bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forensic discrimination between traditional and compressive imaging by blurring kernel investigation
Authors: Taimori Ali , Zayyani Hadi , Marvasti Farokh 
Published: October 2023

AbstractImage forensics encompasses a set of scientific tests to investigation of a suspected event via intrinsic clues of imaging pipeline. Traditional image sensing at the Nyquist-Shannon rate as well as the new modality of compressive imaging below the rate are two main types of sensing in photography and imaging applications. Hence, for forensic investigators, it would importantly necessitate the ability to discriminate among images captured by them. However, due to the complex nonlinear nature of imaging processes, investigating imagers’ traces is a difficult task. To this intent, we first systematically model the imaging pipelines as an encoder-decoder pair. For exploring distinguishable traces, we mathematically simplify and linearize the pair for compressive imaging and two main forms of traditional image sensing with or without compression. Our theoretical analyses on the approximate linear models reveal blurring kernels of different imagers have discriminability. To validate it in real-world scenarios, we considered the whole imaging process as an inverse problem and estimated the blurring kernel based on a deconvolution approach, where the discriminability is also justified by information visualization. Then, we designed a pipeline classification system, where a deep convolutional neural network is trained by the estimated blurring kernels to be able to classify the three imaging systems. Our results in compressive imaging identification show an accuracy improvement about 3.7 % in comparison to the best result among compared methods. Implementation codes are available for research and development.
Human migrations and volcanic activity: Archaeomagnetic evidence of the probable abandonment of the Tingambato archaeological site due to the eruption of El Metate volcano (Mexico)
Authors: Pérez-Rodríguez Nayeli , Morales Juan , Punzo-Díaz José Luis , Cejudo Rubén , Goguitchaichvili Avto 
Published: October 2023

AbstractThe archaeological site of Tingambato is one of the few evidences of the populations that settled in western Mexico during the Classic and Postclassic periods, before the development of the great Tarascan empire. Therefore, its study is fundamental to know both the characteristics of the culture that preceded the empire, as well as the phenomena that led to its formation. During the last decade, efforts have increased to reveal the history of this site. Thanks to the recent excavations, different archaeological materials belonging to the three construction phases of the place are available, which have served to define their main characteristics. In the present investigation, the magnetic characterization and dating of different archaeological materials belonging to the last occupational stage of Tingambato were carried out using archaeomagnetic methods. Some of these materials show evidence of exposure to fire after their elaboration. According to archaeological investigations, the ancient city of Tingambato was burned before being abandoned, so the ages obtained for four of the analyzed potsherds represent the first available dating for the abandonment of the site. Finally, these ages allow us to propose interpretations about the probable causes that led to its abandonment.
Corallite sizes of reef corals: decoupling of evolutionary and ecological trends
Authors: Dimitrijević Danijela , Raja Nussaïbah B. , Kiessling Wolfgang 
Published: October 2023

AbstractCorallite sizes reflect a continuum in the efficacy of photosymbiosis in colonial reef corals, with smaller corallite sizes generally associated with higher autotrophy. Using a large compilation of reef-coral traits and corallite diameters as a proxy, we test here the hypothesis that photosymbiotic efficacy has increased over the evolutionary history of scleractinian corals. To gain a more comprehensive understanding of the evolutionary versus ecological patterns of corallite sizes of reef corals, we used three analytical methods: (1) occurrences-weighted within-bin analyses as a proxy for abundance or ecological dominance to depict ecological patterns; (2) unweighted range-through analyses; and (3) unweighted sampled-in-bin analyses to represent diversity in terms of taxonomic richness, enabling us to trace evolutionary patterns. By-genus, range-through analysis indicates a slightly positive trend of corallite sizes toward the Recent. However, the occurrences-weighted assessment shows a pronounced negative trend of corallite sizes in colonial corals since the Mesozoic. Random walk and directional evolution are both statistically supported to explain this long-term decrease. A driven trend is evolutionarily plausible, giving reef corals a selective advantage in the oligotrophic environments they largely occupy today.
Quantifying resistance to myxomatosis in wild rabbits produces novel evolutionary insights
Authors: Cooke Brian D. , Taggart Patrick , Patel Kandarp 
Published: October 2023

