Search Results (143)

In this paper, L1 phonemic systems are discussed in the context of their impact on the pronunciation and perception of English diphthongs in PE, drawing on World Englishes and phonetic analysis. The study focuses on speakers whose native languages are Punjabi, Seraiki, Pashto, and Urdu, and examines how changes in local vowel inventories and glide processes influence diphthong production. The controlled production and perception tasks were done on eight English diphthongs by 40 adult speakers (10 speakers per L1 group). The formant trajectories (F1, F2), duration, and intensity were recorded by acoustic analyses, which are used to measure the variation that occurs as the articulatory glide occurs between vowel targets. Perception was measured using diphthong identification tasks to assess listeners’ sensitivity to dynamic spectral movement. The results indicate systematic L1-conditioned restructuring. Deviations were the most pronounced in diphthongs with significant vowel gliding, especially centering diphthongs, characterized by a decrease in spectral movement, a constriction in vowel space, and a general tendency toward monophthongization. Closing diphthongs were generally more stable in production; however, they still exhibited systematic L1-conditioned variation, particularly in glide magnitude, spectral direction, and temporal realization. These patterns of production were highly consistent with the results of perceptual production: the diphthongs with lesser acoustic movement were also found to be less accurately recognized, and diphthongs in their L1s and speakers of phonemically richer vowel systems had partial glide contrasts. The findings demonstrate that the variation in diphthongs in PE is systematic, reflecting predictable relationships between the L1 phonemic system, perceptual assimilation, and sociolinguistic experience. The findings highlight the pedagogical value of L1-sensitive pronunciation instruction and contribute to the phonetic description of Pakistani English as a systematic contact variety. Full article

Perishable products are usually priced in real-time to volatile market environments, thereby optimizing inventory control, minimizing resource wastage, and maximizing corporate profitability. Based on the public dataset from the 2023 Higher Education Press Cup National College Students Mathematical Modeling Competition, this paper addresses the challenge of multi-product joint pricing for perishable fresh produce and proposes an attention-augmented proximal policy optimization algorithm (termed ATT-PPO), which embeds an attention mechanism into the proximal policy optimization (PPO) framework. The integrated attention mechanism confers three core advantages to the model: first, it dynamically captures inter-product interdependencies, enabling an accurate reflection of cross-price elasticity and demand correlations; second, it reduces feature redundancy and computational overhead in multi-product collaborative pricing strategies; third, it enhances both the interpretability and computational efficiency of the model. Experimental results demonstrate that in the scenario of multi-product pricing, the ATT-PPO algorithm achieves competitive performance compared to PPO, DDPG (Deep Deterministic Policy Gradient), SAC (Soft Actor-Critic), and TD3 (Twin Delayed Deep Deterministic Policy Gradient), with the key advantage lying in its ability to provide interpretable attention weights that reveal dynamic cross-product dependencies in pricing decisions. This study not only expands the applicability of DRL (Deep Reinforcement Learning) to practical economic problems in the fresh produce sector but also provides valuable theoretical insights that can be generalized to other short-lifecycle product domains, including fashion apparel and consumer electronics. Full article

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability. Full article

Understanding whether vegetation greening corresponds to changes in estimated forest carbon storage is important for evaluating ecological restoration under coupled climate change and human pressures. However, existing studies often rely on vegetation indices and have limited capacity to examine long-term forest carbon storage patterns or distinguish the roles of climatic and anthropogenic factors. This study integrates long-term remote sensing data with a two-way fixed effects model to examine forest ecosystem carbon storage in Shaanxi Province, China, from 1990 to 2023. Forest carbon storage was estimated by combining historical land-use data with static baseline carbon density coefficients derived from the 2012 field inventory, following an IPCC Tier 1-type approach. The carbon pools considered included aboveground biomass, belowground biomass, litter, and soil organic carbon. The results show that NDVI increased significantly, while estimated forest carbon storage increased by 4.27 × 10⁷ t (21.04%), with evident regional heterogeneity. A mismatch was observed between vegetation greenness and estimated forest carbon storage, and NDVI showed weak and unstable associations with carbon storage after controlling for fixed effects. Nighttime light exhibited a significant negative association with carbon storage, whereas climatic factors were generally insignificant. These findings suggest that vegetation indices alone may not reliably represent land-use-based carbon storage estimates. This study provides empirical evidence for understanding forest carbon storage patterns under ecological restoration and highlights the need for dynamic carbon density parameters in future assessments. Full article

