Variety Of Environments Research Articles

LoCoHD: a metric for comparing local environments of proteins

Protein folds and the local environments they create can be compared using a variety of differently designed measures, such as the root mean squared deviation, the global distance test, the template modeling score or the local distance difference test. Although these measures have proven to be useful for a variety of tasks, each fails to fully incorporate the valuable chemical information inherent to atoms and residues, and considers these only partially and indirectly. Here, we develop the highly flexible local composition Hellinger distance (LoCoHD) metric, which is based on the chemical composition of local residue environments. Using LoCoHD, we analyze the chemical heterogeneity of amino acid environments and identify valines having the most conserved-, and arginines having the most variable chemical environments. We use LoCoHD to investigate structural ensembles, to evaluate critical assessment of structure prediction (CASP) competitors, to compare the results with the local distance difference test (lDDT) scoring system, and to evaluate a molecular dynamics simulation. We show that LoCoHD measurements provide unique information about protein structures that is distinct from, for example, those derived using the alignment-based RMSD metric, or the similarly distance matrix-based but alignment-free lDDT metric.

Long-term declines in chlorophyll a and variable phenology revealed by a 60-year estuarine plankton time series

Long-term ecological time series provide a unique perspective on the emergent properties of ecosystems. In aquatic systems, phytoplankton form the base of the food web and their biomass, measured as the concentration of the photosynthetic pigment chlorophyll a (chl a), is an indicator of ecosystem quality. We analyzed temporal trends in chl a from the Long-Term Plankton Time Series in Narragansett Bay, Rhode Island, USA, a temperate estuary experiencing long-term warming and changing anthropogenic nutrient inputs. Dynamic linear models were used to impute and model environmental variables (1959 to 2019) and chl a concentrations (1968 to 2019). A long-term chl a decrease was observed with an average decline in the cumulative annual chl a concentration of 49% and a marked decline of 57% in winter-spring bloom magnitude. The long-term decline in chl a concentration was directly and indirectly associated with multiple environmental factors that are impacted by climate change (e.g., warming temperatures, water column stratification, reduced nutrient concentrations) indicating the importance of accounting for regional climate change effects in ecosystem-based management. Analysis of seasonal phenology revealed that the winter-spring bloom occurred earlier, at a rate of 4.9 ± 2.8 d decade-1. Finally, the high degree of temporal variation in phytoplankton biomass observed in Narragansett Bay appears common among estuaries, coasts, and open oceans. The commonality among these marine ecosystems highlights the need to maintain a robust set of phytoplankton time series in the coming decades to improve signal-to-noise ratios and identify trends in these highly variable environments.

Smoothing in linear multicompartment biological processes subject to stochastic input

Many physical and biological systems rely on the progression of material through multiple independent stages. In viral replication, for example, virions enter a cell to undergo a complex process comprising several disparate stages before the eventual accumulation and release of replicated virions. While such systems may have some control over the internal dynamics that make up this progression, a challenge for many is to regulate behavior under what are often highly variable external environments acting as system inputs. In this work, we study a simple analog of this problem through a linear multicompartment model subject to a stochastic input in the form of a mean-reverting Ornstein-Uhlenbeck process, a type of Gaussian process. By expressing the system as a multidimensional Gaussian process, we derive several closed-form analytical results relating to the covariances and autocorrelations of the system, quantifying the smoothing effect discrete compartments afford multicompartment systems. Semianalytical results demonstrate that feedback and feedforward loops can enhance system robustness, and simulation results probe the intractable problem of the first passage time distribution, which has specific relevance to eventual cell lysis in the viral replication cycle. Finally, we demonstrate that the smoothing seen in the process is a consequence of the discreteness of the system, and does not manifest in systems with continuous transport. While we make progress through analysis of a simple linear problem, many of our insights are applicable more generally, and our work enables future analysis into multicompartment processes subject to stochastic inputs. Published by the American Physical Society 2024

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.

Dynamic Analysis of an FGM Beam Undergoing Large Overall Motion in Temperature Field

The dynamic analysis of a functionally gradient material (FGM) beam undergoing large overall motion based on meshless methods is studied in the variable temperature field environment. Considering the high-order terms of nonlinear coupling deformation which were often ignored in previous studies, the high-order rigid–flexible coupled (HOC) dynamic model of a rotating FGM beam with a lumped mass in a temperature field is established by employing Lagrange’s equations of the second kind. Compared with the first-order approximate coupled (FOAC) dynamic model, the HOC dynamic model is more accurate and has a wider scope of application. The validity of the point interpolation (PIM) method and radial point interpolation method (RPIM) in this paper is verified by comparison with simulation results of the assumed mode method (AMM) and the finite element method (FEM). On this basis, the factors affecting the dynamic characteristics of the FGM beam such as the form of temperature field, the functionally gradient index, and the location of added lumped mass are discussed. In this paper, the meshless method with higher computational efficiency and the HOC dynamic model with more accurate results are adopted. This study will provide guidance and reference for dealing with problems in engineering, machinery, aerospace, and other fields in complex environment.

