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Risk identification method for automotive styling design tasks based on an improved FAHP-VIKOR approach

To address the difficulty of quantifying risks in current automotive styling-design task management, this study proposes a risk identification method for automotive styling design tasks based on an improved FAHP-VIKOR approach. First, the work breakdown structure (WBS) is constructed based on the principle of task modularity, and the risk breakdown structure (RBS) is established through the analysis of factors influencing risk, thereby forming the WBS-RBS coupling matrix for automotive styling design. Then, the improved fuzzy analytic hierarchy process (FAHP) is employed to determine the weights of risk factors such as technical, schedule, and cost risks, while the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is applied to perform comprehensive ranking that adapts to the multidimensional and dynamic characteristics of styling design risks, thereby enabling quantitative risk assessment and identification. Finally, the design tasks involved in the exterior styling concept-design phase of a certain sport utility vehicle (SUV) model are taken as a case study and compared with the single FAHP method, the FAHP-TOPSIS method and the FAHP-VIKOR method. The results indicate that the proposed approach can classify the 27 core design tasks into high, medium and low risks, and can effectively identifies high-risk nodes such as the front bumper surface, lidar surface, and rear bumper surface. Compared with the other methods, the proposed method produces more stable and reasonable risk identification results and can provide decision-making support for risk management and optimal resource allocation in automotive styling design projects.

Intelligent compensation method for measurement errors in optical fiber current sensor caused by temperature variation based on the Levy-Weighted-QPSO-NN algorithm

Temperature variations significantly degrade the measurement accuracy of fiber optic current sensors (FOCS) in critical power systems applications such as high-voltage transmission and renewable energy integration. To address this, we propose an intelligent error compensation method based on an improved Quantum-behaved Particle Swarm Optimization-Neural Network (Levy-Weighted-QPSO-NN) algorithm. The approach leverages easily measurable state parameters—sensing ring temperature, received optical power, half-wave voltage, SLD temperature, and SLD current—as inputs to predict temperature-induced current ratio difference. Experimental validation involved three sensing rings subjected to temperature cycling (−45 °C to 70 °C), emulating harsh substation environments. The Levy-Weighted-QPSO-NN model achieved 91.11% average prediction accuracy for ratio difference with a correlation coefficient (R²) of 0.9223, outperforming QPSO-NN (85.69%) and Weighted-QPSO-NN (88.31%). Key metrics (MAE: 0.0784; RMSE: 0.0819) confirmed superior stability and accuracy. Robustness testing demonstrated consistent performance across varying population sizes (25–70) and iterations (90–150). Using predicted ratio differences for real-time compensation reduced measurement errors from 0.82% to 0.13%, meeting IEC 61869–6/8 and GB/T standards for Class 0.2S accuracy. This method eliminates reliance on complex hardware modifications, offering a generic, algorithm-driven solution for temperature-dependent FOCS errors.

Diagnostic performance of machine learning models based on dual-phase 99mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules

Purpose To evaluate the diagnostic value of machine learning models based on dual-phase 99 mTc-MIBI SPECT/CT semiquantitative parameters for differentiating benign and malignant pulmonary nodules. Methods This retrospective study included 132 patients with pulmonary nodules, including 30 benign and 102 malignant lesions. All patients underwent dual-phase 99 mTc-MIBI SPECT/CT at approximately 20 minutes and 2 hours after tracer injection. Semiquantitative parameters, including early and delayed tumor-to-normal ratios (T/N) and retention indices (RI), were calculated. Clinical variables and imaging parameters were analyzed using univariable and multivariable logistic regression, and selected variables were further used to develop machine learning models. Results Malignant nodules showed significantly higher early-phase uptake and lower retention index values than benign nodules. Multivariable analysis identified elevated CEA and RImax as independent predictors of malignancy. Machine learning models built on these simple semiquantitative parameters showed promising diagnostic performance, with an AUC of 0.944 (95% CI: 0.883–0.990) for SVM on the training set, 0.805 (95% CI: 0.678–0.912) for Logistic Regression (LR), 0.881 (95% CI: 0.800–0.949) for Artificial Neural Network (ANN), and 0.979 (95% CI: 0.951–0.995) for Random Forest (RF), demonstrating their effectiveness in classifying pulmonary nodules. Conclusion Dual-phase 99 mTc-MIBI SPECT/CT semiquantitative parameters provide useful information for distinguishing benign from malignant pulmonary nodules. A machine learning strategy based on simple and interpretable parameters may offer a practical tool for pulmonary nodule assessment, especially in settings where complex imaging analysis is not feasible.