Urban expressway projects are critical components of modern transportation infrastructure, yet their construction quality is often threatened by multi-source, latent, and dynamic risks. Traditional expert-driven risk identification methods frequently suffer from subjective bias and low efficiency, failing to meet the rigorous management requirements of complex engineering environments. To address these challenges, this study proposes a robust risk assessment framework integrating Large Language Models (LLMs) and the Delphi method within a Bayesian Network (BN) structure. First, LLM technology is leveraged to perform semantic mining on extensive engineering texts, including construction specifications and project reports, to pre-identify potential risk factors. Second, the Delphi method is applied through multiple rounds of expert consultation to refine a comprehensive inventory comprising 32 risk factors across five dimensions: personnel, machinery, materials, methods, and environment. Finally, a BN-based evaluation model is developed, utilizing forward inference, backward diagnosis, and sensitivity analysis to quantify risk levels and pinpoint critical risk drivers. The framework was empirically validated using the T Expressway Project in Hangzhou as a case study. Results demonstrate that the model effectively transforms empirical management into precise, data-driven diagnosis, providing project managers with a quantitative tool for optimizing construction quality control and decision making in complex urban bridge projects. Full article

Wildfires have historically shaped Mediterranean ecosystems, fostering the adaptation of fire-resilient species such as Pinus halepensis Mill. Assessing post-fire resilience is essential to understand landscape recovery and guide forest management. This requires evaluating the speed, intensity, and trajectory of vegetation recovery relative to a defined baseline, although the influence of control point selection and baseline configuration remains unclear, despite its critical role in shaping the interpretation of recovery dynamics. This study proposes a methodological framework to assess the resilience of P. halepensis using 14-year Landsat time series following wildfire events, combined with image segmentation algorithms and Object-Based Image Analysis (GEOBIA). The analysis integrates two complementary vectors: (i) temporal evolution of NDVI and (ii) spectral probability of assignment to P. halepensis. Results indicate that NDVI suggests an average vegetation recovery time of seven years; however, spectral probability remains below 40% during this period, indicating slower tree cover recovery. Field inventories confirm that full recovery requires more than 15 years, with early stages dominated by shrublands, mainly Quercus coccifera. These findings show that NDVI alone overestimates resilience and that control selection and baseline configuration strongly influence assessments. GEOBIA enhances the ecological precision of resilience evaluation. Full article

Inventory policy design in modern supply chains must balance cost efficiency, service reliability, and responsible resource utilization under significant demand and supply uncertainty. In many real-world supply chains, both customer demand and replenishment lead time exhibit substantial variability, making the design of continuous-review (s, S) inventory policies challenging. Although stochastic inventory models have been widely studied, many existing approaches rely on simplified assumptions or single-objective formulations, which may limit their applicability under simultaneous demand and lead-time uncertainty. This study proposes a data-driven multi-objective simulation–optimization framework for designing sustainable (s, S) inventory policies under dual uncertainty. The framework integrates empirical stochastic modeling, Monte Carlo simulation, and evolutionary multi-objective optimization to evaluate trade-offs between expected inventory cost and service performance. To enhance methodological rigor, statistical reliability control is incorporated into the simulation-based evaluation process to ensure that Pareto dominance relationships are not distorted by simulation noise. Historical operational data are used to estimate probability distributions for demand and lead time, which are incorporated into a stochastic simulation model representing inventory system dynamics. A multi-objective evolutionary algorithm (NSGA-II) is employed to identify Pareto-efficient policy parameters. An empirical case study from a health supplement supply chain demonstrates how the framework identifies efficient replenishment policies under realistic uncertainty conditions. The results reveal structural trade-offs between inventory cost and service level and show that data-driven policy design can improve decision transparency compared with heuristic replenishment rules. The proposed approach provides a structured decision-support tool for selecting replenishment policies that balance service continuity and inventory sustainability in shelf-life-constrained supply chains. 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

