Optimal Zone Research Articles

The impact of northern red oak (Quercus rubra; Fagaceae) on the forest phytodiversity in Ukraine

The impact of Quercus rubra on the plant diversity of native forests of Ukraine has been investigated. The plant communities of artificial plantations dominated by northern red oak have such general features as poor species composition, with projective coverage values of other species not exceeding 5%, and a simplified vertical structure of the tree layers. In natural forest plant communities with the participation of Q. rubra, there are no rare and typical nemoral dominant species, which results in disruption of consortial relations in ecosystems. Such changes are due to the biological and ecological peculiarities of this species, in particular, its dense crown, abundant biomass of leaves, which reduces lighting, and allelopathic properties; all those factors affect the physical and chemical characteristics of biotopes. Quercus rubra artificial plantations are more adaptive to changes of natural conditions and are able to withstand harsher winters and lack of precipitation. The ecological amplitude of this species is wide, and even with an increase in the average annual temperature by 3 °C most of the ecological indicators do not go beyond the optimum zone of the species, except for the soil moisture and thermal regime. The amplitudes of main indicators of ecological factors of temperate forests and Q. rubra artificial plantations overlap. The dominance of Q. rubra reduces the quality of ecosystem services of native forests. The high competitive advantages of Q. rubra are the reason for the impossibility of natural restoration of the typical broad-leaved or coniferous forests on those land plots where they were replaced by artificial plantations of northern red oak or its tree layers were formed as the results of the spontaneous distribution of this species. In the successions, the communities with the participation of northern red oak are potentially able to replace the native forest phytocoenoses.

Zoning strategies for human–robot collaborative picking

AbstractDuring the last decade, several retailers have started to combine traditional store deliveries with the fulfillment of online sales to consumers from omni‐channel warehouses, which are increasingly being automated. A popular option is to use autonomous mobile robots (AMRs) in collaboration with human pickers. In this approach, the pickers' unproductive walking time can be reduced even further by zoning the storage system, where the pickers stay within their zone periphery and robots transport order totes between the zones. However, the robotic systems' optimal zoning strategy is unclear: few zones are particularly good for large store orders, while many zones are particularly good for small online orders. We study the effect of no zoning (NZ) and progressive zoning strategies on throughput capacity for balanced zone configurations with both fixed and dynamic order profiles. We first develop queuing network models to estimate pick throughput capacity that correspond to a given number of AMRs and picking with a fixed number of zones. We demonstrate that the throughput capacity is dependent on the chosen zoning strategy. However, the magnitude of the gains achieved is influenced by the size of the orders being processed. We also show that using a dynamic switching strategy has little effect on throughput performance. In contrast, a fixed switching strategy benefiting from changes in the order profile has the potential to increase throughput performance by 17% compared to the NZ strategy, albeit at a higher robot cost.

Multifractal characteristics of neuronal activity of the <i> globus pallidus </i> in patients with dystonia

