To win the talent war, companies offer valuable employees customized employment arrangements. These idiosyncratic deals (i-deals), while confidential, may elicit diverse reactions from the recipients' colleagues. We propose a contextual cue-focused dual-path contingency model suggesting that employees perceive and react to coworker i-deals through either a self-improvement or an image-enhancement path. Specifically, when perceiving a merit-based system, individuals will engage in personal thriving to improve themselves, resulting in constructive outcomes. Alternatively, if they perceive their supervisors as having low integrity, they are likely to engage in leader-targeted impression management, which can lead to unfavorable outcomes. In the pilot study, 98 employees participated in a scenario-based study to test hypotheses. The main study was a time-lagged field study involving 219 dyads of employees and their supervisors. Our results show that perceiving coworkers' i-deals can prompt employees' proactive responses with different motives and strategies, depending on the contextual cues they extract. Based on the findings, we discuss theoretical contributions as well as practical implications.
The Trump Administration has pursued most-favored nation (MFN) drug pricing agreements with major pharmaceutical manufacturers, raising concerns about potential effects on pharmaceutical revenues, profitability, and innovation incentives. We examined whether equity markets interpreted these developments as positive or negative for the pharmaceutical sector. We conducted an event study of 16 publicly traded pharmaceutical firms announcing MFN agreements. Abnormal returns and cumulative abnormal returns were estimated using standard market-model regressions based on each firm's historical relationship to the S&P 500. We analyzed market reactions surrounding "Liberation Day," the date MFN letters were announced, and subsequent agreement announcements. The strongest market response was observed following the first MFN agreement announced by Pfizer, with largely positive market reactions across most of the pharmaceutical firms analyzed. In contrast, "Liberation Day" generated limited market reaction, while the MFN letters produced more modest negative effects. Investors did not interpret the announced MFN agreements as materially value destructive, likely because the deals primarily targeted Medicaid and the limited TrumpRx platform, while also resolving some regulatory and tariff uncertainty. Future expansion of MFN may lead to different market reactions.
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To characterize venture capital (VC) investments in orthopaedic surgery over the past 25 years to evaluate trends in global innovation. All VC investments related to orthopaedic surgery between January 2000 and December 2024 were retrospectively evaluated using the PitchBook database (PitchBook Platform, PitchBook Data. Seattle, WA). The headquarter location of each company was reviewed. The year of each investment deal and investment size (US dollars) were aggregated. Deals were also evaluated with respect to funding category (surgical devices, biotechnology, drug discovery, hospital management/technology, medical equipment). Descriptive statistics, compound annual growth rate, 2-sample t-tests, and regression analysis were conducted. A total of $9.7 billion (in 2025 USD) in VC funding was invested in orthopaedic surgery, consisting of 1506 distinct deals. VC funding has steadily increased since 2000, most notably between 2020 and 2024, with 49.8% (750) of deals (P < .001) and 71% of total funding ($7.8 billion) (P = .024). Privately held companies made 98% (1481) of deals. Surgical devices led VC investment with 34% (514) of deals totaling $3.5 billion, with biotechnology following with 30% (457) of deals for $3.3 billion. These 2 categories saw significantly more investments than the remaining categories (P < .001). Orthopaedic companies physically based in the United States of America (USA) received the most funding (811 deals, $6.9 billion). China, Japan, the United Kingdom, Canada, and South Korea followed, though with significantly fewer investments than the USA (P < .001). VC funding in orthopaedic surgery has steadily increased over the past 25 years, largely concentrated in the United States, with a dramatic increase in the past 5 years driven by interest in orthobiologic therapies and digital health. VC investment trends in orthopaedic surgery appear to mirror many of the broader macroeconomic forces seen in the health care sector and have a direct impact on future development, innovation, and clinical practice within orthopaedic surgery.
