Essential trace elements are micronutrients whose deficiency has been associated with altered fertility and/or adverse pregnancy outcomes, while surplus may be toxic. The concentrations of eight essential trace elements were measured using inductively coupled mass spectrometry (ICP-MS) and assessed with respect to clinical in vitro fertilization (IVF) outcomes in a population of 51 women undergoing IVF with intracytoplasmic sperm injection (ICSI), pre-implantation genetic screening for aneuploidy (PGT-A), and single frozen euploid embryo transfer (SET/FET). Specifically, copper (Cu), zinc (Zn), molybdenum, selenium, lithium, iron, chromium, and manganese were quantified in follicular fluid and whole blood collected the day of vaginal oocyte retrieval (VOR) and in urine collected the day of VOR and embryo transfer. We found that the whole blood Cu/Zn ratio was significantly associated with superior responses to ovarian stimulation. Conversely, the whole blood zinc and selenium concentrations were significantly associated with poor ovarian response outcomes. Higher levels of whole blood zinc and selenium, urinary selenium, lithium, and iron had significant negative associations with embryologic outcomes following IVF. Regarding clinical IVF outcomes, higher urinary molybdenum concentrations the day of VOR were associated with significantly lower odds of implantation and live birth, while higher urinary Cu/Mo ratios on the day of VOR were associated with significantly higher odds of implantation, clinical pregnancy, and live birth. Our results suggest that essential trace element levels may directly influence the IVF outcomes of Spanish patients, with selenium and molybdenum exerting negative effects and copper-related ratios exerting positive effects. Additional studies are warranted to confirm these relationships in other human populations. Full article

Southern Min is generally known for not using classifiers [CL] for expressing definiteness/indefiniteness as it is associated with the bare classifier construction [CL N]. This paper offers evidence from Xiamen Southern Min (XSM) that the use of a specific classifier vs. the general classifier é contributes to referentiality in an alternative way by supporting object identification as it is due to the semantic specificity present in specific classifiers and absent in the general classifier. In a dialogic cognitive experiment adapted from the “Hidden color-chips” task (Enfield and Bohnemeyer 2001), 18 participants had to manipulate their addressees’ attention toward various objects situated in their immediate physical space through language as well as deictic gestures. The objects were associated with different specific classifiers or with the general classifier, and they were arranged according to the factors of (a) distance from speaker, (b) visibility for speaker, and (c) uniqueness (adjacency of similar items). The results show, among other things, that there is a higher tendency to use the specific CL in the [demonstrative CL N] construction if adjacent similar objects [−unique] are too far away from the speaker for clear identification by a demonstrative or a pointing gesture. This is seen as a last-resort strategy for creating contrast. Further corroboration comes from the use of specific classifiers in later mentions after the general CL failed to achieve clear identification. These findings can be situated in the broader context of other languages with classifiers in contrastive function (Thai, Vietnamese, and Ponapean) and they show the relevance of using dialogic texts for modeling classifier selection in contrast to narrative texts. Finally, dialogic contexts may serve as bridging contexts for grammaticalization from numeral classifiers to definiteness markers. Full article

Hyperspectral images (HSIs) provide valuable spatial–spectral information for ground analysis. However, in few-shot (FS) scenarios, the limited availability of training samples poses significant challenges in capturing the sample distribution under diverse environmental conditions. Semi-supervised learning has shown promise in exploring the distribution of unlabeled samples through pseudo-labels. Nonetheless, FS HSI classification encounters the issue of high intra-class spectral variability and inter-class spectral similarity, which often lead to the diffusion of unreliable pseudo-labels during the iterative process. In this paper, we propose a simple yet effective progressive pseudo-label selection strategy that leverages the spatial–spectral consistency of HSI pixel samples. By leveraging spatially aligned ground materials as connected regions with the same semantic and similar spectrum, pseudo-labeled samples were selected based on round-wise confidence scores. Samples within both spatially and semantically connected regions of FS samples were assigned pseudo-labels and joined subsequent training rounds. Moreover, considering the spatial positions of FS samples that may appear in diverse patterns, to fully utilize unlabeled samples that fall outside the neighborhood of FS samples but still belong to certain connected regions, we designed a matching active learning approach for expert annotation based on the temporal confidence difference. We identified samples with the highest training value in specific regions, utilizing the consistency between predictive labels and expert labels to decide whether to include the region or the sample itself in the subsequent semi-supervised iteration. Experiments on both classic and more recent HSI datasets demonstrated that the proposed base model achieved SOTA performance even with extremely rare labeled samples. Moreover, the extended version with active learning further enhances performance by involving limited additional annotation. Full article

