A review on preamble-based channel estimation method for FBMC/OQAM toward 6G: Advantages, challenges and future works

Filter bank multicarrier/offset quadrature amplitude modulation (FBMC/OQAM) is a multicarrier modulation that is expected to replace orthogonal frequency division multiplexing (OFDM) in future sixth-generation (6G) networks. FBMC/OQAM has high spectrum efficiency, cyclic prefix (CP)-free transmission, decreased out-of-band emission (OOBE), and asynchronous environment robustness. However, the orthogonality criteria of FBMC/OQAM are onlyin the real field. Therefore, imaginary components of complex-valued OQAM symbols cause imaginary interferences among subcarriers, affecting channel estimation (CE) processing operations. Channel estimation is a critical component of wireless communication systems. Channel estimate allows the receiver to approximate channel impulse response (CIR) to determinethe impacts of the communication channel on the sent symbols. So, an accurate channel estimate is critical in FBMC/OQAM. In this review, we focus on the Preamble-based method, one of the basic methods for channel estimation in FBMC. Three preamble-based approaches have been studied: the interference approximation method (IAM), the interference cancellation method (ICM), and pairs of pilots (POP) using a single antenna and multiple input multiple outputs (MIMO). Compare them regarding bit error rate (BER) and mean square error. Also, it compares different interference approximation methods in terms of bit error rate (BER), magnitude, and peak average power ratio (PAPR). The review found the superiority of M-IAM and NPS in spectrum efficiency and PAPR. Future work that can help the researcher in this field

Delamination assessment via acoustic wave propagation and an optical sensor network

The preservation of the integrity of composite structures necessitates the monitoring of their structural health. A considerable body of research has been dedicated to investigating the use of traditional electrical sensors for the purpose of collecting acoustic waves in order to detect delamination. In contrast, electrical sensors possess several limitations. This research endeavors to evaluate delamination by employing an optical sensor network that relies on a fiber Bragg grating (FBG) sensor. In the experiment, composite plates were fabricated with varying sizes of delamination. The composite specimen has been equipped with a sensor network consisting of four Fiber Bragg Gratings (FBGs) placed linearly. This network enables the detection of acoustic wave propagation resulting from an impact in the middle of the composite plate. Upon analysis of the acoustic waves, it is seen that the average time delay for various delamination circumstances is 68.2% for a delamination size of 10 cm x 4 cm and 116.7% for a delamination size of 10 cm x 6 cm. The findings of the study also demonstrate that the reduction in wave speed is 40.54% and 53.85% for delamination sizes of 10 cm x 4 cm and 10 cm x 6 cm, respectively. The results indicate that the utilization of a network of Fiber Bragg Grating (FBG) sensors for the purpose of delamination detection in plate-like composite structures holds promise in the field of health monitoring

Bioactive compounds and antioxidant activity in various citrus peels: a significant systematic review

Citrus peels, traditionally considered byproducts in fruit processing, have garnered increasing attention due to their bioactive compounds and antioxidant characteristics. This systematic literature review aims to comprehensively evaluate and consolidate existing research on the bioactive compounds and antioxidant properties found in various citrus peel varieties. Citrus fruits, particularly their peels, have emerged as substantial repositories of bioactive compounds, including flavonoids, phenolics, and essential oils. Nevertheless, the fragmented nature of existing research on various citrus species, cultivars, and extraction methods underscores the need for a systematic approach. This is essential to provide a cohesive understanding of the inherent bioactive profiles and antioxidant capabilities present within these peels. Employing the Pre-Recording Systematic Reviews and Meta-Analysis (PRISMA) approach, this study executed a systematic search using academic strategies on Scopus and Web of Science databases to identify and select pertinent studies. Advanced search strategies utilizing keywords (1) citrus peel and (2) antioxidant was employed. A rigorous evaluation of methodological and analytical procedures was undertaken, and data were systematically collected for analysis, resulting in a final dataset of studies. This systematic review covered three key themes: (1) Citrus Fruit and Peel Utilization for Health and Nutrition, (2) Anticancer Properties and Chemical Composition of Citrus Products, and (3) Citrus By-Products and Waste Utilization. The review reveals substantial variability in bioactive compounds and antioxidant activities across different citrus peels. This review offers a comprehensive examination of each study's methodology and findings. Ultimately, the findings emphasize the importance of citrus peels as abundant sources of bioactive compounds and antioxidants, with far-reaching implications for nutrition, health, and biotechnological applications. While providing a comprehensive overview of the diverse bioactive profiles in different citrus peels, this review also advocates for further research to unlock their full potential and encourage sustainable practices in the food industry

