bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards efficient waste management: identification of waste flow chains in micro-regional detail through monitored data
Authors: Němcová Lucie , Pluskal Jaroslav , Šomplák Radovan , Kůdela Jakub 
Published: June 2024

AbstractCountries around the world are gradually implementing the transition to a circular economy in waste management. This effort should be initiated already at the waste producers. It is necessary to plan and monitor waste management in as much detail as possible, i.e. at the level of micro-regions. At present, only indicators at the national level are analysed, as more detailed data at the micro-regional level are often not available or are burdened with significant errors and inconsistencies. The calculation of waste management indicators for micro-regions will allow to identify the potential for increasing material or energy recovery and to plan the necessary infrastructure directly to these locations instead of blanket and often ineffective legislative actions. This paper presents an approach for determining the producer-treatment linkage, i.e., provides information about each produced waste, where it was treated, and in what way. Such information is often not available based on historical waste management data as there are repeated waste transfers and often aggregated within a micro-region. The network flow approach is based on an iterative procedure combining a simulation with multi-criteria optimization. The chosen criteria replicate expert estimates in investigated issue such as minimum flow splitting, and minimum transfer micro-regions. A data reconciliation is performed where the deviation from all simulations is minimized, given that the capacity constraints of nodes and arcs resulting from the database must be satisfied. The approach is tested on a generated sample task to evaluate the precision and time complexity of the developed tool. Finally, the presented approach is applied to address a case study in the Czech Republic, within which it is possible to identify treatment location and methods for waste from individual regions.
Factors associated with self-rated health in people with late-stage parkinson’s and cognitive impairment
Authors: Pigott Jennifer S. , Armstrong Megan , Davies Nathan , Davis Daniel , Bloem Bastiaan R. , Lorenzl Stefan , Meissner Wassilios G. , Odin Per , Ferreira Joaquim J. , Dodel Richard , Schrag Anette 
Published: June 2024

Abstract
Purpose
To investigate the contributors to self-rated health in people with late-stage Parkinson’s disease (PD) and cognitive impairment.

Methods
A secondary analysis of baseline data from the international Care of Late-Stage Parkinsonism (CLaSP) cohort study was conducted. Participants with PD and either dementia or mild cognitive impairment or MMSE < 24/30 in the absence of major depression were included if they had completed the EQ-5D-3L assessment (n = 277). Factors associated with self-rated health (EQ-5D-3L Index and Visual Analogue Scale) were investigated through multivariable linear regression.

Results
More severe PD (motor and non-motor) was associated with worse self-rated health. The EQ-5D-3L dimensions of Mobility, Self-Care and Usual Activities were almost universally affected; the latter two particularly severely. Being unable to perform usual activities or having moderate to extreme anxiety or depression were significantly associated with EQ-5D-3L Visual Analogue Scale, suggesting these are particularly valued. Worse motor impairment and function and the non-motor symptom domains of mood, perception, sexual function, and miscellaneous (e.g., pain) were associated with worse self-rated health, whereas greater burden of gastrointestinal symptoms was associated with better self-rated health in multivariate analysis. Better self-rated health was associated with recent PD nurse consultation, and higher doses of dopaminergic medication.

Conclusion
Improvement of activities of daily living, mood and anxiety should be prioritised in clinical practice, with consideration of perception and sexual function in this population. Recent nurse consultations and higher antiparkinsonian doses are associated with better self-rated health, suggesting there is no room for a therapeutic nihilism in this population of people within a complex phase of PD.
Residues of veterinary antibiotics in solid natural and organic fertilizers—method development and sample analysis
Authors: Patyra Ewelina , Osiński Zbigniew , Kwiatek Krzysztof 
Published: June 2024

