Comprehensive Comparative Analysis Research Articles

Comparison of complications in isolated aesthetic liposuction procedures across different techniques: a systematic review and meta-analysis

BackgroundLiposuction has emerged as one of the most common cosmetic procedures globally, increasing by more than two-fold since 1997. While different liposuction techniques exist, each with distinct advantages and potential sequelae, a comprehensive comparative analysis of complication rates across various techniques in purely aesthetic cases remains unexplored. We aimed to (1) systematically review the literature on complications of distinctliposuction techniques, and (2) compare complication rates between different techniques.MethodsPubMed, EMBASE, and Web of Science databases were searched for studies reporting on complications of isolated cosmetic liposuction techniques in specific anatomical regions. Complication rates were pooled, and heterogeneity was assessed using the I² statistic. Publication bias was evaluated using the Luis Furuya-Kanamori index and Doi plot.ResultsTwenty-three studies were included. Laser-assisted liposuction (LAL) demonstrated the lowest overall complication rates for hemorrhage, infection, seroma formation, thermal injury, and cutaneous necrosis (all 0.13%). Power-assisted liposuction (PAL) exhibited the highest rates for hemorrhage (0.72%), infection (1.34%), and cutaneous necrosis (0.72%). Radiofrequency-assisted liposuction (RFAL) showed the highest rates for seroma formation (3.93%) and thermal injuries/blistering (1.64%). Suction-assisted liposuction (SAL) presented the highest rate of cutaneous irregularities (3.36%). Heterogeneity varied widely across complication types and techniques, with I² values ranging from 0 to 91.4%.ConclusionsThis study provides the first comprehensive comparison of complication rates across different liposuction techniques in aesthetic cases. LAL demonstrated superior safety profiles, while PAL and RAL showed higher rates for specific complications. These findings can inform surgical decision-making and patient counseling.Level of EvidenceNot gradable.

Predicting Alzheimer's disease subtypes and understanding their molecular characteristics in living patients with transcriptomic trajectory profiling.

Deciphering the diverse molecular mechanisms in living Alzheimer's disease (AD) patients is a big challenge but is pivotal for disease prognosis and precision medicine development. Utilizing an optimal transport approach, we conducted graph-based mapping of transcriptomic profiles to transfer AD subtype labels from ROSMAP monocyte samples to ADNI and ANMerge peripheral blood mononuclear cells. Subsequently, differential expression followed by comparative pathway and diffusion pseudotime analysis were applied to each cohort to infer the progression trajectories. Survival analysis with real follow-up time was used to obtain potential biomarkers for AD prognosis. AD subtype labels were accurately transferred onto the blood samples of ADNI and ANMerge living patients. Pathways and associated genes in neutrophil degranulation-like immune process, immune acute phase response, and IL-6 signaling were significantly associated with AD progression. The work enhanced our understanding of AD progression in different subtypes, offering insights into potential biomarkers and personalized interventions for improved patient care. We applied an innovative optimal transport-based approach to map transcriptomic data from different Alzheimer's disease (AD) cohort studies and transfer known AD subtype labels from ROSMAP monocyte samples to peripheral blood mononuclear cell (PBMC) samples within ADNI and ANMerge cohorts. Through comprehensive trajectory and comparative analysis, we investigated the molecular mechanisms underlying different disease progression trajectories in AD. We validated the accuracy of our AD subtype label transfer and identified prognostic genetic markers associated with disease progression, facilitating personalized treatment strategies. By identifying and predicting distinctive AD subtypes and their associated pathways, our study contributes to a deeper understanding of AD heterogeneity.

Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review

The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs.

Deep Fake Detection: A Comprehensive Survey and Comparative Analysis

Deep Fake Detection: A Comprehensive Survey and Comparative Analysis

Exploring potentially synthetic genes related to diarrhetic shellfish toxins production in Prorocentrum sp. via comparative transcriptomics.

Harmful algal blooms (HABs), exacerbated by climate change and environmental disturbances, pose global challenges due to marine toxin contamination, particularly diarrhetic shellfish toxins (DSTs). DSTs are prevalent marine toxins, and understanding their synthesis is vital for managing fisheries and mitigating environmental triggers. This study delves into the synthesis mechanisms of DSTs in Prorocentrum arenarium and Prorocentrum lima, which vary in toxin types and concentrations. We conducted a comprehensive comparative transcriptomic analysis to identify potential toxin-related genes, focusing on polyketide synthases (PKSs) and fatty acid synthases (FASs). Our research predicted 96 PKSs and 91 FASs genes, with a detailed examination of their sequences to elucidate dinophysistoxins (DTXs) synthesis. Additionally, we analyzed differential gene expression of PKSs in P. arenarium under nitrogen and phosphorus-limited conditions, revealing a correlation between specific PKSs gene expression patterns and okadaic acid (OA) content variations. These findings suggest a potential role of the fatty acid biosynthesis pathway in DSTs synthesis. While not completely uncovering the biosynthetic pathway of DSTs, our study offers crucial insights and genomic resources for future research on dinoflagellate toxin production mechanisms.

