Search Results (22,594)

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

This study examines the factors influencing the adoption of enterprise resource planning (ERP) systems within Romanian organizations. The objective is to develop a comprehensive framework for ERP adoption decisions, thereby advancing the field of knowledge and offering managerial insights. To accomplish this research goal, a questionnaire is envisioned, employing various research hypotheses, and distributed to a representative sample. Quantitative econometric regression analysis is employed, considering potential factors such as user training and education, competitive pressures, user involvement and participation, decentralized ERP features, top management support, data quality, the quality of the ERP system, cost and budget considerations, and business process reengineering. Of the 12 factors analyzed, 9 were found to be relevant in terms of influence on the decision to adopt ERP systems, in the context of the Romanian market. The other three factors were found to be irrelevant, thus obtaining results partially different from other areas of the world. By validating the hypotheses and answering the research questions, this work addresses a research gap regarding the lack of a comprehensive understanding of the influencing factors that shape the adoption process of ERP systems in Romania. Full article

In this study, the distribution patterns of the nitrifying and denitrifying microbiome in a large-scale biofilter (587.24 m³) in a cold freshwater recirculating aquaculture system (RAS) was investigated. Previous studies have revealed that the water quality, nitrification, and denitrification rates in the front (BFF), middle (BFM), and back (BFB) of this biofilter are different. The results showed the highest diversity of the denitrifying microbiome in the BFB, followed by BFF and BFM, whereas nitrifying microbiome diversity remained consistent across different positions. Two genera, Nitrosomonas and Nitrosospira, dominated the nitrifying microbiome, while Pseudomonas, Thauera, Cupriavidus, Dechloromonas, Azoarcus, and Paracoccus comprised the top six denitrifying genera. Principal coordinate analysis indicated a distinct spatial distribution pattern of the denitrifying microbiome but not the nitrifying microbiome. The genera Pseudomonas and Dechloromonas were the biomarkers of the BFF and BFB, respectively. Redundancy analysis showed that nitrite, nitrate, dissolved oxygen, and soluble reactive phosphorus influenced the functional microbiome distribution pattern. Network correlation analysis identified one nitrifying hub (Nitrosospira) in the BFF, five denitrifying hubs (Aromatoleum, Dechloromonas, Paracoccus, Ruegeria, and Thauera) in the BFM, and three denitrifying hubs (Azoarcus, Magnetospirillum, and Thauera) in the BFB. Exclusively negative correlations were found between hubs and its adjacent nodes in the BFF and BFB. This study demonstrates that habitat can shape the distribution patterns of the nitrifying and denitrifying microbiome in the biofilter of the RAS, with the BFF exhibiting greater benefits for the nitrification process. Full article

Background/Objective: The use of artificial intelligence to assist in medical applications is an emerging area of investigation and discussion. The researchers studied whether there was a difference between homeopathy guidance provided by artificial intelligence (AI) (automated) and live professional practitioners (live) for acute illnesses. Additionally, the study explored the practical challenges associated with validating AI tools used for homeopathy and sought to generate insights on the potential value and limitations of these tools in the management of acute health complaints. Method: Randomly selected cases at a homeopathy teaching clinic (n = 100) were entered into a commercially available homeopathic remedy finder to investigate the consistency between automated and live recommendations. Client symptoms, medical disclaimers, remedies, and posology were compared. The findings of this study show that the purpose-built homeopathic remedy finder is not a one-to-one replacement for a live practitioner. Result: In the 100 cases compared, the automated online remedy finder provided between 1 and 20 prioritized remedy recommendations for each complaint, leaving the user to make the final remedy decision based on how well their characteristic symptoms were covered by each potential remedy. The live practitioner-recommended remedy was included somewhere among the auto-mated results in 59% of the cases, appeared in the top three results in 37% of the cases, and was a top remedy match in 17% of the cases. There was no guidance for managing remedy responses found in live clinical settings. Conclusion: This study also highlights the challenge and importance of validating AI remedy recommendations against real cases. The automated remedy finder used covered 74 acute complaints. The live cases from the teaching clinic included 22 of the 74 complaints. Full article

Background/Objectives: Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents; the survival rate is as low as 24%. Accurate prediction of clinical outcomes remains a challenge due to tumor heterogeneity and the complexity of pediatric cases. This study aims to improve predictions of progressive disease, therapy response, relapse, and survival in pediatric OS using MRI-based radiomics and machine learning methods. Methods: Pre-treatment contrast-enhanced coronal T1-weighted MR scans were collected from 63 pediatric OS patients, with an additional nine external cases used for validation. Three strategies were considered for target region segmentation (whole-tumor, tumor sampling, and bone/soft tissue) and used for MRI-based radiomics. These were then combined with clinical features to predict OS clinical outcomes. Results: The mean age of OS patients was 11.8 ± 3.5 years. Most tumors were located in the femur (65%). Osteoblastic subtype was the most common histological classification (79%). The majority of OS patients (79%) did not have evidence of metastasis at diagnosis. Progressive disease occurred in 27% of patients, 59% of patients showed adequate therapy response, 25% experienced relapse after therapy, and 30% died from OS. Classification models based on bone/soft tissue segmentation generally performed the best, with certain clinical features improving performance, especially for therapy response and mortality. The top performing classifier in each outcome achieved 0.94–1.0 validation ROC AUC and 0.63–1.0 testing ROC AUC, while those without radiomic features (RFs) generally performed suboptimally. Conclusions: This study demonstrates the strong predictive capabilities of MRI-based radiomics and multi-region segmentations for predicting clinical outcomes in pediatric OS. Full article

