bluuebunny/crossref_metadata_2025_split huggingface dataset:
Facility staffing associated with potentially avoidable hospitalizations in nursing home residents in Japan: A retrospective cohort study.
Authors: Hamasaki Yoko , Sakata Nobuo , Jin Xueying , Sugiyama Takehiro , Morita Kojiro , Uda Kazuaki , Matsuda Shinya , Tamiya Nanako 
Published: April 2023

Abstract
Background Wide variations in facility staffing may lead to differences in care, and consequently, adverse outcomes such as hospitalizations. However, few studies focused on types of occupations. Therefore, we aimed to examine the association between a wide variety of facility staffing and potentially avoidable hospitalizations of nursing home residents in Japan.Methods In this retrospective cohort study using long-term care and medical insurance claims data in Ibaraki Prefecture from April 2018 to March 2019, we identified individuals aged 65 years and above who were newly admitted to nursing homes. In addition, facility characteristic data were obtained from the long-term care insurance service disclosure system. Subsequently, we conducted a multivariable Cox regression analysis and evaluated the association between facility staffing and potentially avoidable hospitalizations.Results A total of 2909 residents from 235 nursing homes were included. The cumulative incidence of potentially avoidable hospitalizations at 180 days was 14.2% (95% confidence interval [CI] 12.7–15.8). Facilities with full-time physicians (adjusted hazard ratio [HR]: 0.59, 95% CI: 0.37–0.94) and a higher number of dietitians (HR: 0.72, 95% CI: 0.54–0.97) were significantly associated with a lower likelihood of potentially avoidable hospitalizations. In contrast, having nurses or trained caregivers during the night shift (HR: 1.72, 95% CI: 1.25–2.36) and a higher number of care managers (HR: 1.37, 95% CI: 1.03–1.83) were significantly associated with a high probability of potentially avoidable hospitalizations.Conclusions We revealed that variations in facility staffing were associated with potentially avoidable hospitalizations. The results suggest that optimal allocation of human resources, such as dietitians and physicians, may be essential to reduce potentially avoidable hospitalizations. To provide appropriate care to nursing home residents, it is necessary to establish a system to effectively allocate limited resources. Further research is warranted on the causal relationship between staff allocation and unnecessary hospitalizations, considering the confounding factors.
Pricing Multi-event Triggered Catastrophe Bonds Based on Copula-POT Model
Authors: Tang Yifan , Ling Chengxiu , Wen Conghua , Zhang Yuqing 
Published: April 2023

Abstract
The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative framework for pricing catastrophic bonds triggered by multiple events with extreme dependence structure. Due to the bond's low cash flow contingencies and the CAT bond's high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce moral hazard and improve bond attractiveness with CIR stochastic rate, displaying the co-movement of the wiped-off coupon and principal, the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulted from the potential catastrophic disaster might be associated with heavy tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct a statistical analysis of pricing a three-event rainstorm CAT bond based on the resulted losses of rainstorms in China during 2006--2020. Monte Carlo simulations are carried out to analyse the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.
Mechanical and Durability Assessment of Rockwool-Bamboo Reinforced Ceiling Boards
Authors: Atoyebi Olumoyewa , Kolajo Oluwafemi O. , Alabi Olayinka O. , Aina Salome O 
Published: April 2023

Abstract
It has become important to explore means of reducing deforestation in our environment by sourcing alternatives for timber in the forest. Bamboo (Bambusa vulgaris) as a type of grass grows abundantly in tropical regions around the world. It is known for its strength and flexibility, which makes it a popular material for construction, furniture, and other uses which are still being explored. This paper evaluated the use of bamboo for the design of ceiling boards based on the physical and mechanical properties of bamboo. The research explored the use of bamboo powder, bamboo fibre, and bamboo sticks which are usually wasted during construction activities. Rock wool was used as a fire-resistance agent, and urea-formaldehyde was used as a binding agent. The bamboo ceiling boards were tested for their physical (water absorption, thickness, swelling, and density) and mechanical (modulus of rupture and modulus of elasticity) properties. The results showed the resulting board has the lowest value of 41.885% and the highest value of 86.37% of water absorption and the highest value of 97.564% and the lowest value of 41.885% of water absorption when immersed in water for 2hrs and 24hrs respectively, the modulus of rupture recorded was 78.915 N/mm2 and highest density value of 0.424kg/m3. It was concluded that bamboo is a suitable material for the production of particle and ceiling boards.
Neoadjuvant PD-1/PD-L1 inhibitors combined with chemotherapy had a higher ORR than mono-immunotherapy in untreated HNSCC: Meta-analysis
Authors: Chen Shaoshi , Yang Yifan , Wang Ru , Fang Jugao 
Published: April 2023

