Search Results (184,163)

This study investigates the contemporaneous associations between investor sentiment, market microstructure, and daily sectoral returns on Boursa Kuwait. The analysis utilizes daily data spanning from January 2021 to May 2025, integrating sectoral returns, trading volume, liquidity, and volatility proxies alongside a manually constructed sentiment index derived from financial news and market announcements. Ordinary least squares (OLS) regressions were estimated for each sector. Baseline specifications incorporate the High–Low Range, Parkinson volatility, and trading volume, while expanded models include the Log_Amihud illiquidity measure, CC_Volatility, Brent crude oil returns, the Boursa Kuwait All Share Index (AllShare_Return), and the first difference of Central Bank of Kuwait discount rate (D_CBK_DiscountRate). Initial results indicate that investor sentiment is significantly associated with returns in several sectors, though the direction and magnitude of these associations are heterogeneous. Upon controlling for broader market and macroeconomic conditions, the sentiment coefficient remains statistically significant in the Technology and Health Care sectors, with the Technology coefficient changing from positive in the baseline specification to negative in the expanded specification. Liquidity and volatility measures continue to exhibit sector-specific associations. AllShare_Return is positively and statistically significantly associated with returns across most sectors, whereas Brent_Return and the D_CBK_DiscountRate show limited, sector-specific relationships. These findings suggest that broader market conditions account for an important part of daily sectoral return variation, whereas the associations of investor sentiment and market microstructure with returns continue to differ across sectors. Full article

The central challenge related to the development of electric vehicles (EVs) involves the effective integration of multiple input sources to create a robust and efficient power system. Recent advancements have focused on optimizing the power distribution within hybrid systems that combine batteries, supercapacitors, and renewable sources like solar PV. Conventionally, energy management systems (EMSs) have relied on rule-based algorithms or deterministic optimization methods. However, these techniques often lack adaptability under real-world driving conditions and face significant challenges regarding their generalizability and computational complexity when applied to dynamic driving cycles. Machine learning approaches are capable of modeling the complex, non-linear interactions between multiple energy sources to ensure intelligent power coordination. This paper proposes a novel Extreme Gradient Boosting (XGBoost)-based intelligent EMS for a BLDC motor-driven electric vehicle (e-bike) utilizing a hybrid battery–solar configuration with a supercapacitor for regenerative braking. The proposed system integrates a super-boost converter for efficient multi-source power delivery. The results show accurate energy source identification, effective multi-source coordination, improved energy utilization, reduced battery stress, and a rapid decision-making capability, which prove the feasibility of the proposed method for real-time electric bicycle energy management. The simulation was executed using the MATLAB/Simulink platform, and the obtained results are outlined. Full article

Modern manufacturing enterprises are increasingly transitioning to multi-variety, small-batch production. This shift introduces significant scheduling challenges, particularly due to the job hierarchy constraints inherent in assembling multi-level intermediate parts. Furthermore, non-machining preparation times—such as tool switching, material handling, and equipment standby—significantly impact production efficiency. To address these challenges, this paper investigates the flexible job shop batch scheduling problem by integrating order and operation consolidation under strict job hierarchy constraints. To mathematically formulate the scheduling problem with non-serial operation precedence networks and dynamic batching, we develop a mixed-integer programming model. The primary objective is to simultaneously minimize the maximum completion time (makespan) and total tardiness. To solve this efficiently, an Improved Grey Wolf Optimization (IGWO) algorithm is proposed. The algorithm features a novel two-tier coding scheme tailored for consolidation logic and employs a hybrid population initialization strategy to enhance initial solution quality. Moreover, it improves the standard hunting mechanism, utilizes Variable Neighborhood Search (VNS) for local exploitation, and independently applies a Simulated Annealing (SA) dynamic acceptance mechanism to balance global exploration and local exploitation. Extensive experiments using small-, medium-, and large-scale industrial data from a power station valve manufacturer validate the effectiveness of the proposed model and algorithm in optimizing complex batch scheduling schemes. Full article

