bluuebunny/crossref_metadata_2025_split huggingface dataset:
High expression of RUVBL1 and HNRNPU is associated with poor overall survival in stage I and II non-small cell lung cancer patients
Authors: Durślewicz Justyna , Jóźwicki Jakub , Klimaszewska-Wiśniewska Anna , Zielińska Aleksandra , Antosik Paulina , Grzanka Dariusz , Braun Marcin 
Published: October 2022

AbstractThe present study aimed to investigate expression levels and prognostic significance of RUVBL1 and HNRNPU in stage I and II non–small-cell lung cancer (NSCLC) patients. Therefore, we evaluated immunohistochemical staining of RUVBL1 and HNRNPU, as well as RNA-seq data from public sources, and the results were evaluated concerning overall survival (OS) and clinicopathological features. We found that RUVBL1 and HNRNPU proteins and mRNA levels were higher in tumor tissues as compared to adjacent/normal tissues. RUVBL1 (p = 0.013) and HNRNPU (p = 0.021) high protein levels were independent prognostic factors for poor OS. Also, the multivariate analysis in the TCGA dataset revealed that high RUVBL1 (p = 0.064) and HNRNPU (p = 0.181) mRNA levels were not significantly associated with prognosis. However, the co-expression status of these markers (R + H +) was independently associated with poor OS both in the TCGA dataset (p = 0.027) and in our cohort (p = 0.001). In conclusion, combined and individual expression of RUVBL1 and HNRNPU proteins, as well as R + H + mRNA status, may serve as potential prognostic biomarkers for NSCLC. This study adds to the previous observations that RUVBL1 and HNRNPU might be novel and promising therapeutic targets and markers for prognostic evaluation.
A novel metaheuristic optimisation approach for text sentiment analysis
Authors: Hosseinalipour Ali , Ghanbarzadeh Reza 
Published: October 2022

AbstractAutomated sentiment analysis is considered an area in natural language processing research that seeks to understand a text author's mood, thoughts, and feelings. New opportunities and challenges have arisen in this field due to the popularity and accessibility of a variety of resources of ideas, such as online review websites, personal blogs, and social media. Feature selection, which can be conducted using metaheuristic algorithms, is one of the steps of sentiment analysis. It is crucial to use high-performing algorithms for feature selection. This paper applies the Horse herd Optimisation Algorithm (HOA) for feature selection in text sentiment analysis. HOA is a metaheuristic algorithm and uses six key behaviours to simulate the social performance of horses of various ages, to solve high-dimensional optimisation problems. In order to improve HOA, this paper adds another behaviour of horses to the basic algorithm; thus, the new algorithm uses seven key behaviours of horses of different ages to imitate their social performance. It is then discretised and converted to a multi-objective algorithm. The improved algorithm's performance is evaluated using 15 CEC benchmark functions, and the results are compared to the Binary Social Spider Algorithm, the Binary Grey Wolf Optimizer, and the Binary Butterfly Optimization Algorithm. The new algorithm, the Multi-objective Binary Horse herd Optimisation Algorithm (MBHOA), excels at solving high-dimensional complex problems. To evaluate the algorithm's performance in feature selection, as a practical example, it is employed in text sentiment analysis and examined on various data sets. The simulation results indicate that MBHOA has a better performance in analysing sentiment compared to similar approaches.
Wetland Conservation and Its Effects on Mosquito Populations
Authors: Dworrak Tarja V. , Sauer Felix G. , Kiel Ellen 
Published: October 2022

AbstractTo stop the worldwide decline of wetlands, conservation measures like restoration, protection and construction of these ecosystems are indispensable. However, wetland conservation could influence mosquito populations. We analysed how conservation measures affect the species composition and abundance of mosquitoes by conducting a systematic literature review and generated results from 113 selected articles. Thereby, we separately assessed conservation measures in constructed, for example polders, and natural, non-constructed, wetlands. An increase in overall mosquito abundance was more prevalent in constructed wetlands, but not in studies conducted in non-constructed wetlands. Besides assessing overall mosquito abundance, we developed a scheme to rank mosquito species-specific nuisance after conservation measures. Mosquito species can differ in their nuisance potential according to their biting and host-seeking behaviors. We further assessed the effects of mosquito management practices on specific mosquito species and discussed different practices between constructed and non-constructed wetlands. Whereas in constructed wetlands more management practices could be applied, practices in non-constructed wetlands were limited. In conclusion, we were not able to reject entirely the hypothesis that mosquito populations change after conservation measures in wetlands.
Somatosensation and the First Person
Authors: Serrahima Carlota 
Published: October 2022

