bluuebunny/crossref_metadata_2025 huggingface dataset:
Carbon Dots@Upconversion Nanoparticles: Conjugate Manipulation and Luminophor Confinement Enabling Aqueous‐Phase Orthogonal Multicolor Phosphorescence‐Upconversion Luminescence
Authors: Shao Kang , Jin Yuqian , Pan Zaifa , Shi Yike , Chen Jiahong , Ye Shiyi , Wang Jing , She Yuanbin
Published: January 2025

Developing single‐particle nanocomposite with aqueous‐phase orthogonal multicolor phosphorescence or multimodal luminescence holds great significance for optical coding, anti‐counterfeiting encryption, bioimaging, and biosensing. However, it faces challenges such as a limited range of emission wavelengths and difficulties in controlling the synthesis process. In this work, a conjugate structure manipulation integrated luminophor confinement strategy is proposed to prepare carbon dots@upconversion nanoparticles (CDs@UCNPs) featuring aqueous‐phase orthogonal multicolor room‐temperature phosphorescence‐upconversion luminescence (RTP‐UCL) through wet‐chemical synthetic methods. Four types of CDs are synthesized by introducing molecules with varying degrees of conjugation, while the intersystem crossing process is enhanced by constructing charge‐transfer states to narrow the energy gap between the excited singlet and triplet states. Aqueous‐phase orthogonal multicolor RTP (green, yellow, and orange) and UCL (blue, green, yellow, and red) are achieved by confining CDs with different conjugation degrees within a NaBiF4 matrix doped with various lanthanide ions. Notably, NaBiF4 UCNPs can crystallize at low temperatures, serving as a matrix to immobilize CDs, thereby preventing their vibration and rotation, and minimizing interference from water and oxygen. Additionally, the versatility of this strategy is demonstrated by constructing multicolor CDs@Cs2NaGdCl6: 5%Yb3+,10%Er3+ perovskite nanocomposites. This strategy offers valuable guidance for the preparation of advanced aqueous‐phase orthogonal multicolor materials.
Nucleophilic Reactions of Phosphorothioate Oligonucleotides
Authors: Jin Jin , Tian Cheng , Chen Chunfa , Huang Cheng Zhi , Zuo Hua
Published: January 2025

The nucleophilic reaction between phosphorothioate oligonucleotides and electrophilic reagents has become a cost‐effective and efficient approach for oligonucleotide functionalization. This method allows for the precise incorporation of desired chemical structures at specific sites on the phosphorothioate backbone through conjugation with electrophilic groups. The reaction is characterized by its high reactivity and yield, as well as its ability to enhance the hydrophilicity of otherwise hydrophobic compounds. Importantly, this modification preserves the structural and functional integrity of the oligonucleotides, making it a topic of significant interest in nucleic acid research. This article reviews recent advancements in the covalent conjugation of phosphorothioate oligonucleotides with various electrophilic compounds. The article starts with an overview of the mechanisms and general reaction conditions involved in nucleophilic reactions. It then proceeds to examine the distinctive properties and benefits of various electrophilic reagents, offering insights that can inform the rational design of phosphorothioate oligonucleotide functionalization. Finally, the article addresses both the challenges and opportunities in this field, providing perspectives on future theoretical and practical developments to enhance the application of phosphorothioate oligonucleotides in areas like structural analysis, drug develop, drug delivery, fluorescent labeling, and nucleic acid nanotechnology.
Analysis of Giant‐Shell CdSe/CdS Quantum Dots via Analytical Ultracentrifugation Combined with Spectrally Resolved Photoluminescence
Authors: Stiegler Lisa M. S. , Wegner K. David , Weigert Florian , Peukert Wolfgang , Resch‐Genger Ute , Walter Johannes
Published: January 2025

Knowledge of the structure–property relationships of functional nanomaterials, including, for example, their size‐ and composition‐dependent photoluminescence (PL) and particle‐to‐particle variations, is crucial for their design and reproducibility. Herein, the Angstrom‐resolution capability of an analytical ultracentrifuge combined with an in‐line multiwavelength emission detection system (MWE‐AUC) for measuring the sedimentation coefficient‐resolved spectrally corrected PL spectra of dispersed nanoparticles is demonstrated. The capabilities of this technique are shown for giant‐shell CdSe/CdS quantum dots (g‐QDs) with a PL quantum yield (PL QY) close to unity capped with oleic acid and oleylamine ligands. The MWE‐AUC PL measurements are calibrated and validated with certified fluorescence standards. The spectrally corrected and size‐dependent PL spectra of the g‐QDs derived from a single MWE‐AUC experiment are then analyzed and compared with the results of single‐particle spectroscopic studies, yielding the PL spectra, decay kinetics, and blinking behavior of individual g‐QDs. This study underlines the vast potential of MWE‐AUC with in‐line optical detection for the characterization of advanced nanomaterials with a complex structure.
Determining the strength of 3D printed concrete with the modified slant shear test
Authors: Licciardello Lucia , Soto Alejandro Giraldo , Kaufmann Walter , Metelli Giovanni
Published: January 2025