Meta-Heuristics for Feature Selection: A Comprehensive Survey and Comparative Analysis

Meta-Heuristics for Feature Selection: A Comprehensive Survey and Comparative Analysis

Nitinol Reactive Oxygen Assist Gas Laser Micromachining: Demystifying the Implications Imposed Upon Pseudoelasticity and the Shape Memory Effect for Nickel-Titanium Alloys

Herein, a novel investigation examines a long-pulse microsecond laser cutting process with an oxygen assist gas and the subsequent effects on martensitic phase transformations for binary nickel-titanium alloys. To establish a comprehensive comparative analysis, a reactive oxygen assist gas process, a standard inert argon assist gas process, and athermal femtosecond laser micromachining are studied in order to form a complete analytical assessment of how laser processing can inflict implications upon pseudoelasticity and shape memory. In-situ thermography reveals an approximate 1.5-times increase in thermal area for an argon assist gas when compared to oxygen. In conjunction with thermal analysis, electron backscatter diffraction and optical microscopy demonstrates 1.7 to 25 μm of epitaxial grain recrystallization associated with the elevated level of thermal propagation. Low cycle fatigue testing of pseudoelastic performance realizes a 12.7 % decrease in the achievable upper plateau stress when comparing long-pulse cutting to femtosecond cutting. Furthermore, when the laser power exceeds 30 W, the ultimate tensile strength suffers notable reductions of 25 % and 40 % for the oxygen and argon processes respectively when compared to femtosecond micromachining. Bend and free recovery testing provides insights into shape memory performance of the endothermic phase transformation from B19’ martensite to B2 austenite, revealing marginal ∼1.5 ˚C reductions in the active austenite finish (Af) temperature as laser power was increased from 10 W to 60 W. Lastly, long-pulse laser processing results in notable ∼15 ˚C shifts in the austenite start (As) temperature during the reverse martensite phase transformation from B19’→R→B2.

An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.

Comparative analysis of different types of pulse width modulation techniques for multilevel inverters

Multilevel inverters have gained significant attention in recent years due to their ability to achieve higher voltage and lower harmonic distortion compared to conventional two-level inverters. Pulse width modulation (PWM) techniques play a crucial role in controlling multilevel inverters by generating the required switching signals for their power electronic devices. This paper presents a comprehensive comparative analysis of various PWM techniques employed in multilevel inverters, including sinusoidal pulse width modulation (SPWM), space vector pulse width modulation (SVPWM), carrier-based pulse width modulation (CBPWM), and selective harmonic elimination (SHEPWM). Each PWM technique's advantages, limitations, and suitability for different multilevel inverter topologies are discussed. Furthermore, recent advancements and hybrid PWM techniques are also examined to explore potential improvements in performance and efficiency. This paper aims to provide researchers, engineers, and practitioners with valuable insights into selecting the most appropriate PWM technique for their specific multilevel inverter applications, considering factors such as performance requirements, cost constraints, and ease of implementation.

Toward Understanding Temperature and Bias Instabilities of Anti‐ambipolar Transistors via Low‐Frequency Noise Spectroscopy

AbstractAnti‐ambipolar transistors (AATs) featuring heterojunctions of n‐ and p‐type semiconductors have garnered significant research interest owing to their unique electrical characteristics. With the nonlinear current response, AATs hold great promise for a wide range of next‐generation electronic applications, further enhancing advanced logic and in‐memory computing functionality. However, the seamless integration of AATs into these applications hinges upon addressing their susceptibility to temperature and bias instabilities, a challenge that has yet to be systematically explored. Here, the origin of these instabilities is reported in AATs composed of indium‐gallium‐zinc oxide (IGZO) and dinaphtho[2,3‐b:2′,3′‐fjthieno[3,2‐b]thiophene (DNTT) through low‐frequency noise (LFN) spectroscopy. The findings reveal that the AATs exhibit a notable reduction in peak current with temperature instability and an abrupt decrease in drain current under applied DC bias. It is examined that these instabilities stem from defect‐related carrier transport mechanisms at the n/p heterojunction, evidenced by the observation of 1/f 4 noise. Furthermore, a comprehensive comparative analysis is provided of 1/f 4 noise behavior with and without the insertion of an insulative layer of AAT. This provides the microscopic origin of how the LFN generation mechanism changes the defect‐related carrier conduction at the interface and mitigates the bias and temperature instabilities.

