Search Results (22,383)

This article offers a conceptual and historical analysis of the rabbinic term maḥloqet, arguing that it functions not merely as a descriptor of disagreement but as a culturally embedded legal category with shifting meanings and purposes across rabbinic history. The article traces maḥloqet through two key moments: its institutionalization in the Mishnah and its attempted elimination in Maimonides’ legal writings. In the Mishnah, maḥloqet is presented as a legitimate and even constructive feature of halakhic discourse, enabling pluralism, preserving dissenting voices, and fostering a collective sense of legal authorship. By contrast, Maimonides views maḥloqet as a symptom of a dysfunctional legal system and seeks to eliminate it through his codificatory project in the Mishneh Torah. Drawing on both legal and philosophical writings, the article argues that for Maimonides, the eradication of maḥloqet is necessary for halakhah to fulfill its unifying social function. The article concludes that the term maḥloqet cannot be fully translated into terms like “dispute” or “controversy,” as it carries distinct legal, political, and epistemological valences unique to rabbinic culture. Its layered function across time highlights the complex interplay between law, authority, and dissent in the Jewish legal tradition. Full article

Tarski’s first-order axiom system ${\mathcal{E}}_2$ for Euclidean geometry is notable for its completeness and decidability. However, the Pythagorean theorem—either in its modern algebraic form $a^2+b^2=c^2$ or in Euclid’s Elements—cannot be directly expressed in ${\mathcal{E}}_2$, since neither distance nor area is a primitive notion in the language of ${\mathcal{E}}_2$. In this paper, we introduce an alternative axiom system ${\mathcal{E}}_d$ in a two-sorted language, which takes a two-place distance function d as the only geometric primitive. We also present a conservative extension ${\mathcal{E}}_{da}$ of it, which also incorporates a three-place angle function a, both formulated strictly within first-order logic. The system ${\mathcal{E}}_d$ has two distinctive features: it is simple (with a single geometric primitive) and it is quantitative. Numerical distance can be directly expressed in this language. The Axiom of Similarity plays a central role in ${\mathcal{E}}_d$, effectively killing two birds with one stone: it provides a rigorous foundation for the theory of proportion and similarity, and it implies Euclid’s Parallel Postulate (EPP). The Axiom of Similarity can be viewed as a quantitative formulation of EPP. The Pythagorean theorem and other quantitative results from similarity theory can be directly expressed in the languages of ${\mathcal{E}}_d$ and ${\mathcal{E}}_{da}$, motivating the name Quantitative Euclidean Geometry. The traditional analytic geometry can be united under synthetic geometry in ${\mathcal{E}}_d$. Namely, analytic geometry is not treated as a model of ${\mathcal{E}}_d$, but rather, its statements can be expressed as first-order formal sentences in the language of ${\mathcal{E}}_d$. The system ${\mathcal{E}}_d$ is shown to be consistent, complete, and decidable. Finally, we extend the theories to hyperbolic geometry and Euclidean geometry in higher dimensions. Full article

The purpose of this work is twofold: first, it aims to derive an exact analytical form of the Pareto index based on the already developed model of the economy as a scale-free network comprising a given amount of either wealth or income (total number of links, each link representing a non-zero amount or quantum of income or wealth) distributed among its variable number of actors (nodes), all of whom have equal access to the system), and second, it aims to employ the derived analytical form of the Pareto index to determine the degree to which the observed inequality in wealth and in income as measured by the respective empirical values of the Pareto index is inherent in the economic system rather than the result of externally imposed factors invariably reflecting a lack of equal access. The derived analytical form of the Pareto index for wealth or for income is described by an exponential function whose exponent is the inverse of the average number of wealth or of income per actor (one-half of the average number of links per node) in the economic model. This exponent features prominently in the scale-free model of the economy and has a numerical value of 0.69 when the Pareto index attains a numerical value of 2, which signifies the optimal, albeit still unequal, distribution of wealth or of income in the economy under the condition of equal access. Because of the correspondence of the scale-free model of the economy to a physical system comprising quantum particles such as photons in thermodynamic equilibrium or state of maximum entropy in accordance with the laws of statistical mechanics, the inverse of the exponent is proportional to the temperature of the economic system, and a new parameter introduced to describe in a comprehensible manner the deviation in the economic system from its optimal distribution of wealth or income. A comparison of the empirical wealth and income Pareto indexes based on economic data for the four largest economies in the word, i.e., USA, China, Germany, and Japan, which account for over 50% of the global GDP, versus the corresponding optimal values per the scale-free model of the economy reveals interesting trends that can be explained away by the prevailing degrees of equal access, as manifested by inadequate education, health care, and housing, as well as the existence of rules and institutions favoring certain actors over others, particularly with regard to the accumulation of wealth. It has also been determined that the newly introduced parameters in the scale-free model of the economy of temperature as well as the quanta of wealth and of income should be expressed in power purchase exchange rates for meaningful comparisons among national economies over time. Full article