This study deals with the assessment of the load‐bearing capacity of layered 3D printed concrete (3DPC) using a modified slant shear test (MSST) to define a Mohr's envelope. To this aim, the established modified Coulomb yield condition of the concrete is complemented by a Coulomb failure criterion of the layer joints (cold or dry joints). Due to the anisotropy inherent to 3DPC elements, caused by the interlayer of printed concrete, the test setups commonly used for conventional concrete are inadequate to assess the load‐bearing capacity of 3DPC. A total of 45 3DPC specimens with different inclinations of the concrete printed layers, including a cold joint with 30 min time gap were tested using the MSST. The results showed a high strength of the interfaces with two different failure modes, depending on the inclination of the layers: (i) concrete matrix failure and (ii) layer interface failure, where the latter governed for layer inclinations of 60° and 75° with respect to the horizontal plane. The test campaign confirms that the proposed MSST is a highly practical test method that enables determining the parameters governing a Mohr's envelope of layered 3DPC and can thus be used to reliably characterize its load‐bearing capacity.
Green Synthesis and Biological Aspect of Seven‐Membered Azepine Hybrids: A Recent Update”
Authors: Bhardwaj Annu , Jaiswal Shivangi , Verma Kanika , Bhardwaj Khushboo , Sharma Manali , Jain Sonika , Dwivedi Jaya , Sharma Swapnil
Published: January 2025

Seven‐membered nitrogen‐containing heterocycles, particularly azepine‐based compounds, represent an intriguing class of molecules with vast arrays of applications. These compounds have garnered considerable attention in synthetic and medicinal chemistry due to their non‐planar, non‐aromatic features, which offer structural flexibility and diversity to design new drugs with improved pharmacological properties. This review summarizes the recent advances in the synthesis of azepine derivatives, including eco‐friendly methodologies that align with the principles of green chemistry, which emphasize atom economy, sustainability, and waste reduction. Besides, the present article highlights diverse biological activities, viz. anticancer, antibacterial, antifungal, antiviral, anti‐inflammatory, and neuroprotective effects of azepine derivatives. Additionally, the review discusses key aspects such as molecular docking studies, structure‐activity relationships (SAR), and mode of action evident through preclinical and clinical trials. The information presented in the current review article would assist researchers in designing and developing novel azepine‐based leads for varied therapeutic applications.
Rehumanizing Academic Belonging: Shifting Student Perceptions From a Belonging‐Performance Dependence to a Belonging‐Competence Mindset
Authors: Buckley Jessica Belue
Published: January 2025

Drawing from Scholarship of Teaching and Learning alongside belongingness research, this manuscript examines why students perceive the dependence of academic belonging on performance, especially early in an academic program. It then offers suggestions for crafting learning environments that (a) grow student capacities to (re‐)envision perceptions of competence and (b) broaden student perceptions of sources that support their academic belonging. Suggestions provide possibilities for shifting students’ belonging‐performance dependence to a belonging‐competence mindset, thereby rehumanizing learning for students and facilitating welcoming, equitable, and learning‐centered (vs. performance‐centered) environments.
Authentic Hope During Troubling Times
Authors: Grande Steve , Staton A. Renee
Published: January 2025

The push toward efficiency in higher education is occurring as increasing numbers of faculty and students are struggling with mental health concerns and the world appears progressively polarized. However, education, at its core, can foster hope and effect positive change. This chapter presents a pedagogy of authentic hope that relies on constructivist, community‐engaged teaching. As students become involved in responding to community issues, they develop a first‐hand understanding of societal issues that fosters self‐efficacy. These experiences can ultimately inspire students to be aware that they are part of shared, collaborative efforts that have the potential to dramatically improve our societies.
Application of Urea Nanoparticles in Low‐Land Rice Fields to Boost Crop Production While Maintaining Soil Fertility and Rhizospheric Health
Authors: Singh Pushplata Prasad , Priyam Ayushi , Yadav Natasha , Das Ajoy , Biswas Sujan
Published: January 2025

Nano‐fertilizers are becoming promising in developing novel fertilization strategies while reducing the requirements of traditional fertilizers. Nano‐fertilizers have high nutrient use efficiency (NUE) and are required in small doses as compared to bulk fertilizers. The use of nano fertilizers in conjunction with a reduced dose of traditional fertilizers improves nutrient availability to plants and minimizes leaching and environmental contamination. The impact of the application of such an integrated fertilization approach on the soil fertility and microbiological population also needs to be understood. The present study investigates the influence of seven field treatments, including biogenic urea nanoparticles, as a source of N nutrient developed by TERI (referred as Teri's nano urea [TNU]), with and without conventional urea in an integrated manner. These treatments include the following: (T1)—100% recommended dose of nitrogen (RDN), (T2)—TNU alone, (T3) 100% RDN combined with TNU, (T4)—TNU at 50% concentration alone, (T5)—75% RDN combined with TNU, (T6) 50% RDN combined with TNU, and (T7) 50% RDN alone. RDF for N, P, and K used in the experiment is 140 kg/ha of urea for nitrogen fertilizer, 60 kg/ha of phosphorus, and 70 kg/ha of potassium in accordance with the local agronomic guidelines. A variable amount of urea was applied in different treatments but the recommended dose of phosphorous and potash fertilizers was applied consistently across the treatments. In different treatments, urea nanoparticles were applied as a nitrogen source via root dipping and foliar spray. The study also incorporates the economics of using nanoparticles to supplement nitrogen in an additive manner along with non‐nano macronutrient sources. Further, the influence of this novel approach of fertilization was also evaluated on the native microorganism species found in the rice fields. The results for the evaluated growth parameters, yield parameters, microbial population of soil, plant uptake, and cost economics suggest that at least 25% of the conventional urea requirement can be substituted with urea nanoparticles without causing harm to the bacterial and mycological species found in the rice agricultural fields.
Iterations of symplectomorphisms and $p$-adic analytic actions on the Fukaya category
Authors: Kartal Yusuf Barış
Published: January 2025

