bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contextual information extraction in brain tumour segmentation
Authors: Zia Muhammad Sultan , Baig Usman Ali , Rehman Zaka Ur , Yaqub Muhammad , Ahmed Shahzad , Zhang Yudong , Wang Shuihua , Khan Rizwan 
Published: July 2023

AbstractAutomatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.
Demand‐side price‐responsive flexibility and baseline estimation through end‐to‐end learning
Authors: Shi Yuanyuan , Xu Bolun 
Published: July 2023

AbstractTime‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand.
Proposed ethical framework for software requirements engineering
Authors: Biable Seblewongel E. , Garcia Nuno M. , Midekso Dida 
Published: July 2023

AbstractRequirements engineering is a fundamental process in software development phases. At the same time, it is a difficult phase and exposed many ethical violations. The main purpose is proposing an ethical framework for software requirements engineering that addresses the identified concerns. These concerns include problems associated with a knowledge gap, requirements identification, quality‐related concerns, unwillingness to give requirements, and practicing forbidden activities. These concerns are grouped into a category as the proposed framework components. Each of the categories encompasses more than one problem domain. The proposed framework suggests resolving mechanisms as collections of clauses for each of those concerns. An expert evaluation technique is used to validate the proposed framework. The experts are purposefully selected from software industries and institutions. Questionnaires and focus group discussions were used as data‐gathering tools for the validation of the proposed framework. The validity (face validity, content validity, and construct validity) and the reliability of the proposed framework were checked. The evaluation results show that the proposed framework has an acceptable range of validity and reliability. The proposed framework can be used as a guideline for software engineers to minimise the occurrence of those identified concerns during the requirements engineering process.
Digital transformation and digital literacy in the context of complexity within higher education institutions: a systematic literature review
Authors: Farias-Gaytan Silvia , Aguaded Ignacio , Ramirez-Montoya Maria-Soledad 
Published: July 2023

AbstractThe incessant changes in technology generate new products and services, presenting multiple opportunities for the complex educational environment. Consequently, higher education institutions must be attentive to these changes to ensure that students have the knowledge and skills necessary for the work environment. This research aimed to identify studies related to digital transformation and digital literacy in higher education institutions through a systematic study of literature. The search resulted in 830 articles published in the Scopus and Web of Science databases from 2015 to 2022. Quality questions, inclusion and exclusion criteria were applied where 202 articles were selected for the study. The results show (a) interest of educational institutions in empirical studies where technologies are incorporated for didactic purposes, (b) challenges of opportunity in training programs to develop digital competences of teachers and students, (c) little interest in the development of media literacy, (d) the methodological aspects of the studies allow exploring new perspectives of digital transformation in higher education. This article may be of interest to academics, decision-makers and trainers of future professionals to introduce educational technology into learning processes in line with the complex demands of the world of work and society.
Eye in outer space: satellite imageries of container ports can predict world stock returns
Authors: Yu Honghai , Hao Xianfeng , Wu Liangyu , Zhao Yuqi , Wang Yudong 
Published: July 2023

AbstractForecasting stock returns is challenging. Traditional economic data that are available to all investors are published with lags and suffer from the problem of frequent revisions. Consequently, they often fail to forecast stock returns. For this reason, investors are increasingly interested in seeking alternative data. This paper forecasts stock returns using satellite-based information on shipping containers, which can capture economic activity in real-time. The container coverage area in each port is identified from 83,672 satellite images via the U-Net method and used as a proxy for the number of containers. Forecast combination over univariate predictive regression is used to generate return forecasts. The results indicate that the number of containers in ports can significantly predict stock index returns in 27 out of 33 countries at a daily frequency for the 2019–2021 period. An investor making use of satellite data on marine ports can, on average, receive an annualized return of 16.38%. The predictability can be explained by the predictive relationship between port container numbers and economic activity. In future studies, satellite data can be applied to monitor and forecast other economic indicators.
Design of a beam line for simultaneous dual-beam ion implantation
Authors: Cai Shixian , Wang Kedong , Huang Wei , Wang Kai , Easton Matthew J. , Li Jie , Zhang Caijie , Zhu Tingru , Wang Hao , Ge Huilin , Xie Xicheng , Lan Haoyang , Li Yingjie , Wei Guohui , Zhu Kun , Yan Xueqing 
Published: July 2023