Abstract

Background: HNSCC is one of the most common types of cancer worldwide and immune checkpoint inhibitor has shown favorable therapeutic effect in R/M HNSC. However, the application of immunotherapy in untreated HNSCC still needs to be discovered since most R/M HNSCC patients have been treated before and their drug susceptibility and immune tumor microenvironment have changed. This meta-analysis tries to compare immunotherapy and immunochemotherapy in untreated HNSCC and give a reference for clinic application.
Methods: Electronic databases, including PubMed, Embase, and Web of Science, were systematically searched from inception through August 31, 2022. The primary outcomes were efficacy, evaluated by objective response rate, 1-year OS and 1-year PFS, and safety, evaluated by grade 3–4 adverse reaction rate.
Results: A total of 1092 patients from twenty-four studies were included, 282 (25.8%) of which had ORR reported. The average ORR was 37% (95%CI = 26%-49%). Immunochemotherapy could have higher ORR than immunotherapy patients (ORR: 61% vs 22%), and favorable 1-year overall survival from PD-L1 inhibitor (OS = 84%, 95%CI 76%-93%). Radiotherapy after neoadjuvant immunotherapy was equal with the other treatments like chemotherapy and surgery (84% vs 88%, subgroup df p = 0.7). There was no apparent difference between immunotherapy and immunochemotherapy (32% vs 42%, subgroup df p = 0.60).
Conclusion: HNSCC patients could benefit more from neoadjuvant immunochemotherapy.
AMST2: Aggregated Multi-Level Spatial and Temporal Context-Based Transformer for Robust Aerial Tracking
Authors: Park Hasil , Lee Injae , Jeong Dasol , Paik Joonki 
Published: April 2023

Abstract
Recently, many existing visual trackers have made significant progress by incorporating either spatial information from multi-level convolution layers or temporal information for tracking. However, the complementary advantages of both spatial and temporal information cannot be leveraged when these two types of information are used separately. In this paper, we present a new approach for robust visual tracking using a transformer-based model that incorporates both spatial and temporal context information at multiple levels. To integrate the refined similarity maps through multi-level spatial and temporal encoders, we propose an aggregation encoder. Consequently, the output of the proposed aggregation encoder contains useful features that integrate the global contexts of multi-level spatial and the temporal contexts. The contrasting yet complementary representation of multi-level spatial and temporal contexts provided by this feature can help prevent tracking failures in a range of complex aerial scenarios, including occlusion, motion blur, and scale variations. Additionally, the proposed architecture can achieve more robust object tracking against significant variations by updating the features of the latest object while retaining the initial template information. Extensive experiments on five challenging short-term and long-term aerial tracking benchmarks have demonstrated that the proposed tracker outperforms state-of-the-art tracking methods in terms of both real-time processing speed and performance.
Effect of using corn industry wastewater as irrigation water in potato crops (Solanum tuberosum)
Authors: Bacame-Valenzuela Francisco Javier , García-Méndez Liliana Monserrat , Sandoval-Salas Fabiola , Perez-García Jesús Alberto , Aceves-Diez Angel , Reyes-Vidal Yolanda 
Published: April 2023

Abstract
Water scarcity affects more than 40% of the population, in addition, 70% of all water extracted from aquifers is used for irrigation, reducing its availability for human consumption. Therefore, irrigation of crops with wastewater helps reduce water scarcity. In Mexico, high COD-value wastewater is generated by the cooking process of corn (Zea mays). In this work, the use of this effluent for the irrigation of potato crops (Solanum tuberosum) is proposed. Four treatments were applied (A) water, (B) water with fertilizer, (C) 100% residual water, and (D) 50% residual water. In the determination of biomass, in the number of leaves there were no differences between the treatments, instead for the height of the plants, A and B were higher for day 32. Treatment B obtained the highest production of tubers, followed by treatment A. Treatment C achieved 80% of the production of treatment A, suggesting that the components of the wastewater can be used as nutrients by potato plants. Being necessary a study on the affectation on soil fertility.
Adjusting N-doping to balance conductivity and polarization for superb electromagnetic wave absorption properties of SiO2
Authors: He Yunfei , Li Xinyu , Liu Dongdong , Fu Wenbo , Su Qiang , Zhong Bo , Xia Long , Huang Xiaoxiao 
Published: April 2023

