Miracle-dz/UniFAR-FTUs huggingface dataset:
No Evidence That Siblings’ Gender Affects Personality Across Nine Countries

Does growing up with a sister rather than a brother affect personality? In this article, we provide a comprehensive analysis of the effects of siblings’ gender on adults’ personality, using data from 85,887 people from 12 large representative surveys covering nine countries (United States, United Kingdom, The Netherlands, Germany, Switzerland, Australia, Mexico, China, and Indonesia). We investigated the personality traits of risk tolerance, trust, patience, locus of control, and the Big Five. We found no meaningful causal effects of the gender of the next younger sibling and no associations with the gender of the next older sibling. Given the high statistical power and consistent results in the overall sample and relevant subsamples, our results suggest that siblings’ gender does not systematically affect personality.
First report of Banana bunchy top virus on heliconia (Heliconia spp.) in Bali, Indonesia

Banana bunchy top virus (BBTV) mostly infects Musa spp. which causes banana bunchy top disease (BBTD), the most devastating viral disease in banana cultivation all over the world. During field survey in Sekar Bumi Tropical Farm located in Kerta Village, 15 heliconia plant samples showed green streak on lamina leaves that similar to common BBTD symptoms were collected. Further research was then conducted to identify molecular characteristic of BBTD in heliconia showing green streak. Molecular identification was conducted using polymerase chain reaction (PCR). Specific primers to amplified DNA target were used in this study: mRep/F (5’-GCGTGAAACGCACAAAAGGCC-3’) and mRep/R (5’-GCATACGTTGTCAAACCTTCTCCTC-3’). The expected 240 bp fragment target of partial master replication (mRep) gene was successfully amplified from ten out of 15 samples. Sequence analysis confirmed that the symptomatic heliconia samples were infected with BBTV and falls into the same clade with BBTV from the Asian Group.
Quality and traffic management in multimedia era

This paper will describe the criteria related to the quality of communication services, as well as the measurement/management methods used. Classification methods for multimedia traffic, and a mechanism that discloses information on quality and allows customers to experience quality will also be mentioned. Finally, standardization of criteria, and the present development of network technology that aims to improve quality will be discussed.
Weweni zhichge: wise practices in urban Indigenous education in Northern Ontario

The following examines factors associated with Indigenous secondary student success with a focus on wise practices in quality education in a northern Ontario city, Canada. Wise practices are an Indigenous perspective on best practices that recognizes there are a diversity of Indigenous ways of knowing and being, and that local histories, communities, and cultural practices are relevant. Results of qualitative research with Indigenous students, staff, teachers, and graduates suggest that parents and the social environment are determinants in Indigenous student success and that Indigenous students negotiate their identity in the pursuit of success. Furthermore, Indigenous curriculum and Indigenous teachers and administrative staff are essential in supporting student success and nurturing the educational journeys of students. Wise practices in Indigenous education in northern Ontario reflect localized land-based learning and teaching in collaboration with urban Indigenous organizations and Elders, and Knowledge holders and administrative officials.
Programmed Death-Ligand 1 (PD-L1) Positivity and Factors Associated with Poor Prognosis in Patients with Gastric Cancer: An Umbrella Meta-Analysis

Gastric cancer (GC) is one of the most common malignancies throughout the world with late diagnosis and poor prognosis. The expression of programmed death-ligand 1 (PD-L1) in GC is attributed to immune evasion and tumor progression. PD-L1 positivity has both predictive and prognostic biomarker potential. Aiming to summarize a large amount of research and to provide a definitive conclusion to the conflicting results on the prognostic significance of PD-L1 expression in GC, we performed an umbrella review based on existing meta-analyses which were published recently (2016-2021) and indexed in the PubMed database. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was used in August 2021 to screen articles, and data extraction with quality assessment was performed on the selected meta-analyses. Review Manager (RevMan) 5.3 software was used to analyze the HR and OR with a 95% confidence interval (CI) among PD-L1 positive GC patients. We also assessed the between-study heterogeneity (I2). Forest and Funnel plots were obtained, and a P-value of <0.05 was considered statistically significant. A total of 567 articles were screened, and we selected three meta-analyses with a total of 40 studies conducted over a period of 14 years. In our umbrella review, a total of 8,419 GC patients with an average PD-L1 positivity of 39% were analyzed. We found that PD-L1 positivity in GC patients is associated with poor prognosis (pooled HR =1.44, 95% CI: 1.24-1.68, P<0.00001) having higher mortality reducing the chances of overall survival (OS). However, there are no significant differences in PD-L1 expression among different lymph node (LN) metastases (OR=1.31, 95% CI: 0.98-1.74, P=0.07) and tumor, node, and metastasis (TNM) stages (OR=1.13, 95% CI: 0.80-1.58, P=0.50). Early identification of PD-L1 expression may help tailor cost-effective and targeted immunotherapy among GC patients. More research is needed to further understand how PD-L1 affects LN metastasis and tumor invasion.
Autonomous Vehicle Intelligent System: Joint Ride-Sharing and Parcel Delivery Strategy

