A review published in Cyber-Physical Energy Systems examines how IoT and machine learning technologies can transform manufacturing energy management. By enabling real-time monitoring, prediction, and optimization, these approaches offer new pathways for energy-efficient and sustainable factories while addressing key challenges in industrial adoption.

Freeze–thaw stress reduces baker’s yeast viability, delays fermentation restart, and weakens frozen-dough bread quality. Here, three independent Saccharomyces cerevisiae lineages were evolved for 300 generations under repeated freeze–thaw selection. Evolved populations showed markedly improved survival, with the best lineage reaching ~96% post-thaw viability, while retaining baseline growth capacity. In frozen dough, evolved lineages improved loaf expansion and texture retention after freezing. Physiological assays showed faster post-thaw recovery, stronger fermentation-associated activity, lower intracellular reactive oxygen species accumulation, altered antioxidant capacity, and better preservation of cell-envelope morphology. Transcriptomic, metabolomic, and cross-omics analyses of ALE1 linked these phenotypes to coordinated remodeling of carbon recovery metabolism, nitrogen redistribution, sulfur/glutathione-associated redox pathways, and membrane-related metabolites. These results show that ALE improves frozen-dough yeast performance through coordinated physiological and multi-omics remodeling rather than a single stress-response pathway.

Threedimensional pointcloud technology can capture plant architecture in vivid detail, but no existing model has been able to reliably separate stems from leaves across an entire crop’s life cycle—from the first true leaf to full reproductive maturity. 

Accurate river water level estimation is crucial for flood prevention, disaster mitigation and water resource management, yet strong non-stationarity and nonlinearity in complex plain river-network regions pose significant challenges to conventional approaches.

Aqueous zinc-ion batteries are promising candidates for large-scale energy storage, yet their development is severely hindered by the interfacial instability of zinc anodes. Distinct from strategies employing pre-formed polymers, this work proposes an innovative monomer-induced in situ interface engineering strategy. By leveraging the preferential adsorption of acrylamide monomers on the Zn surface, a locally high-concentration region is created, which subsequently enables the in situ construction of a stable hydrated network interphase (HNI) triggered synergistically by Zn²⁺ and SO₄²⁻ during electrochemical cycling. The HNI precisely regulates Zn deposition via a triple synergistic mechanism: Lewis acid–base coordination (C = O···Zn²⁺) provides fixed nucleation sites; dynamically anchored SO₄²⁻ within the interphase forms negatively charged microregions that homogenize Zn²⁺ flux via Coulombic repulsion; and a dense hydrogen-bonding network effectively confines free water and suppresses side reactions. Benefiting from this multifunctional interphase, the Zn//Zn symmetric cell achieves an ultra-long cycling life of 8650 h (over 360 days) at 1 mA cm⁻² with excellent reproducibility, the Zn//Ti cell delivers a high average Coulombic efficiency of 99.71% at 5 mA cm⁻². The Zn//I₂ full cell retains 89.15% of its capacity after 12,000 cycles. This work provides a novel paradigm for interfacial construction toward high-performance zinc metal anodes.

The commercialization of liquid lithium-ion batteries has revolutionized the consumer electronics industry. However, conventional lithium-ion batteries with graphite anodes and organic electrolytes are approaching their intrinsic performance limits and struggle to meet the growing demands for higher energy density, reliability, and safety in electric vehicles and large-scale energy storage. Solid-state batteries utilizing lithium or sodium metal anodes are considered promising next-generation energy storage solutions. Despite this potential, the formation of dendrites during charge–discharge cycling remains a critical challenge. Dendrite growth can initiate a destructive feedback loop of crack propagation and further dendrite intrusion, ultimately leading to battery failure and performance degradation. Previous studies have predominantly focused on single physical domains, such as electrochemical, stress, or thermal fields. However, such single-physics approach limits the understanding of dendrite evolution under realistic, coupled multiphysics conditions. This review first compares the morphological characteristics of dendrites in liquid and solid-state metal batteries. It then critically examines the key factors and predictive models of dendrite formation, initially from single-physics and subsequently from an integrated multiphysics perspective. Finally, strategies for mitigating dendrite growth via multiphysics field regulation are summarized. By establishing a comprehensive framework that integrates morphology evolution, multiphysics modeling, and suppression strategies, this work provides a foundational theoretical understanding for addressing dendrite formation in solid-state lithium and sodium metal batteries.

Electronic skin (e-skin), a stretchable, conformable, and multimodal sensing platform, is rapidly advancing in healthcare, robotics, and human–machine interaction (HMI). However, prolonged wear and sweat accumulation at the e-skin-skin interface can introduce signal artifacts, cause discomfort, and eventually lead to interfacial failure. Inspired by the unidirectional liquid transport of the Nepenthes peristome, we constructed an intelligent breathing e-skin (SPTL) featuring a Janus bilayer structure that creates a “liquid diode” effect for active sweat transport, achieving a remarkable cumulative one-way transport index of 956.36 while maintaining a dry interface even under profuse perspiration. This device exhibits a high tensile strain of 627%, a breathability of 20.02 mm s−1, and a pressure sensitivity of 7.39 kPa−1. Furthermore, the SPTL demonstrates versatile multimodal sensing capabilities, including real-time Morse code and non-contact capacitive sensing, while sustaining stable performance over 10,000 pressing cycles (capacitance decay < 3%). As a bio-integrated electrode, SPTL enables high-fidelity acquisition of electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG) signals, significantly outperforming commercial Ag/AgCl electrodes. Integrated with machine learning algorithms and EMG signals captured by the SPTL, it facilitates precise teleoperation of a quadruped robot and handwritten letter recognition with over 95% accuracy. This bionic strategy provides a versatile solution for intelligent, breathable, and multimodal bio-integrated interfaces.

Agrophotovoltaics (APV), as an innovative model integrating agricultural production with solar photovoltaic power generation, is an important pathway to address the dual pressures of food security and low-carbon energy. As the global energy transition accelerates and agricultural modernization advances, its advantages of “dual land use and food–energy co-production” have become increasingly prominent. It can achieve efficient land use while accommodating clean electricity output and crop growth, aligning with the needs of sustainable development. However, for a long time, traditional models have been constrained by bottlenecks such as imbalanced light distribution, lagging temperature and humidity regulation, inefficient manual operation and maintenance, and data fragmentation, making it difficult to balance energy and agricultural benefits and seriously hindering large-scale promotion.