AbstractExperientialism about the sense of bodily ownership is the view that there is something it is like to feel a body as one’s own. In this paper I argue for a particular experientialist thesis. I first present a puzzle about the relation between bodily awareness and self-consciousness, and introduce a somewhat underappreciated view on the sense of bodily ownership, Implicit Reflexivity, that points us in the right direction as to how to address this puzzle. I argue that Implicit Reflexivity, however, does not provide a full solution to the puzzle. I then introduce a novel view on the sense of bodily ownership that inherits a central tenet, Reflexivity, from the above view, without having its flaws. According to Reflexivity, the sense of bodily ownership consists in the reflexive character of bodily sensations, namely in the fact that bodily sensations have experience-dependent properties as part of their content. Cashed out this way, Reflexivity is an attractive way of explicating the notion that bodily sensations are experiences of the body as subject. Reflexivity also highlights a central, but so far neglected, connection between the sense of bodily ownership and the sense of experience ownership.
Should organic animals be slaughtered differently from non-organic animals? A cluster analysis of German consumers
Authors: Kühl Sarah , Bayer Elisa , Busch Gesa 
Published: October 2022

AbstractPublic criticism of livestock husbandry is increasing, and better animal welfare is a main buying motive for organic animal products. The slaughter process is a crucial point for animal welfare impacts, but there are no specific regulations regarding slaughtering methods according to the European organic label. Research is missing on what (organic) consumers expect and what they demand from slaughter when considering organic meat. We examined these questions in a survey with 1,604 consumers in Germany. We identified three groups of participants based on their different views on the treatment of organic and non-organic animals at slaughter. The results show that especially organic consumers expect specific regulations for the slaughter of organic animals to be in place already, and they therewith perceive advantages for meat quality. However, the cluster analysis reveals one group (36.8%) that is for a different slaughtering of organic animals and simultaneously favors the equal treatment of all animals at slaughter. Consumers in this ambivalent cluster justify their meat consumption by buying organic meat more often, which might be a coping strategy with the moral dilemma of meat consumption (“meat paradox”). A second cluster (32.4%) rejects different slaughtering methods and is highly interested in animal welfare. These consumers seem to plead for general animal welfare improvements. A third cluster (30.8%) is not interested in the slaughter methods at all. The results show that specific regulations for the slaughter of organic animals might reduce the risk of losing consumer trust and open up further differentiation strategies.
The challenge of recruiting multimorbid older patients identified in a hospital database to a randomised controlled trial
Authors: Biegus Karol R. , Frobell Richard B. , Wallin Åsa K. , Ekdahl Anne W. 
Published: October 2022

Abstract
Background
Research involving multimorbid older patients is gaining momentum. However, little is known about how to plan a randomised controlled trial (RCT) involving this group of patients. An evidence-based approach to the challenges of a recruitment process could guide researchers and help prevent underpowered trials.

Aim
To define the number of multimorbid older patients that need to be identified and the number of eligible patients that need to be invited to achieve the desired recruitment number to a RCT.

Method
We used recruitment data from the GerMoT trial, a RCT comparing proactive outpatient care based on Comprehensive Geriatric Assessment with usual care. Multimorbid older patients with high healthcare utilisation were recruited to the trial.

Results
Of the 1212 patients identified in a database as meeting the inclusion criteria 838 (70%) could be invited to participate in the trial. The rest could not be invited for a variety of reasons; 162 had moved out of area or into nursing homes and 86 had died before they could be contacted. 113 could not be reached. 450 (54%) of the invited patients agreed to participate.

Conclusions
In our study, we have shown that it is possible to achieve a good consent rate despite older participants with multimorbidity. This can be used when planning an RCT for this patient group, who are often excluded from clinical trials. Our results are specific to a context that provides similar abilities to identify and recruit patients as can be seen in Sweden.
An efficient method for train–track–substructure dynamic interaction analysis by implicit-explicit integration and multi-time-step solution
Authors: Xu Lei , Zhai Wanming , Zhu Shengyang , Liu Weizheng 
Published: October 2022

AbstractIn this work, a method is put forward to obtain the dynamic solution efficiently and accurately for a large-scale train–track–substructure (TTS) system. It is called implicit-explicit integration and multi-time-step solution method (abbreviated as mI-nE-MTS method). The TTS system is divided into train–track subsystem and substructure subsystem. Considering that the root cause of low efficiency of obtaining TTS solution lies in solving the algebraic equation of the substructures, the high-efficient Zhai method, an explicit integration scheme, can be introduced to avoid matrix inversion process. The train–track system is solved by implicitly Park method. Moreover, it is known that the requirement of time step size differs for different sub-systems, integration methods and structural frequency response characteristics. A multi-time-step solution is proposed, in which time step size for the train–track subsystem and the substructure subsystem can be arbitrarily chosen once satisfying stability and precision demand, namely the time spent for m implicit integral steps is equal to n explicit integral steps, i.e., mI = nE as mentioned above. The numerical examples show the accuracy, efficiency, and engineering practicality of the proposed method.
Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time
Authors: Guo Jun , Liu Wenjun , Peng Zhao , Du Baigang 
Published: October 2022

