bluuebunny/crossref_metadata_2025 huggingface dataset:
Houston We Have a Problem: How Debriefing Method Impacts Open Communication and the Depth of Team Reflexivity
Authors: Smith-Jentsch Kimberly A. , Sierra Mary Jane
Published: October 2023

Team debriefings are structured interventions in which teams reflect on their past performance, adapt, and plan for future events. Results from meta-analyses indicate that team debriefings are effective in improving task performance (Keiser & Arthur, Journal of Applied Psychology, 106(7), 1007–1032, 2021, Journal of Business and Psychology, 37(5), 953–976, 2022; Tannenbaum & Cerasoli, Human Factors: The Journal of the Human Factors and Ergonomics Society, 55(1), 231–245, 2013). Although far less often studied, there is also some evidence to suggest that team debriefings (compared to no debriefings) can be used to develop norms for open communication (Jarrett et al., Human Performance, 29(5), 408-427, 2016; Villado & Arthur, Journal of Applied Psychology, 98(3), 514-528, 2013). However, there is currently a dearth of quantitative evidence to guide practitioners in selecting from the myriad methods available to achieve this purpose. Grounded in theory and research on episodic models of team performance (Marks et al., Academy of Management Review, 26(3), 356-376, 2001) and the Motivated Information Processing in Groups model (MIP-G) (De Dreu et al., Personality and Social Psychology Review, 12(1), 22–49, 2008), we conducted a quasi-experiment which compared two debriefing methods. The first, a chronological debriefing, emphasizes outcome accountability and makes competitive interdependence salient, whereas the second method, Team Dimensional Training (TDT), emphasizes process accountability and makes cooperative interdependence salient. Data from 76 flight controllers at Johnson Space Center indicated that the communication climate in TDT debriefings was perceived to be more open than was the climate in chronological debriefings. Analyses of coded transcripts from 69 debriefings revealed that teams engaged in deeper reflexivity when the TDT method was used than they did when the chronological method was used.
Forensic discrimination between traditional and compressive imaging by blurring kernel investigation
Authors: Taimori Ali , Zayyani Hadi , Marvasti Farokh
Published: October 2023

Image 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

The 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

Corallite 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

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

Background:

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

This 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

My 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

More 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

The 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

We 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

Recognition 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

Carbon 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

Corn-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

Post-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

The 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.
Evaluation of STEM students' spatial abilities based on a novel net cube imagination test
Authors: Pawlak-Jakubowska Anita , Terczyńska Ewa
Published: October 2023

This study aimed to determine the level of spatial ability among STEM students. A universal multiple-choice test was prepared. The validity of the test and the effectiveness of its application were tested. The test is an extension of those currently in use. It contains tasks on spatial perception, spatial visualization, mental folding, rotation of spatial elements, and representation of spatial elements on a plane. The test consists of 16 tasks showing a cube with lines located on the walls. The student's task was to determine the development of the cube and mentally construct a cube based on the development. The results of the test determined the level of progress of the group (105 participants), and showed that a significant number of students have difficulties in perceiving and working with a three-dimensional object. On average 55% of the questions were answered correctly. For the group tested, reading a flat drawing and determining axonometry proved easier than other task. Students who attended technical high school or had design experience scored better. During the course, measures to improve teaching were introduced. Spatial model work was strengthened and initial tasks were adjusted according to the level of the group. Emphasis on teamwork and consultation was introduced for those with the lowest scores. The applied modifications in classroom management had a good effect. The average of the final grade was B. The test is a useful tool for academics and students to study spatial ability and improve teaching activities for STEM students.
Clinically informed machine learning elucidates the shape of hospice racial disparities within hospitals
Authors: Khayal Inas S. , O’Malley A. James , Barnato Amber E.
Published: October 2023

