Science and Technology

Placebo and nocebo: the power of expectation in health

Health outcomes and expectation: exploring placebo and nocebo

Expectations influence physiology, and the terms placebo and nocebo describe the corresponding beneficial or adverse results shaped by those expectations. A placebo effect arises when an inert intervention or therapeutic context leads to an improvement in health, whereas a nocebo effect appears when harmful outcomes or unwanted symptoms emerge due to negative expectations. These responses are not imaginary; they trigger observable shifts in symptoms, biological indicators, neural activity, and behavior. Grasping these effects is essential for clinical practice, research design, public health strategies, and responsible communication.Essential Terms and Clear DistinctionsPlacebo: improvement attributable to psychological and contextual factors rather than the…
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Obesity: why the approach is changing

Redefining the approach to obesity

Obesity is increasingly understood not as a matter of willpower or aesthetics, but as a multifaceted, long‑term medical condition shaped by biological, behavioral, social, and environmental influences. This broader understanding has prompted major shifts in prevention strategies, clinical practice, public policy, and scientific research. This article outlines the factors behind this change, reviews supporting evidence and examples, presents emerging tools and care models, and examines the challenges and consequences for patients, healthcare professionals, and communities.Understanding obesity and its significanceObesity is usually defined by body mass index (BMI) thresholds (BMI ≥30 kg/m² for adults), but BMI is a crude measure that…
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How are quantum sensors impacting navigation and medical imaging research?

Unpacking the ethical questions in AI science

Artificial intelligence systems are increasingly used to generate scientific results, including hypotheses, data analyses, simulations, and even full research papers. These systems can process massive datasets, identify patterns faster than humans, and automate parts of the scientific workflow that once required years of training. While these capabilities promise faster discovery and broader access to research tools, they also introduce ethical debates that challenge long-standing norms of scientific integrity, accountability, and trust. The ethical concerns are not abstract; they already affect how research is produced, reviewed, published, and applied in society.Authorship, Attribution, and AccountabilityOne of the most pressing ethical issues centers…
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What trends are reshaping software development with AI code generation?

What’s new in AI code generation for software development?

AI code generation has shifted from experimental tooling to a foundational layer of modern software development. What began as autocomplete for snippets now influences architecture decisions, testing strategies, security reviews, and team workflows. The most significant change is not just speed, but a redefinition of how humans and machines collaborate across the software lifecycle.Copilots Everywhere: From IDEs to the Entire ToolchainEarly AI coding assistants focused on in-editor suggestions. Today, copilots are embedded across the stack, including requirements gathering, code review, testing, deployment, and observability.IDE copilots generate functions, refactor legacy code, and explain unfamiliar codebases in real time.Pull request copilots summarize…
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How are serverless and container platforms evolving for AI workloads?

Future of AI: Serverless & Container Platforms

Artificial intelligence workloads have transformed the way cloud infrastructure is conceived, implemented, and fine-tuned. Serverless and container-based platforms, which previously centered on web services and microservices, are quickly adapting to support the distinctive needs of machine learning training, inference, and data-heavy pipelines. These requirements span high levels of parallelism, fluctuating resource consumption, low-latency inference, and seamless integration with data platforms. Consequently, cloud providers and platform engineers are revisiting abstractions, scheduling strategies, and pricing approaches to more effectively accommodate AI at scale.How AI Workloads Put Pressure on Conventional PlatformsAI workloads differ from traditional applications in several important ways:Elastic but bursty compute…
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Weight-loss medications: benefits, risks, and realistic expectations

Obesity Drugs: Pros, Cons, and Practical Outcomes

Obesity and excess weight are long‑term, often recurrent conditions shaped by intertwined biological, environmental, and behavioral factors, and medications used for weight management have become increasingly valuable tools that can deliver significant weight reduction, enhance metabolic wellbeing, and lessen overall disease impact when incorporated into a comprehensive treatment strategy; this article outlines how these therapies function, reviews the supporting evidence, highlights major risks, and offers grounded expectations for both patients and clinicians.How weight-loss medications operateMedications influence multiple physiological systems involved in appetite control, fullness signals, digestive processes, and overall energy regulation:Appetite-modulating incretin receptor agonists (GLP-1 and dual GLP-1/GIP agonists) curb…
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Why are vision-language-action models important for next-gen robots?

How Vision-Language-Action Models Drive Robotic Innovation

Vision-language-action models, commonly referred to as VLA models, are artificial intelligence frameworks that merge three fundamental abilities: visual interpretation, comprehension of natural language, and execution of physical actions. In contrast to conventional robotic controllers driven by fixed rules or limited sensory data, VLA models process visual inputs, grasp spoken or written instructions, and determine actions on the fly. This threefold synergy enables robots to function within dynamic, human-oriented settings where unpredictability and variation are constant.At a broad perspective, these models link visual inputs from cameras to higher-level understanding and corresponding motor actions, enabling a robot to look at a messy…
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How is liquid cooling evolving to handle AI data center heat loads?

The Future of Databases: Vector Search at its Core

Vector search has moved from a specialized research technique to a foundational capability in modern databases. This shift is driven by the way applications now understand data, users, and intent. As organizations build systems that reason over meaning rather than exact matches, databases must store and retrieve information in a way that aligns with how humans think and communicate.From Exact Matching to Meaning-Based RetrievalTraditional databases are optimized for exact matches, ranges, and joins. They work extremely well when queries are precise and structured, such as looking up a customer by an identifier or filtering orders by date.However, many modern use…
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Gluten: when avoiding it makes sense—and when it doesn’t

Navigating Gluten: When to Cut It Out, When to Keep It

What gluten is—and why it mattersGluten refers to a group of structural proteins mainly present in wheat, barley, rye, and their hybrids, contributing to dough elasticity and allowing baked products to rise and maintain their form; while it is harmless for most individuals, a smaller group experiences immune, allergic, or digestive reactions that lead to genuine health issues, so determining whether to avoid it depends on proper diagnosis, specific symptoms, and long-term nutritional considerations.Situations where steering clear of gluten is plainly justifiedCeliac disease: an autoimmune disorder in which ingestion of gluten damages the small intestine. Prevalence is about 1% worldwide.…
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What is the current state of practical quantum computing for businesses?

Quantum Computing’s Business Reality: A Current Assessment

Quantum computing has moved from theoretical physics labs into early commercial experimentation, but it is not yet a general-purpose replacement for classical computing. For businesses, the current state of practical quantum computing is best described as exploratory, hybrid, and use-case specific. Organizations can already experiment with quantum technologies, gain strategic insight, and achieve limited advantages in niche problems, while widespread operational deployment remains several years away.How Quantum Computing Stands Apart for Modern BusinessesTraditional computers handle data with bits that hold either a zero or a one, while quantum machines rely on qubits, capable of occupying several states at once thanks…
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