AbstractThis paper presents a novel integrated distributed production and distribution scheduling problem in group manufacturing with uncertain travel time (IDPDSP-GM-UTT), in which products are firstly produced in several distributed hybrid flow shops and then delivered to several retailers in batches. The proposed model considers both geographical dispersion of multi-factories and variable travel time between factories and retailers caused by time-varying dynamics of road network, which describes the production environment more authentic. Additionally, a mathematical model is developed to find the optimal quantity of raw material, delivery plan, and punishment of earliness and tardiness with the objective of minimizing total costs. Then, an improved genetic algorithm with two-stage heuristic mutation scheduling strategy and tabu search for local optimization (GA-2HMS&TS) is designed to solve the proposed model. To verify the performances of the proposed method, several experiments by adopting test experimental examples with different scales are performed. The computational results exhibit that the GA-2HMS&TS not only significantly reduces the total cost of production and distribution, but also outperforms all of its rivals. In addition, the robustness of the proposed models is also analyzed with regard to the different road conditions.
Wind speed and global radiation forecasting based on differential, deep and stochastic machine learning of patterns in 2-level historical meteo-quantity sets
Authors: Zjavka Ladislav 
Published: October 2022

AbstractAccurate forecasting of wind speed and solar radiation can help operators of wind farms and Photo-Voltaic (PV) plants prepare efficient and practicable production plans to balance the supply with demand in the generation and consumption of Renewable Energy (RE). Reliable Artificial Intelligence (AI) forecast models can minimize the effect of wind and solar power fluctuations, eliminating their intermittent character in system dispatch planning and utilization. Intelligent wind and solar energy management is essential in load scheduling and decision-making processes to meet user requirements. The proposed 24-h prediction schemes involve the beginning detection and secondary similarity re-evaluation of optimal day-data sequences, which is a notable incremental improvement against state-of-the-art in the consequent application of statistical AI learning. 2-level altitude measurements allow the identification of data relationships between two surface layers (hill and lowland) and adequate interpretation of various meteorological situations, whose differentiate information is used by AI models to recognize upcoming changes in the mid-term day horizon. Observations at two professional meteorological stations comprise specific quantities, of which the most valuable are automatically selected as input for the day model. Differential learning is a novel designed unconventional neurocomputing approach that combines derivative components produced in selected network nodes in the weighted modular output. The complexity of the node-stepwise composed model corresponds to the patterns included in the training data. It allows for representation of high uncertain and nonlinear dynamic systems, dependent on local RE production, not substantially reducing the input vector dimensionality leading to model over simplifications as standard AI does. Available angular and frequency time data (e.g., wind direction, humidity, and irradiation cycles) are combined with the amplitudes to solve reduced Partial Differential Equations (PDEs), defined in network nodes, in the periodical complex form. This is a substantial improvement over the previous publication design. The comparative results show better efficiency and reliability of differential learning in representing the modular uncertainty and PDE dynamics of patterns on a day horizon, taking into account recent deep and stochastic learning. A free available C++ parametric software together with the processed meteo-data sets allow additional comparisons with the presented model results.
Progress of Materials and Devices for Neuromorphic Vision Sensors
Authors: Cho Sung Woon , Jo Chanho , Kim Yong-Hoon , Park Sung Kyu 
Published: October 2022

AbstractThe latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies. "Image missing"
Effects of combined sewer overflow on water quality: a case study of Hatirjheel Lake in Dhaka
Authors: Atauzzaman M. , Ali M. A. 
Published: October 2022

Abstract
This paper presents a case study focusing on the impacts of combined sewer overflows on the water quality of the receiving water body, Hatirjheel. Hatirjheel, the largest surface water body in Dhaka City with an area of about 1.012 km2, receives discharges from nine combined sewer overflow (CSO) structures. The water quality of Hatirjheel is poor throughout the year, but particularly during the wet season (June to October) near the CSO structures through which significant rainwater-sewage overflows. The water has been found to contain high concentrations of 5-day biochemical oxygen demand (BOD5) and chemical oxygen demand; some of the BOD5 values exceed the national discharge standards for treated effluents. Total ammonia concentration in Hatirjheel water increases during the wet season, often exceeding 20 mg/l; the concentration continues to increase after the end of the wet season, most likely due to the ammonification process. Nitrate concentration in Hatirjheel water increases at the end of the wet season, possibly due to nitrification; subsequent reduction in nitrate and ammonia concentration is possibly due to incorporation of nitrogen into algal mass. Excessive phosphorus in Hatirjheel promotes eutrophication, resulting in the visible greenish color of the water. This study highlights the significant adverse impact of combined sewer overflows, particularly for a densely populated city like Dhaka, where most of the rainfall occurs within a relatively short period during monsoon.

