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Conventional fire monitoring systems frequently exhibit high false alarm rates, delayed response times, and a lack of closed-loop control capabilities, which severely constrain their deployment in complex real-world environments. To address these issues, this paper proposes an embedded fire detection, tracking, and extinguishing system based on multimodal information fusion and a lightweight neural model. The system follows a “Perception–Decision–Execution–Feedback” closed-loop paradigm and is implemented on a heterogeneous cooperative computing architecture comprising OpenMV4 H7 Plus and STM32F103C8T6 microcontrollers. The perception layer implements a decision-level RGB-infrared fusion mechanism that incorporates a pruned, INT8-quantized lightweight FOMO model, enabling real-time fire detection with an inference latency of 210 ms and a model size of merely 1.8 MB under resource-constrained embedded conditions. The decision layer employs a Bayesian inference-based multimodal fusion framework that effectively suppresses spurious fire interference. The vision-only false detection rate is 15.3%. After infrared fusion verification, the system-level false alarm rate is reduced to 2.0% on the interference test set. In the execution layer, a sixth-degree polynomial jet trajectory model was established and combined with an improved PID–PI dual-loop controller to enable dynamic optimization of spray angle and flow rate in real time. Experimental results demonstrate that the proposed system achieves an average fire recognition accuracy of 95.6% with a false alarm rate as low as 1.4%. Furthermore, it realizes an extinguishing accuracy better than ±5 cm within an effective operating range of 10–60 cm and completes the entire perception-to-extinguishing cycle within 8.5 s under illumination conditions ranging from 50 to 100,000 lux. These results demonstrate the excellent real-time capability, robustness, and energy efficiency of the proposed system, providing a practical and scalable solution for autonomous embedded fire-fighting applications in household, industrial, and warehouse environments. Full article

Digital trade reflects the convergence of the new technological revolution and traditional trade. Investigating its effectiveness in pollution control and carbon mitigation (PCCM) is crucial for addressing global environmental challenges. This research exploits the rollout of cross-border e-commerce comprehensive pilot zones (CECPZs) as an exogenous policy shock, leveraging double machine learning (DML) methods to assess the impact of digital trade on PCCM using panel data from 280 Chinese prefecture-level cities from 2011 to 2023. The results reveal that digital trade significantly enhances PCCM, mainly by promoting technological innovation, intelligent industrial transformation, and public participation; government emphasis on new quality productive forces and digital government construction positively moderates the link between digital trade and PCCM, while intensified environmental regulation exerts a counteracting inhibitory effect. Heterogeneous outcomes reveal that the promoting effects of digital trade are more evident in large areas, as well as in cities that are neither traditional industrial bases nor resource-based. Further analysis shows that digital trade can deliver a triple dividend in the form of reduced pollution, lower carbon emissions, and sustained economic growth. These findings provide meaningful guidance for promoting a balanced and sustainable relationship between human activities and the natural environment in the digital era. Full article

Incentive-based demand response is an important flexibility resource for power systems with high-renewable energy penetration. However, practical incentive allocation depends not only on flexible capacity and user response uncertainty, but also on the credibility of customer baseline load (CBL), which directly affects response measurement, verification, and incentive settlement. To address this issue, this paper proposes a constrained reinforcement learning method with customer baseline credibility for dynamic resource allocation in incentive-based demand response. Based on user-side load measurements and demand response event records, the proposed framework evaluates user resources using flexible capacity, response reliability, response cost, and CBL credibility. The CBL credibility score reflects the measurement quality of the delivered response and is used as a pre-event allocation factor. Users are then grouped into different resource levels, and a group-level reinforcement learning agent dynamically determines incentive multipliers and response task allocation ratios. To improve feasibility, an action correction module revises raw policy outputs under budget, price, response capacity, and CBL risk constraints before implementation. Case studies are conducted using public industrial demand response measurements and open electricity-system time-series data. The results show that the proposed CBL-CRL method reduces the normalized total operating cost to 0.897, reduces the response tracking error to 0.108, and lowers CBL risk exposure to 0.087 under the normal scenario. Relative to the No-DR reference, CBL-CRL reduces the normalized total operating cost by 10.3 percent. Compared with MAPPO, the strongest learning-based baseline, CBL-CRL reduces the response tracking error by 10.7 percent and the CBL risk exposure by 40.8 percent, while maintaining the same renewable accommodation rate of 0.970. Compared with rule-based and learning-based baselines, CBL-CRL achieves a better balance between operational performance, incentive efficiency, action feasibility, and baseline-related settlement reliability. The results demonstrate that CBL credibility should not only be used for post-event settlement, but can also serve as an effective pre-event resource allocation factor for measurement-driven demand response programs. Full article

