bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rural electrification subsidy estimation: a spatial model development and case study
Authors: Maqelepo Lefu , Williams Nathan , Taneja Jay 
Published: October 2022

Abstract
A global push to achieve universal electricity access, paired with drastic reductions in the cost of decentralized electricity technologies, has led to significant research on how best to roll out access to rural communities in sub-Saharan Africa. Various geospatial electrification models have been developed to aid the decision-making process considering decentralized grid alternatives such as mini-grids and solar home systems. Despite these tools suggesting that in many cases, decentralized systems are a more cost-effective electricity access pathway, grid extension still predominates in practice. This is due, at least in part, to institutional structures in most countries that provide significant direct and indirect subsidies to grid extension projects, commonly through publicly-owned utilities. These sources of finance are generally not available to primarily privately operated off-grid energy service providers. However, the subsidy provided for grid extension projects is not well understood. In this paper, we employ utility grid extension costs and revenue data, and geospatial grid infrastructure data to estimate the size and distribution of subsidy implicitly provided to rural grid extension projects for 129 communities in Mombasa County, Kenya. We also estimate subsidies for hypothetical off-grid electricity systems in the same communities that would deliver equivalent services to the grid. We allocate the cost of shared medium voltage (MV) distribution infrastructure using a marginal and an average cost method for grid extension and compare these with subsidies for off-grid systems. We find that the average of average subsidy per customer across communities for grid extension is US$5,118 and US$5,330 for the two MV cost allocation methods respectively, while for the off-grid systems the corresponding average of average subsidies are US$3,380, using a real discount rate of 1.3% evaluated from a nominal discount rate of 8% and inflation rate of 6.7%. Our results show that in the communities in our case study, 40% and 37% of the communities would command less subsidy while served by minigrids over the grid, and the switch would save 50% and 54% of the total cost for average and marginal cost allocation methods respectively. We also show that by using a multi-model approach to electrification and by reallocation of implicit subsidies that have been exclusive to grid extension to other technology options utilities can cast the net wider, without an increase in budgets.
High-dimensional genomic feature selection with the ordered stereotype logit model
Authors: Seffernick Anna Eames , Mrózek Krzysztof , Nicolet Deedra , Stone Richard M , Eisfeld Ann-Kathrin , Byrd John C , Archer Kellie J 
Published: October 2022

AbstractFor many high-dimensional genomic and epigenomic datasets, the outcome of interest is ordinal. While these ordinal outcomes are often thought of as the observed cutpoints of some latent continuous variable, some ordinal outcomes are truly discrete and are comprised of the subjective combination of several factors. The nonlinear stereotype logistic model, which does not assume proportional odds, was developed for these ‘assessed’ ordinal variables. It has previously been extended to the frequentist high-dimensional feature selection setting, but the Bayesian framework provides some distinct advantages in terms of simultaneous uncertainty quantification and variable selection. Here, we review the stereotype model and Bayesian variable selection methods and demonstrate how to combine them to select genomic features associated with discrete ordinal outcomes. We compared the Bayesian and frequentist methods in terms of variable selection performance. We additionally applied the Bayesian stereotype method to an acute myeloid leukemia RNA-sequencing dataset to further demonstrate its variable selection abilities by identifying features associated with the European LeukemiaNet prognostic risk score.
Machine learning alternative to systems biology should not solely depend on data
Authors: Yeo Hock Chuan , Selvarajoo Kumar 
Published: October 2022

Abstract
In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar ‘AI winters’ that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.
Domestic Violence Survivors Who Became Anti-domestic Violence Volunteers: A Qualitative Study in China
Authors: Wu Lei , Huang Yunong , Gao Yiyuan , Chen Qiang , McLaren Helen 
Published: October 2022