Autonomous vehicle (AV) integration poses a significant challenge for intelligent transportation systems (ITSs). The ability to automatically coordinate complex AV operations at scale is crucial for advancing the quality of core transportation services, such as ride-sharing and parcel delivery. However, existing studies have only considered either of these two services independently from the other, disregarding the potential benefits of their combined optimization. To address this open problem, we design an autonomous vehicle intelligent system (AVIS) providing joint ride-sharing and parcel delivery services under realistic ride and route constraints. We formulate the joint optimization problem through the scope of mixed-integer linear programming and solve it using the Lagrangian dual decomposition method to ensure scalability. We conduct extensive case studies to evaluate the performance of the proposed AVIS and its constituting components. Our experimental results demonstrate that AVIS can effectively provide both ride-sharing and parcel delivery services while satisfying service requests in transportation networks of various scales. In addition, the distributed method is shown to generate near-optimal solutions in reduced computation time.
Modification of Cellulose Micro- and Nanomaterials to Improve Properties of Aliphatic Polyesters/Cellulose Composites: A Review

Aliphatic polyesters/cellulose composites have attracted a lot attention due to the perspectives of their application in biomedicine and the production of disposable materials, food packaging, etc. Both aliphatic polyesters and cellulose are biocompatible and biodegradable polymers, which makes them highly promising for the production of “green” composite materials. However, the main challenge in obtaining composites with favorable properties is the poor compatibility of these polymers. Unlike cellulose, which is very hydrophilic, aliphatic polyesters exhibit strong hydrophobic properties. In recent times, the modification of cellulose micro- and nanomaterials is widely considered as a tool to enhance interfacial biocompatibility with aliphatic polyesters and, consequently, improve the properties of composites. This review summarizes the main types and properties of cellulose micro- and nanomaterials as well as aliphatic polyesters used to produce composites with cellulose. In addition, the methods for noncovalent and covalent modification of cellulose materials with small molecules, polymers and nanoparticles have been comprehensively overviewed and discussed. Composite fabrication techniques, as well as the effect of cellulose modification on the mechanical and thermal properties, rate of degradation, and biological compatibility have been also analyzed.
Changing the planning for agile and lean software development : from roadmapping to continuous planning

This thesis investigates how planning has changed for agile and lean software development from roadmap-based planning towards continuous planning. This thesis provides empirical evidence of how large and global software development companies are conducting planning. The empirical data were collected by conducting an initial inquiry consisting of both a questionnaire study and semi-structured interviews, and then, by conducting a multiple-case study involving three case companies.
PD with neuro-adaptive compensation control using the signed power function

This paper presents a neuro-adaptive control scheme dedicated to solve the motion trajectory tracking problem of robot manipulators under uncertain parameters and external disturbances. A two degrees of freedom direct-drive robot manipulator was taken as a case of study. The proposed controller is able to guarantee asymptotic convergence of the position and velocity tracking errors, and the weights of the artificial neural network are bounded. Artificial neural network weights are updated online using filtered error approach, adaptive laws and signed power function. This scheme does not require any offline training. The neuro-adaptive controller is experimentally validated and compared with a classical one-layer neuro-adaptive controller; the proposed scheme obtains better quantitative metrics related with the RMS values of the position and velocity errors, and a good robust behaviour against external disturbances.
Performance Evaluation of CL-PHB Admission and Termination Algorithms

Pre-Congestion Notification [I-D.briscoe-tsvwg-cl-architecture] approach proposes Admission Control to limit the amount of real-time PCN traffic to a configured level during the normal operating conditions, and Flow Termination used to tear-down some of the flows to bring the PCN traffic level down to a desirable amount during unexpected events such as network failures, with the goal of maintaining the QoS assurances to the remaining flows. Preliminary performance evaluation results on example admission and termination mechanisms were presented in [I-D.briscoe-tsvwg-cl-phb] and in earlier versions of this draft. This draft presents the results of a follow-up simulation study.
Learning From Noisy Data: An Unsupervised Random Denoising Method for Seismic Data Using Model-Based Deep Learning