Inspired by the work of Bell on the dynamical Mordell-Lang conjecture, and by family Floer cohomology, we construct

$p$

p

-adic analytic families of bimodules on the Fukaya category of a monotone or negatively monotone symplectic manifold, interpolating the bimodules corresponding to iterates of a symplectomorphism

$\phi $

ϕ

isotopic to the identity. This family can be thought of as a

$p$

p

-adic analytic action on the Fukaya category. Using this, we deduce that the ranks of the Floer cohomology groups

$HF(\phi ^{k}(L),L';\Lambda )$

H
F
(

ϕ
k

(
L
)
,

L
′

;
Λ
)

are constant in

$k\in {\mathbb{Z}}$

k
∈
Z

, with finitely many possible exceptions. We also prove an analogous result without the monotonicity assumption for generic

$\phi $

ϕ

isotopic to the identity by showing how to construct a

$p$

p

-adic analytic action in this case. We give applications to categorical entropy and a conjecture of Seidel.
PARADIGM-PV: a randomized, multicenter phase 4 study to assess the efficacy and safety of ropeginterferon alfa-2b in patients with low- or high-risk polycythemia vera
Authors: Yacoub Abdulraheem , Abu-Zeinah Ghaith , Qin Albert , Tashi Tsewang , Da’na Waleed , Shih Weichung Joe , Zagrijtschuk Oleh , Tsai Chan-Yen , Geller Robert , Komatsu Norio , Mesa Ruben , Gill Harinder
Published: January 2025

Polycythemia vera (PV) is characterized by clonal hematopoietic stem or progenitor cells with constitutively active somatic mutation(s) in the Janus kinase 2 gene. Phlebotomy (Phl) and aspirin are often used alone for low-risk PV patients. However, data from the Low-PV study demonstrated that Phl and aspirin may not be adequate for patients. Therapeutic intervention with disease-modifying treatment appears to be beneficial for patients with PV regardless of the risk category. Ropeginterferon alfa-2b (ropeg) is a novel interferon-based therapy with favorable dosing schedules. A higher starting-dose (250 µg) regimen with simpler dose titrations was found to have a potent disease-modifying effect with respect to inducing a molecular response. PARADIGM-PV is a randomized, phase 4 study with the primary goal of assessing the efficacy of ropeg at this dosing regimen in alleviating Phl-dependence in both low- and high-risk patients with PV. The secondary endpoints include complete hematologic response, molecular response, symptom improvement, maintenance of median hematocrit (Hct) values < 45% without disease progression, and safety. Patients will be randomized equally to receive either ropeg every two weeks or to continue their current treatment with Phl or other cytoreductive agents (e.g., hydroxyurea, other interferons, or ruxolitinib) as applicable. All patients will receive Phl if their Hct values are elevated to ≥45% according to the National Comprehensive Cancer Network guidelines. The study will enroll approximately 70 patients internationally, including patients in the US. This study will provide new efficacy data, measured as the ability of ropeg to reduce Phl eligibility and modify the disease.
Basal forebrain innervation of the amygdala: an anatomical and computational exploration
Authors: Tuna Tuğçe , Banks Tyler , Glickert Gregory , Sevinc Cem , Nair Satish S. , Unal Gunes
Published: January 2025

Theta oscillations of the mammalian amygdala are associated with processing, encoding and retrieval of aversive memories. In the hippocampus, the power of the network theta oscillation is modulated by basal forebrain (BF) GABAergic projections. Here, we combine anatomical and computational approaches to investigate if similar BF projections to the amygdaloid complex provide an analogous modulation of local network activity. We used retrograde tracing with fluorescent immunohistochemistry to identify cholinergic and non-cholinergic parvalbumin- or calbindin-immunoreactive BF neuronal subgroups targeting the input (lateral and basolateral nuclei) and output (central nucleus and the central bed nucleus of the stria terminalis) regions of the amygdaloid complex. We observed a dense non-cholinergic, putative GABAergic projection from the ventral pallidum (VP) and the substantia innominata (SI) to the basolateral amygdala (BLA). The VP/SI axonal projections to the BLA were confirmed using viral anterograde tracing and transsynaptic labeling. We tested the potential function of this VP/SI-BLA pathway in a 1000-cell biophysically realistic network model, which incorporated principal neurons and three major interneuron groups of the BLA, together with extrinsic glutamatergic, cholinergic, and VP/SI GABAergic inputs. We observed in silico that theta-modulation of VP/SI GABAergic projections enhanced theta oscillations in the BLA via their selective innervation of the parvalbumin-expressing local interneurons. Ablation of parvalbumin-, but not somatostatin- or calretinin-expressing, interneurons reduced theta power in the BLA model. These results suggest that long-range BF GABAergic projections may modulate network activity at their target regions through the formation of a common interneuron-type and oscillatory phase-specific disinhibitory motif.
Collision avoidance and routing based on location access (CARLA) of mobile robots
Authors: ElSayyad Shimaa Ezzat , Saleh Ahmed I. , Ali Hesham A. , Saraya M. S. , Rabie Asmaa H. , Abdelsalam Mohamed M.
Published: January 2025