AbstractLivestock excrement is used around the world as natural fertilizers or, after processing, as organic fertilizers for crops and grasslands. But due to the presence of veterinary antibiotics in them, they may pose a threat not only to the natural environment, mainly to soil microorganisms, but also to human and animal health. This article describes a method for detecting 21 antibacterial substances in solid natural and organic fertilizers. Antibiotics from fertilizers were extracted with a mixture of acetonitrile and McIlvain-Na2EDTA buffer, twice. The extracts were purified by solid phase extraction technique on Strata-X cartridges and analyzed with the use UHPLC-MS/MS technique. The method was validated in accordance with EU Commission Implementing Regulation 2021/808; the obtained recovery ranged from 93.6 to 116.6% (depending on the analytes), and the linearity ranged from 50 to 1000 µg/kg. The developed method was used to analyze 73 samples of solid natural and organic fertilizers. Our research has shown that over 38% of natural fertilizers were contaminated with antibiotics, mainly doxycycline in concentrations reaching several dozen milligrams per kilogram of fertilizers. In the case of processed organic fertilizers, the presence of antibiotics was found in over 37% of the analyzed samples. The research results showed that the developed and validated analytical method may be useful for assessing the presence and content of antibacterial substances in solid natural and organic fertilizers.
Home-based monitoring of cerebral oxygenation in response to postural changes using near-infrared spectroscopy
Authors: Klop Marjolein , Claassen Jurgen A. H. R. , Floor-Westerdijk Marianne J. , van Wezel Richard J. A. , Maier Andrea B. , Meskers Carel G. M. 
Published: June 2024

AbstractOrthostatic hypotension (OH) is prevalent in older adults and can cause falls and hospitalization. Diagnostic intermittent blood pressure (BP) measurements are only a proxy for cerebral perfusion and do not reflect daily-life BP fluctuations. Near-infrared spectroscopy (NIRS)-measured cerebral oxygenation potentially overcomes these drawbacks. This study aimed to determine feasibility, face validity, and reliability of NIRS in the home environment. Ten participants with OH (2 female, mean age 77, SD 3.7) and 11 without OH (5 female, mean age 78, SD 6.7) wore a NIRS sensor at home on two different days for 10–11 h per day. Preceded by a laboratory-situated test, cerebral oxygenation was measured during three standardized supine-stand tests per day and during unsupervised daily life activities. Data availability, quality, and user experience were assessed (feasibility), as well as differences in posture-related oxygenation responses between participants with and without OH and between symptomatic (dizziness, light-headedness, blurred vision) and asymptomatic postural changes (face validity). Reliability was assessed through repetitive supine-stand tests. Up to 80% of the standardized home-based supine-stand tests could be analyzed. Oxygenation recovery values were lower for participants with OH (p = 0 .03–0.15); in those with OH, oxygenation showed a deeper maximum drop for symptomatic than asymptomatic postural changes (p = 0.04). Intra-class correlation coefficients varied from 0.07 to 0.40, with no consistent differences over measurements. This proof-of-concept study shows feasibility and face validity of at-home oxygenation monitoring using NIRS, confirming its potential value for diagnosis and monitoring in OH and OH-related symptoms. Further data are needed for conclusions about reliability.
Methodology for social life cycle impact assessment enhanced with gender aspects applied to electric vehicle Li-ion batteries
Authors: Sánchez Anna , Benveniste Gabriela , Ferreira Víctor José , Bulfaro Isabella , Igualada Lucia , Corchero Cristina 
Published: June 2024

Abstract
Purpose
The objective of this study is to assess the potential social risks and benefits of EV Li-ion batteries by combining the S-LCA framework with gender aspects throughout all the life cycle phases of the battery.

Methods
The social life cycle assessment (S-LCA) methodology has been applied to determine social concerns about a lithium-ion (Li-ion) battery pack design for electric vehicles (EVs) from cradle to grave. A questionnaire based on UNEP S-LCA guidelines and literature case studies of S-LCA on batteries and the energy industry has been prepared for each of the stakeholder categories and distributed among experts in the Li-ion battery sector (more than 21 industrial and academic experts representing the whole battery value chain). Furthermore, the social assessment also includes updated gender aspects to provide wider and more comprehensive social impacts to ensure a gender-neutral approach.