Dystonia is a movement disorder characterized by involuntary muscle contractions resulting in anomalous postures, often accompanied by dystonic tremors. Among the indicative dystonic postures is an involuntary tonic tilt or rotation of the head. Currently, the etiology and pathogenesis of the disease remain theories. Currently, only low-frequency (theta-alpha) oscillations in the globus pallidus are regarded as the possible biomarker of pathological activity in dystonia. In many patients with dystonia, the absence of rhythmic neuronal activity in the basal ganglia renders this biomarker unusable for surgical treatment of the disease. Additionally, the mechanisms underlying the rearrangement of temporary patterns of neural activity that result in pathological symptoms remain unclear. We propose that the appearance of these pathological rhythms is associated with the reduction of dynamic complexity in the pattern of neural activity within the globus pallidus. We aim to use multifractal analysis in this study to evaluate changes in the globus pallidus neural activity pattern’s dynamic complexity and their correlation with the clinical manifestations of dystonia. The study examined the electrical activity of the globus pallidus in 23 patients receiving deep brain stimulation (DBS) treatment. We used the microelectrode registration technique and local field potential (LFP) registration during intraoperative neuron monitoring to precisely identify the specific locations of the external and internal segments of the globus pallidus (GPe and GPi). In four patients, an additional postoperative recording of local field potentials (LFP) was conducted while performing DBS and vibrational impact on the neck muscles. A neurologist evaluated the patients to determine the severity of the disease using the Burke-Fahn-Mardsen Dystonia Rating Scale (BFMDRS). The LEAD DBS software package was employed for postoperative reconstruction of the DBS stimulating electrode positions and for simulating the optimal stimulation zone, selected by the neurologist (i.e., volume of tissue activated, VTA). Subsequently, we identified the region that presented the most significant variation in multifractal spectrum parameters following DBS to understand its correlation with the optimal VTA. A total of 745 local potential records were analyzed, with 433 in GPe and 312 in GPi. The severity of dystonia was found to be correlated with the parameters of the multifractal spectrum (rho: –0.5630882, p=0.005149 for GPe and rho: –0.5180609, p=0.01133 for GPi). Moreover, as the dystonia severity increased along the BFMDRS scale, the multifractal spectrum became narrower and more asymmetric. In post-surgery recordings, we observed changes in the shape of the multifractal spectrum during DBS stimulation or vibration. In particular, when exposed, the multifractal spectrum significantly widened, and its symmetry was restored. We identified the region with the greatest alteration in the width of the multifractal spectrum following DBS and discovered that this region considerably intersected or fully matched the neurologist’s chosen zone for optimal DBS stimulation. We demonstrated that multifractal analysis can serve as an ancillary approach to evaluate the extent of proprioceptive feedback impairment and as a biomarker of dystonia. The noteworthy and substantial correlation with dystonia severity underscores the considerable potential of multifractal spectrum features as a biomarker of dystonia. Additionally, the overlapping areas with the most significant DBS impact and the widest variation in the multifractal spectrum in DBS can be used as a tool for identifying the most favorable region for DBS stimulation.

Evaluation of Different Work Zone Road-Occupation Schemes for Monorail Construction

Due to the increasing demand for transportation, road renovation is inevitable, and the emergence of work zones has become the most common problem in traffic control. There are many research results on the impact of work zones on traffic operation, but most of them focus on various capacity theoretical models or traffic flow control strategies and are mostly concentrated in highway work areas and urban road subway construction work areas. The location of these work zones is often unable to be changed and is mostly considered reasonable. At present, there is still a gap in selecting and evaluating the location of the work zone. Therefore, this article studied a bidirectional six-lane intersection in Xi’an and investigated the situation of traffic flow distortion caused by the setting of work zones at the intersection. Two different positions and forms of work zones were designed for evaluation and analysis. Firstly, we used VISSIM (2022 student) to simulate the original and proposed work zone road-occupation schemes and analyzed six indicators. Finally, in order to determine the optimal work zone locations under different traffic situations, we applied the entropy weight method (EWM) to assign weights to multiple indicators, thereby achieving a comprehensive assessment of various schemes. The results showed that the highest improvement level among the six indicators was 50.2%, and different schemes adapted to different traffic situations. However, occupying two lanes of the median opposite the exit lane performed better under high traffic volume. Occupying the median and lanes on both sides of the median is suitable for low traffic volume. Occupying two lanes of the entrance lane and using the opposite lane as the left-turn entrance lane is suitable for situations with high traffic volume in the opposite lane.

Automatic Selection of the Optimal Zone for Laser Exposure According to the Fundus Images for Laser Coagulation

Automatic Selection of the Optimal Zone for Laser Exposure According to the Fundus Images for Laser Coagulation

Study on the building zoning of regional energy planning based on a dynamic programming theory

Building zoning is crucial for regional energy planning. Appropriate building zoning can effectively optimize the peak load, daily load rate and load standard deviation of buildings. However, a clear quantitative standard for building zoning is currently lacking, and zoning methods based on minimum distance or load fluctuation are often employed, which often fail to achieve optimal zoning and energy-saving effects. To clarify building zoning in regional energy planning, this study proposes a dynamic programming approach and employs the analytic hierarchy process (AHP) to improve the zoning method. This is achieved by using the daily load rate, load standard deviation and building distance of each building group as the criterion layer, and assigns each peripheral building group to the appropriate building group. Taking a university campus as an example, this method reduces the peak heating load of 6136.61 kW and the peak cooling load of 15507.88 kW. The standard deviation of heating and cooling load of the building group has been reduced by 1272.53 kW and 2352.54 kW, respectively. Furthermore, the daily load rate of the building has been stabilized, and the staggered peak has been adjusted, resulting in a more uniform distribution frequency of the building load.