The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.
Assessment of hard tissue changes after horizontal guided bone regeneration with the aid of deep learning CBCT segmentation
Authors: Palkovics Daniel , Hegyi Alexandra , Molnar Balint , Frater Mark , Pinter Csaba , García-Mato David , Diaz-Pinto Andres , Windisch Peter
Published: January 2025

Objectives

To investigate the performance of a deep learning (DL) model for segmenting cone-beam computed tomography (CBCT) scans taken before and after mandibular horizontal guided bone regeneration (GBR) to evaluate hard tissue changes.

Materials and methods

The proposed SegResNet-based DL model was trained on 70 CBCT scans. It was tested on 10 pairs of pre- and post-operative CBCT scans of patients who underwent mandibular horizontal GBR. DL segmentations were compared to semi-automated (SA) segmentations of the same scans. Augmented hard tissue segmentation performance was evaluated by spatially aligning pre- and post-operative CBCT scans and subtracting preoperative segmentations obtained by DL and SA segmentations from the respective postoperative segmentations. The performance of DL compared to SA segmentation was evaluated based on the Dice similarity coefficient (DSC), intersection over the union (IoU), Hausdorff distance (HD95), and volume comparison.

Results

The mean DSC and IoU between DL and SA segmentations were 0.96 ± 0.01 and 0.92 ± 0.02 in both pre- and post-operative CBCT scans. While HD95 values between DL and SA segmentations were 0.62 mm ± 0.16 mm and 0.77 mm ± 0.31 mm for pre- and post-operative CBCTs respectively. The DSC, IoU and HD95 averaged 0.85 ± 0.08; 0.78 ± 0.07 and 0.91 ± 0.92 mm for augmented hard tissue models respectively. Volumes mandible- and augmented hard tissue segmentations did not differ significantly between the DL and SA methods.

Conclusions

The SegResNet-based DL model accurately segmented CBCT scans acquired before and after mandibular horizontal GBR. However, the training database must be further increased to increase the model’s robustness.

Clinical relevance

Automated DL segmentation could aid treatment planning for GBR and subsequent implant placement procedures and in evaluating hard tissue changes.
ESG rating and ambiguity: an informative and distorted signal-based approach
Authors: Bongermino Giorgio , Romagnoli Silvia
Published: January 2025

In an increasingly uncertain, complex socio-economic, and geopolitical environment, the significance of information signals and their perception becomes more crucial, especially in assessing sustainability and environmental impact. The challenges arise from businesses’ lack of transparency and reported data, making it difficult for investors and rating agencies to evaluate and manage risks, costs, sustainability, risk-adjusted performance, greenwashing, fiduciary duty clarification, and scoring. This emphasizes the substantial impact of a high level of ambiguity on the market. Considering the three pillars of ESG parameters, we propose a novel model for assessing an ESG Rating based on (i) the level of disclosure, representing the quality of the signal and released information, and (ii) the subjective perception of the signal itself. This perception can be influenced by factors such as personal risk aversion and ESG disagreement arising from controversies in the rating process. Recognizing the identified distortion in the ESG rating as having predictive power, where ambiguity can been seen as a way to represent the market’s sentiment, the distortion turns out to play the role of a policy driver capable of identifying sectors where ESG is under/overestimated and testing the robustness of a scoring method
“It’s Like Doctor Jekyll and Mister Hyde”: The Construction of Moral Identity by Israeli Men Who Pay Women for Sex
Authors: Prior Ayelet , Peled Einat
Published: January 2025

This study examined how Israeli men who pay women for sex (MPWS) construct and sustain a moral identity within the social context that often portrays them as deviants, perpetrators, and abusers, thereby challenging their ability to maintain a respectful and dignified image. Twenty-three Israeli MPWS participated in in-depth semi-structured interviews, which were then analyzed using constructivist grounded theory method. Using the theoretical framework moral reflexivity, we conceptualize three central dynamics of constructing and sustaining a moral identity in the context of paying for sex: Maintaining an intact moral self by resisting the moral conflict; presenting a reflexive agonizing moral self; and constructing a moral self through identity fragmentation. The findings reveal that these facets of the moral reflexivity process can overlap, coexist, and circulate in a messy manner, thus promoting an ecological understanding of how morality is shaped by various societal forces, rather than discovering what a moral identity is. We therefore call for a morally sensitive approach in studies in the field of MPWS and sex work. Such an approach encourages researchers to be aware of moral issues, moral questions, and moral processes, and to treat morality as a socially context-dependent trait that is highly relevant to the study of the sex industry.
Time-Scaling, Ergodicity, and Covariance Decay of Interacting Particle Systems
Authors: Głuchowski Maciej , Menz Georg
Published: January 2025