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review

Background Sickle cell disease (SCD) stands as one of the most prevalent genetic disorders in the United States (U.S.) that causes severe consequences such as organ damage and excruciating pain. Alarmingly, recent literature indicates a decline in the number of people living with SCD (PLWSCD) seeking professional care – hinting at an avoidance of the healthcare system. Therefore, this scoping review synthesizes the evidence regarding barriers and facilitators influencing healthcare utilization among PLWSCD within the U.S. Methods To map the current literature on SCD management and provide a comprehensive overview of the current knowledge gaps regarding healthcare utilization for PLWSCD, a scoping review was conducted. A systematic search of articles reporting on the utilization of healthcare among PLWSCD in the U.S using seven data sources was conducted on March 24, 2023, without any restrictions on publication date and language. To capture any additional articles, the search was updated on March 4, 2024. Two reviewers independently assessed studies for inclusion, data extraction, and risk of bias (RoB). Main results A total of 708 articles were screened; 70 met the study criteria. Results indicated that the four most common barriers were social ( n  = 25) interpersonal ( n  = 23), economic ( n  = 15), and institutional level factors ( n  = 11). The top four most common facilitators were technology ( n  = 9), education ( n  = 7), autonomy ( n  = 6), and a positive patient-provider relationship ( n  = 6). The most common forms of healthcare utilization were inpatient or hospital admissions ( n  = 19) and emergency department (ED) visits ( n  = 18). Evidence-based interventions (EBI) found to decrease healthcare avoidance included individualized pain plans (IPPs) ( n  = 4) and quality improvement (QI) strategies ( n  = 3). Conclusion This scoping review identified complex multilevel barriers that impede healthcare utilization, and facilitators likely to promote healthcare utilization among PLWSCD in the U.S. Future research should prioritize developing and evaluating comprehensive, multi-level interventions that address identified barriers while leveraging facilitators to improve healthcare engagement and outcomes for this vulnerable population. Healthcare systems and health policies must urgently adopt and integrate evidence-based strategies to rebuild trust and ensure equitable, accessible care for PLWSCD.

From noticing to reflection: A qualitative exploration of rapid cycle deliberate practice effects on electrocardiographic monitoring judgment in critical cardiac care nurses

Background Electrocardiographic interpretation competency among critical cardiac care nurses remains inadequate despite technological advances, creating patient safety concerns and alarm fatigue. Traditional nursing education utilizing didactic knowledge transmission has proven insufficient for developing time-critical electrocardiographic interpretation skills. Rapid cycle deliberate practice emerges as an innovative methodology integrating deliberate practice principles, immediate feedback, and psychologically safe environments through pause-coach-resume mechanisms. Methods Semi-structured interviews utilized Tanner’s clinical judgment model as the questioning framework, supplemented by field observations. Interviews explored experiences around the noticing-interpreting-responding-reflecting continuum, with analysis conducted using NVivo software following van Manen’s methodology. Results Four interconnected themes emerged: shifting perceptual focus from fragmented data perception toward holistic pattern recognition; shifting cognitive models from mechanical matching toward integrating intuition and evidence; evolving response patterns from hesitant reactivity toward confident proactivity; and redefined professional role encompassing new professional identity, ethical responsibility, and continuous learning commitment. Conclusions Analysis through Tanner’s clinical judgment framework suggested perceived multidimensional shifts within critical cardiac care nurses’ clinical judgment processes, as reflected in participants’ descriptions of changes transcending technical skill acquisition to encompass reconceptualizations of clinical reasoning, professional identity, and ethical responsibility. Notably, the psychologically safe environment created through the pause-coach-resume mechanism appeared central to enabling these perceived shifts. These findings indicate that effective competency development requires educational approaches addressing cognitive, behavioral, and identity dimensions simultaneously, with implications for institutional integration of rapid cycle deliberate practice into staff development and future longitudinal investigation of its effectiveness.