Fly ash from municipal solid waste incineration is a hazardous waste whose safe disposal and resource recovery are obstructed by a fundamental trilemma: no single treatment simultaneously immobilizes heavy metals permanently, destroys persistent organic pollutants, and valorizes the residual matrix. Here we resolve this trilemma through a speciation-driven cascade strategy, a design principle in which chemical, thermal, and physical unit operations are precisely matched to the distinct chemical fractions and volatilities of each metal. We systematically review the speciation and occurrence of heavy metals in fly ash and propose a classification of multi-coupled technologies based on how they manipulate the acid-soluble, reducible, and residual fractions. We distill the four recurring interaction mechanisms (i.e., phase-transfer promotion, kinetics alteration, secondary-pollutant co-control, and resource–recovery balancing) that enable synergistic performance, demonstrate that the cascade framework explains why certain couplings succeed while others fail, and derive general rules for process integration. Bottlenecks in multi-variable regulation, secondary pollution, long-term stability evaluation, and engineering scale-up are dissected. We then chart a roadmap toward intelligent, low-carbon, and zero-waste valorization via selective recovery and all-component utilization. This review reframes fly ash treatment as a tunable sequence of speciation-modifying steps and provides a structured framework for hazardous-waste-to-resource conversion across diverse industrial residues. Full article

Population growth in urban food systems has led to a decline in food resources and the emergence of social imbalances, while consumers’ shift toward a healthy and sustainable lifestyle, against the backdrop of a growing carbon footprint and negative health impacts, highlights the need to promote fish-based products as part of a balanced diet, alongside the implementation of circular strategies for utilizing byproducts from processing, which has led to increased interest in reformulating these products in accordance with the principles of the circular bioeconomy. Aquaculture and the agri-food industry generate a large amount of byproducts that are underutilized, even though they could be extremely useful as functional food ingredients. Recycling these streams into ingredients is an effective way to reduce food waste, make better use of resources, and lessen the environmental impact of food systems. In this context, by-products can be strategically incorporated into food reformulation processes, where their bioactive compounds simultaneously improve both the nutritional value and the technological function of foods. However, such approaches can only be effectively utilized if there is a coordinated framework that ensures food safety, supports scalable processing technologies, and complies with regulatory standards. This analysis takes an integrated approach to examine how specific processing strategies, quality control measures, and risk assessment protocols can work together to ensure nutritional optimization, guarantee food safety, and promote circular use. This paper also addresses how these interconnected systems contribute to maintaining public health and increasing consumer confidence, particularly in urban food systems. By highlighting these connections, the research offers a clearer operational perspective on applying circular economy principles to sustainable food production. Even with these opportunities, issues related to scalability, regulatory compliance, and the limited number of applied studies remain major challenges. In this context, this paper examines how the coordinated implementation of these mechanisms can contribute to public health, strengthen consumer confidence, and facilitate the transition to sustainable urban food systems. Finally, this paper offers a clearer operational perspective on how the principles of the circular economy can be effectively put into practice by harmonizing nutrition, safety, and value-added recovery. Full article

The Chinese longsnout catfish is a typical carnivorous fish with a relatively weak ability to utilize glucose. However, the genomic basis for its glucose metabolic adaptation remains unclear. In this study, we used comparative genomics methods to systematically analyze the evolutionary characteristics of glucose metabolism-related genes in the Chinese longsnout catfish, focusing on gene family evolution, patterns of expansion and contraction, and selective pressures. The results indicate that glucose metabolism-related genes have undergone significant reshaping during evolution. Genes involved in glucose digestion, absorption, and insulin signaling pathways demonstrate a tendency toward contraction, while those associated with protein and lipid metabolism exhibit expansion. This pattern is consistent with the species’ long-term adaptation to a high-protein, high-fat diet. Comparative analysis further revealed that, compared to fish with different dietary habits, certain key genes involved in glycolysis in the Chinese longsnout catfish exhibit a reduction in copy number. Molecular evolutionary analysis showed that key genes involved in glycolysis and gluconeogenesis (including hexokinase 2 (hk2), phosphofructokinase, muscle/platelet (pfkm/p)) exhibit signs of accelerated evolution or positive selection. Notably, the PFK gene family exhibits complex evolutionary characteristics resulting from the combined effects of gene contraction, rapid evolution, and positive selection. In summary, this study reveals the genomic evolutionary basis for the glucose metabolic adaptation of the Chinese longsnout catfish and identifies the PFK gene family as a key candidate for elucidating its unique glucose metabolic characteristics. Full article

