bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wearable sensor and machine learning estimate tendon load and walking speed during immobilizing boot ambulation
Authors: Kwon Michelle P. , Hullfish Todd J. , Humbyrd Casey Jo , Boakye Lorraine A. T. , Baxter Josh R. 
Published: October 2023

AbstractThe purpose of this study is to develop a wearable paradigm to accurately monitor Achilles tendon loading and walking speed using wearable sensors that reduce subject burden. Ten healthy adults walked in an immobilizing boot under various heel wedge conditions (30°, 5°, 0°) and walking speeds. Three-dimensional motion capture, ground reaction force, and 6-axis inertial measurement unit (IMU) signals were collected. We used a Least Absolute Shrinkage and Selection Operator (LASSO) regression to predict peak Achilles tendon load and walking speed. The effects of altering sensor parameters were also explored. Walking speed models (mean absolute percentage error (MAPE): 8.81 ± 4.29%) outperformed tendon load models (MAPE: 34.93 ± 26.3%). Models trained with subject-specific data performed better than models trained without subject-specific data. Removing the gyroscope, decreasing the sampling frequency, and using combinations of sensors did not change the usability of the models, having inconsequential effects on model performance. We developed a simple monitoring paradigm that uses LASSO regression and wearable sensors to accurately predict (MAPE ≤ 12.6%) Achilles tendon loading and walking speed while ambulating in an immobilizing boot. This paradigm provides a clinically implementable strategy to longitudinally monitor patient loading and activity while recovering from Achilles tendon injuries.
Research on improved algorithm for helmet detection based on YOLOv5
Authors: Shan Chun , Liu HongMing , Yu Yu 
Published: October 2023

AbstractThe continuous development of smart industrial parks has imposed increasingly stringent requirements on safety helmet detection in environments such as factories, construction sites, rail transit, and fire protection. Current models often suffer from issues like false alarms or missed detections, especially when dealing with small and densely packed targets. This study aims to enhance the YOLOv5 target detection method to provide real-time alerts for individuals not wearing safety helmets in complex scenarios. Our approach involves incorporating the ECA channel attention mechanism into the YOLOv5 backbone network, allowing for efficient feature extraction while reducing computational load. We adopt a weighted bi-directional feature pyramid network structure (BiFPN) to facilitate effective feature fusion and cross-scale information transmission. Additionally, the introduction of a decoupling head in YOLOv5 improves detection performance and convergence rate. The experimental results demonstrate a substantial improvement in the YOLOv5 model's performance. The enhanced YOLOv5 model achieved an average accuracy of 95.9% on a custom-made helmet dataset, a 3.0 percentage point increase compared to the original YOLOv5 model. This study holds significant implications for enhancing the accuracy and robustness of helmet-wearing detection in various settings.
Elucidating the modified performance of high nuclearity of Cu nanostructures-PTFE thin film
Authors: Che Lah Nurul Akmal , Murthy Puhanes 
Published: October 2023

AbstractThe aim of this study is to attain an extensive insight on the performance mechanism that is associated with the formation of Cu nanostructures- polytetrafluoroethylene (PTFE) thin film. The work presented Cu nanostructures synthesised via microwave-assisted method at different Cu precursor concentrations to observe the influence of different average particle diameter distribution, $${d}_{m}$$

d
m

of Cu nanostructures on the fabricated Cu nano thin film. The thin films of Cu nanostructures with a layer of PTFE were fabricated using the Meyer rod coating method. Evaluating the effect of Cu nanostructures at different $${d}_{m}$$

d
m

with overcoated PTFE layer showed that the resistance of fabricated thin film coated with PTFE is not significantly different from that of the uncoated thin film. The results implicate the influence of the PTFE layer towards the output performance, which can maintain a stable and constant resistance over time without affecting the original properties of pure Cu nanostructures, although some of the Cu nanostructures seep into the layer of PTFE. The novelty of this study lies in the effect of the intrinsic interaction between the layer of Cu nanostructure and PTFE, which modulate the performance, especially in photovoltaic cell application.
Temporal dynamics of statistical learning in children’s song contributes to phase entrainment and production of novel information in multiple cultures
Authors: Daikoku Tatsuya 
Published: October 2023

