Search Results (79)

The Iridaceae family comprises approximately 1800 species, including Iris pallida Lam., which is widely recognized for its ornamental and aromatic properties and particularly adopted in the perfume industry. In this study, we evaluated the effects of planting density and maturity age on biomass production, morphological traits, rhizome biomass, and orris concrete yield in Iris pallida grown in Tuscany (Italy). The experiment consisted of four agricultural parcels, each one containing six plots arranged to test combinations of two planting densities (low density [LD], 8 plants/m² and high density [HD], 15 plants/m²) and harvesting age (2, 3, and 4 years). Results indicated that planting density significantly influenced biomass variables—including rhizome, bud, and stem biomass—with the low planting density (LD) exhibiting higher total biomass (5.48 ± 0.59 kg/m²) compared to that observed under high planting density (HD) (1.82 ± 0.54 kg/m²). Orris concrete yield varied significantly across planting densities and harvesting age, consistently favoring LD (0.055 ± 0.01%) over HD (0.045 ± 0.01%). Also, orris concrete yield showed a positive correlation with floral stem number (r = 0.73, p < 0.001), root biomass (r = 0.66, p < 0.01) and floral stem biomass (r = 0.63, p < 0.01), while no significant correlations were found between orris concrete yield and total biomass or rhizome biomass. A shorter production cycle under low-density planting may improve orris concrete yield without compromising biomass productivity. Full article

Infrastructure facility development and modernization highly contribute to national economic growth, but at the same time, such development also causes local negative impacts on the use of specific land plots, creating losses for their right holders. In Russia, some prerequisites have already been laid down on the issue of compensation for the losses associated with restrictions on the rights and prohibitions of economic activity within zones with special territory use conditions (ZSTUCs). However, the impacts of such facilities lead to environmental pollution and land use disadvantages, such as irregular parcels. The aim of this work is to substantiate a set of approaches to compensating for the cumulative negative impact of infrastructure facilities. The factors causing the negative impacts of infrastructure facilities are grouped into three areas: rights restrictions, territorial deficiencies and environmental pollution. This work uses the SWOT analysis method with the possibility of element-by-element analysis, as a result of which the approaches to the compensation for negative impacts under different external and internal conditions are determined. As a result of this study, a justification for a set of approaches to compensating for the negative impacts of infrastructure facilities on land use was executed, and a new algorithm to compensate the right holders of the land, industry sector or state for such negative impacts was developed. The following approaches to compensating for negative impacts were identified: loss assessment; the establishment of environmental payments; cadastral value adjustment; compensation for industry sector losses; and the use of state regulation tools. The first two approaches were identified as the main ones. The proposed algorithm can be realized only with the help of the abovementioned methodological approaches, which form a basis for further research. Full article

Resource-exhausted cities face dual crises of economic stagnation and ecological degradation, which is primarily attributable to the inefficient use of industrial land. The redevelopment of industrial land has emerged as a crucial solution to the “resource depletion-urban decline” dilemma. The issue of inefficient industrial land use in resource-exhausted cities is of great significance as it directly impacts both economic development and ecological protection. Therefore, finding effective ways to redevelop this land is essential for the sustainable development of these cities. This research takes Hegang, a representative resource-exhausted city in China, as a case study. A multi-dimensional evaluation framework and an adaptive redevelopment strategy system are constructed in this research. By integrating data related to land use status, land use efficiency, policy constraints, and development potential, a parcel-scale assessment model is established. This model consists of 4 primary indicators and 13 secondary indicators. Through this model, 11.01 km² of inefficient industrial land in the main urban area of Hegang is identified. Standard deviation ellipse and kernel density analysis are employed to reveal the spatial pattern of inefficient land. The results show that the inefficient industrial land in Hegang exhibits a pattern of “overall dispersion with localized agglomeration”. It is found that idle and abandoned land are the dominant types of inefficient industrial land in Hegang’s main urban area, accounting for 69.7% of the total. This finding provides a clear understanding of the nature of the inefficient land use problem in resource-exhausted cities. A strategic framework is proposed, which incorporates classified governance, dynamic restoration, and multi-stakeholder collaboration. This framework offers a governance toolkit with both theoretical depth and practical value for resource-exhausted cities. Breaking the locked relationship between industrial land and resource dependence promotes the deep integration of spatial restructuring and sustainable transformation. The findings of this research provide significant scientific insights for similar cities worldwide to address the challenges they face and achieve harmony between human activities and land use. Future research could focus on further refining the evaluation framework and redevelopment strategies based on different regional characteristics and resource endowments. Full article