Abstract
This research examined the life experiences of domestic violence survivors in China, specifically the actions taken, experiences and meanings they ascribed in becoming anti-domestic violence volunteers. Data were collected via in-depth interviews in 2020 with ten such volunteers. Theories of posttraumatic growth and theories of volunteering provided an analytical framework. The findings showed that the volunteers’ experiences during and following their escape from violence went through three stages: from shock, self-blame and acceptance to recognition of the abuser as wholly responsible for the violence, followed by connecting domestic violence experiences to gender equality, social justice and human rights. Their actions against domestic violence also went through three stages: from endurance to breaking-away, followed by becoming anti-domestic violence volunteers. Participation in anti-domestic violence volunteer work contributed to rebuilding their perceptions of the self, the family and intimate relationships and the world. It also provided them with effective ways to recover from domestic violence and to foster resilience and experience posttraumatic growth. The findings indicated that whilst domestic violence resulted in a variety of negative impacts on survivors, they had resilience that aided recovery and they achieved posttraumatic growth through participating in anti-domestic violence volunteer work.
Sex difference in trait empathy is encoded in the human anterior insula
Authors: Wu Xiao , Lu Xuejing , Zhang Huijuan , Bi Yanzhi , Gu Ruolei , Kong Yazhuo , Hu Li 
Published: October 2022

Abstract
Females are considered the more empathic sex. This conventional view, however, has been challenged in the past few decades with mixed findings. These heterogeneous findings could be caused by the fact that empathy is a complex and multifaceted construct. To clarify whether sex differences exist in certain dimensions of empathy and whether they are associated with specific neural bases, this study measured trait empathy using the interpersonal reactivity index (IRI) and collected brain structural and functional magnetic resonance imaging data in a large sample of healthy participants (206 males vs. 302 females). We found that females scored higher in the personal distress (PD) subscale than males, but they were comparable to males in other IRI subscales. Sex difference in PD was encoded by brain structural (e.g. gray matter volume in left anterior insula [AI]) and functional (e.g. resting-state functional connectivity between left AI and temporoparietal junction/inferior frontal gyrus) characteristics. Notably, the relationship between sex and PD was indirect-only and serially mediated by AI-associated structural and functional characteristics. Altogether, our results suggested that sex difference existed in self-oriented affective empathy (i.e. PD) and highlighted the importance of the AI, both structurally and functionally, in mediating the sex difference in trait empathy.
A Semantic Embedding Enhanced Topic Model For User-Generated Textual Content Modeling In Social Ecosystems
Authors: Zhang Peng , Liu Baoxi , Lu Tun , Gu Hansu , Ding Xianghua , Gu Ning 
Published: October 2022

Abstract
The development of Information and Communication Technologies (ICT) and Web 2.0 promotes the emergence of diverse social ecosystems like social Internet of Things (IoT), social media and online communities. User-generated textual content (UGTC), which consists of unstructured texts, is the most important and common type of user-generated content in social ecosystems. UGTC in social ecosystems is generated according to two types of context information—global context (topics) and local context (semantic regularities). For UGTC modeling, topic models just consider global context but ignore semantic regularities, while semantic embedding models are on the opposite. So only utilizing topic models or semantic embedding models to model UGTC suffers from some drawbacks. For this problem, we propose a semantic embedding enhanced topic model named SEE-Twitter-LDA for accurately modeling UGTC in social ecosystems. The core of SEE-Twitter-LDA is that words are generated according to mutual semantic information of topics and semantic regularities. So global context and local context are jointly considered for UGTC modeling. By utilizing 553 098 tweets sampled from Twitter and 211 233 posts sampled from Weibo, we validate SEE-Twitter-LDA’s better performance on perplexity, topic divergence and topic coherence versus existing related models.
Efficient Robustness Verification of the Deep Neural Networks for Smart IoT Devices
Authors: Zhang Zhaodi , Liu Jing , Zhang Min , Sun Haiying 
Published: October 2022

