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  • CSR linked to sustainable competitive advantage in Ghanaian public sector

    What the study found

    The study found that corporate social responsibility was positively related to sustainable competitive advantage, green human resource management, and technological adaptability. It also found that green human resource management and technological adaptability were each positively related to sustainable competitive advantage.

    Why the authors say this matters

    The authors conclude that corporate social responsibility, green human resource management, and technological adaptability may be useful strategies for achieving sustainable competitive advantage. They describe this as actionable guidance for managers.

    What the researchers tested

    The researchers used Institutional Theory and the Resource-Based View to examine whether green human resource management and technological adaptability mediate the relationship between corporate social responsibility and sustainable competitive advantage. They analyzed data from 283 managers in the Ghanaian public sector using partial least squares structural equation modeling.

    What worked and what didn't

    Corporate social responsibility showed a significant positive relationship with sustainable competitive advantage, green human resource management, and technological adaptability. Green human resource management and technological adaptability also positively influenced sustainable competitive advantage and each partially mediated the relationship between corporate social responsibility and sustainable competitive advantage. The serial mediation roles of green human resource management and technological adaptability were confirmed.

    What to keep in mind

    The abstract does not describe detailed limitations. The findings are based on managers in the Ghanaian public sector, so the scope is limited to that sample.

    • Corporate social responsibility was positively related to sustainable competitive advantage.
    • Corporate social responsibility was also positively related to green human resource management and technological adaptability.
    • Green human resource management and technological adaptability each positively influenced sustainable competitive advantage.
    • Both factors partially mediated the link between corporate social responsibility and sustainable competitive advantage.
    • The serial mediation of green human resource management and technological adaptability was confirmed.

  • Higher-order statistics improve inference of reionization history

    What the study found

    The study found that combining power spectra with higher-order statistics can improve how well the average neutral hydrogen fraction is constrained from 21-cm observations of the Epoch of Reionization, the period when the first luminous sources ionized the intergalactic medium. Betti numbers were, on average, more informative than the power spectra alone, while the bispectrum provided limited constraints.

    Why the authors say this matters

    The authors conclude that using power spectra together with higher-order statistics could increase the information retrieved from Epoch of Reionization observations and help maximize the scientific return of future 21-cm data. They present this as relevant for upcoming Square Kilometre Array Observatory measurements.

    What the researchers tested

    The researchers studied the information content of the average neutral hydrogen fraction in several summary statistics of the 21-cm signal: Gaussian statistics using spherical and cylindrical power spectra, and non-Gaussian statistics using Betti numbers and the bispectrum. They generated mock 21-cm observations with the Square Kilometre Array Observatory low-frequency telescope AA* configuration, using noise levels corresponding to 100 and 1000 hours, and inferred posterior distributions at redshifts centered at 8.0, 7.2, and 6.5 with an implicit inference framework.

    What worked and what didn't

    Betti numbers alone were on average more informative than the power spectra. The bispectrum gave limited constraints, but combining higher-order statistics with the cylindrical power spectrum improved the mean figure of merit by about 0.25 dex, corresponding to roughly a 33 per cent reduction in the uncertainty in the average neutral hydrogen fraction. The relative contribution of each statistic changed with the stage of reionization.

    What to keep in mind

    The summary is based on mock observations, not real telescope data. The abstract does not provide detailed limitations beyond noting that the contribution of each statistic varies with reionization stage and that results were checked with calibration tests.

    • Betti numbers were, on average, more informative than the power spectra alone.
    • The bispectrum provided limited constraints on the average neutral hydrogen fraction.
    • Combining higher-order statistics with the cylindrical power spectrum improved the mean figure of merit by about 0.25 dex.
    • That improvement corresponded to roughly a 33 per cent reduction in uncertainty in the average neutral hydrogen fraction.
    • The relative usefulness of each statistic changed with the stage of reionization.

  • Microgravity ESA experiments reveal soft matter rheology behaviors

    What the study found

    The review finds that European Space Agency (ESA) microgravity experiments have helped reveal behaviors in soft matter that gravity can hide on Earth. These studies covered complex fluids, foams, emulsions, granular materials, colloidal glasses, soft particles, and red blood cells.

    Why the authors say this matters

    The authors conclude that ESA’s microgravity platforms are relevant for expanding soft matter rheology, which is the study of how matter flows and deforms. They suggest these platforms complement Earth-based experiments by reducing gravity-related effects such as sedimentation and drainage.

    What the researchers tested

    This is a short review of past and present ESA-led projects of interest to rheologists. The paper summarizes interfacial rheology experiments using capillary pressure tensiometers, studies of foam coarsening and emulsion droplet dynamics, granular material investigations, experiments on thermally driven perturbation of soft colloidal glasses, and work on aggregation and migration of soft particles and red blood cells under flow.

