Smart manufacturing has emerged as a key enabler of industrial digital transformation, fostering intelligent, interconnected, and adaptive production systems. At the same time, production flexibility has become a strategic imperative for managing demand volatility, supply chain disruptions, and mass customization requirements. Despite substantial advances in Industry 4.0 and the transition toward Industry 5.0, the literature remains conceptually fragmented and largely technology-driven, with limited integration of organizational, human-centric, and sustainability perspectives. This study presents a systematic literature review of smart manufacturing for production flexibility, synthesizing existing research across major enabling technologies and industrial application domains. The review identifies three critical gaps in the current body of knowledge: (i) the lack of a unified and multidimensional conceptualization of production flexibility, (ii) insufficient integration between cyber–physical infrastructures and socio-technical systems, and (iii) the limited incorporation of human-centricity and sustainability as core design principles. The findings demonstrate that production flexibility should be viewed not as a direct technological outcome, but as an emergent system-level capability arising from the dynamic interaction of digital technologies, organizational structures, and human intelligence. To address these gaps, the study proposes a seven-stage Smart Manufacturing–Production Flexibility (SM–PF) transformation framework encompassing digital connectivity, system integration, intelligent analytics, adaptive automation, autonomous systems, human–AI collaboration, and ecosystem integration. The framework conceptualizes the evolution of flexibility from conventional operational adaptability toward anticipatory, reconfigurable, cognitive, and ecosystem-level capabilities. This study contributes an integrated theoretical foundation and a structured roadmap for future research and industrial transformation in smart manufacturing.
Production collapse in brewery operations is a major industrial challenge marked by sustained declines in output, efficiency, and capacity utilization due to interacting technical, operational, managerial, and external constraints. This systematic review synthesizes existing literature on the root causes of production decline in the brewery and beverage industry, with emphasis on developing economies. Guided by the PRISMA framework and drawing from major scientific databases, the study examines empirical evidence on critical production bottlenecks. The review compares traditional mathematical models with advanced Machine Learning (ML) techniques for root cause identification, highlighting their complementary strengths in interpretability and predictive accuracy. It further evaluates optimization and what-if scenario analysis as decision-support tools for translating predictive insights into practical production improvements. Evidence shows that scenario-based optimization can enhance output, reduce downtime, and improve resource allocation in brewery systems. Despite progress, gaps remain, particularly the absence of integrated root-cause, ML, and optimization frameworks and limited validation rigor. By consolidating fragmented findings and outlining future research directions, this review provides a structured foundation for developing robust, data-driven productivity recovery strategies and strengthening sustainable performance in brewery operations.
With the rapid development of the aluminium electrolysis industry, large amounts of lithium-containing electrolyte residue are generated, posing environmental risks and wasting lithium resources. This study proposes an efficient lithium leaching method from spent carbon anode (SCA) electrolytic aluminium carbon slag using NaOH. The leaching rate of lithium reaches 89.46% at a NaOH concentration of 10 mol/L, a leaching temperature of 90 °C, and a leaching time of 2 h. Thermodynamic calculations concluded that during alkaline leaching, most phases in SCA can react spontaneously with NaOH to release soluble ions. The kinetic results suggested that the leaching behavior of Li+ follows the ‘unreacted shrinkage nucleus model’, controlled by both mixing and diffusion. NaOH concentration and leaching temperature are the key factors governing the effectiveness of Li+ leaching. Medusa simulations showed that the dissociated Al3+ in alkaline leach solution would first form an Al(OH)3 complex and continue to react with OH− to form Al(OH)4−, while lithium exists in the form of Li+ and LiOH. Mechanistic analysis via SEM-EDS and XRD indicates that NaOH breaks Na–Al–F bonds, releasing Li+ and forming NaF. This approach offers an eco-friendly pathway for resource recovery from SCA, supporting cryolite regeneration and minimizing the environmental impacts of hazardous waste.
River ecosystems sustain socio-economic development via the provision of essential ecosystem services, which are of direct relevance to achieving the Sustainable Development Goals (SDGs). A paradigm shift in river management over the last 30 years, away from engineered channels that predominantly increase drainage efficiency, towards more restorative and holistic approaches that integrate hydrological, geomorphological, and ecological systems, makes this an ideal time to reflect on both the successes and future trajectories in river ecosystem management. Therefore, we synthesize published research on river ecosystems within the SDG framework using a suite of knowledge visualization tools. Co-occurrence analysis reveals that research in river ecosystem science can be broadly split into three themes: water quality, water flow, and aquatic organisms, and that most published work spans more than one of these themes. Co-word network evolution reveals a significant increase over the past decade in research on climate change, emerging pollutants, and the dynamics of riparian communities. Regions with different levels of socio-economic development exhibit markedly different research priorities. Correlation analysis between article keywords and the SDGs reveals synergies and trade-offs between river ecosystems and the achievement of 130 of the targets. Under the SDGs framework, these findings highlight frontier research priorities and provide a knowledge base to support the sustainable management of river ecosystems in the face of future challenges.
Acoustic waves can affect two important components of multi-rotor drones, more formally called multi-rotor unmanned aerial vehicles (UAV). The first is located in the electronic board, the so-called IMU (Inertial Measurement Unit), which can be influenced by intense sound waves at resonant frequency. The second is the motor-propeller unit of drones. Multi-rotor drones generate low-frequency acoustic emissions during flight; if external acoustic waves achieve resonance with these blade-induced vibrations, they can cause structural fatigue or mechanical failure in the motor-propeller unit. The paper addresses the following issues: first, the influence of resonant frequency sound waves on these two design elements and their performance evaluation; second, the feasibility of an integrated counter-UAV system comprising acoustic Direction of Arrival (DoA) estimation and Blade Passage Frequency (BPF) detection; and third, a new solution for a long-range directional sound effector. This proposed solution includes determining the operating frequency as the 3rd to 5th harmonics of the BPF. Furthermore, it introduces a new concept that, instead of using a standard array of sound drivers, utilizes a limited quantity of powerful drivers arranged skeletally according to a Vicsek fractal topology. This configuration generates a powerful, needle-like acoustic beam capable of delivering effective mechanical disruption multi-rotor drones at long ranges.
