Search Results (8,549)

Forensic age estimation is a fundamental component of biological profiling for unidentified skeletal remains, particularly in mass casualty incidents where specimens are frequently fragmented or incomplete. This review evaluates the diagnostic utility of craniofacial suture closure—specifically across four facial regions—as a non-invasive methodology for age determination in adults. By analyzing the predictable fusion patterns of ectocranial and endocranial sutures, forensic practitioners can derive approximate age ranges when postcranial indicators are absent or unreliable. Despite its utility, the reliability of suture-based estimation remains a subject of academic debate. The rate of closure is influenced by a complex interplay of environmental and biological factors, including nutritional status, hormonal influences, and mechanical loading. Historically, the method has faced criticism due to significant inter-individual variability and limited sample sizes in cadaveric studies. To improve precision and novel detail, this review explores the integration of emerging technologies such as artificial intelligence (AI) and machine learning (ML). These tools can process extensive cranial datasets to identify subtle morphological patterns that may elude human observation. While craniofacial suture analysis remains an essential resource in the forensic toolkit, its accuracy is contingent upon accounting for multi-factorial biological factors. The authors emphasize the necessity for further external validation across diverse global populations to ensure the generalizability and refinement of the technique in forensic medicine and osteology. Full article

Pluripotent stem cells (PSCs) have proven outstanding potential to revolutionize biomedical research. Specifically, their capacity to form 3D multicellular systems that recapitulate organ development and biology, known as organoids, has transformed basic and translational research. The groundbreaking technology is also being applied to the intricate hypothalamus–pituitary (HP) axes, including the target organs (such as gonads, thyroid and adrenal glands). These HP axes govern critical physiological processes, including reproduction, metabolism and stress. Here, we provide an overview of PSC-derived organoid models that are part of the HP axes, both as individual and multi-organ systems, and evaluate their culturing conditions, phenotypic characteristics, advantages, drawbacks and challenges, as well as their potential for disease modeling and therapeutic discovery. By offering this wide perspective, our review will also serve as a key resource for researchers navigating the evolving landscape of PSC-derived organoid technologies in endocrinology. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article

Universities play a critical role in knowledge creation and technological innovation, serving as key drivers of regional development. However, existing research has paid limited attention to the mechanisms through which university innovation inputs translate into firm-level performance, particularly in the context of science and technology corridors in emerging economies. This study investigates how university innovation resources affect enterprise performance in the G60 Science and Technology Corridor within China’s Yangtze River Delta, one of the country’s most dynamic innovation regions. Using a panel dataset of 55 universities across nine cities from 2008 to 2017, we employ spatial analysis and fixed-effects panel regression models to examine the relationship between university innovation inputs and firm performance and further explore the mediating roles of local human capital and firm R&D investment. The results show that university innovation inputs significantly enhance enterprise performance, although excessive human resource inputs exhibit a negative effect on both short-term and long-term outcomes. Local human capital and firm R&D investment serve as key mediating mechanisms, with input and output resources influencing enterprise performance through distinct pathways. Heterogeneity analysis reveals that non-state-owned enterprises and small- and medium-sized enterprises derive greater long-term benefits from university resources. These findings contribute to the literature by clarifying the conceptual distinction between university innovation inputs and outputs, and by demonstrating the micro-level mechanisms—R&D investment and human capital—through which university-generated knowledge affects firm performance. The results also provide empirical evidence from an emerging economic context, extending the applicability of knowledge spillover and absorptive capacity theories. Policy implications include optimizing university human resource allocation, strengthening university–enterprise collaboration, and providing targeted support for non-state-owned enterprises and SMEs. Future research may extend the analysis to include institutional factors and university heterogeneity. Full article

