Traditional Techniques Research Articles

Monitoring climate change effects on coral reefs using edge-based image segmentation

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors.

Advances in understanding the pathogenesis of epilepsy: Unraveling the molecular mechanisms: A cross-sectional study.

Epilepsy is characterized by having two or more unprovoked seizures. Understanding the pathogenesis of epilepsy, requires deep investigation into the molecular mechanisms. This helps develop diagnostic techniques, treatments, and pharmacotherapy. It also enhances precision medicine and individualized treatment processes. This article reviews all the molecular mechanisms predisposing to epileptogenesis, presents the current diagnostic techniques and drug therapy, and suggests future perspectives in treating Epilepsy in a more comprehensive and holistic approach. Four authors searched keywords concerning epilepsy at a molecular level, Epilepsy diagnostic techniques and technologies, and antiepileptic drug therapy and precision medicine. Separate search strategies were conducted for each concern and retrieved articles were reviewed for relevant results. The traditional diagnostic techniques for Epilepsy and its pathogenesis are insufficient in highlighting dynamic brain changes. For this, emerging technologies including genetic sequencing and profiling, and functional neuroimaging techniques are prevailing. Concerning treatment, the current approach focuses on managing symptoms and stopping seizures using antiseizure medications. However, their usage is limited by developing resistance to such drugs. Some therapies show promise, although most antiseizure drugs do not prevent epilepsy. Understanding epileptogenesis at a molecular and genetic level aids in developing new antiepileptic pharmacotherapy. The aim is to develop therapies that could prevent seizures or modify disease course, decreasing the severity and avoiding drug resistance. Gene therapy and precision medicine are promising but applications are limited due to the heterogeneity in studying the Epileptic brain, dynamically. The dynamic investigation of the epileptic brain with its comorbidities works hand-in-hand with precision medicine, in developing personalized treatment plans.

Concentration‐number (C‐N) fractal models reveal the distribution pattern of the elements in ancient nephrite measured by portable X‐ray fluorescence: Based on nephrite objects excavated from different sites in Nanyang, Henan Province

AbstractChanges in the composition of various chemical elements in ancient nephrite artifacts due to prolonged burial are critical factors that should not be underestimated. However, the increasingly stringent heritage management has made many techniques impractical. Consequently, portable X‐ray fluorescence (pXRF) has become an indispensable nondestructive field analysis method. This paper aims to make use of the pXRF dataset to distinguish which elements tested are endogenous or exogenous, as well as to gauge which elements have been affected by stronger burial effects. More specifically, we carry out this work on the example of 103 pieces of nephrite excavated from different cemeteries in the Nanyang area. In addition to traditional statistical techniques, a new tool, the concentration‐number (C‐N) fractal method can shed new light on the analysis of the distribution patterns of different elements in excavated nephrite. The anomalous boundaries generated by the method have clear geochemical significance and can be delineated between background zone and disturbed regions. The degree of resistance of different elements to fluctuations in external factors was assessed, which has a direct relationship with the content of the buried soil. Considering the richness of the model, it has the potential to be used in archaeometrics.

Subsurface Imaging Using Interferometry of Distributed Acoustic Sensing Ambient Noise Measurement along a Dark Fiber Line: A Case Study in Downtown Reno, Nevada

ABSTRACT Distributed acoustic sensing (DAS) technology is an emerging field of seismic sensing that enables recording ambient noise seismic data along the entire length of a fiber-optic cable at meter-scale resolution. Such a dense spatial resolution of recordings over long distances has not been possible using traditional methods because of limited hardware resources and logistical concerns in an urban environment. The low spatial resolution of traditional passive seismic acquisition techniques has limited the accuracy of the previously generated velocity profiles in many important urban regions, including the Reno-area basin, to the top 100 m of the underlying subsurface. Applying the method of seismic interferometry to ambient noise strain rate data obtained from a dark-fiber cable allows for generating noise cross correlations, which can be used to infer shallow and deep subsurface properties and basin geometry. We gathered DAS ambient noise seismic data for this study using a 12 km portion of a dark-fiber line in Reno, Nevada. We used gathered data to generate and invert dispersion curves to estimate the near-surface shear-wave velocity structure. Comparing the generated velocity profiles with previous regional studies shows good agreement in determining the average depth to bedrock and velocity variations in the analyzed domain. A synthetic experiment is also performed to verify the proposed framework further and better understand the effect of the infrastructural cover along the cable. The results obtained from this research provide insight into the application of DAS using dark-fiber lines in subsurface characterization in urban environments. It also discusses the potential effects of the conduit that covers such permanent fiber installations on the produced inversion results.

