Original Structure Research Articles

Analytical Evaluation of Midjourney Architectural Virtual Lab: Defining Major Current Limits in AI-Generated Representations of Islamic Architectural Heritage

In artificial intelligence (AI), generative systems, most notably Midjourney, have tremendous power to generate creative images of buildings and sites of Islamic architectural heritage through text-to-image generation based on the internet. The AI-generated representations have significant potential for architects, specialists, and everyday users. However, the system has considerable limitations when generating images for some buildings and sites where the representations appear too far from their original represented structures. Evaluating Midjourney as an architectural virtual lab, this research article attempts to define the major current limitations of using Midjourney’s AI system in producing images similar to the actual buildings and sites of Islamic architectural heritage. This research employs prompt engineering techniques based on historical sources as inputs to examine the accuracy of the output of the AI-generated images of selected examples of structures of the Islamic tradition. To validate the research results, it compares the Midjourney output with the original look by employing direct observation through critical analysis of human intelligence (HI) facilitated by the analysis of the latest version of 3M Visual Attention Software and an online survey. It concludes that while Midjourney can represent high-end AI-generated images inspired by the Islamic tradition, it currently falls short of presenting the actual appearance of some of its original structures. Furthermore, it categorizes these limitations into four groups: (1) limits of the prompt, (2) limits of fame, (3) limits of regionality and historical styles, and (4) limits of architectural elements and details. The significance of this research lies in providing timely evaluation factors to architectural heritage practitioners and software developers. This practical article is the second in a series of two sequential articles in the Buildings journal; the first (theoretical) article introduces the concept of Artificial Intelligence Islamic Architecture (AIIA), answering the question: what is Islamic architecture in the age of artificial intelligence?

Numerical Simulation of Surface Subsidence and Fracture Evolution Caused by Pulang Copper Mine Mining

Subsidence of the earth’s surface induced by mining activities has always been a critical concern in the relevant research fields. This subsidence disrupts the original geological structures and can lead to secondary geological hazards, environmental degradation, and threats to human lives and property. An in-depth investigation of this issue led to us using the three-dimensional finite-difference numerical simulation software FLAC3D 6.0 in this study. The research focuses on the Prang Copper Mine subsidence area in Yunnan Province, China, with a particular emphasis on the comprehensive analysis of the formation mechanisms of a large-scale crack appearing on the south side of the subsidence area. The study also includes a predictive analysis of the future development trends of this crack. The simulation results indicate that the crack formation was a consequence of the combined effects of uneven surface subsidence induced by underground structural interfaces and underground mining activities. As mining activities continued, the non-uniform subsidence of the surface intensified. The northward (Y-axis) displacement difference of characteristic points A and B on both sides of the crack continuously increased, signifying the widening of the crack. Mining activities also influence the displacement in the X-axis direction, potentially posing risks to support structures on either side of existing drainage channels. Therefore, effective control measures are warranted. Furthermore, this study highlights the possibility that new mining activities may further exacerbate subsidence on the south side of the subsidence area. This research provides valuable insights into the complexity of surface subsidence and its associated risks, offering guidance for mining activity planning and safety measures.

A conceptual design approach for mega-latticed structures based on combinatorial equilibrium modelling

In this paper, a novel equilibrium-based form-finding approach for a mega-latticed structure is presented based on vector-based 3D graphic statics and the Combinatorial Equilibrium Modelling (CEM). This approach can be effectively applied to the conceptual design phase of the structures with various design objectives under conservative loads. It allows us to adjust the design parameters in real time during the design phase and to obtain the resultant forms immediately. Based on a certain selection strategy, the forms have a better state of internal membrane forces compared to the original spherical structure. An ANSYS Parameter Design Language (APDL)-based automatic geometric modelling method for mega-latticed structures with various surface forms is developed. The proposed method solves the problem that the existing modelling methods are only available for spherical structures. It is highly applicable for modelling mega-latticed structures with spherical, cylindrical, and other heterogeneous surfaces. Furthermore, a complete design workflow that combines the proposed form-finding approach (for conceptual design), the automatic geometric modelling method (for transformation), and the finite element method (for deepening design and analysis), is discussed. This paper provides a reference for the design of mega-latticed structures and similar truss-based spatial structures.

