bluuebunny/crossref_metadata_2025_split huggingface dataset:
Crop waste management: perspectives on alternative uses in India
Authors: Devi Randhi Uma , Balakrishna Kolli 
Published: August 2022

Abstract
India, which is primarily agricultural, produces approximately 683 million tonnes (MT) of crop waste. This waste is mostly used for animal feed and fuel. In India, roughly 178 MT of surplus crop leftovers and 92 MT of crop trash are burned each year because of a lack of sustainable agricultural management practices,  resulting in severe air pollution.  This has grown into a significant environmental issue, contributing to global warming. This crop residue burning can be prevented by adopting sustainable crop management practices through the support of government initiatives and policies. Crop waste generation, demand and feasibility differ from region to region.  As a result, crop residue management plan tailored to the needs of each region should be developed. The central and state governments in India have launched a number of strategies to deal with excess crop leftovers. The majority of innovations have focused on rice husk, straw, maize waste, bagasse and banana crop waste, which account for 56% of India’s crop waste. In addition, several millet crops and cereals such as bajra, sorghum and others generate more than 352 MT of crop waste annually. These wastes are potentially available in most of the Indian states and cover 44% of crop waste, and these are not being used effectively. This review suggests advanced and practical research to develop millet crop residue management using conservation agricultural methods.
Work Integrated Learning (WIL) in the Indo-Pacific: A Village Stay Model
Authors: Tarrant Michael A. , Gleason Mikell , Sykes Helen , Boyd Steven , Lyons Kevin , Herriman Nick 
Published: August 2022

Abstract
This ongoing Work Integrated Learning (WIL) programme began in Fiji in the mid-2010s and was most recently extended to include Indonesia. The project, to establish a commercially viable village stay aligned with a resort, has operated in conjunction with several universities in Australia and the USA and has received external funding. Through outbound mobility programmes, and using an established business toolbox, students work with existing village group structures (i.e. youth, women, elders and church groups) to create a collaborative venture (a cultural immersion and educational stay) between the village and a local eco-resort. To date, five enterprises including four in Fiji and one in Indonesia have been created. Student evaluations attest to the transformational nature of the WIL programme in providing a unique business acumen and global skill set that can position graduates to live and work in the Indo-Pacific region. The long-term goal is to enable an operational system that can be adapted to include multiple village–resort partnerships, producing a replicable business model (and associated tourism toolkit) that can be applied throughout the developing world.

Information
In the Work Integrated Learning (WIL) programmes described here, university students collaborate with eco-resorts and local villagers to create a commercially viable and culturally experiential village stay.
© CAB International 2022
Nucleon–nucleon correlations in the extreme oxygen isotopes
Authors: Wang S M , Nazarewicz W , Charity R J , Sobotka L G 
Published: August 2022

Abstract
There has been an upsurge of interest in two-nucleon decays thanks to the studies of nucleon–nucleon correlations. In our previous work (2021 Phys. Rev. Lett.
126 142501), based on a novel time-dependent three-body approach, we demonstrated that the energy and angular correlations of the emitted nucleons can shed light on the structure of nucleonic pairs formed inside the nucleus. In this work, we apply the new framework to study the decay dynamics and properties of some extreme proton-rich and neutron-rich oxygen isotopes, including two-proton (2p) decays of 11,12O and two-neutron (2n) decay of 26O. Here we show that the low-ℓ components of 11,12O wave functions, which are affected by continuum and configuration-interaction effects, strongly impact decay dynamics and asymptotic correlations. In the calculated wave functions of 11,12O, diproton and cigarlike structures merge together during the tunneling process and the resulting energy and angular correlations are very consistent with the experimental data. The asymptotic correlations of the 2n decay of 26O dramatically change as the 2n decay energy approaches the zero-energy threshold. The small reported value of Q
2n suggests that the 2n decay of this nucleus can be understood in terms of the universal phase-space limit.
Personal dosimetry for positron emitters, and occupational exposures from clinical use of gallium-68
Authors: Eakins J , Hager L , O’Connor U , Cooke J , O’Reilly G , Walsh C , Willson T 
Published: August 2022

