Search Results (1,791)

Predicting garlic prices is difficult because the crop behaves as both an agricultural commodity and a speculative asset. Unlike staple grains, which follow more predictable seasonal supply cycles, garlic can be stored for over a year, its production is geographically concentrated, and its demand remains inelastic. This industry structure makes it susceptible to speculative hoarding, where even minor harvest deficits may trigger sharp price spikes. A typical example is the “Suan Ni Hen” (crazy garlic) phenomenon in the Chinese market: during the 2009–2010 and 2016 periods, speculative capital repeatedly exploited expectations of harvest reduction to engage in large-scale hoarding. According to data released by China’s National Development and Reform Commission (NDRC) at the end of October 2016, national wholesale garlic prices surged by 90% year-on-year, with purchase prices in some major producing areas doubling or multiplying within a short period. Such short-term price bubbles, together with severe volatility and abrupt regime shifts, can make standard forecasting models unreliable in this uncertain environment. Existing methods, ranging from traditional seasonal algorithms to deep learning networks, often overlook the need to decouple the local trend-weekly-seasonal baseline from the dynamic effects of multi-source external signals. This paper proposes GPF-EVMoLE, a compositional multi-step forecasting framework built on an explicit division of labor. The framework first extracts an interpretable local trend and weekly-seasonal baseline through an ETS decomposition module. Two specialized components then process the residual signal: a temporal fusion Transformer-style variable selection network (VSN) uses multi-source external features to identify informative macroeconomic and environmental signals at each forecasting step, while a Mixture of Linear Experts (MoLE) models phase-wise regime shifts within the residual series. Together, these modules adaptively integrate heterogeneous information. This study evaluates the framework on a custom daily evaluation dataset containing 17,685 records across six major producing regions in three provinces. At 7-day and 14-day forecasting horizons, GPF-EVMoLE consistently outperforms eight representative statistical, machine learning, and deep learning baselines across MAE, RMSE, and MAPE metrics. Ablation studies verify the necessity of each component, showing that structural separation of the forecasting tasks helps overcome the limitations of monolithic models and provides an accurate and interpretable solution for complex agricultural markets. Full article

Bioplastics are increasingly expected to function not only as alternatives to fossil-derived plastics but also as components of circular plastic systems. However, currently bioplastics remain limited by cost, feedstock availability, achievable biomass content, and end-of-life compatibility. This review examines these limitations by organizing recent technological and policy trends in bioplastics, with particular attention to Japan’s social and industrial infrastructure. On this basis, we discuss a systems-level framework for circular plastics that integrates regionally underutilized non-edible biomass, decentralized production concepts, and the emerging possibility of cell-plastics based on unicellular green algae. We argue that the practical dissemination of biomass plastics requires not only material development but also compatibility with molding processes, recycling and biodegradation pathways, and regional collection and treatment systems. In this context, cell-plastics derived from Chlamydomonas reinhardtii are positioned as an emerging technological platform for direct biomass utilization and interfacial material design, although their large-scale implementation remains limited by current cultivation and manufacturing constraints. We propose that circular biomass-plastics systems in Japan should be developed as regionally adapted production frameworks with clearly defined end-of-life pathways, rather than as simple substitutes for petroleum-derived plastics. Full article

Graph-based entity deduplication has proven effective for resolving duplicate records when identifying information is sparse and heterogeneous, yet in continuously evolving industrial data systems, it remains insufficient on its own. As new records and relationships are added incrementally, previously separate entity components can merge, causing instability and inconsistency in entity identifiers that undermine downstream analytics, auditing, and system integration, requiring persistent, interpretable identifiers over time. This work introduces StableID, an iterative graph-based framework for persistent entity identification in dynamic environments. StableID treats identifiers as long-lived system assets rather than transient outputs, incorporating historical grouping results into subsequent graph constructions through a feedback mechanism to ensure previously resolved entities retain consistent identifiers. When components merge, a deterministic dominance rule assigns the identifier from the largest prior component to the unified entity, minimizing churn, while time-scoped identifier generation with execution-level prefixes prevents collisions and guarantees global uniqueness during incremental updates. Implemented with distributed graph processing, StableID was evaluated through iterative executions on large-scale, multi-state voter registration data lacking global identifiers and featuring heterogeneous schemas. Results demonstrate strong identifier stability, progressive convergence in the number of entity identifiers, and a clear trend toward a stable identity state as relational connectivity increases. Overall, StableID transforms graph-based deduplication into a production-ready identity management solution suitable for continuously updating industrial data. Full article