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

This research communication presents an analysis of dynamic supply chains (DSCs). The main goal of model-based cybertronics is to approximate, via a mathematical model from a dynamical system, the dynamics and behavior of dynamic supply chains. This considers that is at the operational level, where automation and control theory approaches take an insight —in this case, via Lyapunov stability—as a way to extend the use of mechatronic systems. Three case studies are presented: Firstly, the mathematical modeling and stability analysis of the ball-and-beam problem, as an approximation of a two echelon supply chain. Secondly, the mathematical modeling and stability analysis of a cold chain with temperature monitoring, and its relationship to inventory levels, are presented. From a theoretical perspective, applying model-based cybertronics in DSCs has direct practical implications: it improves operational control, enhances decision-making, and optimizes inventory management, particularly in cold chains. By treating high-volume supply chains as dynamical systems, managers can anticipate fluctuations and quantify efficiency. Finally, Lyapunov stability analysis ensures that models reliably reflect real-world behavior, enabling automation and predictable performance at an operational level in DSCs. Full article

Background: In the last few decades, a growing body of evidence has confirmed that vestibular rehabilitation (VR) can improve the symptoms of many unilateral and bilateral vestibular disorders, by facilitating vestibular compensation mechanisms, such as adaptation, substitution, and habituation. However, the usefulness of the vestibular rehabilitation approach in Persistent Postural–Perceptual Dizziness (PPPD) is currently highly debated and unclear. The aim of the present study was to evaluate the efficacy of VR using computerized dynamic posturography in PPPD patients as a single treatment and without other associated psychological or pharmacological therapies. Results were compared with patients with unilateral and bilateral vestibular disfunction, in order to define the role of our rehabilitation model within a framework of personalized therapy for different disorders. Methods: We evaluated 44 patients (23 F, 21 M; ranged from 28 to 82 years; mean age 63.72) affected by unilateral vestibular vestibulopathy (UVP) (n = 19), bilateral vestibular vestibulopathy (BVP) (n = 10) and PPPD (n = 15). For each patient, a comprehensive clinical bedside vestibular assessment was carefully performed by expert clinicians, as well as Bithermal caloric tests with videonystagmography (VNG), Video Head Impulse Test (vHIT) and Computed Dynamic Posturography (CDP). The impact of dizziness on quality of life (QoL) was assessed by the Italian Dizziness Handicap Inventory (DHI). All subjects evaluated in this study underwent five rehabilitative therapy sessions in our centre, once a week for 45 min and exercised daily for 30 min at home. All the exercises progressively became more difficult each week. Results: Our study showed that vestibular rehabilitation improved quality of life and reduced the level of self-perceived handicap in patients affected by unilateral and bilateral vestibular dysfunction, with significant improvement in DHI total score and posturographic parameters. In PPPD patients, rehabilitation did not significantly modify posturographic performances and the improvement in total DHI score did not reach statistical significance, although a significant change was observed in the functional sub-score. Conclusions: Vestibular rehabilitation confirmed its effectiveness in unilateral and bilateral peripheral vestibulopathies. In patients with PPPD, rehabilitation performed with computerized dynamic posturography may reduce subjective handicap and improve some aspects of daily functioning, although the small sample size and the absence of a control group do not allow definitive conclusions about its efficacy. Full article