Nano- and microplastic contamination poses a growing challenge to aquatic environments, driving the need for efficient and sustainable removal technologies. In this study, carbon–silica hybrid nanoparticles (CSNPs) were synthesized from rice husk-derived black liquor via controlled lignin–silica self-assembly followed by thermal carbonization, providing a waste-recycling biorefinery route for value-added material production. Structural characterizations revealed that carbonization generates a hierarchically porous carbon–silica hybrid with enhanced surface area. The CSNPs exhibited rapid and size-dependent adsorption toward nano- and microplastics (200–1000 nm), with optimal performance observed for 500 nm particles. Microscopic observations further demonstrated a size-adaptive capture mechanism, involving pore filling and surface adsorption for nanoplastics and aggregate-assisted encapsulation for larger microplastics. This study highlights CSNPs as low-cost and effective adsorbents for broad-spectrum plastic removal while offering a sustainable pathway for the high-value utilization of black liquor and rice husk biomass in water purification applications. Full article

The increasing need for energy-efficient and environmentally friendly electricity generation has led to the extensive use of hybrid electric systems. These systems integrate different energy sources in an effort to take advantage of the positives of each technology, as using a single source of energy comes with many limitations and disadvantages; hence, the popularity of hybrids has increased in recent times. In this regard, this paper proposes a lithium-ion battery (LIB) and ultracapacitor (UC)-based hybrid architecture considering an optimal energy management framework. In the transportation sector, hybrid vehicles (LIB and UC-based vehicles) effectively utilize the high energy density and power density of LIBs and UCs. This LIB and UC-based hybrid architecture provides an efficient power management solution considering the high power density of the LIB for smooth road profiles, and the high power density of the UC is driven during sudden spikes in load demand because the LIB will not function optimally during the sudden spikes due to lower power density. Furthermore, in order to achieve efficient utilization of the proposed hybrid system, an optimal energy management framework is used. In this regard, in this study, a fractional-order proportional–integral–derivative (FOPID) controller has been designed for effective and optimal energy management. Furthermore, the designed FOPID has been optimized using a metaheuristic technique, namely particle swarm optimization (PSO), to enhance LIB and UC-based hybrid electric vehicle energy management performance. Employing dynamic and optimal energy flow control, the FOPID-based system improves energy consumption, extends LIB life, and improves overall system performance and reliability. Full article

The countries in the Belt and Road Initiative (BRI) significantly influence global carbon emissions, and the spatial correlation and driving mechanisms of their emissions are crucial for regional emission reduction and global climate governance. This study constructs a carbon emission spatial correlation network, where links represent pairwise spatial correlations derived from a modified gravity model, using data from 54 BRI countries (2011–2020). It applies social network analysis (SNA) to examine the network structure and uses the Temporal Exponential Random Graph Model (TERGM) to identify influencing factors. The main findings are as follows: (1) The BRI carbon emission network has become more interconnected and cohesive, with stronger regional connectivity and reduced inequality. (2) The network shows a core–periphery structure with notable spatial association patterns. Countries like Qatar, Israel, India, China, and the UAE have rapidly established carbon emission links, positioning them at the core due to their high connectivity and influence. (3) The network displays temporal dependence, with reciprocity associated with stronger mutual connections and transitivity associated with more cohesive network structures. Technological innovation and industrial structure optimization are positively associated with the formation of carbon emission connections, while energy structure and foreign investment are negatively associated with it. Economic development and technological innovation are associated with a country’s greater involvement in carbon emission connections, and countries with similar urbanization rates, energy, and industrial structures, but large economic disparities are more likely to form carbon emission associations, reflecting potential complementarities in the network structure. Full article

Global demand for sustainable protein has intensified amid environmental, public health, and ethical concerns surrounding conventional animal agriculture. Edible mushrooms have emerged as promising next-generation protein sources, delivering 19–35% protein (dry weight) with complete essential amino acid profiles and digestibility rates of 60–80%. Beyond protein, mushrooms provide bioactive compounds, including β-glucans, ergothioneine, phenolic acids, and vitamin D₂, supporting immunomodulatory, antioxidant, and anti-inflammatory functions. Enzymatically derived bioactive peptides further demonstrate antihypertensive and antimicrobial activity. This review systematically examines mushroom protein properties, processing technologies, and product performance across three application categories: meat analogs, functional snacks, and beverages. Advanced processing technologies including high-moisture extrusion, ultrasonic-assisted extraction, and microencapsulation have improved bioactive preservation and digestibility. From an environmental perspective, mushroom cultivation requires 85–90% less water and land than animal agriculture, with 80% fewer greenhouse gas emissions. However, critical gaps remain: extraction efficiency varies 3-fold across studies, only 15–23% of commercial products are supported by clinical trials, and techno-economic analyses are largely absent. Standardized processing protocols, large-scale clinical validation, and harmonized quality standards are essential to establish mushrooms as viable, commercially scalable protein alternatives. Full article

