Search Results (4,195)

This study constructs a research model with regulation and mediation based on the resource dependence theory to explore the nonlinear relationship between ESG responsibility fulfillment and firm value. This study uses a sample of Chinese A-share listed manufacturing firms from 2015 to 2022 and conducts empirical analysis using STATA version 18.0. The results indicate a U-shaped relationship between ESG responsibility fulfillment and firm value. Stakeholders’ interests play a partial mediating role in the above relationship. Moreover, institutional investors’ shareholding further strengthens the positive association between ESG responsibility fulfillment and stakeholder interests. The firm life cycle has a heterogeneous effect on the relationship between ESG responsibility fulfillment and stakeholder interests. Specifically, firms in the maturity stage exhibit the most pronounced protection of stakeholder interests, whereas firms in the decline stage show relatively weaker protection effects. Additionally, there is a complementary interaction between the firm life cycle and institutional investors’ shareholding. This combination significantly enhances the positive moderating effect of institutional investors’ shareholding on the relationship between ESG responsibility fulfillment and stakeholder interests only when firms are in the growth or decline stages. This study not only expands the boundaries of resource dependence theory, but also provides management insights for sustainable practices in the real economy. Full article

Thallium is a soft metal with a grey or silvery hue. It commonly occurs in two oxidation states in chemical compounds: Tl⁺ and Tl³⁺. Thermodynamically, Tl⁺ is significantly more stable and typically represents the dominant form of thallium in environmental systems. However, in this chemical form, thallium remains highly toxic. This study focuses on the modification of a glassy carbon electrode (GCE) with silver nanostructures stabilised by potato starch derivatives. The modified electrode (GCE/AgNPs-E1451) was used for the determination of trace amounts of thallium ions using anodic stripping voltammetry. Emphasis was placed on assessing the effect of surface modification on key electrochemical performance parameters of the electrode. Measurements were carried out in a base electrolyte (EDTA) and in a real soil sample collected from Bali. The stripping peak current of thallium exhibited linearity over the concentration range from 19 to 410 ppb (9.31 × 10⁻⁸ to 2.009 × 10⁻⁶ mol/dm³). The calculated limit of detection (LOD) was 18.8 ppb (9.21 × 10⁻⁸ mol/dm³), while the limit of quantification (LOQ), corresponded to 56.4 ppb (2.76 × 10⁻⁷ mol/dm³). The GCE/AgNPs-E1451 electrode demonstrates several significant advantages, including a wide detection range, reduced analysis time due to the elimination of time-consuming pre-concentration steps, and non-toxic operation compared to mercury-based electrodes. Full article

Background/Objectives: The diagnosis of Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacterial (NTM) infections is accomplished by three main diagnostics methods: smear microscopy, culture, and molecular testing. Diagnostic algorithms used by laboratories can significantly impact clinical and infection control management. Current Canadian Tuberculosis Standards recommend the use of nucleic acid amplification testing (NAAT) for smear-positive patients and smear-negative patients upon request. An alternative algorithm is to utilize NAAT in the Panel approach on all samples, pulmonary and extrapulmonary, to potentially reduce time to diagnosis and treatment. This alternative approach was implemented in November 2019 at the Newfoundland and Labrador Public Health and Microbiology Laboratory (NL PHML) using a laboratory-developed multiplex real-time PCR (LDT m-qPCR) assay targeting Mycobacterium spp. (Myco spp.) and MTBC, performed in parallel with smear and culture. Methods: To investigate the impact of this alternate testing approach, we conducted an observational retrospective analysis of laboratory diagnostic and treatment data, recognizing that temporal changes in epidemiology, clinical practice, and laboratory workflow may also have influenced outcomes. To complete this, study data from three years before and four years after implementation were gathered. Results: The sensitivity/specificity of the smear, m-LDT qPCR-MTBC, m-LDT qPCR-Myco spp., and culture assays in this study were 18.1%/100%, 96.7%/99.8%, 47.6%/99.0%, and 96.8%/100%, respectively. The gold standard utilized for these calculations was clinical diagnosis for active MTBC disease and culture for NTM infections, recognizing that the use of clinical diagnosis may introduce subjectivity. The Panel approach reduced the time to diagnosis of tuberculosis MTBC by 29 days (p < 0.0001) for NL PHML, and when modelled for a laboratory with rapid culture identification, diagnosis was reduced by 14 days (p = 0.003). Among non-empirically treated tuberculosis patients, the time to treatment was decreased by 25.5 days (p < 0.001). For NTM infections, rapid diagnostics only affected one patient’s treatment. This finding agrees with clinical management guidelines, which do not routinely utilize rapid diagnostics for the diagnosis of disease or treatment decisions. The cost implications of additional NAAT testing were calculated to be an increase of CAD 23.62 per sample. Conclusions: Our findings support the adoption of a molecular assay for MTBC as an initial diagnostic tool to decrease time to diagnosis and time to treatment, depending on local epidemiology and irrespective of smear status. Utilizing a molecular assay for genus level identification of NTM had minimal impact on clinical management suggesting its limited diagnostic utility in a broad population setting. Full article