Amino acids are organic compounds that enter the protein structure, being involved in the proper functioning of the body. The role of amino acids in the onset of autism spectrum disorder (ASD) is yet to be established. Our aim was to identify correlations between urine amino acids and their derivatives and ASD. Methods: We designed a case–control study that consisted of 75 boys and girls, aged between 2 and 12 years. For amino acid profile, we used urine samples that were analyzed using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Results: Descriptive analysis showed higher values for glutamine, hydroxyproline, tyrosine, aspartic acid, and tryptophan and lower values for serine in the autism group than in the control group. Also, we found that boys with autism had higher values than the boys in the control group for serine, threonine, and aspartic acid. For girls from both groups, we did not find statistically significant values. In terms of age groups, we found significantly higher values for histidine, threonine, valine, methionine, aspartic acid, glutamic acid, alpha amino-adipic acid, sarcosine, alanine, and beta-alanine and significantly lower values for proline for both the autism and control groups under 5 years. Conclusions: The findings of this study support the assumption that amino acids may have a role in the expression of ASD. Full article

This paper presents the process of digital data flow in RE/CAD/RP/CAI systems to develop models for planning surgical procedures in the craniofacial area. At the first RE modeling stage, digital data processing, segmentation, and the reconstruction of the geometry of the anatomical structures were performed. During the CAD modeling stage, three different concepts were utilized. The first concept was used to create a tool that could mold the geometry of the cranial vault. The second concept was created to prepare a prototype implant that would complement the anterior part of the mandibular geometry. And finally, the third concept was used to design a customized prototype surgical plate that would match the mandibular geometry accurately. Physical models were made using a rapid prototyping technique. A Bambu Lab X1 3D printer was used for this purpose. The process of geometric accuracy evaluation was carried out on manufactured prototypes of surgical plates made of ABS+, CPE, PLA+, and PETG material. In the geometric accuracy evaluation process, the smallest deviation values were obtained for the ABS plus material, within a tolerance of ±0.1 mm, and the largest were obtained for CPE (±0.2 mm) and PLA plus (±0.18 mm). In terms of the surface roughness evaluation, the highest value of the Sa parameter was obtained for the PLA plus material, which was 4.15 µm, and the lowest was obtained for the CPE material, equal to 3.62 µm. The knowledge of the flow of digital data and the identification of factors determining the accuracy of mapping the geometry of anatomical structures allowed for the development of a procedure that improves the modeling and manufacturing of anatomical structures within the craniofacial region. Full article

Hybrid composite materials have been widely used to advance the mechanical responses of fiber-reinforced composites by utilizing different types of fibers and fillers in a single polymeric matrix. This study incorporated three types of fibers: basalt woven fiber and steel (AISI304) wire meshes with densities of 100 and 200. These fibers were mixed with epoxy resin to generate plain composite laminates. Three fundamental mechanical tests (tensile, compression, and shear) were conducted according to the corresponding ASTM standards to characterize the steel wire mesh/basalt/epoxy FRP composites used as plain composite laminates. To investigate the flexural behavior of the hybrid laminates, various layer configurations and thickness ratios were examined using a design of experiments (DoE) matrix. Hybrid samples were chosen for flexural testing, and the same procedure was employed to develop a finite element (FE) model. Material properties from the initial mechanical testing procedure were integrated into plain and hybrid composite laminate simulations. The second FE model simulated the behavior of hybrid laminates under flexural loading; this was validated through experimental data. The results underwent statistical analysis, highlighting the optimal configuration of hybrid composite laminates in terms of flexural strength and modulus; we found an increase of up to 25% in comparison with the plain composites. This research provides insights into the potential improvements offered by hybrid composite laminates, generating numerical models for predicting various laminate configurations produced using hybrid steel wire mesh/basalt/epoxy FRP composites. Full article

The reduction in PV prices and interest in energy independence accelerate the adoption of residential battery storage. This storage can support various functions of an energy system undergoing decarbonization. In this work, operative benefits of storage from the system perspective, namely, generation cost reduction and congestion mitigation, are investigated. Germany is chosen as a case study due to its strong reliance on variable renewable energy. For the analysis, an economic dispatch model with a high spatial resolution is coupled with a pan-European transmission grid model. It is shown that the system’s generation costs are highest when the assets are used only to maximize PV self-consumption, and the costs are lowest when the storage also reacts to the market dynamics. This amounts to a 6% cost reduction. Both operation strategies result in an equal level of grid congestion and infrastructure loading. This is improved with a strategy that accounts for regional peak reduction as a secondary objective. The high congestion level emphasizes that grid expansion needs to keep pace with the generation and electrification expansion necessary to decarbonize other sectors. Lastly, policymakers should enable multipurpose utilization, e.g., via the introduction of market-oriented retail electricity prices with intervention options for grid operators. Full article