Pencil‐Shaped Necking of Thick Al Whisker Grown by Stress‐Induced Migration and Enhancement of Tensile Strength

This study aims to characterize the mechanical response of thick Al whiskers under tension. Wire‐shaped whiskers are fabricated by stress‐induced migration using an original sample structure. Straight and single‐crystal whiskers are suspended between a W probe tip attached to a micromanipulator and an atomic force microscope tip attached to a nanostage using electron beam irradiation hardening glue. Tensile tests are conducted by pulling the probe tip. A tensile strength of 1 GPa is obtained for a whisker with a diameter of 500 nm. Observations using a high‐voltage electron microscope show that the whisker is deformed by pencil‐shaped sharp necking with signs of recrystallization at the tip, which displays a novel mechanical response of Al whiskers. The obtained tensile strength is more than twice that of whiskers with similar diameters reported in the literature, indicating that the tensile strength of not only thin, but also thick whiskers can be enhanced by activating a different mode of deformation.

Image Segmentation Based On U-Net and Adjusted U-Nets

Image segmentation, the process of dividing an image into various areas is quite crucial recently. Numerous industries, including robotics, remote sensing, and medical imaging, have applications for this task. In recent years, deep learning techniques, especially U-shaped networks (U-nets), have shown remarkable success in solving image segmentation problems. This paper provides an overview of image segmentation using neural networks, introduces different types of adjusted U-nets used for this task, including the implementation of attention gates and the use of residual neural network as the encoder path based on the original encoder-decoder structure, and then uses U-net and adjusted U-nets to conduct image segmentation on the black sea sprat. The study uses dice similarity coefficient and binary cross entropy loss function to compare the model training results and further judges the functionality of the models by the predicted segmented images. According to the test results, the Res34-UNet with attention gates performs most efficiently in segmenting this image dataset, although it's more unstable compared to the basic U-Net.

Visualized detection of water by modified metal organic framework-199 and its portable test paper with reversible color change

Herein, we fabricate a melamine modified metal organic framework-199 composite (MOF-199@melamine), of which the structure is affected by the dynamics of the guest H2O molecule with significant color change. It realizes the visualized quantitative detection of water in a variety of organic solvents within 30 s. Moreover, DMF restored the original structure by replacing H2O molecules, realizing the regeneration of the materials. On this basis, PTFE-MOF-199@melamine test paper is developed to portably detect water content in organic solvents (maximum 0 %-98 % (v/v) water content) and ambient relative humidity (11–85 %). The test paper can be recycled four times with a regeneration rate higher than 90 %. The results are expected to solve the problems of existed electrochemical or fluorescence strategy such as the complicated operation process and signal output/reading system.

Dataset of the impacts of invasive alien species in Brazil

AbstractThis dataset summarizes the research on the impacts of Invasive Alien Species (IAS) documented in Brazil from 1981 to 2022 and was used to subsidize the Brazilian Platform on Biodiversity and Ecosystem Services. Impacts of non‐native plants and animals, but not microorganisms and fungi, on terrestrial and aquatic (freshwater and marine) environments were included in this review. Most of the literature reviewed consisted of published articles, plus a few book chapters, dissertations, theses, and reports. We found 1003 records of ecological and economic impacts (970 negative and 33 positive) caused by 239 different species, as well as effects on people's well‐being. Understanding and assessing all ecological interactions of IAS in nature, as well as evaluating their contributions to humans, can be a complex task. However, the current scientific evidence from Brazil suggests that negative impacts of IAS are far more common than positive impacts, highlighting the importance of avoiding novel introductions. From a conservationist perspective, the simple presence of IAS may cause the negative impact of changing the original structure and processes of nature. This is of special concern in megadiverse countries like Brazil, where interactions among species are multiple and complex. Therefore, we believe this extensive review is an important contribution to the national knowledge of IAS and the improvement of global databases, which must be periodically reviewed. The complete dataset for this abstract published in the Data Article section of the journal is available in electronic format in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2024-01.1/jalter-en.