This paper investigates the relationship between the benefits achieved by predictive maintenance implementation and the related costs incurred intentionally or not. Specifically, we seek to establish an evaluation method for predictive maintenance that includes prediction model performance, which will better indicate that a predictive maintenance strategy built on the model will succeed in providing benefits compared to a pre-existing maintenance strategy. This will help justifying the costs and complexities of implementing new maintenance procedures and ensure positive financial outcomes. We demonstrate this method with a case study based on scheduled maintenance of an offshore wind farm where a declarative modeling approach is used to simulate service costs and operations, while a deep learning model provides insights on imminent downtime events. The results of this case study show that predictive maintenance is only profitable under specific conditions, such as limited service resources and high prediction model performance, and a profitable performance threshold for the underlying model is obtained. Full article

Microalgae are promising platforms for producing biomass and biochemicals, but changes in component concentrations may reflect biomass accumulation, shifts in composition, or both. This study developed a low-dimensional X–ρ–A allocation model for jointly describing and predicting dry biomass (X), protein (P), and lipid (L) dynamics. The model was parameterized and evaluated using mixotrophic Tris–acetate–phosphate (TAP) batch cultures of Chlamydomonas reinhardtii strain CC-400. The model uses two composition-derived indices: ρ, the fraction of dry biomass represented by the measured protein–lipid module, and A, the lipid fraction within this module. Model comparison favored a formulation in which ρ was constant over time within each condition. The light-response model was calibrated at 30, 100, and 300 μE m⁻² s⁻¹ and evaluated against an independently generated 500 μE m⁻² s⁻¹ dataset from the same experimental system to assess predictive performance beyond the calibration range. Validation R² values were 0.9374, 0.9616, and 0.9321 for dry biomass, protein, and lipid, respectively. Under nitrogen starvation, biomass and protein accumulation were suppressed, whereas A increased, indicating lipid enrichment within the measured protein–lipid module. Within this experimental system, the model provides an interpretable framework for predicting light-dependent biomass, protein, and lipid dynamics and distinguishing biomass-driven increases in component concentrations from shifts in protein–lipid allocation. Full article

The large-scale integration of renewable energy has compelled coal-fired power plants to operate under deep peak-shaving conditions, significantly increasing the failure risk of Circulating Fluidized Bed (CFB) boiler heating surfaces due to severe thermal and pressure fluctuations. To address the limitations of traditional static risk evaluations, this study proposes a novel dynamic risk assessment framework integrating a Spatial–Temporal Graph Convolutional Network (STGCN) and a Dynamic Bayesian Network (DBN). The STGCN, enhanced with an operation-adaptive dynamic cross-attention delay module, predicts spatiotemporal temperature and pressure variations across the high-temperature heating surfaces. The predicted variables are incorporated into the DBN as dynamic evidence, which utilizes Noisy-OR logic and an embedded Weibull physical degradation model to continuously quantify cumulative failure probabilities. Case study results demonstrate that the STGCN outperforms traditional LSTM and RNN baselines in prediction accuracy. Furthermore, the DBN effectively maps the distinct degradation characteristics of individual boiler components, accurately identifying the water wall and superheater as having the highest failure risks and the largest fluctuations in marginal failure probability during rapid load cycling. This integrated data-driven approach provides highly accurate, real-time risk predictions, offering essential decision-making support for the predictive maintenance and safe flexible operation of CFB boilers. Full article

This work reports the synthesis and characterization of sustainable, multifunctional chitosan/graphene oxide hybrid nanoparticles (GOCS_NPs) prepared via a low-temperature ionotropic gelation method utilizing sodium tripolyphosphate as a green crosslinker. Combined DLS and TEM analyses confirmed the successful formation of submicron spherical nanoparticles with a mean diameter of 295 ± 15 nm (PDI 0.26), and GOCS_NPs were evaluated as a dual-action platform for the adsorption of a model cationic dye, Methylene Blue (MB), and for antimicrobial remediation against Staphylococcus aureus and Escherichia coli. Equilibrium adsorption studies revealed that incorporating GO dramatically increased the maximum monolayer adsorption capacity from 2.78 mg g⁻¹ (for CS_NPs) to 37.16 mg g⁻¹ (for GOCS_NPs), closely following the Langmuir and Sips models through a pseudo-second order sorption mechanism. Furthermore, desorption investigations demonstrated that the GOCS_NPs maintained substantial adsorption performance over multiple adsorption–desorption cycles under controlled conditions. Concurrently, GOCS_NPs exhibited a dose-dependent enhancement in antibacterial efficacy, showing greater activity against Gram-negative E. coli (MIC of 1.25 mg mL⁻¹) than against Gram-positive S. Aureus (MIC of 2.50 mg mL⁻¹). Overall, these findings elucidate the structure–property–performance relationships of these carbon–biopolymer hybrid nanocomposites, validating their suitability as an advanced, eco-friendly, and reusable platform for comprehensive and sustainable wastewater remediation. Full article