Results and discussion
The Li-ion battery presents a positive social impact in all the stakeholder categories evaluated, where the worker category has the best social performance driven by the highest score (scores range from 0 to 1, where 0 is the worst social performance and 1 is the best) in 13 indicators out of 23. Furthermore, local community, consumers, and society categories have a good social performance attributed to the absence of involuntary resettlement of individuals, the possibility of the product being reused for other purposes and technology accessible and affordable to developing countries, among others. Four out of seven indicators to evaluate the gender aspects and impacts have the highest score, demonstrating a commitment to fostering an inclusive and equitable work environment. The end-of-life phase presents a positive social performance with a score of 0.77 out of 1 attributed to the presence of infrastructure to dispose of product components other than landfill and incineration responsibly, the possibility of the product to be reused for other purposes and clear information provided to consumers on end-of-life options, among others.

Conclusions
The study presents generally good social impact and gender neutrality on the battery pack design. It gives an insight into the actual status of Li-ion battery social and gender impacts, and the results can be useful to policymakers to design and implement strategies for the welfare of various stakeholders.
Rising Tides of Knowledge: Exploring China’s Higher Education Landscape and Human Capital Growth
Authors: Xiao Shumei , Sheng Jun , Zhang Guangtao 
Published: June 2024

AbstractThis study examines the status of human capital in China’s higher education system by analyzing the quantity of higher education institutions as a metric. The results show a general enhancement in human capital in all regions, along with an optimized structure. This is demonstrated by a decline in the primary education demographic, a consistent growth in secondary education, and a rapid surge in the higher education demographic. The percentage of junior college students has remained steady, whereas the ratios of undergraduate and graduate students have been consistently increasing. The Malmquist index method is used to analyze changes in total factor productivity (TFP) among provinces and regions by breaking it down into technical level and efficiency changes. From 2005 to 2010, there was a general increase in total factor productivity (TFP) and technical proficiency, although technical efficiency showed only slight enhancement. TFP and technical level growth decelerated from 2011 to 2020, while technical efficiency stayed above 1. These findings indicate that China’s total factor productivity (TFP) growth is mainly driven by technological advancements, with minimal improvements in technical efficiency.
A four-season exploration of surface water quality and trophic status in the highly dynamic waters of Karun-4 Dam Lake, SW Iran
Authors: Cheraghpour-Ahmadmahmoodi Nader , Saadat Mohsen , Zamani-Ahmadmahmoodi Rasool , Avokh Avid 
Published: June 2024

AbstractThis study explores seasonal fluctuations in water quality and phytoplankton communities within Karun-4 Dam Lake, the largest double-arch dam in the Middle East situated in Iran. Employing a systematic approach, 26 sampling stations were strategically selected to collect surface water samples at a depth of 50 cm during the midpoints of each season throughout 2019. Significant seasonal variations in water quality parameters and phytoplankton composition were observed. Predominant species included Chrysophyceae (38%) and Bacillariophyceae (32%), indicative of nutrient-rich conditions, particularly during spring and summer, as evidenced by the eutrophic state (Carlson Trophic State Index: 59.43 and 53.96, respectively). Summer exhibited the highest diversity (Shannon–Wiener Index = 2.27) and lowest evenness (Pielou’s Evenness Index = 0.21). PCA and CCA analyses revealed season-specific preferences for nutrients and ions among phytoplankton species. Water temperature emerged as a crucial factor in spring and summer, while environments with elevated bicarbonate and alkalinity levels were less favorable during winter and fall. This study provides essential insights into Karun-4 Lake's dynamic ecological conditions, underscoring the necessity for ongoing monitoring to discern long-term trends and anthropogenic impacts for effective ecosystem management.
Water Quality Assessment with Artificial Neural Network Models: Performance Comparison Between SMN, MLP and PS-ANN Methodologies
Authors: Isık Hakan , Akkan Tamer 
Published: June 2024

