertung.

Ergebnisse
Zwischen April und Juli 2023 erhielten 193 Patienten eine erweiterte Einschätzung. Das mediane Alter betrug 43,5 Jahre, 45,1 % der Patienten waren männlich. 88,6 % (95 %-KI (Konfidenzintervall) 84,1–93,1) der Hilfesuchenden erhielten eine Empfehlung zur vertragsärztlichen Behandlung und das Angebot zur Weiterleitung in eine von acht KP. Davon wollten 15,2 % (95 %-KI 9,8–20,6) in der ZNA verbleiben und für 17 % (95 %-KI 11,4–22,6) war keine KP verfügbar. Insgesamt wurden 60,1 % (95 %-KI 53,2–67,0) der Studienteilnehmer in eine KP weitergeleitet und 56 % (95 %-KI 49,0–63,0) abschließend versorgt. 6,9 % (95 %-KI 2,3–11,5) der weitergeleiteten Patienten stellten sich erneut in der ZNA vor. Die Anwender waren mit dem Einsatz von IVENA eHealth zufrieden und berichteten von einer hohen Akzeptanz der Patienten.

Schlussfolgerung
Eine Weiterleitung Hilfesuchender mit weniger dringlichen Beschwerden ist unter Alltagsbedingungen umsetzbar und sicher. Die Machbarkeitsstudie stellt damit eine potenzielle Anwendung gemäß § 120 Abs. 3b SGB V dar.
Bringing the field into the lab: a novel virtual reality outdoor march simulator for evaluating cognitive and physical performance
Authors: Kimel Naor Shani , Ketko Itay , Yanovich Ran , Gottlieb Amihai , Bahat Yotam , Ben-Gal Oran , Heled Yuval , Plotnik Meir 
Published: June 2024

AbstractSoldiers, athletes, and rescue personnel must often maintain cognitive focus while performing intense, prolonged, and physically demanding activities. The simultaneous activation of cognitive and physical functions can disrupt their performance reciprocally. In the current study, we developed and demonstrated the feasibility of a virtual reality (VR)-based experimental protocol that enables rigorous exploration of the effects of prolonged physical and cognitive efforts. A battery of established neurocognitive tests was used to compare novel cognitive tasks to simulated loaded marches. We simulated a 10-km loaded march in our virtual reality environment, with or without integrated cognitive tasks (VR-COG). During three experimental visits, participants were evaluated pre- and post-activity, including the Color Trail Test (CTT), the Synthetic Work Environment (SYNWIN) battery for assessing multitasking, and physical tests (i.e., time to exhaustion). Results show that Strong or moderate correlations (r ≥ 0.58, p ≤ 0.05) were found between VR-COG scores and scores on the cognitive tests. Both the SYNWIN and CTT showed no condition effects but significant time effects, indicating better performance in the post-activity assessment than in the pre-activity assessment. This novel protocol can contribute to our understanding of physical-cognitive interactions, since virtual environments are ideal for studying high performance professional activity in realistic but controlled settings.
Optimization and development of a new rotary magnetic refrigerator
Authors: Bocanegra Johan Augusto , Scarpa Federico , Fanghella Pietro , Marchitto Annalisa , Tagliafico Luca Antonio 
Published: June 2024

AbstractMagnetic refrigeration is becoming increasingly relevant as a means to achieve sustainability in refrigeration, thanks to its durability, efficiency, and low environmental impact. This paper regards the design of a new rotary magnetic refrigerator feasible for domestic use at room temperature. An interdisciplinary approach was necessary to perform the design, construction, and optimization of the device through mechanical design, thermo-hydraulic design, and integration into a proper test rig. Optimization of critical parts, such as the regenerator, the magnet structure, and the external heat exchangers, was performed using numerical models. The optimal values of control parameters were determined using a parametric sweep study. The results of all these steps of the study resulted in significant progress in designing and constructing an efficient rotary magnetic refrigerator. This work presents the insights obtained from the project implementation, including the design and construction aspects, presented in a practical way that can guide future projects in the field of magnetic refrigeration.
Graphical abstract
Urine Proteomic Signatures of Mild Hypothermia Treatment in Cerebral Ischemia–Reperfusion Injury in Rats
Authors: Zhang Dandan , Li Dapeng , Wang Xueting , Sui Yanyan , Ma Fuguo , Dai Yuting , Wang Mingshan , Qin Weiwei 
Published: June 2024