This paper deals with the design of a single-element and [Formula: see text] bandwidth microstrip patch array antennas for N77/N78 band applications. The presented antennas are designed on a low-cost FR4 epoxy substrate with a dielectric constant of 4.3 and a thickness of 1.6mm while considering manufacturability issues. In order to evaluate the antenna's characteristics, an equivalent circuit model of the proposed microstrip patch array will be built in Keysight ADS, and the reflection coefficient will be compared with the simulated result obtained with CST Studio Suite. An implemented prototype is also measured for the validity of the simulated results, and good agreement between the measurement and simulation is obtained. Besides the classical electromagnetic simulation, supervised machine learning (ML) algorithms are used to estimate the main antenna parameters, such as the bandwidth and the center resonant frequency of the investigated antenna. 203 simulation-based data samples are generated by using CST Microwave Studio and employed to train five regression models, including GB regression, ET regression, DT regression, RF regression, and XGB regression. The models are evaluated in terms of variance score, coefficient of determination (R[Formula: see text]), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Among all tested models, the Random Forest regression model produces the best results in terms of error and accuracy for predicting both bandwidth and center resonant frequency. The proposed work concludes as an effective antenna design method through integration of full-wave simulation, measurement, equivalent circuit modeling, and machine learning-based prediction for robust N77/N78 band implementation.
An important component of medical care in line with demand is the prevention of overuse. Both internationally and in Germany, there are numerous examples of low-value care, i.e., measures that contribute neither to the health of the individual nor to the health status of entire patient or population groups in a meaningful way. These measures waste large shares of resources in healthcare and have harmful effects from a medical perspective for individuals as well as for the community. The driving forces behind overuse are diverse and lie both in systemic factors and in the personal behavior of doctors and patients. Since 2012, internationally with "Choosing Wisely," and since 2015, in Germany with "Klug entscheiden," initiatives have been in place aiming to effect behavioral change at a personal level. The tools are thematically well-focused recommendations that identify individual measures as low-value care, aiming to position doctors and patients at a critical distance from their application. The present article deals with the driving forces for medical overuse, describes the "Choosing Wisely" and "Klug entscheiden" campaigns, and attempts to estimate their potential effectiveness. Eine wichtige Komponente von Bedarfsgerechtigkeit in der Medizin ist die Vermeidung von Überversorgung. International wie auch in Deutschland gibt es zahlreiche Beispiele von „low-value care“, d. h. Maßnahmen, die weder zur Gesundheit des Einzelnen noch zum Gesundheitszustand ganzer Patienten- oder Bevölkerungsgruppen einen wertvollen Beitrag leisten. Diese Maßnahmen vergeuden große Anteile der Ressourcen im Gesundheitswesen und haben aus medizinischer Sicht schädliche Folgen für Einzelne wie für die Gemeinschaft. Die treibenden Kräfte für Überversorgung sind vielfältig und liegen sowohl in Systemfaktoren als auch im persönlichen Verhalten von Ärzt:innen und Patient:innen. Seit 2012 gibt es international mit Choosing Wisely und seit 2015 in Deutschland mit Klug entscheiden Initiativen, die auf der persönlichen Ebene Verhaltensänderungen bewirken sollen. Werkzeug sind thematisch eng fokussierte Empfehlungen, die einzelne Maßnahmen als „low-value care“ kennzeichnen und Ärzt:innen und Patient:innen auf kritische Distanz zu ihrer Anwendung bringen sollen. Der vorliegende Beitrag beschäftigt sich mit den treibenden Kräften für Überversorgung, schildert die Kampagnen Choosing Wisely und Klug entscheiden und versucht eine Einordnung ihres Wirksamkeitspotenzials.
The quest for clean water is as ancient as civilization itself, evolving from the stochastic pores of clay vessels to the precisely engineered nanochannels of modern two-dimensional (2D) materials. Unlike previous reviews that focus primarily on material synthesis or specific applications, this review uniquely traces the conceptual lineage from ancient purification principles to modern 2D materials, demonstrating how atomic-scale engineering augments rather than replaces historical mechanisms. The survey provides a critical comparative framework that systematically evaluates performance across contaminant classes, identifies key research gaps, and proposes specific targets for practical implementation. This historical-to-horizontal perspective provides researchers with both contextual understanding and practical guidance. The study examines how carbon-based 2D platforms, graphene, graphene oxide (GO), reduced graphene oxide (rGO), and MXenes, have transformed water decontamination from a macroscale filtration art into an atomic-scale separation science. It reveals how these materials refine and augment historical purification mechanisms like size-exclusion sieving progresses from micron-scale randomness to ångström-level precision, adsorption shifts from general electrostatic capture to targeted chemical scavenging, and disinfection advances from leaching biocides to contact-based photothermal and catalytic destruction. Beyond inheriting ancient principles, 2D materials introduce novel functionalities including electrocatalytic degradation, plasmonic disinfection, and tunable ionic selectivity, enabling unprecedented removal of heavy metals, organic pollutants, pathogens, and emerging contaminants. However, despite remarkable laboratory performance, critical challenges in scalability, cost, stability, fouling, and environmental safety persist. This review not only deals with the state of the art but also provides a forward-looking framework for transitioning these advanced materials from bench-scale innovation to sustainable, real-world water treatment solutions.
