Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios

This contribution presents a base case modeling and calibration for simulation studies of last-mile parcel delivery in urban areas to be compared with future sustainable and livable alternative scenarios. We formulate a two-stage mixed-integer programming optimization framework. In the first stage, stopping points are identified using a facility location problem. In the second stage, these stopping points are served through multiple vehicle tours by solving a capacitated vehicle routing problem. The parameterization and calibration are based on real data for a part of the Hanover Linden-Nord district, which includes the street network, the parcel demand of a large logistics service provider, and stopping distances derived from trajectories. The model is the basis for follow-up work in the area of 5G-enabled dynamic space management.
Anticipating and transforming futures: a literature review on transdisciplinary coastal research in the Global South

ABSTRACT Anticipation of futures using transdisciplinary approaches is critical to provide the basis for appropriate action to cope with current and future risks and to foster sustainability transformations. Coasts in the Global South in particular are subjected to severe environmental and societal challenges exacerbated by climate change. Yet, traditional research methods and epistemologies may not reflect the need for envisioning radically different sustainable futures. To gain an overview of and identify gaps in the current practices of transformational transdisciplinary research in coastal regions of the Global South, we conducted a systematic literature review of empirical English-language research articles (n = 256). Our results showed that most of the articles reviewed focused on past and current state analysis. Those articles using anticipation methods rarely analysed or established a link between anticipation and sustainability transformation. Yet, transdisciplinary and anticipation research have synergistic effects to foster sustainability transformation. A combination of these approaches may integrate pluralistic voices and values of stakeholders and foster potential alternative visions to counter unsustainable narratives. Thereby, the visions for possible futures may become more inclusive and reflective of realities in the Global South. Anticipation of the future using transdisciplinary approaches can provide a basis for adaptive management of future environmental and societal challenges. It may provide the knowledge-base which can be used to identify, reduce or prevent governance actions that result in undesirable states of the future. The inclusion of anticipation and foresight in transdisciplinary research creates the potential for achieving or progressing towards innovative and sustainable visions of the future.
Long-Horizon Manipulation by a Single-arm Robot via Sub-goal Network based Hierarchical Reinforcement Learning

In this work, we present an approach of long-horizon intelligence that utilizes Sub-goal network based hierarchical reinforcement learning (HRL) for long-horizon tasks by a single-arm robot. Long-horizon (LH) tasks are complicated due to their longer complex sequences and the large number of environmental variables. We attempt to solve the LH learning problem by the Sub-goal network based HRL. The proposed approach is tested in both simulation and hardware environments by a LH task of opening a drawer, grasping and relocating an object, and closing a drawer. Our Sub-goal network based HRL achieves a success rate of 90.3% in completing the LH tasks. Whereas the conventional deep reinforcement learning solution could not complete the LH task.
DEGAT: A Graph Neural Network Algorithm Based on Dynamic Attention Mechanism for Molecular Property Prediction

Accurately predicting the physicochemical, biophysical, and physiological properties of molecules plays a vital role in drug design and drug discovery, and it is also one of the fundamental tasks in computer-aided drug design(CADD). In recent years, graph neural networks have developed rapidly in the application of molecular property prediction tasks, but existing methods pay less attention to the feature information of edges (chemical bonds), so the obtained molecular representations have certain limitations. We propose a dynamic attention-based message passing neural network (DEGAT), which changes the static attention of GAT to dynamic attention, and describes the importance of different edges by applying weights to the edges. On this basis, we also introduce a feature enhancement module, which effectively highlights the role of nodes with strong information. Our experiments show that on ten molecular property datasets, the performance of our proposed model has been significantly improved compared with the advanced baseline models in recent years, which indicates that the DEGAT model has learned molecular representations that are more suitable for molecular property prediction tasks.
Substrate recruitment via eIF2γ enhances catalytic efficiency of a holophosphatase that terminates the Integrated Stress Response