Controlling Exsolution Dynamics in High‐Entropy Oxides for Highly Active and Selective Acetylene Semi‐Hydrogenation

ABSTRACT Exsolution‐derived catalysts feature robust metal–support interactions that enhance catalytic performance; yet achieving precise control over exsolution dynamics in multicomponent oxides remains challenging. In this study, we demonstrate that exsolution behavior in high‐entropy oxides (HEOs) can be rationally tuned through coupled lattice‐ and valence‐engineering to create a highly active and selective catalyst for acetylene semi‐hydrogenation. Incorporation of Li + into a rock salt‐structured HEO (LiNiMgCuZnCoO x and LiHEO) induces local lattice distortion, generates oxygen vacancies, and partially oxidizes Co sites from Co 2+ to Co 3+ , collectively modulating local charge redistribution. This strategy enables facilitated Cu nanoparticle exsolution and alters the exsolution sequence from Cu 0 > Ni 0 > Co 0 in pristine HEO to Cu 0 > Co 0 > Ni 0 in the LiHEO. The resulting catalyst via controlled exsolution exhibits superior activity and ethylene selectivity, outperforming state‐of‐the‐art transition metal systems. This work establishes entropy‐enabled lattice and valence engineering as a facile route to programmable exsolution for enhanced catalysis.

Retraction: Anti-vibration method for sensing ring of fiber optical current transformer integrating structural design and adaptive signal processing

The role of SALL1-MGST1 axis-mediated ferroptosis inhibition in chemoresistance of retinoblastoma

Retinoblastoma (RB) is the most prevalent intraocular malignant tumor in infants and young children, predominantly driven by the biallelic inactivation of the RB1 gene. Although multimodal treatment strategies have markedly improved the survival rates of pediatric patients, chemotherapy resistance, particularly carboplatin (CBP) resistance, remains a major clinical hurdle. In recent years, ferroptosis, an iron-dependent and lipid peroxide-driven form of programmed cell death, has garnered significant attention for its role in tumor drug resistance. This study investigates the functional mechanisms of the transcription factor Spalt-Like Transcription Factor 1 (SALL1) and its downstream target gene Microsomal Glutathione S-Transferase 1 (MGST1) in RB. Bioinformatics analysis revealed a significant upregulation of SALL1 in RB, accompanied by enhanced activity of its regulatory network. Experimental evidence from ChIP-qPCR and luciferase reporter assays indicated that SALL1 binds to the promoter region of MGST1 and enhances its promoter transcriptional output. Functionally, knockdown of SALL1 suppressed cell proliferation and induced ferroptosis, as evidenced by increased levels of lipid peroxidation, elevated malondialdehyde (MDA), and decreased glutathione (GSH) levels. These effects were reversible by the ferroptosis inhibitor Fer-1 or overexpression of MGST1. In the CBP-resistant cell line Y79-R, knockdown of SALL1 notably reduced the IC50, enhanced chemosensitivity, and promoted cell death, a phenotype that could be rescued by overexpression of MGST1. Mechanistically, the SALL1-MGST1 axis promotes RB cell survival and drug resistance by inhibiting lipid peroxidation and ferroptosis. This study is the first to elucidate that the SALL1-MGST1 axis mediates CBP resistance in RB through the regulation of ferroptosis, providing a novel therapeutic target for reversing drug resistance.