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. Full article

In the era of new productive forces, the efficient utilization of industrial land in high-tech zones is critical for fostering technological innovation, intelligent manufacturing, and green development. However, constrained by limited land reserves, inefficient stock utilization, and sluggish industrial upgrading, high-tech zones must establish a scientific early warning mechanism for industrial land redevelopment. This study constructs a four-tier early warning system (normal, alert, warning, and response) based on three key dimensions: enterprise life cycle, enterprise–park compatibility, and industrial land use efficiency. Using the Jinan High-Tech Zone as a case study, this study conducts an empirical analysis of 360 industrial land parcels from 2020 to 2022, employing DEA, fixed effects models, GIS visualization, and MCDA methods. The results indicate a strong correlation between enterprise life cycle and land use efficiency, with significant spatial differentiation in enterprise–park compatibility. Efficient land use is concentrated in areas with well-defined functions and high industrial agglomeration. This study identifies 360 land use scenarios, with 12% classified as normal, 28% requiring monitoring, 52% requiring optimization, and 8% necessitating redevelopment. Based on these findings, a “warning–monitoring–regulation” closed-loop management model is proposed, providing decision-making support for dynamic land optimization and sustainable development in high-tech zones. Full article

Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; they offer real opportunities for energy sustainability, using technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). In this context, this study focuses on improving energy efficiency in smart spaces within the context of Industry 4.0 by utilizing the SmartParcels framework. This framework creates a detailed and cost-effective plan for equipping specific areas of smart communities, commonly referred to as parcels. By adapting this framework, we propose an integrated model for planning and implementing IoT applications that optimizes service utilization while adhering to operational and deployment cost constraints. The model considers multiple layers, including sensing, communication, computation, and application, and adopts an optimization approach to meet the needs related to IoT deployment. In simulated industrial environments, it demonstrated scalability and economic viability, achieving high service utility and ensuring broad geographic coverage with minimal redundancy. Furthermore, the use of heuristics for device reuse and geophysical mapping selection promotes cost-effectiveness and energy sustainability, highlighting the framework’s potential for large-scale applications in diverse industrial contexts. Full article

The paper provides an in-depth investigation of techniques for improving small parcel delivery services in a private logistics company, addressing significant difficulties in customer logistics service, particularly in the growing e-commerce industry. The study addresses a gap in the existing literature by assessing 170 documented customer complaints, with an emphasis on recurring issues such as improper delivery, delays, and damaged parcels. The methodological approach uses statistical tools to determine the magnitude of delivery challenges, integrating a review of the scientific literature with real data analysis. There are 28% complaints about faulty delivery and 26% about delays, according to the statistics. It is clear that systemic improvements are urgently needed. One strategy to improve service reliability and efficiency is to use automation technologies, such as drones, smart route optimization systems, and constant human training programs. While ensuring operational sustainability, these strategies aim to address the underlying causes of consumer dissatisfaction. Full article

Background: Extensive research highlights the economic benefits of collaboration among parcel delivery service providers, yet mutually advantageous cooperative arrangements remain limited in Hungary’s online shopping sector. Service providers typically prefer to operate independently rather than cooperate with competitors. However, the COVID-19 pandemic significantly altered industry dynamics, leading to increased collaboration. Methods: Against this backdrop, this study explores two key research questions. First, it examines the role of economic narratives in shaping market dynamics. Second, it investigates the cognitive biases influencing decision-makers during the pandemic, based on an analysis of Hungary’s parcel delivery sector. Semi-structured interviews were conducted with key actors in the logistics industry, and the data were analyzed using abductive thematic analysis. Results: The findings reveal that specific economic narratives, such as those emphasizing efficiency and safety, indirectly influenced market mechanisms during the COVID-19 pandemic. Notably, the shift in consumer demand towards contactless parcel lockers created new incentives for collaboration. Additionally, this study demonstrates that decision-makers exhibited cognitive biases such as risk aversion, which affected their willingness to cooperate. Conclusions: The research concludes that strong economic incentives can override these biases, fostering collaboration among service providers. Full article

