External Input Research Articles

Four-Dimensional Printing of Multi-Material Origami and Kirigami-Inspired Hydrogel Self-Folding Structures.

Four-dimensional printing refers to a process through which a 3D printed object transforms from one structure into another through the influence of an external energy input. Self-folding structures have been extensively studied to advance 3D printing technology into 4D using stimuli-responsive polymers. Designing and applying self-folding structures requires an understanding of the material properties so that the structural designs can be tailored to the targeted applications. Poly(N-iso-propylacrylamide) (PNIPAM) was used as the thermo-responsive material in this study to 3D print hydrogel samples that can bend or fold with temperature changes. A double-layer printed structure, with PNIPAM as the self-folding layer and polyethylene glycol (PEG) as the supporting layer, provided the mechanical robustness and overall flexibility to accommodate geometric changes. The mechanical properties of the multi-material 3D printing were tested to confirm the contribution of the PEG support to the double-layer system. The desired folding of the structures, as a response to temperature changes, was obtained by adding kirigami-inspired cuts to the design. An excellent shape-shifting capability was obtained by tuning the design. The experimental observations were supported by COMSOL Multiphysics® software simulations, predicting the control over the folding of the double-layer systems.

One-pot synthesis of phenyl- and biphenyl-linked bis-pyrrolo[3,4-b]pyridin-5-ones via a pseudo-repetitive Ugi-Zhu-5CR coupled to a double cascade process (aza-Diels-Alder/N-acylation/decarboxylation/dehydration)

Fifteen new bis-pyrrolo[3,4-b]pyridin-5-ones were synthesized via a one-pot process composed by a pseudo-repetitive Ugi-Zhu-5CR coupled to a double cascade sequence (aza-Diels-Alder cycloaddition/N-acylation/decarboxylation/dehydration) in 39–77 % yields, and in short reaction times (only one hour per compound) despite the big size, symmetry and high complexity of the products. The reaction conditions were first optimized step-by-step, and then, the bis-heterocyclic products were synthesized in one-pot manner. It was observed that, formation of the corresponding imines and the double cascade process were carried out without external heating inputs, instead that only under constant stirring at room temperature. Reaction conditions turned out to be the friendliest (in terms of energy) with the environment in comparison with all previously published methodologies involving post-Ugi-Zhu reactions. Therefore, this work shows that pseudo-repetitive MCRs are more thermodynamically favorable compared to classic MCRs, and that they also allow rapid access to highly structurally complex molecules, exhibiting a high degree of symmetry that may play an important role in several fields of knowledge like optics, material science, agrochemistry, and medicinal chemistry.

Operation regimes of spinal circuits controlling locomotion and the role of supraspinal drives and sensory feedback

Locomotion in mammals is directly controlled by the spinal neuronal network, operating under the control of supraspinal signals and somatosensory feedback that interact with each other. However, the functional architecture of the spinal locomotor network, its operation regimes, and the role of supraspinal and sensory feedback in different locomotor behaviors, including at different speeds, remain unclear. We developed a computational model of spinal locomotor circuits receiving supraspinal drives and limb sensory feedback that could reproduce multiple experimental data obtained in intact and spinal-transected cats during tied-belt and split-belt treadmill locomotion. We provide evidence that the spinal locomotor network operates in different regimes depending on locomotor speed. In an intact system, at slow speeds (<0.4 m/s), the spinal network operates in a non-oscillating state-machine regime and requires sensory feedback or external inputs for phase transitions. Removing sensory feedback related to limb extension prevents locomotor oscillations at slow speeds. With increasing speed and supraspinal drives, the spinal network switches to a flexor-driven oscillatory regime and then to a classical half-center regime. Following spinal transection, the model predicts that the spinal network can only operate in the state-machine regime. Our results suggest that the spinal network operates in different regimes for slow exploratory and fast escape locomotor behaviors, making use of different control mechanisms.

Operation regimes of spinal circuits controlling locomotion and the role of supraspinal drives and sensory feedback.