Cultivated lettuce (Lactuca sativa L.) is a major leafy crop and an emerging model for functional genomics within the Asteraceae family, supported by high-quality reference genomes and efficient transformation systems. Although CRISPR/Cas technology offers powerful opportunities for crop improvement, editing efficiency depends on optimized construct architecture and reliable guide RNA (gRNA) validation. However, a rapid platform for evaluating CRISPR reagents in lettuce is still lacking. Here, we developed an efficient hairyroot-based system to accelerate CRISPR/Cas genome editing optimization in L. sativa. Four Agrobacterium rhizogenes strains were compared for hairy root induction in two cultivars, ‘Saladin’ and ‘Osiride’, identifying strain ATCC15834 as the most effective based on transformation frequency and root production. Using this platform, we evaluated multiple CRISPR construct configurations, including alternative promoters for nuclease and gRNA expression. A plant-derived promoter combined with At-pU6-26 variant significantly improved editing efficiency. As a proof of concept, we targeted LsHB2, the putative ortholog of Arabidopsis thaliana ATHB2, a key regulator of the shade avoidance response using SpCas9, SaCas9, and LbCas12a nucleases. The system enabled rapid genotyping and quantitative indel profiling. Overall, this workflow provides a robust framework for efficient guide selection and construct optimization in lettuce genome editing. Full article

Peptaibols, predominantly secreted by Trichoderma species, are a class of linear peptides composed of five to twenty amino acid residues, synthesized non-ribosomally and enriched with α-amino isobutyric acid. These unique peptides appear to be highly effective in mediating the interactions between Trichoderma and plant pathogenic fungi. In this study, Ultra-Performance Liquid Chromatography–Quadrupole Time-Of-Flight Mass Spectrometry/Mass Spectrometry (UPLC-QTOF-MS/MS) technology was used to detect peptaibols profiles of Trichoderma strains during their interactions with the pathogen Fusarium graminearum. MS investigations of crude extracts derived from in vitro confrontations of Trichoderma atroviride T23 and its genetically modified counterparts, dual-culture assays of Mlae1, Mvel1, OElae1, and OEvel1 with F. graminearum were performed to shed light on the regulatory role of the velvet complex composed of LAE1&VEL1 in the synthesis of peptaibols during the microbial interaction. These results revealed intriguing variations in the total peptaibols produced during the interactions, as well as some differences in the specific peptaibol profiles between the confrontation and control tests. The overexpression strains, OElae1 and OEvel1, distinguished themselves by their proficiency in inducing long-residue peptaibols synthesis, attaining an impressive biocontrol index of up to 76%. The crude extracts containing peptaibols of OElae1 and OEvel1 demonstrated a capability to enhance cell membrane permeability and decrease DON toxin production in F. graminearum, and the crude extracts of OElae1 strains exhibited more effectiveness in reducing DON toxin production. In conclusion, the interaction with F. graminearum significantly impacted the peptaibol production in the examined Trichoderma strain, emphasizing the intricate interplay and reciprocal influence of genetic factors and environmental stimuli. Full article

The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through embedding-based semantic analysis. Using abstracts from 86,674 mobile industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise in policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based changepoint detection with topic lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