Multi-view clustering via pseudo-label guide learning and latent graph structure recovery

Multi-view clustering (MvC) accomplishes sample classification tasks by exploring information from different views. Currently, researchers have paid greater attention to graph-based MvC methods. However, most existing methods only consider the original graph structure and pay relatively little attention to the graph structure in the latent space. In addition, most methods need to pay more attention to the consistency of information on different labels. Otherwise, this may lead to the loss of label information. This paper presents a new multi-view clustering framework to address the above issues. The proposed method considers both the information in the latent space and the original data space, which firstly obtains the pseudo-label by latent representation learning and then lets the pseudo-label guide the learning of the complementary information between the raw data views. To ensure the integrity of the data structure, a latent graph structure recovery strategy is designed in the latent space. Finally, an enhanced label fusion strategy is designed to fusion the different types of labels, yielding an information-rich label matrix for clustering. Experimental results demonstrate the proposed method’s effectiveness compared to other advanced approaches.

Bayesian variable selection for matrix autoregressive models

A Bayesian method is proposed for variable selection in high-dimensional matrix autoregressive models which reflects and exploits the original matrix structure of data to (a) reduce dimensionality and (b) foster interpretability of multidimensional relationship structures. A compact form of the model is derived which facilitates the estimation procedure and two computational methods for the estimation are proposed: a Markov chain Monte Carlo algorithm and a scalable Bayesian EM algorithm. Being based on the spike-and-slab framework for fast posterior mode identification, the latter enables Bayesian data analysis of matrix-valued time series at large scales. The theoretical properties, comparative performance, and computational efficiency of the proposed model is investigated through simulated examples and an application to a panel of country economic indicators.

Chiral Memory in Dynamic Transformation from Porous Organic Cages to Covalent Organic Frameworks for Enantiorecognition Analysis.

The preservation of chirality during a transformation process, known as the "chiral memory" effect, has garnered significant attention across multiple research disciplines. Here, we first report the retention of the original chiral structure during dynamic covalent chemistry (DCC)-induced structural transformation from porous organic cages into covalent organic frameworks (COFs). A total of six two-dimensional chiral COFs constructed by entirely achiral building blocks were obtained through the DCC-induced substitution of chiral linkers in a homochiral cage (CC3-R or -S) using achiral amine monomers. Homochirality of these COFs resulted from the construction of 3-fold-symmetric benzene-1,3,5-methanimine cores with a propeller-like configuration of one single-handedness throughout the cage-to-COF transformation. The obtained chiral COFs can be further utilized as fluorescence sensors or chiral stationary phases for gas chromatography with high enantioselectivity. The present study thus highlighted the great potential to expand the scope of functional chiral materials via DCC-induced crystal-to-crystal transformation with the chiral memory effect.

Sodium-alginate-based indicator film containing a hydrophobic nanosilica layer for monitoring fish freshness

In this study, hydrophobic sodium alginate/anthocyanin/cellulose nanocrystal indicator films were fabricated by incorporating nanosilica (NS) as a waterproofing layer. The concentrations and formation methods (spraying (S), coating (C), and impregnation (I)) of the NS layer (denoted as NSS, NSC, NSI, respectively) were optimized. The results indicated that the optimum concentration of the NS layer was 5 % at a water contact angle (WCA) 110.5°. Further, Fourier transform infrared spectra showed the presence of SiOSi and SiCH3 groups in the NSS, NSC, and NSI films, and X-ray diffraction spectra indicated that original structures of these films were disordered. Moreover, the surface morphology, mechanical properties, and light transmission were affected by the NS layer, and the optimal layer was found to be NSI. After 10 days of storage at 100 % humidity, the NSI film exhibited low water vapor adsorption (37.22 g) and permeability (0.1484 g/m·s·Pa·10−11) and a high WCA (110.2°). In addition, the NSI film exhibited a visible color shift with an increasing pH of the buffer solution. A monitoring test of fish freshness showed that the NSI film displayed a distinctive color change corresponding to fish spoilage during 14 days of storage. This indicates that NSI has high potential in indicator film applications.