Abstract
Due to its low relative permittivity and basically non-electromagnetic loss characteristics, SiO2, a typical electrical insulator, is frequently employed as a wave-transparent material. Hence, using SiO2 as a wave-absorbing material is a novel concept. In this study, a composite (SiO2-Nx) was created using a one-step solvothermal process, doped with N atoms and bound by Si-N bonds. Based on SiO2's imbalanced polarization ability, this research modifies the material's conductivity loss by doping it with N, which successfully balances the polarization and conductivity and turns SiO2-Nx into a wave-absorbing material. The resulting SiO2-N4 composite has a low matched thickness and a wide absorption band of 5.28 GHz with a minimum reflection loss value of -34.48 dB (2.5 mm). The addition of doped-N atoms, which on the one hand improves the impedance matching properties of SiO2-Nx, results in the balanced polarization relaxation and conductivity. The two dielectric loss mechanisms of polarization and conductivity loss, on the other hand, are enhanced to varying degrees by N-doping, which completes the improvement of the wave absorption capability. These outstanding findings offer original suggestions for the creation of SiO2-based electromagnetic wave absorption materials.
Low Coverage of Long Graphene Nanoribbons by On-surface Double Layer Polymerization on Au(111)
Authors: THUPAKULA Umamahesh , Soe We-Hyo , Joachim Christian , Dujardin Erik 
Published: April 2023

Abstract
The controlled surface annealing by steps of 50°C of graphene nanoribbon (GNR) precursors on Au(111) is characterized, during the GNR on-surface synthesis, using low-temperature ultrahigh vacuum scanning tunneling microscopy and dI/dV spectroscopy. The initial monomer coverage is increased up to 3 monolayers (MLs) and annealed at every 50°C. After the first annealing step, the monomers self-organize into 2 ML islands and, then, the Ullmann coupling reaction takes place in both 1st and 2nd MLs. An optimal initial monomer coverage of ~ 1.5 ML is necessary for reaching a final GNR length distribution up to 50 nm and a low surface coverage of 0.4 ML required for single GNR molecule experiments.
Preliminary archaeomagnetic studies of ancient bricks from northern Poland
Authors: Nawrocki Jerzy , Rosowiecka Olga , Wójcik Krystian , Chadima Martin , Werner Tomasz , Wasik Bogusz , Wiewióra Marcin 
Published: April 2023

Abstract
24 brick samples for archaeomagnetic studies were taken from teen historical buildings constructed between c.1280 AD and 1630 AD in northern Poland. Eight of them are from the gothic period. The Thellier-Thellier archaeointensity protocol was used in order to determine the ancient intensity and inclination registered by the bricks. 28 representative specimens from 16 bricks gave successful archaeointensity determination with category B of results quality. For 25 of them the corrections for anisotropy of thermoremanent magnetization and cooling rate were introduced. A large number of specimens classified as category C (48%) is due to a high value of relative additivity check error d(AC) caused most probably by the presence of multi-domain magnetite. Corrected data are convergent with the Central European master curve of archaeointensity. The corrections of raw data reduce their dispersion at specimen/sample level in most of sites.
Estimating Mortality Indicators and Causes of Death Using ANACoD Tool from Civil Registration Data of Iganga-Mayuge HDSS in Uganda
Authors: Habaasa Gilbert , Kajungu Dan 
Published: April 2023

Abstract
Background: Every country requires key population and mortality data to plan for its citizens. Using ANACoD tool, the current study presents the population structure changes for Iganga Mayuge HDSS site in Uganda for the years 2007-2016, computes basic mortality indicators and generates the leading causes of death by age and gender from the civil registration data.
Methods: Secondary data on verbal autopsies as well as the socio-economic data was analysed using Analysis of Mortality levels And Causes-of-Death (ANACoD) tool freely provided by the World Health Organisation.
Results: 3,947 deaths occurred with majority deaths for males (51.5%). Most deaths occurred among the elderly 60+ years (28.1%) and under one (infants) children (23.5%). The population structure has shifted from more infant mortalities (32.1 per 1000 live births) in the year 2007 to less infant mortality (28.1 per 1000 live births) in 2016. Crude Death rate reduced from 4.4 to 3.1 per 1000 population. Life expectancy has increased from 73.7 to 78.7 years. Malaria, HIV, accidents and NCDs remain the major causes of death among adults. In Children, malaria, birth trauma, prematurity and malnutrition are the leading causes of death.
Conclusion: In conclusion, malaria remains the main leading cause of death especially for under-five children for the IMHDSS in Uganda. There is need for strong public health programmes such as sleeping in treated insecticide mosquito nets. Among adults, injury and the Non-Communicable Diseases (NCDs) of Hypertensive disease and Diabetes mellitus have become the more life-threatening cause of death in recent years especially for the older population in the IMHDSS. Improvement in lifestyle among the population through balanced diet feeding and regular exercises is therefore key. Lastly, the paper calls for a strong Civil Registration and Vital Statistics System in Uganda where all deaths in the community and health facilities are effectively registered.
Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics
Authors: Lu Jiazhong , Lan Jin , Huang Yuanyuan , Song Maojia , Liu Xiaolei 
Published: April 2023