Postactivation performance enhancement in the vertical jump using loads above or below the optimum-power load for a jump squat.

Postactivation performance enhancement (PAPE) is an acute response of increased muscle performance following a conditioning activity (CA), generally prescribed based on the percentage of a one-repetition maximum. However, it is unknown how the PAPE response is affected when the CA is performed near the optimum power zone. The purpose of this study was to examine PAPE using loads that were 20% above or below the optimum-power load (OPL). Fifteen recreationally trained subjects, with at least one-year resistance training experience participated in this study. First, the OPL for the JS was determined. Then, subjects performed two protocols in a counterbalanced order: 20% above (+20%OPL) or below (-20%OPL). To examine PAPE on performance, the vertical jump was performed prior to and seven times following each jump squat protocol, with a 2-min rest interval between trials. The two-way ANOVA revealed main effects for condition (F=4.978; P<0.001) for jump height and jump power (F=2.589; P=0.017), but post-hoc comparisons did not show differences. Between baseline and the best trial following each jump squat protocol, two-way ANOVA did not reveal main effects (F=3.048; P=0.103) or interactions (F=0.304; P=0.590). Paired t-tests did not show significant differences between conditions for relative changes in jump height (P=0.543) or jump power (P=0.233). This study revealed similar results between 20% above or below the OPL on subsequent vertical jump performance.

Emotional Processes and Performance

This study investigates the interplay between stress perception and its intensity concerning performance in a professional setting. Contrary to our initial hypothesis of an interaction between stress perception and intensity, our results reveal that individuals perceiving stress as a challenge exhibit higher performance levels, while those viewing stress as a threat experience significantly lower performance, irrespective of stress intensity. This underscores the importance of how individuals interpret stress in the workplace. Our findings have crucial implications for management, employees, and human resources, suggesting that fostering a positive perception of stress as a challenge could enhance overall performance.&#x0D; Drawing on Lazarus and Folkman's transactional model, stress is viewed as a dynamic relationship between individuals and environmental demands. Stress emerges from the subjective appraisal of events rather than the events themselves. This study distinguishes between threat and challenge appraisals, positing that threat perceptions decrease performance, while challenge perceptions enhance it. Additionally, Hanin's stress intensity theory suggests an optimal performance zone unique to each individual.&#x0D; Eighty-five participants (62 men, 25 women) completed a questionnaire assessing stress perception, intensity, and professional performance. Stress perception was manipulated using the Appraisal of Life Events Scale, measuring threat, challenge, and loss. Intensity was assessed with a single item. Professional performance was measured using the Attainment of Sport Achievement Goals Scale.Contrary to expectations, there was no interaction between stress perception and intensity. Threat perception correlated with decreased performance, while challenge perception correlated with increased performance. No significant effects were observed for loss perception. Satisfaction with performance did not align with increased performance levels.&#x0D; Our hypothesis of an interaction effect was not supported, highlighting the impact of stress perception on performance. Positive stress appraisal correlated with enhanced performance, aligning with Lazarus and Folkman's appraisal approach. However, high-performance levels did not necessarily translate into satisfaction, potentially influenced by self-critical tendencies associated with positive perfectionism. Limitations include subjective performance measurement and reliance on self-reporting, suggesting the need for future research combining subjective and physiological measures.This study emphasizes the influence of stress perception on workplace performance, urging managers to consider employees' interpretations of situations. Fostering a challenge perception and providing clear goals may enhance performance and well-being, acknowledging the unique responses of individuals to stress.