This study examines whether China’s Ecological Civilization Demonstration Zones (ECDZs) promote urban green total factor productivity (GTFP). Using panel data for 282 prefecture-level cities from 2011 to 2022, when six batches of policy pilots were implemented, the paper employs a super-efficiency SBM model to estimate GTFP and a difference-in-differences (DID) model to identify the policy effects. The results indicate that ECDZs significantly improve urban GTFP. Specifically, the baseline estimates show that the implementation of ECDZs increases GTFP by approximately 6.52% relative to the sample means. Potential transmission channels further show that technological innovation and industrial structure upgrading are important channels through which ECDZs promote green productivity growth. In addition, significant regional and city-type heterogeneity is observed. The positive policy effects are more pronounced in central regions and in non-resource-based cities, whereas the effects are relatively weak in eastern regions, western regions, and resource-based cities. These findings suggest that differences in economic foundations, industrial structures, and innovation capacities may influence the effectiveness of ECDZs. Overall, this study provides empirical evidence on the green development effects of ECDZs and offers policy implications for improving differentiated environmental governance and promoting high-quality sustainable development in China. Full article

Harvesting remains one of the main bottlenecks in microalgae-based technologies. Although microalgae hold great promise for industrial biotechnology, their growth in dilute suspensions makes biomass recovery challenging. Conventional harvesting methods are often energy-intensive and costly, limiting large-scale implementation. This study applies a life cycle assessment (LCA) to evaluate the environmental performance of a laboratory-scale magnetic harvesting process of Chlorella vulgaris (C. vulgaris) using Fe₃O₄ microparticles in combination with polyaluminum chloride (PAC) and polyacrylamide (PAM), followed by magnetic oscillation for particle detachment and subsequent reuse. Electricity consumption was identified as the dominant environmental hotspot across most impact categories, with the detachment step accounting for nearly two-thirds of the total energy demand, a step often overlooked in previous LCA studies. The global warming potential (GWP) is consistent with typical laboratory-scale assessments and is mainly driven by energy inefficiencies associated with small processing volumes. The values obtained and the scale-up literature indicate that further optimization and future industrial-scale production will decrease these values into a realistic and competitive range. Sensitivity analysis showed that replacing grid electricity with photovoltaic power significantly reduces environmental impacts. The use of NaOH as a reagent also contributed substantially to environmental impacts. Reusing magnetic particles (4 cycles) reduced material resource depletion by up to fourfold, which is a very relevant result bearing in mind the principles of sustainability and circularity. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

For over 70 years, microalgae have been considered promising ingredients for developing sustainable, nutritionally rich foods. Their high protein content, presence of essential amino acids, fatty acids, natural pigments, and a myriad of bioactive compounds position them as potential alternatives to conventional ingredient sources. However, despite their significant potential, the large-scale incorporation of microalgae into food products remains limited. This study presents a critical analysis of the main challenges associated with the use of microalgae in the food industry. Key bottlenecks include high production costs, technological difficulties related to biomass processing, and challenges in extracting desirable compounds. Additionally, the strong flavor, odor, and intense coloration of microalgal biomass can negatively affect sensory acceptance in food products. Other limitations involve scalability issues in cultivation systems, risks of contamination during production, and regulatory constraints related to food safety approval. Consumer perception and limited familiarity with microalgae-based foods also contribute to slower market adoption. Therefore, although microalgae represent a promising and sustainable food resource, overcoming technological, economic, and sensory barriers is essential for their broader integration into the food industry and for achieving successful market consolidation. Full article

