bluuebunny/crossref_metadata_2025 huggingface dataset:
The HIV Empowering Adults’ Decisions to Share: UK/Uganda (HEADS-UP) Study—A Randomised Feasibility Trial of an HIV Disclosure Intervention for Young Adults with Perinatally Acquired HIV
Authors: Evangeli Michael , Gnan Georgina , Musiime Victor , Fidler Sarah , Seeley Janet , Frize Graham , Uwizera Annette , Lisi Matteo , Foster Caroline
Published: March 2024

Young adults with perinatally acquired HIV (PAH) face numerous challenges, including antiretroviral therapy (ART) adherence, managing onward HIV transmission risks and maintaining wellbeing. Sharing one’s HIV status with others (onward HIV disclosure) may assist with these challenges but this is difficult. We developed and tested the feasibility of an intervention to help HIV status sharing decision-making for young adults with PAH. The study used a randomised parallel group feasibility design with 18–25-year-olds in Uganda and 18–29 year-olds in the UK. Participants were randomly assigned to intervention or standard of care (SOC) condition. The intervention consisted of four sessions (3 group, 1 individual) with follow-up support, delivered in person in Uganda and remotely in the UK. Assessments were carried out at: Pre-intervention /baseline; Post-intervention (intervention group only); Six-month follow-up. 142 participants were recruited (94 Uganda, 48 UK; 89 female, 53 male). At six-month follow-up, 92/94 (98%) participants were retained in Uganda, 25/48 (52%) in the UK. Multivariate analysis of combined data from both countries, showed a non-significant effect of intervention condition on HIV disclosure cognitions and affect (p = 0.08) and HIV disclosure intention (p = 0.09). There was a significant intervention effect on well-being (p = 0.005). This study addressed important gaps in understanding acceptable and feasible ways of delivering HIV status sharing support for young people living with PAH across two very different settings. The intervention was acceptable in both countries and feasible in Uganda. In the UK, retention may have been affected by its remote delivery.

Trial registration: ISRCTN Registry, ISRCTN31852047, Registered on 21 January 2019.
Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market
Authors: Ahelegbey Daniel Felix , Casarin Roberto , Fianu Emmanuel Senyo , Grossi Luigi
Published: March 2024

Operating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.
Learning evolving relations for multivariate time series forecasting
Authors: Nguyen-Thai Binh , Le Vuong , Tieu Ngoc-Dung T. , Tran Truyen , Venkatesh Svetha , Ramzan Naeem
Published: March 2024

Multivariate time series forecasting is essential in various fields, including healthcare and traffic management, but it is a challenging task due to the strong dynamics in both intra-channel relations (temporal patterns within individual variables) and inter-channel relations (the relationships between variables), which can evolve over time with abrupt changes. This paper proposes ERAN (Evolving Relational Attention Network), a framework for multivariate time series forecasting, that is capable to capture such dynamics of these relations. On the one hand, ERAN represents inter-channel relations with a graph which evolves over time, modeled using a recurrent neural network. On the other hand, ERAN represents the intra-channel relations using a temporal attentional convolution, which captures the local temporal dependencies adaptively with the input data. The elvoving graph structure and the temporal attentional convolution are intergrated in a unified model to capture both types of relations. The model is experimented on a large number of real-life datasets including traffic flows, energy consumption, and COVID-19 transmission data. The experimental results show a significant improvement over the state-of-the-art methods in multivariate time series forecasting particularly for non-stationary data.
A comparative user study of human predictions in algorithm-supported recidivism risk assessment
Authors: Portela Manuel , Castillo Carlos , Tolan Songül , Karimi-Haghighi Marzieh , Pueyo Antonio Andres
Published: March 2024

In this paper, we study the effects of using an algorithm-based risk assessment instrument (RAI) to support the prediction of risk of violent recidivism upon release. The instrument we used is a machine learning version of RiskCanvi used by the Justice Department of Catalonia, Spain. It was hypothesized that people can improve their performance on defining the risk of recidivism when assisted with a RAI. Also, that professionals can perform better than non-experts on the domain. Participants had to predict whether a person who has been released from prison will commit a new crime leading to re-incarceration, within the next two years. This user study is done with (1) general participants from diverse backgrounds recruited through a crowdsourcing platform, (2) targeted participants who are students and practitioners of data science, criminology, or social work and professionals who work with RisCanvi. We also run focus groups with participants of the targeted study, including people who use RisCanvi in a professional capacity, to interpret the quantitative results. Among other findings, we observe that algorithmic support systematically leads to more accurate predictions from all participants, but that statistically significant gains are only seen in the performance of targeted participants with respect to that of crowdsourced participants. Among other comments, professional participants indicate that they would not foresee using a fully-automated system in criminal risk assessment, but do consider it valuable for training, standardization, and to fine-tune or double-check their predictions on particularly difficult cases. We found that the revised prediction by using a RAI increases the performance of all groups, while professionals show a better performance in general. And, a RAI can be considered for extending professional capacities and skills along their careers.
The potential of algae as a source of cellulose and its derivatives for biomedical applications
Authors: Machado Bárbara , Costa Sofia M. , Costa Isabel , Fangueiro Raul , Ferreira Diana P.
Published: March 2024