Enhancing chemistry education through technology-enhanced learning: Impact on student outcomes

This scientific poster presentation highlights the student-led feedback and modifications made to improve chemistry subjects, along with the subsequent impact on student outcomes. Our team has embraced technology-enhanced learning as an effective approach to enhance the learning experiences of a diverse student cohort. The literature emphasizes that blending face-to-face, online, and self-paced learning tools lead to increased student engagement and improved learning outcomes (Serrano et al., 2019). Our chemistry subjects have undergone significant evolution to incorporate technology-enhanced elements, such as custom-made resources like short lightboard videos and virtual laboratories, which are integrated with our active classroom environment. This integration of theory and practice fosters meaningful, dynamic, and student-centered learning, drawing upon Lev Vygotsky's Zone of Proximal Development (ZPD) to provide differentiated instruction through scaffolding. Our diverse student cohort consists of individuals with varying chemistry backgrounds and enrolled in different degree programs, yet they all undertake the same chemistry subjects. To cater to the varying entry-level knowledge, scaffolding resources have been provided to guide students with lower proficiency through complex concepts, while additional resources engage and challenge more experienced students. This approach places active classes within the optimal zone, where the average student is challenged beyond their comfort zone but not to the extent of disengagement. This instructional approach is complemented by a sociocultural structure that encourages critical engagement among students working in peer groups as well as with the educator. In the absence of our team, virtual educators, via our lightboard videos, support this sociocultural, student-centered approach between classes. The presentation will showcase innovative initiatives that embody the evolution of technology-enhanced learning, with a particular focus on the impact of lightboard videos and virtual laboratory experiments on students' overall learning experiences and outcomes. The influence of these resources is evident locally, nationally, and internationally, as demonstrated by usage statistics and invitations to showcase our work. These resources have garnered strong support from peer reviewers, academics, high school teachers, and key stakeholders. Through this abstract, we aim to provide a concise overview of our innovative approach to chemistry education, emphasizing the positive impact of technology-enhanced learning on student outcomes. Our findings contribute to the growing body of knowledge on effective instructional strategies in science education, and we believe they have the potential to inform and inspire educators in their pursuit of enhancing student learning experiences.

Evaluation of pasture degradation through vegetation indices of the main livestock micro-watersheds in the Amazon region (NW Peru)

Pastures provide different ecosystem services, such as carbon dioxide fixation, are indicators of climate change and prevent soil erosion. However, anthropogenic activities degrade and decrease soil diversity. Therefore, the objective was to evaluate pasture degradation using vegetation indices. For this purpose, the floristic composition was evaluated by means of linear transects (50 m) to determine the percentage of area covered by forages and weeds; the Normalized Vegetation Index (NDVI) and the Pasture Vegetation Cover (PVC) assessment methodology were used to determine pasture degradation, and the chemical properties of the soils were characterized. The degradation map showed that the range "S4-Strongly degraded (CVP, <40%) for the Pomacochas micro-watershed is 2.60% (93.43 ha); however, in Ventilla, 0.40% (8.95 ha) was obtained. An equation was generated from a multiple linear regression model and a principal component analysis (PCA) of the main regressor variables. The results were obtained for both micro-watersheds with an R2 greater than 60% determined by CVP + Bands 3, 4, 5, 6, 8 and pH. In addition, the PCA showed that at acidic pH, the percentage of degradation increased, reaching the level of advanced degradation in both micro-watersheds under study, and when the pH values were close to neutrality, they indicated optimal zones for pasture production. This study provides a new source of information regarding the degradation of high Andean grasslands. It is also the basis for further research in the agricultural sector.