Identifying high-value tactical livestock decisions on a mixed enterprise farm in a variable environment

Context Australia is renowned for its climate variation, featuring years with drought and years with floods, which result in significant production and profit variability. Accordingly, to maximise profitability, dryland farming systems need to be dynamically managed in response to unfolding weather conditions. Aims The aim of this study is to identify and quantify optimal tactical livestock management for different weather-years. Methods This study employed a whole-farm optimisation model to analyse a representative mixed enterprise farm located in the Great Southern region of Western Australia. Using this model, we investigated the economic significance of five key livestock management tactics. These included timing of sheep sales, pasture-area adjustments, rotational grazing, crop grazing and sheep nutrition adjustments. Key results The results showed that, on the modelled dryland mixed-enterprise farm in the Great Southern region of Western Australia, short-term adjustments to the overall farm strategy in response to unfolding weather conditions increased expected profit by approximately 16%. Each tactic boosted profit by between A$7704 and A$53,171. However, we outline several complexities that farmers must consider when implementing tactics. Conclusions The financial gains from short-term tactical management highlighted their importance and farmers’ need to develop and apply those skills. The tactical skills promote business resilience and adaptability in the face of climate uncertainties. Implications The study highlighted the economic value of dynamic livestock management in response to climate variations, offering farmers in the Great Southern region the means to underpin profitable and sustainable farm practices.

Design, simulation and analog/RF performance evaluation of a hetero-stacked source dual metal T-shaped gate tunnel-FET in thermally variable environments

Design, simulation and analog/RF performance evaluation of a hetero-stacked source dual metal T-shaped gate tunnel-FET in thermally variable environments

Enhanced mechanical endurance and power conversion efficiency in flexible Cu2ZnSnS4 solar cells using ZnO/Ag-Ag2O/ZnO film

Cu2ZnSnS4 (CZTS) solar cells occupy an important direction in the field of clean energy research due to their environmental friendliness, low cost, and industrial technology compatibility. The complex and variable usage environment also accelerates the development of flexible CZTS solar cells. However, the inferior flexibility of ITO films limited their development. ZnO/Ag/ZnO (ZAZ) films are potential choices for transparent conductive layers of CZTS solar cells due to their high transmittance, conductivity, and flexibility. To further enhance the photovoltaic performance and mechanical durability of ZAZ films and solar cells, Ag2O was introduced into ZAZ film by magnetron sputtering. An ultra-high average transmittance of 96.6% in 400-800nm was achieved for ZnO/Ag-Ag2O/ZnO (ZAAZ) films at Ag2O sputtering power of 40W, which was attributed to the fact that Ag2O effectively inhibited the growth of 3D islands of Ag film, and formed a more uniform Ag film. ZAAZ films offered a superior figure of merit value of 0.1041 Ω-1. The power conversion efficiency (PCE) of ZAAZ solar cells was 6.94%, higher than that of ZAZ solar cells (6.69%). Remarkably, the PCE of ZAAZ solar cells was 7.9% higher than that of ZAZ solar cells after 1,000 bending cycles. Therefore, Therefore, this approach may provide a promising way to improve the photovoltaic performance and mechanical durability of flexible solar cells.

Analysis of High-Order Bright–Dark Rogue Waves in (2+1)-D Variable-Coefficient Zakharov Equation via Self-Similar and Darboux Transformations

This paper conducts an in-depth study on the self-similar transformation, Darboux transformation, and the excitation and propagation characteristics of high-order bright–dark rogue wave solutions in the (2+1)-dimensional variable-coefficient Zakharov equation. The Zakharov equation is instrumental for studying complex nonlinear interactions in these areas, with specific implications for energy transfer processes in plasma and nonlinear wave propagation systems. By analyzing bright–dark rogue wave solutions—phenomena that are critical in understanding high-energy events in optical and fluid environments—this research elucidates the intricate dynamics of energy concentration and dissipation. Using the self-similar transformation method, we map the (2+1)-dimensional equation to a more tractable (1+1)-dimensional nonlinear Schrödinger equation form. Through the Lax pair and Darboux transformation, we successfully construct high-order solutions that reveal how variable coefficients influence rogue wave features, such as shape, amplitude, and dynamics. Numerical simulations demonstrate the evolution of these rogue waves, offering novel perspectives for predicting and mitigating extreme wave events in engineering applications.This paper crucially advances the practical understanding and manipulation of nonlinear wave phenomena in variable environments, providing significant insights for applications in optical fibers, atmospheric physics, and marine engineering.