Locomotion in mammals is directly controlled by the spinal neuronal network, operating under the control of supraspinal signals and somatosensory feedback that interact with each other. However, the functional architecture of the spinal locomotor network, its operation regimes, and the role of supraspinal and sensory feedback in different locomotor behaviors, including at different speeds, remain unclear. We developed a computational model of spinal locomotor circuits receiving supraspinal drives and limb sensory feedback that could reproduce multiple experimental data obtained in intact and spinal-transected cats during tied-belt and split-belt treadmill locomotion. We provide evidence that the spinal locomotor network operates in different regimes depending on locomotor speed. In an intact system, at slow speeds (<0.4 m/s), the spinal network operates in a non-oscillating state-machine regime and requires sensory feedback or external inputs for phase transitions. Removing sensory feedback related to limb extension prevents locomotor oscillations at slow speeds. With increasing speed and supraspinal drives, the spinal network switches to a flexor-driven oscillatory regime and then to a classical half-center regime. Following spinal transection, the model predicts that the spinal network can only operate in the state-machine regime. Our results suggest that the spinal network operates in different regimes for slow exploratory and fast escape locomotor behaviors, making use of different control mechanisms.

Emergy-Based Evaluation of Xiaolangdi Reservoir’s Impact on the Ecosystem Health and Services of the Lower Yellow River

The disturbance in river ecosystems caused by reservoirs and dams has become a critical topic, attracting increasing attention. However, the extent to which reservoir and dam construction and operation impact downstream river ecosystem health and ecosystem service functions is not fully understood. This research examines the Xiaolangdi Reservoir and the Lower Yellow River (LYR) ecosystem in China as a case study. We analyzed the complex material and energy flows in the LYR ecosystem using emergy theory and developed a set of emergy-based indicators for the quantitative assessment of river ecosystem health and services under reservoir operation interference. The results indicate that the total natural capital and environmental endowments of the LYR ecosystem have remained relatively stable after the operation of the Xiaolangdi Reservoir, with an increase in renewable emergy input. The ecosystem’s vigor decreased slightly, while the biomass emergy diversity index remained stable. However, the total emergy inputs increased significantly, with external feedback inputs becoming the most important emergy source for the LYR ecosystem. The resilience of the LYR ecosystem improved, with a significant increase in emergy density and a decrease in the emergy sustainability index. These findings suggest that although the river ecosystem continues to provide supporting services to human society, the extent of these services has diminished compared to pre-perturbation levels. In this research, a methodology for analyzing the impact of key reservoir operations on the ecosystem health and services of a large river is proposed to provide support for large river sustainable development studies.

Assessing the external atmospheric input of microplastics: Two strategies based on polymer composition and aging characteristics

Microplastics (MPs) can be transported over long distances in the environment, hence, distinguishing between MPs generated locally and those introduced from external sources is necessary for regional MP pollution management. In this study, MPs pollution in the dust of Siziwang banner (Sizi), a sparsely populated area on the Mongolian Plateau, and Hohhot, a city with large populations, was observed. The high proportion of small MPs in Sizi (<25 μm), combined with the fact that most air masses reaching the area have undergone long-distance transport, supports the presence of external input through atmosphere. Based on the significantly different composition distributions and surface characteristics of the small sized MPs in Sizi and Hohhot, a composition-based Bray-Curtis similarity index (Comp-BCs) and a carbonyl index-based BCs index (CI-BCs) were established. Contributions of the external MPs input to small MPs in Sizi were estimated as 23–36 %, indicating that the role of atmospheric input on MPs pollution in sparsely populated areas should not be overlooked.

Striatal lateral inhibition regulates action selection in a mouse model of levodopa-induced dyskinesia.

Striatal medium spiny neurons (MSNs) integrate multiple external inputs to shape motor output. In addition, MSNs form local inhibitory synaptic connections with one another. The function of striatal lateral inhibition is unknown, but one possibility is in selecting an intended action while suppressing alternatives. Action selection is disrupted in several movement disorders, including levodopa-induced dyskinesia (LID), a complication of Parkinson's disease (PD) therapy characterized by involuntary movements. Here, we identify chronic changes in the strength of striatal lateral inhibitory synapses in a mouse model of PD/LID. These synapses are also modulated by acute dopamine signaling. Chemogenetic suppression of lateral inhibition originating from dopamine D2 receptor-expressing MSNs lowers the threshold to develop involuntary movements in vivo , supporting a role in motor control. By examining the role of lateral inhibition in basal ganglia function and dysfunction, we expand the framework surrounding the role of striatal microcircuitry in action selection.