In cross-corpus scenarios, inappropriate feature-processing methods tend to cause the loss of key emotional information. Additionally, deep neural networks contain substantial redundancy, which triggers domain shift issues and impairs the generalization ability of emotion recognition systems. To address these challenges, this study proposes a cross-corpus speech emotion recognition model based on attention-driven feature refinement and spatial reconstruction. Specifically, the proposed approach consists of three key components: first, an autoencoder integrated with a multi-head attention mechanism to enhance the model’s ability to focus on the emotional components of acoustic features during the feature compression process of the autoencoder network; second, a feature refinement and spatial reconstruction module designed to further improve the extraction of emotional features, with a gating mechanism employed to optimize the feature reconstruction process; finally, the Charbonnier loss function adopted as the loss metric during training to minimize the difference between features from the source domain and target domain, thereby enhancing the cross-domain robustness of the model. Experimental results demonstrated that the proposed method achieved an average recognition accuracy of 46.75% across six sets of cross-corpus experiments, representing an improvement of 4.17% to 14.33% compared with traditional domain adaptation methods. Full article

Tomato is one of the most important economic crops in the world, but it is prone to diseases during the growth process, so the detection of tomato diseases is very important. However, when detecting tomato diseases in natural environments, existing models are easily affected by environmental factors such as occlusion and illumination, as well as the small size of lesions. In response to these challenges, this paper proposes a tomato leaf disease detection framework LDW-DETR based on multi-scale fusion. First, the local-global feature fusion (LGFF) module is designed by referring to the idea of the PPA module, which can effectively capture local and global features, thereby enhancing the detection ability of small lesions in complex backgrounds. Second, the CSPDarknet architecture is introduced as the backbone network of LDW-DETR to improve the efficiency of feature extraction. In addition, the bottleneck layer of the C2f component is improved by integrating Strip Block and Contextualized Gated Linear Unit (CGLU) to enhance the perception ability of lesion edges and textures. Finally, the WIoU v3 loss function is used to optimize the bounding box regression process. The experimental results show that compared with RT-DETR, the LDW-DETR model improves mAP@0.5 and mAP@0.5–0.95 by 2.6% and 3.7%, respectively, while the number of parameters is reduced by 17.9%. In addition, it still maintains high robustness and generalization ability in cross-dataset experiments. These results show that LDW-DETR has good detection performance and generalization ability in the tomato leaf disease detection task. Full article

Binary code similarity detection serves as a critical front-line defense mechanism in cybersecurity, playing an indispensable role in identifying known vulnerabilities, detecting emergent malware families, and preventing intellectual property theft via code plagiarism. However, existing methods based on Control Flow Graphs (CFGs) often suffer from two major limitations: the inadequate capture of deep semantic information within CFG nodes, and the neglect of structural relationships across different functions. To address these issues, we propose Breg, a novel framework that synergistically integrates pre-trained semantic features with cross-graph structural features. Breg employs a BERT model pre-trained on a large-scale binary corpus to capture nuanced semantic relationships, and introduces a Cross-Graph Neural Network (CGNN) to explicitly model topological correlations between two CFGs, thereby generating highly discriminative embeddings. Extensive experimental validation demonstrates that Breg achieves leading F1-scores of 0.8682 and 0.8970 on Dataset3. In real-world vulnerability search tasks on Dataset4, Breg achieves an MRR@10 of 0.9333 in the challenging MIPS32-to-x64 search task, a clear improvement over the 0.8533 scored by the strongest baseline. This underscores its superior effectiveness and robustness across diverse compilation environments and architectures. To the best of our knowledge, this is the first work to integrate a pre-trained language model with cross-graph structural learning for binary code similarity detection, offering enhanced effectiveness, generalization, and practical applicability in real-world security scenarios. Full article

