bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence and correlates of fatigue in amyotrophic lateral sclerosis: A systematic review and meta-analysis
Authors: Hamad Abdullah Ashraf , Amer Basma Ehab , Abbas Nagham Bushara , Alnajjar Asmaa Zakria , Meshref Mostafa 
Published: October 2023

Abstract
Objectives
This systematic review and meta-analysis aimed to determine the frequency and correlates of fatigue in patients with amyotrophic lateral sclerosis (ALS).

Methods
Three databases were searched up to 2nd May 2023 to identify studies reporting fatigue frequency in ALS. Studies included had to identify ALS patients through one of ALS diagnostic criteria and measure fatigue by a validated tool with a specific cut-off value. Meta-analysis was conducted using RStudio's "meta" package with a random-effects model. Subgroup analyses and meta-regression explored the relationship between fatigue frequency in ALS and different covariates.

Results
Eleven studies, compromising 1072 patients, met the inclusion criteria and were included in our analysis. The pooled frequency of fatigue across all studies was 48% (95% CI = 40% to 57%). Our subgroup analysis based on the ALSFRS-R revealed a higher frequency of fatigue in studies with lower scores (< 30) compared to those with higher scores (≥ 30), with a pooled frequency of 62% (95% CI = 43% to 79%) and 43% (95% CI = 37% to 49%), respectively. Also, the meta-regression analysis showed a significant negative association between fatigue and ALSFRS-R mean (P = 0.02). The included studies reported an association between fatigue and lower functional status and poorer quality of life in patients with ALS.

Conclusion
Our findings suggest that fatigue is prevalent in almost half of ALS patients and is associated with lower functional status and poorer quality of life, highlighting the importance of assessing and managing fatigue in ALS patients.
Path to success: female leaders in German neurosurgery
Authors: Weiss Miriam , Dogan Rabia , Eisenberg Ulrike , Velalakan Aruni , Krüger Jutta , Moritz Ina , Nistor-Gallo Dorothea , Flueh Charlotte , Janz Claudia , Ahmadi Rezvan , Hakvoort Karlijn , Forster Marie-Thérèse ,   “Women in Neurosurgery –Open for all” –Official Commission of the German Society of Neurosurgery (DGNC)
Published: October 2023

AbstractDespite advances in gender equality, only 6% of German neurosurgical departments are currently led by women. With regard to their pioneering work and the importance of their role model effect, we aimed at reporting on the career pathways of the present and former female chairs of neurosurgical departments in Germany. We approached current and former female chairs in German neurosurgery and gathered descriptive information on their ways into leadership positions through structured interviews. Data were obtained from 16/22 (72.7%) female neurosurgical chairs, aged between 44 and 82 years. They completed their training within 6.5 ± 0.6 years, and it took them further 14.5 ± 5.9 years between training completion and chair acquisition. Having obtained their chair positions between 1993 and 2020, six (37.5%) of them have retired or changed career tracks. Of ten (62.5%) chairs still practicing, two are directors of university departments. Twelve (75.0%) hold professorships. Nine chairs (56.3%) are married, eight (50.0%) having children. Five chairs reported having experienced gender-based discrimination. Twelve had a male mentor or role model, two had a female role model, while only one had a female mentor. This study characterizes the to date small number of female neurosurgical chairs in Germany and their paths to neurosurgical leadership positions. In future, these should become historical in order to perceive the presence of women in leadership positions as self-evident normality, reflecting our society. However, further analyses comparing paths of both female and male neurosurgical chairs are necessary to explore gender-based differences in achieving neurosurgical leadership positions.
Some characterizations of resolute majority rules
Authors: Freixas Josep , Samaniego Dani 
Published: October 2023

AbstractIn this paper we consider decision functions in which voters have three options and one of them is abstention or indifference and the output is a binary decision between two alternatives so that a tie is not possible. These resolute decision functions appear frequently in grading, in some sport competitions, in voting situations in which the status quo is put to the vote, etc. It is a more restricted case of the voting context considered in the seminal article by Kenneth May on decision functions, published in Econometrica in 1952, because the output set does not admit a tie. Among these resolute decision functions we focus on the study of majority functions, in which the number of favorable votes to an alternative must be strictly greater than the number of votes against it. This work provides an axiomatic characterization for the set of majority functions and for the relative majority function with status quo bias. Both characterizations are based on weaker versions of neutrality. Other complementary characterizations are also provided.
Transformation and Leaching Simulation of 6:2 and 8:2 diPAP and Related Metabolites in Unsaturated Soil Column Studies with MACRO
Authors: Weidemann Eva , Gassmann Matthias 
Published: October 2023