This review deals with the questions why humans want to live with other animals and how this is at all possible. In fact, it has become common sense that living and working with companion animals entails major benefits for human wellbeing and health, albeit with the caveat that the positive experience of private keepers and practitioners in pedagogy and therapy is not always backed by scientific scrutiny. The present focus is on relevant aspects of the "Darwinian continuum," which provides humans and other animals with a shared social toolbox, including brain, physiology and behavioral organization. It is discussed why domesticated animals are particularly suitable companions, why dogs are a special case, and why anthropomorphizing other animals may be as much an asset as a burden. In fact, living with companion animals is a human universal, which seems basically motivated by biophilia (the human-typical interest in nature and animals), and by striving for social homoeostasis (for social contexts supporting wellbeing and health). Between-species socializing is possible because of common phylogeny and functional convergence, resulting in matching social mindsets and behavioral systems. It is based on shared principles of behavioral organization, of thinking and decision making, on shared neuronal, physiological and psychological mechanisms, on virtually identical basic affects, and on the shared stress and calming systems. Finally, the social toolbox shared between humans and other animals also suggests a relatively moderate socio-cognitive gap between humans and other animals.
This work deals with the taxonomy of genus Compsoctena from India and Bangladesh, based on fresh material and historical collections housed in the Lepidoptera section of the Zoological Survey of India, Kolkata. Three new species, Compsoctena kushabhadrae sp. nov., C. kamarajisp. nov. (India), and C. faridahsani sp. nov. (Bangladesh) are described as new. The following new combinations are proposed based on primary types housed in the Natural History Museum, London: Compsoctena accurata (Meyrick, 1922), comb. nov.; Compsoctena autochthonia (Meyrick, 1931) comb. nov.; Compsoctena certatrix (Meyrick, 1916), comb. nov.; Compsoctena coagulata (Meyrick, 1919), comb. nov.; Compsoctena colonica (Meyrick, 1916) comb. nov.; Compsoctena cremata (Meyrick, 1916), comb. nov.; Compsoctena deposita (Meyrick, 1919) comb. nov.; Compsoctena devincta (Meyrick, 1916) comb. nov.; Compsoctena expedita (Meyrick, 1907) comb. nov.; Compsoctena exsecrata (Meyrick, 1937) comb. nov.; Compsoctena gregaria (Meyrick, 1916) comb. nov.; Compsoctena infensa (Meyrick, 1916) comb. nov.; Compsoctena isopeda (Meyrick, 1907) comb. nov.; Compsoctena jactata (Meyrick, 1937) comb. nov.; Compsoctena lignosa (Meyrick, 1917) comb. nov.; Compsoctena meliphaea (Meyrick, 1916) comb. nov.; Compsoctena multiplex (Meyrick, 1917) comb. nov.; Compsoctena nota (Meyrick, 1919) comb. nov.; Compsoctena obtrectans (Meyrick, 1930) comb. nov.; Compsoctena paraclasta (Meyrick, 1922) comb. nov.; Compsoctena phaeogenes (Meyrick, 1919) comb. nov.; Compsoctena pericrossa (Meyrick, 1907) comb. nov.; Compsoctena praecepta (Meyrick, 1919), comb. nov.; Compsoctena ptyalistis (Meyrick, 1937) comb. nov.; Compsoctena semota (Meyrick, 1938) comb. nov.; Compsoctena subacta (Meyrick, 1919) comb. nov.; Compsoctena tylota (Meyrick, 1916) comb. nov., and Compsoctena vorticosa (Meyrick, 1930) comb. nov. Although all these species were originally described under the genus Melasina, their current taxonomic placement is reviewed and discussed in detail. Additionally, three new country records, Compsoctena thwaitesii (Walsingham, 1887), and C. anasactis (Meyrick, 1907) from India, and Compsoctena pulla Sobczyk & Breithaupt, 2023 from Bangladesh are provided. An updated checklist of Compsoctena from India and Bangladesh is given arranging the species into ten species groups based on male wing maculation.