ArticleHighlights

Combined sewer overflows could significantly deteriorate the water quality of the receiving water bodies.

Sewer overflows create a significant spatiotemporal variation of water quality, with higher pollution close to the overflowing combined sewer overflow structures.

Because of its significant adverse impact on water quality and ecology, combined sewer systems may not be viable for high-density urban areas.
A Novel Visual-Based Terrain Relative Navigation System for Planetary Applications Based on Mask R-CNN and Projective Invariants
Authors: Del Prete Roberto , Renga Alfredo 
Published: October 2022

AbstractIn the framework of autonomous spacecraft navigation, this manuscript proposes a novel vision-based terrain relative navigation (TRN) system called FederNet. The developed system exploits a pattern of observed craters to perform an absolute position measurement. The obtained measurements are thus integrated into a navigation filter to estimate the spacecraft state in terms of position and velocity. Recovering crater locations from elevation imagery is not an easy task since sensors can generate images with vastly different appearances and qualities. Hence, several problems have been faced. First, the crater detection problem from elevation images, second, the crater matching problem with known craters, the spacecraft position estimation problem from retrieved matches, and its integration with a navigation filter. The first problem was countered with the robust approach of deep learning. Then, a crater matching algorithm based on geometric descriptors was developed to solve the pattern recognition problem. Finally, a position estimation algorithm was integrated with an Extended Kalman Filter, built with a Keplerian propagator. This key choice highlights the performance achieved by the developed system that could benefit from more accurate propagators. FederNet system has been validated with an experimental analysis on real elevation images. Results showed that FederNet is capable to cruise with a navigation accuracy below 400 meters when a sufficient number of well-distributed craters is available for matching. FederNet capabilities can be further improved with higher resolution data and a data fusion integration with other sensor measurements, such as the lunar GPS, nowadays under investigation by many researchers.
Valorisation of alum sludge to produce green and durable mortar
Authors: Jia Qiong , Zhuge Yan , Duan Weiwei , Liu Yue , Yang Jing , Youssf Osama , Lu Jinsuo 
Published: October 2022

AbstractAlum sludge is a typical by-product of drinking water treatment processes. Most sludge is disposed of at landfill sites, and such a disposal method may cause significant environmental concern due to its vast amount. This paper assessed the feasibility of reusing sludge as a supplementary cementitious material, which could efficiently exhaust stockpiled sludge. Specifically, the pozzolanic reactivity of sludge at different temperatures, the reaction mechanism of the sludge–cement binder, and the resistance of sludge-derived mortar to microbially induced corrosion were investigated. The obtained results indicated that 800 °C was the optimal calcination temperature for sludge. Mortar containing sludge up to 30% by weight showed comparable physical properties at a curing age of 90 days. Mortar with 10% cement replaced by sludge can significantly improve the resistance to biogenic corrosion due to the formation of Al-bearing phases with high resistance to acidic media, e.g., Ca4Al2O7·xH2O and strätlingite.
Graphical abstract
Identifying relevant topics and training methods for emergency department flow training
Authors: Young Christina , Patey Christopher , Norman Paul , Chan Teresa , Hurley Oliver , Swab Michelle , Asghari Shabnam 
Published: October 2022

Abstract
Purpose
Despite the importance of patient flow to emergency department (ED) management, there is a need to strengthen and expand training in flow strategies for practicing ED staff. To date, there has been limited academic inquiry into the skills and training that ED staff require to improve patient flow. As part of a quality improvement initiative, our team aimed to identify the topics and training methods that should be included in flow training for ED staff.

Methods
We conducted an integrative review and modified Delphi. For the integrative review, we sought to identify appropriate skills, training strategies, and training modalities to include in a curriculum for ED staff. The findings from the review were compiled and distributed to Canadian experts in ED efficiency through a modified Delphi, including physicians, nurses, and nurse practitioners.

Results
Our literature search retrieved 8359 articles, of which 46 were included in the review. We identified 19 skills, 9 training strategies, and 12 training modalities used to improve ED efficiency in the literature. For the modified Delphi, we received responses from 39 participants in round one and 28 in round two, with response rates of 57% and 41%, respectively. The topics chosen by the most respondents were: “flow decisions,” “teamwork,” “backlog and surge management,” “leadership,” and “situational awareness.”

Conclusion
Our findings suggest that flow training should teach ED staff how to make decisions that improve flow, work more effectively as a team, manage patient backlog and surge, improve leadership skills, and develop situational awareness. These findings add to a gap in the academic literature regarding the training ED staff require to improve patient flow.