Dairy cows are economically significant ruminants in China, and the dairy industry is closely linked to food safety and the agricultural economy. However, various factors such as pathogenic microorganisms often lead to frequent diseases in dairy cows. Furthermore, as potential hosts for diverse viruses, dairy cows can harbor zoonotic pathogens, which pose a threat to public health. The Shaanxi–Gansu–Ningxia region boasts abundant natural resources and extensive pastures. It is a major animal husbandry base in Northwest China, and dairy farming plays a significant role in the local economy. However, research on dairy cow virus diversity in this region remains limited; epidemic prevention and control capabilities are constrained, and the risk of disease outbreaks is elevated. In this study, 790 dairy cow samples were collected from 13 large-scale farms and free-range households in the Shaanxi–Gansu–Ningxia region from 2021 to 2023. Sample types consisted of nasal and anal swabs. Six viral metagenomic libraries were constructed and analyzed using high-throughput sequencing and bioinformatics methods, leading to the identification of 51 viral families. These comprised 16 positive-sense single-stranded RNA virus families, one Retroviridae family, four double-stranded RNA virus families, 21 double-stranded DNA virus families, and nine single-stranded DNA virus families. Among these, RNA viruses were represented by families such as Astroviridae, Coronaviridae, Caliciviridae, Picornaviridae, and Picobirnaviridae; DNA viruses were primarily detected in Circoviridae, Papillomaviridae, Genomoviridae, and Smacoviridae. Alpha diversity analysis revealed no significant differences in viral diversity and abundance among the three regions (p > 0.05); however, significant differences were observed in the read counts and proportions of RNA and DNA viruses across the provinces. Phylogenetic analysis further indicated that viruses carried by dairy cows exhibit considerable genetic diversity and pose potential cross-species transmission risks. This study established a reference database for the dairy cow virome in the Shaanxi–Gansu–Ningxia region, elucidated the phylogenetic relationships of key viruses, and provided a scientific basis for future monitoring and prevention of dairy cow viruses. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

Intrusion detection serves as a core technology for securing heterogeneous edge networks, including IoT, industrial edges, and 5G networks. However, existing federated learning-based intrusion detection systems suffer from environmental heterogeneity, limited sample availability, and severe class imbalance—issues that result in inefficient resource allocation and compromised detection performance against rare attacks. In this paper, we propose a novel lightweight intrusion detection model for heterogeneous edge networks, named FedNIDS-CNN, which is based on dynamic distillation-aided federated learning with a CNN backbone. In the data preprocessing phase, a two-level class balancing strategy integrating nearest-neighbor interpolation augmentation and adaptive synthetic sampling is employed to ensure distortion-free sample synthesis. For feature and model optimization, principal component analysis (PCA) is used to reduce the dimensionality of traffic features, while a lightweight 1D-CNN is adopted as the base model to alleviate computational overhead on edge devices. During federated training and knowledge aggregation, a dynamic weight distillation loss mechanism is designed to enhance the model’s ability to recognize minority-class attacks. Meanwhile, the federated framework supports client-side local training and server-side weighted soft-label aggregation, enabling effective knowledge fusion across heterogeneous models. Experimental results on the CICIDS2017 dataset demonstrate that the proposed method achieves an accuracy of 98.55% and an F1-score of 98.40%. Benefiting from the soft-label transmission and parameter-free aggregation design, the framework gets rid of the constraint of homogeneous model architecture and natively supports heterogeneous network models and edge devices with different computing capabilities. It also significantly reduces communication traffic and per-round training latency, confirming its excellent real-time performance and applicability in resource-constrained edge environments. Full article

Low-carbon operation and reasonable carbon responsibility allocation are essential for improving source-load coordinated emission reduction in park integrated energy systems (PIESs). Existing allocation methods usually trace carbon emissions or calculate marginal contributions, but they still have difficulty distinguishing heterogeneous park users with different load rigidity, demand response (DR) capability, payment capability and real carbon-reduction potential. To address this problem, this paper proposes a carbon responsibility allocation method for PIESs considering user heterogeneity and develops a carbon-cost-feedback-based bi-level low-carbon scheduling model. First, park users are classified into high-energy-consuming industrial users, commercial and public service users, and energy infrastructure users according to quantitative criteria related to energy consumption scale, load continuity, adjustable load proportion and distributed-resource interaction capability. A heterogeneity indicator system is then established, including DR elasticity, electricity utilization efficiency, payment capability, DR potential and actual carbon-reduction potential. Second, an improved Shapley value allocation model is constructed by combining coalition marginal contribution with entropy-weighted heterogeneity correction. The allocation results are converted into user-side carbon responsibility cost signals and embedded into a bi-level optimal scheduling model, where the upper level minimizes the system operating cost and the lower level minimizes users’ integrated energy-use cost. Case studies show that, compared with the conventional economic scheduling scenario, the proposed model reduces the total system cost from CNY 5.0782 million to CNY 4.3258 million and decreases carbon emissions from 14,994.39 t to 10,874.62 t, corresponding to reductions of 14.82% and 27.47%, respectively. The results indicate that the proposed method can coordinate fairness-oriented carbon responsibility allocation with incentive-oriented low-carbon scheduling, supporting both SDG 11 and SDG 12. Full article