Light‐Activated Isolation of High‐Quality Mitochondria for Therapeutic Transplantation

ABSTRACT Artificial mitochondrial transplantation (AMT) holds great promise for reprogramming cellular metabolism and restoring cell function. Its clinical translation, however, relies on access to mitochondria that are both of high purity and metabolically active, requirements that current isolation techniques struggle to meet. Conventional differential centrifugation (DC) method yields heterogeneous and low‐activity mitochondria, whereas magnetic bead (MB)‐based immuno‐isolation leaves non‐biodegradable beads permanently attached. Herein, we present a Light‐Activated Mitochondrial Isolation ( LAMI ) platform comprising programmable mitochondria‐targeting MBs and a photo‐responsive release mechanism for the selective, efficient, and non‐destructive extraction of high‐quality mitochondria. LAMI employs magnetic nanoparticles decorated with a branched, modular probe architecture that supports systematic variation in mitochondria‐targeting ligand type, ligand density, and optical tracking elements. Incorporation of a photo‐cleavable linker allows on‐demand, mild, and reagent‐free release of captured mitochondria. Compared with DC method, LAMI produces mitochondria with markedly improved purity, structural integrity, and functionality. In an ischemia‐reperfusion injury (IRI) model, LAMI ‐isolated mitochondria‐based AMT exhibits superior therapeutic performance. Together, LAMI provides a non‐destructive, efficient, and versatile mitochondrial isolation strategy that overcomes long‐standing limitations of current methods, offering a robust platform to advance AMT and its future biomedical applications.

Caesarean section epidemic in India: Is private sector to blame? A multivariate logistic regression analysis of the National Family Health Survey

Background C-sections (CS) can be lifesaving in certain medical situations, but their prevalence has surged beyond recommended levels globally, raising concerns about inappropriate medical interventions and healthcare delivery quality. Objectives The study explores whether medical conditions alone determine C-sections or if socio-economic and institutional factors (i.e., Private/Public) also play a significant role. Method Using WHO and World Bank data, the relationship between C-section rates and income was analysed at the global level. Furthermore, at the national and state levels in India, the same analysis was conducted using data from the Ministry of Statistics and Programme Implementation and NFHS-5. Additionally, utilising the Birth recode datasets of NFHS-4 and 5, multivariate logistic regression was performed with C-section as the outcome variable and socio-economic and institutional variables, such as place of residence, levels of education, wealth index, and place of delivery, as predictor variables. Results Throughout the analysis, we found the institutional setting as the most significant influencing factor of CS rates, at both the national (OR: 4.11, 95% CI 3.98–4.24; NFHS-5) and state level for Bihar (OR: 16.19, 95% CI 13.76–19.05; NFHS-5), Uttar Pradesh (OR: 8.62, 95% CI 7.84–9.47; NFHS-5), Tamil Nadu (OR: 3.08, 95% CI 2.70–3.52; NFHS-5) and Andhra Pradesh (OR: 4.16, 95% CI 3.40–5.09, NFHS-5). On the contrary, we found that socioeconomic factors influenced the likelihood of CS only in states where medical infrastructure was lacking, indicating that socioeconomic factors are not directly responsible for CS rates; they are complicit only in determining institutional access. Additionally, an inverted U-shaped relationship was found between national per capita income and CS rates, indicating global inequality in the quality of healthcare. Within India, this relationship increasingly mirrors the global trend, possibly due to disparities in healthcare access and quality. Conclusion Our analysis confirms that the increase in CS rates is not solely caused by medical conditions, but is also significantly influenced by non-medical factors, particularly institutional factors. Economic incentives strongly drive private healthcare providers to prefer CS deliveries. The results suggest the need for targeted policy interventions to mitigate perverse incentives for private facilities and enhance public medical infrastructure, particularly in underserved regions.

Boosting Formic Acid Production in Methane Oxidation by Zeolite Confined Single‐Site Rh Catalyst

ABSTRACT The selective oxidation of methane to value‐added oxygenates remains a long‐standing challenge in catalysis, often constrained by poor reactivity (and single‐pass methane conversion) and limited product selectivity. In this study, we report a Rh‐Beta catalyst featuring atomically dispersed Rh δ+ species embedded in the aluminosilicate *BEA zeolite matrix that enables highly selective methane oxidation in a CH 4– O 2– CO–H 2 O system. This catalyst achieves a remarkable formic acid space‐time yield of 210 mol mol Rh −1 h −1 at 7.84% methane conversion, with formic acid selectivity of 96%—surpassing all previously reported catalyst systems. Mechanistic investigations combining spectroscopy and theory reveal that hydroxyl radicals, generated via acid‐promoted water‐gas shift and in situ H 2– O 2 reactions, are responsible for methane activation, while the zeolite backbone plays a vital role in stabilizing formaldehyde intermediate and promoting its further oxidation to formic acid product. This work offers a new perspective on the conversion–selectivity relationship in methane oxidation and demonstrates a feasible pathway for the selective functionalization of methane under mild conditions.