The energy produced by the non-renewable resources is facing challenges due to high energy consumption. So, to compensate this depletion in energy production, scientists are focusing on the production of energy from renewable sources like solar energy, etc. The current study is concentrated to the solar energy enhancement which can be achieved by improving the efficiency of the energy producing devices like solar thermal collectors and photovoltaic-thermal systems by passing the ternary nanofluid in these energy producing systems. The present work deals with the Maxwell ternary nanofluid flow and heat transfer past inclined linearly permeable stretching sheet embedded in porous media. Effects of Lorentz force, solar radiation and suction of the surface are incorporated into the current mechanism. The entropy generation analysis is carried out to optimize the cooling process inside the thermal systems. The solutions of the transformed ordinary differential equations are computed using boundary value problem 4th order collocation technique-based solver. The results indicate the increasing nanoparticles volume fraction enhances temperature of fluid and controls velocity of fluid. Growing Maxwell fluid parameter and magnetic field parameter reduces the velocity of the fluid and raises the temperature of the fluid as well solar radiation improves the temperature of the fluid flow domain. The suction parameter controls the boundary layer thickness. The increasing Brinkman number enhances entropy generation and decreases Bejan number. The numerical computations were performed using boundary value problem 4th order collocation technique-based solver for different ranges of the dimensionless parameters, namely, 0.6 ≤ Br ≤ 1.0, 0.1 ≤ S ≤ 1.5, 1.1 ≤ M ≤ 6.1, 0.1 ≤ λ ≤ 0.9, 0.01 ≤ ϕ1, ϕ2, ϕ3 ≤ 0.05, 0.1 ≤ λ1 ≤ 3.1, 1.0 ≤ Pr ≤ 7.0, 1.1 ≤ Rd ≤ 6.1, 0.1 ≤ K ≤ 5.1, 0.1 ≤ α1 ≤ 0.5 and α = π/6. Sensitivity analysis to determine the variations in output by changing the parameters as input. The grid independent test has been carried out to guarantee grid independent convergence of the numerical solutions. Recent results are equated with already existing outcomes for the validation of the present modeled code.
Forensic odontology is a sub-discipline of forensic science, that deals with examination, handling and demonstration of dental evidence for personal identification. Morphological dental traits (MDTs) can be used for the classification of population groups that may play a vital role in forensic identification. The objective of the present study was to classify the population affinity in two population groups on the basis of the MDTs using Machine Learning (ML) models and to comparatively analyze which ML model is the best for the classification. In the present study, 434 participants (207 Males and 227 females) ranging in age from 18 to 40 years were enrolled from two major population groups of North India i.e. the Khasas and the Kolis. Dental casts of the participants were prepared. MDTs were observed and noted in comma separated value (csv) excel sheet. The feature selection was done using Recursive Feature Elimination (RFE) to identify the most informative morphological trait of teeth for population classification. Machine learning models were trained on the prevalence and incidence of these MDTs, including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GBM); the models achieved accuracy rates of 74.7%, 75.8%, 64.37%, 68.97%, and 64.37%, respectively. Of all the models, logistic regression (LR) has the highest accuracy (75.8%), followed by SVM. Further, ROC-AUC and F1 score analysis confirmed Logistic Regression as the best-performing model, outperforming Decision Tree and Gradient Boosting which both recorded the lowest accuracy of 64.3%. The present study demonstrates how machine learning approaches can be used to support population affinity estimation using dental features. Findings of the present study may be useful in disaster victim identification, crime scene investigation, and other forensic examinations where dental remains are presented for forensic analysis.