Allium victorialis L. is a significant plant traditionally used as both food and medicine in Northeast China. However, the structural and functional studies of its polysaccharide components remain limited. In the present study, we investigated the anti-inflammatory potential of AVP-1-2 (DP 27), an inulin-type fructan isolated from A. victorialis, using an LPS-induced RAW264.7 macrophage model. Its structural characterization was determined through methylation analysis and nuclear magnetic resonance spectroscopy. AVP-1-2 is an inulin-type fructan composed of fructose and glucose. The backbone consists mainly of →1)-β-D-Fruf-(2→, with minor glucose residues and limited branching. The qPCR and ELISA results indicated that AVP-1-2 effectively suppressed the mRNA expression and protein secretion of pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) in a cellular model. Overall, this work provides new structural and functional insights into A. victorialis polysaccharides and offers a theoretical foundation for the comprehensive utilization of A. victorialis resources. Full article

Background/Objectives: The management of edentulism and severe alveolar bone loss in older adults remains a significant challenge in implant dentistry. Given the potential biological and therapeutic burden associated with extensive regenerative procedures, increasing interest has been directed toward treatment strategies that utilize available anatomy and reduce surgical complexity. This narrative review examined contemporary augmentation-avoidance approaches for implant rehabilitation in geriatric patients. Methods: A structured narrative literature search was performed in PubMed/MEDLINE, Scopus, and Web of Science, including studies published up to 30 May 2026. Clinical studies, systematic, umbrella and narrative reviews, meta-analyses that evaluate augmentation-avoidance approaches in implant dentistry were considered. The review examined evidence relevant to older adults, including clinical, prosthetic, functional, and patient-centered outcomes. Results: The available literature describes multiple strategies for managing reduced bone availability, including short and ultra-short implants, narrow-diameter implants, tilted implant concepts, pterygoid and trans-sinus implants, zygomatic implants, immediate loading protocols, flapless and computer-guided surgery, and implant-retained overdentures. These approaches have broadened the range of treatment options available for patients with reduced bone availability and may reduce the need for extensive regenerative procedures in selected clinical situations. In addition to implant and prosthetic outcomes, increasing attention has been directed toward oral health-related quality of life, masticatory function, treatment satisfaction, and maintenance requirements. However, evidence specifically focused on geriatric populations remains limited. Conclusions: Contemporary implant dentistry offers several alternatives to extensive bone regeneration in carefully selected geriatric patients, although current evidence is largely extrapolated from mixed adult populations. Treatment planning should integrate anatomical conditions with systemic health, functional status, frailty-related factors, and long-term maintenance considerations. Further geriatric-focused research is needed to support patient-centered decision-making and optimize long-term rehabilitation outcomes in aging populations. Full article

In the industrial edge-to-cloud continuum, data is often privacy-sensitive and spans multiple organizations that do not fully trust one another, making central aggregation of raw data undesirable and often non-compliant with regulations such as the General Data Protection Regulation (GDPR). Federated Learning (FL) addresses this by sharing model updates rather than raw data, but conventional FL assumes a central coordinator, leaving it exposed to poisoning and inference attacks and to a single point of trust and failure. Decentralized Federated Learning (DFL) couples FL with Distributed Ledger Technologies (DLTs), removing the coordinator and enabling verifiable aggregation in trustless, cross-organizational environments. In this work, we assess the applicability of DFL to on-device spoken-command recognition—a representative edge audio task underpinning voice-driven industrial interfaces—by comparing decentralized and centralized training under idealized and adversarial conditions. Using a Convolutional Neural Network (CNN) replicated across edge nodes, we evaluate resilience to inter-node data imbalance, to poisoning attacks, and to a privacy-preserving noise-injection defense against inference attacks, together with model compression for resource-constrained edge devices. The system pairs this comparison with a validation-based poisoning defense in which each node scores its peers’ updates on its own held-out data, and an update is aggregated only if a majority of nodes report a weighted F1-score above a threshold—requiring neither a shared validation set nor a trusted validator. Our results indicate that the DFL system achieves accuracy comparable to centralized baselines in most scenarios (weighted F1-score 0.762 across nine nodes, against 0.896 centralized), and that a cross-node validation mechanism reliably excludes poisoned updates as long as fewer than half of the nodes are compromised (within 3.54% of the unpoisoned model). Noise-based inference defenses reduce accuracy substantially (44.7% on average at a noise standard deviation of 1.0), exposing a sharp privacy–utility trade-off, whereas model compression preserves performance (0.765 against 0.762 for pruning and format conversion, with 8-bit quantization costing up to a further 13.3%). These findings clarify both the promise and the current limitations of decentralized, privacy-preserving learning for the industrial edge-to-cloud continuum. Full article

