Search Results (192)

Battery-powered automatic guided vehicles (B-AGVs) serve as crucial horizontal transportation equipment in terminals and significantly impact the terminal transportation efficiency. Imbalanced B-AGV availability during terminal peak and off-peak periods is driven by dynamic vessel arrivals. We propose a flexible dual-threshold charging (FDTC) strategy synchronized with vessel dynamics. Unlike the static threshold charging (STC) strategy, FDTC dynamically adjusts its charging thresholds based on terminal workload intensity. And we develop a collaborative B-AGV scheduling and routing optimization model incorporating FDTC. A tailored Dijkstra-Partition neighborhood search (Dijkstra-Pns) algorithm is designed to resolve the problem in alignment with practical scenarios. Compared to the STC strategy, FDTC strategy significantly reduces the maximum B-AGV running time and decreases conflict waiting delays and charging times by 25.04% and 24.41%, respectively. Moreover, FDTC slashes quay crane (QC) waiting time by 40.78%, substantially boosting overall terminal operational efficiency. Full article

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

Tropical forests continue to experience high levels of habitat loss and degradation, with wildfires becoming a frequent component of human-modified landscapes. Here we investigate the response of palm species to the conversion of old-growth forests to successional mosaics, including forest patches burned during wildfires. Palms (≥50 cm height) were recorded once in 2023–2024, across four habitat classes: terra firme old-growth stands, regenerating forest stands associated with slash-and-burn agriculture, old-growth stands burned once and twice, and active cassava fields, in the Tapajós-Arapiuns Extractive Reserve, in the eastern Brazilian Amazon. The flammability of palm leaf litter and forest litter were also examined to assess the potential connections between palm proliferation and wildfires. A total of 10 palm species were recorded in this social forest (including slash-and-burn agriculture and resulting successional mosaics), with positive, negative, and neutral responses to land use. Species richness did not differ among forest habitats, but absolute palm abundance was greatest in disturbed habitats. Only Attalea spectabilis Mart. (curuá) exhibited increased relative abundance across disturbed habitats, including active cassava field. Attalea spectabilis accounted for almost 43% of all stems in the old-growth forest, 89% in regenerating forests, 90% in burned forests, and 79% in crop fields. Disturbed habitats supported a five-to-ten-fold increment in curuá leaves as a measure of habitat flammability. Although curuá litter exhibited lower flame temperature and height, its lower carbon and higher volatile content is expected to be more sensitive to fire ignition and promote the spread of wildfires. The conversion of old-growth forests into social forests promotes the establishment of palm-dominated forests, increasing the potential for a forest transition further fueled by wildfires, with effects on forest resilience and social reproduction still to be understood. Full article

To tackle the inefficiencies in 3D mine tunnel modeling and the tedious task of drawing centerlines, this study introduces a faster method for generating centerlines using CAD secondary development. Starting with the tunnel centerline, the research then dives into techniques for creating detailed 3D tunnel models. The team first broke down the steps and logic behind tunnel modeling, designing a 3D tunnel framework and its data structure—complete with key geometric components like traverse points, junctions, nodes, and centerlines. By refining older centerline drawing techniques, they built a CAD-powered tool that slashes time and effort. The study also harnessed advanced algorithms, such as surface fitting and curve lofting, to swiftly model tricky tunnel sections like curves and crossings. This method fixes common problems like warped or incomplete surfaces in linked tunnel models, delivering precise and lifelike 3D scenes for VR-based mining safety drills and simulations. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