Although this work deals with well-established methods and a well-known model system, it demonstrates innovative findings. These groundbreaking findings will be of interest to chemists, biologists, and medical professionals alike. This investigation is pure and provides a dataset for gaining an in-depth understanding of the interactions of the MS2 bacteriophage with inorganic and organic ions. The nanoparticle surface can be viewed as an unstructured continuum characterized by a zeta potential that governs interspecies interactions and depends on the ionic composition of the aqueous medium. Interactions with inorganic and organic ions should involve considering the surface as a discontinuum. Recently, empirical evidence has led to a patch-like model of the surface of MS2 with discrete positive and negative values of surface charge density. This work involves concepts of the solvatochromism of malachite green as a polarity-dependent factor, the metachromasia of crystal violet as an association-dependent factor, and the chemical kinetics of alkaline fading of these dyes to investigate interparticle interactions and ion exchange. Such combined techniques provide broadly applicable data for studying bacterial interactions with various charged species or other biological interfaces.
This work deals with the synthesis of metallocene-phosphorus based radical coordination polymers. The radical polymeric chains were synthesized by a single step reaction between commercially available group 4 metallocene dichlorides [Cp2MCl2] (M = Ti, Zr, Hf; Cp = η5-C5H5), pentaphosphaferrocene [Cp*Fe(η5-P5)] (Cp* = η5-C5Me5) and potassium metal. These reactions selectively gave polymeric chains of radical, [K{Cp2M}2{(η4-P5)FeCp*}2] n (M = Ti, Zr, Hf). The four membered P2M2 core is the first example of such a phosphorus-based ring with a radical anionic nature. Moreover, it is also a rare example of a main group element based radical polymeric chain. The radical nature of the metallopolymer is further established by EPR, XANES and quantum chemical calculations. The potassium ion acts as a linker ion between two [{Cp2M}{(η4-P5)FeCp*}] fragments. The reaction of [K{Cp2M}2{(η4-P5)FeCp*}2] n with [2.2.2]-cryptand in THF resulted in chain cleavage into charge separated monomeric complexes [(K[2.2.2]-cryptand){Cp2M}2{(η4-P5)FeCp*}2] (M = Ti, Zr, Hf).
Radar technology in the microwave and millimeter-wave frequency range is the subject of current research for structural health monitoring of composite materials, e.g., damage detection in wind turbine blades. Performance assessment, enabling widespread practical application of this promising and non-contact sensing approach, can be realized via probability of detection (POD) theory, which is a statistical method for determining the detectability of damage through response metrics as a function of flaw size. This paper deals with the experimental investigation of a delamination model represented by two parallel glass fiber reinforced polymer plates separated from each other from 0mm to 1mm in steps of 0.01mm. Experimental studies with a frequency modulated continuous wave radar are performed under laboratory conditions in the frequency range from 57GHz to 65GHz. The signal response is represented by two damage indicators (DIs), according to the root mean square deviation and Mahalanobis distance. Since the reflection of electromagnetic waves exhibits a nonlinear behavior, this also implies a nonlinear response in the DI characteristic. The novelties in this work are the successful implementation of a nonlinear regression model, combined with an optimal threshold decision through receiver operating characteristic curves for a high-resolution POD representation. The POD with 95% confidence bounds indicates the flaw size at which the delamination can be detected reliably. Depending on the radar distance in experimental studies, the binary structural condition (damaged or undamaged) was correctly assessed from 95% to 100%. The minimum detectable size ranges from 0.01mm to 0.08mm.