Pine-YOLO: A Method for Detecting Pine Wilt Disease in Unmanned Aerial Vehicle Remote Sensing Images

Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: MAP@0.5 at 90.69%, mAP@0.5:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment.

Event-Driven Day-Ahead and Intra-Day Optimal Dispatch Strategy for Sustainable Operation of Power Systems Considering Major Weather Events

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method’s effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.

Boom-bust population dynamics drive rapid genetic change

Increasing environmental threats and more extreme environmental perturbations place species at risk of population declines, with associated loss of genetic diversity and evolutionary potential. While theory shows that rapid population declines can cause loss of genetic diversity, populations in some environments, like Australia's arid zone, are repeatedly subject to major population fluctuations yet persist and appear able to maintain genetic diversity. Here, we use repeated population sampling over 13 y and genotype-by-sequencing of 1903 individuals to investigate the genetic consequences of repeated population fluctuations in two small mammals in the Australian arid zone. The sandy inland mouse (Pseudomys hermannsburgensis) experiences marked boom-bust population dynamics in response to the highly variable desert environment. We show that heterozygosity levels declined, and population differentiation (FST) increased, during bust periods when populations became small and isolated, but that heterozygosity was rapidly restored during episodic population booms. In contrast, the lesser hairy-footed dunnart (Sminthopsis youngsoni), a desert marsupial that maintains relatively stable population sizes, showed no linear declines in heterozygosity. These results reveal two contrasting ways in which genetic diversity is maintained in highly variable environments. In one species, diversity is conserved through the maintenance of stable population sizes across time. In the other species, diversity is conserved through rapid genetic mixing during population booms that restores heterozygosity lost during population busts.

Evaluation of variation in seedling root architectural traits and their potential association with nitrogen fixation and agronomic traits in field pea accessions

AbstractRoot system architecture (RSA) plays a central role in water and nutrient acquisition in plants. Plasticity and genetic variation in RSA can be used as an adaptive strategy to optimize plant performance under variable environments. We quantified phenotypic variation for seedling RSA among 44 diverse pea (Pisum sativum L.) genotypes, including breeding lines and germplasm accessions, grown under controlled conditions for 14 days using two‐dimensional hydroponic root imaging. Root image analysis revealed significant genotypic variability among the lines for all root traits, namely root length (RL), root diameter (RD), root volume, root surface area, number of tips, network width (NW), network depth (ND), and network convex area. Significant positive correlations were observed among the evaluated root traits, ranging from 0.5 to 0.9. Pea lines were ranked based on estimated means for root traits, with lines E20, F1, and F8 showing high rankings, while E4 and F5 received low rankings for most traits. To associate root traits with nitrogen (N) fixation and field agronomic performance, we performed redundancy analysis (RDA). The quantified root traits accounted for significant variation in the agronomic traits (R2 = ∼30%, p < 0.001). RDA showed a positive association between lodging susceptibility and root system NW and between plant height and root system ND. RD was positively associated with grain yield and N fixation. N fixation was positively associated with the number of lateral roots. The findings of this study indicate that variation for seedling root traits in pea could aid selection for N fixation and other important agronomic traits.

Obstacle Detection Technology for Autonomous Driving Based on Deep Learning

With the rapid growth of artificial intelligence (AI) technology, traditional obstacle detection equipment faces multiple challenges such as high cost, low real-time performance, non normalization, dependence on manual operation, and time-consuming and labor-intensive. To address these shortcomings, this article proposes a deep learning (DL) based obstacle detection technology for autonomous driving on the road surface. As a complex system that integrates multiple key components such as environmental perception, positioning and navigation, path planning, and motion control, one of the core technologies of autonomous vehicles is accurate perception of the surrounding environment. In practical applications, autonomous vehicles often face complex and variable road environments, which may lead to a decrease in the quality of images captured by cameras, resulting in blurry and unclear phenomena. The DL method, especially the object detection algorithm, has shown unique advantages in visual perception and recognition in autonomous driving scenes. This paper deeply studies the obstacle detection technology of automatic driving road based on DL, aiming to achieve efficient and accurate obstacle recognition, improve the safety and reliability of auto drive system, and promote the further growth of automatic driving technology.

Output-only identification of time-varying structural modal parameters under thermal environment

Obtaining the input load presents a significant challenge for system identification problems with time-varying characteristics in aerospace engineering. Therefore, the modal parameter is determined through output-only identification techniques. Considering rapid time-varying characteristics in thermal environment, a Recursive Subspace Method (RSM) is used to enhance the precision of identifying modal parameters. A three-degree-of-freedom system with time-varying stiffness is first investigated to validate the identification method. The effect of noise on the robustness of the algorithm is discussed. Then, the experimental investigation is undertaken considering a composite beam under a time-varying thermal environment though the thermal-vibration experimental system. Experimental results indicate that the change of structural modal frequencies under variable temperature environment is mainly affected by the thermal stress induced in the structure. Comparing the experimental and simulation result, the output-only identification approach has good efficiency and convergence for time-varying system.