Addressing the urgent need to decarbonise aviation and valorise agricultural waste, this paper investigates the production of Sustainable Aviation Fuel (SAF) from corn stover. A preliminary evaluation based on a literature review indicates that among various conversion technologies, fast pyrolysis (FP) emerged as the most promising option, offering the highest fuel yield (22.5%) among various pathways, a competitive potential minimum fuel selling price (MFSP) of 1.78 USD/L, and significant greenhouse gas savings of up to 76%. Leveraging Aspen Plus simulation, SAF production via FP was rigorously designed and optimised, focusing on the heat integration strategy within the process to minimise utility consumption and ultimately the total cost. Consequently, the produced fuel exceeded the American Society for Testing and Materials (ASTM) limit for the final boiling point, rendering it unsuitable as a standalone jet fuel. Nevertheless, it achieves regulatory compliance when blended at a rate of up to 10% with conventional jet fuel, marking a practical route for early adoption. Energy optimisation through pinch analysis integrated four hot–cold stream pairs, eliminating external heating, reducing cooling needs by 55%, and improving sustainability and efficiency. Economic analysis revealed that while heat integration slashed utility costs by 84%, the MFSP only decreased slightly from 2.35 USD/L to 2.29 USD/L due to unchanging material costs. Sensitivity analysis confirmed that hydrogen, catalyst, and feedstock pricing are the most influential variables, suggesting targeted reductions could push the MFSP below 2 USD/L. In summary, this work underscores the technical and economic viability of corn stover-derived SAF, providing a promising pathway for sustainable aviation and waste valorisation. While current limitations restrict fuel quality during full substitution, the results affirm the feasibility of SAF blending and present a scalable, low-carbon pathway for future development. Full article

In the quest for stable, lead-reduced perovskites, this study unravels the structural and electronic evolution of CsZnI₃ across its α, β, and γ phases. DFT calculations spotlight the tetrahedral γ phase—with elongated Zn–I bonds (3.17 Å)—as the most stable, sidestepping the octahedral distortions of its metallic α and β counterparts. Pb²⁺ doping (>50%) drives a transformation to mixed octahedral–tetrahedral coordination, slashing the wide 3.15 eV bandgap to a solar-optimal 2.20 eV via lattice shrinkage. Above 50% doping, an optimum emerges—balancing structural integrity with efficient light absorption. These findings elevate Zn-doped or Zn-Pb-based compounds as promising and tunable perovskites for next-gen photovoltaics. Full article

The nitrogen (N) and phosphorus (P) additions were commonly used to improve plantation quality. However, the balance between nutrient uptake in the underground part and nutrient utilization in the aboveground part of Pinus elliottii (Slash pine) plantation in subtropical regions after N and P addition is still unclear. We conducted the experiment using a randomized complete block design with four treatments: N (50 kg N ha⁻² yr⁻¹, P (100 kg P ha⁻² yr⁻¹), NP (N + P), and a control (CK). Nutrient transport dynamics of underground (rhizosphere soil and roots) and aboveground (twigs and needles) parts of a 10-year-old Pinus elliottii plantations were evaluated. The trial was maintained for three consecutive growing seasons. The results showed that N and P additions significantly increased the N, P, and potassium (K) contents of soils and plant tissues in subtropical slash pine plantation forests, and showed a significant and gradual increase in interannual variations over the observation period (except for TN in soils, which increased first and then decreased). In terms of nutrient transport and reabsorption efficiency, N addition promoted the transport of elemental P from the translocating root system to the twigs, whereas P addition inhibited this process. P addition significantly increased the nitrogen reabsorption efficiency (NRE) of the needles, but decreased the phosphorus reabsorption efficiency (PRE), showing an element-specific response to the nutrient reabsorption process. Structural equation modeling further revealed that N or P addition had direct positive effects on soil N, P, and K content (path coefficients r: 0.54, 0.71, 0.41). N addition indirectly negatively affected N resorption efficiency (NRE) and K resorption efficiency (KRE) (r: −0.62, −0.51) but positively affected PRE (r: 0.44). Conversely, P addition had an indirect negative effect on PRE (r: −0.59). These results reveal that in subtropical regions, slash pine plantations adapt to N or/and P addition by adjusting nutrient absorption, transport, and resorption efficiency. This provided new insights into nutrient transport and distribution strategies in underground and aboveground parts of plants under N or/and P additions. Full article