Artificial intelligence-detected HER2 strong-positive tumor proportion predicts FISH positivity and treatment response in breast cancer

Human epidermal growth factor receptor 2 (HER2)-targeted therapies have revolutionized breast cancer treatment, necessitating standardized HER2 testing. However, current immunochemistry-based HER2 assessment faces challenges due to subjectivity and variability among observers, prompting the guidance of artificial intelligence (AI). We evaluated AI’s efficacy in HER2 status assessment and treatment response prediction, especially focusing on complete and intense circumferential HER2-positive (3+) tumor cells. An AI-powered HER2 analyzer (Lunit SCOPE HER2, Lunit Inc., Seoul, South Korea) and three pathologists independently assessed HER2 3 + tumor cell proportions from whole-slide images of 191 breast cancer cases from Kyung Hee University Hospital. Logistic regression and receiver operating characteristic (ROC) curves determined predictive accuracy. AI-detected 3 + tumor cell proportions strongly correlated with fluorescent in situ hybridization (FISH) positivity (area under ROC curve [AUC]: 0.783) and pathological complete response (pCR) rates (odds ratio [OR]: 1.003–1.052), outperforming pathologists’ assessments. Combining AI improved prediction accuracy of pathologists, with an AUC increased from 0.712–0.813 to 0.790–0.821 for predicting FISH positivity and median OR increased from 0.996–1.061 to 1.003–1.055 for predicting pCR. Overall, this preliminary study suggests that AI could enhance HER2 status determination and treatment response prediction, complementing traditional pathological evaluation of HER2 immunohistochemistry.

Developing a contracts law keyword list (CLKL) for academic legal education: A corpus-based, keyness-informed study

This study introduces the Contracts Law Keyword List (CLKL), a discipline-specific, genre-focused resource of pedagogically useful lexical items in contract law, an understudied legal subfield at the nexus of law and commerce. Items in the CLKL are drawn from authentic language data using predetermined frequency, range, and keyness parameters. The CLKL includes 747 keywords (KWs) typical of written contract law textbooks, covering 4.22% of all words in the study corpus and 4.19% in the law section of the British National Corpus (BNC). Structural analysis revealed the pervasive presence of nouns, which account for nearly half of all grammatical categories. Adjectives and verbs constitute the second- and third-largest groups of KWs. There are rare instances of adverbs, archaic terms, prepositions, and words with dual structural functions. These findings underscore the importance of creating a field-specific vocabulary list that can address the lexical demands of a growing number of law students and international practitioners. The CLKL may also be used to deepen knowledge of domain-specific vocabulary, enhance engagement with authentic language examples, and foster awareness of expert-authorized conventions typical of legal contracts. Law educators can draw on the list while designing instructional materials or planning classroom activities.

Mechanically Adaptive Polyrotaxane Interlayers for Low‐Pressure High Energy Density Sulfide‐Based All‐Solid‐State Batteries

ABSTRACT All‐solid‐state batteries (ASSBs) employing lithium (Li) metal anodes or an anode‐less configuration, despite their superior energy density, suffer from performance degradation under low stack pressure, hindering their practical application. To address this, we design a mechanically adaptive anode interface that leverages an elastic polymer incorporating mechanically interlocked polyrotaxane (PR). This interface synergistically combines the elastic resilience—derived from the unique ring‐sliding motion of PR—with indium fluoride (InF 3 ), which undergoes spontaneous conversion to form a chemically stable interface. This approach enables robust cycling stability and reliable operation under commercially relevant conditions (25°C, 0.8 MPa), even in an anode‐less configuration (N/P = 0), thus demonstrating the potential of mechanically interlocked molecular architectures for maintaining void‐free interfaces in low‐pressure ASSBs with high energy densities.