Abstract
Wild rabbits in Australia developed genetic resistance to the myxoma virus, which was introduced as a biological control agent. However, little is known about the rate at which this evolutionary change occurred. We collated data from challenge trials that estimated rabbit resistance to myxomatosis in Australia and expressed resistance on a continuous scale, enabling trends in its development to be assessed over 45 years up to 1995. Resistance initially increased rapidly, followed by a plateau lasting ten years, before a second rapid increase occurred associated with the introduction of European rabbit fleas as myxoma virus vectors. By contrast, in the United Kingdom, where rabbit flea vectors were already present when the myxoma virus initially spread, resistance developed more slowly. No estimates of rabbit resistance to myxomatosis have been made for almost 30 years, despite other highly lethal rabbit pathogens becoming established worldwide. Continued testing of wild-caught rabbits in Australia to determine current levels of resistance to myxomatosis is recommended to assess its current effectiveness for managing pest rabbits. Given the economic and environmental significance of invasive rabbits, it would be remiss to manage such biological resources and ecosystem services poorly.
Cognitive change before sudden gains in cognitive behavioural therapy for panic disorder
Authors: Lee Rachel , McMillan Dean , Delgadillo Jaime , Alexander Rachael , Lucock Mike 
Published: October 2023

AbstractBackground:Sudden gains occur in a range of disorders and treatments and are of clinical and theoretical significance if they can shed light on therapeutic change processes. This study investigated the relationship between sudden gains in panic symptoms and preceding cognitive change during cognitive behavioural therapy (CBT) for panic disorder.Method:Participants with panic disorder completed in session measures of panic symptoms and catastrophic cognitions. Independent samples t-tests were used to compare the post-treatment score of those who met criteria for one or more sudden gain during treatment with those who did not, and to compare within-session cognitive change between pre-sudden gain sessions and the previous (control) session.Results:Twenty-two (42%) of 53 participants experienced a sudden gain during treatment. Participants demonstrating a sudden gain showed more improvement in panic symptoms from pre- to post-treatment than those without a sudden gain. The within-session cognitive change score in the pre-gain session was significantly greater than in the control session.Conclusions:Sudden gains occurred in individual CBT for panic disorder and within-session cognitive change was associated with sudden gains. This is consistent with the cognitive model of panic disorder and highlights how sudden gains can help to identify key change processes.
Temporary prosperity or sustainable development: the long-run impact of developing pollution-intensive industries
Authors: Nian Hongyu , Xu Zhiwei , Yin Haitao 
Published: October 2023

AbstractThis paper proposes a dynamic model to capture the interaction among the environment, human capital accumulation, and economic growth. We emphasize the mechanism that pollution stock depresses human capital accumulation, which has received increasing support from empirical studies. The model predicts that the development of pollution-intensive industries can help an economy gear up a short-run prosperity, but it impairs the capability for long-run economic growth, trapping the economy at a low development level. The cost for a dirty economy to switch is expensive and even infeasible if the environmental degradation is irreversible. Policy interventions, such as tax on pollution and subsidy on human capital, can help alleviate but cannot eradicate the economic stagnation.
Reflections of Reason: Kant on Practical Judgement
Authors: Dunn Nicholas 
Published: October 2023

AbstractMy aim in this article is to provide an account of practical judgement, for Kant, that situates it within his theory of judgement as a whole – particularly, with regards to the distinction between the determining and reflecting use of judgement. I argue that practical judgement is a kind of determining judgement, but also one in which reflecting judgement plays a significant role. More specifically, I claim that practical judgement arises from the cooperation of the reflecting power of judgement with the faculty of reason – the former assisting the latter in the application of its principle. I conclude by considering a possible role for feeling in practical judgement.
Evaluating cover crop tolerance to corn residual herbicides using field-treated soil in greenhouse bioassay
Authors: Severo Silva Tatiane , Arneson Nicholas John , Silva Daniel Valadão , Werle Rodrigo 
Published: October 2023

AbstractMore growers across the U.S. Midwest are considering interseeding or overseeding cover crops into corn for soil health purposes. One challenge of this practice is the potential injury from soil residual herbicides applied preemergence (PRE) for weed control in corn to the interseeded and overseeded cover crop species. Field-treated soil was collected in 2021 and 2022 at Janesville, WI, and Lancaster, WI, to investigate the impact of PRE residual herbicides on establishment of interseeded and overseeded cover crops via greenhouse bioassay. Soil samples (0 to 5 cm depth) were collected from field experiments at 0, 10, 20, 30, 40, 50, 60, and 70 days after treatment (DAT). Treatments consisted of 14 single and multiple sites of action (SOAs) PRE herbicides plus a nontreated check (NTC). Four bioindicator cover crop species were used in the greenhouse bioassay: annual ryegrass, cereal rye, radish, and red clover. Cover crop biomass was collected 28 d after bioassay seeding. Cover crop species responded differently across herbicide treatments. Annual ryegrass and cereal rye were sensitive to treatments containing herbicide Group 15, whereas Groups 2, 4, 5, 14, and 27 had minimal impact on their establishment when field soil was collected at 30 DAT (interseeding scenario) and 70 DAT (overseeding scenario) compared to the NTC. Radish and red clover were sensitive to herbicide Groups 2, 4, and 27, whereas Groups 5, 14, and 15 had minimal impact on their establishment. Annual ryegrass, radish, and red clover were more sensitive to PRE herbicides containing two and three SOAs than to herbicides with a single SOA. On the basis of these greenhouse bioassay results, cover crop species should be carefully selected depending on the soil residual herbicide when interseeded and overseeded into corn. Field studies will be conducted to validate these results and support recommendations to growers interested in this system.
Constraining the First Year of Ice‐Free Arctic: Importance of Regional Perspective
Authors: Paik Seungmok , Kim Daehyun , An Soon‐Il , Ham Yoo‐Geun 
Published: October 2023