Cellulose has been intensively investigated for biomedical applications, because of its excellent properties, like biodegradability, biocompatibility, abundant availability of renewable resources and cost-effectiveness. Among all sources of cellulose, marine macroalgae or seaweeds, are acquiring tremendous attention, due to their high availability around the world. Moreover, the atypical proliferation of some exotic macroalgae species represents a serious problem to the ecosystems, since their accumulation threatens native oceanic species and resources worldwide. Several studies already reported the successfully extraction of cellulose and its derivatives from brown, green and red macroalgae. The extracted cellulose properties vary according to the type of algae, their maturity and the used extraction methods. This review will cover the main methods used to extract cellulose from algae, focusing on more sustainable ones, as well as its further processing into the various cellulose derivatives. Electrospun nanofibers have revealed great potential for biomedical applications, such as delivery of therapeutic agents, tissue engineering, wound dressings and enzyme immobilization. Pure cellulose presents some drawbacks, such as limited solubility in organic solvents and its inability to fuse due to inter and intra-molecular hydrogen bonding. To overcome these limitations, cellulose derivatives, which includes microcrystalline cellulose, cellulose nanocrystals, cellulose nanofibers, cellulose acetate and carboxymethyl cellulose, have been extensively studied to generate electrospun fibers. Therefore, this review aims to explore the marine seaweeds as a promising source of cellulose and its derivatives, the extraction methodologies of these compounds, as well as to demonstrate the potential of nanofibers developed by electrospinning with algae-based cellulose for biomedical applications.
Humanoid robot-empowered language learning based on self-determination theory
Authors: Fung Ka Yan , Lee Lik Hang , Sin Kuen Fung , Song Shenghui , Qu Huamin
Published: March 2024

With the ability to provide feedback and assistance, humanoid educational robots have been proven effective in assisting students to overcome learning challenges and enhancing individual learning outcomes. However, the strength of humanoid robots in promoting social and emotional skills has not been well investigated. Socially supportive behaviour can contribute more to students’ learning engagement than knowledge transfer. This study focuses on the design of humanoid robots to engage students from functional and affective perspectives. To this end, a pilot test is conducted on 64 primary school students in Hong Kong, comprising a control group (N = 33) and an experimental group (N = 31). Questionnaires, observations, and language proficiency test are done to ensure the validity of the findings. The results show that the experimental group, which learned with the humanoid robots, significantly improved their behavioural engagement (+ 13.24%), emotional engagement (+ 13.14%), cognitive engagement (21.56%), and intrinsic motivation (12.07%). The impact of utilizing humanoid robots in education is elucidated through the lens of the self-determination theory (SDT), which pertains to students’ learning motivation and engagement. These findings can provide valuable insights into the development of more captivating humanoid robots for extensive learning purposes.
Understanding vessel noise across a network of marine protected areas
Authors: McKenna Megan F , Rowell Timothy J , Margolina Tetyana , Baumann-Pickering Simone , Solsona-Berga Alba , Adams Jeffrey D , Joseph John , Kim Ella B , Kok Annebelle CM , Kügler Anke , Lammers Marc O , Merkens Karlina , Reeves Lindsey Peavey , Southall Brandon L , Stimpert Alison K , Barkowski Jack , Thompson Michael A , Van Parijs Sofie , Wall Carrie C , Zang Eden J , Hatch Leila T
Published: March 2024