Holocene landscape changes in response to sea level oscillations and agricultural intensification in a coastal plain of southeastern South America

This study identified landscape changes in response to variations in mean sea level (MSL) and anthropogenic intervention, from the Early to Late Holocene in Southestern South America. Ananalysis based on palynology and sedimentary bacterial DNA from two sediment cores from the Patos-Mirim System (PMS) is introduced for the first time, to achieve an integrated paleoenvironmental and ladscape reconstruction. The data indicated an east-west migration of the freshwater and forest environments set in the lowlands during the transgressive period (6000–5000 cal yr BP) and an increase in external total biomass input during this MSL rise. A consolidated vegetation onset was inferred, especially of forest environments, after the Holocene Climatic Optimum (5000 cal yr BP). During the last century, landscape changes especially associated with anthropogenic factors were clearly identified from the palynological assemblages. That is, an alternation between Poaceae and Cyperaceae, the first occurrence of Pinus spp. and an increase in the influx of herbs and trees flagged the increase in cultivated areas due to the agricultural and agroforestry intensification. Extreme values of palynological influx appear to be a good proxy for the impact of El Niño events on the sedimentary record, which were associated to increased precipitation. The study also clearly demonstrates the high environmental stress generated by human activities, e.g. loss of plant diversity and changes in the bacterial abundance due to the higher agrochemicals input.

Postural control strategies with alterations in visual input conditions in a standing position.

Mediated by balance systems, postural control relies heavily on visual input. However, the precise relationship between various visual conditions and postural strategies for balance remains unclear. This study investigated alterations in the center of pressure (COP) and ankle muscle activity during standing positions in healthy individuals under different visual conditions. Fifty-three healthy university students participated in the study. Stabilometry was conducted to evaluate the total path length and outer circumferential area for the COP. Further, surface electromyography was utilized to assess the activity of the bilateral tibialis anterior (TA) and lateral head of gastrocnemius muscles. Participants were instructed to maintain an upright posture under three visual conditions: binocular (eyes open) and monocular (either closing the dominant or nondominant eye). The three conditions were compared using a one-way analysis of variance. No significant differences were observed in the COP across visual conditions. However, in the TA of the nondominant leg, the mean values for the binocular, dominant eye, and nondominant eye conditions were 0.91, 1.08, and 1.14 times that of the resting standing position. Notably, the TA of the nondominant leg was significantly lower in the binocular condition than the dominant and nondominant eye conditions. The study findings indicate that the COP remain consistent despite variations in external visual input. This stability is likely maintained through compensatory adjustments, such as increased TA muscle activity in the nondominant leg under the nondominant eye condition. These observations highlight the complex interplay between the visual perception and postural stability.

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China

Atmospheric ozone (O3) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O3. However, traditional experimental methods for determining O3 concentrations using automatic monitoring stations cannot predict O3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O3 displayed a high all-day peak from February to May. A correlation analysis between O3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (−0.72), and was negatively correlated with O3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O3 during periods of significant O3 pollution. After negating the effects of meteorological parameters, the predicted O3 concentrations decreased significantly, whereas they increased in the absence of NOx. Although individual VOCs were found to greatly affect the O3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O3 concentrations and distinguish how different features affect O3 variations.

Exploring the origins of switching dynamics in a multifunctional reservoir computer.

The concept of multifunctionality has enabled reservoir computers (RCs), a type of dynamical system that is typically realized as an artificial neural network, to reconstruct multiple attractors simultaneously using the same set of trained weights. However, there are many additional phenomena that arise when training a RC to reconstruct more than one attractor. Previous studies have found that in certain cases, if the RC fails to reconstruct a coexistence of attractors, then it exhibits a form of metastability, whereby, without any external input, the state of the RC switches between different modes of behavior that resemble the properties of the attractors it failed to reconstruct. In this paper, we explore the origins of these switching dynamics in a paradigmatic setting via the "seeing double" problem.