Robust face recognition using quaternion interval type II fuzzy logic-based feature extraction on colour images.

In this paper, we propose a new robust and fast learning technique by investigating the effect of integration of quaternion and interval type II fuzzy logic along with non-iterative, parameter free deterministic learning machine (DLM) pertaining to face recognition problem. The traditional learning techniques did not account colour information and degree of pixel wise association of individual pixel of a colour face image in their network. Therefore, this paper presents a new technique named quaternion interval type II based deterministic learning machine (QIntTyII-DLM), which considers the interrelationship between three colour channels viz. red, green, and blue (RGB) by representing each colour pixel of a colour image in quaternion number sequence. Here, quaternion vector representation of a colour face image is fuzzified using interval type II fuzzy logic. This reduces the redundancy between pixels of different colour channels and also transforms colour channels of the image to orthogonal colour space. Thereafter, classification is performed using DLM. Experiments performed (on four standard datasets AR, Georgia Tech, Indian, face (female) and faces 94 (male) face datasets) and comparison done with other existing techniques proves that the proposed technique gives better results in terms of percentage error rate (reduces approximately 10-12%) and computational speed.

Effective removal of personal care product residues from aqueous media using hydrophobic deep eutectic solvents: Experimental and computational approach

Personal care product (PCP) residues - classified under micropollutants (MPs)- are widely found in aquatic environments, posing trace-level toxicity. Traditional water treatment techniques struggle to remove these toxins effectively. Deep eutectic solvents (DESs), the liquid mixtures typically composed of hydrogen bond donors (HBDs) and hydrogen bond acceptors (HBAs), have recently emerged as a viable alternative for extracting pollutants from water. Among DESs, hydrophobic deep eutectic solvents (HDESs) exhibit low water miscibility and unique solvent properties. The main objective of this study is to synthesize HDESs to effectively eliminate 2-naphthol and methylparaben- two of the significant chemical contaminants in PCPs- from aqueous media. HDESs were prepared by mixing different terpenes (menthol and thymol) and long-chain carboxylic acids at varying molar ratios. They were characterized using different spectroscopic and thermal techniques. The density and viscosity of the pure DESs were measured as afunction of temperature. Experimental studies were carried out using the liquid–liquid extraction (LLE) method, and the factors influencing the efficiency of extraction were optimized.Considering the limited number of studies on the extraction mechanism of MPs from aqueous media, this study employs comprehensive computational methods, including density functional theory (DFT) and the COnductor-like Screening MOdel for Real Solvents (COSMO-RS), to provide a detailed interpretation of the extraction mechanism. A series of successive extractions were conducted to reduce the DES usage to ensure economic viability. The reusability of the best-performed DES was explored. Additionally, the regeneration of one of the DESs was accomplished through activated carbon-based recovery.---------------------------------------------------------------------------------------------------------------------

On the feed-forward neural network for analyzing pantograph equations

Ordinary differential equations (ODEs) are fundamental tools for modeling and understanding a wide range of chemistry, physics, and biological phenomena. However, solving complex ODEs often presents significant challenges, necessitating advanced numerical approaches beyond traditional analytical techniques. Thus, a novel machine learning (ML)-based method for solving and analyzing ODEs is proposed in the current investigation. In this study, we utilize a feed-forward neural network (FNN) with five fully connected layers trained on data samples generated from the exact solutions of specific ODEs. To show the efficacy of our suggested method, we will conduct a thorough evaluation by comparing the anticipated solutions of the FNN with the exact solutions for some ODEs. Furthermore, we analyze the absolute error and present the loss functions for some ODE examples, providing valuable insights into the model’s performance and potential areas for further development.

Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.

LEAF: A Federated Learning-Aware Privacy-Preserving Framework for Healthcare Ecosystem

Over the last decades, the healthcare industry has been revolutionized like anything, especially after the Covid-19 surge. Various artificial intelligence approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimum robustness to unseen or untrained data. So, there is a requirement for new techniques which can counterfeit the issues mentioned earlier. Federated learning (FL) can help to make specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making using FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% more than that of the traditional centralized and FL-based systems advantages without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4-5 times.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets.