Self-medication practice with anti-malarial drugs and its associated factors among patients with fever attending public health facilities in Dera District, Northwest Ethiopia

Background The issue of self-medication with anti-malarial drugs is a critical public health challenge. Despite national guidelines promoting diagnostic-confirmed treatment, evidence addressing the prevalence of self-medication with anti-malarial drugs and drivers in Ethiopia is scarce. Therefore, this study aimed to bridge this gap and to provide meaningful insights into ongoing efforts to curb the misuse of anti-malarial drugs and helping the strategy of malaria eliminationby investigating the prevalence of self-medication with anti-malaria drugs and its associated factors. Methods An institution-based cross-sectional study was conducted in Dera District, Northwest Ethiopia, from June 1–30, 2025. A total of 591 febrile patients were selected using a stratified multistage sampling technique. Data were collected through interviewer-administered structured questionnaires developed by reviewing different related literatures. Data entry and analysis were done using Epi-Data version 4.6 and STATA 17. Multivariable binary logistic regression analysis was performed to identify factors associated with self-medication. Statistical significance was declared at a p-value less than 0.05. Result The prevalence of self-medication with anti-malarial drugs was 42.8% (95% CI: 38.9%–46.8%). Factors positively associated with self-medication included: monthly income of ≥5000 ETB (AOR = 1.64, 95% CI: 1.04–2.59), poor knowledge about malaria (AOR = 1.68, 95% CI: 1.12–2.55), poor risk perception towards self-medication (AOR = 2.10, 95% CI: 1.40–3.14), and distance ≤5 km from private drug sellers (AOR = 1.65, 95% CI: 1.10–2.47). Community-based health insurance membership was negatively associated with self-medication (AOR = 0.60, 95% CI: 0.41–0.88). Conclusion Self-medication with anti-malarial drugs was moderate (33.4%_66.6%) in the study area based on percentile classification. The findings highlighted the need for designing different strategies to reduce inappropriate drug use and promoting community-based health insurance enrollment.

Burnout subtypes in the German working population: Differentiation by symptomatology, work-related factors and structural impairment according to the OPD

Farber’s typological approach expands burnout models by identifying three burnout subtypes: frenetic, underchallenged, and worn-out. Despite its potential, research on this approach is limited, especially in the German working population. This study translated and validated the Burnout Questionnaire for Clinical Subtypes (BCSQ-12) into German and examined these burnout subtypes in the German working population. Using a non-probabilistic online quota sampling method, 616 employees were surveyed. The BCSQ-12 was translated and evaluated for its psychometric properties (EFA, Varimax rotation). Burnout subtypes were identified through hierarchical and K-means cluster analysis and analysed for symptomatologic, work-related and individual differences, particularly structural impairment (ANOVA). Additional questionnaires assessed burnout (MBI-GS-D), depression (PHQ-9), work engagement (UWES-9), job demands (COPSOQ), social support (MSPSS), and structural impairment (OPD-SQ). The BCSQ-12 demonstrated satisfactory psychometric properties and confirmed its three-factor structure. The cluster analyses revealed four profiles: non-burned-out, frenetic, underchallenged and worn-out, which differed in terms of the symptomatologic, work-related and individual factors. The pattern of resources and demands between the burnout subtypes indicates a deterioration from the frenetic to the underchallenged to the worn-out employee. The present results emphasize possible advantages of including burnout profiles into the general assessment of burnout and indicate the need for tailored interventions.

Porous Organic Cages for CO ₂ Capture and Confined Reduction

ABSTRACT Porous organic cages (POCs) are discrete molecular materials that combine intrinsic porosity with solution processability and well‐defined, chemically tunable cavities. These features make them attractive for CO 2 capture and selective gas separation, and more recently for chemical transformations under confinement. Across amorphous and crystalline solids, as well as membrane and composite systems, POC‐based materials enable selective CO 2 uptake. However, their performance is governed by how molecular structure translates into accessible pore environments through solid‐state organization. In catalysis, POCs can promote local CO 2 enrichment and facilitate interaction with active sites, but their role extends beyond simple concentration effects. Even when not directly involved in the reaction, the POC's cavity can influence catalytic performance through confinement effects or host–guest interactions, as illustrated by hybrid systems and porphyrinic cages incorporating guest species. This Minireview examines recent advances in the use of POCs to integrate CO 2 capture with confined reduction. We discuss how cage structure, cavity size, internal functionality, solid‐state packing, and processing strategies govern gas binding, transport, and accessibility, and how confinement influences catalytic behavior. These studies highlight emerging design principles but also current limitations, and point to clearer structure–function relationships to enable applications under realistic conditions.