Dephosphorylation of pSer51 of the α subunit of translation initiation factor 2 (eIF2αP) terminates signalling in the integrated stress response (ISR). A trimeric mammalian holophosphatase comprised of a PP1 catalytic subunit, the conserved C-terminally located ∼70 amino acid core of a substrate-specific regulatory subunit (PPP1R15A/GADD34 or PPP1R15B/CReP) and G-actin (an essential co-factor) efficiently dephosphorylate eIF2αP in vitro. Unlike their viral or invertebrate counterparts, with whom they share the conserved 70 residue core, the mammalian PPP1R15s are large proteins of more than 600 residues. Genetic and cellular observations point to a functional role for regions outside the conserved core of mammalian PPP1R15A in dephosphorylating its natural substrate, the eIF2 trimer. We have combined deep learning technology, all-atom molecular dynamics simulations, X-Ray crystallography and biochemistry to uncover binding of the γ subunit of eIF2 to a short helical peptide repeated four times in the functionally-important N-terminus of human PPP1R15A that extends past its conserved core. Binding entails insertion of Phe and Trp residues that project from one face of an α-helix formed by the conserved repeats of PPP1R15A into a hydrophobic groove exposed on the surface of eIF2γ in the eIF2 trimer. Replacing these conserved Phe and Trp residues with Ala compromises PPP1R15A function in cells and in vitro. These findings suggest mechanisms by which contacts between a distant subunit of eIF2 and elements of PPP1R15A distant to the holophosphatase active site contribute to dephosphorylation of eIF2αP by the core PPP1R15 holophosphatase and to efficient termination of the ISR in mammals.
Learning regular cross-trial shifts of the target location in serial search involves awareness – an eye-tracking study

People can learn, and utilize, not only static but also dynamic (cross-trial) regularities in the positioning of target items in parallel, ‘pop-out’ visual search. However, while static target-location learning also works in serial search, acquiring dynamic regularities seems prevented by the demands imposed by item-by-item scanning. Also, questions have been raised regarding a role of explicit awareness for utilizing (at least) dynamic regularities to optimize performance. The present study re-investigated whether dynamic regularities may be learned in serial search when regular shifts of the target location occur frequently, and whether such learning would correlate with awareness of the dynamic rule. To this end, we adopted the same regularity used by Yu et al. (2023) to demonstrate dynamic learning in parallel search: a cross-trial shift of the target location in, e.g., clockwise direction within a circular array in 80% of the trials, which was compared to irregular shifts in the opposite (e.g., counterclockwise; 10%) or some other, random direction (10%). The results showed that ⅔ of participants learned the dynamic regularity, with their performance gains correlating with awareness: the more accurately they estimated how likely the target shifted in the frequent direction, the greater their gains. Importantly, part of the gains accrued already early during search: a large proportion of the very first and short-latency eye movements was directed to the predicted location, whether or not the target appeared there. We discuss whether this rule-driven behavior is causally mediated by conscious control. (248 words).
Benchmarking DNA binding affinity models using allele-specific transcription factor binding data

Transcription factors (TFs) bind to DNA in a highly sequence-specific manner. This specificity can manifest itself in vivo at heterozygous loci as a difference in TF occupancy between the two alleles. When applied on a genomic scale, functional genomic assays such as ChIP-seq typically lack the statistical power to detect allele-specific binding (ASB) at the level of individual variants. To address this, we propose a framework for benchmarking sequence-to-affinity models for TF binding in terms of their ability to predict allelic imbalances in ChIP-seq counts. We show that a likelihood function based on an over-dispersed binomial distribution can aggregate evidence for allelic preference across the genome without requiring statistical significance for individual variants. This allows us to systematically compare predictive performance when multiple binding models for the same TF are available. We introduce PyProBound, an easily extensible reimplementation of the ProBound biophysically interpretable machine learning framework. Configuring PyProBound to explicitly account for a confounding sequence-specific bias in DNA fragmentation rate yields improved TF binding models when training on ChIP-seq data. We also show how our likelihood function can be leveraged to perform de novo motif discovery on the raw allele-aware ChIP-seq counts.
Regional design: a transformative approach to planning

the Ombrone River. The study evaluates the performance of regional design in four key areas, notably perception, networks, frameworks, and local action. The research demonstrates that regional design can effectively transform how a river basin is perceived, enhance regional soft governance networks, offer planning frameworks for regional development, and drive the implementation of local projects. This research underscores the value of regional design in regions with fragmented planning landscapes. It highlights how the approach can contribute to shaping the discourse and decisions in large-scale territorial and governance processes, offering a viable methodology to guide Regional Agreement initiatives. Although taking a comprehensive perspective on the performances of regional design, iteration and reflexivity stand out as the central feature of the tested regional design methodology.
Characteristics and impacts of extratropical Rossby Wave breaking during the Atlantic hurricane season

AbstractThis study investigates the characteristics of extratropical Rossby wave breaking (RWB) during the Atlantic hurricane season and its impacts on Atlantic tropical cyclone (TC) activity. It was found that RWB perturbs the wind and moisture fields throughout the troposphere in the vicinity of a breaking wave. When RWB occurs more frequently over the North Atlantic, the Atlantic main development region (MDR) is subject to stronger vertical wind shear and reduced tropospheric moisture; the basinwide TC counts are reduced, and TCs are generally less intense, have a shorter lifetime, and are less likely to make landfalls. A significant negative correlation was found between Atlantic TC activity and RWB occurrence during 1979–2013. The correlation is comparable to that with the MDR SST index and stronger than that with the Nino-3.4 index. Further analyses suggest that the variability of RWB occurrence in the western Atlantic is largely independent of that in the eastern Atlantic. The RWB occurrence in the.
Investigation of Hybrid Deep Learning Networks in Forecasting Energy Supply