AbstractMild hypothermia (MH) is an effective measure to alleviate cerebral ischemia–reperfusion (I/R) injury. However, the underlying biological mechanisms remain unclear. This study set out to investigate dynamic changes in urinary proteome due to MH in rats with cerebral I/R injury and explore the neuroprotective mechanisms of MH. A Pulsinelli’s four-vessel occlusion (4-VO) rat model was used to mimic global cerebral I/R injury. Liquid chromatography-tandem mass spectrometry (LC–MS/MS) was employed to profile the urinary proteome of rats with/without MH (32 °C) treatment after I/R injury. Representative differentially expressed proteins (DEPs) associated with MH were validated by western blotting in hippocampus. A total of 597 urinary proteins were identified, among which 119 demonstrated significant changes associated with MH. Gene Ontology (GO) annotation of the DEPs revealed that MH significantly enriched in endopeptidase activity, inflammatory response, aging, response to oxidative stress and reactive oxygen species, blood coagulation, and cell adhesion. Notably, changes in 12 DEPs were significantly reversed by MH treatment. Among them, 8 differential urinary proteins were previously reported to be closely associated with brain disease, including NP, FZD1, B2M, EPCR, ATRN, MB, CA1and VPS4A. Two representative proteins (FZD1, B2M) were further validated by western blotting in the hippocampus and the results were shown to be consistent with urinary proteomic analysis. Overall, this study strengthens the idea that urinary proteome can sensitively reflect pathophysiological changes in the brain, and appears to be the first study to explore the neuroprotective effects of MH by urinary proteomic analysis. FZD1 and B2M may be involved in the most fundamental molecular biological mechanisms of MH neuroprotection.
Graphical Abstract
In a 4-VO rat model, 119 urinary proteins demonstrated significant changes associated with MH. MH is enriched in endopeptidase activity, inflammatory response, oxidative stress, etc, and significantly reversed changes in 12 DEPs. FZD1 and B2M are thought to be involved in the most fundamental molecular biological mechanisms of MH neuroprotection.
177Lu−SN201 nanoparticle shows superior anti−tumor efficacy over conventional cancer drugs in 4T1 orthotopic model
Authors: Lekmeechai Sujinna , Pietras Kristian , Axelsson Oskar 
Published: June 2024

AbstractIn the current in-vivo study we demonstrate the potential of the radiolabeled nanoparticle 177Lu-SN201 as an effective anticancer treatment, as evidenced by significantly prolonged survival and reduced tumor burden in the aggressive, triple negative 4T1 murine breast cancer model. We show with high statistical significance that 177Lu-SN201 is superior at suppressing the tumor growth not only compared to vehicle but also to the commonly used cancer drugs paclitaxel, niraparib, carboplatin, and the combination of the immune checkpoint inhibitors anti PD-1 and anti-CTLA-4. The dosing of the standard drugs were based on examples in the literature where good effects have been seen in various mouse models. The treatment is reasonably well-tolerated, as indicated by clinical chemistry of liver and renal function through the measurement of glutamate pyruvate alanine aminotransferase, alanine amino transferase, blood urea nitrogen, and creatinine levels in plasma samples, despite some weight loss. Overall, 177Lu-SN201 presents as a promising therapeutic candidate for cancer treatment.
Server-side rescoring of spoken entity-centric knowledge queries for virtual assistants
Authors: Zhang Youyuan , Gondala Sashank , Fraga-Silva Thiago , Gysel Christophe Van 
Published: June 2024

AbstractOn-device virtual assistants (VAs) powered by automatic speech recognition (ASR) require effective knowledge integration for the challenging entity-rich query recognition. In this paper, we conduct an empirical study of modeling strategies for server-side rescoring of spoken information domain queries using various categories of language models (LMs) (N-gram word LMs, sub-word neural LMs). We investigate the combination of on-device and server-side signals, and demonstrate significant word error rate improvements of 23%-35% relative on various entity-centric query subpopulations by integrating various server-side LMs compared to performing ASR on-device only. We also perform a comparison between LMs trained on domain data and a generative pre-trained (GPT) (a variant GPT-3) offered by OpenAI as a baseline. Furthermore, we also show that model fusion of multiple server-side LMs trained from scratch most effectively combines complementary strengths of each model and integrates knowledge learned from domain-specific data to a VA ASR system.
Improving search and rescue planning and resource allocation through case-based and concept-based retrieval
Authors: Nasar Wajeeha , Torres Ricardo da Silva , Gundersen Odd Erik , Karlsen Anniken Susanne Thoresen 
Published: June 2024