AbstractStatistical learning is thought to be linked to brain development. For example, statistical learning of language and music starts at an early age and is shown to play a significant role in acquiring the delta-band rhythm that is essential for language and music learning. However, it remains unclear how auditory cultural differences affect the statistical learning process and the resulting probabilistic and acoustic knowledge acquired through it. This study examined how children’s songs are acquired through statistical learning. This study used a Hierarchical Bayesian statistical learning (HBSL) model, mimicking the statistical learning processes of the brain. Using this model, I conducted a simulation experiment to visualize the temporal dynamics of perception and production processes through statistical learning among different cultures. The model learned from a corpus of children’s songs in MIDI format, which consists of English, German, Spanish, Japanese, and Korean songs as the training data. In this study, I investigated how the probability distribution of the model is transformed over 15 trials of learning in each song. Furthermore, using the probability distribution of each model over 15 trials of learning each song, new songs were probabilistically generated. The results suggested that, in learning processes, chunking and hierarchical knowledge increased gradually through 15 rounds of statistical learning for each piece of children’s songs. In production processes, statistical learning led to the gradual increase of delta-band rhythm (1–3 Hz). Furthermore, by combining the acquired chunks and hierarchy through statistical learning, statistically novel music was generated gradually in comparison to the original songs (i.e. the training songs). These findings were observed consistently, in multiple cultures. The present study indicated that the statistical learning capacity of the brain, in multiple cultures, contributes to the acquisition and generation of delta-band rhythm, which is critical for acquiring language and music. It is suggested that cultural differences may not significantly modulate the statistical learning effects since statistical learning and slower rhythm processing are both essential functions in the human brain across cultures. Furthermore, statistical learning of children’s songs leads to the acquisition of hierarchical knowledge and the ability to generate novel music. This study may provide a novel perspective on the developmental origins of creativity and the importance of statistical learning through early development.
A combined experimental and modeling approach to elucidate the adsorption mechanism for sustainable water treatment via In2S3-anchored chitosan
Authors: Mishra Soumya Ranjan , Roy Prerona , Gadore Vishal , Ahmaruzzaman Md. 
Published: October 2023

AbstractA novel Chitosan/Indium sulfide (CS/In2S3) nanocomposite was created by co-precipitating Chitosan and InCl3 in solution, resulting in In2S3 agglomeration on the Chitosan matrix with a remarkable pore diameter of 170.384 Å, and characterized it for the physical and chemical properties. Under optimal conditions (pH = 7, time = 60 min, catalyst dosage = 0.24 g L−1, and dye concentration = 100 mg L-1), the synthesized nanocomposite demonstrated remarkable adsorption capabilities for Victoria Blue (VB), attaining a removal efficiency of 90.81%. The Sips adsorption isotherm best matched the adsorption process, which followed pseudo-second-order kinetics. With a rate constant of 6.357 × 10–3 g mg−1 min−1, the highest adsorption capacity (qm) was found to be 683.34 mg g−1. Statistical physics modeling (SPM) of the adsorption process revealed multi-interaction and multi-molecular adsorption of VB on the CS/In2S3 surface. The nanocomposite demonstrated improved stability and recyclability, indicating the possibility for low-cost, reusable wastewater dye removal adsorbents. These results have the potential to have practical applications in environmental remediation.
Unleashing the power of the Sun: the increasing impact of the solar cycle on off-season super typhoons since the 1990s
Authors: Wu Chau-Ron , Lin Yong-Fu , Lin I-I , Yu Jin-Yi 
Published: October 2023

AbstractThe occurrence of super typhoons outside the normal typhoon season can result in devastating loss of life and property damage. Our research reveals that the 11-year solar cycle can affect the incidence of these off-season typhoons (from November to April) in the western North Pacific by influencing sea surface temperature (SST) through a footprint mechanism. The solar cycle, once amplified by atmospheric and ocean interactions, generates a noticeable SST footprint in the subtropical North Pacific during winter and spring, which eventually intrudes into the tropical central Pacific and affects the atmospheric conditions, resulting in an increase or decrease in the occurrence of super typhoons during active or inactive solar periods. This mechanism has become more effective since the Atlantic Multi-decadal Oscillation (AMO) shifted to a warm phase in the 1990s, intensifying the subtropical Pacific couplings. An example of this type of off-season super typhoon during an active solar period is Typhoon Haiyan in 2013. By incorporating information about the solar cycle, we can anticipate the likelihood of super typhoon occurrences, thus improving decadal disaster preparation and planning.
An efficient Julia framework for hierarchical equations of motion in open quantum systems
Authors: Huang Yi-Te , Kuo Po-Chen , Lambert Neill , Cirio Mauro , Cross Simon , Yang Shen-Liang , Nori Franco , Chen Yueh-Nan 
Published: October 2023