UPLC-Q-TOF-MS With Chemometrics Approach Analysis of Nonvolatile Compounds for Medicinal Citrus reticulata With Cultivar and Areas Variations.

Citri Reticulatae Pericarpium (CRP), also known as Chenpi in Chinese, is the dry mature peel of Citrus reticulata Blanco or its cultivated varieties. CRP as the health-care food and dietary supplement has been widely used in various diseases. The quality of CRP can be affected by various factors, which are closely related to the metabolite composition of CRP. The objective of this study is to conduct a comprehensive comparative analysis on the chemical profiling of 51 C. reticulata samples of eight medicinal varieties, grown in different areas, and provide a methodological reference for the study of pharmacodynamic material bases and quality control of C. reticulata. Initially, a comprehensive characterization was performed using quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), and a heatmap visualization was employed for clarifying the distribution of the annotated active ingredients. Furthermore, obtained chemical profiles data were employed in multivariate statistical methods, comprising principal component analysis (PCA), and orthogonal partial least-squares-discrimination analysis (OPLS-DA). A total of 42 chemical components were annotated in positive ion mode. The relative contents were evident differences in the active ingredients of medicinal varieties of C. reticulata; mostly, polymethoxy flavones (PMFs) in C. reticulata "Dahongpao" were more abundant; among them, nobiletin and tangeretin are the main active ingredients in CRP. In addition, the relative contents of chemical constituents of C. reticulata "Dahongpao" and C. reticulata "Unshiu" from different areas were less variable. Compared with production origins, the varieties of C. reticulata had a greater impact on quality. This work obtains a better understanding of the chemical profiles of medicinal varieties of C. reticulata, facilitated the reasonable applicability and quality control of medicinal varieties of C. reticulata.

The First Report of Cod Gill Poxvirus in Gills of Atlantic Cod (Gadus morhua L.) Suffering From Cardiorespiratory Disease.

Atlantic cod farming experiences renewed growth in Norway, and increased awareness is essential to address emerging diseases in this species. There are few reports on gill diseases in cod, and to date, no viral gill infections of cod have been documented. In this study, we collected samples from three sequential time points in summer 2023 from farmed cod suffering from cardiorespiratory disease. We document severe heart and gill pathology, and a novel double-strand DNA virus was discovered in the gills. Through comprehensive genetic characterisation and comparative sequence analysis, this virus was classified as a new species in the genus Poxvirus, designated cod gill poxvirus (CGPV). We demonstrate disease causality with severe gill lesions as shown by histopathology and RNA scope insitu hybridisation, and poxvirus particles were identified in gill epithelial cells by transmission electron microscopy. Further, another gill pathogen not previously described in cod, Candidatus. Branchiomonas cysticola, was identified by pcr and insitu hybridisation. Our findings provide strong evidence for poxvirus in Atlantic cod and underscore the imminent threat posed to Atlantic cod farm industry.

Comprehensive evaluation of sodium dichloroisocyanurate (NaDCC) tablets as a novel solid-state alternative to conventional membrane cleaning agents in gravity-driven filtration systems.

Gravity-driven membrane (GDM) systems are increasingly recognized as sustainable and energy-efficient solutions for decentralized water treatment. However, membrane fouling, particularly by organic matter, remains a significant operational challenge, necessitating regular chemical cleaning to maintain performance. The present study was undertaken to investigate the cleaning efficiency of sodium dichloroisocyanurate (NaDCC) tablets, a novel solid-state alternative to conventional liquid cleaning agents such as sodium hypochlorite (NaOCl), sodium lauryl sulfate (SLS), acetic acid, and citric acid. NaDCC tablets, originally developed for drinking water disinfection, offer advantages in terms of transport, storage, and safety compared with conventional liquid formulations. A comparative evaluation of cleaning agents was conducted on hollow fiber membranes used in GDM systems, with the concentration and contact times optimized for each chemical. NaOCl demonstrated the highest permeability recovery, reaching 48.29% at 500 mg L-1 after 12 h, followed closely by NaDCC, with a recovery of 46.55% under similar conditions. Conversely, SLS, acetic acid, and citric acid presented significantly lower recovery rates, with maximum flux restorations of 14.57%, 14.90%, and 16.73%, respectively. These results highlight the comparable performance of NaDCC and NaOCl in addressing organic fouling while offering practical advantages such as greater stability and reduced chemical handling risks. This study highlights the efficacy of NaDCC as a viable detergent for GDM systems, and also provides a comprehensive comparative analysis of the water permeability performances of commercial detergents such as NaOCl, which cause various ecotoxicities, and suggests the feasibility of NaDCC as a chemical detergent in practical membrane processes. Our findings contribute to the development of more sustainable and cost-effective membrane-cleaning protocols that enhance long-term operational efficiency and minimize environmental impacts.

