CES 2026 showed that humanoid and embodied AI systems still have a long way to go before delivering real-world value, particularly in homes. At the same time, there is a growing sense that the path to deployment is becoming clearer. A consensus has emerged across platforms: multi-camera perception, often wrist-mounted, paired with VLA models, is sufficient for most tasks. Increasingly, tactile hands and VTLA software are added. There was a clear split between industrial and home-care humanoids.

Systematic logistics, conveyance amenities and facilities as well as warehousing information play a key role in fostering profitable development in a supply chain. The aim of transformation in industries is the improvement of the resiliency regarding the supply chain. The resiliency policies are required for companies to affect the collaboration with logistics service providers positively. The decrement of air pollutant emissions is a persistent advantage of the efficient management of logistics and transportation in supply chain. The management of shipment type is a significant factor in analyzing the sustainability of logistics and supply chain. An automatic approach to predict the shipment type, logistics delay and traffic status are required to improve the efficiency of the supply chain management. A hybrid graphsage network (H-GSN) is proposed in this paper for multi-task purpose of logistics management in a supply chain. The shipment type, shipment status, traffic status, logistics ID and logistics delay are the objectives in this article regarding three different databases including DataCo, Shipping and Smart Logistcis available on Kaggle as supply chain logistics databases. The average accuracy of 97.8% and 100% are acquired for 10 kinds of logistics ID and 3 types of traffic status prediction in Smart Logistics dataset. The average accuracy of 98.7% and 99.4% are obtained for shipment type prediction in DataCo and logistics delay in Shipping database, respectively. The evaluation metrics for different logistics scenarios confirm the efficiency of the proposed method to improve the resilience and sustainability of the supply chain.

Fruits and vegetables form a vital component of the global economy; however, their distribution poses complex logistical challenges due to high perishability, supply fluctuations, strict quality and safety standards, and environmental sensitivity. In this paper, we propose an adaptive optimization model that accounts for delays, travel time, and associated temperature changes impacting produce shelf life, and compare it against traditional approaches such as Robust Optimization, Distributionally Robust Optimization, and Stochastic Programming. Additionally, we conduct a series of computational experiments using Internet of Things (IoT) sensor data to evaluate the performance of our proposed model. Our study demonstrates that the proposed adaptive model achieves a higher shelf life, extending it by over 18\% compared to traditional optimization models, by dynamically mitigating temperature deviations through a temperature feedback mechanism. The promising results demonstrate the potential of this approach to improve both the freshness and efficiency of logistics systems an aspect often neglected in previous works.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian forces have spread rapidly through Pokrovsk, the city in Ukraine's east where the warring sides have concentrated their manpower and tactical ingenuity during the past week, in what may be a final culmination of a 21-month battle. Geolocated footage placed Russian troops in central, northern and northeastern Pokrovsk, said the Institute for the Study of War (ISW), a Washington-based think tank. It set its sights on the city almost two years ago, after capturing Avdiivka, 39km (24 miles) to the east.

The State of AI: Is China about to win the race? In this conversation, the FT's John Thornhill and MIT Technology Review's Caiwei Chen consider the battle between Silicon Valley and Beijing for technological supremacy. Viewed from abroad, it seems only a matter of time before China emerges as the AI superpower of the 21st century. Here in the West, our initial instinct is to focus on America's significant lead in semiconductor expertise, its cutting-edge AI research, and its vast investments in data centers. The legendary investor Warren Buffett once warned: "Never bet against America." He is right that for more than two centuries, no other "incubator for unleashing human potential" has matched the US.

Abstract-- Efficiently delivering items to an ongoing surgery in a hospital operating room can be a matter of life or death. In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. We propose the Operating Room Bot (ORB), a robot framework to automate logistics tasks in hospital operating rooms (OR). ORB leverages a robust, hierarchical behavior tree (BT) architecture to integrate diverse functionalities of object recognition, scene interpretation, and GPU-accelerated motion planning. The contributions of this paper include: (1) a modular software architecture facilitating robust mobile manipulation through behavior trees; (2) a novel real-time object recognition pipeline integrating YOLOv7, Segment Anything Model 2 (SAM2), and Grounded DINO; (3) the adaptation of the cuRobo parallelized trajectory optimization framework to real-time, collision-free mobile manipulation; and (4) empirical validation demonstrating an 80% success rate in OR supply retrieval and a 96% success rate in restocking operations. These contributions establish ORB as a reliable and adaptable system for autonomous OR logistics.

Driverless semitrucks raise questions about safety, reliability and the future of the trucking industry. A bold new pilot program is bringing autonomous trucking to the heart of Texas. That means robots are about to hit some of the country's busiest shipping lanes, with doors in tow. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts and exclusive deals delivered straight to your inbox. Plus, you'll get instant access to my Ultimate Scam Survival Guide -- free when you join my CYBERGUY.COM/NEWSLETTER

These authors contributed equally to this work. Abstract --With growing interest in sustainable logistics, electric vehicle (EV)-based deliveries offer a promising alternative for urban distribution. This depends on factors such as the charging point (CP) availability, cost, proximity, and vehicles' state of charge (SoC). We propose CARGO, a framework addressing the EV-based delivery route planning problem (EDRP), which jointly optimizes route planning and charging for deliveries within time windows. After proving the problem's NP-hardness, we propose a mixed integer linear programming (MILP)-based exact solution and a computationally efficient heuristic method. Using real-world datasets, we evaluate our methods by comparing the heuristic to the MILP solution, and benchmarking it against baseline strategies, Earliest Deadline First (EDF) and Nearest Delivery First (NDF). The results show up to 39% and 22% reductions in the charging cost over EDF and NDF, respectively, while completing comparable deliveries. Delivery systems form the backbone of modern logistics, facilitating the movement of goods across regional, inter-city, and urban networks [1]. These systems face increasing pressure to remain cost-efficient, responsive, and scalable amid growing demand for fast, flexible services.

The increasing demand for fast and cost effective last mile delivery solutions has catalyzed significant advancements in drone based logistics. This research describes the development of an AI integrated drone delivery system, focusing on route optimization, object detection, secure package handling, and real time tracking. The proposed system leverages YOLOv4 Tiny for object detection, the NEO 6M GPS module for navigation, and the A7670 SIM module for real time communication. A comparative analysis of lightweight AI models and hardware components is conducted to determine the optimal configuration for real time UAV based delivery. Key challenges including battery efficiency, regulatory compliance, and security considerations are addressed through the integration of machine learning techniques, IoT devices, and encryption protocols. Preliminary studies demonstrate improvement in delivery time compared to conventional ground based logistics, along with high accuracy recipient authentication through facial recognition. The study also discusses ethical implications and societal acceptance of drone deliveries, ensuring compliance with FAA, EASA and DGCA regulatory standards. Note: This paper presents the architecture, design, and preliminary simulation results of the proposed system. Experimental results, simulation benchmarks, and deployment statistics are currently being acquired. A comprehensive analysis will be included in the extended version of this work.

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.