AbstractPFAS (perfluoroalkyl substances) are widespread in the environment and are found in almost every medium. Besides persistent PFAS, their transforming precursors gained attention since they may be a reservoir for persistent PFAS in soil. Only little information is available about the behavior of these precursors and the simultaneous leaching of their transformation products. However, this information is urgently needed to improve estimates of contamination levels. For this purpose, two precursors and their non-degradable metabolites were simulated using the MACRO model, with parameter values optimized using the evolutionary algorithm caRamel. A constant first-order transformation rate did not adequately represent the leaching of transformation products, but a variable transformation rate that decreased at the date of an equilibrium soil moisture provided a good representation of the precursor transformation and leaching of transformation products. The results are new evidence that variable environmental parameters such as soil moisture may affect the transformation rate of precursors. The simulation study is a step towards describing the behavior of PFAS precursors through physically-based models.
Myofilament dysfunction in diastolic heart failure
Authors: Aboonabi Anahita , McCauley Mark D. 
Published: October 2023

AbstractDiastolic heart failure (DHF), in which impaired ventricular filling leads to typical heart failure symptoms, represents over 50% of all heart failure cases and is linked with risk factors, including metabolic syndrome, hypertension, diabetes, and aging. A substantial proportion of patients with this disorder maintain normal left ventricular systolic function, as assessed by ejection fraction. Despite the high prevalence of DHF, no effective therapeutic agents are available to treat this condition, partially because the molecular mechanisms of diastolic dysfunction remain poorly understood. As such, by focusing on the underlying molecular and cellular processes contributing to DHF can yield new insights that can represent an exciting new avenue and propose a novel therapeutic approach for DHF treatment. This review discusses new developments from basic and clinical/translational research to highlight current knowledge gaps, help define molecular determinants of diastolic dysfunction, and clarify new targets for treatment.
On the physiological and structural contributors to the overall balance of excitation and inhibition in local cortical networks
Authors: Shirani Farshad , Choi Hannah 
Published: October 2023

AbstractOverall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a network’s baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. Our study based on extensive bifurcation analyses thus reveal the functional optimality and criticality of structural and physiological parameters in establishing the baseline operating state of local cortical networks.
Safe Feedback Motion Planning in Unknown Environments: An Instantaneous Local Control Barrier Function Approach
Authors: Li Cong , Zhang Zengjie , Ahmed Nesrin , Liu Qingchen , Liu Fangzhou , Buss Martin 
Published: October 2023

AbstractMobile robots are desired with resilience to safely interact with prior-unknown environments and finally accomplish given tasks. This paper utilizes instantaneous local sensory data to stimulate the safe feedback motion planning (SFMP) strategy with adaptability to diverse prior-unknown environments without building a global map. This is achieved by the numerical optimization with the constraints, referred to as instantaneous local control barrier functions (IL-CBFs) and goal-driven control Lyapunov functions (GD-CLFs), learned from perceptional signals. In particular, the IL-CBFs reflecting potential collisions and GD-CLFs encoding incrementally discovered subgoals are first online learned from local perceptual data. Then, the learned IL-CBFs are united with GD-CLFs in the context of quadratic programming (QP) to generate the safe feedback motion planning strategy. Rather importantly, an optimization over the admissible control space of IL-CBFs is conducted to enhance the solution feasibility of QP. The SFMP strategy is developed with theoretically guaranteed collision avoidance and convergence to destinations. Numerical simulations are conducted to reveal the effectiveness of the proposed SFMP strategy that drives mobile robots to safely reach the destination incrementally in diverse prior-unknown environments.
Solids containing Si-O-P bonds: is the hydrolytic sol-gel route a suitable synthesis strategy?
Authors: Imparato Claudio , Bifulco Aurelio , Malucelli Giulio , Aronne Antonio 
Published: October 2023

