Search Results (1,803)

This study examines how the allocation of export expansion across sectors affects economy-wide outcomes in Bangladesh. Using a Social Accounting Matrix (SAM) framework, we combine linkage analysis with simulation to evaluate how sectoral export growth propagates through the production network. The results show that the impact of export diversification depends critically on sectoral allocation rather than export intensity alone. While aggregate differences between scenarios are modest, reallocating export growth toward sectors with stronger intersectoral linkages generates larger economy-wide gains in GDP and labor income. In particular, sectors with low initial export shares but high network connectivity—such as agriculture, hunting, forestry, and fishing; retail trade; other community, social and personal services; and inland transport—produce stronger multiplier effects than most export-intensive sectors. These findings highlight a key distinction between export intensity and network centrality, demonstrating that sectors with limited direct export participation can play a central role in transmitting economic gains. The results provide a network-based perspective on export diversification and offer policy-relevant insights for designing strategies that promote more inclusive and efficient economic growth. Full article

Understanding how the brain processes complex real-world experiences remains a central challenge in cognitive neuroscience. Naturalistic movie neuroimaging has gained prominence by using temporally continuous stimuli that approximate everyday perception. However, cinematic experience is not equivalent to real-world cognition. Films are systematically constructed through film forms such as editing, camera movement, and sound, which diverge from natural perceptual conditions and shape cognitive processing. In this review, we rethink naturalistic movie neuroimaging by foregrounding film form as a central explanatory factor. We propose a conceptual framework for studying human cognition through film form, in which film form is conceptualized as a mediating layer between naturalistic movie neuroimaging and cognitive processing. We synthesize behavioral and neuroimaging evidence showing that multiple film forms exert domain-specific influences on attention, emotion, and memory. To organize these findings, we propose the Film Cognition Matrix, which maps film forms onto core cognitive domains and supports comparative research. Finally, we argue that interpretations of naturalistic movie neuroimaging should explicitly model film form as a mediator. Future directions include computationally modeling to isolate film-form effects on neural activity, expanding film-form–cognition mapping, exploring interactive and immersive media, and clarifying the boundary between real-world cognition and cinematic aesthetics. Full article

Bone tissue plays a vital role in the human body and possesses intrinsic self-repair mechanisms; however, large defects or pathological fractures may exceed its natural healing capacity. Bone tissue engineering provides promising strategies to restore bone integrity through the use of scaffolds, growth factors, and stem cells. While calcium phosphate (CaP)-based ceramics, such as hydroxyapatite (HAp) and tricalcium phosphate (TCP), represent the current benchmark, their limitations, including slow degradation (HAp) and limited osteoinductivity (TCP), have driven the development of alternative biomaterials. In this context, magnesium phosphate (MgP)-based materials have gained increasing attention due to their tunable resorption rate, improved biodegradability, and ability to stimulate osteogenesis and angiogenesis through the release of magnesium (Mg²⁺) ions. This study reports on composite scaffolds based on electrospun poly(ε-caprolactone) (PCL) fibres coated with MgP layers doped with lithium (Li) and zinc (Zn), designed to mimic the nanofibrous architecture of the extracellular matrix. Lithium and zinc were selected due to their known ability to modulate cellular response, with lithium promoting osteogenic activity and zinc contributing to improved cell proliferation and antibacterial potential. The phosphate phases obtained by coprecipitation were deposited onto the PCL fibres using Matrix-Assisted Pulsed Laser Evaporation (MAPLE), enabling controlled surface functionalization. Following thermal treatment, the formation of the crystalline magnesium pyrophosphate (Mg₂P₂O₇) phase was confirmed by chemical and structural characterization. The combination of a slowly degrading PCL matrix, providing sustained structural support, and a bioactive MgP coating, enabling rapid and controlled ion release, results in improved scaffold performance in terms of biocompatibility, biodegradability, and bioactivity. While the slow degradation rate of PCL ensures mechanical stability over an extended period, the surface-deposited MgP phase allows immediate interaction with the biological environment, facilitating faster ion release and enhancing cell–material interactions. These findings highlight the potential of the developed composites as promising candidates for trabecular bone regeneration and as viable alternatives to conventional CaP-based scaffolds in regenerative medicine. Full article

This work investigates the attitude stabilization and synchronization problem for multi-combined spacecraft with time-varying inertia parameters, external disturbances, and input constraints. First, the comprehensive disturbance is reconstructed considering the influence of inertia uncertainties for controller system design. And then, a novel disturbance observer is developed, and a state feedback controller developed through comprehensive disturbance estimation is proposed. The characteristic of uniform ultimate boundedness for the closed-loop attitude system is proved according to Lyapunov stability analysis, producing the sufficient linear matrix inequality (LMI) condition for the disturbance observer and state feedback controller designs. It is worth noting that the observer and controller gain matrices are solved simultaneously. The feasibility of the attitude stabilization control strategy is demonstrated through numerical simulations. Full article

Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article

Background: Translocation morphology renal cell carcinoma (tRCC) accounts for nearly half of all pediatric RCC cases. Biological study AREN14B4-Q aimed to characterize the molecular landscape of tRCC using samples acquired from patients enrolled in the Children’s Oncology Group Risk Classification and Biobanking study AREN03B2. Methods: From 2006 to 2014, patients <30 yr old with renal tumors were prospectively enrolled in AREN03B2, a Central IRB-approved biobanking study. All pediatric RCC cases underwent a detailed central pathology review and molecular diagnostics to accurately classify RCC subtypes. Samples with confirmed tRCC and appropriate informed consent were identified with adequate tissue for RNA and DNA extraction, along with germline DNA, for whole-genome sequencing (WGS), RNA sequencing, and DNA methylation analyses. Results: From 41 patients, high-quality samples allowed for 18 tumors and non-tumor DNA to be analyzed via WGS, 19 via DNA methylation, and 36 RNA samples via transcriptome sequencing. Consistent with and extending clinical cytogenetic findings, WGS and fusion transcript analyses confirmed very few additional mutations beyond the tRCC translocation. No recurrent genomic copy number gains/losses were found. RNA and WGS analyses enabled sub-classification of tRCC, closely aligning with the different TFE3 fusion partners. DNA methylation analyses demonstrated less tRCC sub-stratification compared with RNA analyses. Pathways activated in tRCC were involved in epithelial differentiation, extracellular matrix organization, apoptosis, immune regulation, signal transduction, and angiogenesis. Conclusions: Arrested epithelial differentiation is the overarching driver in tRCC and is strongly correlated with the specific subclasses of fusion transcript generated by the genetic translocation TFE fusion partner. Negative regulation of apoptosis, increased M2 macrophage expression, and enhanced angiogenesis also appear to be functional features of tRCCs, as are increased expression of matrix metalloproteinases, PI3K-AKT/mTOR/MAPK signaling, and mitochondrial metabolism, highlighting potential therapeutic options beyond direct targeting of the oncogenic driver fusions. Full article

Plant-derived exosomes (PDEs) are gaining attention owing to their key implications in cross-kingdom communication, facilitating bioactive entities among plants and animals. PDEs are tiny nanoscale vesicles generally comprised of RNAs, proteins, and secondary metabolites and are involved in the regulation of physiological processes (immune modulation, cell regeneration, and stress response). An important feature of PDEs is to enable cross-kingdom regulation in skin wound repair. This is because PDEs can modulate several signaling pathways (PI3K-Akt, TGF-β, and mitogen-activated protein kinase) that further direct inflammatory, cell migratory, angiogenic, and extracellular matrix remodeling. Key features of PDEs, including modest immunogenicity, easy crossing of biological barriers, and natural biocompatibility, make them novel alternatives to synthetic wound-healing agents. Therefore, this review disparagingly examines the biogenesis, molecular composition, and diversified biological functions of PDEs, particularly with reference to potential implications in wound healing and overall skin health. The current challenges pertaining to PDE isolation, scalability, and bioavailability and regulatory hurdles for their clinical translation were also explored. In addition, the epigenetic effects of PDEs on human skin cells and wound healing are explained in detail. Finally, this review presents a comprehensive investigation of PDEs in skin wound repair, identifies research gaps, and outlines future directions for dermatological applications. Full article