EPAKA: An efficient and privacy-preserving authenticated key agreement scheme based on physical security for VANET

Vehicular ad hoc network (VANET) has been a promising technology in smart transportation system, which can enable information exchange between vehicles and roadside units (RSUs). However, the privacy of vehicles and RSUs is a critical challenge in VANET, as they may expose sensitive information to malicious attackers or unauthorized parties. Many existing authenticated key agreement (AKA) schemes aim to protect the privacy of vehicles and RSUs, but they often neglect the physical security of the devices involved in the communication. Therefore, we propose an efficient and privacy-preserving AKA scheme in VANET, which embeds physical unclonable function (PUF) and fuzzy extraction (FE) technology. PUF is a physical device that generates random strings based on their intrinsic characteristics and external inputs, which can protect the secrets in the devices from being stolen by attackers. FE can compensate for the drawbacks of PUF affected by environmental factors. Our scheme preserves the identity privacy of legitimate RSUs and vehicles, as well as intercepts and traces the identity of malicious attackers. In addition, we eliminate the involvement of the third party (TP) in the AKA phase to better meet the high-speed driving of vehicles. Finally, we conduct formal and informal security analyses in random oracle model (ROM), which prove that our scheme can resist various attacks. We also show in the performance analysis that our scheme has the lowest computational cost, communication overhead, and total energy consumption.

Nitrogen excretion and utilisation of dairy cows grazing temperate semi-natural grasslands

Diets reliant on grazed, temperate herbage are prone to greater nitrogen (N) losses via urine than balanced stall-fed diets which poses a greater risk for N emissions. Measures for improving the N utilisation in grazing-based dairy cattle systems are predominantly investigated on homogenous clover-ryegrass pastures with high herbage yields and nutritional quality. In contrast, grazing-based systems reliant on less external inputs (e.g., synthetic fertilisers or concentrates) using semi-natural grassland as main feed source, such as in large parts of Central Europe, received less attention. The N utilisation and excretion of grazing cows in low-input dairy farms was, thus, investigated on nine commercial organic dairy farms in South Germany across one to four periods per farm. The dataset captured a diverse set of dairy production systems comprising 323 individual animal observations. A mean (± one standard deviation) milk production, DM intake (DMI), and pasture DMI of 23.9 kg (± 5.35), 21.0 kg (± 3.21), and 11.3 kg/d (± 4.83), respectively, was determined using titanium dioxide and faecal crude protein concentration as markers of faecal excretion and diet digestibility, respectively. Milk N use efficiency (MNE; i.e., milk N secretion as share of N intake) averaged 24.7 g/100 g N intake (± 5.91), which is greater than observations in temperate, high-input grazing systems but lower than in cows receiving balanced diets in the barn. The MNE and another seven indicators of N utilisation and excretion displayed a wide range of values. The grazing management factors explaining this variation were, thus, identified via backward elimination. The supplementation strategy had the greatest potential for manipulating N utilisation and excretion of dairy cows. Increasing shares of fresh forages (i.e., meadow grass or clover-grass leys) as well as of hay in supplement DMI increased N utilisation (e.g., MNE) and decreased urinary N excretion (e.g., urinary N to creatinine ratio), while increasing shares of concentrates in supplement DMI are related to lower N losses via urine. At the same time, increases in total supplement DMI reduced N utilisation and increased urinary N excretion. Hence, full-time grazing combined with supplementation of fresh forage and hay in the barn is a viable option for low-input, grazing-based dairy operations with moderate levels of N losses.