AbstractMaterials based on silicon-phosphorus mixed oxides have traditionally attracted interest in electronics, optics, catalysis, and related fields. The preparation of a solid containing stable Si–O–P linkages is a huge challenge due to their intrinsic instability to hydrolysis in a wet atmosphere. On the other hand, most technological applications of these materials, such as protonic conductive membranes in fuel cells and water-tolerant solid acid catalysts, are related to their interaction with water; consequently, suitable synthesis procedures that positively face this tradeoff are mandatory. Besides the traditional high-temperature techniques, sol-gel synthetic methods represent a viable, low-cost alternative, allowing for the preparation of high-purity materials with a homogeneous distribution of the components at the atomic scale. Si–O–P linkages are easily obtained by nonhydrolytic sol-gel routes, but only in inert and dry atmosphere. Conversely, hydrolytic routes offer opportunities to control the structure of the products in a wide range of processing conditions. The present review aims at providing an overall picture of the research on the sol-gel synthesis of phosphosilicate and related materials and theisr different applications, emphasizing how the interest in these systems is still lively, considering both conventional and emerging applications, such as flame retardance. The incorporation of Si–O–P nanostructures in polymer composites, coatings, and textiles is indeed a promising strategy to improve properties like thermal stability and fire resistance; however, their in-situ synthesis brings about additional difficulties related to the reactivity of the precursors. The perspectives linked with the development of Si–P-based materials are finally outlined.
Graphical Abstract
Utilisation of coal tar naphthalene oil fractions for the synthesis of value-added chemicals: alternative paths to mono- and di-methylnaphthalenes
Authors: Güleç Fatih , Koçkan Aysun , Karaduman Ali 
Published: October 2023

AbstractThis study investigates the utilisation of coal tar naphthalene oil fraction (CTNOF), an economical by-product derived from the iron-steel industry, for the production of valuable chemicals, with a particular focus on methylnaphthalenes (MNs) and dimethylnaphthalenes (DMNs). Of specific interest is 2,6-dimethylnaphthalene (2,6-DMN), a pivotal component in the manufacture of polyethylene naphthalate (PEN). The intricate and costly nature of 2,6-DMN production currently poses challenges to the commercial viability of PEN. This study provides the potential heterogeneous reaction pathways for the synthesis of MNs and DMNs via methylation, disproportionation, and isomerisation of CTNOF. The utilisation of CTNOF was investigated in a laboratory-scale fixed bed reactor operating at atmospheric pressure using a mixture of CTNOF: methanol having 1:5 mass ratio over HBeta zeolite catalyst at a temperature of 400 °C and weight hourly space velocity of 2 h−1. The results reveal the successful methylation of CTNOF over the HBeta zeolite catalyst, initially achieving high naphthalene conversion, particularly into 2-MN. This highlights the potential of CTNOF as an alternative feedstock for the production of value-added chemicals. While naphthalene conversion initially reaches 99 wt% within 0.5 h of operation, it gradually decreases to approximately 10 wt% over extended run times. Notably, coke deposition significantly deactivates the HBeta zeolite catalyst during CTNOF methylation, impacting naphthalene conversion. A substantial proportion of naphthalene compounds convert to methylnaphthalenes early in the reaction, predominantly 2-MN, increasing from 14 wt% (in CTNOF feedstock) to 87 wt%. Among DMNs, selectivity for 2,6-DMN, 2,7-DMN, 1,3-DMN, and 1,7-DMN increases, while other DMN isomers exhibit a sharp decrease in selectivity. The distribution of 2,6-triad DMNs rises from 38 wt% in feedstocks to 52–55 wt% with extended reaction times, attributed to MN conversion to DMNs and potential isomerisation from other DMNs. This study underscores the feasibility of using CTNOF for the direct synthesis of valuable chemicals, specifically 2,6-DMN and 2-MN, through methylation over HBeta zeolite catalysts. However, it emphasises the critical role of residence time in coke deposition and the need for optimisation, particularly regarding this parameter, to ensure the efficiency of this catalytic process.
Students’ experiences of the research-teaching nexus
Authors: Macheridis Nikos , Pihl Annika Fjelkner , Paulsson Alexander , Pihl Håkan 
Published: October 2023