Spatial preferences of small and medium knowledge based enterprises in Tehran new business area

In recent decades, there has been a growing emphasis on the importance of small and medium-sized enterprises (SMEs) as a significant driver of economic growth and enhancer of competitiveness. They constitute an estimated 90% of the total population of businesses, and contribute significantly, up to 40% of Gross Domestic Product (GDP), in emerging markets. Furthermore, these enterprises yield a fundamental function in cultivating job prospects, offering employment to approximately half of the worldwide labor force. However, SMEs often face unique challenges related to their size and resources, including accessing finance, developing new products and services, and identifying optimal locations for their businesses. This study seeks to address the latter issue by analyzing the spatial preferences of small and medium knowledge-based enterprises in Tehran's new business area, utilizing the spatial analyst tools by GIS. The research methodology employed in this study is non-experimental in character. It involves the distribution of a social survey to proprietors of SMEs in order to ascertain the key criteria they prioritize when selecting a location for their business. For the purpose of achieving the objective, a conceptual-operational model was developed, comprising four index categories. These categories are delineated as the personal motivations of the entrepreneur, the characteristics of the internal business environment, the spatial environment surrounding the business, and the institutional factors shaping the space. These components constitute critical determinants of the proposed model. The chosen variables were subjected to spatial analysis, and a business environment assistance map was developed in four zones. Following thorough analysis, the northwestern part of the area under consideration has been identified as the optimal zone, and subsequent recommendations for appropriate strategies have been proposed. The study culminated by offering suggestions for improvement, consisting of the implementation of a contextually-oriented model pertaining to new Iranian business improvement districts (BIDs) as a means of augmenting the status of SMEs.

Hot Deformation Behavior and Processing Maps of 26CrMo7S Steel Used in Oil Exploration.

The hot deformation behavior and flow stress characteristics of experimental 26CrMo7S steel were analyzed using a thermal simulator under a range of conditions, including a strain rate range of 0.01~10 s-1, a temperature range of 850~1250 °C, and a maximum deformation amount of 70%. The Arrhenius constitutive model was built for the corresponding conditions, and the model's accuracy was verified through error analysis. Additionally, hot processing maps were constructed to analyze the processing zone of the steel under different hot deformation conditions. Finally, the microstructure of the processing zones was observed and verified using the electron backscattered diffraction (EBSD). The results indicate that the interaction of work hardening and dynamic softening influences the hot deformation behavior of 26CrMo7S steel. The Arrhenius constitutive equation with a value of the correlation coefficient (r = 0.99523) accurately predicts the flow behavior of 26CrMo7S steel under different strains. The optimal processing zone obtained with the hot processing maps is within a deformation range of 1010~1190 °C and a strain rate range of 0.01~10-1.5 s-1, and the obtained microstructure is in good agreement with the analysis results.

Towards resilience effectiveness: Assessing its patterns and determinants to identify optimal geographic zones

Resilience has a critical role to promote human well-being in turbulent environments. However, extensive interventions and stimulus measures not only achieve resilience goals with numerous unacceptable consequences, but also highlight the narrow definition of resilience. The study proposes the concept of resilience effectiveness to constrain the unsustainability of resilience in concept and practice. The resilience effectiveness of Chinese cities in the early stages of the pandemic is first implemented for evaluation based on input-output relationship, and then patterns and determinants are investigated using the GIS methods and the geographically optimal zones-based heterogeneity (GOZH) model. The study finds that the average resilience effectiveness is not high, indicating that China's strong resilience is the loss of safety, greenness, inclusiveness, and efficiency in the early stage of the pandemic. Resilience effectiveness has significant spatial heterogeneity and is dominated by innovation ability. The optimal geographic zones of Chinese cities for resilience effectiveness are identified, revealing that it is a complex and multifactorial process. This paper broadens resilience research and provides implications for city planning in the post-pandemic era.

Pendekatan Spasial dalam Perencanaan Lanskap Kampung Wisata Jamburea di Kabupaten Garut

Spatial Approach in Landscape Planning for Jamburea Tourism Village in Garut Regency. Jamburea, located in the Banyuresmi District of Garut, is a potential tourist haven with its unique blend of natural beauty and rich cultural traditions. Dominated by wetland landscapes influenced by the seasonal Situ Rancakukuk lake, residents adapt by converting its periphery into rice fields during the dry season and fish ponds during the rainy season. Furthermore, the kampong region is a repository of intangible cultural assets, such as culinary speciality, Pencak silat, traditional children’s games, 'Asgar' barber shops, and bamboo handicrafts. Employing ArcGIS tools, this study used a spatial approach to analyze physical-biophysical, non-physical, and tourism aspects for optimal zoning. This paper has two objectives: firstly, to plan the landscape of the Jamburea tourism village using a spatial approach in the analytical process. The second objective is to examine the application of spatial methods in landscape planning. Results suggest 72.09% of the kampong is suitable for tourism development, 13.68% is moderately suitable, and 14.23% requires conservation. Proposed zones include natural and cultural tourism, tourism support, welcome areas and buffer zones. This study underscores the efficacy of the spatial approach in devising comprehensive plans for Jamburea's tourism potential.