AbstractThe ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies.
Modeling the Development of Plasmasphere Ducts and Irregularities With SAMI3/WACCM‐X
Authors: Huba J. D. , Liu H.‐L. 
Published: October 2023

AbstractWe show that atmospheric gravity waves can generate plasma ducts and irregularities in the plasmasphere using the coupled SAMI3/WACCM‐X model. We find the equatorial electron density is irregular as a function of longitude which is consistent with CRRES measurements (Clilverd et al., 2007, https://doi.org/10.1029/2007ja012416). We also find that plasma ducts can be generated for L‐shells in the range 1.5–3.0 with lifetimes of ∼ 0.5 hr; this is in line with observations of ducted VLF wave propagation with lifetimes of 0.5–2.0 hr (Clilverd et al., 2008, https://doi.org/10.1029/2007ja012602; Singh et al., 1998, https://doi.org/10.1016/s1364-6826(98)00001-7).
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks
Authors: Halvagal Manu Srinath , Zenke Friedemann 
Published: October 2023

AbstractRecognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object representations in computational models, raising the question of how the brain achieves such processing. In the present study, we show that combining Hebbian plasticity with a predictive form of plasticity leads to invariant representations in deep neural network models. We derive a local learning rule that generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity. Finally, our model accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Thus, we provide a plausible normative theory emphasizing the importance of predictive plasticity mechanisms for successful representational learning.
CO2 concentration forecasting in smart cities using a hybrid ARIMA–TFT model on multivariate time series IoT data
Authors: Linardatos Pantelis , Papastefanopoulos Vasilis , Panagiotakopoulos Theodor , Kotsiantis Sotiris 
Published: October 2023

AbstractCarbon Dioxide (CO$$_{2}$$

2

) is a significant contributor to greenhouse gas emissions and one of the main drivers behind global warming and climate change. In spite of the global economic slowdown due to the COVID-19 pandemic, the global average atmospheric CO$$_{2}$$

2

concentration reached a new record high in 2020 with its year-on-year increase being the fifth highest annual increase in 63 years, according to the National Oceanic and Atmospheric Administration. Furthermore, the years 2020 and 2019 were respectively the second and third warmest, while the decade 2010–2019 was the warmest decade ever recorded. In an attempt to curb this climate emergency, many countries and organizations globally have adopted ambitious goals and announced plans to help dramatically reduce CO$$_{2}$$

2

emissions. As part of these plans, various innovative smart city projects are being developed, focusing on implementing Internet of Things (IoT) technologies. By collecting sensor-based data, such technologies aim towards automating data-driven decision-making around carbon emission management and reduction. In this work, a hybrid machine learning system, aimed at forecasting CO$$_2$$

2

concentration levels in a smart city environment was developed using a multivariate time series dataset containing IoT sensor measurements of CO$$_{2}$$

2

, as well as various environmental factors, taken at every second. The proposed system demonstrated superior performance to similar methods, while also maintaining a high degree of interpretability. More specifically, the approach was empirically compared against other similar approaches in several scenarios and use cases, thus also offering more insight into the predictive capabilities of such state-of-the-art systems. For this comparison, both traditional time series and deep learning approaches were employed, including the current state-of-the-art architectures, such as attention-based, transformer networks. Results demonstrated that, when measured across various settings and metrics, including three different forecasting horizons, the hybrid solution achieved the best overall results, and in some cases, the difference in performance was statistically significant. At the same time, insights from the system’s inner workings were extracted, shedding light on the reasoning behind the model’s predictions and the factors that contribute to them, thus showcasing its transparency. Lastly, throughout the experiments, deep learning approaches illustrated their ability to better handle the multivariate nature of the dataset and in general tended to outperform the traditional time series methods, especially for longer forecasting horizons.
Deciphering the genetic diversity and population structure of wild barley germplasm against corn leaf aphid, Rhopalosiphum maidis (Fitch)
Authors: Maanju Sunny , Jasrotia Poonam , Yadav Surender Singh , Kashyap Prem Lal , Kumar Sudheer , Jat Manoj Kumar , Lal Chuni , Sharma Preeti , Singh Gyanendra , Singh Gyanendra Pratap 
Published: October 2023