Dragon fruit (Hylocereus undatus) is a high-value crop in the Philippines that has gained commercial importance due to its nutritional benefits and profitability. However, determining the optimal maturity stage remains challenging for farmers relying on manual classification. We developed an automated system that integrates You Only Look Once Version 7 (YOLOv7) for dragon fruit detection. A dataset of dragon fruit images across three maturity levels, unripe, ripe, and over-ripe, was collected and used to train the model. The system classifies maturity stages based on external features such as color and shape, and its performance will be evaluated using a confusion matrix. By providing accurate classification, the proposed system aims to assist farmers in harvesting dragon fruits at their optimal stage, improving yield quality and market competitiveness while reducing human error. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with limited treatment options. Patients are treated with DNA damaging chemotherapies which act by inducing DNA damage in rapidly dividing tumor cells. Unfortunately, these tumors frequently develop treatment resistance, underscoring the need to understand resistance mechanisms in order to develop better treatment strategies. DNA damage response (DDR) detects and repairs DNA damage, and the DDR pathway has been shown to contribute to chemoresistance. Another factor known to drive chemoresistance in PDAC is the dense stroma, composed of extracellular matrix proteins secreted by cancer-associated fibroblasts (CAFs). Our recent work identified a CAF-induced resistance mechanism involving N-myc downstream regulated gene 1 (NDRG1). CAF-induced signaling resulted in the phosphorylation of NDRG1 and NDRG1-dependent DNA repair and protection from chemotherapies. Loss of NDRG1 resulted in increased chemotherapy-induced DNA damage and decreased replication fork speed and recovery. Methods: To gain insight into the molecular mechanism of NDRG1-mediated DNA repair and replication, we performed a BioID screen to identify binding partners of NDRG1. We further assessed the mechanistic roles of the identified interaction partners on DNA repair using DNA replication and repair assays such as the Comet assay and DNA fiber assays. Results: Our BioID screen identified meiotic recombination 11 (MRE11) protein, a nuclease involved in DDR, as a putative NDRG1 interacting protein. Interaction between MRE11 and NDRG1 was enriched during the late S/early G2 cell cycle phases and under replication stress. However, this interaction is likely indirect as the interaction only occurred in a cellular context and not with in vitro purified proteins. Blocking NDRG1 phosphorylation or blocking MRE11 exonuclease activity both resulted in protection of newly synthesized DNA at stalled replication forks. In NDRG1 knockout cells, blocking MRE11 led to decreased protection of nascent DNA, suggesting that NDRG1 and MRE11 may be acting in the same pathway and that NDRG1 is required for MRE11’s activity at stalled forks. Conclusions: In summary, our work has uncovered a protein complex between NDRG1 and MRE11 that may play a key role in chemoresistance due to its role in the processing of stalled replication forks. Full article

Platelet-rich plasma (PRP) is widely used as a second-line treatment for chronic tendinopathy that persists despite structured conservative care, yet outcomes and imaging correlates remain heterogeneous. This review outlines PRP biology and preparation, summarises quantitative imaging techniques for monitoring tendon response, and presents the experience of a single centre integrating these methods into routine supraspinatus and lateral elbow PRP workflows. PRP is described as an autologous platelet concentrate with variable leukocyte and fibrin content, with leukocyte-rich formulations commonly selected for chronic tendinopathy. Quantitative approaches—including ultrasound shear-wave elastography and radiomics, MRI T2/T2* mapping, CT-based bone metrics, PET/CT, and optical techniques—offer numerical biomarkers of tendon structure, mechanics, and inflammation but are rarely implemented in PRP trials. At the authors’ centre, leukocyte-rich PRP is injected under ultrasound guidance after failed physiotherapy, and follow-up combines validated questionnaires with grey-level run-length matrix texture analysis of ultrasound and 3.0 T MRI T2 distribution profiling. A pilot ultrasound study in supraspinatus and common extensor tendinosis showed uniform short-term clinical improvement and significant changes in most texture features, with selected parameters correlating with symptom relief. A prospective supraspinatus cohort demonstrated significant six-month clinical gains in both tendinosis and small partial-thickness tears, whereas only the tendinosis group exhibited T2 profile convergence toward asymptomatic patterns. These data indicate that quantitative ultrasound radiomics and whole-length T2 profiling are feasible imaging biomarkers that capture PRP-induced tendon remodelling beyond qualitative imaging and may help tailor PRP protocols to specific tendon phenotypes. Full article

In recent years, scientific and technological development has made trade-in programs for innovative electronic products more and more popular. Many of these innovative companies that continue to launch new products offer trade-in and cash-out sales strategies to stimulate purchase. This paper studies when a company launches these two sales strategies and how to ensure optimal pricing that maximizes profits, while taking into account the degree of the consumer’s strategy, the degree of the new product’s innovation, the residual value of the old products, and the cost. We construct a two-period dynamic pricing joint optimization model with four core decision variables and derive the closed-form optimal solution through strict mathematical derivation including Hessian matrix analysis and KKT condition verification. We have adopted a dynamic pricing strategy that conforms to the actual market. The results show that this study provides new mathematical insights for dynamic pricing research, and reveals the substantive rule that companies are more likely to gain greater benefits when the degree of product innovation is not high and the consumer’s strategy degree is moderate. Statistics show that companies are more likely to gain greater benefits when the degree of product innovation is not high and the consumer’s strategy degree is moderate. Full article