For seismic random noise attenuation, deep learning has attracted much attention and achieved promising performance. However, compared with conventional methods, the denoising performance of supervised learning-based methods heavily depends on massive training samples with high-quality labeled data, which makes their generalization capabilities limited. Even though deep neural networks (DNNs) usually outperform the conventional denoising methods, their performance is not guaranteed since neural networks still lack good mathematical interpretability at present. To alleviate the dependency on labeled data and explore insights into the denoising system, we proposed an unsupervised denoising method based on model-based deep learning, which combined domain knowledge and a data-driven method. We designed a network based on the modified iterative soft threshold algorithm (ISTA), which omitted the soft threshold to alleviate uncertainties introduced by empirically selected thresholds. In this network, we set the dictionary and code as trainable parameters. A loss function with a smooth penalty was designed to ensure that the network training can be implemented in an unsupervised manner. In the proposed method, we set the denoised result by $f-x$ deconvolution as the input for our network, and the further denoised data can be obtained after each epoch of the training, which means that our method does not need the testing procedure. Experiments on synthetic and field seismic data demonstrate that our method exhibits competitive performance compared to the conventional, supervised, and unsupervised methods, including $f-x$ deconvolution, curvelet, the Denoising Convolutional Neural Network (DnCNN), and the integration of neural network and Block-matching and 3-D filtering method (NN + BM3D).
Stereotactic radiotherapy for bone oligometastases

About 60–90% of cancer patients are estimated to develop bone metastases, particularly in the spine. Bone scintigraphy, computed tomography (CT ) and magnetic resonance imaging (MRI ) are currently used to assess metastatic bone disease; positron emission tomography/computed tomography (PET-CT ) has become more widespread in clinical practice because of its high sensitivity and specificity with about 95% diagnostic accuracy. The most common and well-known radiotracer is 18F-fluorodeoxyglucose (18FDG); several other PET-radiotracers are currently under investigation for different solid tumors, such as 11C or 18FDG-choline and prostate specific membrane antigen (PSMA)-PET/CT for prostate cancer. In treatment planning, standard and investigational imaging modalities should be registered with the planning CT so as to best define the bone target volume. For target volume delineation of spine metastases, the International Spine Radiosurgery Consortium (ISRC ) of North American experts provided consensus guidelines. Single fraction stereotactic radiotherapy (SRT ) doses ranged from 12 to 24 Gy; fractionated SRT administered 21–27 Gy in 3 fractions or 20–35 Gy in 5 fractions. After spine SRT, less than 5% of patients experienced grade ≥ 3 acute toxicity. Late toxicity included the extremely rare radiation-induced myelopathy and a 14% risk of de novo vertebral compression fractures.
Influence of vane angle on the effectiveness of air conditioning of wall-mounted split-type air conditioners in residential buildings

The purpose of this study is to accurately evaluate the effect of vane angle on the effectiveness of air conditioning of wall-mounted split-type air conditioners. First, to determine the detailed characteristics of airflow from an air conditioner opening, airflow rate, turbulence characteristics, and air distribution were measured according to the vane angle. Then, the detailed characteristics were calculated using a simplified opening model with boundary conditions obtained from the calculated results of a cross-flow fan model using the overset mesh technique to validate the computational fluid dynamics simulation. Finally, to evaluate the effectiveness of air conditioning, the effectiveness of air change was calculated under isothermal conditions by varying the vane angle and the setting position of the air conditioner by using the validated computational fluid dynamics model. The measurement results showed that when the vane was set at the middle position (rather than at the left or right positions), the airflow rate increased by 19.0%–20.9% in the high airflow rate mode. The simulation results verified that the simplified opening model was consistent with actual findings, and only a slight difference was observed between actual measurements and the computational fluid dynamics simulation results. Furthermore, installing the air conditioner at the center of a wall and setting the vane at the middle position improved the effectiveness of indoor air conditioning.
Does size matter? An analysis of the niche width and vulnerability to climate change of fourteen species of the genus Crotalus from North America

The niche comprises the set of abiotic and biotic environmental conditions in which a species can live. Consequently, those species that present broader niches are expected to be more tolerant to changes in climatic variations than those species that present reduced niches. In this study, we estimate the amplitude of the climatic niche of fourteen species of rattlesnakes of the genus Crotalus to evaluate whether those species that present broader niches are less susceptible to the loss of climatically suitable zones due to the projected climate change for the time period 2021–2040. Our results suggest that for the species under study, the breadth of the niche is not a factor that determines their vulnerability to climatic variations. However, 71.4% of the species will experience increasingly inadequate habitat conditions, mainly due to the increase in temperature and the contribution that this variable has in the creation of climatically suitable zones for most of these species.
Dynamic Electoral Competition with Voter Loss-Aversion and Imperfect Recall