Unsteady-state dynamics and AI in membrane desalination: challenges and emerging opportunities

Membrane desalination is a pivotal technology for addressing global water scarcity, yet most studies focus on steady-state operation. Unsteady and cyclic processes remain comparatively underexplored, although they are central to advancing recovery, fouling resistance, and operational flexibility. This review synthesizes over 100 publications (2015–2025) covering reverse osmosis (RO) configurations such as closed-circuit RO (CCRO), batch RO (BRO), flow-reversal RO (FRRO) and emerging hybrids, as well as flow in MD, FO, ED and PRO. Quantitative comparisons reveal that CCRO can reach up to 98 % recovery but suffers from flushing inefficiency with ∼11 % residual brine per cycle; BRO reduces energy consumption by ∼30 % under brackish water treatment at 95 % recovery; and FRRO retrofits have lowered specific energy consumption by ∼14 % while enabling recoveries of 90–91 %. Beyond mechanistic modeling, the review highlights the integration of computational fluid dynamics (CFD) and machine learning (ML), including explainable AI (e.g., SHAP), reinforcement learning, and physics-informed neural operators, which have demonstrated up to 16 % operating cost reduction and > 100 % membrane life extension in industrial-scale RO. We identify three critical gaps: (i) flushing inefficiency and cycle-to-cycle salt accumulation, (ii) limited pilot-scale and long-term datasets for unsteady operations, and (iii) challenges in integrating CFD with AI frameworks. By bridging mechanistic and data-driven approaches, this review outlines opportunities to develop digital twin frameworks for resilient, efficient, and intelligent unsteady desalinatio

Advancements in earthquake research and seismic risk analysis: An in-depth review centered on Malaysia

This paper provides an in-depth review of recent advancements in earthquake and seismic risk analysis research, specifically focusing on Malaysia. It highlights the need for a deep understanding of seismic risks to create better hazard preparedness and mitigation strategies. Using a systematic literature review method, the paper brings together a wide range of recent studies, modern methods, and key findings. It identifies major knowledge gaps and suggests new directions for future research. Additionally, this paper covers cutting-edge seismic monitoring technologies, advanced fault analysis techniques, and comprehensive seismic hazard assessment models to provide a thorough understanding of Malaysia's complex seismic landscape. It also addresses the economic implications of seismic events, emphasizing the significance of earthquake preparedness. Moreover, the study underscores the need for greater collaboration among scholars, policymakers, and practitioners from various sectors. It advocates for the development of integrated frameworks and policies to address the identified gaps and translate research insights into practical solutions. The conclusion calls for a united effort to boost Malaysia's resilience to seismic hazards through continuous innovation, shared expertise, and strategic policymaking. This thorough analysis not only aims to deepen the scientific understanding of seismic risks in Malaysia but also to inspire effective policies and build a community ready to tackle future seismic threats. By closing knowledge gaps and enhancing teamwork, this study helps pave the way for a more secure and prepared Malaysia

Synthetic image data generation via rendering techniques for training AI-based instance segmentation

Synthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning

Efficient screening of biopolymers for tablet dosage form using the mixture screening design to enhance probiotic viability and gastric resistance

It is widely acknowledged that probiotics are beneficial for health, but harsh gastric conditions can destroy them. This condition highlights the need for protective delivery mechanisms in the stomach. Probiotics must be protected from the harsh gastric environment and provided with the right delivery mechanism for maximum effectiveness. This study aimed to identify significant biopolymer excipients that can be used to produce acid-resistant probiotic tablets using the mixture screening design. In this study, six biopolymers, namely, carboxymethyl cellulose (CMC), pectin, carbopol, hydroxypropyl methylcellulose (HPMC), alginate, and xanthan were screened at different ratios to evaluate their effects on probiotic viability and tablet disintegration time in simulated gastric/intestinal fluids. A 21-run design with three replicates was implemented using a mixture screening approach. Statistical analysis showed that CMC, HPMC, and alginate have significantly improved the viability and dissolution time of the tablets in the gastric environment. These polymers were selected for further optimisation studies to develop protective probiotic tablets. The mixture screening design allowed the efficient screening of polymer combinations to identify key excipients for maximising viability and gastric resistance

