Abstract: In this review, the occurrence in the environment and human surrounding, as well as toxic action of perfluorooctanoic acid (PFOA) and its selected short-chain analogs – perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA) has been described. These substances belong to a group of polyfluoroalkyl substances (PFASs) widely represented in various compartments of the environment, including air, water and soil. They are also present in dust, drinking water and food, which are main sources of the exposure of humans to these compounds. Due to physico-chemical properties PFASs are strongly resistant to degradation in the biosphere and therefore, effectively accumulate in biota, including humans. PFOA has been produced from decades, but due to its toxicity it has been successively replacing by PFASs of shorter chain, including PFHxA and PFBA, which presence in the environment, as well as risk of human exposure and toxicity has been poorly investigated. It has been proven that PFOA reveals hepatotoxic and endocrine-disrupting activities, as well as exhibits prooxidative, immunotoxic, epigenetic and carcinogenic potential. Hitherto conducted researches have shown that PFHxA and PFBA are less toxic than PFOA, nevertheless additional extensive studies should be conducted in order to determine environmental and toxicological status of these compounds.

Abstract: Improving provitamin‑A cassava requires a clear understanding of how key agronomic and nutritional traits respond to seasonal variation and how these traits interact within a multivariate structure. The objectives were to evaluate agronomic and nutritional traits across seasons, apply multivariate analyses to uncover trait domains, and investigate inter‑trait relationships to guide breeding strategies. Forty‑two provitamin‑A cassava accessions were evaluated using a split‑plot randomized complete block design, with genotype as the main‑plot factor and harvest time as the subplot factor, replicated across seasons. Eight traits were measured, with emphasis on DM, FYLD, and TC. Multivariate analyses provided deeper insight into trait structure. Principal component analysis and factor analysis identified distinct trait structures: PCA revealed four trait domains, while factor analysis uncovered three latent trait groupings–Root Productivity (ML2), Vegetative and Compositional Diversity (ML3), and Harvest Efficiency (ML1). Traits such as RTWT and HI exhibited near‑zero uniqueness, reinforcing their roles as anchor indicators of productivity and efficiency, while TC displayed exceptionally high uniqueness, confirming its independence from yield domains and its regulation by distinct metabolic pathways. Biplots highlighted genotype dispersion and trait loadings, hierarchical clustering grouped accessions by combined agronomic and nutritional performance, and a chord diagram confirmed a tightly linked yield complex alongside a separate nutritional domain. Variance component estimates revealed contrasting levels of genetic and environmental influence. DM showed moderate genotypic variance and no significant seasonal effect, underscoring its stability and reliability for processing quality. FYLD exhibited low genotypic variance, high residual variance, and a highly significant season effect, confirming strong environmental sensitivity. TC displayed high genotypic variance and a significant seasonal effect, suggesting that carotenoid accumulation is both genetically controlled and environmentally modulated. The integration of mixed‑model variance partitioning with multivariate and network‑based analyses revealed clear differences in trait stability, genetic control, and inter‑trait relationships. These findings identify DM as a robust trait for selection, FYLD as highly environment‑dependent, and TC as a promising target for biofortification with manageable environmental sensitivity. The results provide a comprehensive model for index‑based selection strategies, guiding breeders toward “stacked” ideotypes that combine high yield potential, stable dry matter content, enhanced carotenoid accumulation, and efficient resource allocation to meet industrial, nutritional, and food security needs across cassava‑growing regions.

Abstract: The mathematization of science is undergoing a structural transformation driven by the rise of computation and data-intensive methods. While classical mathematization relied on explicitly defined laws and formal structures, contemporary scientific practice increasingly encounters mathematical objects that arise as outcomes of dynamical and algorithmic processes. This paper introduces the notion of computationally emergent structures to describe entities generated and stabilized through the interaction of parameterized models, optimization dynamics, and data. We develop a minimal formal framework in which such structures are characterized as asymptotic outcomes of learning dynamics and show that, in over parameterized regimes, they are selected by implicit variational principles not specified a priori. This framework provides a unified account of implicit regularization, kernel regimes, and stability phenomena in modern learning systems. These results show that contemporary learning systems operate according to implicit variational principles in which geometry, dynamics, and data jointly determine effective mathematical structure. They thereby identify a shift from representation to dynamical emergence, extending the scope of mathematization toward a theory of structure formation grounded in computation.