Background: PLACK syndrome is an ultra-rare autosomal recessive disorder caused by biallelic loss-of-function variants in CAST, which encodes calpastatin, an endogenous inhibitor of calpains. The syndrome is classically defined by peeling skin, leukonychia, acral punctate keratoses, cheilitis, and knuckle pads. Although the phenotype has been largely restricted to dermatological manifestations, emerging reports suggest dilated cardiomyopathy (DCM) as a systemic complication. Methods: We investigated five affected children from three sibships of an extended consanguineous family. Clinical evaluation and genome sequencing (GS) followed by segregation analysis of the targeted mutation test (TMT) were performed. Histopathological examination of an explanted heart was conducted in one child who underwent heart transplantation. Results: All affected children exhibited typical dermatological features of PLACK syndrome. Four developed severe DCM, two of whom required orthotopic heart transplantation. GS, performed in three affected children, identified a novel homozygous frameshift variant in CAST (NM_001750.7:c.1177dup, p.Arg393Profs*4), which segregated with the disease within the family. No additional plausible variants in known cardiomyopathy-associated genes were detected. Histopathological examination of the explanted heart demonstrated hypertrophied cardiomyocytes with nuclear enlargement, hyperchromasia, and fibrosis. Conclusions: Our findings expand the phenotypic spectrum of PLACK syndrome to include severe DCM and suggest CAST deficiency as a novel cause of recessively inherited cardiomyopathy. The favorable short-term outcome following transplantation highlights a potential therapeutic option. Given the possibility of age-dependent penetrance, lifelong cardiac surveillance is for the affected individuals suggested. To emphasize cardiomyopathy as a critical and underrecognized component of the syndrome, we propose the consideration of modifying the acronym to PLACK-C. Full article

Background/Objectives: Empathy is essential for successful social functioning, mediating different aspects of social cognition in everyday life. An intriguing aspect is the involvement of empathy even in basic neural mechanisms of action perception, thanks to its association with the Mirror Neuron System (MNS). The present retrospective study explores whether individual differences in cognitive and affective empathy, measured by the Interpersonal Reactivity Index (IRI) questionnaire, can predict motor resonance—the enhancement of motor cortex reactivity during the observation of biological movements—during transitive and intransitive action observation. Methods: Data from 160 healthy subjects who participated in transcranial magnetic stimulation (TMS) experiments assessing corticospinal excitability during action observation were retrospectively analyzed using multiple linear regression models. Participants filled the IRI and observed intransitive single-digit finger movements (n = 80) or grasping actions directed at different targets (intransitive, object-directed, social-directed; n = 80) synchronized with TMS over the primary motor cortex, allowing the investigation of how action features modulate the relationship between participants’ empathic traits and motor resonance magnitude. Results: Results show that empathic traits do not affect motor resonance during intransitive movements, whereas they do when motor resonance is measured during the observation of transitive actions. Cognitive empathy, particularly the perspective-taking scale, significantly predicts motor resonance magnitude when observing goal-directed actions. Meanwhile, affective empathy, specifically the empathic concern scale, predicts motor resonance while observing social action. Conclusions: These findings highlight that different facets of empathy are significantly related to humans’ ability to understand others’ actions through inner simulation mechanisms, particularly concerning action goals and social relevance. Full article

Accurate fault location is crucial for enabling maintenance personnel to quickly reach the fault site for inspection and repair, thereby minimizing power outage duration. To address the low fault location accuracy caused by phase unsynchronization of double-ended recording data and the dependence of traditional algorithms on accurate line parameters, this paper introduces a novel fault location algorithm for hybrid transmission lines. The method integrates a data synchronization approach with a physics-informed neural network (PINN) implemented using a backpropagation (BP) neural network architecture. First, the proposed synchronization algorithm corrects the phase misalignment between double-ended recordings. Second, a distributed-parameter fault location model is developed to derive a location function, which is then used to construct physics-informed input features. This approach reduces the need for large fault datasets, addressing the challenge of the low occurrence of faults in practice. Finally, a BP neural network employing these physics-informed features is utilized to learn the nonlinear mapping to the fault location, allowing for accurate fault location, enabling accurate positioning without requiring precise line parameters. Validation using actual line data confirms the high precision of the synchronization algorithm. Furthermore, simulations show that the proposed fault location algorithm achieves high accuracy and remains robust against variations in fault position, type, transition resistance, inception angle, and load current, making it highly practical for real engineering applications. Full article