Online reviews have a direct bearing on what prospective customers will choose to buy. Consumers need help to make effective use of online reviews for purchasing decisions. The current sentiment analysis methods often overlook the complexity and usability of products in reviews which are critical for potential buyers. The existing approaches solely rely on sentiment polarity but this approach offers a comprehensive way to analyze product' reviews by integrating appraisal theory with hybrid deep learning methods. This research study aims to explore product complexity in customer reviews by utilizing transformer and recurrent neural network-based models. Amazon product reviews dataset is manually annotated according to perceived complexity using attitudinal categories of appraisal theory, i.e., appreciation and judgment and then cross-validated through shapley additive explanations method of eXplainable AI. Appreciation has to do with how much a product is user-friendly or has a learning curve to operate. Judgment deals with evaluation of products on the basis of effectiveness and appropriateness. The proposed model uses a hybrid approach by combining DistilRoBERTa, a pre-trained transformer model, and a bi-directional gated recurrent unit, a recurrent neural network-based model, to learn both contextual and sequential dependencies in product reviews. The proposed approach makes use of fine-tuned DistilRoBERTa embeddings for extracting features, and this model is improved further using a bi-directional gated recurrent unit layer, which takes into account the context of the past and the future. A 5-fold stratified cross-validation method is used to address imbalance learning, with class weighting applied to further balance the impact of sentiment classes in training. The proposed model has achieved a mean fold accuracy of 96.13%,which is higher than that of existing state-of-the-art approaches such as Random Forest (89.93%) [11], DistilBERT with advanced embeddings (92%) [25], XLNet (89.62%) [28], and GPT-based sentiment models (94.5%) [30]. By utilizing Shapley Additive Explanations (SHAP) for explainability, this model provides transparency in understanding emotional tendencies, functional effectiveness, and overall user perceptions, offering insights that traditional models lack. This framework provides a scalable, automated solution for Amazon products' performance evaluation and provides insights into emotional tendencies, functional effectiveness and overall perceptions from the users. It will help in optimizing product development strategies using advanced natural language processing techniques with explainability by setting a new standard for understanding products'product feedback.
Day-by-day monitoring of the environment is gaining attention, thereby leading to the evolution of sustainable conventional probes as a suitable sensing platform. This work deals with the development of low-cost, multifunctional colorimetric sensing of Cu2+ metal ions in the environment with pH monitoring, using Hibiscus rosa-sinensis extract containing anthocyanins that act as an indicator, forming a complex. The UV-visible spectrophotometer shows a reliable anthocyanin content obtained using a water extract. This is further combined with the ratiometric quantitative analysis using smartphone assistance, processed using RGB channels, which acts as a user-friendly portable analytical tool with good reliability. The LOD of detection of Cu2+ through the smartphone technique was found to be 1.31x10-5 M with a feasible linearity of R2 = 0.935. The same phytochemical properties were screened against various microbial organisms, such as E. coli, S. aureus, and A. Niger, for resistance. Therefore, this work provides a multifunctional, sustainable platform for point-of-care sensing of Cu2+ and its antimicrobial resistance.
The paper deals with an application of machine learning algorithms to examine the impact of agricultural land-use types on CO₂ emissions in Vietnam during the period 1990-2019. A four-layer Artificial Neural Network (ANN) model was employed to analyze the relationship between 21 agricultural land-use types (LUTs) and CO₂ emissions. The selected LUTs cover major agricultural products, including crops, meat, and vegetables. The results indicate that most agricultural LUTs are positively associated with CO₂ emissions, including bananas, dry beans, cabbages, cashew nuts in shell, fresh cassava, raw or retted jute, cauliflowers and broccoli, dry chilies and peppers, raw cinnamon and cinnamon-tree flowers, coconuts in shell, green coffee, groundnuts excluding shelled, fresh hen eggs in shell, watermelons, tea leaves, and sweet potatoes. By contrast, fresh or chilled horse meat, unmanufactured tobacco, soya beans, sesame seed, and rice show negative associations with CO₂ emissions. These findings provide empirical evidence to support policymakers in designing targeted strategies for reducing greenhouse gas (GHG) emissions from agricultural production in Vietnam.
