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Who gathers honey? Who designs clothes? Who makes lunch for students?  So many jobs can be found in this delightful and satisfying sliding panel format for preschoolers. The 36 panels feature jobs in various settings: in the orchestra, at the market, at school, and more! Each new spread offers clues to the right respon

Mr. Bear is having a birthday party, and everyone is welcome! It doesn’t matter if you’re big, small, don’t have teeth, or are afraid of the dark . . . you’re invited! In this wonderfully illustrated oversize book that celebrates inclusion as well as a special day, Mr. Bear’s Birthday also includes lots of gifts—with f

Waiting is not easy, whether it’s waiting to play with a toy or to eat a snack. And Little Kangaroo, Little Cat, and their friends are just not sure that they can be patient. But as their grown-ups show them, being patient doesn’t have to be boring or unbearable. There are many fun things they can do while they wait! C

From New York City to Tokyo and Berlin to Sydney, the streets of the 15 cities included in this book are filled with a fascinating showcase of local landmarks and awesome attractions. How will you get from the Brooklyn Bridge to Central Park in New York City? Or find your way to Topkapi Palace in Istanbul? Each spread

Finding murderers is just a day on the job for police captain Nadia Barka. But when a mutilated corpse is discovered in an old baptistery, she’s faced with some difficult questions: Who left a young woman’s body in a high-security museum in the middle of the night? And where is the victim’s heart?Barka has no leads, until several days later when unassuming computer technician Julien Lombard comes forward, claiming to have had a premonition of the woman’s abduction and murder. The ensuing investigation turns up nothing, however, and Julien is just as skeptical about his intel as the police are. But, after another woman goes missing, Barka decides to take a chance and trust in Julien’s gift. Does Julien hold the key to preventing another gruesome crime? Working together, can they find the murderer before he steals another heart?

Ferdinand Brun hasn’t always been a grumpy old man. Many years ago, he was a grumpy young man. Now he’d much rather spend time with his canine companion, Daisy, than any of his nosy neighbors. But as his behavior becomes increasingly peculiar, his daughter grows concerned and begins to consider moving him into a retirement home.In order to maintain his freedom, Ferdinand must submit to an apartment inspection by his longtime enemy, the iron-fisted concierge, Mrs. Suarez. Unfortunately, he’s never tidied up a day in his life. His neighbors, precocious ten-year-old Juliette and vivacious ninety-two-year-old Beatrice, come to the rescue. And once he lets these two into his life, things will never be the same. After an eighty-three-year reign of grouchiness, Ferdinand may finally learn that it’s never too late to start living.

As a former soldier, private investigator Jean Legarec is used to high stakes and sharpshooters. But he doesn’t realize what he’s getting into when he agrees to investigate the disappearance of six-year-old Alexandre, the grandson of a highly influential French politician.For a million euros, Legarec launches a dangerous search that will dig deep into some of the darkest corners of European history. With the help of the boy’s beautiful aunt, Béatrice, he uncovers evidence of a modern-day terrorist plot rooted in a long-buried enterprise of the Third Reich. From Paris to Malta to the Vosges forest, a cast of witnesses—including a death camp survivor and former mercenaries—help Legarec piece together a terrifying truth.Yet even then, the PI worries that his own dark past and his undeniable attraction to Béatrice might be clouding the investigation. Can he put the personal aside in order to do his job? And can he free an innocent child from a web of absolute evil?

The challenge was to make appealing something that was not. To transform this godforsaken city, populated by people as indifferent to art as tourists are to the idea of visiting the only historic building—a vague ruin of a castle—in a glamorous destination. After all, what the Guggenheim did for Bilbao could be reproduced […]

The pandemic of respiratory disease spreading from person-to-person, named “coronavirus disease 2019” (abbreviated “COVID-19”), presents a public health emergency of international concern. This pandemic is occurring during renewed attention to the persistent issue of police brutality against Blacks in the United States. Protests have ensued to highlight perceived and observed injustice against minorities, particularly Black people. Concerns arise that these protests may complicate efforts to adhere to social distancing and increase risks of COVID-19 exposure among Black persons, who are already disproportionality affected by COVID-19 outcomes due to systemic barriers within the healthcare system and society. This article discusses police brutality against Blacks in the United States and subsequent protests, considerations for social distancing and racial disparities in health during COVID-19, and the need for policies to ensure fair and equitable enforcement of social distancing mandates. We also discuss the need for policies to improve access to COVID-19 testing, diagnosis, and management among underserved and minority communities. © 2020 Taylor & Francis Group, LLC.