Abstract: Generative Artificial Intelligence (GenAI) is driving a fundamental paradigm shift in architectural design, transitioning from deterministic drafting to algorithmic curation. While the Architecture, Engineering, and Construction (AEC) sector rapidly adopts these tools, academic curricula face a critical "Techno-Instructional Void." This gap risks inducing a "Zero Order Thinking State" (ZOTS)—a cognitive passivity rooted in Cognitive Load Theory, where students uncritically accept unbuildable machine hallu-cinations. Developed through comprehensive preliminary consultations with academ-ic colleagues and longitudinal studio observations, this study introduces the "Twin Houses" methodology and the "Technical Sealing" protocol. By enforcing "Cognitive Friction," the framework compels students to validate probabilistic GenAI outputs against deterministic physical laws (e.g., Blondel's Formula 2R + T = 63 cm) and safety norms. Crucially, Building Information Modeling (BIM) acts as an automated Proof-Assistant, utilizing visual programming APIs (Revit Dynamo, Allplan Python-Parts) and IFC 4.3 data schemas for rigorous Rule-Based Checking (RBC). To confirm cross-border transferability and optimize the time-costs of curriculum integration via an asynchronous AI-TPACK module, the framework is currently undergoing verifica-tion interviews with a bilateral expert panel (n=8) from Germany and Türkiye. Ulti-mately, this framework provides a structured pedagogical approach, equipping in-structors to guide students in transforming machine hallucinations into legally builda-ble, tectonic realities. Sample videos showcasing student works are available in the Supplementary Materials.

Abstract: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder driven by complex interactions between protein aggregation, oxidative stress, neuroinflammation, and cellular dysfunction. Among plant-derived compounds, curcumin has emerged as one of the most extensively studied polyphenols due to its broad spectrum of biological activities. This review provides a critical synthesis of mechanistic, preclinical, and clinical evidence on curcumin in AD. Experimental studies consistently demonstrate that curcumin modulates key pathogenic processes, including neuroinflammatory signaling, oxidative stress, and amyloid-β aggregation, with more limited evidence for effects on tau pathology. While in vitro studies offer detailed mechanistic insights, in vivo models provide more integrated evidence, including improvements in cognitive performance and reductions in pathological markers. Despite this strong preclinical foundation, clinical evidence remains limited and inconsistent. Randomized controlled trials have not demonstrated clear therapeutic efficacy, with outcomes strongly influenced by formulation, bioavailability, and study design. Poor solubility, rapid metabolism, and limited brain exposure remain key translational barriers. In response, increasing attention has been directed toward formulation strategies and structurally related compounds. Emerging curcuminoids, such as bisdemethoxycurcumin (BDMC), are discussed as potential next-generation candidates. Preliminary evidence suggests that BDMC may modulate oxidative stress, autophagy, astrocyte senescence, and amyloid-related processes, although data remain largely preclinical. Overall, curcumin represents a mechanistically rich and preclinically promising multi-target compound, but with unresolved translational limitations. Future research should prioritize pharmacokinetic optimization, formulation-dependent validation, and exploration of novel curcuminoid strategies to bridge the gap between experimental findings and clinical application in AD.

Abstract: Bismuth tungstate (Bi2WO6) is a typical bismuth-based visible-light-responsive semiconductor photocatalyst that has attracted significant attention in the fields of environment remediation and energy conversion. In this paper, to address the issues of high photogenerated carrier recombination rate and limited visible-light response range of Bi2WO6, various modification strategies are highlighted, including morphology control, element doping, heterojunction construction, carbon material compositing, and coupling with functional materials such as MOFs, COFs, or conductive polymers. Furthermore, the structure-activity relationships are discussed. On this basis, the latest application progress of Bi2WO6-based photocatalysts in fields such as pollutant degradation, antibacterial activity, and energy conversion and storage is summarized. Finally, prospects are put forward regarding the existing shortcomings and future development directions in the application of Bi2WO6-based photocatalysts, aiming to provide a systematic theoretical reference for the design and application of high-performance Bi2WO6-based photocatalysts.