In this paper the intelligent methods for solving the problem of short-and middle-term forecasting of electricity sales to ultimate customers are considered. A hybrid GMDH-neo-fuzzy network, GMDH and ARIMA were studied. Neo-fuzzy neurons are chosen as nodes of a hybrid DL network. The optimal parameters of the hybrid DL network and GMDH were found. The synthesis of the optimal structure of hybrid GMDH-neo-fuzzy network for short-and middle-term forecasting was performed. Experimental studies of hybrid GMDH-neo-fuzzy network, GMDH and ARIMA for short-and middle-term forecasting have been conducted. The accuracy of the obtained forecasts was compared. The expediency of applying the researched methods of artificial intelligence for the considered intervals is substantiated.
H7 influenza A viruses bind sialyl-LewisX, a potential intermediate receptor between species

Influenza A viruses (IAVs) can overcome species barriers by adaptation of the receptor binding site of the hemagglutinin (HA). To initiate infection, HAs bind to glycan receptors with terminal sialic acids, which are either N-acetylneuraminic acid (NeuAc) or N-glycolylneuraminic acid (NeuGc), the latter is mainly found in horses and pigs but not in birds and humans. We investigated the influence of previously identified equine NeuGc-adapting mutations (S128T, I130V, A135E, T189A, and K193R) in avian H7 IAVs in vitro and in vivo. We observed that these mutations negatively affected viral replication in chicken cells, but not in duck cells, and positively affected replication in horse cells. In vivo, the mutations reduced virus virulence and mortality in chickens. Ducks excreted high viral loads for a longer time than chickens, although they appeared clinically healthy. To elucidate why chickens and ducks were infected by these viruses despite the absence of NeuGc, we re-evaluated the receptor binding of H7 HAs using glycan microarray and flow cytometry studies. This revealed that mutated avian H7 HAs also bound to α2,3-linked NeuAc and sialyl-LewisX, which have an additional fucose moiety in their terminal epitope, explaining why infection of ducks and chickens was possible. Interestingly, the α2,3-linked NeuAc and sialyl-LewisX epitopes were only bound when presented on tri-antennary N-glycans, emphasizing the importance of investigating the fine receptor specificities of IAVs. In conclusion, the binding of NeuGc-adapted H7 IAV to sialyl-LewisX enables viral replication and shedding by chickens and ducks, potentially facilitating interspecies transmission of equine-adapted H7 IAVs. (249 words) Importance Influenza A viruses cause millions of deaths and illness in birds and mammals each year. The viral surface protein hemagglutinin initiates infection by binding to host cell terminal sialic acids. Hemagglutinin adaptations affect the binding affinity to these sialic acids and therefore the potential host species targeted. While avian and human IAVs tend to bind N-acetylneuraminic acid (a form of sialic acid), equine H7 viruses prefer binding to N-glycolylneuraminic acid (NeuGc). To better understand the function of NeuGc-specific adaptations in hemagglutinin and to elucidate interspecies transmission potential NeuGc-adapted viruses, we evaluated the effects of NeuGc-specific mutations in avian H7 viruses in chickens and ducks, important economic hosts and reservoir birds, respectively. We also examined the impact on viral replication and found a binding affinity to sialyl-LewisX, another terminal epitope. These findings are important as they contribute to the understanding of the role of sialyl-LewisX in avian influenza infection. (148 words)
Modeling Processes of Seismological Phenomena in the Carpathian Region

The Carpathian region is a seismically active area characterized by a complex geological structure and a history of significant seismic events. Understanding the processes governing seismological phenomena in this region is crucial for assessing seismic hazards and ensuring the safety of local populations and infrastructure. With the goal to deal with ambiguous data and address uncertainties, this study recommends applying fuzzy modeling techniques to seismological research. It specifically aims to use fuzzy sets and fuzzy logic in seismic modeling. In order to increase the accuracy and prediction power of fuzzy models, the research investigates their integration with various computational techniques and data sources.
Data-enabled design : a situated design approach that uses data as creative material when designing for intelligent ecosystems

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
SOAR elucidates disease mechanisms and empowers drug discovery through spatial transcriptomics