Abstract
In the Internet of Things, smart devices are expected to correctly capture and process data from environments, regardless of perturbation and adversarial attacks. Therefore, it is important to guarantee the robustness of their intelligent components, e.g. neural networks, to protect the system from environment perturbation and adversarial attacks. In this paper, we propose a formal verification technique for rigorously proving the robustness of neural networks. Our approach leverages a tight liner approximation technique and constraint substitution, by which we transform the robustness verification problem into an efficiently solvable linear programming problem. Unlike existing approaches, our approach can automatically generate adversarial examples when a neural network fails to verify. Besides, it is general and applicable to more complex neural network architectures such as CNN, LeNet and ResNet. We implement the approach in a prototype tool called WiNR and evaluate it on extensive benchmarks, including Fashion MNIST, CIFAR10 and GTSRB. Experimental results show that WiNR can verify neural networks that contain over 10 000 neurons on one input image in a minute with a 6.28% probability of false positive on average.
AWFC: Preventing Label Flipping Attacks Towards Federated Learning for Intelligent IoT
Authors: Lv Zhuo , Cao Hongbo , Zhang Feng , Ren Yuange , Wang Bin , Chen Cen , Li Nuannuan , Chang Hao , Wang Wei 
Published: October 2022

AbstractCentralized machine learning methods require the aggregation of data collected from clients. Due to the awareness of data privacy, however, the aggregation of raw data collected by Internet of Things (IoT) devices is not feasible in many scenarios. Federated learning (FL), a kind of distributed learning framework, can be running on multiple IoT devices. It aims to resolve the issues of privacy leakage by training a model locally on the client-side, other than on the server-side that aggregates all the raw data. However, there are still threats of poisoning attacks in FL. Label flipping attacks, typical data poisoning attacks in FL, aim to poison the global model by sending model updates trained by the data with mismatched labels. The central parameter aggregation server is hard to detect the label flipping attacks due to its inaccessibility to the client in a typical FL system. In this work, we are motivated to prevent label flipping poisoning attacks by observing the changes in model parameters that were trained by different single labels. We propose a novel detection method called average weight of each class in its associated fully connected layer. In this method, we detect label flipping attacks by identifying the differences of classes in the data based on the weight assignments in a fully connected layer of the neural network model and use the statistical algorithm to recognize the malicious clients. We conduct extensive experiments on benchmark data like Fashion-MNIST and Intrusion Detection Evaluation Dataset (CIC-IDS2017). Comprehensive experimental results demonstrated that our method has the detection accuracy over 90% for the identification of the attackers flipping labels.
A Consensus Diagnosis Utilizing Surface KI-67 Expression as an Ancillary Marker in Low-Grade Dysplasia Helps Identify Patients at High Risk of Progression to High-Grade Dysplasia and Esophageal Adenocarcinoma
Authors: Lee Christina , Hayat Umar , Song Kevin , Gravely Amy A , Mesa Hector , Peltola Justin , Iwamoto Carlos , Manivel Carlos , Bilal Mohammad , Shaheen Nicholas , Shaukat Aasma , Hanson Brian J 
Published: October 2022

Summary
Esophageal adenocarcinoma (EAC) develops in a step-wise manner, from low-grade dysplasia (LGD) to high-grade dysplasia (HGD), and ultimately to invasive EAC. However, there remains diagnostic uncertainty about LGD and its risk of progression to HGD/EAC. The aim is to investigate the role of Ki-67, immune-histochemical marker of proliferation, surface expression in patients with confirmed LGD, and risk stratify progression to HGD/EAC. A retrospective cohort study was conducted. Patients with confirmed LGD and indefinite for dysplasia (IND), with a mean follow-up of ≥1 year, were included. Pathology specimens were stained for Ki-67 and analyzed for evidence of surface expression. Our results reveal that 29% of patients with confirmed LGD who stained positive with Ki-67 progressed to HGD/EAC as opposed to none (0%) of the patients who stained negative, a statistically significant result (P = 0.003). Similarly, specimens from patients with IND were stained and analyzed revealing a nonsignificant trend toward a higher rate of progression for Ki-67 positive cases versus Ki-67 negative, 30% versus 21%, respectively. Ki-67 expression by itself can identify patients with LGD at a high risk of progression.
Safety of same-day discharge after peroral endoscopic myotomy
Authors: Sarkis Yara , Al-Haddad Mohammad A , Siwiec Robert , Kessler William R , Wo John M , Stainko Sarah , Perkins Anthony , DeWitt John M 
Published: October 2022