AbstractThe need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts’ experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions.
Biopolymer patches for the simultaneous incorporation of local anesthetic and anti-inflammatory drugs
Authors: Okolišan Dorinel , Vlase Titus , Vlase Gabriela , Bradu Ionela-Amalia , Pahomi Alexandru , Matichescu Anamaria 
Published: June 2024

AbstractThis study presents a novel type of biopolymer patches in which anti-inflammatory drugs and amide-type anesthetics were simultaneously incorporated. Since the biopolymer matrix is composed of k-carrageenan, hydroxyethyl cellulose, and glycerol, as a plasticizer, the resulting patches have an elasticity and a “stickiness” that can be exploited in the field of transdermal delivery of drugs. Combinations of anesthetic and anti-inflammatory drugs resulted in twenty patches with a unique and distinct physical appearance. Furthermore, as indicated by FTIR, TG/DTG, and DSC analysis data, none of the active substances affects the integrity of the biopolymer matrix; instead, they influence the Tmelting of the patch matrix, either in its value increase or decrease.
A framework for training larger networks for deep Reinforcement learning
Authors: Ota Kei , Jha Devesh K. , Kanezaki Asako 
Published: June 2024

AbstractThe success of deep learning in computer vision and natural language processing communities can be attributed to the training of very deep neural networks with millions or billions of parameters, which can then be trained with massive amounts of data. However, a similar trend has largely eluded the training of deep reinforcement learning (RL) algorithms where larger networks do not lead to performance improvement. Previous work has shown that this is mostly due to instability during the training of deep RL agents when using larger networks. In this paper, we make an attempt to understand and address the training of larger networks for deep RL. We first show that naively increasing network capacity does not improve performance. Then, we propose a novel method that consists of (1) wider networks with DenseNet connection, (2) decoupling representation learning from the training of RL, and (3) a distributed training method to mitigate overfitting problems. Using this three-fold technique, we show that we can train very large networks that result in significant performance gains. We present several ablation studies to demonstrate the efficacy of the proposed method and some intuitive understanding of the reasons for performance gain. We show that our proposed method outperforms other baseline algorithms on several challenging locomotion tasks.
Find the Gap: AI, Responsible Agency and Vulnerability
Authors: Vallor Shannon , Vierkant Tillmann 
Published: June 2024

AbstractThe responsibility gap, commonly described as a core challenge for the effective governance of, and trust in, AI and autonomous systems (AI/AS), is traditionally associated with a failure of the epistemic and/or the control condition of moral responsibility: the ability to know what we are doing and exercise competent control over this doing. Yet these two conditions are a red herring when it comes to understanding the responsibility challenges presented by AI/AS, since evidence from the cognitive sciences shows that individual humans face very similar responsibility challenges with regard to these two conditions. While the problems of epistemic opacity and attenuated behaviour control are not unique to AI/AS technologies (though they can be exacerbated by them), we show that we can learn important lessons for AI/AS development and governance from how philosophers have recently revised the traditional concept of moral responsibility in response to these challenges to responsible human agency from the cognitive sciences. The resulting instrumentalist views of responsibility, which emphasize the forward-looking and flexible role of agency cultivation, hold considerable promise for integrating AI/AS into a healthy moral ecology. We note that there nevertheless is a gap in AI/AS responsibility that has yet to be extensively studied and addressed, one grounded in a relational asymmetry of vulnerability between human agents and sociotechnical systems like AI/AS. In the conclusion of this paper we note that attention to this vulnerability gap must inform and enable future attempts to construct trustworthy AI/AS systems and preserve the conditions for responsible human agency.
Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model
Authors: Aly Mohammed 
Published: June 2024

AbstractThis paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.