Protected areas are typically managed as a network of sites exposed to varying anthropogenic conditions. Managing these networks benefits from monitoring of conditions across sites to help prioritize coordinated efforts. Monitoring marine vessel activity and related underwater radiated noise impacts across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of marine species. Here, we use underwater acoustic detections of vessels to quantify different characteristics of vessel noise at 25 locations within eight marine sanctuaries including the Hawaiian Archipelago and the U.S. east and west coasts. Vessel noise metrics, including temporal presence and sound levels, were paired with Automatic Identification System (AIS) vessel tracking data to derive a suite of robust vessel noise indicators for use across the network of marine protected areas. Network-wide comparisons revealed a spectrum of vessel noise conditions that closely matched AIS vessel traffic composition. Shifts in vessel noise were correlated with the decrease in vessel activity early in the COVID-19 pandemic, and vessel speed reduction management initiatives. Improving our understanding of vessel noise conditions in these protected areas can help direct opportunities for reducing vessel noise, such as establishing and maintaining noise-free periods, enhancing port efficiency, engaging with regional and international vessel quieting initiatives, and leveraging co-benefits of management actions for reducing ocean noise.
Hypocrisy and Epistemic Injustice
Authors: Carey Brian
Published: March 2024

In this article I argue that we should understand some forms of hypocritical behaviour in terms of epistemic injustice; a type of injustice in which a person is wronged in their capacity as a knower. If each of us has an interest in knowing what morality requires of us, this can be undermined when hypocritical behaviour distorts our perception of the moral landscape by misrepresenting the demandingness of putative moral obligations. This suggests that a complete theory of the wrongness of hypocrisy must account for hypocrisy as epistemic injustice.
The influence of habitat heterogeneity on Nematoda communities in Posidonia oceanica meadows
Authors: García-Cobo Marta , González-Casarrubios Alberto , García-Gómez Guillermo , Pardos Fernando , Sánchez Nuria
Published: March 2024

Nematodes typically comprise the most abundant phylum in meiofaunal communities. We aim to characterize the specific ecological conditions that influence Nematoda communities inPosidonia oceanicagrasslands focusing on three habitats: leaves, matte, and unvegetated adjacent sediment. We hypothesized that the constant flux of nutrients under the canopy would result in a higher concentration of metazoans compared to the unvegetated sediment outside; however, the hypothesis was not confirmed by the obtained results. The habitat heterogeneity played an important role in shaping the community, yielding a greater richness in the matte. The resource specificity of the leaves (microepiphytes) was also expected to shape the community by favoring nematodes capable of exploiting this resource (epistratum feeders with scraping-like structures), such as the family Chromadoridae that dominated this habitat. In addition, the high light exposure of the leaves explains the higher concentration of ocellated nematodes in this habitat. Organic carbon and nitrogen contents had an impact on the Nematoda community composition, meanwhile average grain size only affected the abundance of nematodes with elongated/filiform tails.
Exploring the Effects of Technology-Related Informal Mathematics Learning Activities: A Structural Equation Modeling Analysis
Authors: Jiang Haozhe , Chugh Ritesh , Turnbull Darren , Wang Xiaoqin , Chen Suting
Published: March 2024

Despite the burgeoning adoption of informal learning in people’s daily lives, the actual effects of informal learning activities, especially technology-related informal learning activities, are much less reported than those of formal learning. Furthermore, there is a notable lack of research on the effects of technology-related informal mathematics learning activities (TRLA). This study aims to propose and validate a new model which illustrates the effects of TRLA on four constructs: mathematics self-efficacy (MSE), mathematics interest (MI), self-regulation in mathematics learning (SR), and teacher-student relationship (TSR). Adopting a quantitative cross-sectional survey approach, 460 students were investigated. The data were analyzed employing two-step structural equation modeling. Our findings demonstrate the direct effects of TRLA on MI and SR as well as the indirect effects on MI, MSE, and TSR. This study advances the understanding of technology-enhanced informal learning, which is an emerging perspective of technology-enhanced learning.
Exploring Family Obligation as a Buffer Between Parental Differential Treatment and Sibling Hostility
Authors: McClellan Lucy S. , Fry Cassidy M. , Telzer Eva H. , Rogers Christy R.
Published: March 2024

High levels of between-sibling parental differential treatment (PDT) have been associated with several negative outomes, including externalizing behavior, emotional maladjustment, and depressive symptoms, as well as with hostility in sibling relationships. In contrast, high levels of family obligation have been associated with positive adolescent adjustment and family dynamics. Given the substantial risks associated with PDT and the benefits of family obligation for emotional health and family relations, we investigated family obligation as a potential buffer against the negative effects of PDT on sibling relationships. We hypothesized that 1) younger and older siblings experiencing higher levels of PDT would demonstrate greater sibling hostility, and 2) family obligation would buffer against these associations. Adolescent younger siblings (Mage = 12.1; 24 females) and their adolescent older siblings (Mage = 14.5; 21 females) participated in a collaborative problem-solving task, which was coded for directional expressions of hostility. Siblings also independently completed questionnaires on PDT and family obligation. Greater PDT was associated with more hostility expressed from younger sibling to older sibling, and a greater sense of family obligation buffered against this association. Greater PDT was also associated with increased hostility from older sibling to younger sibling, but no significant interaction effect was found with family obligation. Findings highlight the potential of family obligation to improve sibling relationships in the context of PDT and can inform future sibling research and family intervention work; the import of these findings is limited due to the homogeneous nature of the sample and we recommend inclusion of more diverse populations.
Product of Gaussian Mixture Diffusion Models
Authors: Zach Martin , Kobler Erich , Chambolle Antonin , Pock Thomas
Published: March 2024