AbstractIdentifying and measuring potential sources of pollution is essential for water management and pollution control. Using a range of artificial intelligence models to analyze water quality (WQ) is one of the most effective techniques for estimating water quality index (WQI). In this context, machine learning–based models are introduced to predict the WQ factors of Southeastern Black Sea Basin. The data comprising monthly samples of different WQ factors were collected for 12 months at eight locations of the Türkiye region in Southeastern Black Sea. The traditional evaluation with WQI of surface water was calculated as average (i.e. good WQ). Single multiplicative neuron (SMN) model, multilayer perceptron (MLP) and pi-sigma artificial neural networks (PS-ANNs) were used to predict WQI, and the accuracy of the proposed algorithms were compared. SMN model and PS-ANNs were used for WQ prediction modeling for the first time in the literature. According to the results obtained from the proposed ANN models, it was found to provide a highly reliable modeling approach that allows capturing the nonlinear structure of complex time series and thus to generate more accurate predictions. The results of the analyses demonstrate the applicability of the proposed pi-sigma model instead of using other computational methods to predict WQ both in particular and other surface water resources in general.
Refining the integral Jensen inequality for finite signed measures using majorization
Authors: Horváth László 
Published: June 2024

AbstractAmong inequalities that use the concept of convexity, Jensen-type inequalities and majorization-type inequalities are significant and fundamental. An important and widely researched area in the study of Jensen-type inequalities is the refinement of such inequalities. In this paper, we provide a general method for refining the integral Jensen inequality for finite signed measures using integral majorization inequalities. Under the conditions considered in the paper, the results are unique, and even for measures, they give a new approach. We also provide interesting specific refinements, some of which relate to Jensen–Steffensen’s inequality. A new and extended version of this inequality is also obtained in the paper.
Multi-Segment Foot Kinematics during Gait in Adults with Asymptomatic and Symptomatic Flatfoot
Authors: Tsai Wei-Chi , Chen Zong-Rong , Hsu Jui-Tse , Song Chen-Yi 
Published: June 2024

Abstract
Purpose
To investigate the differences in foot kinematics during gait between adults with asymptomatic and symptomatic flatfoot.

Methods
The study included 10 participants (six males and four females, aged 25.7 ± 6.5 years) with symptomatic flatfoot and 10 participants (eight males and two females, aged 21.2 ± 1.0 years) with asymptomatic flatfoot. Multi-segment foot kinematics were captured during barefoot gait analysis using a 3D software. Angles were calculated for the calcaneus with respect to the shank (Sha-Cal), the midfoot with respect to the calcaneus (Cal-Mid), and the metatarsus with respect to the midfoot (Mid-Met) during the stance phase.

Results
Some differences were noted between medium-to-large effect sizes. The symptomatic group had a decreased Mid-Met dorsiflexion angle at the initial contact to 50% of the stance phase compared with the asymptomatic group. The symptomatic group also showed decreased Mid-Met abduction at initial contact, larger Sha-Cal eversion angles at 10% of the stance phase, and larger Cal-Mid eversion angles at 50% and 70% of the stance phase compared to the asymptomatic group. The symptomatic group also had a larger peak Sha-Cal eversion angle than the asymptomatic group.

Conclusion
Adults with symptomatic flatfoot exhibit significant differences in foot kinematics towards decreased forefoot dorsiflexion at initial contact to mid-stance, decreased forefoot abduction at initial contact, and increased rearfoot eversion during the stance phase compared with those with asymptomatic flatfoot during gait. Pain may impair intersegmental motion.
The Care of the Self and the Meaningful Four-Day Workweek
Authors: Pedersen Michael , Muhr Sara Louise , Dunne Stephen 
Published: June 2024

AbstractThose who find their work meaningful often need to be more committed. Over-commitment, in turn, frequently results in stress, personal conflicts, and burnout. Such over-commitment, in other words, leads to employees needing to take more care of themselves. This paper considers the prospects for meaningful self-care in the context of working time reduction. For this, we consider the case of the four-day workweek, asking employees of such organizations to explain how they make meaning out of their newly found time off. Conceptually, we rely upon the work of Michel Foucault, particularly his analysis of the care of the self. On its basis, we coded five self-care practices: (1) rest and recuperation, (2) professional and personal development, (3) domestic work, (4) balancing work, and (5) additional work. We conclude by highlighting the theoretical and practical implications of work reduction for the analytical, ethical, and practical pursuit of meaningful work.
Effect of high laser scanning speed on microstructure and mechanical properties of additively manufactured 316L
Authors: Berghaus Michael , Florian Steffen , Solanki Keyur , Zinn Carolin , Wang Hongcai , Butz Benjamin , Apmann Hilmar , von Hehl Axel 
Published: June 2024