A Literature Review on Anxiety of the Athletes' Performance

In this era of modernization, performance is one of the stress factors that often trigger affective reactions of anxiety, as well as psychophysiological changes in the human autonomic nervous system. In the context of sports performance assessment, athletes experience high levels of anxiety which often have a negative influence on their performance as anxiety tend to be a typical phenomenon experienced by athletes across various sports disciplines. Anxiety can be understood as a negative affective variable associated with a low level of performance (Gómez‐Íñiguez, Rodríguez, &amp; Cantero, 2021). Present study applied narrative review on the previous studies as the research method to examine the factors and effects of the anxiety on the athletes’ performance. By synthesizing the findings from multiple previous studies, this study also aims to provide a deeper understanding of anxiety in athletes' performance and offer insights for future researchers and all other parties who involved in sports. Present study concluded that internal factors are the crucial elements that contribute to the anxiety on the athletes’ performance, and anxiety level tend to be able to increase or decrease the athletes’ performance. In order to ensure the positive effects of anxiety, athletes have to aware and stay in their optimal intensity zone for them to maximize their performance. More research, interventions and application of technology in anxiety awareness and management are recommended for the future researchers.

Improvised energy management control through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in non-plugin parallel hybrid electric vehicle

This work proposed to integrate intelligent techniques in an adaptive equivalent consumption minimization strategy (AECMS) for a robust equivalence factor correction based on the demand torque and battery state of charge (SOC). The two intelligent approaches of fuzzy logic and genetic algorithmically optimized adaptive neuro-fuzzy inference system (ANFIS) have been employed. The entire performance is validated using the prepared three hybrid standard driving cycles and also for training the fuzzy inference system (FIS). The proposed ANFIS-ECMS delivers a narrow battery charging and discharging profile within the optimal battery utilization zone. Also, it delivers the higher fuel economy for the three driving cycles in the D1 4.20%, D2 1.175%, and D3 0.90% improvisation is occurred compared to fuzzy-PI AECMS. Even in the reliability assessment of ANFIS-AECMS, the performance has been tested on the self-developed real-world driving cycle. The fuel economy increases by 1.26% and 0.80% are compared to rule-based and fuzzy-PI AECMS. The entire results of this work evidence that the proposed strategy achieves significant improvement in battery and fuel energy utilization and also the reduction in emissions compared with rule-based, conventional fixed PI ECMS and fuzzy-PI based ECMS.

An approach for optimization of controllable drilling parameters for motorized bottom hole assembly in a specific formation

This study focuses on optimizing drilling parameters when using Positive Displacement Motors (PDMs). In drilling operations involving mud motors, weight-on-bit (WOB) alterations lead to variations in the system's parasitic pressure drop. Consequently, this affects the optimum flow rate and the hydraulic power of the bit. Also, if the flow rate changes, the bit's rotations per minute (RPM) also change. In other words, using PDMs creates a link between the hydraulic system and the drilling speed, such that changing drilling parameters such as the WOB causes changes in the hydraulic system's performance. Therefore, one possible way to optimize the drilling parameters is to consider the drilling rate and hydraulic system simultaneously using a multi-objective approach. This study used an integrated approach encompassing data mining and mathematical modeling, employing a multi-objective framework to identify optimal parameters. The approach was applied to Dariyan Formation drilling data. The data mining approach revealed a well-distributed data set covering optimal and suboptimal zones suitable for optimization. In data mining, the identification of optimal conditions included a WOB of 11500 lb, a rotation speed of 105.8 rev/min, and a flow rate of 843 gpm, leading to an ROP of 44.23 ft/h. In multi-objective optimization, the optimal parameters consisted of a WOB of 14480 lb, a rotation speed of 115 rev/min, and a flow rate of 920.8 gpm, resulting in an ROP of 40.49 ft/h. Comparing optimal results with the drilling data shows a substantial MSE reduction of over 35 %. The results show the good performance of this approach in detecting the optimal and non-optimal drilling variables.