For over 50 years, various ion beam facilities have been utilized to simulate the irradiation effect of neutrons on relevant nuclear materials. However, due to the limitation of single ion beam irradiation, the injection of H2+ and He+ into the sample coaxially is necessary to observe the synergy of hydrogen and helium displacement damage. This study presents a beam line design that is capable of separating ions with different charge mass ratios, screening out unwanted particles, and finally combining the separated beams again. Through this design, H+, H2+, and He+ beams can be completely separated with an axis deviation of less than 0.5 mm after beam combination. Furthermore, the proportion of H2+ and He+ can be adjusted by regulating the inlet air of the ion source, down to the accuracy of a single ion. An accelerating tube is employed to adjust the energy of implanted ions within 10–50 keV. This device also has the capability to efficiently transmit a single type of ion while filtering out extraneous ions (such as O+ beam). The fine-beam optical design not only makes it easy to adjust beam parameters but also eliminates the need for a high-precision power supply. The goal of this work is to have an analytical tool to understand the effect of each electromagnetic component on the ion trajectories.
Informational communication and metacognition
Authors: Proust Joëlle 
Published: July 2023

AbstractProcedural metacognition is the set of affect-based mechanisms allowing agents to regulate cognitive actions like perceptual discrimination, memory retrieval or problem solving. This article proposes that procedural metacognition has had a major role in the evolution of communication. A plausible hypothesis is that, under pressure for maximizing signalling efficiency, the metacognitive abilities used by nonhumans to regulate their perception and their memory have been re-used to regulate their communication. On this view, detecting one’s production errors in signalling, or solving species-specific trade-offs between informativeness, processing effort, clarity, or urgency depend on a form of procedural metacognition, called “metacommunication”. How does this view relate to Gricean theories of human communication? A parallel between procedural trade-offs and conversational maxims is discussed for its evolutionary implications. Rather than accepting radically discontinuist interpretations, in which mindreading operates a full reorganization of pragmatics, it is proposed that procedural forms of regulation are entrenched in all forms of human communication. According to contextual demands, humans adopt and monitor more or less demanding informational goals, such as factual updating, clarifying, explaining, proving, and reaching consensus in collective matters. Under time pressure, only part of these goals require adopting others’ viewpoint. Efficiency in collective decision-making, in particular, might have been considerably raised by an ability to interpret others’ intentions and motivations.
Decision-making algorithms for the identification of crops based on remote sensing data
Authors: Sulyukova L F , Shamsiev R Z 
Published: July 2023

Abstract
The article presents an algorithm for collective management decision-making. The developed algorithm is based on Bayesian mechanisms for resolving contradictions when choosing methods for processing of decisions. The algorithm is used for phased processing of Earth remote sensing data in order to identify agricultural crops. Functional satellite images created using the algorithm increase the accuracy of identifying plant objects. With the help of GPS navigator observation points linked by geographical coordinates, reference points have been created, according to which the classification of functional satellite images is accomplished, with the most accurate results of the distribution of pasture crops in contrast to the original image. The classification was performed according to four types of vegetation in the studied territory of Northern and Southern Nuratau of the Republic of Uzbekistan. Based on the results obtained, field work was carried out in order to test the remote sensing data obtained in relation to the identification of agricultural crops of pasture territories.
Application of artificial intelligence technologies to assess water salinity
Authors: Muhamediyeva D T 
Published: July 2023

Abstract
Topical issues of developing theoretical and methodological tools for constructing a fuzzy logical model for assessing water salinity are considered. When constructing a Sugeno fuzzy logical model for estimating water salinity, a rational number of rules and effective values of their membership functions were chosen. Initially, the membership function parameters were obtained from water industry experts. In the future, it is necessary to adjust the parameters of the membership function using neural networks to obtain the minimum number of fuzzy rules.
Factors affecting the highly dispersed spray when applying fertilizers
Authors: Salikhova M N , Aleksandrov V A , Galchak I P , Kolga A D , Kukhar V S 
Published: July 2023