Abstract
The current status and issues regarding positron dosimetry in nuclear medicine are summarized. The suitability of the United Kingdom Health Security Agency extremity and eye beta-gamma personal thermoluminescence dosemeters are then considered. Monte Carlo modelling is performed to determine their responses and derive sets of calibration factors, along with H
p(0.07) and H
p(3) conversion coefficients, for carbon-11, nitrogen-13, oxygen-15, fluorine-18 and gallium-68 sources, which are commonly used in positron emission tomography (PET) computed tomography; data for these isotopes is assumed extrapolatable to other positron sources. It is found that the dosemeters are adequate for assessing exposures to PET radionuclides, even if their routine calibrations to caesium-137 were maintained. An idealized set of measurements representing gallium-68 exposure scenarios is then described, including reproducible mock-ups of individuals manipulating vials and syringes. Finally, a short case-study is presented that explores occupational doses during routine clinical use of gallium-68. The extremity dosemeter results demonstrated significant variations dependent upon the exposure conditions, with some seen to be comparatively large; whole-body and eye dose rates per activity were found to be lower. The importance of routine dose monitoring of workers is emphasized, with the need for a longer-termed follow-up study demonstrated.
Deep learning based instance segmentation of particle streaks and tufts
Authors: Tsalicoglou C , Rösgen T 
Published: August 2022

Abstract
3D particle streak velocimetry (3D-PSV) and surface flow visualization using tufts both require the detection of curve segments, particle streaks or tufts, in images. We propose the use of deep learning based instance segmentation neural networks Mask region-based convolutional neural network (R-CNN) and Cascade Mask R-CNN, trained on fully synthetic data, to accurately identify, segment, and classify streaks and tufts. For 3D-PSV, we use the segmented masks and detected streak endpoints to volumetrically reconstruct flows even when the imaged streaks partly overlap or intersect. In addition, we use Mask R-CNN to segment images of tufts and classify the detected tufts according to their range of motion, thus automating the detection of regions of separated flow while at the same time providing accurate segmentation masks. Finally, we show a successful synthetic-to-real transfer by training only on synthetic data and successfully evaluating real data. The synthetic data generation is particularly suitable for the two presented applications, as the experimental images consist of simple geometric curves or a superposition of curves. Therefore, the proposed networks provide a general framework for instance detection, keypoint detection and classification that can be fine-tuned to the specific experimental application and imaging parameters using synthetic data.
Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion
Authors: Liu Cang , Tong Jinyu , Zheng Jinde , Pan Haiyang , Bao Jiahan 
Published: August 2022

Abstract
Despite the great achievements of deep learning methods based on a single sensor in fault diagnosis, learning useful information from multi-sensor data is still a challenge. In order to make full use of multi-sensor information and improve the performance of rolling bearing fault diagnosis, a novel multi-sensor information fusion framework is proposed in this paper. First, a multi-sensor-based multi-frequency information fusion method is proposed. The multi-frequency information of each sensor is segmented first to enhance the datasets, and then a weighted fusion rule based on fuzzy entropy is constructed to fuse the information of different frequency components for multi-sensors. Second, a multi-kernel attention convolutional neural network is designed to realize multi-frequency feature capture, fusion, and fault classification of multi-sensors. Finally, two different rolling bearing datasets are used to implement fault diagnosis experiments. Experimental results show that the proposed method outperforms the comparative methods in terms of diagnostic performance and robustness.
Bioinspired micro- and nano-structured neural interfaces
Authors: Mariano Anna , Bovio Claudia Latte , Criscuolo Valeria , Santoro Francesca 
Published: August 2022