Abstract: A systematic experimental investigation was conducted on the motion response (RAO) and mooring performance of a novel disk-shaped buoy (geometric scale 1:10) subjected to combined wind, wave, and current actions. A hybrid experimental strategy was employed, integrating a large-scale wave flume (for long-period waves and currents) with a harbor basin (for short-period waves and wind), aiming to mitigate the scale effects inherent in Froude-scaled models, particularly with regard to drag force measurements. The test matrix included free decay in calm water, RAOs under regular waves, motion and mooring line tension under irregular waves, and measurements of wind and current drag coefficients. Key results indicate a natural roll period of approximately 3.0 s with a notably high dimensionless damping ratio (ζ ≈ 0.14–0.15), which is conducive to rapid motion attenuation. A pronounced resonance peak in the roll RAO (26.6°/m) was observed near the 3 s period. Under an extreme sea state (Hₛ = 13.8 m, Tₚ = 16.1 s), the maximum roll angle and dynamic mooring line tension reached 21.30° and 61.56 kN, respectively, the latter being about 3.0 times the static pretension. The mean wind drag coefficient and current drag coefficient were determined as 0.76 and 0.44. This research provides a validated dataset and critical insights for the design, mooring system optimization, and operational safety assessment of such disk-shaped buoys. The effectiveness of the hybrid testing approach is confirmed, and the favorable damping characteristic of this buoy form is highlighted.

Abstract: Agentic AI systems plan over time, call tools, write and retrieve memory, and coordinate across modules and services. Some of the most consequential failures in such systems are structural before they are behavioral: hidden coordination, trace-mediated lock-in, seam bottlenecks, and the silent erosion of meaningful override. This position paper argues that agentic AI systems should be evaluated and governed for \textbf{structural governability}, not only output alignment. By structural governability, we mean whether consequential coordination remains observable, attributable, interruptible, and steerable at the seams between components before irreversible commitments occur. Output-only evaluation does not capture this property. We propose an evidence ladder for structural risk in place of any single master metric: architecture-time priors over system structure, runtime coupling signals on telemetry graphs, and deeper state-regime diagnostics for high-stakes cases. We then sketch a research agenda for benchmarks that stress structure rather than terminal task success, reporting standards that disclose control geometry, and seam-level interventions including approval gates, permission freezes, trace decay, rollback, and subsystem isolation. The wider stake is cognitive integrity. Once agentic systems mediate what users retrieve, remember, delegate, and act upon, alignment depends on preserving the conditions under which users and operators can still understand, contest, redirect, and refuse those processes.

Abstract: Isavuconazole is increasingly being used for the treatment of invasive fungal disease (IFD), although real-world pediatric data remain limited. We retrospectively reviewed patients aged ≤18 years who received isavuconazole for fungal infection at two tertiary centers in Chile between 2021 and 2025. Twenty patients were included, with a median age of 13.7 years (IQR, 9.4–15.5); 14 (70%) had an underlying malignancy, and 6 (30%) were allogeneic hematopoietic cell transplant recipients. Isavuconazole was used for treatment in 16 patients and for prophylaxis in 4, predominantly as second-line therapy due to prior antifungal intolerance, inadequate response, drug–drug interactions, or QT prolongation. Fifteen patients had IFD, with Mucorales (n=3) being the most frequently identified pathogens in proven cases and pulmonary involvement predominating in probable IFD. A succesful response at 90 days was achieved in 60% (6/10) of evaluable cases. No breakthrough fungal infections occurred during treatment or prophylaxis. Hepatic enzyme elevations were observed in four patients. Isavuconazole was associated with favorable outcomes and an acceptable safety profile, supporting its use as an alternative antifungal in pediatric patients.

Abstract: This study examines the relationships between innovation, green management implementation, and their effects on business sustainability and value creation in Indonesian healthcare companies. Innovation is measured using Value Added Intellectual Capital (VAIC) efficiency, while green management implementation is proxied by Environmental, Social, and Governance (ESG) scores. Business sustainability is operationalized through carbon disclosure practices, and value creation is measured via Market Value Added (MVA). Using panel data from 30 listed firms (2019–2023) and applying fixed and random effects regression models showed that green management implementation significantly and positively influences carbon disclosure, supporting the role of ESG practices in enhancing sustainability transparency. However, innovation capacity demonstrates no significant direct effects on either carbon disclosure or market value creation, challenging conventional assumptions about intellectual capital's impact on sustainability and value performance. Component-level analysis improves explanatory power, showing that structural capital efficiency has a marginally negative effect on carbon disclosure, while social ESG scores emerge as the strongest driver of transparency. However, neither carbon disclosure nor ESG performance translates into immediate value creation, with MVA explained more by firm-specific characteristics and profitability (ROA). These findings extend intellectual capital and transparency theories by showing how social performance channels shape disclosure practices.