The mining industry is going through a big digital change because of the use of new technologies that are meant to make work safer, more productive, and more environmentally friendly. AI-driven digital twins (AI-DTs) are one of these new ideas. They combine real-time data collection with smart analytics to make it possible for decisions to be made in a predictive, adaptive, and autonomous way. This paper provides a thorough systematic literature review (SLR) of AI-DT applications in mining operations, encompassing studies published from 2015 to 2025. According to the PRISMA method, 68 primary studies were chosen and looked at from many angles, such as publication trends, demographic analysis, research methods, data sources, mining domains, and the AI techniques that were used. The findings reveal an increasing scholarly interest in AI-DTs, characterized by a significant prevalence of machine learning and deep learning methodologies, alongside a preference for real-world sensory data to augment model accuracy. Most applications deal with physical assets, processing plants, and operational systems. Subsurface environments, on the other hand, are still not well understood. The review also points out some major problems with data integration, scalability, interoperability, and the fact that there has not been much large-scale industrial validation. Based on these findings, the paper points out important areas of research that need more work and suggests ways to move forward with the development and use of AI-DTs in mining. In conclusion, this study gives researchers and practitioners a clear plan for how to use AI-DTs to make mining operations more efficient, resilient, and long-lasting. Full article

Industrial decarbonisation requires the large-scale integration of renewable energy into energy-intensive processes traditionally characterised by limited flexibility, high heat demands, and strong dependence on fossil fuels. In this context, energy storage, encompassing thermal and electrical storage as well as hydrogen as an energy carrier, emerges as a key enabling solution to reconcile variable renewable supply with industrial process demands. This paper proposes a dynamic techno-economic framework linking sectoral energy profiles to storage sizing and economic performance in industrial renewable integration. Storage technologies are assessed with hydrogen emerging as a long-duration buffer and a solution for decarbonising high-temperature heat. A representative industrial plant with 5 GWh/year energy demand and an 80%/20% thermal-to-electric load split is analysed under increasing solar-to-load ratios (20–60%), with storage technologies evaluated both individually and in hybrid configurations. Results demonstrate that hybrid battery–hydrogen configurations systematically outperform single-technology solutions. Yearly energy cost reductions reach 16.6–33.8% at 20% renewable penetration, 30.0–49.6% at 40%, and 43.4–62.8% at 60%, with advantages over the best standalone option increasing on average from 13.5% to 28.0% as renewable availability rises. Overall, the study identifies scale-dependent feasibility thresholds and highlights small and medium-sized industrial plants as the most actionable deployment context under current technological and market conditions. Full article

The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is one of the most widely used multi-criteria decision-making (MCDM) approaches in industrial, financial, and scientific fields. However, its sequential computational cost of O(m × n), where m denotes the number of alternatives and n the number of criteria, becomes prohibitive when decision matrices have several million rows. Despite its geometric interpretability and simplicity, classical TOPSIS faces two key computational bottlenecks at scale: (i) Euclidean distance calculations O(m × n) dominating the total cost, and (ii) the O(m × log m) sorting step, both inherently sequential and memory-bound on CPUs. To overcome these limitations, we propose GPU-TOPSIS, a fully vectorized and parallel reformulation of TOPSIS based on tensor execution on graphics processing units (GPUs), whose main contributions are: (i) a formally correct reformulation of TOPSIS as a GPU tensor pipeline preserving mathematical fidelity to the original method; (ii) a two-pass fragment-processing algorithm guaranteeing exact mathematical equivalence with monolithic TOPSIS, while reducing the memory footprint from O(m × n) to O(m_t × n), where m_t < m is the size of each independently processed fragment; (iii) three independent implementations on CuPy, PyTorch, and TensorFlow, ensuring the framework’s portability and genericity. Experimental evaluations on real data from the Amazon Products 2023 dataset, using matrices of up to 200 million alternatives (via the 2-pass formulation), demonstrate speedups of up to 4.75× compared to the reference CPU implementation (NumPy), with inter-backend score differences below 5 × 10⁻⁸ and 100% ranking overlap across all tested Top-K thresholds. A perturbation sensitivity analysis of the criteria weights and cross-backend consistency tests confirms that GPU acceleration fully preserves robustness and decision reliability, making GPU-TOPSIS a practical, open, and reproducible solution for large-scale multi-criteria decision making in Big Data environments. Full article