The main focus of this article is the study of ergodicity of Interacting Particle Systems (IPS). We present a simple lemma showing that scaling time is equivalent to taking the convex combination of the transition matrix of the IPS with the identity. As a consequence, the ergodic properties of IPS are invariant under this transformation. Surprisingly, this simple observation has non-trivial implications: It allows to extend any result that does not respect this invariance, which we demonstrate with examples. Additionally, we develop a recursive method to deduce decay of correlations for IPS with alphabets of arbitrary (finite) size, and apply the Time-Scaling Lemma to that as well. As an application of this new criterion we show that certain one-dimensional IPS are ergodic answering an open question of Toom et al.
Fast online feature selection in streaming data
Authors: Hochma Yael , Last Mark
Published: January 2025

The challenge of getting big amounts of high-quality labeled data is compounded by the fact that data labeling is often subjective and requires significant human effort. In many cases, the quality of the labeled data depends entirely on the expertise and experience of human annotators, making it challenging to ensure labeling accuracy in large and dynamic datasets. Moreover, there may be a significant delay between the arrival of a new instance and its manual labeling. This paper explores the use of fully unsupervised feature selection algorithms in non-stationary data streams, where the importance of features may change over time. We introduce a novel feature selection algorithm called Online Fast FEa-ture SELection-OFFESEL, which calculates the feature importance scores in each incoming window based on their mean normalized values and without using any class labels. We evaluate OFFESEL on 17 benchmark data streams, both stationary and non-stationary, using popular online classifiers like PerceptronMask, VFDT, Online Boosting, and Linear SVM. We compare OFFESEL to several other feature selection algorithms, including state-of-the-art supervised ones like FIRES and ABFS, as well as popular unsupervised ones like MCFS, LS, and Max Variance, which we adapted to data streams. Our results indicate that OFFESEL outperforms all supervised and unsupervised feature selection algorithms in terms of classification accuracy. Specifically, OFFESEL preserves the accuracy level of the supervised FIRES algorithm, which proved more accurate than ABFS in our experiments, while maintaining the accuracy level achieved by the unsupervised Max Variance algorithm. Moreover, OFFESEL requires even less computation time than Max Variance and shows high stability on stationary datasets. Overall, our study demonstrates the potential benefits of using unlabeled data for feature ranking and selection in dynamic data streams.
Self explainable graph convolutional recurrent network for spatio-temporal forecasting
Authors: García-Sigüenza Javier , Curado Manuel , Llorens-Largo Faraon , Vicent Jose F.
Published: January 2025

Artificial intelligence (AI) is transforming industries and decision-making processes, but concerns about transparency and fairness have increased. Explainable artificial intelligence (XAI) is crucial to address these concerns, providing transparency in AI decision making, alleviating the effect of biases and fostering trust. However, the application of XAI in conjunction with problems with spatio-temporal components represents a challenge due to the small number of options, which when implemented penalize performance in exchange for the explainability obtained. This paper proposes self explainable graph convolutional recurrent network (SEGCRN), a model that seeks to integrate explainability into the architecture itself, seeking to increase the ability to infer the relationship and dependence between the different nodes, proposing an alternative to explainability techniques, which are applied as a second layer. The proposed model has been able to show in different data sets the ability to reduce the amount of information needed to make a prediction, while reducing the impact on the prediction caused by applying an explainability technique, having managed to reduce the use of information without loss of accuracy. Thus, SEGCRN is proposed as a gray box, which allows a better understanding of its behavior than black box models, having validated the model with traffic data, combining both spatial and temporal components, achieving promising results.
Unveiling Students’ Mental Models and Learning Demands: an Empirical Validation of Secondary Students’ Model Progression on Plant Nutrition
Authors: Pedrera Oier , Barrutia Oihana , Díez José Ramón
Published: January 2025

Identifying the mental models held by students has been widely emphasized as being a pivotal aspect of effective science education. In fact, it allows us to understand students’ conceptions, detect teaching-learning difficulties and tailor instruction accordingly. Hence, in this study, the plant nutrition mental models held by upper secondary students were investigated and empirically validated with the aim of detecting the most pressing learning demands and providing instructional guidelines to improve the teaching-learning of the topic. In order to unveil students’ mental models a 5-question open-ended questionnaire was administered to 122 Spanish upper secondary students. Their responses were analyzed through an innovative approach that merged phenomenography and Item Response Theory. Three distinct models emerge from the analysis investigating the sequential development of students’ reasoning. The first and most basic comprises students with heterotrophic explanatory ideas based on intuitive and naïve conceptions. The intermediate model consists of participants who incorporate photosynthesis and plants’ gas exchanges to their models without fully grasping some key underlying concepts probably due to previous misconception inducing instruction. The upper model encompasses the few students who have autotrophic mental models and ideas which resemble the consensus Scientific Model of Plant Nutrition. The findings also reveal that the overall conceptualization level of the participants is relatively low and that several teaching-learning difficulties are strictly linked to each of the different mental models. Finally, the learning demands of the topic are described, and the implications for the teaching-learning designs aimed at overcoming those difficulties are discussed.
An analysis of the modality and flexibility of the inverse stereographic normal distribution
Authors: Hinz Florian B. , Mahmoud Amr H. , Lill Markus A.
Published: January 2025