Racial disparities in hospice care are well documented for patients with cancer, but the existence, direction, and extent of disparity findings are contradictory across the literature. Current methods to identify racial disparities aggregate data to produce single-value quality measures that exclude important patient quality elements and, consequently, lack information to identify actionable equity improvement insights. Our goal was to develop an explainable machine learning approach that elucidates healthcare disparities and provides more actionable quality improvement information. We infused clinical information with engineering systems modeling and data science to develop a time-by-utilization profile per patient group at each hospital using US Medicare hospice utilization data for a cohort of patients with advanced (poor-prognosis) cancer that died April-December 2016. We calculated the difference between group profiles for people of color and white people to identify racial disparity signatures. Using machine learning, we clustered racial disparity signatures across hospitals and compared these clusters to classic quality measures and hospital characteristics. With 45,125 patients across 362 hospitals, we identified 7 clusters; 4 clusters (n = 190 hospitals) showed more hospice utilization by people of color than white people, 2 clusters (n = 106) showed more hospice utilization by white people than people of color, and 1 cluster (n = 66) showed no difference. Within-hospital racial disparity behaviors cannot be predicted from quality measures, showing how the true shape of disparities can be distorted through the lens of quality measures. This approach elucidates the shape of hospice racial disparities algorithmically from the same data used to calculate quality measures.
A generalized dual-domain generative framework with hierarchical consistency for medical image reconstruction and synthesis
Authors: Zhang Jiadong , Sun Kaicong , Yang Junwei , Hu Yan , Gu Yuning , Cui Zhiming , Zong Xiaopeng , Gao Fei , Shen Dinggang
Published: October 2023

Medical image reconstruction and synthesis are critical for imaging quality, disease diagnosis and treatment. Most of the existing generative models ignore the fact that medical imaging usually occurs in the acquisition domain, which is different from, but associated with, the image domain. Such methods exploit either single-domain or dual-domain information and suffer from inefficient information coupling across domains. Moreover, these models are usually designed specifically and not general enough for different tasks. Here we present a generalized dual-domain generative framework to facilitate the connections within and across domains by elaborately-designed hierarchical consistency constraints. A multi-stage learning strategy is proposed to construct hierarchical constraints effectively and stably. We conducted experiments for representative generative tasks including low-dose PET/CT reconstruction, CT metal artifact reduction, fast MRI reconstruction, and PET/CT synthesis. All these tasks share the same framework and achieve better performance, which validates the effectiveness of our framework. This technology is expected to be applied in clinical imaging to increase diagnosis efficiency and accuracy.
Visible light-induced metal-free chemoselective oxidative cleavage of benzyl C–heteroatom (N, S, Se) bonds utilizing organoboron photocatalysts
Authors: Wei Lanfeng , Bai Wenbo , Hu Zhiyan , Yang Zhiyong , Xu Liang
Published: October 2023

We have established a photooxidation system, which can realize formal carbonylation of C–heteroatom (N, S, Se) bonds.
Interface segregation of iron sintering aid in gadolinium-doped ceria
Authors: Machado Marina , da Silva Andre L. , Moraes Leticia P. R. , Rodrigues Lays N. , Caliman Lorena B. , Gouvêa Douglas , Fonseca Fabio C.
Published: October 2023

Increased sintering activity of Fe-doped CGO is related to the preferential segregation of iron species in CGO grain boundaries.
A promising small-sized near-infrared absorbing zwitterionic dye for DSSC and NLO applications: DFT and TD-DFT approaches
Authors: Sharma Suryapratap J. , Sekar Nagaiyan
Published: October 2023

Small-sized zwitterionic dyes, due to their absorption ranging from ultraviolet to visible to near-infrared regions, are a potential candidate for NLO and an alternative for the co-sensitization technique in DSSCs.
Phosphinoborenium cations stabilized by N-heterocyclic carbenes: synthesis, structure, and reactivity
Authors: Kaniewska-Laskowska Kinga , Ordyszewska Anna , Wojnowski Tomasz , Halenka Hanna , Czapla Marcin , Chojnacki Jarosław , Grubba Rafał
Published: October 2023