Comparative genomics and phylogenetic analysis of six Malvaceae species based on chloroplast genomes.

The Malvaceae family, comprising 9 subfamilies and 4,225 species, includes economically significant taxa, such as Ceiba pentandra, Gossypium ekmanianum, Gossypium stephensii, Kokia drynarioides, Talipariti hamabo, and Durio zibethinus. Chloroplast (cp) genome research is crucial for elucidating the evolutionary divergence and species identification within this family. In this study, we assembled and annotated cp genomes of six Malvaceae species, conducting comprehensive comparative genomic and phylogenomic analyses. The assembled genomes range from 160,495 to 163,970bp in size, with 125-129 genes annotated. Notable differences were observed in the IR (inverted repeat) regions, and SSR analysis revealed that Durio zibethinus has the highest number of specific variation sites. Among the six species, Talipariti hamabo uniquely exhibits more palindromic repeats than forward repeats. Seven highly mutated regions were identified, offering potential markers for species identification. Phylogenetic reconstruction using the maximum likelihood method revealed two primary clades within Malvaceae: Byttneriina and Malvadendrina. Within Malvadendrina, the subfamily Helicteroideae represents the earliest divergence, followed by Sterculioideae. This study provides a robust phylogenetic framework and valuable insights into the classification and evolutionary history of Malvaceae species.

Parameter Extraction for Photovoltaic Models with Flood-Algorithm-Based Optimization

Accurately modeling photovoltaic (PV) cells is crucial for optimizing PV systems. Researchers have proposed numerous mathematical models of PV cells to facilitate the design and simulation of PV systems. Usually, a PV cell is modeled by equivalent electrical circuit models with specific parameters, which are often unknown; this leads to formulating an optimization problem that is addressed through metaheuristic algorithms to identify the PV cell/module parameters accurately. This paper introduces the flood algorithm (FLA), a novel and efficient optimization approach, to extract parameters for various PV models, including single-diode, double-diode, and three-diode models and PV module configurations. The FLA’s performance is systematically evaluated against nine recently developed optimization algorithms through comprehensive comparative and statistical analyses. The results highlight the FLA’s superior convergence speed, global search capability, and robustness. This study explores two distinct objective functions to enhance accuracy: one based on experimental current–voltage data and another integrating the Newton–Raphson method. Applying metaheuristic algorithms with the Newton–Raphson-based objective function reduced the root-mean-square error (RMSE) more effectively than traditional methods. These findings establish the FLA as a computationally efficient and reliable approach to PV parameter extraction, with promising implications for advancing PV system design and simulation.

A Comparative Analysis Of Active And Passive Investing Strategies In Dana Pensiun XYZ (Dapen XYZ)

The study aims to provide a comprehensive comparative analysis of active vs. passive investment strategies in the context of pension funds, aimed at providing insights into the dynamic discourse surrounding active and passive investment. Through an exploration of empirical data and scientific discourses, the study highlights the potential benefits and limitations of each strategy, ultimately contributing to the informed decision-making process for pension fund managers. The study will also use the Markowitz model and efficient boundary methods to analyze and optimize investment strategies, focusing on risk, cost and cost management, and investment strategy. The analysis will also provide insights that can be done for fund managers to target higher investment returns, in terms of influencing company decisions and behavior to implement long-term investment strategies by reallocating some assets to other investment strategies

Comparing Russian and Ukrainian media frames during the war: A mixed-method semantic network approach

During the 2022–2023 phase of the Russo-Ukrainian war, information flows were intensively framed by both sides, predominantly via various Telegram channels. This article implements a mixed-method semantic network approach to analyse media framing in the context of this conflict, exemplified through a case study of Mariupol and Azovstal siege. Although prior research highlighted contrasting framing patterns in Russian and Ukrainian media, a comprehensive comparative analysis of media frames during the ongoing war still needs to be conducted. Our study addresses this gap by investigating and interpreting the inter-conceptual relationships surrounding the frames within the selected four Russian and Ukrainian Telegram channels. We examine variations in framing among conflicting sides and oppositional media and reveal four issue-specific conflict frames, each depicting diverse representations of the main events, actors, and their roles. Our find-ings contribute to a more nuanced understanding of the conflict’s cultural and ideological background and lay a foundation for further exploration of generic frames in Russian and Ukrainian media.