Background and Objectives: Fibromyalgia syndrome (FMS) is frequently accompanied by autonomic symptoms and autonomic dysregulation, which may influence ocular blood flow regulation. The choroid is a densely vascular, autonomically innervated tissue, and optical coherence tomography (OCT)-derived markers have been used to explore potential ocular microvascular changes in FMS, with inconsistent findings. The choroidal vascularity index (CVI), defined as the proportion of luminal area within the total choroidal area, has been proposed as a potentially more robust marker of choroidal vascular status than thickness alone. We aimed to compare CVI and choroidal thickness between patients with FMS and healthy controls and examine the association between autonomic symptom burden and CVI in FMS. Materials and Methods: This single-centre observational cross-sectional case–control study enrolled adults aged 18–65 years. Swept-source OCT was performed; low-quality scans were excluded, and only right eyes were analysed. CVI, subfoveal maximum and mean choroidal thickness were obtained using an artificial intelligence-assisted analysis platform. Autonomic symptom burden, fibromyalgia impact, and central sensitization-related symptoms were assessed using the Composite Autonomic Symptom Score-31 (COMPASS-31), the Revised Fibromyalgia Impact Questionnaire (FIQ-R), and the Central Sensitization Inventory (CSI), respectively. Group comparisons, Spearman correlations, and multivariable linear regression were performed. Results: COMPASS-31, FIQ-R, and CSI scores were higher in the FMS group (all p < 0.001). CVI and choroidal thickness did not differ significantly between groups (CVI p = 0.124; maximum thickness p = 0.136; mean thickness p = 0.097). CVI was not correlated with COMPASS-31, FIQ-R, or CSI within either group. In adjusted models, age was independently associated with CVI (p < 0.001), whereas FMS status and COMPASS-31 total score were not. Conclusions: CVI and choroidal thickness were similar in FMS and controls, and CVI was not associated with self-reported autonomic symptom burden in FMS. Studies incorporating objective autonomic testing and dynamic vascular imaging paradigms are warranted. Full article

Aggregate production planning (APP) helps medium-term production, manpower, inventory, and subcontracting decisions match expected demand. Deterministic planning models are generally ineffective in manufacturing due to demand and operational variability. Fuzzy linear programming (FLP) has been frequently used to describe imprecision using membership functions and satisfaction levels. Despite its versatility, accurate approaches for solving multi-objective FLP-based APP models become computationally expensive as issue size and complexity increase. Thus, metaheuristic algorithms are widely used, although many still have premature convergence, parameter sensitivity, and restricted scalability. This study investigates the Narwhal Optimization Algorithm (NO) as a population-based metaheuristic framework. It proposes two hybrid variants to improve convergence reliability and constraint-handling capability: NO combined with the Super Modified Simplex Method (SMS) for local refinement and NO integrated with a Runge–Kutta-based optimizer (RK) for search stability. These hybrid techniques are tested for solution quality, convergence behavior, and robustness using eight response-surface benchmark functions and four constrained optimization problems. A real-parameter fuzzy APP problem with three goods and a six-month planning horizon uses the best variations. The Elevator Kinematic Optimization (EKO) algorithm, chosen for its compliance with the same mathematical framework and consistent parameter values, is used to compare the offered solutions fairly and controlled. Fuzzy programming uses a max–min satisfaction framework with linear membership functions from positive and negative ideal solutions. Computational experiments assess solution quality, stability, and efficiency for nominal and ±10% demand disturbances. The hybrid NO variants better resist premature convergence, stabilize solutions, and satisfy users more than the original NO and benchmark approaches. For small and medium-sized organizations in dynamic situations, hybrid narwhal-based optimization appears to be a reliable and scalable decision-support solution for APP problems under uncertainty. Full article

Asset discovery is a fundamental but inherently flawed capability in cybersecurity, as current methodologies frequently confuse preliminary discovery observations with definitive asset inventories, thereby obscuring uncertainty, restricting auditability, and eroding trust in security-critical decision-making. This work addresses the issue of inconsistent asset identification in dynamic IT settings by presenting an evidence-based architectural paradigm that clearly distinguishes observation, identity resolution, and inventory representation. The principal research aim is to develop and authenticate an architecture that maintains discovery evidence, facilitates deterministic, verifiable identity resolution, and supports interpretable inventory derivation. In contrast to state-centric and model-driven methodologies, the proposed architecture enhances (i) traceability through the preservation of time-scoped, method-attributed observations, (ii) identity continuity amidst dynamic conditions such as IP reassignment and infrastructure modifications, and (iii) auditability by facilitating the reconstruction of inventory claims from foundational evidence. An examined proof-of-concept implementation in a controlled yet realistic network environment shows superior identity stability, greater discovery traceability, and retention of historical context relative to traditional inventory models. The results validate the practicality and architectural benefits of the strategy; nevertheless, the evaluation is constrained by a lack of formalised performance indicators and adversarial robustness, which are recognised as priorities for further investigation. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article