Identification of structural origins of complex charge heterogeneity in therapeutic ACE2Fc fusion protein facilitated by free-flow isoelectric focusing

Fc Fusion protein represents a versatile molecular platform with considerable potential as protein therapeutics of which the charge heterogeneity should be well characterized according to regulatory guidelines. Angiotensin-converting enzyme 2 Fc fusion protein (ACE2Fc) has been investigated as a potential neutralizing agent to various coronaviruses, including the lingering SARS-CoV-2, as this coronavirus must bind to ACE2 to allow for its entry into host cells. ACE2Fc, an investigational new drug developed by Henlius (Shanghai China), has passed the Phase I clinical trial, but its huge amount of charge isoforms and complicated charge heterogeneity posed a challenge to charge variant investigation in pharmaceutical development. We employed offline free-flow isoelectric focusing (FF-IEF) fractionation, followed by detailed characterization of enriched ACE2Fc fractions, to unveil the structural origins of charge heterogeneity in ACE2Fc expressed by recombinant CHO cells. We adopted a well-tuned 3-component separation medium for ACE2Fc fractionation, the highest allowable voltage to maximize the FF-IEF separation window and a mild Protein A elution method for preservation of protein structural integrity. Through peptide mapping and other characterizations, we revealed that the intricate profiles of ACE2Fc charge heterogeneity are mainly caused by highly sialylated multi-antenna N-glycosylation. In addition, based on fraction characterization and in silico glycoprotein model analysis, we discovered that the large acidic glycans at N36, N73, and N305 of ACE2Fc were able to decrease the binding activity towards Spike (S) protein of SARS-CoV-2. Our study exemplifies the value of FF-IEF in highly complex fusion protein characterization and revealed a quantitative sialylation-activity relationship in ACE2Fc.

Mechanistic insights into interactions between ionizable lipid nanodroplets and biomembranes

Delivery of RNA into cells using lipid nanoparticles (LNPs) has been a significant breakthrough in RNA-based medicine, with clinical applicability expanded through the use of ionizable lipids (ILs). These unique lipids can alter their charge state in response to pH changes, which is crucial for pH-triggered endosomal escape and effective lipid-mediated RNA delivery. In this study, we conducted a comprehensive set of molecular dynamics (MD) simulations to investigate interactions between IL-containing lipid nanodroplets (LNDs) and cell membrane models. Using an atomistic resolution model, we investigated the merging process of LNDs with cell membrane models under neutral conditions relevant to an intercellular environment and acidic pH conditions found in late endosomes. Our observations revealed that at neutral pH, LNDs merged with lipid membranes while preserving the bilayer structure. Under acidic conditions, the LNDs remained attached to the bilayer without fusing into the membranes. Importantly, the presence of ILs did not disrupt the original biomembrane structure during the simulation period. The MD simulations provided valuable atomistic insights into the mechanism of interaction between IL-containing nanodroplets and biomembranes, which could aid the rational design of ILs to develop more efficient LNPs for RNA therapies. Communicated by Ramaswamy H. Sarma

Electrochemical Conversion of CO2 into Formate Boosted by In Situ Reconstruction of Bi-MOF to Bi2O2CO3 Ultrathin Nanosheets.

Substantial emissions of CO2 have presented formidable challenges for global climate dynamics. Electrochemical reduction of CO2 to produce formic acid (HCOOH) is considered to be a promising approach for achieving carbon neutrality. Nevertheless, the development of a catalyst exhibiting both high catalytic activity and selectivity toward desired products remains an arduous task. Herein, we report the synthesis of a unique porous bismuth-based MOF (Bi-BTC) through microwave-assisted agitation. The Bi-BTC MOF has a good catalytic performance in electrochemical CO2RR to formate products. At -0.9 V (vs RHE) potential, the Faradaic efficiency of formate can reach 96%, and the current density of the CO2RR is 25 mA/cm2. Bi-BTC also exhibits good electrochemical stability. FEformate and current density were maintained for 24 h with almost no attenuation. It was found that Bi-BTC was reconstructed in the CO2RR process. The shape of nanocolumn before electrolysis is transformed into an ultrathin nanosheet. The soft and hard acid-base theory (HSAB) proves that the reason for the reconfiguration is that the hard base ions (HCO3-) and the intermediate acid (Bi3+) break in the Bi-O bond in Bi-MOF, resulting in the transition of the original column structure of Bi-BTC to Bi2O2CO3 ultrathin nanosheeets. The DFT calculation shows that the restructured Bi2O2CO3 nanosheet exposes a crystal surface structure, which is conducive to lower the activation energy barrier of the electrochemical CO2RR intermediate *OCHO and stabilizing the reaction intermediate. Therefore, it is more beneficial to improve the selectivity of the electrochemical CO2RR to formate formation. This result proves that irreversible reconfiguration of catalyst is beneficial to electrochemical CO2RR. In addition, coupling a Bi-BTC cathode with a stable anode (IrO2) enables battery-driven high-activity CO2RR and an OER with good activity and efficiency.