NHC-phosphinoborenium cations containing a phosphanyl group directly bonded to the tricoordinate boron center have been isolated.
Cutting “lab-to-fab” short: high throughput optimization and process assessment in roll-to-roll slot die coating of printed photovoltaics
Authors: Wagner Michael , Distler Andreas , Le Corre Vincent M. , Zapf Simon , Baydar Burak , Schmidt Hans-Dieter , Heyder Madeleine , Forberich Karen , Lüer Larry , Brabec Christoph J. , Egelhaaf H.-J.
Published: October 2023

Commercialization of printed photovoltaics requires knowledge of the optimal composition and microstructure of the single layers, and the ability to control these properties over large areas under industrial conditions.
A comprehensive review on isochoric freezing: a recent technology for preservation of food and non-food items
Authors: R. Dhanya , Panoth Abhirami , Venkatachalapathy N.
Published: October 2023

Isochoric freezing has impeccable applications in the frozen food industry since it preserves food products without inducing ice crystal formation and also increases the marketability of various food products currently unsuitable for traditional freezing methods.
Co-SAC catalyzed utilization of methanol and ethanol in the transfer hydrogenation of azo bonds: experimental and theoretical studies
Authors: Panja Dibyajyoti , Dey Sadhan , Saha Rohini , Sahu Rajib , Das Gourab Kanti , Bhobe Preeti , Kundu Sabuj
Published: October 2023

An efficient protocol for the transfer hydrogenation of –NN– bonds using methanol and ethanol as the H2 sources is reported.
Bio-based platform chemicals synthesized from lignin biorefinery
Authors: Hu Rui , Zhan Jiahui , Zhao Yuying , Xu Xinyi , Luo Gang , Fan Jiajun , Clark James H. , Zhang Shicheng
Published: October 2023

Bio-based chemicals synthesized by lignin offer a promising pathway of bioenergy utilization to achieve the target of the Paris Agreement with <2 °C of climate warming temperature.
Pinch-off droplet generator using microscale gigahertz acoustics
Authors: Zhou Yangchao , He Meihang , Zhang Haitao , Liu Bohua , Sun Chongling , Han Ziyu , Duan Xuexin
Published: October 2023

We propose a nozzle-free acoustic-based method for generating droplets with diameters ranging from 2 to 1800 μm using a gigahertz (GHz) bulk acoustic wave (BAW).
A multivariate metal–organic framework based pH-responsive dual-drug delivery system for chemotherapy and chemodynamic therapy
Authors: Akbar Muhammad Usman , Akbar Arslan , Saddozai Umair Ali Khan , Khan Malik Ihsan Ullah , Zaheer Muhammad , Badar Muhammad
Published: October 2023

By combining two different ligands and metals, MOFs can be fine-tuned for effective encapsulation and delivery of two anticancer drugs.
Mechanical properties of TiO2/carboxylic-acid interfaces from first-principles calculations
Authors: Sellschopp Kai , Vonbun-Feldbauer Gregor B.
Published: October 2023

Fundamental insights into the atomistic processes during interface failure in metal-oxide-organic nanocomposites are provided based on ab-initio calculations.
Comparative analysis of two models for phonon polaritons in van der Waals materials: 2D and 3D
Authors: Chen Shuo , Wu Xiaohu , Fu Ceji
Published: October 2023

We investigate the difference between polaritonic properties of phonon polaritons in a vdW material α-MoO3 based on 3D and 2D models. The volume (multiple branches) and surface (single branch)-confined phonon polaritons depend on choice of model.
A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments
Authors: Zhou Zhiqian , Ren Junkai , Zeng Zhiwen , Xiao Junhao , Zhang Xinglong , Guo Xian , Zhou Zongtan , Lu Huimin
Published: October 2023

When deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots. However, until now, efficient and safe robot navigation in such dynamic environments is still an open problem. The critical reason is that the inconsistency between navigation efficiency and motion safety is greatly intensified by the high dynamics and uncertainties of pedestrians. To face the challenge, this paper proposes a safe deep reinforcement learning algorithm named Conflict‐Averse Safe Reinforcement Learning (CASRL) for autonomous robot navigation in dynamic environments. Specifically, it first separates the collision avoidance sub‐task from the overall navigation task and maintains a safety critic to evaluate the safety/risk of actions. Later, it constructs two task‐specific but model‐agnostic policy gradients for goal‐reaching and collision avoidance sub‐tasks to eliminate their mutual interference. Then, it further performs a conflict‐averse gradient manipulation to address the inconsistency between two sub‐tasks. Finally, extensive experiments are performed to evaluate the superiority of CASRL. Simulation results show an average 8.2% performance improvement over the vanilla baseline in eight groups of dynamic environments, which is further extended to 13.4% in the most challenging group. Besides, forty real‐world experiments fully illustrated that the CASRL could be successfully deployed on a real robot.
Design of spread spectrum communication system based on multipath signal synthesis
Authors: Xiaoxu Guo , Zhaobin Xu , Jiacheng Dai , Jia Yang , Xiaojun Jin , Zhonghe Jin
Published: October 2023

A low‐complexity anti‐multipath interference spread spectrum communication system is proposed to address shortcomings in traditional anti‐multipath technology, such as complex structures and high resource consumption. The system utilizes a fast Fourier transform to capture pseudo‐noise code signals and simultaneously detect multipath signals. Moreover, the signal‐to‐noise ratio (SNR) of each path signal is calculated, and the multipath signal is synthesized through the designed optimal SNR synthesis method to improve the SNR of the demodulated signal to reduce the bit error rate. MATLAB is used to perform functional simulations of the system to verify its feasibility. The simulation results show that the system can successfully detect and synthesize multipath signals and achieve lower communication error rates than traditional code division multiple access (CDMA) systems.
Impact of power flow on the apparent impedance calculated by the distance protection in transmission lines
Authors: Leonardo Fonseca , Fernando Chapi , Antonio Fonseca , Franklin Quilumba , Fabian Pérez‐Yauli
Published: October 2023

The reach of primary protection zones of transmission lines distance relays is affected when large power flows circulate through the transmission line prior to a short circuit occurrence. This can cause improper operation of the protection system in certain scenarios. In practice, protection zone adjustments are made by considering the apparent impedances obtained from short‐circuit simulations in the line of interest using standard methods such as IEC 60909‐0 or ANSI/IEEE C37.010. However, these short‐circuit calculation methods omit the existing operating conditions prior to the fault. Therefore, this work proposes a set of equations developed for calculating the apparent impedance seen by the distance relay in view of different types of short circuits, including the effects of power flow prior to the fault. These equations are then used to establish the distance primary protection zone settings. The analysis uses the mathematical expressions proposed of voltages and currents at the relay location, and the impedance diagrams obtained from simulated short circuits. Subsequently, the settings obtained with these equations are compared to those achieved using the IEC 60909‐0 standard, allowing for the differences in settings and their effectiveness. Finally, this procedure is applied to a part of the Ecuadorian Transmission System.
Study on surface discharge of nonlinear conductive epoxy resin/SrTiO3 composites in 80–300 K temperature zone
Authors: Xing Yunqi , Wang Bin , Wang Mingyang , Chi Jiakai , Jia Peng , Huang Rongjin
Published: October 2023

Bisphenol F epoxy resin (EP) is often used in terminal current lead insulation of superconducting equipment because of its good insulation performance, high mechanical strength, good toughness at cryogenic temperatures, and resistance to cold shock and heat shock. However, due to the wide temperature range of 80–300 K and the strong electric field, the EP‐N2 interface is prone to surface flashover, resulting in terminal insulation failure. To improve the reliability of the current lead insulation, non‐linear conductive EP/strontium titanate (SrTiO3) composites were prepared by modification with nano filling. The changes of dielectric, surface discharge, flashover, and trap distribution characteristics of composite materials were studied, and the mechanism of SrTiO3 on the surface flashover of composite materials was analysed. The results show that the conductivity of the composite increases with the rise of SrTiO3 filling content, and the amplitude of improvement is greater under the strong electric field, showing a more significant non‐linearity. The composite has a lower trap energy level and a greater number of shallow traps compared to pure EP, which accelerates surface charge de‐trapping and reduces charge accumulation, effectively enhancing the discharge and surface flashover voltage of the composite.
Blind motion deblurring using improved DeblurGAN
Authors: Ji Wentao , Chen Xing , Li Yihong
Published: October 2023