In this work, we tackle the problem of estimating the density$$ f_X $$fXof a random variable$$ X $$Xby successive smoothing, such that the smoothed random variable$$ Y $$Yfulfills the diffusion partial differential equation$$ (\partial _t - \Delta _1)f_Y(\,\cdot \,, t) = 0 $$(∂t-Δ1)fY(·,t)=0with initial condition$$ f_Y(\,\cdot \,, 0) = f_X $$fY(·,0)=fX. We propose a product-of-experts-type model utilizing Gaussian mixture experts and study configurations that admit an analytic expression for$$ f_Y (\,\cdot \,, t) $$fY(·,t). In particular, with a focus on image processing, we derive conditions for models acting on filter, wavelet, and shearlet responses. Our construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show numerical results for image denoising where our models are competitive while being tractable, interpretable, and having only a small number of learnable parameters. As a by-product, our models can be used for reliable noise level estimation, allowing blind denoising of images corrupted by heteroscedastic noise.
Characteristics of Ce-doped phosphate inorganic glass scintillators for gamma- and X-ray detection
Authors: Nakabayashi Yusuke , Fujimoto Yutaka , Koshimizu Masanori , Kawamoto Hiroki , Asai Keisuke
Published: March 2024

We report on the structural, photoluminescence (PL), and scintillation properties of Ce-doped P2O5–Al2O3–Cs2O–BaO glass scintillators having different Ce concentrations (1.96, 3.85, 7.41, 10.7, and 13.8 mol%) for gamma- and X-ray detection. The highest density (3.57 g/cm3) was achieved with Ce concentration of 13.8 mol%. The PL emission and X-ray-induced radioluminescence spectra showed emission peaks corresponding to the allowed transition from the Ce3+ 5d excited state to the 4f ground states. The highest scintillation light yields (approximately 2200 photons/MeV) were achieved with Ce concentrations of 7.41, 10.7, and 13.8 mol%. These light yields are higher than those previously reported for glasses containing heavy elements, such as Eu-doped SiO2–Al2O3–HfO2 (630 photons/MeV), Ce-doped P2O5–Li2O–GdI3–Al2O3–CaCO3 (1600 photons/MeV), and Ce-doped SiO2–Al2O3–BaF2–Gd2O3 (2050 photons/MeV). For all glasses, short scintillation decay times (28.5–34.0 ns) corresponding to the transition from the Ce3+ 5d excited state to the 4f ground states were observed. These results indicate that the fabricated glass with an optimal Ce concentration of 13.8 mol% is suitable for gamma- and X-ray detection.
A Preliminary Trial of an Early Surveillance Program for Autism and Developmental Delays within General Practices
Authors: Karlov Lisa , Masi Anne , Diaz Antonia Mendoza , Khan Feroza , Winata Teresa , Gilbert Melissa , Nair Radhika , Dissanayake Cheryl , Barbaro Josephine , Eapen Valsamma ,   Autism Surveillance Study Team, Abdullahi Ifrah , Descallar Joseph , Eastwood John , Hasan Iqbal , Jalaludin Bin , Kohlhoff Jane , Liaw S. T. , Lingam Raghu , Ong Natalie , Tam Chun Wah Michael , Unwin Katy , Williams Katrina , Woolfenden Sue
Published: March 2024

