Extracellular vesicles (EVs) are emerging as naturally bioactive nanomaterials with intrinsic biocompatibility and targeting potential. Recent integration of machine learning (ML) into EV research has accelerated advances in molecular profiling, structure–function prediction, and rational design of vesicle-based therapeutics. Yet, the inherent complexity and heterogeneity of EV populations pose major analytical challenges. Concurrently, machine learning is revolutionizing biomedical science by uncovering patterns in high dimensional, multimodal datasets. In EV research, ML has enabled major advances across automated imaging, multi omics integration, disease classification, therapeutic engineering, and standardization. This review presents a comprehensive synthesis of ML-enabled EV studies, organized by data modality (imaging, omics, cytometry), algorithmic paradigm (CNNs, random forests, autoencoders, GNNs), and translational application (diagnosis, prognosis, drug delivery, manufacturing QC). Unlike prior reviews that have typically considered EV biology and AI methods in relative isolation, we introduce a unified three-axis taxonomy that explicitly links EV data modalities, machine learning architectures, and clinical use-cases, thereby providing a structured map of the field. We discuss key technical barriers including data sparsity, batch variability, and model explainability and spotlight frontier developments such as federated learning, self-supervised models, and real-time EV analytics. At the nexus of computational intelligence and nanomedicine, ML-enhanced EV platforms are rapidly progressing from fragmented innovations to clinically actionable systems. This review offers a roadmap for advancing AI-integrated EV technologies in cancer precision medicine.
Increased production volumes and higher demands for operational efficiency have prompted PT X to reduce its reliance on manual material handling activities, as such practices lead to time waste, human error, and disruptions in material flow. These conditions have driven manufacturing industries to transition toward automation technologies. One widely adopted automation solution is the Automated Guided Vehicle (AGV). However, the implementation of AGVs requires a relatively high initial investment, making a comprehensive investment feasibility analysis essential to ensure that the transition delivers sustainable benefits. This study aims to analyze the investment feasibility of implementing AGVs in the material handling system using a Return on Assets (ROA) approach based on operational cost savings. The research employs a quantitative analysis by comparing total investment and maintenance costs with the financial benefits derived from labor cost reductions over the analysis period, while also considering annual increases in benefit values due to inflation. The results indicate that the implementation of AGVs generates cumulative operational cost savings that exceed the total investment cost, with the investment payback period achieved in 2031. This finding shows that AGV investment is financially feasible in the long term. The main conclusion of this study is that AGV implementation contributes significantly to cost efficiency and process stability in material handling operations. The research implications suggest that these findings can serve as a decision-making basis for management in planning investments in automation technologies.
Background: Tax avoidance and financial fraud significantly undermine fiscal integrity, particularly in emerging markets such as Indonesia and Pakistan, where corporate governance frameworks are still evolving. Effective corporate governance is essential for mitigating managerial opportunism (agency problems) that drive this financial misconduct. However, the effectiveness of specific structures, such as independent oversight and CEO duality, remains inconsistent and context-dependent. A critical gap exists in understanding the moderating influence of gender diversity on corporate boards in curbing misconduct within these specific dual-market settings. Objective: This study examines the impact of corporate governance structures, independent commissioners, concentrated ownership, institutional ownership, and CEO duality on tax avoidance and financial fraud in non-financial firms in Indonesia and Pakistan, with gender diversity as a moderating factor. Methodology: This study employs a quantitative, panel data design to analyse the relationships. The sample comprises 2,000 firm-year observations (200 non-financial firms each from the Pakistan Stock Exchange and Indonesia Stock Exchange, covering 2020–2024). Data are sourced from audited financial statements and governance disclosures. Multiple linear regression is used to test the hypotheses, with robust standard errors applied to address potential heteroskedasticity. The model incorporates interaction terms to assess the moderating role of gender diversity and controls for firm-level factors, including Return on Assets (ROA), leverage, and firm size. Findings: The analysis revealed that Independent Commissioners and Institutional Ownership are significant deterrents, resulting in a reduction of both tax avoidance and financial fraud. Concentrated Ownership was found to be negatively associated with misconduct, but with a comparatively weaker effect. Conversely, CEO Duality significantly increases both tax avoidance and financial fraud. Furthermore, the moderation analysis confirmed that Gender Diversity not only directly reduces misconduct but also significantly strengthens the negative effects of effective governance mechanisms while mitigating the adverse impact of CEO Duality on both outcomes. Conclusion: Strong governance structures and gender diversity effectively curb tax avoidance and financial fraud, enhancing ethical oversight in emerging markets. Unique Contribution: This study uniquely integrates gender diversity as a moderator into the governance-misconduct relationship in the understudied emerging markets of Indonesia and Pakistan, thereby contributing to the agency theory literature. Key recommendation: Regulators should promote independent oversight, institutional investment, and gender diversity to curb financial misconduct.
