Modulation Parameters Research Articles

Effects of 12 weeks of functional training vs. Muay Thai on cardiac autonomic modulation and hemodynamic parameters in older adults: a randomized clinical trial

BackgroundThe elevated blood pressure (BP) and lower cardiac autonomic modulation (CAM) are associated with higher morbidity mortality risk among older adults. Although exercise is an important intervention for cardiovascular promotion, it is unclear whether combat sports training could benefit cardiovascular outcomes as much as autonomic in this population. This study compared the effects of 12 weeks of Muay Thai (MT) training against functional training (FT) on CAM and hemodynamic parameters in older adults. MethodsThe sample consisted of 50 older adults (41 women; 66.0 ± 5.3 years old), who were equaly randomized into FT (n = 25) and MT (n = 25) intervention groups. CAM was measured by 30-min rest heart rate variability. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and resting heart rate (RHR) were measured using an automatic oscillometric device. Pulse pressure (PP) and the double product (DP) were also calculated. The interventions were carried out three times a week, with 60-min length per session, during 12 consecutive weeks. The intensity of the interventions was measured using the subjective perception of exertion scale and by accelerometer. Two-factor repeated measures analysis of covariance was used for groups comparison, considering intervention group and body mass as factors. The 95% confidence interval of the difference (95%CIdif) was also calculated and the effect size was measured using partial eta squared (η2p).ResultsCAM indices did not show significant changes across moments and intervention groups. In hemodynamic parameters, only in DBP was there an effect of the moment (F1,39 = 8.206; P = 0.007; η2p = 0.174, large) and interaction effect between group*moment (F1,39 = 7.950; P = 0.008; η2p = 0.169, large). Specifically, the MT group at the post-training moment showed lower DBP (P = 0.010; 95%CIdif = -13.3; -1.89) in relation to the FT group. Furthermore, the MT group showed a decrease in DBP during training (P = 0.002; 95%CIdif = -10.3; -2.6). Also, an increase in training intensity was also found over the 12 weeks in FT, with no difference between the groups.ConclusionAfter 12 weeks of MT practice there was a reduction in DBP compared to FT in older adults.Trial registrationNCT03919968 Registration date: 01/02/2019.

Random thermal-vibration mechanisms of sandwich ventral fin-type plate-shell systems with porous functionally graded core

The stochastic thermal-vibration mechanisms within a sandwich ventral fin-type plate-shell system, featuring a porous functionally graded (FG) core, are exhaustively analyzed under various random loading conditions employing an innovative node-based, meshless computational approach. The studied structure is decoupled into several plates and open cylindrical panel according to the geometric characteristics, and the mechanical relationships at the structural boundaries or connection interfaces are equivalently simulated by using penalty parameters. Following the general Hamilton's principle, the meshless approach combined with the first-order shear deformation theory (FSDT) incorporating thermal effects is employed to derive the vibration equations of the sandwich ventral fin-type plate-shell systems. Also, the pseudo excitation method (PEM) is introduced to calculate stationary and nonstationary random responses. In order to verify the accuracy of the meshless algorithm in this study, the convergence and correctness are studied comprehensively. And then, the effects of some parameters such as temperature variation, porosity parameters, power-law index and random excitations on the thermal vibration characteristics of the sandwich ventral fin-type plate-shell systems with porous FG core are presented. The results show that the power-law index and temperature can increase the frequency parameter of the structure. The smooth power spectral density (PSD) excitation only affects the amplitude of the response curve, and does not affect the frequency corresponding to the peak. In the analysis of non-stationary random vibration, the influence of modulation parameters on response is very significant.