(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational and memory constraints inherent to resource-limited systems. (2) Methods: A scoping review of 52 studies published between 2017 and 2024 was conducted, covering time-domain, frequency-domain, matrix-based, and machine learning approaches. The methods were evaluated according to estimation accuracy, computational complexity, memory footprint, and suitability for on-device implementation. (3) Results: Time-domain peak detection methods consistently provide high accuracy (minimum of $79.25$%, maximum of $99.96$%, and median $\ge 99.69$%) for ECG and reliable heart rate estimation for PPG with linear computational complexity, low memory requirements and low energy consumption. Frequency-domain approaches are suitable for average heart rate estimation from PPG but do not preserve inter-beat intervals (error range of [1.07, 6.4] beats per minute (BPM)). Matrix-based and machine learning methods often entail higher computational cost without proportional performance gains in wearable contexts (error range of [1.07, 6.4] BPM for PPG signals; accuracy in range of $[95.4, 99.96]$% for ECG). (4) Conclusions: Lightweight signal-processing techniques offer the most favorable trade-off between accuracy and efficiency for wearable implementations, whereas computationally intensive approaches are better suited for edge- or cloud-based processing. Full article

Creases in origami reflectarray antennas (ORAs) impose layout exclusion zones that invalidate conventional shaped beam synthesis, assuming continuous periodic apertures. A crease-compatible shaped beam synthesis approach is presented, in which crease-intersecting elements are treated as constrained reflectors by removing only their patches while retaining a continuous ground plane, thereby translating geometric restrictions into explicit amplitude/phase constraints. These constraints are incorporated into a modified alternating projection method (MAPM) via an iteration-updated ternary state matrix and a revised inverse projector, where the amplitudes of internal elements are kept prescribed, and only their phases are iteratively optimized. A 15 GHz hexagonal twist ORA using triangular-ring unit cells is designed to generate a sector beam in the xoz plane and a pencil beam in the yoz plane. Full-wave simulations demonstrate a peak gain of 26.4 dBi with sidelobe levels below −16.1 dB, validating the proposed beam shaping synthesis with crease constraints for ORAs. Full article

Precise curvilinear path tracking remains a persistent challenge for autonomous agricultural machines, where conventional Model Predictive Control (MPC) suffers from poor adaptability to varying curvatures and high computational overhead in unstructured farmland environments. This paper proposes a soft-constrained MPC framework enhanced by a two-layer fuzzy architecture and Recursive Least Squares filtering to address these limitations simultaneously. The first fuzzy layer dynamically adjusts the MPC prediction horizon in response to real-time path curvature, enabling proactive steering on complex curved trajectories. The second fuzzy layer tunes the state weighting matrix online based on lateral and heading deviations, improving transient tracking accuracy without increasing computational cost. Recursive Least Squares filtering is further integrated to suppress sensor noise and compensate for tire slip dynamics inherent to farmland operation. The proposed framework is validated using MATLAB simulations on both constant-curvature semicircular paths and variable-curvature S-curve trajectories at operational speeds of 2.0 and 2.5 m/s, followed by outdoor field trials on a scaled autonomous robot platform. Simulation results demonstrate average tracking error reductions of 52.7–55.9% on constant-curvature paths and 10.8–18.2% on variable-curvature paths compared to fixed-parameter soft-constrained MPC. Field experiments confirm practical viability, achieving an RMS lateral error of 0.131 m over a 50 m curved route on natural terrain. These results demonstrate that the hierarchical decomposition of adaptation objectives yields substantial accuracy gains while preserving real-time feasibility on resource-constrained embedded platforms. Full article

Under the dual carbon strategy and the sweeping tide of digital transformation in education, higher education confronts an urgent imperative: cultivating talent equipped with interdisciplinary skills and sustainable decision-making capabilities. To meet this critical challenge, this study pioneers the PAMD (Patient Capital–Accounting–Matrix–Development) interdisciplinary teaching framework. Rooted firmly in Education for Sustainable Development (ESD) principles, PAMD uniquely weaves together patient capital, carbon asset accounting, and linear algebra matrix modeling. Utilizing a quasi-experimental design with undergraduate business students, we implemented “Carbon Asset Accounting and Low-Carbon Transition Investment Analysis” as a case study. We rigorously evaluated teaching effectiveness across academic performance, competency, and cognitive attitude dimensions using Welch’s t-test, Hedges’ g, and ANCOVA. After controlling for baseline scores, the experimental group significantly surpassed the control group in comprehensive decision-making (81.22 vs. 72.41, g = 0.71) and matrix modeling competency (3.74 vs. 3.22, g = 0.77). The experimental cohort also demonstrated consistent gains in carbon accounting reporting precision and data representation clarity. Cognitive assessments revealed moderate effect sizes for both low-carbon investment literacy and interdisciplinary learning interest. These compelling results demonstrate that embedding a long-term value orientation into accounting representation and matrix modeling powerfully cultivates students’ ability to transfer interdisciplinary knowledge and make sound sustainable decisions within complex contexts. This study offers a robust, evidence-based, and replicable pathway for driving sustainability-oriented interdisciplinary reform within business education. Full article