Abstract
Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection algorithm based on a message-passing neural network with traffic spatiotemporal features. Our algorithm can not only effectively distinguish and correlate upstream and downstream traffics, but also clearly embody the relationship between different traffics from the same source node and destination node by the established graph structure. We also improve the model by utilising the characteristics of the existing network attacks, which indicates thatthe traffic spatiotemporal characteristics could achieve high accuracy on attack traffic detection. Compared to the previous algorithms, extensive experiments show that our proposed model outperforms other similar models in performance. For the multi-classification tasks, our algorithm can effectively detect most of the network attacks and outperform traditional machine learning methods. In terms of algorithm robustness, our algorithm is less affected by adversarial attacks than traditional machine learning algorithms.
Metal concentrations in fish tissues in the Kara, Bolshoi Patok, and Maly Patok River basins, North-Eastern European Russia
Authors: Hughson Claire , Ponomarev Vasily , Kondratenok Boris , Walker Tony 
Published: April 2023

Abstract
Fish and fish tissue are effective bioindicators due to their sensitivity to pollution and are frequently used for assessing aquatic ecosystem health. Establishing baseline metal concentrations in freshwater fish tissues within aquatic ecosystems is important prior to establishing industrial activities to help determine potential future industrial impacts. Historically, North-Eastern European Russia has been an area with relatively low levels of industrial development and is still in pristine condition. In this region the noise to background ratio for industrial contaminants may be disproportionately high. This study measured baseline metal concentrations in freshwater fish tissues collected from three study sites (Bolshoi Patok, Maly Patok and Kara River) in North-Eastern European Russia as bioindicators for overall aquatic ecosystem health. Seven fish species including European Grayling, Arctic Char, Whitefish, Perch, Pike, Roach, and Peled over a five-year period between 2000 to 2005. Fish tissue samples were analyzed for Copper (Cu), Lead (Pb), Cadmium (Cd), and Zinc (Zn) metal concentrations. Metal concentrations measured in fish tissues in this study compared favourably to remote sites in Alaska in the US and Slovenia. Despite small variation between sampling sites, metal concentrations were relatively low and considered in pristine condition. Metal concentrations measured in fish tissues in this study represent baseline conditions which will be important to compare against using monitoring programs should the region experience future industrial development.
Multi-domain-fusion Deep Learning for Automatic Modulation Recognition in Spatial Cognitive Radio
Authors: Hou Shunhu , Dong Yaoyao , Li Yuhai , Yan Qingqing , Wang Mengtao , Fang Shengliang 
Published: April 2023

Abstract
Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio(SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. However, most deep learning models only use a single network. In complex wireless environments, due to the diversity of signal features, it is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time-frequency domain joint recognition model that combines two deep learning networks(DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN(multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component(IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3(three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT(Fast Fourier Transform) is used to obtain frequency domain amplitude and phase(FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.
iBehavior - A Smartphone-Based Ecological Momentary Assessment Tool for the Assessment of Behavior Change in Neurodevelopmental Disorders
Authors: Dakopolos Andrew , Glassman Dana , Scott Haleigh , Bass Michael , Hessl David 
Published: April 2023

Abstract
The purpose of the present study was to describe the content and function of iBehavior, a smartphone-based caregiver-report electronic ecological momentary assessment (eEMA) tool developed to assess and track behavior change in people with intellectual and developmental disabilities (IDDs), and to examine its preliminary validity. Ten parents of children (ages of 5–17 years) with IDDs (n = 7 with fragile X syndrome; n = 3 with Down syndrome) rated their child’s behavior (aggression and irritability, avoidant and fearful behavior, restricted and repetitive behavior and interests, and social initiation) using iBehavior once daily for 14 days. At the conclusion of the 14-day observation period, parents completed traditional rating scales as validation measures, as well as a user feedback survey. Parent ratings using iBehavior showed emerging evidence of convergent validity among domains with traditional rating scales including the Behavior Rating Inventory of Executive Function 2 (BRIEF-2), Aberrant Behavior Checklist – Community (ABC-C), and Conners 3. iBehavior was feasible in our sample, and parent feedback indicated high overall satisfaction. Results of the present pilot study indicate successful implementation and preliminary feasibility and validity of an eEMA tool for use as a behavioral outcome measure in IDDs.