Physics-based machine learning optimization of thermoelectric assembly for maximizing waste heat recovery

With the increasing energy consumption of data centers around the world, the recovery of low-grade waste heat in data centers has become increasingly important. To maximize the waste heat recovery of thermoelectric modules in data centers, physics-based machine learning optimization of the thermoelectric assembly was carried out. A theoretical model and machine learning model of a fan-heat sink-thermoelectric generator system for waste heat recovery were proposed for predicting the performance of a thermoelectric assembly. Genetic algorithms were used to find the best heat sink geometric parameters for the possible greatest power generation. Given a temperature difference constraint of 75 °C between the heat source and the environment, the greatest output power from the thermoelectric assembly was 3.836 (watts), with the optimal plate fin height of 60 (mm), 50 fins, and 1.7 (mm) of fin pitch over a 120 mm × 120 mm area under the determined parameters of the thermoelectric module with a 120 mm cooling fan. In addition, by comparing different machine learning algorithms, the results revealed the superiority of random forest regression in solving optimal problems. This work provides a convenient and practical means for the fast optimization of thermoelectric assembly.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Study on thermal runaway propagation characteristics of the lithium-ion battery module by two-phase flow of nitrogen and water mist in longitudinal ventilation environment

To prevent and control the thermal runaway (TR) and its propagation of lithium-ion batteries (LIBs) is a key problem for its sustainable development and wide application. However, there is still a gap in the research on the characteristics of two-phase flow of nitrogen(N2) and water mist (NWM) controlling TR and its propagation in different longitudinal ventilation environments. In this study, the influence of wind velocity on 0.5 MPa NWM to control TR and its propagation characteristic parameters of LIB module was studied experimentally. The results show that with the increase of wind velocities, the opening time of the safety valve and TR trigger temperature of the LIB module gradually increased. At the same wind velocity, TR trigger temperature of cell 1 to cell 5 was successively reduced after 0.5 MPa NWM was applied. The cooling and asphyxiation effect of 0.5 MPa NWM is the best in natural ventilation environment, but the suppression effect of 0.5 MPa NWM on TR propagation of the LIB module is weakened under the influence of forced ventilation. However, different wind velocities have little effect on TR trigger temperature of the blocked LIB module. At the same time, a quasi-steady state thermal equilibrium of the NWM in a ventilation environment was established for the LIB module, and the influence of the coupling effect of different longitudinal ventilation environments and 0.5 MPa NWM on TR propagation characteristic parameters of the LIB module and the heat transfer mechanism in the thermal runaway propagation process were compared under the two conditions of within or without battery case. It is found that the convection effect of the wind plays a leading role in the heat dissipation of the blocked LIB module when the wind velocity is less than 4.5 m/s. The heat conduction of the battery case plays a leading role when the wind velocity is greater than 4.5 m/s. Moreover, in any ventilation environments, TR propagation time of the blocked LIB module is smaller than that of the unblocked LIB module. Under the same wind velocity, TR trigger temperature of the blocked LIB module and the maximum temperature of each cell are higher than that without battery case. This work provides a new perspective for controlling TR propagation of the LIBs. At the same time, it can provide theoretical guidance for ensuring process safety and firefighters to fight LIB fire accidents.

CATNet: Cascaded attention transformer network for marine species image classification

Complex physicochemical environmental effects result in the underwater species images’ highly intricate and diverse backgrounds, which poses significant challenges for identifying marine species. Deep learning techniques are used extensively for image classification thanks to their exceptional feature extraction capabilities. To address the above issues, we present a cascaded attention transformer network for marine species image classification named CATNet. More specifically, we first utilize the efficient channel attention module with depthwise convolution, which can efficiently extract the essential features of the image and reduce the computational parameters of the traditional attention module. Subsequently, we design a stacked transformer module to focus on the different features among a large amount of feature information, thereby reducing redundancy and improving?the generalization performance of CATNet. To comprehensively exploit the features extracted from different modules, we integrate the efficient channel attention and stacked transformer modules to construct a feature-cascaded module. Meanwhile, we construct a large-scale Marine Species Image Dataset (MSID) including 8605 underwater images with different species. Concretely, it consists of 712 coral reef images, 1270 fish images, 1082 people images, 2414 sea urchin images, 1219 squid images, 837 sea cucumber images, and 1071 turtle images. Comprehensive experimentation on our built dataset underscores the proposed CATNet’s superiority over the existing state-of-the-art methods in marine species image classification. The data is available. https://www.researchgate.net/publication/381961074_2024MSID.