SummaryThere are limited data on the safety of same-day discharge (SDD) after peroral endoscopic myotomy (POEM). The aim of our study is to assess the frequency and relationship to POEM for emergency department (ED) visits and hospitalizations after SDD in these patients. We retrospectively identified consecutive patients between November 2019 and August 2021 who underwent POEM with SDD and at least 6 months follow-up. Criteria for SDD includes: (1) no serious procedure-related adverse event; (2) post-POEM esophagram without leak; (3) stable vital signs; (4) ability to take liquids orally; (5) pain controlled without IV analgesia; (6) adequate social support; (7) American Society of Anesthesiologists (ASA) class I–III. A causative relationship between POEM and ED visits and hospitalizations was assigned by consensus. Out of 185 POEMs performed, 78 (41.7%, 42M, mean 51±16 years) had SDD. Within 30 days of POEM, 8 ED visits occurred in 7/78 (9%) patients and 2 (25%) were considered related to POEM; hospitalization was required in 3 (38%). After 30 days, 11 ED visits occurred in 10/78 (12.8%) patients and 1 (9%) was considered related to POEM; hospitalization was required in 5 (45%). In this study of consecutive SDD patients after POEM, 3/19 (16%) ED visits and 2/8 (25%) hospitalizations within 6 months were considered related to the procedure. Therefore, eligible patients who follow and fulfill a strict protocol after POEM may be safely discharged the same day.
Ion transport peptide regulates energy intake, expenditure, and metabolic homeostasis inDrosophila
Authors: Gáliková Martina , Klepsatel Peter 
Published: October 2022

AbstractIn mammals, energy homeostasis is regulated by the antagonistic action of hormones insulin and glucagon. However, in contrast to the highly conserved insulin, glucagon is absent in most invertebrates. Although there are several endocrine regulators of energy expenditure and catabolism (such as the adipokinetic hormone), no single invertebrate hormone with all of the functions of glucagon has been described so far. Here, we used genetic gain- and loss-of-function experiments to show that the Drosophila gene Ion transport peptide (ITP) codes for a novel catabolic regulator that increases energy expenditure, lowers fat and glycogen reserves, and increases glucose and trehalose. Intriguingly, Ion transport peptide has additional functions reminiscent of glucagon, such as inhibition of feeding and transit of the meal throughout the digestive tract. Furthermore, Ion transport peptide interacts with the well-known signaling via the Adipokinetic hormone; Ion transport peptide promotes the pathway by stimulating Adipokinetic hormone secretion and transcription of the receptor AkhR. The genetic manipulations of Ion transport peptide on standard and Adipokinetic hormone-deficient backgrounds showed that the Adipokinetic hormone peptide mediates the hyperglycemic and hypertrehalosemic effects of Ion transport peptide, while the other metabolic functions of Ion transport peptide seem to be Adipokinetic hormone independent. In addition, Ion transport peptide is necessary for critical processes such as development, starvation-induced foraging, reproduction, and average lifespan. Altogether, our work describes a novel master regulator of fly physiology with functions closely resembling mammalian glucagon.
More Gamer, Less Girl: Gendered Boundaries, Tokenism, and the Cultural Persistence of Masculine Dominance
Authors: Drenten Jenna , Harrison Robert L , Pendarvis Nicholas J 
Published: October 2022

Abstract
How do exclusionary boundaries persist in consumption subcultures amid increased progress, representation, and inclusion? In video gaming, women have come to represent nearly half of the market; yet, this is a limited indicator of gender-based progress. A culture of masculine dominance persists. Extending previous research on boundary work, the authors employ a cultural perspective of tokenism to examine how gendered boundaries in consumption subcultures persist despite efforts to transform or even eradicate them. This qualitative study draws on interviews with 23 gamers who identify as women (ages 19–29 years), coupled with data from social media platforms, news media, and industry reports. Empirical findings capture the recursive process of maladaptive boundary crossing: how women’s efforts to subvert gendered boundaries at the micro level (e.g., through response enactments) get churned through the structuring tokenistic mechanics of boundary work at the meso level and result in the inadvertent cultural persistence of masculine dominance. The analysis offers a conceptual framework that explains how micro–meso level dynamics perpetuate and conceal inequity in consumption subcultures. Implications address the precarious promise of progress and the cultural legacy of tokenism in the marketplace with particular relevance to broader systems of domination.
The probabilistic random forest applied to the QUBRICS survey: improving the selection of high-redshift quasars with synthetic data
Authors: Guarneri Francesco , Calderone Giorgio , Cristiani Stefano , Porru Matteo , Fontanot Fabio , Boutsia Konstantina , Cupani Guido , Grazian Andrea , D’Odorico Valentina , Murphy Michael T , Bongiorno Angela , Saccheo Ivano , Nicastro Luciano 
Published: October 2022