There are inequities in engagement with established early childhood developmental surveillance programs, eclipsing disadvantaged families. The current study sought to address this by dovetailing developmental surveillance with immunization visits and other opportunistic contacts with children at general practices). While 53 General Practices were recruited, significant COVID-19 disruptions resulted in only 81 children being screened (both parent-administered and GP completed). Of the 81 children, 11 screened positive and all of them along with 5% of screen negatives (i.e. 4 children) received clinician-administered reference-standard assessment for autism and developmental delay (DD) using Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview Schedule –Revised (ADI-R), and Mullen Scales of Early Learning (MSEL). All children found by reference-standard assessment to have probable autism and/or DD had screened positive during the screening process, and 90.9% of children who screened positive were found by reference-standard assessment to have probable DD or autism. The findings provide early evidence for the feasibility and usefulness of parent completed and GP administered developmental measures during opportunistic contacts with GPs as a promising method to facilitate early identification of DD or autism.
Numerical Modeling and Open-Source Implementation of Variational Partition-of-Unity Localizations of Space-Time Dual-Weighted Residual Estimators for Parabolic Problems
Authors: Thiele J. P. , Wick T.
Published: March 2024

In this work, we consider space-time goal-oriented a posteriori error estimation for parabolic problems. Temporal and spatial discretizations are based on Galerkin finite elements of continuous and discontinuous type. The main objectives are the development and analysis of space-time estimators, in which the localization is based on a weak form employing a partition-of-unity. The resulting error indicators are used for temporal and spatial adaptivity. Our developments are substantiated with several numerical examples.
Fast Dimension Spectrum for a Potential with a Logarithmic Singularity
Authors: Gohlke Philipp , Lamprinakis Georgios , Schmeling Jörg
Published: March 2024

We regard the classic Thue–Morse diffraction measure as an equilibrium measure for a potential function with a logarithmic singularity over the doubling map. Our focus is on unusually fast scaling of the Birkhoff sums (superlinear) and of the local measure decay (superpolynomial). For several scaling functions, we show that points with this behavior are abundant in the sense of full Hausdorff dimension. At the fastest possible scaling, the corresponding rates reveal several remarkable phenomena. There is a gap between level sets for dyadic rationals and non-dyadic points, and beyond dyadic rationals, non-zero accumulation points occur only within intervals of positive length. The dependence between the smallest and the largest accumulation point also manifests itself in a non-trivial joint dimension spectrum.
Pixels and Pedagogy: Examining Science Education Imagery by Generative Artificial Intelligence
Authors: Cooper Grant , Tang Kok-Sing
Published: March 2024

The proliferation of generative artificial intelligence (GenAI) means we are witnessing transformative change in education. While GenAI offers exciting possibilities for personalised learning and innovative teaching methodologies, its potential for reinforcing biases and perpetuating stereotypes poses ethical and pedagogical concerns. This article aims to critically examine the images produced by the integration of DALL-E 3 and ChatGPT, focusing on representations of science classrooms and educators. Applying a capital lens, we analyse how these images portray forms of culture (embodied, objectified and institutionalised) and explore if these depictions align with, or contest, stereotypical representations of science education. The science classroom imagery showcased a variety of settings, from what the GenAI described as vintage to contemporary. Our findings reveal the presence of stereotypical elements associated with science educators, including white-lab coats, goggles and beakers. While the images often align with stereotypical views, they also introduce elements of diversity. This article highlights the importance for ongoing vigilance about issues of equity, representation, bias and transparency in GenAI artefacts. This study contributes to broader discourses about the impact of GenAI in reinforcing or dismantling stereotypes associated with science education.
Influence of climate-smart technologies on the success of livestock donation programs for smallholder farmers in Rwanda
Authors: Kandulu John M. , Zuo Alec , Wheeler Sarah , Dusingizimana Theogene , Chagunda Mizeck G. G.
Published: March 2024

Climate change threatens the livelihoods of Sub-Saharan African farmers through increased droughts. Livestock donation programs offer a potential solution, but their effectiveness under climate stress remains unclear. This study assesses the economic viability of integrating climate-smart technologies (cowsheds and biogas plants) into these programs in Rwanda. Using a stochastic benefit–cost analysis from the beneficiary perspective, we evaluate the net gains for households receiving heifers compared to the current program. Our findings reveal that integrating climate-smart technologies significantly enhances economic viability. Households with cows and climate-smart technologies can possibly realise net benefits 3.5 times higher than the current program, with benefit–cost ratios reaching 5:1. Beyond economic benefits, adopting biogas reduces deforestation, greenhouse gas emissions, and respiratory illness risks. This study demonstrates that integrating climate-smart technologies into livestock donation programs can generate positive economic, environmental, and health benefits, leading to more resilient and sustainable smallholder systems. However, overcoming implementation challenges requires tailored policy packages addressing local barriers.
Spontaneous self-affirmation predicts more meaning and less boredom
Authors: O’Dea Muireann K. , Igou Eric R. , van Tilburg Wijnand A. P.
Published: March 2024