Abstract
The article is devoted to devices for subsurface fertilization in the form of drip-air mixtures. A comparative analysis of spray mixers with centrifugal and jet nozzles has been carried out. The article presents the results of a study on the spray dependence on the ratio of air and liquid pressure with increasing fluid flow and steady air flow. It has been established that a spray mixer with a centrifugal nozzle gives a thinner spray cone is better in its operational qualities and will more fully meet the requirements for devices for intra-soil application of liquid fertilizers. In addition, it was determined that according to agrotechnical requirements, the average diameter of liquid droplets injected into the soil must be kept constant, and the flow rate must be changed, leaving the device speed and the compressor operating mode unchanged.
Rapid synthesis of nickel ferrite nanoparticles by the molten salt method
Authors: León-Flores J , Pérez-Mazariego J L , Olmedo-Reséndiz E T , Marquina M L , Escamilla R , Gómez R , Arenas-Alatorre J 
Published: July 2023

Abstract
It is presented a ‘shocking thermal treatment’ methodology as a modification to the molten salts process to rapidly obtain pure phase nickel ferrite nanoparticles within a high-scalable green synthesis scheme. Analysis from x-ray diffraction and electron microscopy (SEM and TEM) confirms the formation of the nanostructures, with the most frequent crystallite sizes between 55–85 nm. Room temperature magnetic characterization, from Mössbauer spectroscopy and magnetic measurements M(H), evidences their superparamagnetic distribution with a magnetic saturation of 47.9 emu g−1. An approximated stoichiometry of

Ni

0.03

Fe

0.97

A

Ni

0.97

Fe

1.03

B

O

4

was proposed from Rietveld refinement, Mössbauer and energy-dispersive x-ray spectroscopies. The effect on the electronic structure due to the shocking thermal treatment was elucidated by estimating the optical band gap with values between 1.78 and 2.38 eV.
High-efficiency photon-number-resolving detector for improving heralded single-photon sources
Authors: Stasi Lorenzo , Caspar Patrik , Brydges Tiff , Zbinden Hugo , Bussières Félix , Thew Rob 
Published: July 2023

Abstract
Heralded single-photon sources (HSPS) intrinsically suffer from multiphoton emission, leading to a trade-off between the source’s single-photon quality and the heralding rate. A solution to this problem is to use photon-number-resolving (PNR) detectors to filter out the heralding events where more than one photon pair is created. Here, we demonstrate an improvement of a HSPS by heralding photons using a high-efficiency parallel superconducting nanowire single-photon detector (P-SNSPD) with PNR power. Specifically, we show a reduction in the

g

(
2
)

(
0
)

of the heralded single photon by

(
26.9
±
0.1
)
%

for a fixed pump power, or alternatively, an increase in the heralding rate by a factor of

1.368
±
0.002

for a fixed

g

(
2
)

(
0
)

. We also demonstrate that such a PNR device can reveal thermal photon-number statistics of unheralded photons, which is enabled by our ability to construct its full input–output response function. These results are possible thanks to our P-SNSPD architecture that ensures non-latching operation with no electrical crosstalk, which are essential conditions necessary to obtain the correct photon-number statistics and also faster recovery times, therefore enabling fast heralding rates. These results show that our efficient PNR P-SNSPD architecture can significantly improve the performance of HSPSs and can precisely characterize them, making these detectors a useful tool for a wide range of optical quantum information protocols.
Integral cryptanalysis: a new key determination technique for 3-phase Kuznyechik encryption
Authors: Ilkhom Boykuziev , Khan Angshuman , Bakhtiyor Abdurakhimov , Das Rupayan , Khudoykulov Zarif 
Published: July 2023