Embedded feature importance with threshold-based selection for optimal feature subset in autism screening

The early detection of autism spectrum disorders (ASD) in children poses significant challenges due to the dynamic and progressive nature of the symptoms. To The current screening process involves a lengthy and costly series of questions covering various aspects of a child's development. To address this issue, we adopt the embedded feature selection method based on random forest and threshold-based to produce a simplified version questionnaire for Autism screening. The aim of this paper is to identify the most crucial and effective features from the Quantitative Checklist for Autism in Toddlers (Q-CHAT) by combining the strengths of threshold filtering and embedded random forest feature importance. This integration allows us to significantly reduce the number of screening questions while maintaining reliable and accurate results. Our proposed method yields a streamlined alternative to traditional screening, extracting just eight key features that achieves an impressive 96% accuracy performance. This promising approach holds the potential to revolutionize early detection and intervention programs for toddlers with autism, ultimately leading to improved outcomes

Mixed convection boundary layer flow over a horizontal circular cylinder in a Williamson hybrid ferrofluid with viscous dissipation effect

This research paper focuses on the flow of Williamson hybrid ferrofluid on mixed convection boundary layer flow via a horizontal circular cylinder. The mathematical model discussed reflect to the investigation of flow characteristics and the heat transfer capabilities of a hybrid ferrofluid. The problem starts with a system of partial differential equations which govern the problem together with suitable initial and boundary conditions. In addition, this problem also considered the presence of magnetohydrodynamic (MHD) effect and viscous dissipation. The dimensional governing equations together with its initial and boundary conditions are converted into dimensionless governing equations by using the proper dimensionless variables. The Keller-box technique is used to solve the partial differential equation numerically where these equations are generated from the modification of the dimensionless governing equation and non-similarity transformation. Various pertinent parameters are acquired and discussed in this research. The results showed that the skin friction coefficient and Nusselt number have increased because of the ferrofluid’s increased nanoparticle volume. The Williamson hybrid ferrofluid performed better than Williamson ferrofluid in terms of heat transfer capacity with high friction between the fluid and the cylinder surface

Exploring the association between personality traits and leadership: A systematic literature review

Understanding personality-related factors that influencing leadership is crucial for guiding stakeholders toward achieving long-term organizational goals, as leadership effectiveness is demonstrably shaped by individual dispositions impacting decision-making, interpersonal dynamics, and motivation. Although the association between personality traits and leadership has been extensively examined across diverse contexts, systematic reviews synthesizing recent empirical evidence remain relatively insufficient. Therefore, this study aims to systematically explore how diverse personality frameworks relate to leadership outcomes by reviewing literature published between 2019 and 2024. Utilizing predefined inclusion and exclusion criteria, a systematic review identified, selected, and rigorously analyzed 22 peer-reviewed articles sourced from Scopus, Web of Science, and Google Scholar databases. The findings consistently reveal significant associations between all five Big Five traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) and leadership, with extraversion exhibiting a strong positive correlation and neuroticism demonstrating a prominent negative correlation. Furthermore, traits from alternative models, specifically honesty-humility from the HEXACO framework and enterprising characteristics from the RIASEC model, were also identified as positive contributors to leadership development. These results emphasize the critical importance of integrating insights from multiple personality theories to gain a more detailed and comprehensive understanding of leadership functioning across different organizational and cultural settings. Beyond its theoretical implications, this review offers valuable, evidence-based insights for educators designing leadership syllabuses, organizational consultants developing talent management programs, and policymakers formulating strategies to cultivate leadership pathways. By identifying robust and consistent personality predictors of leadership, this study contributes meaningfully to the expanding literature focused on enhancing leadership capacity at individual and organizational levels, thereby supporting more effective and sustainable goal achievement