Contrasting impacts of climate warming on Himalayan Hemlock growth: Seasonal and elevational variations

Ongoing climate change can have varying impacts on tree growth within the growing season and across their elevation ranges, with important implications for forest ecosystem functions and services. However, our knowledge of these effects on climate-sensitive Himalayan forests is still limited. Here, we explore the elevational changes in climatic factors driving long-term changes in the radial growth of Himalayan Hemlock (Tsuga dumosa), including recent responses to unprecedented climate warming in the central Himalayas. We evaluated several growth parameters, including total ring width, earlywood width, adjusted latewood width, and maximum latewood density, in unique > 400-year-old forests along an elevational gradient of 2500–3100 m on the southern slopes of Dhaulagiri, Nepal. Our findings show that changing climatic conditions, characterized by increasing temperatures and variable precipitation patterns, had a more detrimental effect on Tsuga growth at the edge of its elevation range compared to the optimal mid-elevation zone. Specifically, at lower elevations, the combination of spring and preceding autumn warming restricted earlywood growth, while warmer temperatures in late summer stimulated growth at the mid-elevation site by alleviating the cool growth-limiting conditions caused by high monsoonal precipitation. Furthermore, increased spring temperatures enhance latewood density, while summer warming promotes latewood growth at higher elevations. Additionally, we observed that the recent rise in autumn temperatures has begun to impede Tsuga growth across all elevations. In conclusion, our study reveals that the growth of Tsuga trees is influenced by multiple climatic factors that vary within the growing season and across different parts of its elevation range. Recent spring warming has constrained growth in lower elevations, while higher previous autumn temperatures have reduced growth at both higher and lower boundaries of the species' range. These findings contribute to a better understanding of the complex relationship between climate change and tree growth dynamics, particularly in vulnerable Himalayan forests.

A design methodology of composite scarf repairs using artificial intelligence

Composite Scarf Bonded (CSB) based techniques are highly effective in structural connections and structural repairs. In this article, a preliminary design methodology based on Machine Learning (ML) algorithms trained on databases obtained via a semi-analytical approach is proposed and used to generate the design space for CSB structures under tensile loads. This ML framework introduces the one-hot encoding technology to deal with discrete inputs, such as multiple stacking sequences. Four ML algorithms, Adaptive Boosting, Gradient Boosting Regression, Extreme Gradient Boosting, and Artificial Neural Networks are studied. The best-performing model is then used to generate the damage tolerance-based design space for CSB structures made from fabric and unidirectional prepregs, accounting for material and geometrical uncertainties. Very good representations of the design space and accuracy in structural strength and failure mode are obtained. An optimal scarf angle zone, where laminate and adhesive fail simultaneously, was identified using the proposed framework. This design framework opens new avenues for the selection of material and layup configuration in structural design and enables the fast estimation of the optimal scarf angle range for the preliminary design of CSB structures.

Application of artificial intelligence in ophthalmology for solving the problem of semantic segmentation of fundus images

The paper presents main aspects of the application of artificial intelligence in ophthalmology for the diagnosis and treatment of eye diseases, considering the problem of semantic segmentation of fundus images as an example. The classic approach to semantic segmentation on the basis of textural features is compared to the proposed approach based on neural networks. Basic problems of using the neural network approach in biomedicine are formulated. We propose a new method for selecting an optimal zone of laser exposure for laser coagulation based on two neural networks. The first network is used for detecting anatomical objects in the fundus and the second one is used for selecting the area of macular edema. The region of interest is formed from the edema area while taking into account the location of anatomical objects in it. A comparative analysis of sev-eral architectures of neural networks for solving the problem of selecting the edema area is carried out. The best results in the selection of the edema area are shown by the neural network architecture of Unet++.