Chronic boredom is associated with many negative psychological outcomes, including undermining perceived meaning in life. Meanwhile, emerging research suggests that spontaneous self-affirmation, that is, an inclination to self-affirm, is linked to greater well-being and buffers against psychological threats. We investigated the relationship between spontaneous self-affirmation, perceptions of meaning in life, and boredom proneness with four correlational studies. Study 1a (N = 166) demonstrated that people inclined to self-affirm experience greater perceptions of meaning in life. Study 1b (N = 170) confirmed that spontaneous self-affirmation is associated with lower levels of boredom proneness. Study 2a (N = 214) and Study 2b (N = 105) provided evidence for our central hypothesis, showing that spontaneous self-affirmation predicts lower levels of boredom proneness via greater perceptions of meaning in life. These findings confirm that elevating meaning in life through psychological resources, like spontaneous self-affirmation, may limit boredom. Our work extends the emerging well-being benefits of spontaneous self-affirmation, by demonstrating associations with higher meaning in life and lower boredom proneness.
Fixed points and attractors of additive reaction systems
Authors: Ascone Rocco , Bernardini Giulia , Manzoni Luca
Published: March 2024

Reaction systems are discrete dynamical systems that simulate biological processes within living cells through finite sets of reactants, inhibitors, and products. In this paper, we study the computational complexity of deciding on the existence of fixed points and attractors in the restricted class of additive reaction systems, in which each reaction involves at most one reactant and no inhibitors. We prove that all the considered problems, that are known to be hard for other classes of reaction systems, are polynomially solvable in additive systems. To arrive at these results, we provide several non-trivial reductions to problems on a polynomially computable graph representation of reaction systems that might prove useful for addressing other related problems in the future.
Multi-modal Domain Adaptation Method Based on Parameter Fusion and Two-Step Alignment
Authors: Wu Lan , Wang Han , Gong Lishuang , Yao Yuan , Guo Xin , Li Binquan
Published: March 2024

Due to the well-known domain shift problem, directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance. The existing multi-modal domain adaption methods not only lack the fine-grained information of cross-modal data distribution, but also lack the cross-modal correlation research. Therefore, this paper proposes a multi-modal domain adaption method based on parameter fusion and two-step alignment (PFTS) to solve the related problems. The consistency of network parameters is used to enhance the correlation among modalities, and a higher-order moment measurement is introduced to improve the alignment of data distribution at the fine-grained level. In addition, the weighting of each modality is further carried out to achieve focused transfer. Comprehensive experiments based on multi-modal datasets with different domain adaption settings have been conducted, the results show that the precision of PFTS is 5.38% higher than state-of-the-art multi-modal domain adaption methods.
An Adaptive Learning Rate Deep Learning Optimizer Using Long and Short-Term Gradients Based on G–L Fractional-Order Derivative
Authors: Chen Shuang , Zhang Changlun , Mu Haibing
Published: March 2024

Deep learning model is a multi-layered network structure, and the network parameters that evaluate the final performance of the model must be trained by a deep learning optimizer. In comparison to the mainstream optimizers that utilize integer-order derivatives reflecting only local information, fractional-order derivatives optimizers, which can capture global information, are gradually gaining attention. However, relying solely on the long-term estimated gradients computed from fractional-order derivatives while disregarding the influence of recent gradients on the optimization process can sometimes lead to issues such as local optima and slower optimization speeds. In this paper, we design an adaptive learning rate optimizer called AdaGL based on the Grünwald–Letnikov (G–L) fractional-order derivative. It changes the direction and step size of parameter updating dynamically according to the long-term and short-term gradients information, addressing the problem of falling into local minima or saddle points. To be specific, by utilizing the global memory of fractional-order calculus, we replace the gradient of parameter update with G–L fractional-order approximated gradient, making better use of the long-term curvature information in the past. Furthermore, considering that the recent gradient information often impacts the optimization phase significantly, we propose a step size control coefficient to adjust the learning rate in real-time. To compare the performance of the proposed AdaGL with the current advanced optimizers, we conduct several different deep learning tasks, including image classification on CNNs, node classification and graph classification on GNNs, image generation on GANs, and language modeling on LSTM. Extensive experimental results demonstrate that AdaGL achieves stable and fast convergence, excellent accuracy, and good generalization performance.
Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize
Authors: Memic E. , Trenz J. , Heshmati S. , Graeff S.
Published: March 2024

Crop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg−1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.
A universal parallel simulation framework for energy pipeline networks on high-performance computers
Authors: Han Pu , Hua Haobo , Wang Hai , Xue Fei , Wu Changmao , Shang Jiandong
Published: March 2024