    What worked and what didn't

    The abstract says the microgravity conditions enabled high-precision measurements of interfacial viscoelasticity and revealed plastic rearrangements in colloidal glasses. It also reports roaming bubbles in foams, progressive arrest of droplet motion in emulsions, margination effects in blood cell analogues under flow, and an improved understanding of how gravity affects convection and fluidization in agitated granular matter.

    What to keep in mind

    This summary is based only on the abstract of a review article, so it does not provide detailed methods, quantitative results, or full study-by-study limitations. The abstract also does not describe any negative findings or experimental failures.

    • ESA microgravity experiments helped reveal soft matter behaviors that gravity can mask on Earth.
    • The review covers foams, emulsions, granular materials, colloidal glasses, soft particles, and red blood cells.
    • Microgravity enabled high-precision measurements of interfacial viscoelasticity.
    • The abstract reports plastic rearrangements in colloidal glasses and roaming bubbles in foams.
    • The authors conclude that ESA platforms complement Earth-based rheology experiments.

  • Theory-guided models matched autism status well in a small study

    What the study found

    The study found preliminary evidence that prespecified Pathogenetic Triad models for autism can predict case status well. These models, which combine autistic personality, cognitive capacity, and neuropathological burden, performed among the strongest models of similar size in this dataset.

    Why the authors say this matters

    The authors conclude that the findings support the Pathogenetic Triad as a multilevel architecture of autism liability. They also suggest that prespecified multilevel frameworks can be turned into empirical tests in neuropsychiatric samples.

    What the researchers tested

    The researchers studied 42 people, including 21 autistic participants, using dense multimodal characterization. The data included behavioral phenotyping, psychometric measures, autonomic physiology, structural MRI morphometry, and magnetoencephalographic indices; autistic personality was measured with the Autism-Spectrum Quotient, cognitive capacity with Wechsler intelligence scales, and neuropathological burden with heart-rate variability as a proxy indicator.

    What worked and what didn't

    Across many model specifications, low-dimensional Pathogenetic Triad models ranked among the strongest models of comparable size. Matched comparisons suggested that using all three Pathogenetic Triad domains gave systematic advantages over alternatives with similar univariate input strength, and the models were broadly comparable to higher-dimensional models in this dataset.

    What to keep in mind

    The study was preliminary and based on a small, demographically restricted cohort. The abstract does not describe external validation beyond the reported out-of-sample prediction within this dataset, so the results should be read within that limited scope.

    • The study found preliminary support for a Pathogenetic Triad model of autism liability.
    • The model combined autistic personality, cognitive capacity, and neuropathological burden.
    • Low-dimensional Pathogenetic Triad models ranked among the strongest models of comparable size.
    • Including all three domains was associated with systematic advantages over matched alternatives.
    • The dataset was small and demographically restricted.

  • MRI-based model assessed sarcoma grade and Ki-67 expression

    What the study found

    The study found that an automated MRI-based clinical-radiomics model may assess soft tissue sarcoma grade and Ki-67 expression, which are pathological measures used to describe tumor aggressiveness and cell proliferation. It also found that the model may improve the diagnostic performance of less-experienced radiologists.

    Why the authors say this matters

    The authors say this matters because histological grade and Ki-67 expression are prognostic risk factors in soft tissue sarcoma, and these assessments usually require biopsy, which is invasive and may be affected by tumor heterogeneity. The study suggests that an MRI-based approach could provide a noninvasive alternative for assessment.

    What the researchers tested

    The researchers conducted a retrospective study of 186 patients with pathologically confirmed soft tissue sarcoma from three hospitals. They developed an automatic segmentation model and compared it with manual segmentations, then built clinical-imaging signature models using structural MRI radiomics, structural MRI plus apparent diffusion coefficient (ADC) radiomics, and those features combined with clinical information and MRI semantic features.

    What worked and what didn't

    The segmentation model showed good performance, with Dice coefficients of 0.80 for extremity cases and 0.73 for trunk cases. In validation, the best model for grade was the logistic regression clinical-imaging signature model, and the best model for Ki-67 expression was the support vector machine model, with AUCs of 0.846 and 0.742, respectively. Using the model improved diagnostic performance for the two less-experienced radiologists, but the abstract does not report a comparable improvement for the most experienced radiologist.

    What to keep in mind

    The study was retrospective and included 186 patients, so the findings are based on a specific multicenter sample. The abstract does not describe limitations beyond the reported validation design, and it does not state how the model would perform outside the studied hospitals or in other patient groups.

    • The study evaluated an automated MRI-based clinical-radiomics pipeline for soft tissue sarcoma grade and Ki-67 expression.
    • Soft tissue sarcoma grade and Ki-67 expression are described as prognostic risk factors and usually require biopsy.
    • The segmentation model achieved Dice coefficients of 0.80 in extremity cases and 0.73 in trunk cases.
    • The best validation AUCs were 0.846 for grade and 0.742 for Ki-67 expression.
    • Model use improved diagnostic performance for the two less-experienced radiologists.