AbstractCorn-leaf aphid (CLA-Rhopalosiphum maidis) is a major insect pest of barley (Hordeum vulgare) causing yield loss upto 30% under severe infestation. Keeping in view of the availability of very few sources of CLA resistance in barley, the present investigation was framed to assess the genetic diversity and population structure of 43 wild barley (H. vulgare subsp. spontaneum) genotypes using eight microsatellite markers against R. maidis. Three statistical methods viz. multivariate-hierarchical clustering, Bayesian clustering and PCoA, unanimously grouped genotypes into three subpopulations (K = 3) with 25.58% (SubPop1-Red), 39.53% (SubPop2-Green) and 34.88% (SubPop3-Blue) genotypes including admixtures. Based on Q ≥ 66.66%, 37.20% genotypes formed a superficial “Mixed/Admixture” subpopulation. All polymorphic SSR markers generated 36 alleles, averaging to 4.5 alleles/locus (2–7 range). The PIC and H were highest in MS31 and lowest in MS28, with averages of 0.66 and 0.71. MAF and mean genetic diversity were 0.16 and 89.28%, respectively. All these parameters indicated the presence of predominant genetic diversity and population structure amongst the studied genotypes. Based on AII, only 6 genotypes were found to be R. maidis resistant. SubPop3 had 91.66% (11) of the resistant or moderately resistant genotypes. SubPop3 also had the most pure genotypes (11), the least aphid infestation (8.78), and the highest GS (0.88), indicating its suitability for future R. maidis resistance breeding initiatives.
Impact of COVID infection on lung function test and quality of life
Authors: Toh Ming Ren , Teo Ying Rachel , Poh Li Choo Ruby , Tang Yiting , Soh Rui Ya , Sharma Kiran , Kalyanasundaram Ganesh , Poh Kai Chin 
Published: October 2023

AbstractPost-COVID-19 pulmonary sequalae are well-recognized early in the pandemic. Survivorship clinics are crucial for managing at-risk patients. However, it is unclear who requires pulmonary function test (PFT) and when PFTs should be performed. We aim to investigate for whom and how these interval PFTs should be performed. We performed a single-centre, prospective cohort study on COVID-19 survivors between 1st May 2020 and 31st April 2022. These patients were followed up at 6, 9 and 12 months with interval PFT and Short Form-36 (SF-36) Health Survey. Those with PFT defects were offered a computed tomography scan of the thorax. Of the 46 patients recruited, 17 (37%) had severe/critical illness. Compared to those with mild/moderate disease, these patients were more likely to experience DLCO defects (59% versus 17%, p = 0.005) and had lower SF-36 scores (mean physical component summary score of 45 ± 12 versus 52 ± 8, p = 0.046). These differences were most notable at 6 months, compared to the 9- and 12-months intervals. DLCO defects were also associated with older age, raised inflammatory markers and extensive CXR infiltrates. Besides interstitial-like abnormalities, obesity and undiagnosed lung conditions accounted for 39% of the PFT abnormalities. Interval PFTs can be performed earliest 6 months post-COVID-19. Patients with normal tests were unlikely to develop new abnormalities and would not require repeat PFTs. Abnormal PFTs can be followed-up with repeat PFTs 6 monthly until resolution. Non-COVID-19 differentials should be considered for persistent PFT abnormalities.
Effect of water stress and foliar application of chitosan and glycine betaine on lettuce
Authors: Ibrahim Ehab A. , Ebrahim Noura E. S. , Mohamed Gehan Z. 
Published: October 2023

AbstractThe present study investigated the effect of foliar application of chitosan at 150 ppm and glycine betaine at 700 ppm on lettuce plants cv. Balady grown under well-watered and water deficit conditions in terms of growth, yield, quality, and water usage efficiency. The study was conducted in Qalubia Governorate, Egypt, during the two seasons of 2020/2021 and 2021/2022 on clay soil. Results indicated that water-stressed plants had a reduction in plant fresh weight, plant height, leaf area, and total yield, chlorophyll content and relative water content, while they exhibited an increase in total soluble solids, nitrate, and proline contents as well as water-use efficiency in both seasons. The foliar application of chitosan or glycine betaine to lettuce significantly improved plant performance under limited and normal irrigation conditions in comparison with untreated plants. The maximum positive effect was for chitosan foliar application. Overall, the results of this study indicated that foliar application of chitosan or glycine betaine was a substitute technology for improving the lettuce yield and quality as well as increasing water use efficiency under both irrigation regimes, but may be more efficient in lettuce plants subjected to a water deficit.