An urban agricultural block construction method based on low-carbon concepts: A case study of the Beijing-Tianjin area in China

With the ongoing progress of urbanization, various issues such as resource scarcity, climate change, and carbon emissions overload have gradually emerged. Urban agriculture (UA) is recognized as a pivotal strategy to promote sustainable urban development. In this context, the study proposes a spatial scale hierarchy, encompassing “single building - building cluster - urban block," to formulate spatial strategies for the theoretical model of urban agricultural blocks. The theoretical model integrates production spaces, production methods, and spatial systems based on relevant design principles and the needs of residents. Nankai District's Qingfeng Road East Block in Tianjin is used as the site for applying and simulating the theoretical model. The results of the study show that UA in a 500m × 500m block can ideally eliminate the need for external food inputs by 100% for starchy vegetables, legumes and fruits, and meat/poultry, while achieving a 254.73% self-sufficiency rate for fungi. Additionally, it can reduce energy inputs by 28.33%, water inputs by 38.13%, and completely eliminate waste output for 8754 people. Carbon emission analysis indicates that the urban agricultural block can reduce approximately 2250.01t CO2 emissions from buildings and waste, with greater carbon-neutral potential in transportation and agriculture, forestry, and other land uses (AFOLU). This is significant for tackling various urban challenges and enhancing future development.

Characteristic analysis of rain-wind induced stay-cable vibration based on friction damper in field observation

Abstract Both domestic and foreign scholars have developed various friction dampers with good characteristics. The primary types of friction dampers include conventional friction dampers, Pall friction dampers, Sumitomo friction dampers, and Centripetal friction dampers. The components of these dampers will slip or deform under load effect when the energy is consumed by friction. As the structural vibration period increases while the external energy input decreases, the vibration amplitude is reduced. This vibration reduction method is widely used in the automotive industry. Most of the hysteresis models of dampers are similar to rectangular shapes, making the selection of an accurate initial sliding force crucial for the rectangular hysteresis model. Based on the effective data collected from the field observation test, a self-made friction damper, which has a good vibration reduction effect, was used to observe rain-wind induced stay-cable vibration at Dongting Lake Bridge in Yueyang, Hunan Province. Accordingly, some suggestions are put forward for the research work on rain-wind-induced vibration.

A multifunctional life cycle assessment of durum wheat cropping systems

Agricultural systems strongly impact ecosystems by driving terrestrial degradation, water depletion, and climate change. The Life Cycle Assessment allows for comprehensive analyses of the environmental impacts of food production. Nonetheless, its application still faces challenges due to cropping systems’ increased complexity and multifunctionality. Past research has emphasized the need for more holistic approaches to consider dynamic crop interactions and diverse functions of cropping systems, beyond just meeting the demand for foods and feeds. In this context, this study applied an alternative combined and multifunctional modelling approach to compare the environmental performances of two durum wheat cropping systems. The latter differed in crop rotation schedules, farming methods, tillage techniques, and genotypes grown (including both modern and old ones). Novel methodological choices were adopted in this study, aiming at best representing the complexity and peculiarities of these systems, by considering crop rotation effects and reflecting the main durum wheat stakeholders’ perspectives. The results showed that the organic low-input landrace-growing system (Case 1) had considerably lower environmental impacts than the conventional high-input one (Case 2), regardless of the functional unit. The environmental hotspots were the increased land occupation and the bare fallow for Case 1 and Case 2, respectively. At the endpoint level, the most affected impact categories for both the systems of analysis were land use, fine particulate matter formation, global warming (human health), and human non-carcinogenic toxicity. Also, the midpoint analysis pointed out important differences in terms of other assessed impact categories, with Case 1 better performing for the majority of them. The identified improvement solutions include the following: the enhancement of the yield performances and the optimization of nitrogen provision from the leguminous crop for Case1, the shift toward a more efficient rotational scheme, the reduction of the use of external inputs, and the avoidance of unnecessary soil tillage operations for Case 2.

Long-term dynamics of dissolved oxygen and isotopic composition in Lake Erie and Lake Ontario: Implications for eutrophication and ecosystem health