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.
The Hole Argument without the notion of isomorphism
Authors: Luc Joanna
Published: March 2024

In this paper, I argue that the Hole Argument can be formulated without using the notion of isomorphism, and for this reason it is not threatened by the criticism of Halvorson and Manchak (Br J Philos Sci, 2022. https://doi.org/10.1086/719193). Following Earman and Norton (Br J Philos Sci 38, pp. 515–525, 1987), I divide the Hole Argument into two steps: the proof of the Gauge Theorem and the transition from the Gauge Theorem to the conclusion of radical indeterminism. In the analaysis of the first step, I argue that the Gauge Theorem does not rely on the notion of isomorphism but on the notion of the diffeomorphism-invariance of the equations of local spacetime theories; however, for this approach to work, the definition of local spacetime theories needs certain amendments with respect to Earman and Norton’s formulation. In the analysis of the second step, I postulate that we should use the notion of radical indeterminism instead of indeterminism simpliciter and that we should not decide in advance what kind of maps are to be used in comparing models. Instead, we can tentatively choose some kind of maps for this purpose and check whether a given choice leads to radical indeterminism involving empirically indistinguishable models. In this way, the use of the notion of isomorphism is also avoided in the second step of the Hole Argument. A general picture is that physical equivalence can be established by means of an iterative procedure in which we examine various candidate classes of maps, and, depending on the outcomes, we need to broaden or narrow these classes. The Hole Argument can be viewed as a particular instance of this procedure.
Green biosourced composite for efficient reactive dye decontamination: immobilized Gibberella fujikuroi on maize tassel biomatrix
Authors: Celik Sema , Kurtulus Tas Selen , Sayin Fatih , Akar Tamer , Tunali Akar Sibel
Published: March 2024

Biosorptive treatment with microbial biomass is regarded as an environmentally friendly and effective way to reduce dye contamination in contaminated aquatic environments. Immobilizing microbial cells for use in this process can significantly improve their effectiveness as biosorbents in the water treatment process. The current investigation searches for a sustainable and environmentally friendly approach to decolorization by employing a green biocomposite material sorbent system (ZM@GFC) created by immobilizing fungal cells (Gibberella fujikuroi) on maize tassel tissues to efficiently remove Reactive Yellow 2 (RY2) from contaminated water sources. Batch and dynamic flow tests were performed to evaluate the biodecolorization properties of the newly created immobilized biomaterial as well as the effects of several essential operating conditions factors on the sorption behavior. Biosorption yields of 95.7% and 90.0% in batch and dynamic modes were achieved for experimental dye decolorization. The biosorption of RY2 by ZM@GFC occurred fast and achieved equilibrium within 60 min. The pseudo-second-order kinetic model elucidated the dye biosorption onto ZM@GFC. The Langmuir model provided a more accurate representation of the results than the Freundlich model. At the same time, Redlich-Peterson isotherm demonstrated the best level of agreement with the experimental data. These findings indicate that the biosorption mechanism predominantly involved the formation of a monolayer covering and that the energy properties of the ZM@GFC surface were uniform. The breakthrough capacity at the exhaustion time was 537.32 mg g−1. The predicted cost of generating ZM@GFC was anticipated to be 61.03 USD/kg. The investigations on safe disposal demonstrated that the biosorption process did not generate any secondary pollution. In conclusion, using maize tassel tissue as an immobilized decolorization agent offers a possible method for removing reactive azo dye pollutants from the aquatic medium that is both economical and environmentally benign.
Mixed-Material Feedstocks for Cold Spray Additive Manufacturing of Metal–Polymer Composites
Authors: Schwenger Matthew S. , Kaminskyj Madison S. , Haas Francis M. , Stanzione Joseph F.
Published: March 2024