Abstract: Chalcopyrite and molybdenite exhibit similar surface wettability and high floatability, which has long hindered their efficient and selective separation in mineral processing. In this work, the novel chalcopyrite depressant 2-mercapto-5-benzoimidazole sulfonate dihydrate (2MBI5SA) was investigated for its effect on the flotation behavior of chalcopyrite and molybdenite. Compared with the conventional depressant sodium sulfide (Na₂S), 2MBI5SA exhibited stronger selective depression toward chalcopyrite; under conditions yielding Mo recovery of 81.46% and a Mo grade of 4.46%, the Cu recovery decreased to 13.03%. To clarify the origin of this selectivity, interfacial properties were systematically characterized using adsorption measurements, contact angle measurements, zeta potential measurements, FT-IR, XPS, and SEM-EDS, and the adsorption mechanism was further elucidated using SCC-DFTB calculations. The results demonstrate that 2MBI5SA chemisorbs onto the chalcopyrite surface via bidentate coordination, forming a stable adsorption layer that effectively suppresses chalcopyrite flotation. Moreover, structure-function relationship analysis confirmed that introducing hydrophilic and ionizable functional groups into the collector framework can convert a collector into a selective depressant, thereby providing new insight into the design of environmentally benign flotation depressants.

Abstract: Oncolytic virotherapy offers a promising avenue for solid tumor treatment, yet single-agent approaches are frequently limited by insufficient tumor lysis and inadequate immune activation. Here we report that combined therapy with two recombinant variants of the oncolytic vaccinia virus, armed with either herpes simplex virus thymidine kinase (VV-HSVtk) or the interleukin 15 receptor subunit alpha (VV-mIL15Rα), induces enhanced cytotoxicity and immune stimulation in a murine mammary adenocarcinoma model (4T1). In vitro, VV-HSVtk exhibited dose-dependent cytotoxicity markedly potentiated by ganciclovir (GCV) through HSVtk-mediated phosphorylation into a cytotoxic nucleoside analogue, and co-culture of VV-infected tumor cells with donor-derived NK cells further amplified oncolytic efficiency. In vivo, combined treatment with VV-HSVtk, VV-mIL15Rα, and GCV resulted in significant tumor regression and extended survival relative to monotherapy controls in 4T1 syngeneic mice. Histological examination revealed robust lymphocytic infiltration at tumor sites and absence of hepatic or splenic toxicity. Mechanistically, VV-mIL15Rα-driven IL-15 trans-presentation amplified immune cell activation, while VV-HSVtk/GCV provided targeted tumor debulking and immunogenic cell death, collectively reshaping the immunosuppressive tumor microenvironment. These findings establish a multimodal oncolytic platform combining direct viral cytotoxicity, suicide gene therapy, and cytokine-mediated immune co-stimulation as a translatable strategy for treating solid tumors.

Abstract: Purpose: This study examines whether finance-adapted (FA) phishing detection models improve detection of finance-themed (FT) attacks, whether improvements differ across email and webpage modalities, and whether finance adaptation creates a specialisation–generalisation trade-off. Design/Methodology/Approach: A domain-aware framework is developed using email (164,972 instances) and webpage (11,430 instances) datasets. FT and non-finance-themed (NFT) instances are identified using weighted lexicon-based labelling. Generic models are compared with FA models across Logistic Regression, Linear SVC, and Random Forest using F1-score, MCC, balanced accuracy, ROC-AUC, and PR-AUC. Statistical validation employs bootstrap confidence intervals and McNemar's test, while SHAP and permutation importance interpret webpage model behaviour. Findings: FA models outperform generic models in FT email classification, confirming that finance-specific semantic cues improve detection. However, gains are weaker and less consistent in webpage classification, where models rely mainly on structural indicators (page rank, Google index, hyperlinks). Results reveal a specialisation–generalisation trade-off: FA models improve in-domain detection but do not consistently outperform generic models on NFT instances, with F1-score declines of -0.0057 to -0.0151 on non-finance subsets. Practical Implications: Financial institutions and fintech platforms should deploy domain-adapted detection for email-based threats, where finance-specific linguistic cues yield measurable gains, while maintaining generic or ensemble models for broader webpage phishing coverage. Originality/Value: This study introduces a finance-themed, multi-modal, explainable AI framework for phishing detection, demonstrating that domain adaptation depends critically on data modality and feature representation. It provides a novel systematic comparison of generic versus FA phishing detection across both modalities with statistical validation and explainability analysis.