The development of cost-effective and highly sensitive hydrogen peroxide (H₂O₂) biosensors with robust stability is critical due to the pivotal role of H₂O₂ in biological processes and its broad utility across various applications. In this work, porous ceria hollow microspheres (CeO₂-phm) were synthesized using a solvothermal synthesis method and employed in the construction of an electrochemical biosensor for H₂O₂ detection. The resulting CeO₂-phm featured a uniform pore size centered at 3.4 nm and a high specific surface area of 168.6 m²/g. These structural attributes contribute to an increased number of active catalytic sites and promote efficient electrolyte penetration and charge transport, thereby enhancing its electrochemical sensing performance. When integrated into screen-printed carbon electrodes (CeO₂-phm/cMWCNTs/SPCE), the CeO₂-phm/cMWCNTs/SPCE-based biosensor exhibited a wide linear detection range from 0.5 to 450 μM, a low detection limit of 0.017 μM, and a high sensitivity of 2070.9 and 2161.6 μA·mM⁻¹·cm⁻²—surpassing the performance of many previously reported H₂O₂ sensors. In addition, the CeO₂-phm/cMWCNTs/SPCE-based biosensor possesses excellent anti-interference performance, repeatability, reproducibility, and stability. Its effectiveness was further validated through successful application in real sample analysis. Hence, CeO₂-phm with solvothermal synthesis has great potential applications as a sensing material for the quantitative determination of H₂O₂. Full article

The microbial community in a recirculating aquaculture system (RAS) is pivotal in fish health, contributing significantly to the productive performance during the growing-out phase. Classical and molecular methods using PCR for species-specific amplifications have traditionally been used for bacterial community surveillance. Unfortunately, these approaches mask the real bacterial diversity and abundance, population dynamics, and prevalence of pathogenic bacteria. In this study, we explored the use of Oxford Nanopore Technology to characterize the microbiota and functional metagenomics in a commercial freshwater RAS. Intestine samples from Atlantic salmon (Salmo salar (85 ± 5.7 g)) and water samples from the inlet/outlet water, settling tank, and biofilters were collected. The full-length 16S rRNA gene was sequenced to reconstruct the microbial community, and bioinformatic tools were applied to estimate the functional potential in the RAS and fish microbiota. The analysis showed that bacteria involved in denitrification processes were found in water samples, as well as metabolic pathways related to hydrogen sulfide metabolism. Observations suggested that fish classified as sick exhibited decreased microbial diversity compared with fish without clinical symptomatology (p < 0.05). Proteobacteria were predominant in ill fish, and pathogens of the genera Aeromonas, Aliivibrio, and Vibrio were detected in all intestinal samples. Notably, Aliivibrio wodanis was detected in fish showing abnormal clinical conditions. Healthy salmon showed higher contributions of pathways related to amino acid metabolism and short-chain fatty acid fermentation (p < 0.05), which may indicate more favorable fish conditions. These findings suggest the utility of nanopore sequencing methods in assessing the microbial community in RASs for salmon aquaculture. Full article

Hydrogen energy offers high energy density and carbon-free combustion, making it a promising fuel for next-generation propulsion and power generation systems. Hydrogen offers approximately three times more energy per unit mass than natural gas, and its combustion yields only water as a byproduct, making it an exceptionally clean and efficient energy source. Materials used in hydrogen-fueled combustion engines must exhibit high thermal stability as well as resistance to corrosion caused by high-temperature water vapor. This study introduces a novel ceramic matrix composite (CMC) designed for such harsh environments. The composite is made of yttria-stabilized zirconia (YSZ) fiber-reinforced silicoboron carbonitride [Si(B)CN]. CMCs were fabricated via the polymer infiltration and pyrolysis (PIP) method. Multiple PIP cycles, which help to reduce the porosity of the composite and enhance its properties, were utilized for CMC fabrication. The Si(B)CN precursor formed an amorphous ceramic matrix, where the presence of boron effectively suppressed crystallization and enhanced oxidation resistance, offering superior performance than their counter part. Thermogravimetric analysis (TGA) confirmed negligible mass loss (≤3%) after 30 min at 1350 °C. The real-time ablation performance of the CMC sample was assessed using a hydrogen torch test. The material withstood a constant heat flux of 185 W/cm² for 10 min, resulting in a front-surface temperature of ~1400 °C and a rear-surface temperature near 700 °C. No delamination, burn-through, or erosion was observed. A temperature gradient of more than 700 °C between the front and back surfaces confirmed the material’s effective thermal insulation performance during the hydrogen torch test. Post-hydrogen torch test X-ray diffraction indicated enhanced crystallinity, suggesting a synergistic effect of the oxidation-resistant amorphous Si(B)CN matrix and the thermally stable crystalline YSZ fibers. These results highlight the potential of YSZ/Si(B)CN composites as high-performance materials for hydrogen combustion environments and aerospace thermal protection systems. Full article