Autonomous driving has the potential to revolutionize transportation, but developing safe and reliable systems remains a significant challenge. Reinforcement learning (RL) has emerged as a promising approach for learning optimal control policies in complex driving environments. However, existing RL-based methods often suffer from low sample efficiency and lack explicit safety constraints, leading to unsafe behaviors. In this paper, we propose a novel framework for safe reinforcement learning in autonomous driving that addresses these limitations. Our approach incorporates a latent dynamic model that learns the underlying dynamics of the environment from bird’s-eye view images, enabling efficient learning and reducing the risk of safety violations by generating synthetic data. Furthermore, we introduce state-wise safety constraints through a barrier function, ensuring safety at each state by encoding constraints directly into the learning process. Experimental results in the CARLA simulator demonstrate that our framework significantly outperforms baseline methods in terms of both driving performance and safety. Our work advances the development of safe and efficient autonomous driving systems by leveraging the power of reinforcement learning with explicit safety considerations. Full article

Starting on 2 November 2023, some territories in the Region of Tuscany in Italy were hit by exceptionally intense meteorological and calamitous events. The Region of Tuscany’s government was immediately at the forefront of relief, assisting the population, and aiding restoration. On 3 November, a national state of emergency was declared due to flooding. Journalistic communication is an essential aspect of disaster mitigation, preparedness, response and recovery. In terms of protecting people and reducing damage, journalists and the media have an important role to play. This article reports an Italian case study analysing the behaviour of local media in cases of natural disaster. Nine focus groups were conducted with local journalists covering the flood emergency. The results highlight the role of social and institutional mediation, rather than mere dissemination, played by the local press in emergency situations, a central element in the construction of a community bond, precisely in moments of insecurity and disorientation. The narration of a disaster from the inside seems to have allowed the emergence and representation of hitherto unknown social realities in Tuscany. The goal of the news coverage was widened, making it possible to respond better to the diverse interests of the reading public and to satisfy in a short time and exhaustively the information needs of individual communities in difficulty. Full article

Radio Occultation (RO) is pivotal for profiling the neutral and ionized atmosphere, with the PlanetiQ mission, via its GNOMES satellites, striving to establish an advanced atmospheric observing system. However, an assessment of the spatiotemporal distributions of PlanetiQ observations and comparisons with reliable datasets are lacking. This study addresses this gap by examining the temporal and spatial distribution of RO observations from PlanetiQ during its initial 198 operational days in 2023, alongside comparisons with COSMIC and Numerical Weather Prediction (NWP) models. Data from GN02, GN03, and GN04 satellites, yielding 1099, 1313, and 1843 RO events per day, respectively, were analyzed. The satellite constellation’s observations demonstrate a generally well-distributed pattern, albeit minor deficiencies in equatorial and polar regions. Single-profile comparisons with COSMIC data reveal strong correlations for pressure, temperature, Water Vapor Pressure (WVP), and refractivity profiles, with temperature exhibiting larger variations (RMSE = 1.24 °C). Statistical analyses confirm statistically insignificant differences between the PlanetiQ and COSMIC profiles at the same spatio-temporal coordinates. Comparisons with NWP models show slight differences with GFS, with overall RMSE values of 0.23 mb (WVP), 0.6 mb (pressure), 1.3 (refractivity), and 1.5 °C (temperature). However, assessments against GFS/ECMWF models indicate overall compatibility, with insignificant differences between PlanetiQ profiles and model observations. Full article