Abstract: This study proposes a geometric procedure for robust controller tuning under parametric uncertainty, based on root-contour analysis of the closed-loop control system. For a fixed candidate controller tuning, the set of possible pole locations induced by the admissible variations of the control plant parameters is constructed. Robust admissibility is formulated as a geometric set-inclusion problem, requiring this set to remain inside a prescribed dynamic performance region in the complex s-plane. A distinction is introduced between nominal admissibility, robust stability, and robust admissibility, showing that stability over the entire uncertainty set is not sufficient to guarantee the desired dynamic performance. To quantify the root contours, several indices are defined, including the dispersion along the real and imaginary axes, the maximum pole displacement with respect to the nominal pole locations, and the geometric margin to the boundary of the performance region. The procedure is applied to the selection and verification of PI controller tunings for an uncertain single-input single-output (SISO) control system and is further validated through examples with different structures of parametric uncertainty, including a system with a single uncertain parameter and a PID-controlled system with several uncertain control plant parameters. The results show that root-contour analysis can distinguish tunings that are only robustly stable from tunings that preserve the prescribed dynamic performance over the entire uncertainty set. Thus, the method can be used as a practical tool for the diagnosis, comparison, and selection of controller tunings under parametric uncertainty.

Abstract: This paper provides the first systematic, cross-country empirical comparison of the Recovery and Resilience Facility (RRF) and Cohesion Policy funds (CPF) in the domain of renewable energy deployment. Covering 14 EU Member States, the analysis combines quantitative cross-country evidence on financing volumes, technology mixes, implementation speed, and reported capacity achievements. The findings show that the RRF represents a major amplification of EU renewable energy financing, with planned allocations exceeding Cohesion Policy expenditure by a factor of five to ten. At the same time, claims of superior performance-based delivery require qualification: green transition financial progress lags the general RRF disbursement rate, milestone fulfilment for renewable energy falls short of planned indicative rates in most countries, and reported operational capacity figures raise plausibility concerns. The analysis reveals no meaningful correlation between milestone and target fulfilment and progress with renewable energy Country-Specific Recommendations, suggesting that administrative compliance with milestones does not immediately translate into structural reform outcomes. These findings carry direct implications for the design of the post-2027 EU financial framework, particularly regarding the stabilisation of performance indicators, the introduction of attribution protocols for reform-linked achievements, and the preservation of complementarity between performance-based and non-performance-based approaches.

Abstract: Maritime operations rely on the Automatic Identification System (AIS), an open broadcast protocol whose unauthenticated, self-reported Messages are easily abused. This survey takes an AIS-first, security-focused view, grounded in a comprehensive review of prior AIS-security research. We (i) explain how AIS works and use that to expose fundamental weaknesses; (ii) synthesize from the literature the main threats and their technical and operational impacts; (iii) categorize, from the surveyed works and operational practice, mitigations by the layers they target and, for each mitigation, indicate whether it primarily prevents, detects, responds, or supports recovery; and (iv) provide practical recommendations. Bringing together cybersecurity, maritime operations, and data-science perspectives, we consolidate recommendations for securing AIS-based systems and assess their current use in practice, thus highlighting the gaps that standards and implementations still need to address.

Abstract: This study examines the evolution of research on mergers and acquisitions (M&A) in the banking sector through a bibliometric analysis aimed at identifying the main con-tributors, dominant research themes, and emerging gaps in the literature. The study situates banking M&A research within broader discussions of efficiency, market structure, integration performance, and financial stability, while highlighting the need to better understand how scholarly influence and thematic development shape the field over time. Using bibliometric techniques, the analysis evaluates publication trends, journal con-centration, authorship patterns, international collaboration networks, citation struc-tures, keyword co-occurrence, and thematic mapping. The study synthesizes relation-ships among influential publications, institutions, and countries to assess how ideas circulate and which research themes remain central or underexplored within the liter-ature. The findings reveal a concentrated body of scholarship dominated by a limited number of journals, recurrent author networks, and strong transatlantic collaboration patterns, with relatively limited representation from the Global South. Research themes related to efficiency, firm performance, and market structure occupy the core of the literature, whereas technology integration, sustainability considerations, consumer outcomes, and conduct risk remain comparatively peripheral. Citation patterns are highly une-ven, reflecting a small group of highly influential studies alongside a broader set of context-specific contributions. The analysis also identifies a persistent gap between pre-merger strategic narratives and post-merger integration realities, particularly in relation to operational outcomes and systemic risk considerations. The study concludes that future research would benefit from integrating traditional finance approaches with transaction-level integration measures, governance and op-erational performance indicators, and more robust identification strategies around regulatory and policy shocks. The findings further suggest that banking practitioners should place greater emphasis on integration feasibility, risk-control capabilities, digi-tal transformation readiness, and sustainability considerations when evaluating and implementing merger strategies.