In Internet-of-Things (IoT), several types of random access (RA) protocols have generated significant interest to address the constantly growing access requirements of IoT devices. Considering high traffic loads in a sixth-generation (6G) massive heterogeneous IoT network, this article studies contention-based RA based on a slotted ALOHA with priority access (PA) protocol empowered by uncoordinated uplink rate-splitting multiple access (RSMA), referred to as PA-RSMA-ALOHA. However, prior research created the preconfigured received power model assuming ideal conditions, such as perfect channel state information (CSI) and perfect successive cancellation interference (SIC). Performance degradation may result from the inability to fully remove the decoded signal due to the disregard for imperfect SIC and CSI. This article examines PA-RSMA-ALOHA under imperfect CSI and imperfect CSI. Moreover, an irregular repetition slotted ALOHA (IRSA) protocol is proposed to enhance the throughput by exploiting diversity burst in a medium access control frame, referred to as PA-RSMA-IRSA. First, the bound on the throughput of PA-RSMA-ALOHA with imperfect CSI and SIC is derived using a collision model and Poisson distribution. Next, the effect of packet diversity burst and SIC in PA-RSMA-IRSA is derived to present asymptotic throughput using density evolution graph-based decoding. Finally, simulation results verify the superiority of the proposed PA-RSMA-IRSA over the benchmark RAs.
Generasi muda di era digital menghadapi tantangan kesehatan mental akibat budaya flexing, social comparison, dan Fear of Missing Out (FOMO) di media sosial, yang secara konsisten dilaporkan berasosiasi dengan pexiganan kecemasan (Przybylski dkk., 2013; Shannon dkk., 2022). Nilai-nilai tasawuf Islam klasik, khususnya konsep zuhud dalam Kitab Minhajul Abidin karya Imam Al-Ghazali, berpotensi menjadi strategi mitigasi yang kontekstual namun belum terdiseminasi secara efektif kepada masyarakat luas. Tujuan: Kegiatan ini bertujuan menginternalisasi nilai-nilai zuhud kepada komunitas pemuda Youth Green Villager di Desa Ketapang, Kec. Susukan, Kab. Semarang, sebagai strategi mitigasi kecemasan digital dan penguatan ketahanan mental. Metode: Program dilaksanakan melalui lima tahap terstruktur dengan desain one-group pretest-posttest (pra-eksperimental tanpa kelompok kontrol): sosialisasi dan koordinasi, pengukuran awal (pre-test), diseminasi materi, workshop internalisasi berbasis Focus Group Discussion (FGD), dan pengukuran akhir (post-test). Instrumen kuesioner digunakan untuk mengukur dua dimensi: internalisasi nilai zuhud dan mitigasi kecemasan digital. Hasil: Dari 10 peserta, terjadi peningkatan skor rata-rata secara deskriptif dari 13,7 (pre-test) menjadi 21,8 (post-test) pada kedua dimensi pengukuran, didukung temuan kualitatif dari FGD yang mengindikasikan pergeseran cara pandang sebagian peserta terhadap tekanan digital. Kesimpulan: Model diseminasi nilai spiritual Islam klasik yang dikombinasikan dengan pendekatan kesehatan mental modern menunjukkan indikasi peningkatan pemahaman dan mekanisme koping peserta dalam menghadapi tekanan era digital. Hasil ini bersifat awal (preliminary) mengingat keterbatasan jumlah peserta dan ketiadaan kelompok pembanding, sehingga generalisasi memerlukan kajian lanjutan dengan desain yang lebih kuat.