AbstractThe hierarchical equations of motion (HEOM) approach can describe the reduced dynamics of a system simultaneously coupled to multiple bosonic and fermionic environments. The complexity of exactly describing the system-environment interaction with the HEOM method usually results in time-consuming calculations and a large memory cost. Here, we introduce an open-source software package called HierarchicalEOM.jl: a Julia framework integrating the HEOM approach. HierarchicalEOM.jl features a collection of methods to compute bosonic and fermionic spectra, stationary states, and the full dynamics in the extended space of all auxiliary density operators (ADOs). The required handling of the ADOs multi-indexes is achieved through a user-friendly interface. We exemplify the functionalities of the package by analyzing a single impurity Anderson model, and an ultra-strongly coupled charge-cavity system interacting with bosonic and fermionic reservoirs. HierarchicalEOM.jl achieves a significant speedup with respect to the corresponding method in the Quantum Toolbox in Python (QuTiP), upon which this package is founded.
Decoding the state of stress and fluid pathways along the Andean Southern Volcanic Zone
Authors: Pérez-Estay Nicolás , Ruz-Ginouves Javiera , Pérez-Flores Pamela , Sielfeld Gerd , Roquer Tomás , Cembrano José 
Published: October 2023

AbstractDecoding means decrypting a hidden message. Here, the encrypted messages are the state of stress, fluid pathways, and volcano tectonic processes occurring in volcanoes of the Andean Southern Volcanic Zone (SVZ). To decode these messages, we use earthquake focal mechanisms, fault slip data, and a Monte Carlo simulation that predicts potential pathways for magmatic and hydrothermal fluids. From this analysis, we propose that SVZ volcanoes have three end-member stress patterns: (i) Stress-A, a strike-slip regime coupled with the regional far-field tectonic stress; (ii) Stress-B, an extensional regime that may be promoted by volcanic edifice loading and upward pressure due to magma inflation occurring within the upper brittle-crust; and (iii) Stress-C, a local and transient fluid-driven stress rotated ~90 degrees from Stress-A. Notoriously, Stress-C pattern was observed in most volcanoes with historical eruptions. We propose that volcanoes presenting Stress-B are attractive geothermal targets, while Stress-C could be used as a predicting signal for impending eruptions.
Substantial increase in future fluvial flood risk projected in China’s major urban agglomerations
Authors: Jiang Ruijie , Lu Hui , Yang Kun , Chen Deliang , Zhou Jiayue , Yamazaki Dai , Pan Ming , Li Wenyu , Xu Nan , Yang Yuan , Guan Dabo , Tian Fuqiang 
Published: October 2023

AbstractUrban land will face high fluvial flood risk against the background of climate change and urban expansion. The effect of urban spatial expansion, instead of densification of assets within existing urban cells, on flood risk has rarely been reported. Here, we project the future flood risk of seven urban agglomerations in China, home to over 750 million people. The inundated urban land areas in the future are projected to be 4 to 19 times that at present. Without considering the urban spatial expansion, the inundated urban land areas will be underestimated by 10-50%. Urban land is more likely to be inundated than non-urban land, and the newly-developed urban land will be inundated more easily than the historical urban land. The results demonstrate the urgency of integrating climate change mitigation, reasonable urban land expansion, and increased flood protection levels to minimize the flood risk in urban land.
Vocabulary Instruction Embedded in Narrative Intervention: A Repeated Acquisition Design Study With First Graders at Risk of Language-Based Reading Difficulty
Authors: Spencer Trina D. , Kirby Megan S. , Petersen Douglas B. 
Published: October 2023

Purpose:
The purpose of the current study was to investigate the effect of vocabulary instruction embedded in the narrative intervention on the immediate and retained definitional knowledge of taught words for first graders at risk for language-related reading difficulties.