The effect of corporate ESG performance on firm competitiveness has attracted growing attention from both regulators and market participants. Most studies explore and interpret this effect from the perspective of supply-side factors such as technological innovation; however, the role of customer-side factors remains underexplored. This exploratory study aims to theoretically and empirically analyze the mediation role of the customer-side factors in the impact of corporate ESG on market share. Based on a review of the literature, we develop a theoretical model linking corporate ESG performance to customer purchase behavior. The derived hypotheses are empirically checked using panel data of Chinese listed companies from 2009 to 2023 using two-way fixed-effect regression, three-step mediation analysis, and Sobel test. The results show that the effect of ESG performance on market share is significantly positive, and this relationship is mediated by three variables: corporate reputation, firm visibility, and market coverage. Therefore, we suggest that (i) the Chinese government should strengthen mandatory ESG disclosure requirements and enhance supervision of ESG rating agencies; (ii) corporations should substantially improve their ESG performance and enhance ESG communication capabilities; (iii) customers should pay more attention to public interest, allowing individual benefits to align with social welfare, thereby achieving a win-win outcome for both customers and corporations. Full article

The Taoping paleo-landslide poses a significant risk to local residents and critical infrastructure. However, traditional field surveys and deformation monitoring methods are often inadequate for capturing subtle, localized deformation characteristics—particularly at the head scarp and lateral margins—thereby limiting comprehensive assessments of slope instability. Surface strain data offer direct insights into internal stress redistribution during slope evolution and are essential for interpreting landslide mechanisms and forecasting failure. Given the current limitations in dense and wide-area strain monitoring technologies, this study proposes a novel method for modeling landslide strain fields based on Interferometric Synthetic Aperture Radar (InSAR) phase gradients. Using the phase gradient stacking approach, InSAR-derived phase gradients are transformed into strain-related parameters, enabling estimation of shear strain rates, principal strain rates, and their directional distributions. The application to the Taoping paleo-landslide reveals clear spatial patterns of compressive and tensile strain across the landslide body. Field investigations corroborate the InSAR-derived strain features through corresponding geomorphological evidence observed in both compressional and extensional zones. The proposed method enhances the understanding of landslide deformation behavior, supports evaluation of shear surface continuity and evolution, and offers a robust framework for early warning and risk mitigation in complex landslide-prone areas. Full article

Oocyte competency is a crucial determinant of fertilisation success and the initial development of embryos in assisted reproductive technologies. The metabolic and biochemical environment of the ovarian follicle is crucial for determining oocyte developmental potential, alongside genetic integrity. The follicular microenvironment includes a complex network of signalling chemicals that regulate mitochondrial activity, steroidogenesis, oxidative balance, and cellular energy metabolism. Recently, metabolic hormones originating from adipose tissue and skeletal muscle, namely, adipokines and myokines, have received considerable focus as crucial regulators of ovarian physiology. Adiponectin, irisin, and the recently identified hormone asprosin have emerged as crucial metabolic regulators influencing granulosa cell activity, mitochondrial bioenergetics, insulin signalling pathways, and redox homeostasis inside the follicular niche. Adiponectin mostly provides metabolic protection by activating AMP-activated protein kinase (AMPK) and improving insulin sensitivity, which in turn enhances mitochondrial efficiency and steroidogenic function in granulosa cells. Irisin, derived from the breakdown of fibronectin type III domain-containing protein 5 (FNDC5), aids the developing oocyte by facilitating mitochondrial biogenesis, augmenting oxidative phosphorylation, and altering cellular defence mechanisms against oxidative stress. Conversely, asprosin has been associated with glucogenic signalling, metabolic stress, and probable mitochondrial malfunction, suggesting a possible relationship between systemic metabolic problems and negative reproductive consequences. Clinical and experimental research indicate that the levels of these metabolic regulators in follicular fluid may correlate with ovarian response, oocyte quality, fertilisation rates, and embryo development during in vitro fertilisation cycles. This review consolidates current molecular, cellular, and clinical information, clarifying the pathways by which adipokines and myokines influence follicular metabolism and impact oocyte competency. Understanding the metabolic connections between systemic endocrine signals and the follicular milieu may provide novel indicators for reproductive prognosis and provide new treatment targets to improve assisted reproduction outcomes. Full article