Argemone mexicana L. has been used in traditional Mexican medicine. Among its bioactive constituents, berberine (BER) has garnered attention for its cytotoxic properties against different tumor cell lines. This study investigates the in vitro toxicity against HEP-G2 (human hepatocellular carcinoma) and murine lymphoma (L5178Y-R) cells using the MTT assay of the methanol extract (AmexM), sub-partitions of A. mexicana, and BER. Selectivity indices (SIs) were determined by comparing their cytotoxic effects on VERO (monkey kidney epithelial) and PBMC (human peripheral blood mononuclear) non-tumoral cells. Additionally, the anti-hemolytic effect of these treatments was assessed using the AAPH method. The treatment with the most promising activity against tumor cells and anti-hemolytic efficacy underwent further evaluation for toxicity in Artemia salina and antioxidant activities using DPPH, ABTS, and FRAP assays. BER demonstrated an IC₅₀ = 56.86 µg/mL in HEP-G2 cells and IC₅₀ < 5.0 µg/mL in L5178Y-R cells, with SI values of 15.97 and >5.40 in VERO and PBMC cells, respectively. No significant hemolytic effects were observed, although AmexM and BER exhibited the highest anti-hemolytic activity. BER also demonstrated superior antioxidant efficacy, with lower toxicity in A. salina nauplii compared to the control. Additionally, BER significantly attenuated nitric oxide production. This study highlights the antiproliferative effects of A. mexicana, particularly BER, against HEP-G2 and L5178Y-R tumor cell lines, along with its selectivity towards normal cells. Furthermore, its anti-hemolytic and antioxidant potentials were demonstrated, suggesting that BER is a promising candidate for potent chemotherapeutic agents. Full article

A number of image dehazing techniques depend on the estimation of atmospheric light intensity. The majority of dehazing algorithms do not incorporate a physical model to estimate atmospheric light, leading to reduced accuracy and significantly impacting the effectiveness of dehazing. This article presents a novel approach for estimating atmospheric light using the polarization state and polarization degree gradient of the sky. We utilize this approach to enhance the outcomes of image dehazing by applying it to pre-existing dehazing algorithms. Our study and development of a real-time dehazing system has shown that the approach we propose has a clear advantage over previous methods for estimating ambient light. After incorporating the proposed approach into existing defogging methods, a significant improvement in the effectiveness of defogging was noted through the assessment of various criteria such as contrast, PSNR, and SSIM. Full article

With an estimated 2.4 million cases of foodborne illnesses recorded annually in the UK alone, food safety has become a paramount concern among stakeholders. Modern technology has positioned streaming platforms as pivotal conduits for disseminating information. Channels such as YouTube offer detailed recordings of the food production process, granting consumers extensive visibility of the food journey from farm to table. This increased transparency not only promotes vigilant monitoring of food safety practices but also solicits consumer feedback regarding the public exposure to food processing videos. Based on the Theory of Planned Behavior (TPB), this study augments its framework with constructs, such as perceived trust, perceived risk, community experience, and brand identity, to evaluate Taiwan’s Generation Z consumer behavioral intentions. With 226 valid responses amassed, structural equation modeling facilitated elucidation of the relationships among the constructs. This analysis yielded three salient insights. First, Generation Z’s engagement with food processing videos on streaming platforms is positively correlated with their subsequent purchasing behavior. Second, enriched community experience was correlated with strengthened brand identification. Third, both perceived trust and perceived risk had a constructive impact on behavioral intentions within Gen Z’s demographic data. Based on these outcomes, food industry enterprises should proactively develop and bolster community experiential value, thereby encouraging streaming platform users to transform into brand consumers and advocates. Full article

(1) Background: In Cambodia, Aedes albopictus is an important vector of the dengue virus. Vector control using insecticides is a major strategy implemented in managing mosquito-borne diseases. Resistance, however, threatens to undermine the use of insecticides. In this study, we present the levels of insecticide resistance of Ae. albopictus in Cambodia and the mechanisms involved. (2) Methods: Two Ae. albopictus populations were collected from the capital, Phnom Penh city, and from rural Pailin province. Adults were tested with diagnostic doses of malathion (0.8%), deltamethrin (0.03%), permethrin (0.25%), and DDT (4%) using WHO tube assays. Synergist assays using piperonyl butoxide (PBO) were implemented before the pyrethroid assays to detect the potential involvement of metabolic resistance mechanisms. Adult female mosquitoes collected from Phnom Penh and Pailin were tested for voltage-gated sodium channel (VGSC) kdr (knockdown resistance) mutations commonly found in Aedes sp.-resistant populations throughout Asia (S989P, V1016G, and F1534C), as well as for other mutations (V410L, L982W, A1007G, I1011M, T1520I, and D1763Y). (3) Results: The two populations showed resistance against all the insecticides tested (<90% mortality). The use of PBO (an inhibitor of P450s) strongly restored the efficacy of deltamethrin and permethrin against the two resistant populations. Sequences of regions of the vgsc gene showed a lack of kdr mutations known to be associated with pyrethroid resistance. However, four novel non-synonymous mutations (L412P/S, C983S, Q1554STOP, and R1718L) and twenty-nine synonymous mutations were detected. It remains to be determined whether these mutations contribute to pyrethroid resistance. (4) Conclusions: Pyrethroid resistance is occurring in two Ae. albopictus populations originating from urban and rural areas of Cambodia. The resistance is likely due to metabolic resistance specifically involving P450s monooxygenases. The levels of resistance against different insecticide classes are a cause for concern in Cambodia. Alternative tools and insecticides for controlling dengue vectors should be used to minimize disease prevalence in the country. Full article