Neurodevelopmental changes in Drosophila melanogaster are restored by treatment with lutein-loaded nanoparticles: Positive modulation of neurochemical and behavioral parameters

Neurodevelopmental disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), are characterized by persistent changes in communication and social interaction, as well as restricted and stereotyped patterns of behavior. The complex etiology of these disorders possibly combines the effects of multiple genes and environmental factors. Hence, exposure to insecticides such as imidacloprid (IMI) has been used to replicate the changes observed in these disorders. Lutein is known for its anti-inflammatory and antioxidant properties and is associated with neuroprotective effects. Therefore, the aim of this study was to evaluate the protective effect of lutein-loaded nanoparticles, along with their mechanisms of action, on Drosophila melanogaster offspring exposed to IMI-induced damage. To simulate the neurodevelopmental disorder model, flies were exposed to a diet containing IMI for 7 days. Posteriorly, their offspring were exposed to a diet containing lutein-loaded nanoparticles for a period of 24 h, and male and female flies were subjected to behavioral and biochemical evaluations. Treatment with lutein-loaded nanoparticles reversed the parameters of hyperactivity, aggressiveness, social interaction, repetitive movements, and anxiety in the offspring of flies exposed to IMI. It also protected markers of oxidative stress and cell viability, in addition to preventing the reduction of Nrf2 and Shank3 immunoreactivity. These results demonstrate that the damage induced by exposure to IMI was restored through treatment with lutein-loaded nanoparticles, elucidating lutein's mechanisms of action as a therapeutic agent, which, after further studies, can become a co-adjuvant in the treatment of neurodevelopmental disorders, such as ASD and ADHD.

Structure-Activity Relationship of Oxacyclo- and Triazolo-Containing Respiratory Syncytial Virus Polymerase Inhibitors.

Despite the availability of medicines preventing respiratory syncytial virus (RSV) infection, post-exposure treatment options are needed for addressing patient's needs. RSV non-nucleoside polymerase inhibitors (NNI) have emerged as a promising asset for which our group previously disclosed JNJ-8003 with potent in vitro antiviral activity and pronounced in vivo efficacy. In this work, a structural-guided design to modify the linker vector of JNJ-8003 resulted in the identification of 2-oxacyclo pyridine-containing derivatives whose various ring closing strategies are described. In addition, bioisosteric replacement of an amide bond with triazole retained potency, and cryo-electron microscopy (cryo-EM) confirmed binding in the capping domain. Subsequent NMR conformational analysis suggested a correlation between the potency and conformations. Our efforts have fulfilled the aim of identifying linker modifications with maintained biological activity while enriching structural diversity and allowing modulations of other parameters.

Nothing to lose? Neural correlates of decision, anticipation, and feedback in the balloon analog risk task.

Understanding the subprocesses of risky decision making is a prerequisite for understanding (dys-)functional decisions. For the present fMRI study, we designed a novel variant of the balloon-analog-risk task (BART) that measures three phases: decision making, reward anticipation, and feedback processing. Twenty-nine healthy young adults completed the BART. We analyzed neural activity and functional connectivity. Parametric modulation allowed assessing changes in brain functioning depending on the riskiness of the decision. Our results confirm involvement of nucleus accumbens, insula, anterior cingulate cortex, and dorsolateral prefrontal cortex in all subprocesses of risky decision-making. In addition, subprocesses were differentiated by the strength of activation in these regions, as well as by changes in activity and nucleus accumbens-connectivity by the riskiness of the decision. The presented fMRI-BART variant allows distinguishing activity and connectivity during the subprocesses of risky decision making and shows how activation and connectivity patterns relate to the riskiness of the decision. Hence, it is a useful tool for unraveling impairments in subprocesses of risky decision making in people with high risk behavior.