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly. In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification. Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well. The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

DeepLS: Local Search for Network Optimization Based on Lightweight Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being investigated as a competitive alternative to traditional techniques for solving network optimization problems. A promising research direction lies in enhancing traditional optimization algorithms by offloading low-level decisions to a DRL agent. In this study, we consider how to effectively employ DRL to improve the performance of Local Search algorithms, i.e., algorithms that, starting from a candidate solution, explore the solution space by iteratively applying local changes (i.e., moves), yielding the best solution found in the process. We propose a Local Search algorithm based on lightweight Deep Reinforcement Learning (DeepLS) that, given a neighborhood, queries a DRL agent for choosing a move, with the goal of achieving the best objective value in the long term. Our DRL agent, based on permutation-equivariant neural networks, is composed by less than a hundred parameters, requiring only up to ten minutes of training and can evaluate problem instances of arbitrary size, generalizing to networks and traffic distributions unseen during training. We evaluate DeepLS on two illustrative NP-Hard network routing problems, namely OSPF Weight Setting and Routing and Wavelength Assignment, training on a single small network only and evaluating on instances 2x-10x larger than training. Experimental results show that DeepLS outperforms existing DRL-based approaches from literature and attains competitive results with state-of-the-art metaheuristics, with computing times up to 8x smaller than the strongest algorithmic baselines.

Research on medical image segmentation technology based on deep learning

With economy expanding quickly, people's demand for medical services has become higher and higher, and medical image as an important basis for medical diagnosis has naturally received widespread attention. However, traditional image segmentation methods are easily affected by noise and unable to meet the complex and changing practical clinical applications. The increasing utilization of deep learning technology enables effective resolution of these problems. In this paper, we will first introduce the traditional image segmentation techniques, and describe the main methods to realize traditional image segmentation and its limitations. Immediately after that, it is proposed that deep learning methods can solve the challenges of medical image segmentation with traditional methods, and then the structure, algorithms and applications of several of the most commonly used deep learning methods are introduced. This paper proposes that medical image segmentation based on deep learning can segment the image more robustly and with high accuracy, and it can automatically obtain the most suitable features. The research in this paper will be of great value to the research and application of medical image segmentation technology based on deep learning.

Research on Traditional Reinforcement Techniques for Rammed Earth Walls in China

ABSTRACT Rammed earth is typically a material with a high compressive capacity and low tensile and shear strength. In traditional Chinese architecture, reinforcing rammed-earth walls with ties and beams is common. This study offers insights into the characteristics of reinforcement methods applied to rammed earth walls in houses and forts. After analyzing the mechanical failure of the walls, the article suggests that reinforcement can enhance mechanical performance by reducing shrinkage cracks, enhancing the bearing capacity of walls, and increasing the integrity of the entire building.

An Integrated Approach to Boosting Sales through Digital Marketing Strategies: An Exploration of Synergies between Traditional and Modern Marketing Techniques

Abstract: Integrating traditional and digital marketing approaches provides several advantages for businesses aiming to create a comprehensive marketing strategy. By using a consistent message and visual identity across all channels, businesses can create a strong and memorable brand. Digital campaigns allow you to reach a wider audience, while traditional tactics such as TV, radio and print expand brand awareness beyond just online channels. Furthermore, integrating your digital and traditional marketing channels can boost conversions by making the most out of touchpoints, which is critical to your lead nurturing process. Combining these approaches opens up opportunities for increased engagement and response rates, which can be particularly advantageous for traditional marketing channels. For example, participating in local events and promoting them online, sponsoring a local event or setting up a table at a local convention can yield positive outcomes for businesses aiming to create a comprehensive and effective marketing campaign. The secret to great marketing is the combination of both digital and traditional marketing and using them in an integrated way to enhance brand awareness and increase sales. Traditional and digital come together in beautiful harmony for the perfect connection between your customers and brand. Businesses that merge traditional and digital marketing strategies can reap the benefits of both approaches, creating a comprehensive marketing campaign that yields positive outcomes and increased sales.

Preparation and Characterisation of Nanolignin-Gelatine-Glycerol Composite (NLGGCs) Thin Film for Food Coating Application

Recognising the importance of preventing rapid food deterioration and prolonging the shelf life of fruits and vegetables from oxidation, we successfully created thin films composed of Nanolignin-Gelatine-Glycerol Composites (NLGGCs) through a traditional blending technique. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the successful preparation of nanolignin structures, with characteristic peaks observed at 513 cm-1 (C-C stretching in aromatic), 1222 cm-1 (phenolic OH), and 1107 cm-1 (Ar-H and syringyl group). Ultraviolet-visible spectroscopy (UV-Vis) revealed an absorption capacity within the 280 to 300 nm range. Film opacity increased with a greater nanolignin composition in the thin film, attributed to the presence of chromophore structures. Thermogravimetric Analysis (TGA) demonstrated a thermal degradation temperature exceeding 300°C. Differential Scanning Calorimetry (DSC) analysis unveiled two distinct glass transition temperatures (Tg) at approximately 60°C and 80°C, indicating microphase separation and immiscibility between gelatine and nanolignin particles. The nanolignin content significantly influenced solubility and water uptake, with higher nanolignin content leading to reduced solubility and water absorption. The application of NLGGCs film coatings on banana surfaces extended their shelf life compared to control samples after 10 days. Furthermore, NLGGCs underscore the pivotal role in enhancing the performance as a promising bio-based food coating alternative for future applications.