The decarbonization of the electricity system coupled with the electrification of transport, heat, and industry represents a practical and cost-effective approach to deep decarbonization. A key question is as follows: where to build new solar and wind farms? This study presents a cost-based approach to evaluate land parcels for solar and wind farm suitability using colour-coded heatmaps that visually depict favourable locations. An indicative cost of electricity is calculated and classified for each pixel by focusing on key factors including the resource availability, proximity to transmission infrastructure and load centres, and exclusion of sensitive areas. The proposed approach mitigates the subjectivity associated with traditional multi-criteria decision-making methods, in which both the selection of siting factors and the assignment of their associated weightings rely highly on the subjective judgements of experts. The methodology is applied to Australia, South Korea, and Indonesia, and the results show that proximity to high-voltage transmission and load centres is a key factor affecting site selection in Australia and Indonesia, while connection costs are less critical in South Korea due to its smaller land area and extensive infrastructure. The outcomes of this study, including heatmaps and detailed statistics, are made publicly available to provide both qualitative and quantitative information that allows comparisons between regions and within a region. This study aims to empower policymakers, developers, communities, and individual landholders to make informed decisions and, ultimately, to facilitate strategic renewable energy deployment and contribute to global decarbonization. Full article

The Delottococcus aberiae is a mealybug pest known as Cotonet de les Valls in the province of Castellón (Spain). This tiny insect is causing large economic losses in the Spanish agricultural sector, especially in the citrus industry. The European Copernicus program encourages the progress of Earth observation (EO) in relation to the development of agricultural monitoring tools. In this context, this work is based on the analysis of the temporal evolution of spectral surface reflectance data from Sen2Like, analyzing healthy and fields affected by the mealybug. The study area is focused on the surroundings of Vall d’Uixó (Castellón, Spain), involving an approximate area of 25 ha distributed in a total of 21 fields of citrus trees with different mealybug incidence, classified as healthy or unhealthy, during the 2020–2021 season. The relationship between the mealybug infestation level and the Normalized Difference Vegetation Index (NDVI) and other optical bands (Red, NIR, SWIR, derived from Sen2Like) were analyzed by studying the time-series evolution of each parameter across the time period 2017–2022. In this study, we also demonstrate that evergreen fruit trees such as citrus, show a seasonality across the EO-based time series, which is linked to directional effects caused by the sensor–sun geometry. This can be mitigated by using a Bidirectional Reflectance Distribution Function (BRDF) model such as the High-Resolution Adjusted BRDF Algorithm (HABA). To study the infested fields separately from healthy ones and avoid mixing fields with very different spectral responses caused by field type, separation between rows, or age, we studied the evolution of each parcel separately using monthly linear regressions, considering the 2017–2018 seasons as a reference when the pest had not developed yet. The observations indicate the feasibility of the distinction between affected and healthy plots during a year utilizing specific spectral ranges, with SWIR proving a notably effective channel, enabling separability from mid-summer to the fall. Furthermore, the anomaly inspection demonstrates an increase in the effects of the pest from 2020 to 2022 in all spectral regions and enables a first approximation for identifying healthy and affected fields based on negative anomalies in the red and SWIR channels and positive anomalies in the NIR and NDVI. This work contributes to the development of new monitoring tools for efficient and sustainable action in pest control. Full article

In recent years, parcel volumes have reached record highs, prompting the logistics industry to explore innovative solutions to meet growing demand. In densely populated areas, delivery robots offer a promising alternative to traditional truck-based delivery systems. These autonomous electric robots operate on sidewalks and deliver time-sensitive goods, such as express parcels, medicine and meals. However, their limited cargo capacity and battery life require a return to a depot after each delivery. This challenge can be modeled as an electric vehicle-routing problem with soft time windows and single-unit capacity constraints. The objective is to serve all customers while minimizing the quadratic sum of delivery delays and ensuring each vehicle operates within its battery limitations. To address this problem, we propose a mixed-integer quadratic programming model and introduce an enhanced formulation using a layered graph structure. For this layered graph, we present two solution approaches based on relaxations that reduce the number of nodes and arcs compared to the expanded formulation. The first approach, Iterative Refinement, solves the current relaxation to optimality and refines the graph when the solution is infeasible for the expanded formulation. This process continues until a proven optimal solution is obtained. The second approach, Branch and Refine, integrates graph refinement into a branch-and-bound framework, eliminating the need for restarts. Computational experiments on modified Solomon instances demonstrate the effectiveness of our solution approaches, with Branch and Refine consistently outperforming Iterative Refinement across all tested parameter configurations. Full article