Abstract
The development of a functional nervous system requires neurons to interact with and promptly respond to a wealth of biochemical, mechanical and topographical cues found in the neural extracellular matrix (ECM). Among these, ECM topographical cues have been found to strongly influence neuronal function and behavior. Here, we discuss how the blueprint of the architectural organization of the brain ECM has been tremendously useful as a source of inspiration to design biomimetic substrates to enhance neural interfaces and dictate neuronal behavior at the cell-material interface. In particular, we focus on different strategies to recapitulate cell-ECM and cell–cell interactions. In order to mimic cell-ECM interactions, we introduce roughness as a first approach to provide informative topographical biomimetic cues to neurons. We then examine 3D scaffolds and hydrogels, as softer 3D platforms for neural interfaces. Moreover, we will discuss how anisotropic features such as grooves and fibers, recapitulating both ECM fibrils and axonal tracts, may provide recognizable paths and tracks that neuron can follow as they develop and establish functional connections. Finally, we show how isotropic topographical cues, recapitulating shapes, and geometries of filopodia- and mushroom-like dendritic spines, have been instrumental to better reproduce neuron–neuron interactions for applications in bioelectronics and neural repair strategies. The high complexity of the brain architecture makes the quest for the fabrication of create more biologically relevant biomimetic architectures in continuous and fast development. Here, we discuss how recent advancements in two-photon polymerization and remotely reconfigurable dynamic interfaces are paving the way towards to a new class of smart biointerfaces for in vitro applications spanning from neural tissue engineering as well as neural repair strategies.
An inception network for positron emission tomography based dose estimation in carbon ion therapy
Authors: Rutherford Harley , Saha Turai Rohan , Chacon Andrew , Franklin Daniel R , Mohammadi Akram , Tashima Hideaki , Yamaya Taiga , Parodi Katia , Rosenfeld Anatoly B , Guatelli Susanna , Safavi-Naeini Mitra 
Published: August 2022

Abstract

Objective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates of the dose distribution with comparable or improved accuracy and computational efficiency compared to the iterative method and other similar PET-based direct dose quantification techniques.
Personalized mid-course FDG-PET based adaptive treatment planning for non-small cell lung cancer using machine learning and optimization
Authors: Ajdari Ali , Liao Zhongxing , Mohan Radhe , Wei Xiong , Bortfeld Thomas 
Published: August 2022

Abstract

Objective. Traditional radiotherapy (RT) treatment planning of non-small cell lung cancer (NSCLC) relies on population-wide estimates of organ tolerance to minimize excess toxicity. The goal of this study is to develop a personalized treatment planning based on patient-specific lung radiosensitivity, by combining machine learning and optimization. Approach. Sixty-nine non-small cell lung cancer patients with baseline and mid-treatment [18]F-fluorodeoxyglucose (FDG)-PET images were retrospectively analyzed. A probabilistic Bayesian networks (BN) model was developed to predict the risk of radiation pneumonitis (RP) at three months post-RT using pre- and mid-treatment FDG information. A patient-specific dose modifying factor (DMF), as a surrogate for lung radiosensitivity, was estimated to personalize the normal tissue toxicity probability (NTCP) model. This personalized NTCP was then integrated into a NTCP-based optimization model for RT adaptation, ensuring tumor coverage and respecting patient-specific lung radiosensitivity. The methodology was employed to adapt the treatment planning of fifteen NSCLC patients. Main results. The magnitude of the BN predicted risks corresponded with the RP severity. Average predicted risk for grade 1–4 RP were 0.18, 0.42, 0.63, and 0.76, respectively (p < 0.001). The proposed model yielded an average area under the receiver-operating characteristic curve (AUROC) of 0.84, outperforming the AUROCs of LKB-NTCP (0.77), and pre-treatment BN (0.79). Average DMF for the radio-tolerant (RP grade = 1) and radiosensitive (RP grade ≥ 2) groups were 0.8 and 1.63, p < 0.01. RT personalization resulted in five dose escalation strategies (average mean tumor dose increase = 6.47 Gy, range = [2.67–17.5]), and ten dose de-escalation (average mean lung dose reduction = 2.98 Gy [0.8–5.4]), corresponding to average NTCP reduction of 15% [4–27]. Significance. Personalized FDG-PET-based mid-treatment adaptation of NSCLC RT could significantly lower the RP risk without compromising tumor control. The proposed methodology could help the design of personalized clinical trials for NSCLC patients.
Interstitial fluid streaming in deep tissue induced by ultrasound momentum transfer for accelerating nanoagent transport and controlling its distribution
Authors: Yuan Baohong 
Published: August 2022