Reverse-U Aortotomy (Kırali Incision) for Aortic Valvular Interventions.

Surgical incisional approach to the ascending aorta is the main strategic step during valvular and/or subvalvular aortic interventions. Classic aortotomy incisions (transverse or oblique) can be challenging and can cause suboptimal exposure of the aortic root especially for the patients with small aortic annulus or for redo coronary artery bypass patients with patent proximal grafts interposed to the ascending aorta. The Kırali incision was used in 91 patients (including 13 reoperations) who underwent an aortic intervention for valvular and subvalvular pathologies. Aortic root was exposed by forming inverted "U" shape incision starting from approximately 3 cm above the right coronary ostium toward the center of the noncoronary annulus and the top of the left-right commissure like a tongue. The aortic valve was replaced with a mechanical prosthesis in 45 patients and with a bioprosthesis in 39 patients including 14 sutureless and 16 stentless prostheses. A total of 29 patients received a concomitant procedure per the following: coronary artery bypass grafting on 8 patients and left ventricular assist device on 7 patients. There was no any problem related to aortotomy incision technique such as bleeding, rupture, dehiscence, or laceration perioperatively. There was no complication related to the procedure during 5-year follow-up. This new aortotomy incision technique is a safe procedure that provides good exposure for all kinds of aortic valve interventions and protects grafts and can facilitate aortic root enlargement or aortoplasty easily. This incision has the potential to be an alternative to traditional techniques.

Impact of Spencer Technique on Pain, Range of Motion, and Functional Disability in Patients With Frozen Shoulder: A Pilot Study.

Background and objective The shoulder is the most flexible ball- and socket-type joint in the human body. The pathological condition that can commonly affect this joint is the frozen shoulder. This condition is marked by pain and stiffness in the area surrounding the shoulder complex. This leads to difficulty in doing the daily activities of living. Exercise and physical therapy are mostly recommended to decrease pain and improve and maintain the range of motion (ROM). Mainly traditional techniques such as Mulligans, Maitland, and Kaltenborn are used, along with electrotherapy and exercises, for the treatment of this condition. The effect of the Spencer technique is seen in baseball players' shoulder function. Thus, the purpose of this study is to determine how the Spencer approach affects patients with frozen shoulders in terms of pain, ROM, and functional impairment. Methodology This study included 20 patients aged between 40 and 60 years with stage 2 and 3 diagnosed frozen shoulder. This is a single-group pilot study that received the Spencer technique along with a moist heat pack and Codman's exercises on the affected shoulder for three weeks. Outcome measures used for assessment before and after treatment were the visual analogscale (VAS), shoulder ROM, and shoulder pain and disability index (SPADI). After the second, third, and sixth months, a follow-up was conducted.Two patients were lost to follow-up; consequently, statistical analysis was performed on the data from 18 patients. Results The current study's results suggested that there was an improvement in the mean values of VAS, ROM, and SPADI at post-three weeks, anda sustained effect was observed at the second, third, and sixth months. A statistically significant difference (P< 0.05) was found. Conclusions The study's conclusions demonstrated improved pain, ROM, and SPADI scores post-intervention. Treatment effects persisted, as seen by follow-up at the two, three, and six-month marks. As a result, the Spencer technique utilized in this pilot study on frozen shoulder patients proved effective. Also, the outcome effects were sustained, which suggests its utility in frozen shoulder rehabilitation.

Chemical durability and shielding study of borosilicate glass systems from solid municipal waste ash for radiation shielding applications