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: a scoping review

ObjectiveTherapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies. MethodsMEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers. ResultsOf 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback. ConclusionsThere is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback. SignificanceThe expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.

Golay complementary sequence and constant envelope orthogonal frequency‐division multiplexing‐based for integrated sensing and communication with mutual information analysis

AbstractThe design of waveforms plays a critical role in integrated sensing and communication (ISAC) systems. An ISAC waveform with a 0dB peak‐to‐average power ratio (PAPR) is designed by combining a Golay complementary sequence with a constant envelope orthogonal frequency‐division multiplexing. By adjusting the phase modulation parameters, this waveform allows for a trade‐offs between communication and sensing capabilities. The authors focus on several key performance metrics for the proposed ISAC waveform, notably using mutual information as a holistic performance indicator to assess both sensing and communication effectiveness. Through extensive numerical simulations, the authors demonstrate that the ISAC waveform significantly enhances detection probability compared to traditional phase‐modulated waveforms. The findings suggest that this approach is beneficial for designing low PAPR phase‐modulated ISAC waveforms, enhancing both the functionality and efficiency of ISAC systems.

The diffusion behaviour of coupled particle rings driven by self-propelled particles in a two-dimensional reflection channel

Investigated the diffusion behavior of self-propelled coupled particle rings in a two-dimensional channel considering particle collisions. The channel geometry and noise regulation play crucial roles in directing transport within the system. Observed a significant alteration in the diffusion behavior of the particle rings at specific stages of the collision process, accompanied by corresponding changes in the diffusion coefficient. As the modulation phase shift increases, the mean square displacement (MSD) of the particle rings displays periodic fluctuations. The binding force between the particle rings partially restricts the growth of the MSD. An increase in white noise intensity enhances the diffusion behavior. The impact of self-propulsion speed is influenced by the modulation parameters. The sign of the modulation parameter dictates the correlation of the self-propulsion speed. Furthermore, the number of particle rings in the channel introduces a complex effect on the diffusion behavior.

Online nonlinearity elimination for fringe projection profilometry using slope intensity coding

The nonlinearity effect in the system of fringe projection profilometry can cause the non-sinusoidal deviation of the fringe patterns, inducing ripple-like phase errors and further affecting measurement accuracy. This paper presents an online nonlinearity elimination method based on slope intensity coding. Two sequences of sinusoidal phase-shifting fringe patterns with different frequencies, and one slope intensity pattern with one uniform intensity pattern are projected. The equations for the nonlinearity response are established using the defined mean and modulation parameters, the captured uniform intensity and two extracted background intensities. The nonlinearity response coefficients determined by solving the equations are used for pixel-wise nonlinearity correction on the captured images, which are employed for computing the wrapped phase, and further obtaining continuous phase by the multi-frequency phase unwrapping method. Experimental results demonstrate that the proposed method can eliminate the nonlinearity-induced phase error online by using fewer images and maintain the reliability of phase unwrapping in the measurement of isolated objects with complex surfaces.

Feed-forward compensation for emulator-type testing facilities

Defining the required trackability level of the target condition for the testing facility reconditioning unit represents an unresolved challenge in improving the reproducibility of load-based tests and corresponding performance rating standards development. To enhance the reproducibility of such testing methodology, this paper presents and discusses a new feed-forward compensation technique based on the development of a transfer function model for the delay and offset characteristics of the psychrometric room's air temperature and humidity modulations with reference to the target signal from the room emulator. It is demonstrated that the proposed methodology enables offset and delay reduction in the trackability of the return air condition within 60 s at different testing conditions, enhances the reproducibility of the test results to limit performance deviations to within 2 %, and achieves closely matched controlled parameter modulations during load-based tests.