  • Mamba-based BOA compressor matches or exceeds standard lossless tools

    What the study found

    The study found that BOA Constrictor, a lossless neural compressor built on the Mamba state space model, achieved competitive compression on several scientific datasets. It sometimes matched or exceeded standard lossless compressors such as LZMA, ZSTD, and ZLIB, and it performed well on some high-energy physics data, computational fluid dynamics data, and cosmology data.

    Why the authors say this matters

    The authors say this matters because petabyte-scale data from high energy physics experiments creates a storage challenge. They conclude that BOA is a first step toward improving compression for next-generation scientific data.

    What the researchers tested

    The researchers tested BOA, a pseudo-streaming lossless neural compressor, on structured scientific datasets. These included ATLAS Open Data in HDF5 format, simulated particle collision records in HepMC v3, CMS Open Data in NanoAOD format, as well as datasets from computational fluid dynamics and cosmology.

    What worked and what didn't

    BOA achieved an effective compression ratio of 7.23× on ATLAS Open Data and 9.13× on simulated particle collision records when model size was included. In those tests, it outperformed the next-best traditional algorithms, and on CMS Open Data it obtained comparable or improved effective compression ratios within 5% of the next-best traditional algorithm. Its throughput was reported as about 3.5 to 45 MB/s for compression and about 1.5 to 25 MB/s for decompression, which the abstract says is not yet competitive with optimized algorithms such as ZSTD or LZMA.

    What to keep in mind

    The results come from a proof-of-principle implementation, so the abstract does not present them as a finished production system. The abstract also notes that BOA is strongest on high-entropy float32 payloads, that FP16 weights reduce model size without reducing predictive accuracy, and that the available summary does not describe other limitations beyond slower throughput.

    • BOA is a lossless neural compressor based on the Mamba state space model.
    • It achieved 7.23× effective compression on ATLAS Open Data and 9.13× on simulated particle collision records.
    • It outperformed the next-best traditional algorithm on those two datasets when model size was included.
    • On CMS Open Data, it was comparable to or better than the next-best traditional algorithm within 5%.
    • The abstract says BOA is slower than optimized compressors such as ZSTD and LZMA.
    • FP16 weights reduced the model footprint without degrading predictive accuracy.

  • Three illness perception profiles found in maintenance hemodialysis patients

    What the study found

    The study identified three distinct illness perception profiles among patients receiving maintenance hemodialysis: Low Perception-Optimistic, Moderate Perception-Peaceful, and High Perception-Pessimistic. The findings also show that these profiles were associated with different sociodemographic, clinical, psychological, and coping-related factors.

    Why the authors say this matters

    The authors conclude that the heterogeneity in illness perceptions suggests a need for personalized interventions. They say the profiles and their associated factors may help support targeted interventions aimed at improving treatment adherence, self-management, and quality of life in patients receiving maintenance hemodialysis.

    What the researchers tested

    The researchers used an explanatory mixed-methods design. In the quantitative phase, they studied 291 hemodialysis patients in China from May to July 2024 using the Brief Illness Perceptions Questionnaire and latent profile analysis, then examined predictors with univariate and multivariate logistic regression. In the qualitative phase, they interviewed 16 participants from August to September 2024 and analyzed the interviews with the Colaizzi seven-step method.

    What worked and what didn't

    Latent profile analysis identified three groups: Low Perception-Optimistic (39.2%), Moderate Perception-Peaceful (16.5%), and High Perception-Pessimistic (44.3%). The profiles differed significantly in age, education, employment, income, dialysis duration, primary disease, comorbidity count, anxiety and depression, symptom severity, health literacy, self-efficacy, coping style, and perceived social support. The qualitative analysis found four main themes: illness experience, emotional experience, illness impact, and illness coping.

    What to keep in mind

    The study was conducted at a single hospital in China, so the sample is limited in scope. The abstract also does not describe intervention testing, and it does not report limitations beyond the call for future research on interventions and broader contextual factors.

    • Three illness perception profiles were identified in 291 maintenance hemodialysis patients.
    • The largest group was High Perception-Pessimistic, at 44.3%.
    • Profiles were linked with age, education, income, dialysis duration, symptoms, health literacy, self-efficacy, coping style, and social support.
    • Avoidance or surrender coping strategies were associated with greater likelihood of some less favorable profiles.
    • The qualitative interviews produced four themes: illness experience, emotional experience, illness impact, and illness coping.

  • Health marketing communications centers on health communication and social marketing

    What the study found

    The study found that health marketing communications research is anchored in health communication, health marketing, and social marketing. It also identified stronger thematic streams around persuasion, message framing, prevention-oriented communication, and co-design.