Based on municipal solid waste (MSW) ash as a main raw material, three novel borosilicate glasses with the composition of 70 waste + 20 borax + 10 Na2O + x ZrO2, where x = 0, 0.1 or 0.3 (wt. %),were prepared by the traditional melting-annealing technique. The prepared glasses were analyzed by EDX analysis, revealing the rich compositions of the prepared glasses correlated to the MSW ash used by 70 wt.% in preparing glasses. Some optical, chemical and radiation shielding properties of the prepared glasses were investigated. Either Zr addition or 80 kGy of gamma radiation revealed improvement of the glasses optical transmittance and chemical durability in neutral dis H2O, alkaline 0.1 N NaOH and acidic 0.1 N HCl leaching media for 70 days. Electron spin resonance (ESR) revealed the same spectra before and after irradiation, referring to the prevention of free radical formation by irradiation.The shielding parameters were measured by the experimental gamma spectroscopy (NaI detector) and the theoretical Phy-X/PSD software e.g., linear attenuation coefficients (LAC) and the findings revealed high unanimity among them at photon energies 0.662, 1.173 and 1.333 MeV. Another shielding parameters were also studied e.g., mass attenuation coefficients (MAC), effective atomic number (Zeff), effective electron density (Neff) and effective conductivity (Ceff). Presence of various metal oxides and the host trigonal BO3 and tetrahedral BO4 and SiO4 units, and ZrO2 provide the glasses compactness and effectual stability against ionizing irradiation. The prepared borosilicate glasses have highly strong and compacted structures that can inhibit the passage of radiation photons, because of the variety of many glass network formers, intermediates and modifiers present in the used waste ash. The results indicate the highly economic benefit of the prepared glasses, where the useless MSW ash are used mainly by 70 wt.% to produce effective borosilicate glass systems for promising radiation shielding purposes, especially 0.3 Zr borosilicate glass that has the best radiation shielding properties.

A novel non-segmented inverted water outline rendering method can improve the tracking of responsible blood vessels for hemifacial spasm.

This study aimed to explore a novel, non-segmented based on inverted water outline, and rapid 3D rendering method for identifying the responsible blood vessels for hemifacial spasm. First, the software was developed using the free and open-source 3D Slicer to process magnetic resonance images. Outlines of the water region were extracted and rendered in a three-dimensional space. The traditional image re-slicing technique (IMRT) was used for the control group, while non-segmented inverted water outline rendering (NSIWR) was used to observe the relevant blood vessels in the root entry/exit zone (REZ) of patients with hemifacial spasm. The intraoperative exploration results were considered the gold standard for comparing the differences in identifying relevant blood vessels between the two methods. Twenty-five patients were included, and the reconstruction effect evaluation suggested that NSIWR could effectively reconstruct the responsible blood vessels of the cochlea, facial nerve, and REZ. Compared with IMRT, NSIWR effectively improved the diagnosis of the responsible blood vessels in the REZ, clarified their sources and directions, and was consistent with intraoperative results. This study introduced a novel rapid rendering method based on NSIWR, which was successfully applied for hemifacial spasm. The method enhances accuracy in identifying responsible blood vessels in the REZ without needing multi-modal techniques. It has the potential to improve surgical effectiveness and reduce exploration time in treating hemifacial spasm.

Single isocenter versus dual isocenter treatment using flattening filter-free and jaw-tracking volumetrically modulated arc therapy for boot-shaped lung cancer: Evaluation of dosimetric and feasibility.

To determine whether a dual-isocenter volumetrically modulated arc therapy (VMAT) technique results in lower normal pulmonary dosage compared to a traditional single isocenter technique for boot-shaped lung cancer. A cohort of 15 patients with advanced peripheral or central lung cancer who had metastases in the mediastinum and supraclavicular lymph nodes was randomly selected for this retrospective study. VMAT plans were generated for each patient using two different beam alignment techniques with the 6-MV flattening filter-free (FFF) photon beam: single-isocenter jaw-tracking VMAT based on the Varian TrueBeam linear accelerator (S-TV), and dual-isocenter VMAT based on both TrueBeam (D-TV) and Halcyon linear accelerator (D-HV). For all 45 treatment plans, planning target volume (PTV) dose coverage, conformity/homogeneity index (CI/HI), mean heart dose (MHD), mean lung dose (MLD) and the total lung tissue receiving 5, 20, 30Gy (V5, V20, V30) were evaluated. The monitor units (MUs), delivery time, and plan quality assurance (QA) results were recorded. The quality of the objectives of the three plans was comparable to each other. In comparison with S-TV, D-TV and D-HV improved the CI and HI of the PTV (p<0.05). The MLD was 13.84±1.44Gy (mean±SD) for D-TV, 14.22±1.30Gy and 14.16±1.42Gy for S-TV and D-HV, respectively. Lungs-V5Gy was 50.78±6.24%, 52.00±7.32% and 53.36±8.48%, Lungs-V20Gy was 23.72±2.27%, 26.18±2.86% and 24.96±3.09%, Lungs-V30Gy was 15.69±1.76%, 17.20±1.72% and 16.52±2.07%. Compared to S-TV, D-TV provided statistically significant better protection for the total lung, with the exception of the lungs-V5. All plans passed QA according the gamma criteria of 3%/3mm. Taking into account the dosimetric results and published clinical data on radiation-induced pulmonary injury, dual-isocenter jaw-tracking VMAT may be the optimal choice for treating boot-shaped lung cancer.