Influence of Exogenous Melatonin Treatment on the Ram Fertility of the North-east Bulgarian Fine-wool Sheep Breed

The potential of the epiphyseal hormone melatonin to effect on seasonally breeding species has been extensively studied, but the data remain inconclusive. Levels of reproductive parameters modulation are important to monitoring, given the breed affiliation, as example analyzed breed, which has a pronounced seasonal type of reproduction. The objective of this study was to determine whether application of exogenous melatonin has an effect on ram fertility of the North-east Bulgarian fine-wool sheep breed, as estimated by the reproductive performances of ewes, in the non-breeding season. In addition, the study quantified the effects of exogenous melatonin on ejaculate volume and concentration of sperm in rams with melatonin implants (n = 5) and per os treated rams (n = 5). The application of exogenous melatonin improves the fertility of rams of the studied breed. As a result of the current study, in the 2nd experimental group there was a positive trend on ejaculate volume and concentration of sperm throughout the experiment compared to control group. Although rams exhibited an improvement in sperm parameters, a significantly increased birth rate was found in sheep mating with male receiving melatonin both through implants (P < 0.05) and per os (P < 0.05). It was found to increase the total percentage of birth lambs in 1st (28%) and 2nd (29%) experimental groups compared to control. The results are guiding for North-east Bulgarian fine-wool sheep farmers and breeders, in terms of opportunities to improve the breeding process and obtain more lambs.

Marine Algae and Deriving Biomolecules for the Management of Inflammatory Bowel Diseases: Potential Clinical Therapeutics to Decrease Gut Inflammatory and Oxidative Stress Markers?

The term "inflammatory bowel disease" (IBD) describes a class of relapse-remitting conditions that affect the gastrointestinal (GI) tract. Among these, Crohn's disease (CD) and ulcerative colitis (UC) are two of the most globally prevalent and debilitating conditions. Several articles have brought attention to the significant role that inflammation and oxidative stress cooperatively play in the development of IBD, offering a different viewpoint both on its etiopathogenesis and on strategies for the effective treatment of these conditions. Marine ecosystems may be a significant source of physiologically active substances, supporting the search for new potential clinical therapeutics. Based on this evidence, this review aims to comprehensively evaluate the activity of marine algae and deriving biomolecules in decreasing pathological features of CD and UC. To match this purpose, a deep search of the literature on PubMed (MEDLINE) and Google Scholar was performed to highlight primary biological mechanisms, the modulation of inflammatory and oxidative stress biochemical parameters, and potential clinical benefits deriving from marine species. From our findings, both macroalgae and microalgae have shown potential as therapeutic solutions for IBD due to their bioactive compounds and their anti-inflammatory and antioxidant activities which are capable of modulating markers such as cytokines, the NF-κB pathway, reactive oxidative and nitrosative species (ROS and RNS), trefoil factor 3 (TFF3), lactoferrin, SIRT1, etc. However, while we found promising preclinical evidence, more extensive and long-term clinical studies are necessary to establish the efficacy and safety of marine algae for IBD treatment.

Tuning Superconductivity in Nanosecond Laser‐Annealed Boron‐Doped Si1–xGex Epilayers

Superconductivity in ultradoped Si1−xGex:B epilayers is demonstrated by nanosecond laser doping, which allows introducing substitutional B concentrations well above the solubility limit and up to 7 at%. A Ge fraction x ranging from 0 to 0.21 is incorporated in Si:B: 1) through a precursor gas, by gas immersion laser doping; 2) by ion implantation, followed by nanosecond laser annealing; and 3) by ultrahigh‐vacuum‐chemical vapor deposition growth of a thin Ge layer, followed by nanosecond laser annealing. The 30 and 75 nm‐thick Si1−xGex:B epilayers display superconducting critical temperatures Tc tuned by B and Ge between 0 and 0.6 K. Within Bardeen Cooper Schrieffer (BCS) weak‐coupling theory, Tc evolves exponentially with both the density of states and the electron–phonon potential. While B doping affects both, through the increase of the carrier density and the tensile strain, Ge incorporation allows addressing independently the lattice deformation influence on superconductivity. To estimate the lattice parameter modulation with B and Ge, Vegard's law is validated for the ternary SiGeB bulk alloy by density functional theory calculations. Its validity is furthermore confirmed experimentally by X‐ray diffraction. A global linear dependence of Tc versus lattice parameter, common for both Si:B and Si1−xGex:B, with δTc/Tc ≈ 50% for δa/a ≈1%, is highlighted.