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery.
Nonparametric point cloud filter
Authors: Sun Yefa , Wang Jinli
Published: October 2023

This paper proposes a nonparametric point cloud filter to address the issue that existing point cloud filtering methods cannot retain important point cloud features after filtering and often require complex parameter adjustments. Firstly, a nonparametric clustering method is proposed to cluster various features of the point cloud and filter out isolated outliers. Then, the manifold distance truncation method is adopted to remove the outlier cluster generated by the point cloud clustering to complete point cloud filtering. Additionally, the proposed nonparametric clustering algorithm is compared with four of the latest clustering algorithms, including K‐means clustering and OPTICS clustering to verify the rationality of the clustering features. Finally, the filtering results of the nonparametric point cloud filter are compared with those of statistical filtering and two other recently proposed point cloud filters to demonstrate its improved filtering effect and algorithm stability. The experimental results indicate that the proposed nonparametric point cloud filter can achieve better‐filtering results and retain more point cloud features without adjusting parameters.
Parameter optimization of reactive power device planning in hybrid AC/DC networks based on objective‐oriented scenario selection method
Authors: Niu Tao , Wu Wenguo , Fang Sidun , Li Fan , Chen Guanhong
Published: October 2023

As one of the most common voltage/var‐related contingencies, commutation failures in hybrid AC/DC networks deteriorate the voltage profiles. Thus, systems may suffer from large‐scale cascading risks induced by the lack of dynamic reactive power support. Conventional power system planning usually aims to optimize the device capacity and location, ignoring the influences of intrinsic device parameters on high‐voltage direct current (HVDC) dynamics during commutation failures. If the intrinsic device parameters are also taken into account in the planning problem, it would allow for faster dynamic voltage/var support during system faults or disturbances to recover from voltage drops. However, considering the intrinsic parameters, allocations and capacities of a device, the optimal planning problem brings several challenges, such as scenario selections and computational burdens. To overcome these issues, an objective‐oriented scenario selection based power system planning approach is proposed in this paper. First, the detailed operations of converter stations are formulated both considering the steady state and system dynamics. Next, the quantitative relation between the power losses and deviation in system conditions is then derived to approximately describe the operational characteristics. Then, an objective‐oriented representative scenario selection method is introduced, aiming to reduce the number of selected scenarios, guaranteeing both computational accuracy and efficiency. Finally, a modified IEEE‐118 system is used to test the proposed algorithm, and the superiority and computational efficiency are observed by the simulation results.
Reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration
Authors: Wang Haipeng , Lu Hongchang , Sun Kai , Wu Xuewei , He Yuling , Du Xiaodong
Published: October 2023

This paper presents a reliability evaluation method for distribution network with distributed generations considering feeder fault recovery and network reconfiguration, and mainly addresses issues: 1) insufficient consideration of the characteristics for the components and distributed generations (DGs) of distribution network such as time sequences, randomness and intermittency; 2) changes in the feeder area of the distribution network after the access of DGs; and 3) multi‐dimensional reliability index for distribution network with DGs has not been formed perfectly. Firstly, Markov reliability model for distribution network components/DGs is established in our study. Then, considering the fault recovery and network reconfiguration of the feeder area, island partitioning method of distribution network with DGs is also proposed. Moreover, a reliability evaluation model is presented for distribution network with DGs based on sequential Monte Carlo method. Case study is performed on the improved IEEE RBTS BUS6 F4 feeder system, and the research results indicate the validity and effectiveness of the proposed method. In addition, the system reliability indicators, such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), and average system availability index (ASAI), are studied and discussed in detail from three dimensions: the access of DGs, the types of DGs, and the capacity of DGs.