Molecular Bridge Enables Dual‐Intermediate Synergy for Selective CO ₂ Electroreduction to Multicarbon Products

ABSTRACT CO 2 electroreduction to multicarbon products offers a sustainable pathway for chemical synthesis, yet its practical efficiency has long been hindered by the kinetically mismatched *CO and *H intermediates, a fundamental bottleneck in multicarbon formation. Here, we address this challenge through a new molecular‐bridge‐enabled dual‐intermediate synergy strategy. By integrating sulfonated cobalt phthalocyanine molecules with two‐dimensional Cu nanosheets, we construct a cooperative catalytic interface in which the molecular bridge not only activates CO 2 to generate *CO but also reorganizes the interfacial water network to facilitate proton transfer for *H feeding. This synchronized CO*─H* delivery to the Cu active sites dramatically enhances C─C coupling and subsequent hydrogenation. As a result, the Cu─CS nanosheets achieve 81% Faradaic efficiency for C 2+ products at 400 mA cm −2 and maintain stable operation for > 105 h. Importantly, Cu─CS nanosheets shift the reaction pathway from the CO/H 2 ‐dominated output of pristine Cu nanosheets to a C 2+ ‐selective profile, boosting the C 2+ :(CO + H 2 ) ratio from 0.7 to 4.6, an over sixfold improvement. In situ spectroscopy reveals enriched high‐frequency atop‐bound *CO and increased proton‐transfer‐active 2‐HB·H 2 O species, synergistically accelerating C 2+ intermediate formation and indicating the effectiveness of molecular intermediate synergy in steering electrocatalytic pathways and product distribution.

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications

This paper proposes a new grid-tied transformer-less boost switched capacitor topology (TLBSCT) that employs three capacitors and twelve switches to generate seven levels with a gain of three times. The salient features of the TLBSCT are its boosting capacity, zero leakage current, minimum switching devices and lower voltage stress. The capacitors of the proposed TLBSCT have self-balancing characteristics. The proposed TLBSCT offers a brief discussion of the configuration, principle of working and the design of the parameter, as well as its control scheme. In addition, a comparative study of the proposal against the current transformerless inverter (TLI) shows the better performance of the proposed approach(PA). Also, the theoretical concept and viability of the suggested design have been demonstrated by simulations and experiments. This work contributes to SDG 7: Affordable and Clean Energy by improving efficient and reliable grid-connected solar power conversion systems.

Development and validation of a multidimensional organizational dehumanization scale: Evidence from higher education

Organizational dehumanization has become a salient concern in contemporary academic work, particularly in higher education systems shaped by managerial and metric-driven governance. This study aimed to develop and validate a psychometrically robust instrument to assess academics’ perceived organizational dehumanization. Using two independent samples of academics in Türkiye (EFA sample: n = 318; CFA sample: n = 263; total N = 581), we examined the scale’s factor structure, reliability, and validity evidence. Exploratory and confirmatory factor analyses supported a two-factor model reflecting Devaluation and Instrumentality, yielding a final 34-item scale. The model demonstrated acceptable-to-strong fit indices (χ²/df = 2.28; CFI = .99; NFI = .97; RMSEA = .074; SRMR = .045), high internal consistency (α = .93−.96; overall α = .96), and evidence for discriminant validity, with mixed evidence for convergent validity at the subdimension level. Overall, the findings suggest that the proposed scale provides a reliable, multidimensional measure of academics' organizational dehumanization perceptions, validated in a higher education sample, and offers a tool for future research and institutional diagnostics in university contexts.