Investigation of solution structures in a nonlinear electric transmission network incorporating dissipative elements through physical phenomena analysis

This paper presents an innovative approach to solving the voltage equation in a multi-coupled nonlinear transmission network with dissipative elements. By employing the G′G2 -expansion method and fractional derivatives, we explore the behavior of current and voltage, considering memory effects. Our investigation delves into the impact of fractional calculus on signal transmission, revealing insights into period, amplitude, and propagation direction. Additionally, we analyze the influence of obliqueness angles and various fractional-order derivatives on voltage waveforms Stability analysis demonstrates the significant role of dissipative elements and modulation parameters on signal stability and network resistance to interference. Furthermore, we examine the effects of free parameters on signal transmission characteristics. Our findings highlight the intricate interplay between fractional order, wave obliqueness, and physical parameters on voltage propagation, addressing gaps in previous research.

Enhancement of the photothermoelectric effect through synergistic modulation of multiple parameters

The performance of photothermoelectric materials is severely limited by various intrinsic parameters that are difficult to simultaneously modulate, thereby hindering their applications in the fields of photodetectors, solar cells, thermoelectric therapy and so on. In this study, we have prepared suspended films composed of carbon nanotube (CNT) and molybdenum disulfide quantum dots (MoS2 QDs), which demonstrate a remarkable maximum photothermal conversion efficiency of 2201 K·mm2·W−1. This outstanding performance is attributed to the formation of a heterojunction between CNT and MoS2 QDs. Furthermore, the light absorption, temperature coefficient of resistance, and Seebeck coefficient of this suspended film are simultaneously modulated in a direction conducive to enhancing the photothermoelectric effect. Consequently, we have fabricated CNT-CNT/MoS2 QDs p-n junction photodetectors to showcase the improved photoresponse performance. This study presents an innovative approach for modulating multiple intrinsic parameters of photothermoelectric materials, which offers valuable insights for enhancing the performance of corresponding photothermoelectric devices.

Formation and mechanical properties of AZ31B magnesium alloy welds by power-modulated oscillating laser

an energy-space modulated laser was proposed in welding workpieces with AZ31B magnesium alloy to enhance the efficiency of heat transfer and reduce defects of welds, and the proposed laser was power-modulated oscillating laser (PMOL). To validate the performance of the proposed technique, non-penetration welding and butt welding were conducted on the workpieces with a thickness of 5 mm. The impact of power-modulation parameters was investigated at the aspects of micro-structures, appearance, and mechanical properties of welds. The results showed that in comparison with a conventional laser (CL) or circular oscillation laser (COL), an energy-space modulated laser led to (1) an increased depth of weld penetration and (2) an enhanced coupling efficiency of laser energy. The parameters of power modulation could be optimized to reduce the defects of welds such as spatter, underfill, and thick fish scales significantly reduced; it refined the equiaxed grains in the center area of weld. When the frequency and amplitude of power-modulation were set as 50 Hz and 500 W, respectively, the mechanical properties of the weld were improved with the ultimate tensile strength of 238 MPa and an elongation ratio of 13.9 % which were 96.1 % and 58.7 % of those of the base material (BM), respectively. In comparison with the results welded by CL and COL, the ultimate tensile strength by PMOL was increased by 13.9 % and 4.4 %, and the elongation was increased by 54.4 % and 8.6 %, respectively.