ABSTRACT
Several recent works have focused on the search for bright, high-z quasars (QSOs) in the South. Among them, the QUasars as BRIght beacons for Cosmology in the Southern hemisphere (QUBRICS) survey has now delivered hundreds of new spectroscopically confirmed QSOs selected by means of machine learning algorithms. Building upon the results obtained by introducing the probabilistic random forest (PRF) for the QUBRICS selection, we explore in this work the feasibility of training the algorithm on synthetic data to improve the completeness in the higher redshift bins. We also compare the performances of the algorithm if colours are used as primary features instead of magnitudes. We generate synthetic data based on a composite QSO spectral energy distribution. We first train the PRF to identify QSOs among stars and galaxies, then separate high-z quasar from low-z contaminants. We apply the algorithm on an updated data set, based on SkyMapper DR3, combined with Gaia eDR3, 2MASS, and WISE magnitudes. We find that employing colours as features slightly improves the results with respect to the algorithm trained on magnitude data. Adding synthetic data to the training set provides significantly better results with respect to the PRF trained only on spectroscopically confirmed QSOs. We estimate, on a testing data set, a completeness of $\sim 86{{\ \rm per\ cent}}$ and a contamination of $\sim 36{{\ \rm per\ cent}}$. Finally, 206 PRF-selected candidates were observed: 149/206 turned out to be genuine QSOs with z &gt; 2.5, 41 with z &lt; 2.5, 3 galaxies and 13 stars. The result confirms the ability of the PRF to select high-z quasars in large data sets.
A panchromatic view of infrared quasars: excess star formation and radio emission in the most heavily obscured systems
Authors: Andonie Carolina , Alexander David M , Rosario David , Laloux Brivael , Georgakakis Antonis , Morabito Leah K , Villforth Carolin , Avirett-Mackenzie Mathilda , Rivera Gabriela Calistro , Del Moro Agnese , Fotopoulou Sotiria , Harrison Chris , Lapi Andrea , Petley James , Petter Grayson , Shankar Francesco 
Published: October 2022

ABSTRACT
To understand the active galactic nuclei (AGNs) phenomenon and their impact on the evolution of galaxies, a complete AGN census is required; however, finding heavily obscured AGNs is observationally challenging. Here we use the deep and extensive multiwavelength data in the COSMOS field to select a complete sample of 578 infrared (IR) quasars ($L_{\rm AGN,IR}\gt 10^{45}\rm \: erg\: s^{-1}$) at z &lt; 3, with minimal obscuration bias, using detailed UV-to-far-IR spectral energy distribution (SED) fitting. We complement our SED constraints with X-ray and radio observations to further investigate the properties of the sample. Overall, 322 of the IR quasars are detected by Chandra and have individual X-ray spectral constraints. From a combination of X-ray stacking and $L_{\rm 2-10\rm \,kev}$ – $L_{\rm 6\: \mu m}$ analyses, we show that the majority of the X-ray faint and undetected quasars are heavily obscured (many are likely Compton thick), highlighting the effectiveness of the mid-IR band to find obscured AGNs. We find that 355 (≈61 per cent) IR quasars are obscured ($N_{\rm H}\gt 10^{22}\rm \: cm^{-2}$) and identify differences in the average properties between the obscured and unobscured quasars: (1) obscured quasars have star formation rates ≈3 times higher than unobscured systems for no significant difference in stellar mass and (2) obscured quasars have stronger radio emission than unobscured systems, with a radio-loudness parameter $\approx 0.2 \rm \: dex$ higher. These results are inconsistent with a simple orientation model but in general agreement with either extreme host-galaxy obscuration towards the obscured quasars or a scenario where obscured quasars are an early phase in the evolution of quasars.