Circular data arises in various fields including robotics, biology, geology and material sciences. Modelling such data requires flexible distribution families on the hypertorus. Common choices are the von Mises and the wrapped normal distributions. In this work we investigate the inverse stereographic normal distribution as an alternative distribution family both from a theoretical and applied perspective. We first generalize unimodality results to arbitrary dimensions by proving that the inverse stereographic normal distribution is unimodal if and only if all eigenvalues of the covariance matrix are less than or equal to 0.5. We then analyze the flexibility by fitting mixtures of shifted inverse stereographic normal distributions via gradient descent to several examples of toroidal data.
An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models
Authors: Ajala Adewole Adetoro , Adeoye Oluwatosin Lawrence , Salami Olawale Moshood , Jimoh Ayoola Yusuf
Published: January 2025

Human-induced global warming, primarily attributed to the rise in atmospheric CO2, poses a substantial risk to the survival of humanity. While most research focuses on predicting annual CO2 emissions, which are crucial for setting long-term emission mitigation targets, the precise prediction of daily CO2 emissions is equally vital for setting short-term targets. This study examines the performance of 14 models in predicting daily CO2 emissions data from 1/1/2022 to 30/9/2023 across the top four polluting regions (China, India, the USA, and the EU27&UK). The 14 models used in the study include four statistical models (ARMA, ARIMA, SARMA, and SARIMA), three machine learning models (support vector machine (SVM), random forest (RF), and gradient boosting (GB)), and seven deep learning models (artificial neural network (ANN), recurrent neural network variations such as gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional-LSTM (BILSTM), and three hybrid combinations of CNN-RNN). Performance evaluation employs four metrics (R
2, MAE, RMSE, and MAPE). The results show that the machine learning (ML) and deep learning (DL) models, with higher R
2 (0.714–0.932) and lower RMSE (0.480–0.247) values, respectively, outperformed the statistical model, which had R
2 (− 0.060–0.719) and RMSE (1.695–0.537) values, in predicting daily CO2 emissions across all four regions. The performance of the ML and DL models was further enhanced by differencing, a technique that improves accuracy by ensuring stationarity and creating additional features and patterns from which the model can learn. Additionally, applying ensemble techniques such as bagging and voting improved the performance of the ML models by approximately 9.6%, whereas hybrid combinations of CNN-RNN enhanced the performance of the RNN models. In summary, the performance of both the ML and DL models was relatively similar. However, due to the high computational requirements associated with DL models, the recommended models for daily CO2 emission prediction are ML models using the ensemble technique of voting and bagging. This model can assist in accurately forecasting daily emissions, aiding authorities in setting targets for CO2 emission reduction.
Direct measurement of brake wear particles from a light-duty vehicle under real-world driving conditions
Authors: Al Wasif-Ruiz Tawfiq , Suárez-Bertoa Ricardo , Sánchez-Martín José Alberto , Barrios-Sánchez Carmen Cecilia
Published: January 2025

As tailpipe emissions have decreased, there is a growing focus on the relative contribution of non-exhaust sources of vehicle emissions. Addressing these emissions is key to better evaluating and reducing vehicles’ impact on air quality and public health. Tailoring solutions for different non-exhaust sources, including brake emissions, is essential for achieving sustainable mobility. Studying emissions from vehicles in real-world scenarios provides a better understanding of their environmental impact compared to laboratory testing alone. This study presents findings on the direct measurement of brake particles and the characterization of this source of particulate matter in real-world conditions using a mobile laboratory. In situ measurements of particle concentration and size distribution showed good agreement with previous laboratory studies, indicating the suitability of the approach to investigate break particle emissions during real-world operation. The study demonstrates that particle size distributions can vary based on the temperature of the brake disk, which is influenced by the initial braking speed, with significant variations observed between speeds of 60, 80, 100, and 120 km/h. Particles with sizes between 6 and 523 nm were released into the air from the brake system, although it is likely that larger particles were also emitted but not captured due to the upper detection limit of the Engine Exhaust Particle Sizer. During harsh braking events, such as decelerations of 4.2 m/s2 from 120 km/h, a concentration of up 106 (#/cm3) was measured for particles under 8 nm. Moreover, scanning electron microscope analysis revealed that nanoparticles are present in the form of agglomerates, whose shape can change depending on the formation process. Elements present in the particles comprised mainly iron, copper, and aluminium, indicating wear of the brake pad materials and disk components.
Early Results of Implementing Rapid Methadone Titration for Hospitalized Patients: A Case Series
Authors: Martin Marlene , Baisey Taylor , Skinner Sasha , Ly Leslie , Slown Kristin , Harter Kristin , Nguyen Oanh Kieu , Steiger Scott , Suen Leslie W.
Published: January 2025

Objectives

With the increase in illicit fentanyl use in the USA, hospitals face challenges managing opioid withdrawal and opioid use disorder (OUD). To improve opioid withdrawal and OUD treatment among hospitalized patients with daily fentanyl use, we developed a rapid methadone titration (RMT) protocol. We describe development, implementation, and outcomes during the first 12 weeks.