This paper investigates the problem of exponential $H_{\infty }$ output control for switching fuzzy systems, considering both impulse and non-impulse scenarios. Unlike previous research, where the average dwell time (ADT: ${\tau }_a$) and the upper bound of inter-event intervals (IEIs: T) satisfy the condition ${\tau }_a\ge \frac{ln\mu +(\alpha +\beta )T}{\alpha }=\frac{ln\mu +\beta T}{\alpha }+T$, implying that frequent switching is difficult to achieve, this paper demonstrates that by adopting the mode-dependent event-triggered mechanism (ETM) and a switching law, frequent switching is indeed achieved. Moreover, the question of deriving the normal $L_2$ norm constraint is solved through the ADT method, although only a weighted $L_2$ norm constraint was obtained previously. Additionally, by constructing a controller-mode-dependent Lyapunov function and adopting logarithmic quantizers, the sufficient criteria of exponential $H_{\infty }$ output control problem are presented. The validity of established results is demonstrated by a given numerical simulation. Full article

Sunscreen products are essential for shielding the skin from ultraviolet (UV) radiation, a leading cause of skin cancer. While existing products serve this purpose, there is a growing need to enhance their efficacy while minimizing potential systemic absorption of UV filters and associated toxicological risks. Liposomal-based formulations have emerged as a promising approach to address these challenges and develop advanced photoprotective products. These vesicular systems offer versatility in carrying both hydrophilic and lipophilic UV filters, enabling the creation of broad-spectrum sunscreens. Moreover, their composition based on phospholipids, resembling that of the stratum corneum, facilitates adherence to the skin’s surface layers, thereby improving photoprotective efficacy. The research discussed in this review underscores the significant advantages of liposomes in photoprotection, including their ability to limit the systemic absorption of UV filters, enhance formulation stability, and augment photoprotective effects. However, despite these benefits, there remains a notable gap between the potential of liposomal systems and their utilization in sunscreen development. Consequently, this review emphasizes the importance of leveraging liposomes and related vesicular systems as innovative tools for crafting novel and more efficient photoprotective formulations. Full article

Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server. Full article

Multi-cropping is becoming an increasingly popular technique in agriculture to tackle major and complex agroecosystem problems such as biodiversity and soil fertility loss, erosion and degradation, increased greenhouse gas emissions, etc. Comprehensively assessing the impact of multi-cropping intensity on agroecosystems is a new and still under-researched approach that can provide a better understanding of the impact of individual indicators on the overall functioning of biodiverse agroecosystems. Data from a stationary field experiment using multi-cropping at the Vytautas Magnus University Experimental Station between 2020 and 2022 were used to carry out this study. The study included maize, hemp, and faba bean as single, binary, and ternary crops. A complex assessment approach (CEI value) was used to determine the impact of these crops on the agroecosystem, the interrelationships between the main indicators, and the strength of their effects. It was found that the ternary maize–hemp–faba bean crop had the most positive effect on the agroecosystem. The effectiveness of other crops was 2 to 35% less. The lowest value was calculated for the maize–faba bean crop. Full article

This paper proposes a method for conducting quantitative inductive research on survey data when the variable of interest follows an ordinal distribution. A methodology based on novel and traditional penalising models is described. The main aim of this study is to pedagogically present the method utilising the new penalising methods in a new application. A case was employed to outline the methodology. The case aims to select explanatory variables correlated with the target debt level in Swedish listed companies. The survey respondents were matched with accounting information from the companies’ annual reports. However, missing data were present: to fully utilise penalising models, we employed classification and regression tree (CART)-based imputations by multiple imputations chained equations (MICEs) to address this problem. The imputed data were subjected to six penalising models: grouped multinomial lasso, ungrouped multinomial lasso, parallel element linked multinomial-ordinal (ELMO), semi-parallel ELMO, nonparallel ELMO, and cumulative generalised monotone incremental forward stagewise (GMIFS). While the older models yielded several explanatory variables for the hypothesis formation process, the new models (ELMO and GMIFS) identified only one quick asset ratio. Subsequent testing revealed that this variable was the only statistically significant variable that affected the target debt level. Full article