Abstract

Objective. This study aims to theoretically investigate the dynamics of ultrasound-induced interstitial fluid streaming and tissue recovery after ultrasound exposure for potentially accelerating nanoagent transport and controlling its distribution in tissue. Approach. Starting from fundamental equations, the dynamics of ultrasound-induced interstitial fluid streaming and tissue relaxation after an ultrasound exposure were modeled, derived and simulated. Also, both ultrasound-induced mechanical and thermal effects were considered in the models. Main results. The proposed new mechanism was named squeezing interstitial fluid via transfer of ultrasound momentum (SIF-TUM). It means that an ultrasound beam can squeeze the tissue in a small focal volume from all the directions, and generate a macroscopic streaming of interstitial fluid and a compression of tissue solid matrix. After the ultrasound is turned off, the solid matrix will recover and can generate a backflow. Rather than the ultrasound pressure itself or intensity, the streaming velocity is determined by the dot product of the ultrasound pressure gradient and its conjugate. Tissue and nanoagent properties also affect the streaming and recovery velocities. Significance. The mobility of therapeutic or diagnostic agents, such as drugs, drug carriers, or imaging contrast agents, in the interstitial space of many diseased tissues, such as tumors, is usually extremely low because of the inefficiency of the natural transport mechanisms. Therefore, the interstitial space is one of the major barriers hindering agent deliveries. The ability to externally accelerate agent transport and control its distribution is highly desirable. Potentially, SIF-TUM can be a powerful technology to accelerate agent transport in deep tissue and control the distribution if appropriate parameters are selected.
Constraints on τNLfrom Planck temperature and polarization
Authors: Marzouk Kareem , Lewis Antony , Carron Julien 
Published: August 2022

AbstractWe update constraints on the amplitude of the primordial trispectrum, using the finalPlanckmission temperature and polarization data. In the squeezed limit, a cosmological local trispectrum would be observed as a spatial modulation of small-scale power on the CMB sky. We reconstruct this signal as a source of statistical anisotropy via quadratic estimator techniques. We systematically demonstrate how the estimated power spectrum of a reconstructed modulation field can be translated into a constraint onτNLvia likelihood methods, demonstrating the procedures effectiveness by inferring knownτNLsignal(s) from simulations. Our baseline results constrainτNL< 1700 at the 95% confidence level, providing the most stringent constraints to date.
Dark photon stars: formation and role as dark matter substructure
Authors: Gorghetto Marco , Hardy Edward , March-Russell John , Song Ningqiang , West Stephen M. 
Published: August 2022