Methods

We analyzed electronic health record data of hospitalizations seen by the Addiction Consult Team (ACT) for methadone initiation between 9/11/23 and 12/3/23. Adults aged 18–64 reporting daily fentanyl use, desiring methadone, and without end-stage organ damage or critical illness were RMT eligible. We characterized patients who received RMT, abstracting demographic and clinical characteristics, adverse events graded by the Common Terminology Criteria for Adverse Events (CTCAE), and methadone and additional full agonist opioid (FAO) dosing. Our primary outcome was adverse events. Secondary outcomes included median time to 100 mg of methadone, FAO dosing, and self-directed discharge.

Results

ACT assessed 55 hospitalizations representing 47 patients for RMT eligibility. Among these, 19 (34.5%) hospitalizations representing 17 patients were eligible for and received RMT. Four (21.2%) hospitalizations that received RMT had sedation events, and all were mild or moderate grade by CTCAE. Hospitalizations achieved a median methadone dose of 100 mg by day 6, with FAO doses peaking on day 5. One (5.3%) hospitalization had a self-directed discharge.

Conclusions

With careful patient selection and ACT evaluation, a RMT protocol for hospitalized patients with fentanyl use disorder experienced few adverse events other than mild-moderate sedation, even among those receiving FAO and those with concurrent substance use disorders.
Genetic diversity of closely related Calligounum species collected from Saudi habitats by analyzing the matK and rpoC1 genes, and SCoT and IRAP markers
Authors: AlGarawi Amal Mohamed , Abd-Elgawad Magda Elsayed
Published: January 2025

The genus Calligonum L. Her (Polygonaceae) comprises 158 species with widespread distribution in regions such as India, China, North Africa, Pakistan, Afghanistan, Saudi Arabia, and South Europe. Calligonum L. is a prominent shrubby psammophyte found in deserts, known for its traditional medicinal uses. The aim of this study is to use Start Codon Target (SCoT) and Inter-Retrotransposon Amplified Polymorphism (IRAP) markers to evaluate the genetic diversity of Calligonum comosum and Calligonum tetrapterum in Saudi Arabia. In addition, it uses the raxmlHPC algorithm and DNA barcoding techniques (matK and rpoC1) to examine phylogenetic relationships. SCoT and IRAP markers revealed distinct genetic patterns, clustering Calligonum species based on their genetic similarities. DNA barcoding of matK and rpoC1 genes provided valuable insights into the evolutionary relationships within Calligonum species. Phylogenetic analyses highlighted well-supported structures with outgroup sequences showing early divergence. Conserved motifs analysis confirmed the presence of shared motifs in both isolated and identified genes, validating the potential use of isolated genes as biomarkers. This comprehensive genetic analysis enhances our understanding of Calligonum species’ genetic relationships, contributing valuable information for taxonomic classification and molecular marker validation. We are the first to add sequence of rpoC1 gene to Calligonum comosum and Calligonum tetrapterum in Gen-Bank. The identified conserved motifs and phylogenetic insights underscore the potential applications of Calligonum genes in various fields, including medicine and biodiversity conservation.
Fostering lithium-ion battery remanufacturing through Industry 5.0
Authors: Neri Alessandro , Butturi Maria Angela , Tomasin da Silva Leandro , Lolli Francesco , Gamberini Rita , Sellitto Miguel Afonso
Published: January 2025

The rise of electric vehicles (EVs) has resulted in notable environmental benefits, yet challenges persist regarding battery disposal and recovery. The increasing demand for EVs heightens concerns about the environmental impact of lithium-ion battery (LIB) waste, which threatens both ecosystems and public health. Although remanufacturing is seen as a sustainable solution to these issues, current research does not thoroughly examine the role of Industry 5.0 technologies in optimising this process. This study aims to compare and assess the potential of various Industry 5.0 technologies and approaches to enhance the remanufacturing of lithium-ion batteries. Using the AHP-PROMETHEE method, we identify the most critical and influential Industry 5.0 prospects that should be prioritised for addressing key challenges such as diagnostic accuracy, safe disassembly, and high-quality reassembly. The multi-criteria analysis highlights key Industry 5.0 imperatives that can facilitate efficient and effective remanufacturing processes. The study identifies Digital Product Passport (DPP), Digital Twin (DT), and the Internet of Everything (IoE) as critical enablers in optimizing the LIB remanufacturing process. The analysis reveals that DPP stands out as the top enabler, significantly enhancing transparency, traceability, and lifecycle management for LIBs. DT and IoE follow closely, contributing to real-time monitoring, predictive maintenance, and seamless data integration across the supply chain. This paper delves in the emerging concept of the Digital Battery Passport (DBP), a DPP mandated by recent European regulations aimed at improving battery management and circularity. The DBP facilitates access to critical data throughout the battery’s lifecycle, including its origin, composition, and state of health. This information is crucial for optimising remanufacturing processes, ensuring compliance with sustainability standards, and extending battery life. The paper highlights the potential of DBP to transform the EV battery value chain by enhancing transparency and enabling more informed decision-making across stakeholders. Our findings offer significant insights for policymakers, battery manufacturers, and remanufacturing firms.
Dating of mortars from Kastelli Hill, Chania, Crete, Greece, based on their compositional analyses
Authors: Maravelaki Pagona-Noni , Andreadaki-Vlazaki Maria
Published: January 2025