Method:
We employed a repeated acquisition design with innovative quality features and supplemental statistics with 11 treatment students and three control students. In the context of the school's multitiered system of supports, treatment students received 30-min small group interventions, 4 days a week for 12 weeks. Intervention involved story retelling and personal story generation lessons, both of which emphasized the learning and practicing of target vocabulary words in each story. Pre- and postprobes of the taught definitions were conducted every week.

Results:
According to visual analysis conventions of single-case research, there was a consistent pattern of improvement from pre- to postprobes for all treatment participants, but for none of the control participants. Retention was also consistently observed, when measured at Week 13. Supplemental statistics confirmed that large effects were associated with the intervention.

Conclusion:
Vocabulary instruction embedded in narrative intervention led to meaningful acquisition and retention of taught vocabulary for students at risk of language-based reading difficulty.
The quantum internet: A synergy of quantum information technologies and 6G networks
Authors: Rozenman Georgi Gary , Kundu Neel Kanth , Liu Ruiqi , Zhang Leyi , Maslennikov Alona , Reches Yuval , Youm Heung Youl 
Published: October 2023

AbstractThe quantum internet is a cutting‐edge paradigm that uses the unique characteristics of quantum technology to radically alter communication networks. This new network type is expected to collaborate with 6G networks, creating a synergy that will fundamentally alter how we communicate, engage, and trade information. The improved security, increased speed, and increased network capacity of the quantum internet will lead to the emergence of a broad variety of new applications and services. The current state of quantum technology and its integration with 6G networks are summarised in this study, with an emphasis on the key challenges and untapped possibilities. The main goal is to get knowledge about how the quantum internet might impact communication in the future and alter several economic and societal sectors.
A new simulation methodology for generating accurate drone micro‐Doppler with experimental validation
Authors: Moore Matthew , Robertson Duncan A. , Rahman Samiur 
Published: October 2023

AbstractUnmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro‐Doppler information from radars. Classifiers capable of exploiting differences in micro‐Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro‐Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro‐Doppler signatures. This is followed by the description of a purpose‐built validation radar that has been developed specifically to gather high‐fidelity experimental drone micro‐Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro‐Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro‐Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro‐Doppler signatures for training classification algorithms.
Privacy‐preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering
Authors: Semmelmann Leo , Resch Oliver , Henni Sarah , Weinhardt Christof 
Published: October 2023

AbstractIn modern power systems, predicting the time when peak loads will occur is crucial for improving efficiency and minimising the possibility of network sections becoming overloaded. However, most works in the load forecasting field are not focusing on a dedicated peak time forecast and are not dealing with load data privacy. At the same time, developing methods for forecasting peak electricity usage that protect customers' data privacy is essential since it could encourage customers to share their energy usage data, leading to more data points for the effective management and planning of power grids. Hence, the authors employ a dedicated Learning to Rank XGBoost algorithm to forecast peak times with only ranks of loads instead of absolute load magnitudes as input data, thereby offering potential privacy‐preserving properties. We show that the presented Learning to Rank XGBoost model yields comparable results to a benchmark XGBoost load forecasting model. Additionally, we describe our extensive feature engineering process and a state‐of‐the‐art Bayesian hyperparameter optimisation for selecting model parameters, which leads to a significant improvement of forecasting accuracy. Our method was used in the context of the final round of the international BigDEAL load forecasting challenge 2022, where we consistently achieved high‐ranking results in the peak time track and an overall fourth rank in the peak load forecasting track with our general XGBoost model.
Berthold Kihn (1895–1964): Profil eines
NS-„Euthanasie“-Täters
Authors: Braun Birgit , Knoepffler Nikolaus 
Published: October 2023

