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"Thieves."
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The thief
\"The old farmhouse in the French countryside is a refuge for former jewel thief Josephine Deveraux. Admittedly, there aren't many men in the vicinity, but she has her cat to cuddle up with. It's a far cry from her former life, constantly running from the law, and she's enjoying her peace... until the intruder in the three-piece suit tackles her. He wants her back in the game, helping with a heist -- and he's not above making threats to get his way. Little does Josephine know that [the] notorious -- and notoriously charming -- theif, Xavier Lambert, is after the very same 180-carat prize she's being blackmailed to steal. To his chagrin, he's doing it not as a free agent, but as a member of the Elite Crimes Unit -- the team he was forced to join when his brilliant career came to a sudden end. And little does Xavier know that his comeback is about to include a stranger's kiss, a stinging slap, and a hunt for missing treasure -- along with the infuriatingly sexy woman who's outfoxing him...\"--Back cover.
Book
A novel meta-heuristic algorithm for solving numerical optimization problems: Ali Baba and the forty thieves
This paper presents a novel meta-heuristic algorithm called Ali Baba and the forty thieves (AFT) for solving global optimization problems. Recall the famous tale of Ali Baba and the forty thieves, where Ali Baba once saw a gang of forty thieves enter a strange cave filled with all kinds of treasures. The strategies pursued by the forty thieves in the search for Ali Baba inspired us to design ideas and underlie the basic concepts to put forward the mathematical models and implement the exploration and exploitation processes of the proposed algorithm. The performance of the AFT algorithm was assessed on a set of basic benchmark test functions and two more challenging benchmarks called IEEE CEC-2017 and IEEE CEC-C06 2019 benchmark test functions. These benchmarks cover simple and complex test functions with various dimensions and levels of complexity. An extensive comparative study was performed between the AFT algorithm and other well-studied algorithms, and the significance of the results was proved by statistical test methods. To study the potential performance of AFT, its further development is discussed and carried out from five aspects. Finally, the applicability of the AFT algorithm was subsequently demonstrated in solving five engineering design problems. The results in both benchmark functions and engineering problems show that the AFT algorithm has stronger performance than other competitors’ algorithms.
Journal Article
Defence compounds in pollen
Pollen plays two important roles in angiosperm reproduction, serving as a vehicle for the plant’s male gametes, but also, in many species, as a lure for pollen-feeding animals. Despite being an important food source for many pollinators, pollen often contains compounds with known deterrent or toxic properties, as documented in a growing number of studies. Here we review these studies and discuss the role of pollen defensive compounds in the coevolutionary relationship between plants and bees, the preeminent consumers of pollen. Next, we evaluate three hypotheses that may explain the existence of defensive compounds in pollen. The pleiotropy hypothesis, which proposes that defensive compounds in pollen merely reflect physiological spillover from other plant tissues, is contradicted by evidence from several species. Although plants may experience selection to defend pollen against poor-quality pollinators, we also find only partial support for the protection-against-pollen-collection-hypothesis. Finally, pollen defences might protect pollen from colonisation by antagonistic microorganisms (antimicrobial hypothesis), although data to evaluate this idea are scarce. Further research on the effects of pollen defensive compounds on pollinators, pollen thieves, and pollen-colonising microbes will be needed to understand why many plants have chemically defended pollen, and the consequences of those defences for pollen consumers.
Journal Article
Theft of swords
\"Two thieves in the wrong place at the wrong time are on the run in this fast-paced adventure fantasy\"--Provided by publisher.
Book
Economist video. The footballer who stole \The scream\
He was the Norwegian footballer who became the art thief renowned for stealing Edvard Munch's 'The Scream'. The Economists' obituary editor, Ann Wroe, explores the unusual life and criminal history of Pål Enger.
Streaming Video
Just watch me : a novel
\"From the author of the wildly successful Dexter series, comes a new mesmerizing bad guy we can root for: Riley Wolfe is a master thief, expert at disguise and violent when he needs to be. It's no accident, though, that Riley targets only the .1% and is willing to kill when necessary: He despises the degenerate and immoral possessors of the objects that he steals. In this series launch, Riley targets an extraordinary set of crown jewels, a heist that will hopefully land him with a sensational collection of heirlooms and leave their owners bereft and embarrassed--or worse. Yes, these jewels are worth millions, but the true attraction for grabbing the jewels comes down to one simple fact: These jewels are absolutely unstealable. There is just no way anyone could get past the airtight security and dream of getting away with even a single diamond from the Imperial Collection. Riley Wolfe has always liked a challenge. But the heist is only the beginning. Riley is pursued by a worthy opponent, a cop barely a step behind him. And dancing around them both is a woman, a master forger and sometimes ally of our murdering Robin Hood protagonist.\"-- Publisher's description.
Book
Metaheuristic-Enhanced SVR Models for California Bearing Ratio Prediction in Geotechnical Engineering
Soil resistance characteristics, particularly the California Bearing Ratio (CBR), play a pivotal role in pavement and subgrade design. However, conventional laboratory-based CBR testing is often time-consuming, labor-intensive, and costly. This study presents a novel machine learning framework that combines Support Vector Regression (SVR) with three recent metaheuristic optimization algorithms—Dingo Optimization Algorithm (DOA), Alibaba and the Forty Thieves Optimization (AFT), and Adaptive Opposition Slime Mold Algorithm (AOSMA)—to predict CBR values efficiently and accurately. A dataset consisting of 220 soil samples with eight geotechnical input parameters was used to develop and evaluate the hybrid models. The predictive performance of each model was assessed using multiple evaluation metrics, including R², RMSE, MSE, RSR, and WAPE. Results indicate that the SVR–AFT (SVAF) hybrid model outperformed the others, achieving an R² of 0.9968 and an RMSE of 0.7946 in the testing phase, demonstrating high generalization ability and predictive precision. The integration of SVR with metaheuristic algorithms significantly enhances model robustness and accuracy, offering a practical and cost-effective alternative to empirical CBR testing methods. This work highlights the potential of hybrid AI models in solving complex geotechnical prediction problems and contributes to the growing body of research at the intersection of civil engineering and artificial intelligence.
Journal Article
The dagger's path
\"When sailors came to Ardhi's homeland, they plundered not only its riches, but its magic, too. Now disgraced islander Ardhi must retrieve what was stolen, but there are ruthless men after this power, men who will do anything to possess it ...\"--Page 4 of cover.
Book
Crimes against nature
Crimes against Nature reveals the hidden history behind three of the nation's first parklands: the Adirondacks, Yellowstone, and the Grand Canyon. Focusing on conservation's impact on local inhabitants, Karl Jacoby traces the effect of criminalizing such traditional practices as hunting, fishing, foraging, and timber cutting in the newly created parks. Jacoby reassesses the nature of these \"crimes\" and provides a rich portrait of rural people and their relationship with the natural world in the late nineteenth and early twentieth centuries.
eBook