AbstractStudents experience the research-teaching nexus differently as they progress through their first three years of undergraduate study depending on the discipline. The question is if students, within the same discipline, experience the nexus differently depending on the profile of the institution where they study. The present study explored students’ experiences of the research-teaching nexus (RT-nexus) during their undergraduate studies at one research-intensive and one teaching-intensive university. A survey (n = 340) was distributed among business students at two Swedish universities. One finding is that students from both universities reported on a progression in how they experience the nexus, and in learning outcomes. Students also rated what teachers do and what they themselves do higher, than what their peers do. The main gap between students from the two universities was that students from the research-intensive university generally perceived a stronger connection between teaching and research than did students from the teaching-intensive university. They also to a higher extent found that a close connection between research and teaching in their education would be important to them in their future work life, whereas students from the teaching-intensive university were more unsure.
Naturalistic Scene Modelling: Deep Learning with Insights from Biology
Authors: Appiah Kofi , Jin Zhiyong , Shi Lei , Kwok Sze Chai 
Published: October 2023

AbstractAdvances in machine learning coupled with the abundances of training data has facilitated the deep learning era, which has demonstrated its ability and effectiveness in solving complex detection and recognition problems. In general application areas with elements of machine learning have seen exponential growth with promising new and sophisticated solutions to complex learning problems. In computer vision, the challenge related to the detection of known objects in a scene is a thing of the past. With the tremendous increase in detection accuracies, some close to that of human detection, there are several areas still lagging in computer vision and machine learning where improvements may call for more architectural designs. In this paper, we propose a physiologically inspired model for scene understanding that encodes three key components: object location, size and category. Our aim is to develop an energy efficient artificial intelligent model for naturalistic scene understanding capable of deploying on a low power neuromorphic hardware. We have reviewed recent advances in deep learning architecture that have taken inspiration from human or primate learning systems and provided direct to future advancement on deep learning with inspiration from physiological experiments. Upon a review of areas that have benefitted from deep learning, we provide recommendations for enhancing those areas that might have stalled or grinded to a halt with little or no significant improvement.
Potential toxicity of Schisandra chinensis to water environment: acute toxicity tests with water crustaceans
Authors: Valíčková Jana , Zezulka Štěpán , Maršálková Eliška , Kotlík Josef , Maršálek Blahoslav , Opatřilová Radka 
Published: October 2023

AbstractFruits of Schisandrachinensis, an East Asian liana plant, are currently more and more used to produce nutrient supplements that positively affect human health due to the content of various secondary metabolites. On the other hand, these substances because of their bioactivity can cause possible allelopathic or toxic effects concerning other organisms (algae, plants, animals). But the ecotoxicological properties of S. chinensis outside its area of origin have yet to be sufficiently verified. Two crustaceans, Daphnia magna and Thamnocephalus platyurus, were selected as model aquatic organisms to test the potential impact of S. chinensis active compounds on the aquatic environment. Crude water extract from S. chinensis fruits, simulating the natural leakage of active substances in water, was tested in treatments from 0.0045 to 45 mg/L (according to the content of schisandrin as the dominating lignan). Effective concentration (EC50) causing 50% lethal effect for D. magna was established to 0.0448 mg/L after 24 h and 0.0152 mg/L after 48 h. EC50 for T. platyurus reached 0.4572 mg/L after 24 h, i.e. more than ten times higher than for D. magna. This study showed that the potential environmentally relevant concentrations of S. chinensis bioactive compounds could represent a severe risk to aquatic ecosystems.
Generalized global solar radiation forecasting model via cyber-secure deep federated learning
Authors: Moradzadeh Arash , Moayyed Hamed , Mohammadi-Ivatloo Behnam , Aguiar António Pedro , Anvari-Moghaddam Amjad , Abdul-Malek Zulkurnain 
Published: October 2023

AbstractRecently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data.
The determinants of compensation report transparency: manager incentives and firm characteristics
Authors: Pfeiffer Iris , Jarchow Svenja 
Published: October 2023

AbstractThis paper analyses the determinants of disclosure in compensation reports. Using a hand-collected dataset of 429 observations we assess which compensation, governance and ownership variables influence the quality of disclosure in compensation reports from 2006 to 2014 in a German setting. Managers have incentives to conceal compensation disclosure leading to a conflict of interest with shareholders. The overall findings suggest that opportunistic reporting incentives, as proposed by the managerial power theory, cannot explain a lack of more detailed disclosure. Managers rather avoid these disclosures because they would require additional effort. The empirical analyses reveal four major disclosure determinants: company size, age, family members in the boards and verticality. Other variables such as proprietary costs, governance variables and performance show no or no stable influence. The absence of disclosure is therefore a confluence of company resources (company size and forecasts increase disclosure), owner interests (family members in the board decreases disclosure), and concerns about social equity infringement (higher pay inequity leads to lower disclosure).