In this paper, a miniaturized balanced bandpass filter with flexible input/output (I/O) functionality is initially designed based on parallel-coupled microstrip lines. Unlike conventional balanced bandpass filters, the proposed filter features two distinct I/O configurations. In these two states, the I/O ports of the developed balanced filter are symmetrically arranged in either horizontal or vertical directions. Moreover, the developed balanced filter demonstrates excellent differential-mode and common-mode suppression in both states. To further enhance the common-mode suppression without compromising the differential-mode performance, an asymmetrical quarter-wavelength open-circuited stub is introduced in the middle of the filter when the I/O ports are vertically symmetric. The inclusion of this stub significantly broadens the common-mode suppression bandwidth. More importantly, the developed balanced filters achieve highly compact sizes, which is essential for integration into modern compact RF front-end modules. To verify the feasibility of the proposed design concept, two prototypes are designed and fabricated, whose simulated and measured results are in good agreement. Full article

Endometrial tissue-derived mesenchymal stem/stromal cells (eMSCs) have potential therapeutic properties partially exerted via their secreted extracellular vesicles (EVs). eMSC-EVs contain cargos with regenerative and immunomodulatory properties. Specifically, the miRNA profile of CD146High eMSC-EVs has been shown to promote anti-inflammatory M2 macrophage polarization in vitro. Herein, we aimed to characterize the lncRNA profile of CD146High and CD146Low eMSC-EVs and further assess their immunomodulatory and anabolic therapeutic function in osteoarthritis (OA). We hypothesized that the CD146High eMSC-EVs lncRNA profile is enriched with potent anti-inflammatory and pro-anabolic cartilage effects when compared to the CD146Low eMSC-EVs lncRNA profile. Human endometrial tissue was collected, and the eMSCs were magnetically sorted to yield the CD146High and CD146Low eMSC subpopulations. The eMSC-EVs were isolated via ultracentrifugation and CD63 magnetic immunoselection methods and characterized by nanosight and flow cytometry analyses. Our results showed that CD146High eMSC-EVs display an lncRNA profile with both anabolic and catabolic features, exerting a more dynamic effect on chondrocyte gene expression than CD146Low eMSC-EVs, suggesting a potential benefit of using CD146High eMSC-EVs to attenuate the negative effects of inflammation in OA. CD146High eMSC-EVs also demonstrated greater endothelial repair capacity under inflammatory stress. In conclusion, cell-free CD146High eMSC-EV has therapeutic potential through its protective anti-inflammatory effects, warranting further pre-clinical investigation. Full article

Inherited platelet function disorders (IPFD) are characterized by normal platelet count and morphology but impaired function due to pathogenic variants in genes encoding membrane receptors, granule constituents, or intracellular signaling proteins. Glanzmann’s thrombasthenia, the most representative IPFD, results from ITGA2B or ITGB3 mutations that disrupt the αIIbβ3 integrin complex, producing severe mucocutaneous bleeding. Advances in molecular genetics have expanded the IPFDs landscape to include defects in other platelet receptors (Glycoprotein (GP)-VI, P2Y₁₂, and thromboxane A₂[TxA₂]-R), signaling mediators (RASGRP2, FERMT3, G-protein regulators, PLC, and TxA₂ pathway enzymes), and granule biogenesis disorders such as Hermansky–Pudlak and Chediak–Higashi syndromes. High-throughput sequencing technologies, including long-read approaches, have greatly improved diagnostic yield and clarified genotype–phenotype correlations. Clinically, bleeding severity varies from mild to life-threatening, and management relies on antifibrinolytics, desmopressin, or platelet transfusion; recombinant activated factor VII and hematopoietic stem cell transplantation are reserved for selected cases. Emerging strategies such as gene therapy and bispecific antibodies that link platelets and coagulation factors represent promising advances toward targeted and preventive treatment. A better knowledge of the clinical features and understanding molecular pathogenesis of IPFDs not only enhances diagnostic precision and therapeutic options but also provides key insights into platelet biology, intracellular signaling, and the broader mechanisms of human hemostasis. Full article