Abstract
Due to its ease of processing, the stainless steel 316L is a widely used material for the laser powder bed fusion (PBF-LB/M) process. Compared to other additive manufacturing technologies PBF-LB/M has a lot of advantages such as design freedom and high resolution of details. However, PBF-LB/M also has some disadvantages, such as a reduced build-up rate. In general, 316L provides a wide range of parameter settings used for PBF-LB/M. In this study, the manufacturing limits were approached allowing a maximum build-up rate along with a high relative density > 99% without compromising the required mechanical properties. Microstructure analyses as well as tensile tests were performed to validate this approach. This article also provides insights on defects and relative density for scanning speed above 3000 mm/s. Furthermore, it was shown that the scanning speed has a major influence on the grain size and on the texture of the specimens. For the first time, the relative density, microstructure and mechanical properties of additively manufactured 316L were determined in relation to each other for high scanning speed. A set of parameters has been found that works best with a laser power of 285 W and a scanning speed of 1250 mm/s which results in a specimen relative density of 99.2%, a yield strength of 425 MPa, a tensile strength of 586 MPa and a build-up rate of 4.64 mm3/s. The findings can be further used to enhance the mechanical properties of PBF-LB/M 316L in terms of high build-up rates.
Context discovery for anomaly detection
Authors: Calikus Ece , Nowaczyk Slawomir , Dikmen Onur 
Published: June 2024

Abstract
Contextual anomaly detection aims to identify objects that are anomalous only within specific contexts, while appearing normal otherwise. However, most existing methods are limited to a single context defined by user-specified features. In practice, identifying the right context is not trivial, even for domain experts. Moreover, for high-dimensional data, the notion of meaningful contexts that can unveil anomalies becomes substantially more complex. For instance, multiple useful contexts can often capture different phenomena. In this work, we introduce Con
Quest, a new unsupervised contextual anomaly detection approach that automatically discovers and incorporates multiple contexts useful for detecting and interpreting anomalies. Through experiments on 25 datasets, we show that Con
Quest outperforms various state-of-the-art methods. We also demonstrate its benefits in terms of increased direct interpretability.
Sub-ppm of toxic gases detection on Ag-doped WO3 nanosensor
Authors: Al-Sarraj Ahmad , Al Soubaihi Rola , Saoud Khaled M. , Ayesh Ahmad I. , Bermak Amine 
Published: June 2024

AbstractRecent investigations reveal an increasing interest in detecting toxic substances that, if present in the environment at low concentrations, can cause serious health conditions. Moreover, some of these toxic substances can be found as gases in human breath due to disease. Nanomaterial-based sensors have emerged as a crucial area of research for this purpose. This study focuses on silver-doped tungsten oxide nanoparticles (Ag/WO3) as nanosensors capable of detecting trace amounts of toxic gases at room temperature. These gases include Hydrogen sulfide (H2S), as well as other toxic gases like acetone, Ammonia (NH3), Ethanol (C2H5OH), and Acetone ((CH3)2CO). The gas-sensing behavior of Ag/WO3 nanosensors was investigated at extremely low concentrations of these gases. X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM) were employed to analyze the material's structure and chemical state. The sensor exhibited sensitivity to gas concentrations as low as 0.25 ppm, with a robust response of up to 80%. Notably, it showed the highest selectivity toward H2S gas compared to ethanol, ammonia, and acetone. The sensor's performance was also evaluated under varying temperatures and humid conditions, demonstrating reliable responses at room temperature. Heron, the synthesis of Ag/WO3 sensors with excellent sensitivity at extremely low gas concentrations is reported, making this sensor a promising tool for detecting toxic gases that threaten human health. Furthermore, the potential implications of this research on human health are significant, as detecting low concentrations of these gases can be a potential tool for the diagnostic process as well as health and environmental monitoring.