Zusammenfassung
Einleitung In der Geschichtswissenschaft besteht noch immer kein
vollständiger Konsens, wie sich der Übergang von eugenisch
begründeter Sterilisationspraxis zur „Euthanasie“
interpretieren lässt. Ziel des vorliegenden Artikels ist es daher,
dieser konkreten Fragestellung kritisch-historisch nachzugehen. Hierzu dient das
Täterprofil Berthold Kihns, wobei vor allem hervorzuheben ist, dass sich
Kihns Weg in den „Euthanasie“-„Abgrund“ ohne
eine primäre eugenische Ausrichtung entwickelte.
Methoden Es erfolgte Literatur- und Archivstudium. Erstmalig wurden auch
relevante fränkische und familienarchivarische Quellen einbezogen. Zudem
wurden Dokumente des Universitätsarchivs Jena mit solchen der
Konzentrationslager Buchenwald und Oranienburg verglichen.
Ergebnisse Nach beruflichen und privaten Dilemmata setzte der
Neuroinfektiologe Kihn einen neuen pseudowissenschaftlichen Schwerpunkt hin zur
„Ausschaltung der Minderwertigen aus der Gesellschaft“. Neben
Kihns etwa seit der Jahrtausendwende zweifelsfrei wissenschaftlich belegter
Rolle bei der zentralen „Euthanasie“ und der
„Kinder-Euthanasie“ finden sich aktuell auch Hinweise auf seine
Beteiligung an der dezentralen „Euthanasie“. Dies wird anhand
des prominenten Patienten Felix von Papen aufgezeigt.
Diskussion Kihn gehörte weder zu den etablierten Rassenhygienikern
noch zu den wissenschaftlich angesehenen Erbbiologen und Eugenikern.
Karrierestrategisch sprang er auf eine sich abzeichnende staatspolitisch
geförderte Bewegung auf. Hierfür mitverantwortlich mag seine
berufliche sowie private Sackgassen-Situation gewesen sein.
Schluss Weiterführend vergleichende Profilerstellung von
NS-„Euthanasie“-Tätern ist ein Forschungs-Desiderat. Sie
kann dazu beitragen, der immer noch unzureichend geklärten Frage der
Eugenik-„Entartung“ hin zur „Euthanasie“
differenzierter zu begegnen.
MASLD – Prävention und ernährungsmedizinische
Behandlungsmöglichkeiten
Authors: Roeb Elke 
Published: October 2023

ZusammenfassungDiese Übersicht nutzt die neue Nomenklatur der Fettlebererkrankungen, die
im Juni 2023 von der amerikanischen Lebergesellschaft AASLD und der
europäischen Lebergesellschaft EASL in einem umfangreichen
Konsensusverfahren etabliert wurde. Die Deutsche Gesellschaft für
Gastroenterologie, Verdauungs- und Stoffwechselkrankheiten (DGVS)
unterstützt die neue Namensgebung mit Nachdruck.Körperliche Inaktivität, zunehmendes Körpergewicht und
höheres Lebensalter haben in den letzten Jahrzehnten zu einem Anstieg
der Prävalenz der metabolischen Dysfunktion assoziierten
Fettlebererkrankung (MASLD) geführt. In vielen Industrieländern
ist die MASLD inzwischen die häufigste chronische Lebererkrankung. MASLD
ist eine Form der steatotischen Lebererkrankung, die eng mit
Stoffwechselkrankheiten wie Typ-2-Diabetes, Dyslipidämie und Adipositas
verbunden ist. Sie wird daher auch als metabolisch assoziierte Fettleber
bezeichnet 1.
Analyse der BTHG-bezogenen Chancen und Grenzen für die
Suchthilfe
Authors: Gellert-Beckmann Stefanie 
Published: October 2023

ZusammenfassungMit dem Bundesteilhabegesetz (BTHG) sind die Leistungen der Eingliederungshilfe
(EGH) aus dem Sozialhilfegesetz SGB XII in das Rehabilitations- und
Teilhaberecht (SGB IX) überführt worden. Sie sind somit keine
Fürsorgeleistungen mehr, sondern müssen den Rechtsanspruch auf
Selbstbestimmung, Teilhabe und Partizipation einlösen. Zudem soll die
Ausgabendynamik der EGH gebremst werden. Zur Umsetzung der Leitideen dienen u.
a. vertragliche Regelungen, die den allgemein anerkannten Stand der fachlichen
Erkenntnisse der EGH bei der Leistungserbringung gewährleisten und
parallel eine finanzielle Steuerung ermöglichen sollen. Die Suchthilfe
muss den Paradigmenwechsel des BTHG fachlich integrieren. Im vorliegenden
Beitrag werden wesentliche Konzepte und Ansätze der Suchthilfe und aus
angrenzenden Fachgebieten in Bezug auf die Prämissen und Ziele des BTHG
diskutiert und die sich ergebenden Chancen vorgestellt.