Spatial transcriptomics provides researchers with a better understanding of gene expression within the tissue context. Although large volumes of spatial transcriptomics data have been generated, the lack of systematic curation and analysis makes data reuse challenging. Herein, we present Spatial transcriptOmics Analysis Resource (SOAR), a resource with an extensive, systematically compiled collection of spatial transcriptomics data across tissues, organs, and pathological conditions. SOAR is a comprehensive database with uniformly processed and annotated samples, facilitating future benchmark studies and method development. SOAR also offers multi-pronged analysis capability, including an integrative approach toward drug discovery that allows for efficient exploration of novel and targeted therapeutic uses for existing compounds.
Meta-Heuristic Ant Colony Algorithm for Multi-Tasking Assignment on Collaborative AUVs

Multiple Unmanned Underwater Vehicles Is a typical combinatorial optimization problem, to achieve multiple AUV, coordinated, collaborative tasks to complete complex jobs subsea. Through analyzing the ant colony optimization algorithm, the paper proposed An Meta-heuristic ant colony optimization algorithm the Implementation to solve the multi AUVs to achieve the task allocation problem, and had simulation test based on the consolidated analyze the advantages of multiple unmanned underwater vehicle . results show that the ant colony optimization algorithms in solving multi-task allocation problem of multiple unmanned underwater vehicle showed a good performance.
GSI demo: multiuser gesture/speech interaction over digital tables by wrapping single user applications

Most commercial software applications are designed for a single user using a keyboard/mouse over an upright monitor. Our interest is exploiting these systems so they work over a digital table. Mirroring what people do when working over traditional tables, we want to allow multiple people to interact naturally with the tabletop application and with each other via rich speech and hand gestures. In previous papers, we illustrated multi-user gesture and speech interaction on a digital table for geospatial applications -- Google Earth, Warcraft III and The Sims. In this paper, we describe our underlying architecture: GSI Demo. First, GSI Demo creates a run-time wrapper around existing single user applications: it accepts and translates speech and gestures from multiple people into a single stream of keyboard and mouse inputs recognized by the application. Second, it lets people use multimodal demonstration -- instead of programming -- to quickly map their own speech and gestures to these keyboard/mouse inputs. For example, continuous gestures are trained by saying "Computer, when I do [one finger gesture], you do [mouse drag]". Similarly, discrete speech commands can be trained by saying "Computer, when I say [layer bars], you do [keyboard and mouse macro]". The end result is that end users can rapidly transform single user commercial applications into a multi-user, multimodal digital tabletop system.
Material and Methods

A cute cerebral ischemia is a major cause of mortality and disability worldwide, but no successful pharmacotherapy has been established that can benefit patients beyond the time window of thrombolysis with recombinant tissue-type plas-minogen activator (tPA). 1 Because of risks related to hemorrhage , this window is within 3 to 4.5 hours after the onset of symptoms. 2 The tPA has also shown neurotoxicity in experimental models of cerebral ischemia. 3 Therefore, a combination treatment that would reduce the deleterious effects of tPA, while maintaining the benefits of recanalization, might extend the usability and efficacy of tPA. Fingolimod has emerged as a new treatment for multiple sclerosis. The effectiveness of fingolimod has also been shown by several groups in rodent models of ischemic brain injury. These studies suggest that antiinflammatory mechanisms and vasculoprotection possibly underlie the beneficial effects of fingolimod after stroke. 12 We therefore hypothesized that combining fingolimod with tPA might reduce hemorrhagic transformation associated with delayed administration of tPA. In this study, we evaluated the effect of fingolimod in a mouse model of thromboembolic stroke, in which the beneficial effect of tPA-induced reperfusion and hemorrhagic transformation associated with delayed administration are similar to those occurring in humans. Animals C57BL/6 male mice (25–30 g, Charles River Laboratory) were maintained on a 12/12 hours light/dark cycle and fed ad libitum. Experiments were conducted according to protocols approved by the Background and Purpose—The sphingosine 1-phosphate receptor agonist fingolimod reduces infarct size in rodent models of stroke and enhances blood–brain barrier integrity. Based on these observations, we hypothesized that combination of fingolimod with tissue plasminogen activator (tPA) would reduce the risk of hemorrhagic transformation associated with delayed administration of tPA. Methods—We evaluated the effects of fingolimod in a mouse model of thromboembolic stroke, in which both the beneficial effect of reperfusion associated with early tPA treatment and hemorrhagic transformation associated with delayed administration mimic clinical observations in humans. Results—Our results demonstrate that fingolimod treatment attenuates the neurological deficit and reduces infarct volume after in situ thromboembolic occlusion of the middle cerebral artery. Combination of fingolimod and tPA improves the neurological outcome of the thrombolytic therapy and reduces the risk of hemorrhagic transformation associated with delayed administration of tPA. Conclusion—This study confirms the protective efficacy of fingolimod as a treatment against ischemic stroke in another rodent model of stroke (thromboembolic occlusion), and suggests that fingolimod could potentially be used in combination with tPA …