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a practical means of providing computation and communication support for geographically dispersed Internet of Things (IoT) terminals. However, the broadcast nature of wireless links makes offloading data vulnerable to cooperative eavesdropping. Moreover, the limited onboard energy of UAVs and the latency-sensitive characteristics of MEC services lead to a challenging trade-off between the secrecy rate, energy consumption, and latency. To address this issue, we formulate a utility maximization problem to jointly optimize UAV trajectory and task-offloading decisions in UAV-assisted MEC systems against multiple eavesdroppers. Due to the strong coupling among optimization variables and the non-convexity of the problem, an enhanced twin-delayed deep deterministic policy gradient (TD3) framework integrating Hindsight Experience Replay (HER) and Prioritized Experience Replay (PER) is proposed to improve convergence efficiency and learning stability. Furthermore, a system utility-driven reward function is designed to balance the secrecy rate, energy consumption, and processing latency under different application requirements. The simulation results demonstrate that the proposed approach consistently outperforms DDQN, DDPG, and conventional TD3 in terms of system utility, secrecy performance, convergence speed, and adaptability to different scenarios. Full article

Air compressors are among the most energy-intensive cross-cutting technologies used in the industry, making accurate performance evaluation essential for improving energy efficiency. ISO 1217, the internationally recognized standard for displacement compressor acceptance testing, has remained largely unchanged since its last revision in 2009. The standard specifies that flow-rate measurement should be performed as indicated in ISO 5167-1 and ISO 9300. However, it does not prescribe a specific flow-meter technology, allowing different technologies to be selected for compressor performance measurements. Since flow-rate measurement is one of the most critical parameters for compressor performance assessment, the choice of flow-meter technology can directly influence the evaluated compressor performance. Furthermore, the importance of energy efficiency has increased, while flow-measurement technologies have continued to develop since the last revision of ISO 1217 in 2009. Therefore, the influence of flow-meter technology and its measurement accuracy should be considered more explicitly in the ongoing revision of the standard to support reliable performance evaluation and the development of more energy-efficient displacement compressors. To investigate the influence of flow-meter technology, this paper reviews the physical operating principles of commonly used flow-meter technologies for compressed-air applications and discusses their respective advantages and limitations. In addition, four flow meters from different brands, utilizing different technologies, were experimentally compared on a dedicated laboratory test bench under controlled conditions at compressor discharge pressures between 5 and 8 ${\mathrm{bar}}_{\mathrm{e}}$, representing typical industrial operating pressures. To ensure comparability, all measured flow rates were normalized to common reference conditions according to ISO 2533. The results reveal substantial differences among the investigated technologies. The rotary displacement meter showed the closest agreement with the Venturi reference meter, with deviations generally below 3%. In contrast, thermal and ultrasonic flow meters exhibited larger systematic deviations, particularly when operated at low load factors relative to their measurement ranges. Based on the test results obtained with different flow-meter brands, the choice of flow-meter technology was found to significantly influence compressor performance calculations according to ISO 1217, resulting in calculated isentropic efficiencies ranging from approximately 58% to 72% under the same compressor operating conditions. These findings demonstrate that both flow-meter technology and sensor sizing can substantially affect the result of compressor performance evaluations. The results highlight the importance of appropriate flow-meter selection and indicate a need for clearer guidance regarding flow-measurement instrumentation in revisions of ISO 1217. Full article