Technology Engineering Afraid your fish is too fishy? Microneedles can tell when things start getting rancid long before we notice smells. Breakthroughs, discoveries, and DIY tips sent every weekday. A new biosensor made out of needles most commonly seen in dermatology clinics and medspas could make the fresh fish " smell test " seem antiquated. For as long as humans have eaten fish, we've identified rot or spoilage by looking for a handful of physical signs .

Human cultural complexity did not arise in a vacuum. Scholars in the humanities and social sciences have long debated how ecological factors, such as climate and resource availability, enabled early hunter-gatherers to allocate time and energy beyond basic subsistence tasks. This paper presents a formal, interdisciplinary approach that integrates theoretical modeling with computational methods to examine whether conditions that allow lower spoilage of stored food, often associated with colder climates and abundant large fauna, could indirectly foster the emergence of cultural complexity. Our contribution is twofold. First, we propose a mathematical framework that relates spoilage rates, yield levels, resource management skills, and cultural activities. Under this framework, we prove that lower spoilage and adequate yields reduce the frequency of hunting, thus freeing substantial time for cultural pursuits. Second, we implement a reinforcement learning simulation, inspired by engineering optimization techniques, to validate the theoretical predictions. By training agents in different $(Y,p)$ environments, where $Y$ is yield and $p$ is the probability of daily spoilage, we observe patterns consistent with the theoretical model: stable conditions with lower spoilage strongly correlate with increased cultural complexity. While we do not claim to replicate prehistoric social realities directly, our results suggest that ecologically stable niches provided a milieu in which cultural forms could germinate and evolve. This study, therefore, offers an integrative perspective that unites humanistic inquiries into the origins of culture with the formal rigor and exploratory power of computational modeling.

We consider a practically motivated variant of the canonical online fair allocation problem: a decision-maker has a budget of perishable resources to allocate over a fixed number of rounds. Each round sees a random number of arrivals, and the decision-maker must commit to an allocation for these individuals before moving on to the next round. The goal is to construct a sequence of allocations that is envy-free and efficient. Our work makes two important contributions toward this problem: we first derive strong lower bounds on the optimal envy-efficiency trade-off that demonstrate that a decision-maker is fundamentally limited in what she can hope to achieve relative to the no-perishing setting; we then design an algorithm achieving these lower bounds which takes as input $(i)$ a prediction of the perishing order, and $(ii)$ a desired bound on envy. Given the remaining budget in each period, the algorithm uses forecasts of future demand and perishing to adaptively choose one of two carefully constructed guardrail quantities. We demonstrate our algorithm's strong numerical performance - and state-of-the-art, perishing-agnostic algorithms' inefficacy - on simulations calibrated to a real-world dataset.

Ginni Rometty, chief executive officer of International Business Machines Corp. (IBM), speaks during the IBM Think Conference in San Francisco, California, U.S. Photographer: David Paul Morris/Bloomberg photo credit: 2019 Bloomberg Finance LP 2019 Bloomberg Finance LP The title of this article comes from one of many memorable (and tweetable) lines I heard from one of the speakers at IBM's THINK 2019 conference in San Francisco. This is one of the major conferences focused on AI, data, and technology that drives business today. I attended sessions that had universal business lessons for the forward-thinking company. Here is a summary of some takeaways. Ginni Rometty, IBM's CEO, came out strong in her "Chairman's Presentation" with another provocative statement: "Data is the basis for the competitive advantage."

To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling better designs for enterprises, analysis of their performance, and management of their operations. This article motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modeling and considers in detail the Toronto virtual enterprise effort at the University of Toronto. It can be both descriptive and definitional--spanning what is and what should be.

That's what Jeff Welser, lab director of IBM Research-Almaden told me on a recent visit. Given that IBM's Watson technology was initially a system designed to play the TV game show Jeopardy, but is now a general-purpose machine learning system thought to be the fastest-growing part of IBM's business, it's not surprising that the company is hoping another wild seed will bear profitable fruit. But where do these seeds come from? Welser told me that research projects often originate from open mike sessions, held once a year. These are serious events and, at the same time, entertainment along the lines of Shark Tank meets American Idol.

Understanding how different factors affect the supply chain remains a top priority for research firms around the globe. This unwavering drive represents the continued interest in advancing today's capabilities with state-of-the-art technology and adaptability. From artificial intelligence to refocusing on procurement, the state of supply chain continued to explode throughout 2016, and you need to understand why. Artificial intelligence (AI) is among the most well-recognized ideas in science fiction. However, it's true applications are becoming more apparent daily.

To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling better designs for enterprises, analysis of their performance, and management of their operations. This article motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modeling and considers in detail the Toronto virtual enterprise effort at the University of Toronto.