Low-temperature stress severely restricts the engineering application of anaerobic ammonia oxidation (anammox) technology in municipal mainstream wastewater treatment, leading to its slower large-scale implementation relative to industrial wastewater and reject water treatments. The inhibitory effects of low temperatures on the anammox process cannot be merely ascribed to conventional microbial metabolic responses. Elucidating the specific mechanisms underlying low-temperature impacts on anammox bacteria is therefore critical for formulating targeted mitigation strategies. In this study, a meta-analysis was performed to compare the response patterns of specific anammox activity (SAA) and nitrogen removal rate (NRR) to temperature variations. SAA declines gradually with decreasing temperature, while NRR displays a more dramatic and stepwise reduction. The T₅₀ values (temperature corresponding to 50% of the performance at 30 °C) for these two parameters are 20 °C and 15 °C, respectively. Low-temperature inhibition of anammox is a multifaceted process, encompassing direct physiological disturbances to individual anammox cells and impaired nitrite bioavailability within the microbial community. To address these temperature-related bottlenecks, a conceptual hybrid nitrogen removal system was rationally optimized by integrating conventional strategies with an innovative split-flow influent regulation strategy. This hybrid system is anticipated to enhance the stability and treatment efficiency of anammox under low-temperature conditions, thus facilitating its broader engineering application in cold climate regions. Full article

Propylene hydroformylation is a typical large-scale gas–liquid reaction. Nevertheless, exorbitant costs and theoretical studies lagging far behind practical industry have prevented advancements in reactor efficiency. In this work, the flow, mass transfer, and reaction processes within the gas–liquid reactor were simulated using a three-dimensional CFD-PBM coupled model. The coupling processes between the flow field, mass transfer, and reaction in the gas–liquid reactor are clarified in this study. It provides precise direction for further process optimization by introducing the Hatta number as a quantitative criterion to determine the reaction’s controlling step (mass transfer-controlled or reaction-controlled). The constraints of conventional single-point analysis were overcome by visualizing a Ha number distribution contour, which showed that about 65% of the volume inside the propylene hydroformylation reactor is in a mass transfer-limited state. Based on this, operational parameter optimization was carried out, and the findings show that reaction efficiency may be successfully increased by reasonably raising the superficial gas velocity and system pressure within a certain range. The conversion rate increased by 23% when the superficial gas velocity doubled and by two times when the pressure doubled. Additionally, the effects of the stirring device and rotational speed were investigated, resulting in a 19% increase in conversion rate after optimization. The design and process optimization of similar hydroformylation gas–liquid reactors can benefit from this research. Full article

Heavy metal contamination of foods remains a persistent global challenge for food safety and public health, driven by industrialization, mining activities, intensive agriculture, and ongoing environmental degradation. This scoping review synthesizes peer-reviewed literature on the occurrence of priority toxic metals—arsenic, cadmium, lead, mercury, and nickel—in food matrices, with emphasis on contamination pathways, analytical detection strategies, and documented human health effects. The reviewed studies reveal widespread accumulation of heavy metals in staple foods, including cereals, vegetables, seafood, and processed products, with concentrations frequently approaching or exceeding international regulatory limits, particularly in regions exposed to strong anthropogenic pressure. Conventional laboratory-based techniques, such as atomic absorption spectrometry and inductively coupled plasma methods, remain the reference standards for quantitative determination and regulatory compliance; however, their application to large-scale or continuous monitoring is often constrained by cost, infrastructure, and operational complexity. Consequently, increasing attention has been directed toward emerging detection approaches, including portable X-Ray fluorescence, Raman/SERS spectroscopy, electrochemical biosensors, electronic tongues, and in situ magnetic measurements, as complementary tools for rapid screening and field-based surveillance. Among these, environmental magnetism and in situ magnetic techniques stand out as non-destructive, low-cost proxies capable of identifying metal-associated particulate contamination linked to food production systems. Chronic dietary exposure to heavy metals is consistently associated with neurotoxicity, nephrotoxicity, carcinogenicity, and oxidative stress, underscoring the need for integrated, multi-tiered monitoring frameworks to support early detection, risk assessment, and prevention. Full article