The paper explores the pressing issue of energy consumption in machine learning (ML) models and their environmental footprint. As ML technologies, especially large-scale models, continue to surge in popularity, their escalating energy demands and corresponding CO₂ emissions are drawing critical attention. The article dives into innovative strategies to curb energy use in ML applications without compromising—and often even enhancing—model performance. Key techniques, such as model compression, pruning, quantization, and cutting-edge hardware design, take center stage in the discussion. Beyond operational energy use, the paper spotlights a pivotal yet often overlooked factor: the substantial emissions tied to the production of ML hardware. In many cases, these emissions eclipse those from operational activities, underscoring the immense potential of optimizing manufacturing processes to drive meaningful environmental impact. The narrative reinforces the urgency of relentless advancements in energy efficiency across the IT sector, with machine learning and data science leading the charge. Furthermore, deploying ML to streamline energy use in other domains like industry and transportation amplifies these benefits, creating a ripple effect of positive environmental outcomes. The paper culminates in a compelling call to action: adopt a dual-pronged strategy that tackles both operational energy efficiency and the carbon intensity of hardware production. By embracing this holistic approach, the artificial intelligence (AI) sector can play a transformative role in global sustainability efforts, slashing its carbon footprint and driving momentum toward a greener future. Full article

In this study, we introduce a novel distribution related to the gamma distribution, referred to as the generalized incomplete gamma distribution. This new family is defined through a stochastic representation involving a linear transformation of a random variable following a distribution derived from the upper incomplete gamma function. As a result, the proposed distribution exhibits a probability density function that effectively captures data exhibiting asymmetry and both mild and high levels of kurtosis, providing greater flexibility compared to the conventional gamma distribution. We analyze the probability density function and explore fundamental properties, including moments, skewness, and kurtosis coefficients. Parameter estimation is conducted via the maximum likelihood method, and a Monte Carlo simulation study is performed to assess the asymptotic properties of the maximum likelihood estimators. To illustrate the applicability of the proposed distribution, we present two case studies involving real-world datasets related to mineral concentration and the length of odontoblasts in guinea pigs, demonstrating that the proposed distribution provides a superior fit compared to the gamma, inverse Gaussian, and slash-type distributions. Full article

Global warming has driven a marked increase in forest fire occurrences, underscoring the critical need for timely and accurate detection to mitigate fire-related losses. Existing forest fire detection algorithms face limitations in capturing flame and smoke features in complex natural environments, coupled with high computational complexity and inadequate lightweight design for practical deployment. To address these challenges, this paper proposes an enhanced forest fire detection model, YOLOv8n-SMMP (SlimNeck–MCA–MPDIoU–Pruned), based on the YOLO framework. Key innovations include the following: introducing the SlimNeck solution to streamline the neck network by replacing conventional convolutions with Group Shuffling Convolution (GSConv) and substituting the Cross-convolution with 2 filters (C2f) module with the lightweight VoV-based Group Shuffling Cross-Stage Partial Network (VoV-GSCSP) feature extraction module; integrating the Multi-dimensional Collaborative Attention (MCA) mechanism between the neck and head networks to enhance focus on fire-related regions; adopting the Minimum Point Distance Intersection over Union (MPDIoU) loss function to optimize bounding box regression during training; and implementing selective channel pruning tailored to the modified network architecture. The experimental results reveal that, relative to the baseline model, the optimized lightweight model achieves a 3.3% enhancement in detection accuracy (mAP@0.5), slashes the parameter count by 31%, and reduces computational overhead by 33%. These advancements underscore the model’s superior performance in real-time forest fire detection, outperforming other mainstream lightweight YOLO models in both accuracy and efficiency. Full article

The intensification of economic globalization and the growing scarcity of global land resources have magnified the complexity of future land use and land cover (LULC) changes. In Mainland Southeast Asia (MSEA), these transformations are particularly pronounced, yet comprehensive, targeted, and systematic reviews are scant. This research employs bibliometrics and critical literature review methodologies to scrutinize 1956 relevant publications spanning from 1990–2023, revealing key insights into the contributors to land use studies in MSEA, which include not only local researchers from countries like Thailand and Vietnam but also international scholars from the United States, China, Japan, and France. Despite this, the potential for global collaboration has not been fully tapped. This study also notes a significant evolution in data analysis methods, transitioning from reliance on single data sources to employing sophisticated multi-source data fusion, from manual feature extraction to leveraging automated deep learning processes, and from simple temporal change detection to comprehensive time series analysis using tools like Google Earth Engine (GEE). This shift encompasses the progression from small-scale case studies to extensive multi-scale system analyses employing advanced spatial statistical models and integrated technologies. Moreover, the thematic emphasis of research has evolved markedly, transitioning from traditional practices like slash-and-burn agriculture and deforestation logging to the dynamic monitoring of specialized tree species such as rubber plantations and mangroves. Throughout this period, there has been a growing focus on the broad environmental impacts of land cover change, encompassing soil degradation, carbon storage, climate change responses, ecosystem services, and biodiversity. This research not only offers a comprehensive understanding of the LULC research landscape in MSEA but also provides critical scientific references that can inform future policy-making and land management strategies. Full article