PV SYSTEM MPPT CONTROL: A COMPARATIVE ANALYSIS OF P&O, INCCOND, SMC AND FLC ALGORITHMS

The importance of solar energy is manifested in the growing demand for renewable energy sources around the world, which is fueled by environmental concern and the scarcity of conventional energy. Maximum power point trackers (MPPTS) are necessary in solar energy systems due to atmospheric changes that threaten the efficiency of solar energy systems. This article compares MPPT technologies. Including traditional and modern techniques, and despite the results achieved by classical techniques in maximizing and extracting as much energy as possible, they face a big problem in reaching the bone energy point. These provide modern technologies such as FLC and SMC. They provide exceptional accuracy and excellent response in all environmental conditions but come with additional complexity and higher cost. These technologies are ideal in applications that require high performance under continuously changing conditions or in difficult environments (such as large solar power systems or systems dealing with large fluctuations in illumination). This research aims to conduct a comprehensive study and compare of classical technologies (P&O and IncCond) and modern technologies sliding mode control (SMC, Fuzzy Logic Control – FLC), taking into account factors such as efficiency, complexity and response time. Tests were conducted under different climatic conditions to understand and enhance the efficiency of MPPT technologies. Our study highlights the enhanced performance for methods based on modern technologies. This study provides a comprehensive comparative analysis, and by improving the efficiency and reliability of solar energy systems, our research supports the advancement of sustainable energy solutions.

Dynamic Evaluation of Adaptive Product Design Concepts Using m-Polar Linguistic Z-Numbers

Adaptive design focuses on creating flexible products that meet evolving demands and enhance sustainability. However, evaluating adaptive design concepts poses significant challenges due to the dynamic nature of product features over time and the inherent uncertainty in decision-makers’ (DMs’) evaluations. Most traditional frameworks rely on static models that fail to capture the temporal evolution of attributes and often overlook decision-makers’ (DMs’) confidence levels, resulting in incomplete or unreliable evaluations. To bridge these gaps, we propose the m-polar linguistic Z-number (mLZN) to address these issues. This framework uses the dynamic representation capabilities of m-polar fuzzy sets (mFSs) and the symmetrical structure of linguistic Z-numbers (LZNs), which effectively integrate linguistic evaluations with corresponding confidence levels, providing a balanced and robust approach to handling uncertainty. This approach models design characteristics across multiple periods while accounting for DMs’ confidence levels. Based on this framework, we develop mLZN weighted and geometric aggregation operators, computation rules, and ranking methods to support dynamic multi-attribute group decision-making (MAGDM). The proposed framework’s effectiveness is demonstrated through a case study on adaptive furniture design for children, which showcases its ability to dynamically evaluate key attributes, including safety, ease of use, fun, and comfort. Furthermore, we validate its robustness and feasibility through comprehensive sensitivity and comparative analyses.

Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework

Abstract The electromagnetic-gravity optimization (EMGO) framework is a novel optimization technique that integrates the fine-structure constant and leverages electromagnetism and gravity principles to achieve efficient and robust optimization solutions. Through comprehensive performance evaluation and comparative analyses against state-of-the-art optimization techniques, EMGO demonstrates superior convergence speed and solution quality. Its unique balance between exploration and exploitation, enabled by the interplay of electromagnetic and gravity forces, makes it a powerful tool for finding optimal or near-optimal solutions in complex problem landscapes. The research contributes by introducing EMGO as a promising optimization approach with diverse applications in engineering, decision support systems, machine learning, data mining, and financial optimization. EMGO’s potential to revolutionize optimization methodologies, handle real-world problems effectively, and balance global exploration and local exploitation establishes its significance. Future research opportunities include exploring adaptive mechanisms, hybrid approaches, handling high-dimensional problems, and integrating machine learning techniques to enhance its capabilities further. EMGO gives a novel approach to optimization, and its efficacy, advantages, and potential for extensive adoption open new paths for advancing optimization in many scientific, engineering, and real-world domains.