Structure and electrical properties of bilayer zigzag silicene nanoribbons depending on stone-wales and applied electric fields by SCC-DFTB method

The influence of geometry and electrical properties of Stone-Wales (SW) defects and an applied electric field on bilayer zigzag silicene nanoribbons (ZSiNRs) were investigated using the self-consistent charge density functional tight-binding (SCC-DFTB) method. The results show that concave deformation occurs at SW defects, leading to varying degrees of disruption in the original symmetrical structure. The overall stability of the marginal Stone-Wales (MSW) nanoribbons surpasses that of the central Stone-Wales (CSW) nanoribbons, SW-II type defects are prone to form in bilayer nanoribbons. The band gap of MSW defective nanoribbons expands significantly, realizing the transition from metallic to semi-metallic and semi-conductor properties. The electrons are transferred from the inner to the outer side of the nanoribbons, with a higher prevalence of defect nanoribbons compared to those that are perfect. The electric field intensity significantly alters the properties of both MSW-I and MSW-II defective nanoribbons.

Hydrogels in Cutaneous Wound Healing: Insights into Characterization, Properties, Formulation and Therapeutic Potential.

Hydrogels are polymeric materials that possess a set of characteristics meeting various requirements of an ideal wound dressing, making them promising for wound care. These features include, among others, the ability to absorb and retain large amounts of water and the capacity to closely mimic native structures, such as the extracellular matrix, facilitating various cellular processes like proliferation and differentiation. The polymers used in hydrogel formulations exhibit a broad spectrum of properties, allowing them to be classified into two main categories: natural polymers like collagen and chitosan, and synthetic polymers such as polyurethane and polyethylene glycol. This review offers a comprehensive overview and critical analysis of the key polymers that can constitute hydrogels, beginning with a brief contextualization of the polymers. It delves into their function, origin, and chemical structure, highlighting key sources of extraction and obtaining. Additionally, this review encompasses the main intrinsic properties of these polymers and their roles in the wound healing process, accompanied, whenever available, by explanations of the underlying mechanisms of action. It also addresses limitations and describes some studies on the effectiveness of isolated polymers in promoting skin regeneration and wound healing. Subsequently, we briefly discuss some application strategies of hydrogels derived from their intrinsic potential to promote the wound healing process. This can be achieved due to their role in the stimulation of angiogenesis, for example, or through the incorporation of substances like growth factors or drugs, such as antimicrobials, imparting new properties to the hydrogels. In addition to substance incorporation, the potential of hydrogels is also related to their ability to serve as a three-dimensional matrix for cell culture, whether it involves loading cells into the hydrogel or recruiting cells to the wound site, where they proliferate on the scaffold to form new tissue. The latter strategy presupposes the incorporation of biosensors into the hydrogel for real-time monitoring of wound conditions, such as temperature and pH. Future prospects are then ultimately addressed. As far as we are aware, this manuscript represents the first comprehensive approach that brings together and critically analyzes fundamental aspects of both natural and synthetic polymers constituting hydrogels in the context of cutaneous wound healing. It will serve as a foundational point for future studies, aiming to contribute to the development of an effective and environmentally friendly dressing for wounds.

Strain-insensitive micro torsion and temperature sensor based on a helical taper seven-core fiber structure.