Baseline glucocorticoids alone do not predict reproductive success across years, but in interaction with enzymatic antioxidants

AbstractGlucocorticoids are known to adjust organismal functions, such as metabolism, in response to environmental conditions. Therefore, these hormones are thought to play a key role in regulating the metabolically demanding aspects of reproduction, especially in variable environments. However, support for the hypothesis that variation in glucocorticoid concentrations predicts reproductive success is decidedly mixed. Two explanations may account for this discrepancy: (i) Glucocorticoids might not act independently but could interact with other physiological traits, jointly influencing reproduction, and (ii) such an association could become apparent primarily in challenging environments when glucocorticoid concentrations increase. To address these two possibilities, we determined natural variation in circulating baseline glucocorticoid concentrations in parental great tits (Parus major) alongside two physiological systems known to be related with an individual's metabolism: oxidative status parameters (i.e., concentrations of pro‐oxidants, dietary, and enzymatic antioxidants) and body condition. These systems interact with glucocorticoids and can also influence reproductive success. We measured these variables in two breeding seasons that differed in environmental conditions. When accounting for the interaction of baseline glucocorticoids with other physiological traits, we found a positive relationship between baseline glucocorticoids and the number of fledglings in adult great tits. The strength of this relationship was more pronounced for those individuals who also had high concentrations of the enzymatic antioxidant glutathione peroxidase. When studied independently, glucocorticoids were not related to fitness proxies, even in the year with more challenging environmental conditions. Together, our study lend to support the hypothesis that glucocorticoids do not influence fitness alone, but in association with other physiological systems.

Mass Participation and Tournament Event Management for the Team Physician: A Consensus Statement (2022 Update).

Mass participation events include endurance events (e.g., marathon, triathlon) and/or competitive tournaments (e.g., baseball, tennis, football (soccer) tournaments). Event management requires medical administrative and participant care planning. Medical management provides safety advice and care at the event that accounts for large numbers of participants, anticipated injury and illness, variable environment, repeated games or matches, and mixed age groups of varying athletic ability. This document does not pertain to the care of the spectator.

Sex ratio distorting microbes exacerbate arthropod extinction risk in variable environments

AbstractMaternally‐inherited sex ratio distorting microbes (SRDMs) are common among arthropod species. Typically, these microbes cause female‐biased sex ratios in host broods, either by; killing male offspring, feminising male offspring, or inducing parthenogenesis. As a result, infected populations can experience drastic ecological and evolutionary change. The mechanism by which SRDMs operate is likely to alter their impact on host evolutionary ecology; despite this, the current literature is heavily biased towards a single mechanism of sex ratio distortion, male‐killing. Furthermore, amidst the growing concerns surrounding the loss of arthropod diversity, research into the impact of SRDMs on the viability of arthropod populations is generally lacking. In this study, using a theoretical approach, we model the epidemiology of an understudied mechanism of microbially‐induced sex ratio distortion—feminisation—to ask an understudied question—how do SRDMs impact extinction risk in a changing environment? We constructed an individual‐based model and measured host population extinction risk under various environmental and epidemiological scenarios. We also used our model to identify the precise mechanism modulating extinction. We find that the presence of feminisers increases host population extinction risk, an effect that is exacerbated in highly variable environments. We also identified transmission rate as the dominant epidemiological trait responsible for driving extinction. Finally, our model shows that sex ratio skew is the mechanism driving extinction. We highlight feminisers and, more broadly, SRDMs as important determinants of the resilience of arthropod populations to environmental change.

Pengaruh Desain Stasiun Kerja Dengan Pendekatan Participatory Ergonomics untuk Meningkatkan Produktivitas Karyawan: Studi Kasus SMK Az-Zawiyah Tanjung Batu

In the context of education, the success of an institution is not only determined by the quality of curriculum and teaching, but also by adequate working environment conditions. Therefore, this study aims to investigate the effect of workstation design with a participatory ergonomics approach on employee productivity in the teachers' room and principal's room. The result of this study is that there is an influence of workstation design elements, layout and physical environment on the application of Participatory Ergonomics in improving employee productivity of SMK Az-Zawiyah Tanjung Batu. This is because the sig t value of the workstation design variable is 0,007 < α = 0,05, the layout variable is 0,012 < α = 0,05, and the physical environment variable is 0,009 < α = 0,05. There is a joint influence (simultaneously) elements of workstation design, layout, and physical environment on employee productivity SMK Az-Zawiyah Tanjung Batu this is because the sig value F variable workstation design, layout, and physical environment amounted to < 0,001 < α = 0,05.