This study, focusing on Great Lakes Erie and Ontario, Canada, explores dissolved oxygen (DO) dynamics and its stable isotopic composition (δ18ODO) in the Great Lakes. Using historical dissolved oxygen data from 1965 to 2021, combined with synoptic isotopic data collected in the early 2000s, the research examined the spatiotemporal trends in DO and δ18O-DO to assess the effects of biotic and abiotic processes such as photosynthesis, respiration, and atmospheric gas exchange. The study revealed significant seasonal and depth-related variations in DO levels, with hypolimnetic hypoxia and metalimnetic oxygen minima (MOM) observed in both lakes. With its comparatively shallow depth and higher nutrient loads, Lake Erie exhibited more pronounced dissolved oxygen fluctuations, with DO levels reaching as low as 3.65 mg L−1 and δ18ODO values falling below +24.6 ‰. These findings indicated Lake Erie’s more dynamic metabolic environment, particularly during the thermally stratified season. In contrast, Lake Ontario showed more stable oxygen concentration levels, with occasional dissolved oxygen depletions down to 6.80 mg L−1 and δ18ODO values up to +32.1 ‰, suggesting more localised influences of external inputs and biological processes. This study provides a long-term perspective on dissolved oxygen status in these lakes and offers essential insights into their ecological health. This work contributes valuable data for managing and protecting the Great Lakes ecosystem.

Robust Formation Tracking Control for Noncooperative Heterogeneous Multiagent Systems.

The robust formation tracking problems (FTPs) are investigated for noncooperative heterogeneous multiagent systems (MASs). The parameters and state dimensions of each agent in the MASs can be heterogeneous. The leader is noncooperative and has an unknown external input. The followers are affected by uncertainties and disturbances. A distributed extended state observer is designed to estimate the leader's state and the unknown external input. Then a robust formation tracking control method is proposed. It turns out that the output formation tracking error can reduce to any size neighborhood near the origin by selecting appropriate parameters. Finally, the effectiveness of the proposed observer and controller is demonstrated by a numerical simulation example and a hardware-in-the-loop (HITL) simulation example.

Enhancing Autonomous Agents Through Narrative Memory: A Comprehensive Framework for Improved Performance and Interaction

The process of organizing information and experiences into a narrative style is known as narrative memory, and it presents a cutting-edge method for improving the performance of autonomous agents. This essay describes the idea of narrative memory, as well as its elements, construction of narrative structures, and uses. The four main elements of narrative memory are timelines, characters, events, and context. Events are important things that happen, such interactions or acts, and characters are people, other agents, and the autonomous agent. The context gives the underlying knowledge required to comprehend these events and individuals, while the chronology sets the order of occurrences, creating a cause-and-effect link. The process of creating narrative frameworks includes gathering data, identifying events, crafting stories, and refreshing memories. Three methods are used to collect data: external sources, user input, and sensors. This data is used to identify significant events, which are subsequently arranged into logical tales. New situations and experiences are constantly added to the narrative memory. Improved predictive analysis, easier learning and adaptability, and improved human-robot interaction are just a few of the uses of narrative memory in autonomous agents. Robots can interact with people more successfully if they can comprehend human narratives and reply with responses that are contextually relevant. Agents may learn and adapt by identifying patterns in their prior experiences, and they can make better decisions by forecasting future occurrences with the help of historical narratives. Managing the complexity of real-world events, addressing ambiguity and uncertainty, and guaranteeing the ethical use of narrative data are challenges in the implementation of narrative memory. In order to improve narrative memory frameworks, future research options include creating sophisticated story generating tools, integrating narrative memory with other memory systems, and working across disciplines. The goal of this roadmap is to enhance autonomous systems' comprehension, communication, and decision-making skills by utilizing the power of tales.

Reactive molecular dynamics of the fracture behavior in geopolymer: Crack angle effect

Reactive molecular dynamics was applied in this study to construct the sodium aluminosilicate hydrate (N-A-S-H) and tensile fracture models with various crack angles. The impact of crack angle on the behavior of N-A-S-H fractures was explored while considering structural mechanical properties and energy evolution. Furthermore, the fracture toughness and brittleness index for various crack angle models were calculated. The findings indicated that the ultimate strength and elastic modulus of the fracture models grew linearly with the increase in crack angle. The fracture toughness value progressively grew while the model’s elastic energy efficiency and new surface energy efficiency decreased simultaneously. The 45° crack model possessed the largest oblique crack development surface in the fracture process due to the coupling effect of tensile and shear stress. Its elastic energy efficiency decreased as well the most, while the new surface energy efficiency increased and the fracture toughness value dropped sharply. It is crucial to place a stronger emphasis on spotting cracks both in the in-service structures or structures being demolished. This ensures optimal performance and safety by enabling more effective adjustments to the direction of external forces and energy input.