High-performance polymers such as poly(ether ether ketone) (PEEK) are appealing as composite components for a wide variety of industrial and medical applications due to their excellent thermomechanical properties. However, conventional PEEK metallization methods can often lead to poor quality control, low deposition rate, and high cost. Cold spray is a promising potential alternative to produce polymer–metal composites rapidly and inexpensively due to its relatively mild operating conditions and high throughput. In this study, we investigated the deposition characteristics of metal–polymer composite feedstock, composed of PEEK powder and copper flake in varying ratios, onto a PEEK substrate. Copper-PEEK powder blends were prepared by both hand-mixing and cryogenic milling (cryomilling), which predominantly creates composite particles with micron-scale copper domains coating PEEK particle surfaces. This process non-monotonically affects the relative dominance and length scales of the multiple contributing deposition mechanisms present in mixed-material cold spray. In low-pressure cold spray, deposits showed significant changes in deposition efficiency and composition as a result of milling, with improvements in these characteristics most dramatic at lower Cu fractions. Deposits of a cryomilled blend of nominally 30 vol.% copper in PEEK exhibited minimal porosity under scanning electron microscopy, complete retention of powder composition, and the highest deposition efficiency among all samples tested. Notably, neither neat PEEK nor neat Cu meaningfully deposited at the same mild conditions as this 30 vol.% Cu blend, indicating a synergistic departure from linear mixing rules driven by the relative balance of local deposition interactions (e.g., hard–soft, soft–soft, etc.). Intentional powder and process design toward optimizing this balance may facilitate cold spray metallization applications.
Consistently Inconsistent? Clarifying Conflicting Timelines with Aoristic Interview Analysis
Authors: Keatley David
Published: March 2024

Police investigations are complex processes, often accumulating large amounts of interviews. Mapping temporal pathways within and across individuals’ statements is time-consuming and often difficult to interpret after multiple interviews. The current paper provides a novel use of a well-supported timeline method: aoristic analysis. By applying aoristic analysis to interviews, it is shown that consistencies and inconsistencies across multiple interviews can be easily calculated and given probability scores. Important parts of timelines can be highlighted to expedite investigations. This approach uses existing police records and offers an additional layer of statistical analyses to provide quicker insights into the likelihood of activity and event times and durations. While offered as a proof-of-concept study, the current paper provides an opportunity for a range of researchers and academics to assist with interpreting timelines and investigations.
Impact of 1-Methylcyclopropene in Combination with Heat Shock and Dimethyl Dicarbonate on the Physicochemical Profiles of Table Olive Fermentations
Authors: López-García Elio , Benítez-Cabello Antonio , Rodríguez-Gómez Francisco , Martín-Arranz Virginia , Romero-Gil Verónica , Arroyo-López Francisco Noé , Garrido-Fernández Antonio
Published: March 2024

Table olives are the most important fermented vegetable in the Mediterranean countries. In a continuous effort to improve the quality of this food, this work applies an experimental design with four variables to study the use of 1-methylcyclopropene (1-MCP, 2.85 µL/L) in combination with a light heat shock (65 °C, 5 min) and dimethyl dicarbonate (DMDC, 83 mg/L), during the processing of Spanish style and natural table olives. Fruits processed in the Spanish style exhibited higher moisture levels, luminance, and combined acidity. On the contrary, natural olives contained more sugars, had a firmer texture, and displayed a greener colour. Our results show that applying 1-MCP, heat shock, and DMDC can positively impact the processing of Spanish style and natural table olives and influence their physicochemical characteristics. Adding DMDC to the initial brine caused colour degradation but lowered the pH. Using 1-MCP with a light heat shock improved the sugar content and luminance of olives and increased the titratable acidity in the brine. Applying 1-MCP to the Spanish style enhanced the luminosity (brightness) and, when combined with DMDC, led to the lowest pH levels. Further research at an industrial scale is necessary to validate these results and study their influence on microorganisms. These treatments could have applications in developing new table olive processing methods that allow for more predictable products.
Improving strategic planning for nature: Panacea or pandora’s box for the built and natural environment?
Authors: Scott Alister , Kirby Matthew
Published: March 2024

This paper assesses how strategic planning for nature can be improved for England’s built and natural environment using mainstreaming and landscape-scale concepts. Whilst both concepts feature in academic literature, there has been limited attention on their role as catalytic agents for strategic planning. Addressing this gap, evidence is used from two stakeholder workshops involving 62 senior policy experts managing a range of operational and hypothetical strategic spatial planning challenges. The results reveal a significantly weakened strategic planning arena characterised by policy disintegration, short termism and uncertainty. Key findings highlight the fallacy of pursuing strategic planning for nature in isolation from wider policy integration fusing environmental, economic and social components from the outset. Current barriers to progress include institutional inertia, technocratic vocabularies and neoliberalist priorities exacerbated by a weak underlying theory. Conversely opportunities for mainstreaming processes may help knowledge generation and exchange within transdisciplinary partnerships, whilst landscape scale thinking can improve understanding of issues using natures inherent geometry transforming processes and outcomes. The paper recommends the adoption of strategic planning pathways using mainstreaming and landscape-scale approaches working in tandem. Whilst focused on the English context, our findings are transferable to other planning systems in the Global North, especially those championing neoliberal market led policies.