We propose a multimode interference-based optical fiber NHTSN sensor with a helical taper for simultaneous measurement of micro torsion and temperature. The sensor consists of single mode fiber (SMF), no-core fiber (NCF), and seven-core fiber (SCF). A helical taper is fabricated in the SCF using a flame heater, forming the SMF-NCF-Helical Taper SCF-NCF-SMF (NHTSN) structure. Theoretical analysis and experimental results demonstrate that the introduction of helical taper not only imparts directionality to the torsion measurement, but also results in a significant improvement in torsion sensitivity due to the increased inter-mode optical path difference (OPD) and enhanced inter-mode coupling. In the experiment, the torsion sensitivity of the NHTSN sensor reaches -1.255 nm/(rad/m) in the twist rate (TR) range of -3.931 rad/m to 3.931 rad/m, which is a 9-fold improvement over the original structure. Further reduction of the helical taper diameter increases the sensitivity to -1.690 nm/(rad/m). In addition, the sensor has a temperature sensitivity of up to 97 pm/°C from 20 °C to 90 °C, and simultaneous measurement of torsion and temperature is attainable through a dual-parameter measurement matrix. The NHTSN sensor possesses advantages of compact size, high sensitivity, good linearity, and strain-independence, endowing it with potential applications in structural health monitoring (SHM) and engineering machinery.

Inter-site structural heterogeneity induction of single atom Fe catalysts for robust oxygen reduction

Metal-nitrogen-carbon catalysts with hierarchically dispersed porosity are deemed as efficient geometry for oxygen reduction reaction (ORR). However, catalytic performance determined by individual and interacting sites originating from structural heterogeneity is particularly elusive and yet remains to be understood. Here, an efficient hierarchically porous Fe single atom catalyst (Fe SAs-HP) is prepared with Fe atoms densely resided at micropores and mesopores. Fe SAs-HP exhibits robust ORR performance with half-wave potential of 0.94 V and turnover frequency of 5.99 e−1s−1site−1 at 0.80 V. Theoretical simulations unravel a structural heterogeneity induced optimization, where mesoporous Fe-N4 acts as real active centers as a result of long-range electron regulation by adjacent microporous sites, facilitating O2 activation and desorption of key intermediate *OH. Multilevel operando characterization results identify active Fe sites undergo a dynamic evolution from basic Fe-N4 to active Fe-N3 under working conditions. Our findings reveal the structural origin of enhanced intrinsic activity for hierarchically porous Fe-N4 sites.

Middle Eastern carbonate reservoirs – the critical impact of Discrete Zones of Elevated Permeability (DZEP) on reservoir performance

Abstract Middle Eastern carbonate petroleum reservoirs exhibit a range of heterogeneities which consist of variable combinations of primary stratigraphic and secondary diagenetic and structural characteristics. These produce diverse permeability architectures which can exert a profound influence on reservoir performance during secondary recovery. Of particular importance are laterally persistent discrete zones of elevated permeability (DZEP) that typically make up a volumetrically minor proportion of the reservoir yet show disproportionately high fluid inflow or outflow. The stratigraphic, diagenetic, and structural origins of elevated permeability in Middle Eastern carbonate reservoirs are considered here and the consequences of such features for reservoir performance are discussed. The term DZEP denotes geological sources of elevated permeability at least an order of magnitude greater than background reservoir properties. Stratigraphically organised DZEP comprise coarse-grained layers, event beds or parasequence tops or bases in neritic or platform interior settings. Other origins include bioturbated layers, grainy clinothems, and bed-scale, grain-size variations in shoal deposits. Diagenetic DZEP are typically dissolution horizons with mouldic and touching-vug pore networks or dolomitized intervals which often overprint stratigraphic DZEP. Structural DZEP include individual faults, fracture corridors, and fracture concentrations related to mechanical stratigraphy. During production through natural pressure depletion, DZEP may dominate well productivity. Under secondary recovery, the same intervals may dominate inter-well fluid flow, causing flood conformance issues, cross-zone fluid movement, bypassed pay, and earlier-than-expected water or gas breakthrough to production wells. Optimisation of production and ultimate recovery relies on collecting the correct kinds of data at a sufficiently early stage in the reservoir characterisation process to permit their inclusion in static and dynamic reservoir models.

Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔG Predictions.

The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.