Abstract
Any new vector boson with non-zero mass (a 'dark photon' or 'Proca boson') that is present during inflation is automatically produced at this time from vacuum fluctuations and can comprise all or a substantial fraction of the observed dark matter density, as shown by Graham, Mardon, and Rajendran. We demonstrate, utilising both analytic and numerical studies, that such a scenario implies an extremely rich dark matter substructure arising purely from the interplay of gravitational interactions and quantum effects. Due to a remarkable parametric coincidence between the size of the primordial density perturbations and the scale at which quantum pressure is relevant, a substantial fraction of the dark matter inevitably collapses into gravitationally bound solitons, which are fully quantum coherent objects. The central densities of these 'dark photon star', or 'Proca star', solitons are typically a factor 106 larger than the local background dark matter density, and they have characteristic masses of 10-16
M
⊙ (10-5 eV/m)3/2, where m is the mass of the vector.
During and post soliton production a comparable fraction of the energy density is initially stored in, and subsequently radiated from, long-lived quasi-normal modes. Furthermore,
the solitons are surrounded by characteristic 'fuzzy' dark matter halos in which quantum wave-like properties are also enhanced relative to the usual virialized dark matter expectations.
Lower density compact halos, with masses a factor of ∼ 105 greater than the solitons, form at much larger scales. We argue that, at minimum, the solitons are likely to survive to the present day without being tidally disrupted. This rich substructure, which we anticipate also arises from other dark photon dark matter production mechanisms, opens up a wide range of new direct and indirect detection possibilities, as we discuss in a companion paper.
Bound noise-like pulse generation from Yb-doped passively mode-locked fiber lasers
Authors: Gao Zi-Qi , Wu Yun-Feng , Liu Hai-Yan , Zhang Ze-Xin , Tian Jin-Rong , Song Yan-Rong 
Published: August 2022

Abstract
We demonstrated two different types of bound noise-like pulses (NLPs) in ytterbium-doped mode-locked fiber lasers using the nonlinear polarization rotation technique. In the anomalous dispersion area, there were multiple spikes in the intensity autocorrelation (IAC) trace. In the normal dispersion area, the spike presented only in the center the of IAC trace. The results of the experiment show that the bound state NLPs may have partial coherence.
Microwave enhanced precision in 2D and 3D atom localization at nonzero temperatures
Authors: Iqbal Dar Zubair , Kaur Amanjot , Singh Neeraj , Kaur Paramjit 
Published: August 2022

Abstract
Two- and three-dimensional (2D and 3D) atom localization is analyzed by monitoring the probe absorption spectrum in a microwave driven X-type scheme. It is found that for both stationary and moving atom cases, the precision and certainty in atomic position can be significantly improved by proper adjustment of the system parameters. Our results also reveal that the high microwave field strength curbs the Doppler broadening effect to a large extent and enhances detection probability to 100% in 2D and 3D subspace at nonzero temperatures. Our proposed scheme may be helpful for experimental realization of high precision position measurement and atom nanolithography at room temperature.
Influence of the neck parameter on the fission dynamics within the two-center shell model parametrization*
Authors: Liu Li-Le , Wu Xi-Zhen , Chen Yong-Jing , Shen Cai-Wan , Li Zhu-Xia , Ge Zhi-Gang , Shu Neng-Chuan 
Published: August 2022

Abstract
The influence of the neck parameter on the fission dynamics at low excitation energy is studied based on the three-dimensional Langevin approach, in which the nuclear shape is described with the two-center shell model (TCSM) parametrization, and the elongation, mass asymmetry, and fragment deformation are set to be the generalized coordinates of the Langevin equation. We first study the influence of the neck parameter on the scission configuration. We find that there is almost no obvious correlation between the neck parameter

and mass asymmetry η at the scission point, indicating that

has no evident impact on the fragment mass distribution. The elongation

and its correlation with the mass asymmetry η at the scission point are clearly influenced by the neck parameter

, which has a strong effect on the total kinetic energy (TKE) distribution of the fragments. The pre-neutron emission fragment mass distributions for 14 MeV n+

U and

Pu are calculated, and then, based on these results, the post-neutron emission fragment mass distributions are obtained by using the experimental data of prompt neutron emission. The calculated post-neutron emission fragment mass distributions can reproduce the experimental data well. The TKE distributions for 14 MeV n+

U fission are calculated for

=0.25, 0.35, and 0.45, and the results show that the TKE distribution cannot be described very well for the three cases. However, the trend of the calculated TKE distribution with

is just as expected from the scission configuration calculations. The results with

=0.35 present a better agreement with the experiment data compared with the other two cases.