The popularity of online shopping in China has increased significantly, creating new development opportunities for the express delivery industry. However, the rapid expansion of the express industry has also created challenges in the parcel sorting process. The demanding nature of parcel sorting work, which is characterized by intense and prolonged repetitive tasks, makes individuals particularly vulnerable to the effects of fatigue. Fatigue is a complex condition that encompasses both physiological and psychological exhaustion. It often results in reduced energy levels and diminished functionality, significantly impacting an individual’s performance at work and their overall well-being. This study aimed to investigate how physiological and psychological fatigue affects sorting efficiency and to identify appropriate rest periods that will allow employees to maintain their performance levels. The research involved fifteen participants who took part in a 60 min continuous sorting experiment and a similar experiment with scheduled breaks. During both trials, we collected data on participants’ electromyography (EMG) and electrodermal activity (EDA), as well as subjective fatigue ratings (RPE). Signal features such as the median frequency (MF) of EMG and the skin conductance level (SCL) were analyzed to assess physiological and psychological fatigue, respectively. The results show that physiological fatigue mainly affects sorting efficiency in the first 30 min, while psychological fatigue becomes more influential in the following half-hour period. In addition, subjective fatigue levels during the first 30 min are primarily determined by psychological factors, while beyond that point, both physiological and psychological fatigue contribute to subjective fatigue. Rest periods of 415–460 s, based on EDA recovery times, effectively support sorting efficiency and participants’ recovery. This study highlights the complex ways in which fatigue affects parcel sorting performance and provides valuable theoretical and practical insights for establishing labor quotas and optimizing work schedules in the parcel sorting industry. Full article

Plantation distribution information is of great significance to the government’s macro-control, optimization of planting layout, and realization of efficient agricultural production. Existing studies primarily relied on high spatiotemporal resolution remote sensing data to address same-spectrum, different-object classification by extracting phenological information from temporal imagery. However, the classification problem of orchard or artificial forest, where the spectral and textural features are similar and their phenological characteristics are alike, still presents a substantial challenge. To address this challenge, we innovatively proposed a multi-index entropy weighting DTW method (ETW-DTW), building upon the traditional DTW method with single-feature inputs. In contrast to previous DTW classification approaches, this method introduces multi-band information and utilizes entropy weighting to increase the inter-class distances. This allowed for accurate classification of orchard categories, even in scenarios where the spectral textures were similar and the phenology was alike. We also investigated the impact of fusing optical and Synthetic Aperture Radar (SAR) data on the classification accuracy. By combining Sentinel-1 and Sentinel-2 time series imagery, we validated the enhanced classification effectiveness with the inclusion of SAR data. The experimental results demonstrated a noticeable improvement in orchard classification accuracy under conditions of similar spectral characteristics and phenological patterns, providing comprehensive information for orchard mapping. Additionally, we further explored the improvement in results based on two different parcel-based classification strategies compared to pixel-based classification methods. By comparing the classification results, we found that the parcel-based averaging method has advantages in clearly defining orchard boundaries and reducing noise interference. In conclusion, the introduction of the ETW-DTW method is of significant practical importance in addressing the challenge of same-spectrum, different-object classification. The obtained orchard distribution can provide valuable information for the government to optimize the planting structure and layout and regulate the macroeconomic benefits of the fruit industry. Full article

In the e-commerce last-mile delivery process, the asset operators (logistics service providers who own parcel locker facilities) support their delivery service with parcel lockers, while the non-asset operators (logistics service providers without parcel lockers) perform door-to-door delivery. Due to demand fluctuation, asset operators’ parcel-locker slots may be left vacant, while non-asset operators are stuck with the high-cost door-to-door service. The exclusiveness of parcel-locker usage reduces resource utilization and service efficiency in last-mile delivery. Therefore, this paper proposes a parcel-locker-sharing model in which these two parties share the parcel-locker capacity in last-mile delivery. The asset operator rents the unused parcel lockers to the non-asset operator by charging a rental fee,while the non-asset operator rents the parcel lockers for delivery to save logistics costs. The motivation of this alliance is to increase the profits of both parties and that of the total supply chain. This study establishes the supply-chain profit model for the parcel-locker-sharing framework and finds that the profit or loss depends on the comparison of the operation cost savings and delivery-cost savings. A numerical analysis is conducted to validate the final result. The research further suggests the optimal rental quantity and price interval. This paper is the first to study the operational mechanism of sharing the parcel locker between two distinct types of logistics service providers and to offer recommendations for industrial application. Full article

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the “salt and pepper” effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the “salt and pepper” spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. Full article