African swine fever (ASF) remains a major threat to swine production in Central Luzon, Philippines. This study assessed ASF detection and farm-level risk factors in Central Luzon using a risk-based surveillance framework. Pooled blood samples from five pigs per farm were collected in 277 farms across seven provinces and tested by real-time PCR. The analysis yielded an apparent farm-level prevalence of 26.7% (95% CI: 21.6–32.3), defined by one pooled 5-pig blood sample per farm. However, these values reflect risk-based surveillance outcomes rather than population-representative prevalence. Detection varied by province, with high rates in Bataan (80.5%) and Nueva Ecija (55.0%), moderate detection in Zambales (24.3%), lower detection in Pampanga (5.0%) and Tarlac (20.0%), and no positives in Aurora or Bulacan. Survey data were available for 201 farms. Firth-penalized logistic regression identified the absence of perimeter fencing as the only statistically significant predictor of ASFV detection. Veterinary oversight and consultancy showed protective but non-significant trends. These results highlight structural and professional biosecurity gaps, emphasizing the need for expanded veterinary outreach, fencing support, and training to mitigate ASF risk in smallholder-dominated production systems. Full article

Shell-like housing structures for motors and compressors can be found in everyday products. Consumers significantly evaluate acoustic emissions during the first usage of products. Unpleasant sounds may raise concerns and cause complaints to be issued. A prevention strategy is a holistic acoustic design, which includes predicting the emitted sound power as part of end-of-line testing. The hybrid experimental-simulative sound power prediction based on laser scanning vibrometry (LSV) is ideal in acoustically harsh production environments. However, conducting vibroacoustic testing with laser scanning vibrometry is time-consuming, making it difficult to fit into the production cycle time. This contribution discusses how the time-consuming sampling process can be accelerated to estimate the radiated sound power, utilizing adaptive sampling. The goal is to predict the acoustic signature and its uncertainty from surface velocity data in seconds. Fulfilling this goal will enable integration into a product assembly unit and final acoustic quality control without the need for an acoustic chamber. The Gaussian process regression based on PyTorch 2.6.0 performed 60 times faster than the preliminary reference implementation, resulting in a regression estimation time of approximately one second for each frequency bin. In combination with the Equivalent Radiated Power prediction of the sound power, a statistical measure is available, indicating how the uncertainty of a limited number of surface velocity measurement points leads to predictions of the uncertainty inside the acoustical signal. An adaptive sampling algorithm reduces the prediction uncertainty in real-time during measurement. The method enables on-the-fly error analysis in production, assessing the risk of violating agreed-upon acoustic sound power thresholds, and thus provides valuable feedback to the product design units. Full article

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article

Background: Uncontrolled asthma remains a significant clinical challenge, often linked to impaired lung function and increased diaphragmatic workload. Recent studies have shown promising results using a triple inhaled therapy comprising beclomethasone dipropionate/formoterol fumarate/glycopyrronium (BDP/FF/G). This study assessed the real-world efficacy of BDP/FF/G on lung function and diaphragmatic workload in patients with uncontrolled asthma. Methods: A prospective observational study enrolled 21 adult patients diagnosed with uncontrolled asthma despite high-dose ICS/LABA therapy. Patients underwent lung function tests and right diaphragmatic ultrasound assessments at baseline and after three months of treatment with BDP/FF/G (172/5/9 mcg, administered as two inhalations every 12 h). Results: After three months, significant improvements were observed in FEV₁ (from 57.75 ± 12.30% to 75.10 ± 18.94%, p < 0.001) and FEF_25–75 (from 47.80 ± 19.23% to 75.10 ± 36.06%, p < 0.001). Additionally, during the same period, we recorded significant reductions in residual volume (from 130.10 ± 28.20% to 92.55 ± 21.18%, p < 0.001) and total airway resistance (R_tot) (from 164.60 ± 83.21% to 140.70 ± 83.25%, p < 0.05). The mean asthma control test (ACT) score increased by 5.6 points (p < 0.001), surpassing the established minimal clinically important difference (MCID) of 3 points and raising the cohort mean above the well-controlled threshold. The right diaphragmatic workload was significantly decreased, as shown by a reduction in thickening fraction (TF) (from 63.86 ± 17.67% to 40.29 ± 16.65%, p < 0.01). Correlation analysis indicated significant associations between diaphragmatic function and some lung function parameters (FEV₁, FEF_25–75, and R_tot). Conclusions: In this real-world pilot, triple BDP/FF/G was linked to improvements in airflow, hyperinflation, symptoms, and a reduction in diaphragmatic thickening fraction, indicating potential physiological benefit. Due to the small sample size, single-centre design, and 3-month follow-up, these results should be viewed as hypothesis-generating and need to be confirmed in larger, controlled, multicentre studies with longer follow-up. Full article