In this article, we introduce a distribution that is an extension of the Power Maxwell (PM) distribution, which is based on the quotient of two independent random variables. These are the PM and a gamma distribution, respectively. In this way, the result is a model with greater kurtosis than the PM distribution. We study its probability density function and some properties, such as moments, asymmetry and kurtosis coefficient. An EM algorithm is proposed to estimate the parameters via the maximum likelihood method. A simulation study is carried out to study the asymptotic behaviour of our estimators. An application to a real dataset is also included. Full article

This study aimed to evaluate the role of trees outside forests on agricultural land (TOF-AL) in preserving the initial aboveground biomass (AGB) of forests within the agricultural landscape of Mongala province in the Democratic Republic of Congo. In 2024, tree inventories were conducted over four months in the forests and agricultural lands of Mongala province to analyse AGB. The effects of artisanal logging and charcoal production activities on the AGB conservation rate were considered. This study indicates that 78.3% of the trees encountered in agricultural lands were large-diameter trees (diameter at breast height (DBH) ≥ 60 cm). In forest areas, large-diameter trees accounted for 55.9% of tree density. The average AGBs are 66.8 Mg ha⁻¹ for TOF-AL and 373.5 Mg ha⁻¹ for forest trees. The AGB of TOF-AL accounts for 17.9% of the AGB of the total forest trees. The AGB conservation rates vary by region, with Lisala having the highest at 22.1%, Bumba the lowest at 11.2%, and Bongandanga at 20.5%. Artisanal logging and charcoal production reduce the AGB conservation rate of TOF-AL. The AGB conservation rate is positively correlated with the distances to major cities. These results prove that conserving trees in agricultural landscapes can reduce the AGB losses associated with slash-and-burn agriculture and contribute to mitigating climate change effects. Full article

While commercially available lightweight “stab-proof” apparel exists, it offers little resistance to true stabbing as it is primarily designed to withstand slash attacks. Yet, crimes involving the use of a knife or sharp instrument have consistently been rising in the UK over several decades. For the most part, the various proposed solutions to stab-proofing are based on speciality textiles and while these have shown success in slash-proofing, their utility for stab-proofing is still somewhat of a misnomer. Nature showcases a plethora of puncture-resisting materials and structures. At the macro-scale, these include carapaces, egg cases, toughened skin, and more. One of the most effective protective mechanisms known comes through surface scaling, present on animals such as reptiles and fish. Scaled protective armours present in extant fish species include overlapping elasmoid scales, interlocking ganoid scales, placoid scales, tessellating carapace scutes, and interlocking plates. Here, we research overlapping and interlocking scaled structures to ascertain the stab penetration resistance of biomimetic scaled structures against continuum material to obtain the force–time relationship of the impact event as well as ascertaining the penetration depth. We use additive manufacturing methods to manufacture biomimetic armour made of nylon, a common protective artificial material used in slash-proofing textiles. Stab testing to the closely replicated HOSDB body armour standard 2017, we find that biomimetic scales made of nylon offer greater protection against direct stabbing than continuum nylon material sheets. This can be attributed to (a) the heightened flexibility in an interlocked fish scale structure that does not exist in a continuum sheet of the same material; (b) the effect of overlapping of the fish scales, resulting in a greater penetration depth requirement before the structure undergoes perforation; and (c) segmentation into smaller armour plates (of the same thickness) rather than continuum sheets provides a lower span-to-depth ratio, therefore leading to a smaller deflection of the plate upon impact and a greater deceleration and, hence, a greater impact force. Full article