to map how implicit memory has been addressed in international scientific speech-language-hearing production. An electronic search was conducted in the PubMed, Web of Science, Scopus, Embase, CINAHL, and PsycINFO databases, supplemented by a manual search of references of the included studies. The scoping review was conducted based on the Joanna Briggs Institute Evidence Synthesis Manual, following the PRISMA-ScR guidelines. The protocol was registered in the Open Science Framework (10.17605/OSF.IO/PNDWF). criteria: Studies addressing implicit memory in speech-language-hearing contexts, in Portuguese, English, or Spanish, regardless of year of publication, were included. analysis: Two reviewers independently extracted data, considering design, population, objectives, and main outcomes. The studies were grouped by area of application and synthesized by descriptive and narrative analysis. A total of 2,565 documents were identified, of which 39 comprised this review. The studies were grouped into three categories (language, audiology, and voice). The most frequent themes found were the application of implicit learning in speech-language-hearing contexts; the use of implicit tasks for the assessment and characterization of disorders; and investigations of biases, prejudices, and/or implicit perceptions in speech-language-hearing practice. The literature deals with implicit memory in a limited and heterogeneous manner, mainly in language, audiology, and voice, focusing on implicit learning, assessment, and biases. Despite its clinical potential, there is a lack of conceptual and methodological systematization, indicating the need for future research to consolidate its use in the field. mapear como a memória implícita tem sido abordada na produção científica internacional em Fonoaudiologia. Foi realizada busca eletrônica nas bases PubMed, Web of Science, Scopus, Embase, CINAHL e PsycINFO, complementada por busca manual nas referências dos estudos incluídos. A revisão de escopo foi conduzida com base no Manual para Síntese de Evidências do Joanna Briggs Institute, seguindo as diretrizes PRISMA-ScR. O protocolo foi registrado no Open Science Framework (10.17605/OSF.IO/PNDWF). Foram incluídos estudos que abordassem a memória implícita em contextos fonoaudiológicos, em português, inglês ou espanhol, independentemente do ano de publicação. Dois revisores extraíram os dados de forma independente, considerando delineamento, população, objetivos e principais desfechos. Os estudos foram agrupados por área de aplicação e sintetizados por análise descritiva e narrativa. Um total de 2.565 documentos foram identificados, dentre os quais 39 compuseram essa revisão. Os estudos foram agrupados em 3 categorias (linguagem, audiologia e voz). Os temas mais frequentes encontrados foram: a aplicação da aprendizagem implícita em contextos terapêuticos fonoaudiológicos; o uso de tarefas implícitas para avaliação e caracterização de transtornos; as investigações de vieses, preconceitos e/ou percepções implícitas na prática fonoaudiológica. A literatura trata a memória implícita de forma limitada e heterogênea, principalmente em linguagem, audiologia e voz, com foco em aprendizagem implícita, avaliação e vieses. Apesar do potencial clínico, há carência de sistematização conceitual e metodológica, indicando a necessidade de pesquisas futuras para consolidar seu uso na área. mapear como a memória implícita tem sido abordada na produção científica internacional em Fonoaudiologia. Foi realizada busca eletrônica nas bases PubMed, Web of Science, Scopus, Embase, CINAHL e PsycINFO, complementada por busca manual nas referências dos estudos incluídos. A revisão de escopo foi conduzida com base no Manual para Síntese de Evidências do Joanna Briggs Institute, seguindo as diretrizes PRISMA-ScR. O protocolo foi registrado no Open Science Framework (10.17605/OSF.IO/PNDWF). Foram incluídos estudos que abordassem a memória implícita em contextos fonoaudiológicos, em português, inglês ou espanhol, independentemente do ano de publicação. Dois revisores extraíram os dados de forma independente, considerando delineamento, população, objetivos e principais desfechos. Os estudos foram agrupados por área de aplicação e sintetizados por análise descritiva e narrativa. Um total de 2.565 documentos foram identificados, dentre os quais 39 compuseram essa revisão. Os estudos foram agrupados em 3 categorias (linguagem, audiologia e voz). Os temas mais frequentes encontrados foram: a aplicação da aprendizagem implícita em contextos terapêuticos fonoaudiológicos; o uso de tarefas implícitas para avaliação e caracterização de transtornos; as investigações de vieses, preconceitos e/ou percepções implícitas na prática fonoaudiológica. A literatura trata a memória implícita de forma limitada e heterogênea, principalmente em linguagem, audiologia e voz, com foco em aprendizagem implícita, avaliação e vieses. Apesar do potencial clínico, há carência de sistematização conceitual e metodológica, indicando a necessidade de pesquisas futuras para consolidar seu uso na área.
The analysis of the story "The Curse" by Canetti (1977; it could have been a dream) allowed us to address concepts such as early trauma in Ferenczi, trauma in Freud, the body as a register of deep pain that houses perhaps even deeper psychic pain. Trauma occupies a principal place. It is a real trauma that cannot be linked; it is split off. In this sense, Ferenczi proposes thinking about the constitution of the infantile ego as an extension of the adult ego, thus this ego can operate as a structuring or de-structuring of the early trauma. The narration of this story allows us to think about the traumatic events in two stages. One is inferred, and the other is heartbreakingly manifest. One speaks to us about object relations, and the second deals with the real experience and refers to the failure in primary libidinal cathexis.