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security. Full article

This research investigated how detergent type and concentration, solution temperature, and flow rate affect ultrasonic cleaning efficiency in jewelry manufacturing. A silver bracelet without gemstones served as the test sample, and the study combined harmonic response analysis to assess acoustic pressure distribution with computational fluid dynamics to examine fluid flow patterns inside an ultrasonic cleaning machine. Cleaning tests were performed under real factory conditions to verify the simulations. Results showed that cleaning efficiency depends on the combined chemical and ultrasonic effects. Adding detergent lowered surface tension, encouraging cavitation bubble formation; higher temperatures (up to 60 °C) softened dirt, making removal easier; and moderate solution flow improved the cleaning, helping to carry dirt away from jewelry surfaces. Too much flow, however, decreased cavitation activity. The highest cleaning efficiency (93.890%) was achieved with 3% U-type detergent at 60 °C and a flow rate of 5 L/min, while pure water at room temperature (30 °C) without flow had the lowest efficiency (0.815%), confirmed by weighing and scanning electron microscope measurements. Interestingly, maximum ultrasonic power concentration did not always match the highest cleaning efficiency. The study supports sustainable practices by limiting detergent use to 3%, in line with Sustainable Development Goal (SDG) 9 (Industry, Innovation, and Infrastructure). Full article

Background: Colorectal cancer is the third most diagnosed cancer and a leading cause of cancer-related deaths worldwide. Early detection significantly improves patient outcomes, yet many cases are identified only at late stages. The high molecular and genetic heterogeneity of colorectal cancer presents major challenges in accurate diagnosis, prognosis, and therapeutic stratification. Recent advances in gene expression profiling offer new opportunities to discover genes that play a role in colorectal cancer carcinogenesis and may contribute to early diagnosis, prognosis prediction, and the identification of novel therapeutic targets. Methods: This study involved 142 samples: 84 primary tumor samples, 27 liver metastases, and 31 adjacent non-tumor tissues serving as controls. RNA sequencing was performed on a subset of tissues (12 liver metastases and 3 adjacent non-tumor tissues) using a targeted RNA panel covering 395 cancer-related genes. Data processing and differential gene expression analysis were carried out using the DRAGEN RNA and DRAGEN Differential Expression tools. The expression of six genes involved in hypoxia and epithelial-to-mesenchymal transition (EMT) pathways (SLC16A3, ANXA2, P4HA1, SPP1, KRT19, and LGALS3) identified as significantly differentially expressed was validated across the whole cohort via quantitative real-time PCR. The relative expression levels were determined using the ΔΔct method and log2FC, and compared between different groups based on the sample type; clinical parameters; and mutational status of the genes KRAS, PIK3CA, APC, SMAD4, and TP53. Results: Our results suggest that the expression of all the validated genes is significantly altered in metastases compared to non-tumor control samples (p < 0.05). The most pronounced change occurred for the genes P4HA1 and SPP1, whose expression was significantly increased in metastases compared to non-tumor and primary tumor samples, as well as between clinical stages of CRC (p < 0.001). Furthermore, all genes, except for LGALS3, exhibited significantly altered expression between non-tumor samples and samples in stage I of the disease, suggesting that they play a role in the early stages of carcinogenesis (p < 0.05). Additionally, the results suggest the mutational status of the KRAS gene did not significantly affect the expression of any of the validated genes, indicating that these genes are not involved in the carcinogenesis of KRAS-mutated CRC. Conclusions: Based on our results, the genes P4HA1 and SPP1 appear to play a role in the progression and metastasis of colorectal cancer and are candidate genes for further investigation as potential biomarkers in CRC. Full article

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article