This paper explores the implications of voter loss-aversion and imperfect recall for the dynamics of electoral competition in a simple Downsian model of repeated elections. We first establish a benchmark result: when the voters’ reference point is forward-looking, there are a continuum of rational expectations equilibria (REE). When voters are backward-looking i.e. the reference point is last period’s recalled policy, interesting dynamics only emerge when voters have imperfect recall about that policy. Then, the interplay between the median voter’s reference point and political parties’ choice of platforms generates a dynamic process of polarization (or de-polarization). Under the assumption that parties are risk-neutral, platforms monotonically converge over time to a long-run equilibrium, which is always a REE. When parties are risk-averse, dynamic incentives also come into play, and generally lead to more policy moderation, resulting in equilibria that are more moderate than the most moderate REE.
Redefining Monitoring Rules for Intelligent Fault Detection and Classification via CNN Transfer Learning for Smart Manufacturing

Fault detection and classification has been employed to enhance yield and product quality for smart semiconductor manufacturing. For early detection of abnormal events that cause defects, the status variables identification data collected by the sensors embedded in advanced machines can be analyzed to derive the actions for advanced process control and advanced equipment control. However, the validity and effectiveness of fault detection and classification technologies may highly depend on domain knowledge and experience of the process engineers who should redefine the monitoring rules quickly when new process excursion occurred especially when ramping up new technologies and products. Motivated by realistic needs, this study aims to propose a novel strategy to empower intelligent fault detection and classification that employed convolutional neural network to analyze the feature SVID data and determine the conditions of the wafers, while shorten the cycle time for self-learning from domain knowledge and redefining new monitoring rules for fault classification in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. The results have demonstrated the practical viability of the developed solution.
A theoretical study of Mountain Waves on Western Ghats

The paper presents the results of a theoretical investigation on the occurrence of mountain waves on the lee of. Western Ghats. The average E-W vertical cross-section of the ghats is represented by a mathematical expression and the expressions for vertical velocity and displacement of streamlines are established for a two-dimensional motion making appropriate approximation in the le6-wave equation. Computation has been made for six cases for the winter season when the wind is more or less westerly and the atmosphere is dry. It appears wave lengths less than 25 km are not important for the Western Ghats. It is. only the longer waves that give appreciable amplitudes. The various cases are discussed in terms of the distribution of wind speed and stability.
Study of Matter Density Distributions, Elastic Electron Scattering Form factors and Root Mean Square Radii of 9C, 12N, 23Al, 11Be and 15C Exotic Nuclei

The ground state densities of neutron-rich (11Be,15C) and proton-rich (9C,12N,23Al) exotic nuclei are investigated using a two-body nucleon density distribution (2BNDD) with two frequency shells model (TFSM). The structure of the valence one-neutron of 11Be is in pure (1p1/2) and of 15C in pure (1d5/2) configuration, while the structure of valence one-proton configuration is in 9C,12N are to be in a pure (1p1/2) and 23Al in a pure (2s1/2) . For our studied nuclei, an efficient (2BNDD) operator for point nucleon system folded with two-body correlation operator's functions is used to investigate nuclear matter density distributions, elastic electron scattering form factors, and root-mean square (rms) radii. The effect of the strong tensor force (TC) in nucleon-nucleon forces is taken into account in the correlation. The wave functions of a single particle harmonic oscillator are used with two different oscillator size parameters, βc and βv, the former for core (inner) orbits and the latter for valence (halo) orbits. The measured matter density distributions of these nuclei clearly show the long tail results. The plane wave born approximation (PWBA) is used to investigate the elastic electron scattering form factors for these exotic nuclei.
A Novel Strategy for Unveiling Spatial Distribution Pattern of Gallotannins in Paeonia rockii and Paeonia ostii Based on LC–QTRAP–MS

Gallotannins (GTs) are a series of hydrolyzable tannins with multiple health-promoting effects. In this study, an integrated liquid chromatography tandem mass spectrometry (LC–MS/MS) strategy was developed for unveiling the spatial distribution pattern of GTs in the emerging oilseed crops Paeonia rockii and P. ostii. According to the fragmentation behavior of the representative GT (1,2,3,4,6-penta-O-galloyl-β-D-glucose, PGG), the diagnostic neutral loss (NL) of 170 Da was chosen for the non-targeted screening of GT precursors. Simultaneously, the tandem mass spectrometry spectrum (MS/MS) information was acquired through an enhanced product ion (EPI) scan. Nine major GTs were identified in tree peony. To quantify the targeted GTs in different tissues of tree peony, we established a multiple reaction monitoring (MRM)–enhanced product ion (EPI)-based pseudo-targeted approach under the information-dependent acquisition (IDA) mode. The quantitative results show that the GT compounds were ubiquitous in tree peony plants with diverse structures. The typical GT PGG was mainly distributed in roots, leaves, and petals. This strategy can also be utilized for metabolite characterization and quantification in other substrates.