    Why the authors say this matters

    The authors conclude that the mapping offers a foundation for future research in health marketing communications. They also highlight underdeveloped areas in digital health, cultural context, and health equity.

    What the researchers tested

    The researchers carried out a bibliometric analysis of 171 Scopus-indexed articles in the Business, Management, and Accounting domain. They used Bibliometrix and Biblioshiny to map publication trends, influential sources, collaboration patterns, and thematic development.

    What worked and what didn't

    The analysis showed clear publication trends and identified influential sources and collaboration patterns in the field. It also showed that some themes are more developed than others, with digital health, cultural context, and health equity described as underdeveloped.

    What to keep in mind

    The study is limited to 171 articles indexed in Scopus and to the Business, Management, and Accounting domain. The abstract does not describe additional limitations.

    • The field is anchored in health communication, health marketing, and social marketing.
    • Stronger thematic streams appear around persuasion, message framing, prevention-oriented communication, and co-design.
    • Digital health, cultural context, and health equity are described as underdeveloped areas.
    • The study analyzed 171 Scopus-indexed articles using Bibliometrix and Biblioshiny.
    • The authors present the mapping as a foundation for future research.

  • Transformer identifies strongly modified jets in heavy-ion collisions

    What the study found

    The study found that a Transformer classifier can detect information in jet data that is not captured by other models using high-level physical observables. It also identified a class of jets that the authors describe as unequivocally modified, even when medium response and underlying event contamination were included.

    Why the authors say this matters

    The authors say this matters because vacuum-like jets, meaning jets that experienced little interaction with the quark-gluon plasma, dilute the overall observed modification in nucleus-nucleus collision samples. The study suggests that being able to judge jet modification on a jet-by-jet basis would help overcome this limitation.

    What the researchers tested

    The researchers tested a Transformer classifier trained on a low-level representation of jets using the four-momenta of all jet constituents. They compared its performance with other architectures that use high-level physical observables as input. The study also examined the experimentally relevant case in which medium response and underlying event contamination are both accounted for.

    What worked and what didn't

    The Transformer was able to capture discriminating information that other tested architectures did not access. In the setting studied, it could identify a class of jets that were clearly modified. The authors also performed a robust estimate of an upper bound on the fraction of nucleus-nucleus collision jets that are indistinguishable from proton-proton jets.

    What to keep in mind

    The abstract does not provide numerical results in the text available here. It also does not describe detailed limitations beyond the fact that the estimate concerns an upper bound for jets that are, for all purposes, indistinguishable from proton-proton jets.

    • The study says a Transformer can find jet information not captured by models using high-level observables.
    • It identified a class of jets described as unequivocally modified.
    • The authors say vacuum-like jets dilute the observed modification in nucleus-nucleus collision samples.
    • The researchers estimated an upper bound on jets in nucleus-nucleus collisions that are indistinguishable from proton-proton jets.
    • The analysis included medium response and underlying event contamination.

  • High-frequency uncertainty principle for Fourier-Bessel transform

    What the study found

    The study shows a Fourier-Bessel transform analogue of the classical Paneah-Logvinenko-Sereda theorem. For functions whose Fourier-Bessel transform is supported in a frequency interval [R, R+1], the authors prove an L2 bound by the function's size on a relatively dense set.

    Why the authors say this matters

    The abstract says the work is motivated by control theory problems, especially decay rates for the damped wave equation. The authors present the result as relevant to that setting by giving a frequency-localized estimate in the Fourier-Bessel framework.

    What the researchers tested

    They studied functions on the positive real line with respect to the measure dμ_α(x) ≈ x^{2α+1} dx, for α > -1/2. They assumed the set E ⊂ R+ is μ_α-relatively dense and that supp F_α(f) ⊂ [R, R+1], where F_α denotes the Fourier-Bessel transform.

    What worked and what didn't

    Under those assumptions, they prove that the L2_α norm of f on the whole space is controlled by its L2_α norm on E, written as ‖f‖_{L^2_α(R+)} ≲ ‖f‖_{L^2_α(E)}. The abstract does not report cases where the estimate fails or describe negative results.

    What to keep in mind

    The abstract gives only the stated theorem and its setup, not proof details. It also does not describe limitations beyond the conditions already listed: α > -1/2, relative density of E, and frequency support contained in [R, R+1].

    • The paper proves a Fourier-Bessel version of the Paneah-Logvinenko-Sereda theorem.
    • The result applies when the Fourier-Bessel transform is supported in a unit-length interval [R, R+1].
    • A μ_α-relatively dense set E controls the L2_α norm of the function on the whole positive real line.
    • The work is motivated by control theory questions for the damped wave equation.
    • The abstract does not describe failures, exceptions, or proof details.