Intangible cultural heritage (ICH) embodies national memory. China has established a four-level ICH protection system covering national, provincial/autonomous regional, municipal, and county levels. The Ningxia Hui Autonomous Region possesses abundant ICH resources formed by intensive cultural integration. However, existing studies have mostly focused on the national and provincial levels and paid insufficient attention to county-level ICH, which restricts detailed analysis of its spatial characteristics. Based on 1546 four-level ICH items, this study employs GIS spatial analysis and the geodetector method to investigate the spatial distribution characteristics and driving factors of ICH. The results indicate that ICH quantity is the highest in Yinchuan (372) and the lowest in Shizuishan (163). Traditional skills (763) are predominant, while Quyi (15) is the rarest. The imbalance index ($s$ = 0.1553) and the geographic concentration index ($G$ = 46.1) demonstrate that ICH is unevenly distributed and clustered at the municipal scale, showing a pattern of high density in the north and low density in the south. The Hui population ($q$ = 0.5639), cultural industry employees ($q$ = 0.4835), and annual precipitation ($q$ = 0.3809) are the main driving factors, with significant multi-factor interactions. This research provides a theoretical reference and practical paradigm for balanced ICH protection and living heritage in Ningxia. Full article

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts–Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to $1.8\times$ higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet. Full article

Under the concurrent advancement of ecological civilization and resource-dependent region transformation, key state-owned forest areas in northeast China have shifted from timber supply to ecosystem protection. However, while the Natural Forest Protection Program has restored forest resources and increased coverage, it has also led to the contraction of traditional industries, reduced employment, population outflow, and a structural tension between weak economic growth and enhanced ecological functions. This study aims to investigate how labor mobility affects the coordinated development of economic and ecological welfare in these regions. To achieve this, we construct economic and ecological welfare indices using entropy weighting and calculate their coupling coordination degree based on panel data from the China Forestry Statistical Yearbook (2000–2017) and the China Forestry and Grassland Statistical Yearbook (2018–2025). Our key scientific contributions are as follows: (1) we reveal a nonlinear and significantly negative impact of labor mobility on coupling coordination; (2) we identify industrial structure as a partial mediating channel; and (3) we uncover significant regional and developmental stage heterogeneity. Methodologically, we employ fixed-effects, mediation, threshold, and spatial panel models to ensure robustness. The findings provide novel insights into labor–environment trade-offs in forest-dependent regions and offer policy implications for optimizing labor allocation, strengthening ecological compensation and industrial synergy, and improving regional governance to achieve coordinated economic–ecological development. Full article

Sustainability has become a key strategic priority in resource-intensive industries such as food processing, yet limited research has examined how specific internal sustainability governance and management components influence firm financial performance. Drawing on the institutional theory and Resource-Based View, this study investigates the direct effects of sustainability-oriented vision and business policy, sustainability-oriented organizational culture, and sustainability strategies on financial performance in the food processing industry. The empirical analysis is based on survey data from 247 firms in Slovenia and employs multiple regression analysis and structural equation modeling (SEM) to test the proposed relationships. The results indicate that sustainability strategies exhibit the strongest positive and statistically significant effect on firm financial performance, followed by sustainability-oriented organizational culture. In contrast, sustainability-oriented vision and business policy show a statistically significant negative direct association, suggesting that formal sustainability commitments alone do not translate into financial benefits without effective strategic integration and organizational support. These findings demonstrate that sustainability does not influence financial performance uniformly, but through distinct organizational mechanisms. The study contributes to the literature by distinguishing between normative, cultural, and strategic dimensions of sustainability and demonstrating their different direct implications for financial performance. It also provides practical insights for managers by highlighting the importance of embedding sustainability into organizational culture and core strategic processes in order to support long-term financial value creation. Full article