Abstract
The primary objective of this work is to develop an algorithm for detecting the secret round key used in encryption by analyzing a collection of plaintexts and ciphertexts encrypted with the three-round Kuznyechik algorithm. In addition, the fundamentals of using the integral cryptanalysis method for the Substitution-permutation (SP) network-based Kuznyechik encryption have been investigated. It has been found that the Kuznyechik three-round encryption algorithm is useful for creating a balanced set before the third round and an unbalanced set at the end of the round, using a set in which the first byte is active, and the remaining bytes are passive. Based on the findings above, an efficient algorithm to determine the key of the final round for the 3-round Kuznyechik algorithm is proposed utilizing the method of integral cryptanalysis. According to simulation results, the proposed approach offers approximately 90% improvement compared to baseline algorithms in terms of time complexity, data, and memory overhead.
Buccal Fat Pad Augmentation for Midfacial Rejuvenation: Modified Fat Grafting Technique and Ogee Line Remodeling
Authors: Zhu Congxiao , Li Facheng , Tang Xiaojun , Cai Lei , Yin Bo , Zhang Xinyu , Jiang Chanyuan , Han Xuefeng 
Published: July 2023

Abstract

Background
The volume and position of the buccal fat pad (BFP) change with age, which manifests as a hollow midface. Previous studies showed that autologous fat grafting for BFP augmentation could effectively ameliorate midfacial hollowing.

Objectives
The aim of this study was to introduce a modified fat grafting technique for female patients with midfacial hollowing to restore the volume of BFP, and to evaluate the safety and effectiveness of this approach.

Methods
Two cadavers were used for the dissection of the BFP and to demonstrate the surgical procedures. Forty-eight patients were treated for midfacial hollowing with the modified grafting strategy. The BFP was filled through a percutaneous zygomatic incision and an immediate amelioration in the hollow area was observed. Improvements were evaluated from measurements of the ogee line and ogee angle, FACE-Q questionnaires, and 3-party satisfaction ratings. Clinical profiles were reviewed and statistically analysed.

Results
The mean [standard deviation] ogee angle was 6.6° [1.9°] preoperatively and 3.9° [1.4°] postoperatively (average reduction, 2.7°). Patients’ ogee lines were smoother postoperatively, with marked improvements in overall appearance, psychological well-being, and social confidence. Patients reported high satisfaction with decision-making and postoperative outcomes and felt 6.61 [2.21] years younger. Overall, 88%, 76%, and 83% of the cases were graded as good or excellent in improvement by surgeon, patient, and the third party, respectively.

Conclusions
For age-dependent midfacial hollowing in female patients, the modified percutaneous grafting technique described here was safe and efficacious in restoring BFP volume. This technique produced a smoother ogee line and a natural, younger midfacial contour.

Level of Evidence: 4
Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes
Authors: Ji Yanrong , Dutta Pratik , Davuluri Ramana 
Published: July 2023

Abstract

Motivation
Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches.

Results
We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks.

Availability and implementation
Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC).

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
MDSubSampler: a posteriori sampling of important protein conformations from biomolecular simulations
Authors: Oues Namir , Dantu Sarath Chandra , Patel Riktaben Jigarkumar , Pandini Alessandro 
Published: July 2023

Abstract

Motivation
Molecular dynamics (MD) simulations have become routine tools for the study of protein dynamics and function. Thanks to faster GPU-based algorithms, atomistic and coarse-grained simulations are being used to explore biological functions over the microsecond timescale, yielding terabytes of data spanning multiple trajectories, thereby extracting relevant protein conformations without losing important information is often challenging.

Results
We present MDSubSampler, a Python library and toolkit for a posteriori subsampling of data from multiple trajectories. This toolkit provides access to uniform, random, stratified, weighted sampling, and bootstrapping sampling methods. Sampling can be performed under the constraint of preserving the original distribution of relevant geometrical properties. Possible applications include simulations post-processing, noise reduction, and structures selection for ensemble docking.

Availability and implementation
MDSubSampler is freely available at https://github.com/alepandini/MDSubSampler, along with guidance on installation and tutorials on how it can be used.