Vietnam's 1986 doi moi reforms are transforming Vietnam from a socialist economy to a socialist market economy. In addition to liberalized laws allowing

Roads should always be in a reliable con-dition and maintained regularly. One of the problems that should be maintained well is the pavement cracks problem. This a challenging problem that faces road engineers, since maintaining roads in a stable condition is needed for both drivers and pedestrians. Many meth-ods have been proposed to handle this problem to save time and cost. In this paper, we proposed a two-stage method to detect pavement cracks based on Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) to solve this classification problem. We employed a Principal Component Analysis (PCA) method to extract the most significant features with a di˙erent number of PCA components. The proposed approach was trained using a Mendeley Asphalt Crack dataset, which contains 400 images of road cracks with a 480×480 resolution. The obtained results show how PCA helped in speeding up the learning process of CNN.

We present the visual orbits of two long-period spectroscopic binary stars, HD 8374 and HD 24546, using interferometric observations acquired with the CHARA Array and the Palomar Testbed Interferometer. We also obtained new radial velocities from echelle spectra using the APO 3.5 m and Fairborn 2.0 m telescopes. By combining the visual and spectroscopic observations, we solve for the full, three-dimensional orbits and determine the stellar masses and distances to within 3% uncertainty. We then estimate the effective temperature and radius of each component star through Doppler tomography and spectral energy distribution analyses, in order to compare the observed stellar parameters to the predictions of stellar evolution models. For HD 8374, we find masses of M 1 = 1.636 ± 0.050M ⊙ and M 2 = 1.587 ± 0.049M ⊙, radii of R 1 = 1.84 ± 0.05R ⊙ and R 2 = 1.66 ± 0.12R ⊙, temperatures of K and K, and an estimated age of 1.0 Gyr. For HD 24546, we find masses of M 1 = 1.434 ± 0.014M ⊙ and M 2 = 1.409 ± 0.014M ⊙, radii of R 1 = 1.67 ± 0.06R ⊙ and R 2 = 1.60 ± 0.10R ⊙, temperatures of K and K, and an estimated age of 1.4 Gyr. HD 24546 is therefore too old to be a member of the Hyades cluster, despite its physical proximity to the group.

In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure.

According to the CPT theorem, which states that the combined operation of charge conjugation, parity transformation and time reversal must be conserved, particles and their antiparticles should have the same mass and lifetime but opposite charge and magnetic moment. Here, we test CPT symmetry in a nucleus containing a strange quark, more specifically in the hypertriton. This hypernucleus is the lightest one yet discovered and consists of a proton, a neutron and a Λ hyperon. With data recorded by the STAR detector1–3 at the Relativistic Heavy Ion Collider, we measure the Λ hyperon binding energy BΛ for the hypertriton, and find that it differs from the widely used value4 and from predictions5–8, where the hypertriton is treated as a weakly bound system. Our results place stringent constraints on the hyperon–nucleon interaction9,10 and have implications for understanding neutron star interiors, where strange matter may be present11. A precise comparison of the masses of the hypertriton and the antihypertriton allows us to test CPT symmetry in a nucleus with strangeness, and we observe no deviation from the expected exact symmetry.

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently calculating spatially explicit life cycle global warming (GW) and eutrophication (EU) impacts at the county scale over a geographic region are lacking. The objective of this study was to determine the most efficient and accurate model for estimating spatially explicit life cycle GW and EU impacts at the county scale, with corn production in the U.S.’s Midwest region as a case study. This study compared the predictive accuracies and efficiencies of five distinct supervised machine learning (ML) algorithms, testing various sample sizes and feature selections. The results indicated that the gradient boosting regression tree model built with approximately 4000 records of monthly weather features yielded the highest predictive accuracy with cross-validation (CV) values of 0.8 for the life cycle GW impacts. The gradient boosting regression tree model built with nearly 6000 records of monthly weather features showed the highest predictive accuracy with CV values of 0.87 for the life cycle EU impacts based on all modeling scenarios. Moreover, predictive accuracy was improved at the cost of simulation time. The gradient boosting regression tree model required the longest training time. ML algorithms demonstrated to be one million times faster than the traditional process-based model with high predictive accuracy. This indicates that ML can serve as an alternative surrogate of process-based models to estimate life-cycle environmental impacts, capturing large geographic areas and timeframes.