Abstract: Infrared object detection from unmanned aerial vehicles (UAVs) is critically challenged by multi-type composite degradation—including noise, blur, and low contrast—which severely undermines feature discriminability and multi-scale target perception. This study proposes SGW-DETR (Spectral-Guided Graph-structured Wavelet Detection Transformer), a novel framework built upon RT-DETR, incorporating three synergistic modules across the backbone, neck, and encoder. FDSANet (Frequency Domain Spectral Awareness Network) replaces the conventional ResNet backbone, integrating the Multi-Scale Frequency Perception Module (MSFPM), Selective Channel Frequency Decomposition (SCFD), and Dynamic Kernel Spectral Modulation (DKSM) to achieve instance-level adaptive spectral feature extraction without degradation-type supervision. The Graph-Structured Fusion Network (GSFN) combines the Adaptive Semantic Fusion Module (ASFM) with the Graph Structure Perception Module (GSPM), employing Gaussian kernel soft membership and two-stage message passing to explicitly model spatial topological dependencies among object components. The Wavelet-guided Contrast Feature Aggregation module (WCFA) restructures the Attention-based Intra-scale Feature Interaction (AIFI) encoder via a Haar-based Frequency Decomposition Unit (HFDU), decomposing features into foreground-edge and background-thermal components and achieving hierarchical foreground–background decoupling through nested dual-path causal contrastive attention. A UAV infrared degradation dataset comprising 4,686 images spanning six degradation types with component-level annotations was constructed for evaluation. SGW-DETR achieves 75.2% mAP50, outperforming RT-DETR by 3.5%, while simultaneously reducing GFLOPs and parameter count by 16.8% and 9.9% at an inference speed of 85.5 FPS. Sustained performance gains on M3FD and IndraEye benchmarks further demonstrate the framework’s cross-domain generalization capability, offering practical value for UAV-based surveillance, search-and-rescue, and border monitoring under adverse imaging conditions.

Abstract: The Gibbs Paradox (concerning the entropy of mixing and entropic extensivity) was explored in depth by Edwin Jaynes (1992). We take up Jaynes’ treatment, considering the special cases for which entropy is (approximately) extensive, and the general case in which it is not. We also explore the Holographic Principle which (strictly speaking) excludes the extensivity of entropy. The formalism of Quantitative Geometrical Thermodynamics shows that, being isomorphic to energy, it is entropy production (not entropy) that is extensive. As a corollary, Shannon information is also not extensive, although information production is extensive.

Abstract: Supernumerary robotic limbs (SRLs) represent an emerging class of wearable robotic systems designed to augment, rather than replace, human motor capabilities. Unlike prostheses or exoskeletons, SRLs operate as independent kinematic agents that enable users to perform multi-limb tasks, reduce physical workload, and enhance operational efficiency in complex environments. This study presents a systematic review of SRL technologies, focusing on mechanical design configurations, sensing modalities, and control strategies, and their influence on key performance metrics such as payload capacity, positioning accuracy, and human–robot interaction efficiency. A structured literature review methodology was adopted following PRISMA guidelines, covering publications from 2010 to 2025 across major scientific databases. The analysis reveals fundamental trade-offs between degrees of freedom, weight, and payload capacity, where high-dexterity systems often impose increased ergonomic burden. Control strategies have evolved from direct teleoperation toward hybrid and shared-autonomy frameworks integrating vision, bio-signals, and machine learning, although challenges remain in achieving intuitive and low-latency interaction. Application domains span industrial manufacturing, construction, rehabilitation, and assistive daily activities, with growing interest in precision-constrained environments such as healthcare. Despite significant progress, limitations persist in actuator back-drivability, long-term wearability, and robust intention recognition under real-world conditions. This review synthesizes current advancements, identifies critical research gaps, and outlines future directions toward scalable, human-centric SRL systems capable of seamless integration into industrial and clinical workflows.