Hematopoietic stem cell transplantation (HSCT) remains a cornerstone in the management of patients with hematological malignancies. Endothelial injury syndromes, such as HSCT-associated thrombotic microangiopathy (HSCT-TMA), veno-occlusive disease/sinusoidal obstruction syndrome (SOS/VOD), and capillary leak syndrome (CLS), constitute complications after HSCT. Moreover, endothelial damage is prevalent after immunotherapy with chimeric antigen receptor-T (CAR-T) and can be manifested with cytokine release syndrome (CRS) or immune effector cell-associated neurotoxicity syndrome (ICANS). Our literature review aims to investigate the genetic susceptibility in endothelial injury syndromes after HSCT and CAR-T cell therapy. Variations in complement pathway- and endothelial function-related genes have been associated with the development of HSCT-TMA. In these genes, CFHR5, CFHR1, CFHR3, CFI, ADAMTS13, CFB, C3, C4, C5, and MASP1 are included. Thus, patients with these variations might have a predisposition to complement activation, which is also exaggerated by other factors (such as acute graft-versus-host disease, infections, and calcineurin inhibitors). Few studies have examined the genetic susceptibility to SOS/VOD syndrome, and the implicated genes include CFH, methylenetetrahydrofolate reductase, and heparinase. Finally, specific mutations have been associated with the onset of CRS (PFKFB4, CX3CR1) and ICANS (PPM1D, DNMT3A, TE2, ASXL1). More research is essential in this field to achieve better outcomes for our patients. Full article

The vertical transition zone of mountain vegetation is characterized by high species diversity, and the width of the transition zone may serve as an indirect indicator of climate change. However, research into the differential characteristics of vegetation response to climate changes at the boundary of vertical transition zones has been limited. This study employs MODIS and climate data spanning 2001 to 2018 to investigate spatiotemporal trends in precipitation (PRE), temperature (TMP), radiation (RAD), and Normalized Difference Vegetation Index (NDVI) across nine montane deciduous broad-leaved forests in the eastern monsoon region of China. It explores the time-lag and -accumulation effects of climatic variables on NDVI, quantifying their relative contributions to both its short-term and interannual variations. Results show that, notably, with the Qinling-Daba Mountains as a demarcation, northern regions exhibit significant increases in RAD (0.874–2.047 W m⁻²/a), whereas southern regions demonstrate notable rises in TMP (0.59–0.73 °C/10a). Areas of lower annual PRE correspond to the most rapid increases in annual average NDVI (5.045 × 10⁻³/a). NDVI’s lag time and cumulative duration responses to TMP are the shortest (0 and 2~4 periods), while its correlation with RAD is the strongest (0.815–0.975), generally decreasing from higher to lower latitudes. TMP significantly affects NDVI variations, impacting both short-term and interannual trends, with PRE driving short-term fluctuations and RAD dictating long-term shifts. This research provides critical data and a theoretical framework that enhances our understanding of how regional vegetation’s vertical zonation responds to climate change, thereby making a substantial contribution to the study of mountain vegetation’s diverse adaptability to climatic variations. Full article

Conventional path-planning strategies for GMA-WAAM may encounter challenges related to geometrical features when printing complex-shaped builds. One alternative to mitigate geometry-related flaws is to use algorithms that optimise trajectory choices—for instance, using heuristics to find the most efficient trajectory. The algorithm can assess several trajectory strategies, such as contour, zigzag, raster, and even space-filling, to search for the best strategy according to the case. However, handling complex geometries by this means poses computational efficiency concerns. This research aimed to explore the potential of machine learning techniques as a solution to increase the computational efficiency of such algorithms. First, reinforcement learning (RL) concepts are introduced and compared with supervised machining learning concepts. The Multi-Armed Bandit (MAB) problem is explained and justified as a choice within the RL techniques. As a case study, a space-filling strategy was chosen to have this machining learning optimisation artifice in its algorithm for GMA-AM printing. Computational and experimental validations were conducted, demonstrating that adding MAB in the algorithm helped to achieve shorter trajectories, using fewer iterations than the original algorithm, potentially reducing printing time. These findings position the RL techniques, particularly MAB, as a promising machining learning solution to address setbacks in the space-filling strategy applied. Full article