The study of mortars plays a crucial role in determining the chronology of construction phases. According to the database of mortars analysed from historical monuments in Crete and other areas of Greece, significant differences in mortar composition are observed across historical periods. In the present study, 14 mortars from Kastelli Hill and Katre Street in Chania, originating from the fortified wall and neighboring buildings, are analysed. The physico-chemical and mineralogical analysis aims at clarifying the nature of mortars, their manufacturing technology, and, in particular, to compare different construction phases. In addition, comparing historically identified mortars with those under study reveals similarities and differences across construction phases. The indicators considered include the trace elements of aggregates and binder, the characteristics of aggregate grain size, the type of binder (hydraulic or aerial), and the presence of pozzolanic additives. The comparisons indicated that the mortars from the wall were manufactured during the Roman period, since they are successfully compared with the mortars of buildings dating back to that time.

Graphical abstract
Yinchen lipid-lowering tea attenuates lipid deposition in a fatty liver model by regulating mitochondrial dysfunction through activation of AdipoR1/AMPK/SIRT1 signaling
Authors: Luo Xilin , Fang Yuanyuan , Wang Wei , Tong Meiling , Qin Bin , Cao Jinyu , Yang Yinjie
Published: January 2025

This study investigated the ameliorative effects of Yinchen lipid-lowering tea (YCLLT) on Non-alcoholic fatty liver disease (NAFLD), the specific mechanism involved was also studied. We modeled hepatocellular steatosis with HepG2 cells and intervened with different concentrations of YCLLT-containing serum. Lipid deposition was assessed by oil red O staining and AdipoR1 expression was analyzed by Western blot. The hepatocyte steatosis model was further treated with YCLLT-containing serum and/or silencing AdipoR1. Lipid deposition was observed by oil red O staining. Flow cytometry was used to detect apoptosis and mitochondrial membrane potential. The levels of TNF-α, IL-6, MDA, 8-OHdG, and ATP were analyzed by ELISA or the corresponding kits. The mitochondrial structure was observed by transmission electron microscopy. The expression of AdipoR1/AMPK/SIRT1 signaling pathway factors was analyzed by Western blot, and co-localization of SIRT1 and immunofluorescence. The results revealed that YCLLT attenuated lipid deposition, inhibited the levels of inflammatory factors TNF-α and IL-6, reduced the levels of MDA and 8-OHdG, up-regulated the ATP content and mitochondrial membrane potential, and promoted the expression of AdipoR1, p-LKB1, p-AMPKα, SIRT1, and PGC-1a in a cellular model of NAFLD. Further, silencing of AdipoR1 inhibited the ameliorative effect of YCLLT in the NAFLD cell model. Altogether, Yinchen lipid-lowering tea attenuates lipid deposition in a fatty liver model by improving mitochondrial function via activating AdipoR1/AMPK/ SIRT1 signaling.
The Epistemic Cost of Opacity: How the Use of Artificial Intelligence Undermines the Knowledge of Medical Doctors in High-Stakes Contexts
Authors: Schmidt Eva , Putora Paul Martin , Fijten Rianne
Published: January 2025

Artificial intelligent (AI) systems used in medicine are often very reliable and accurate, but at the price of their being increasingly opaque. This raises the question whether a system’s opacity undermines the ability of medical doctors to acquire knowledge on the basis of its outputs. We investigate this question by focusing on a case in which a patient’s risk of recurring breast cancer is predicted by an opaque AI system. We argue that, given the system’s opacity, as well as the possibility of malfunctioning AI systems, practitioners’ inability to check the correctness of their outputs, and the high stakes of such cases, the knowledge of medical practitioners is indeed undermined. They are lucky to form true beliefs based on the AI systems’ outputs, and knowledge is incompatible with luck. We supplement this claim with a specific version of the safety condition on knowledge, Safety*. We argue that, relative to the perspective of the medical doctor in our example case, his relevant beliefs could easily be false, and this despite his evidence that the AI system functions reliably. Assuming that Safety* is necessary for knowledge, the practitioner therefore doesn’t know. We address three objections to our proposal before turning to practical suggestions for improving the epistemic situation of medical doctors.