This paper presents a mathematics-focused AI iterative prediction framework that combines Extreme Gradient Boosting (XGBoost) for nonlinear function approximation with nonlinear autoregressive model with exogenous inputs (NARXs) for time-series modeling, applied to analyzing the Remaining Useful Life (RUL) and availability of Unmanned Aerial Vehicle (UAV) Brushless DC (BLDC) motors. The framework integrates nonlinear regression, temporal recursion, and survival analysis into a unified system. The dataset includes five UAV motor types, each recorded for 10 min at 20 Hz, totaling approximately 12,000 records per motor for validation across these five motor types. Using grouped K-fold cross-validation by motor ID, the framework achieved mean absolute error (MAE) of 4.01 h and root mean square error (RMSE) of 4.51 h in RUL prediction. Feature importance and SHapley Additive exPlanation (SHAP) analysis identified temperature, vibration, and HI as key predictors, aligning with degradation mechanisms. For availability assessment, survival metrics showed strong performance, with a C-index of 1.00 indicating perfect risk ranking and a Brier score at 300 s of 0.159 reflecting good calibration. Additionally, Conformalized Quantile Regression (CQR) enhanced interval coverage under diverse operating conditions, providing mathematically guaranteed uncertainty bounds. The results demonstrate that this framework improves both accuracy and interpretability, offering a reliable and adaptable solution for UAV motor prognostics and maintenance planning. Full article

Background/Objectives: Targeting Hsp90β with isoform-selective inhibitors offers a promising therapeutic strategy with reduced toxicity compared to pan-Hsp90 inhibition. However, mechanisms of resistance to Hsp90β-selective inhibition remain poorly defined. This study aimed to identify molecular determinants of Hsp90β dependency and pharmacologic resistance across cancer types. Methods: We integrated gene dependency, transcriptomic, proteomic, metabolomic, and drug sensitivity data from the Cancer Cell Line Encyclopedia with in vitro validation using the Hsp90β-selective inhibitor, NDNB-25. Comparative and correlation analyses were performed to identify resistance-associated pathways, followed by network and combination drug testing to validate functional interactions. Results: Resistant cell lines exhibited extensive rewiring of Rho GTPase signaling, cytoskeletal remodeling, and metabolic adaptation, including mitochondrial dysfunction and redox imbalance. Integrated analyses linked these phenotypes to aryl hydrocarbon receptor (AHR) activation and compensatory Hsp90α expression. Experimental validation confirmed increased kynurenine levels, a known endogenous AHR ligand, in NDNB-25–acquired resistant cells. Gene–drug network integration revealed collateral sensitivity to carboplatin, which synergized with Hsp90β inhibition in resistant models. Conclusions: This study defines the molecular features and adaptive programs underlying resistance to Hsp90β-selective inhibition and identifies therapeutic vulnerabilities that can be exploited to overcome it. The findings establish a systems-level framework for predicting Hsp90β inhibitor response and support rational combination strategies, including carboplatin co-treatment, for future preclinical development. Full article

Metal–organic frameworks (MOFs) have been increasingly recognized as promising platforms for enzyme modulation, owing to their tunable porosity, high surface area, and versatile chemical functionality. In this review, the potential of MOFs for the inhibition and modulation of protein kinases and phosphatases—key regulators of cellular signaling and disease progression—is examined. The structural fundamentals of MOFs are outlined, followed by a discussion of common synthesis strategies, including solvothermal, microwave-assisted, sonochemical, and mechanochemical methods. Emphasis is placed on how synthesis conditions influence critical features such as particle size, crystallinity, surface chemistry, and functional group accessibility, all of which impact biological performance. Four primary mechanisms of MOF–enzyme interaction are discussed: surface adsorption, active site coordination, catalytic mimicry, and allosteric modulation. Each mechanism is linked to distinct physicochemical parameters, including pore size, surface charge, and metal node identity. Special focus is given to biologically relevant metal centers such as Zr⁴⁺, Ce⁴⁺, Cu²⁺, Fe³⁺, and Ti⁴⁺, which have been shown to contribute to both MOF stability and enzymatic inhibition through Lewis acid or redox-mediated mechanisms. Recent in vitro studies are reviewed, in which MOFs demonstrated selective inhibition of disease-relevant enzymes with minimal cytotoxicity. Despite these advancements, several limitations have been identified, including scalability challenges, limited physiological stability, and potential off-target effects. Strategies such as post-synthetic modification, green synthesis, and biomimetic surface functionalization are being explored to overcome these barriers. Through an integration of materials science, coordination chemistry, and molecular biology, this review aims to provide a comprehensive perspective on the rational design of MOFs for targeted enzyme inhibition in therapeutic contexts. Full article