Wire arc additive manufacturing (WAAM) is an efficient, low-cost technique for fabricating large-scale metallic components and, in particular, functionally graded materials (FGMs). This review focuses on the fabrication of 316L stainless steel–Inconel 625 FGMs by arc-based WAAM processes, examining Gas Metal Arc Welding (GMAW), Gas Tungsten Arc Welding (GTAW) and Plasma Arc Welding (PAW) in terms of their microstructural outcomes, compositional control strategies, residual stress development and mechanical performance. A critical finding emerging from the reviewed literature is that direct compositional interfaces between 316L and Inconel 625 can yield superior tensile strength and ductility and lower residual stresses compared to smooth gradient strategies, owing to the formation of detrimental secondary phases such as δ-phase, Laves phase and MC carbides at intermediate iron–nickel compositions encountered only during graded builds. The potential of Submerged Arc Additive Manufacturing (SAAM) as a future high-deposition-rate alternative for large-scale FGM fabrication is also discussed. Key challenges, including dilution control, Laves phase formation, residual stress management and the corrosion characterization of the graded region, are identified, together with priority research directions for advancing the industrial adoption of arc-based FGM components. Full article

The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber–physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0. Full article

The growing demand for sustainable, decentralized energy solutions has heightened interest in biomass-based technologies for rural applications. In Mexico, the expansion of oil palm cultivation in humid tropical regions has generated large quantities of agro-industrial residues that remain largely underutilized. This review analyzes the potential of oil palm residues as feedstock for small-scale thermochemical conversion, with a particular focus on gasifier stove technologies. Key residues, including empty fruit bunches, mesocarp fiber, and palm kernel shells, exhibit favorable physicochemical properties, including adequate calorific values and high volatile matter content, which support their suitability for gasification processes. However, challenges related to moisture content, ash composition, and tar formation may affect system performance and require appropriate pre-treatment and operational control. Gasifier stoves, especially fixed-bed and top-lit updraft (TLUD) configurations, represent a viable solution for decentralized energy generation in rural settings, improving combustion efficiency and reducing emissions compared to traditional biomass use. Despite their potential, current bioenergy policies in Mexico remain primarily focused on large-scale biofuel production, limiting the deployment of small-scale technologies. Overall, oil palm residues constitute a promising feedstock for gasifier stove applications, although their successful implementation depends on feedstock optimization, appropriate stove design, and the development of policy frameworks that support decentralized bioenergy systems. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article

Microbial lipids have emerged as a promising sustainable alternative to plant- and petroleum-derived oils, with applications spanning biofuels, oleochemicals, nutraceuticals, and specialty materials. Significant advances in metabolic engineering and strain development have increased lipid production capacity across diverse microorganisms. Numerous reviews have summarized the biological and metabolic advances in this field, highlighting significant progress in metabolic engineering and strain development that has increased lipid production capacity across diverse microorganisms. However, translating these gains into economically viable industrial processes remains a major challenge. This review examines process engineering strategies for microbial lipid production across the full bioprocessing pipeline, from laboratory-scale strain evolution to industrial-scale operation. We discuss recent developments in adaptive laboratory evolution, systems-guided strain optimization, and robustness engineering, emphasizing their implications for process performance. Key bioprocess parameters—including substrate selection, nutrient limitation strategies, reactor design, oxygen transfer, and process control—are critically evaluated for their impact on lipid yield, productivity, and scalability. Furthermore, downstream processing considerations and techno-economic constraints are analyzed in the context of large-scale implementation. By integrating strain-level innovations with process engineering principles, this review highlights current bottlenecks, emerging solutions, and future directions for achieving efficient and scalable microbial lipid biomanufacturing. Full article

The environmental challenges posed by heavy metal contamination in sludge leachate are becoming increasingly severe, necessitating the development of highly efficient remediation technologies. Among various treatment approaches, magnetic adsorbents have garnered significant attention as a promising solution due to their outstanding adsorption performance, convenient magnetic separation characteristics, and potential for regeneration. This paper systematically reviews the latest research progress on magnetic adsorbents designed for the complex system of sludge leachate, covering synthesis methods, surface functionalization, adsorption mechanisms, and performance evaluation. Key synthesis strategies are analyzed, including magnetic core preparation, inorganic coating, carbon composites, organic polymer grafting, functional molecule impregnation, and metal–organic framework (MOF) composites. The mechanisms by which these strategies influence material adsorption capacity, selectivity, and stability are elucidated. Despite significant achievements in laboratory studies, practical applications still face challenges such as large-scale synthesis, regeneration efficiency, cyclic stability, and adaptability to complex water bodies. Future research should focus on green synthetic pathways to advance the industrial application of structurally functional magnetic composite materials, providing systematic solutions from material design to process optimization for the sustainable remediation of heavy metal contamination in sludge leachate. Full article