The work presents a comprehensive analysis and costing of sugar beet cultivation in 2020–2022, for individual farms of the Lublin region. About 120 farms were analyzed. Based on this analysis, the criteria for a model farm were determined and adopted for the calculation of sugar beet production costs. ARIMA process modeling was performed, based on which forecasts were determined for several selected parameters. Customs tariffs introduced by the USA have a drastic impact on the economy. The effects of the COVID19 pandemic may also have a significant impact on the current market situation. Forecasting in the current geopolitical situation is very difficult because of the lack of stationarity of parameters. The financial result obtained by growers is mainly influenced by indirect costs absorbing 61.31% of total costs in 2020. In 2021 and 2022, indirect costs were 61.16% and 59.61% of production income, respectively. Among this group of costs, the largest share is accounted for by the costs of sowing services, sugar beet harvesting, and soil liming amounting from 14.27% to 15.92%. During the analyzed period, sugar beet cultivation remained profitable, with a production profitability index of 1.31 in 2020 and 2021, and 1.10 in 2022. The unit cost of production increased every year. In 2020, it was 14.27% and in 2021, it increased to 15.19%. The unit cost of production in 2022 was the highest, at 23.41%. Sugar beet cultivation is one of the profitable activities in agricultural production, but it is characterized by high production costs, which increased during the years analyzed (2020 to 2022), topping out at 90.87% of total revenue. The information and data presented in this study will be used in the development of a farmer-oriented application and will support the creation of an expert system for sugar beet growers. Cost forecasting will enable farmers to plan their production more effectively. Full article

Nitrogen (N) fertilizer management in winter wheat production faces challenges from volatilization losses and sub-optimal application strategies. This is particularly problematic in the Southern Great Plains, where environmental conditions during top-dressing periods favor N losses. This study evaluated the effects of a fertilizer placement method, enhanced-efficiency fertilizers, and application timing on grain yield and protein concentration (GPC) across six site-years in Oklahoma (2016–2018). Treatments included broadcast applications of untreated urea and SuperU^® (urease/nitrification inhibitor-treated urea). These were compared with subsurface placement using single-disc and double-disc drilling systems, applied at 67 kg N ha⁻¹ during January, February, or March. Subsurface placement increased the grain yield by 324–391 kg ha⁻¹ compared to broadcast applications at sites with favorable soil conditions. However, responses varied significantly across environments. Enhanced-efficiency fertilizers showed limited advantages over untreated urea. Benefits were most pronounced during February applications under conditions favoring volatilization losses. Application timing effects were more consistent for GPC than for the yield. Later applications (February–March) increased GPC by 0.8–1.2% compared to January applications. Treatment efficacy was strongly influenced by soil pH, equipment performance, and post-application environmental conditions. This indicates that N management benefits are highly site-specific. These findings demonstrate that subsurface placement can improve nitrogen use efficiency (NUE) under appropriate conditions. However, success depends on matching application strategies to local soil and environmental factors rather than adopting universal recommendations. Full article

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Although organizational resilience is well established, refining the systematic quantitative evaluation of health systems resilience (HSR) remains an ongoing opportunity for advancement. Research either focuses on individual HSR indicators, such as social welfare policy, public expenditure, health insurance, healthcare quality, and technology, or broadly examines socio-economic factors, highlighting the need for a more comprehensive methodological approach. This study employed the Slacks-Based Measure (SBM) within Data Envelopment Analysis (DEA) to analyze efficiency by maximizing outputs. It systematically examined key HSR factors across countries, providing insights for improved policymaking and resource allocation. Taking a five-year (2016–2020) dataset that covered 55 to 56 countries and evaluating 17 indicators across governance, health systems, and economic aspects, the paper presents that all sixteen top-ranked countries with a perfect efficiency score of 1 belonged to the high-income group, with ten in Europe, highlighting regional HSR differences. This paper concludes that adequate economic resources form the foundation of HSR and ensure stability and sustained progress. A properly supported healthcare workforce is essential for significantly enhancing health systems and delivering quality care. Last, effective governance and the equitable allocation of resources are crucial for fostering sustainable development and strengthening HSR. Full article

Background. Physicomechanical properties and clinical service of bulk-fill composites depend on their adequate polymerization and depth of cure. Some manufacturers claim that these composites can be adequately cured when used in bulks exceeding 4 mm. Objective. The aim of this study was to compare Vickers microhardness (VMH) and depth of cure (DOC) of six contemporary bulk-fill resin composites at depths of 4 mm and 6 mm. Material and methods. Six bulk-fill composites were evaluated in this study: 1. Tetric EvoCeram Bulk (Ivoclar Vivadent, Schaan, Liechtenstein), (TEC); 2. Filtek Bulk Fill Posterior (3M ESPE Dental Products Division, St. Paul, MN, USA), (FBF); 3. Filtek One Bulk Fill (3M ESPE Dental Products Division, St. Paul, MN, USA, (FOB); 4. SonicFill 2 (Kerr, Orange, CA, USA), (SF2); 5. Admira Fusion X-tra (Voco, GmbH, Cuxhaven, Germany), (AFX); 6. GrandioSO X-tra (Voco, GmbH, Cuxhaven, Germany), (GSX). The 18 specimens (3 of each composite) were prepared in split Teflon moulds of 4 mm diameter and 6 mm thickness. All composites were cured in standard mode for 20 s using LED LCU (D-Light Duo, RF-Pharmaceuticals Sarl, Geneva, Switzerland; 1200–1300 mW/cm). The VMH was measured using a digital Micro Hardness Tester Shimadzu (HMV-2T E, Shimadzu Corporation, Kyoto, Japan). A 50 g (0.5 N) load force was applied for 30 s. Each specimen was measured at five places selected by chance at each level (N = 15). The hardness ratio or DOC was calculated for all samples as the ratio of bottom and surface microhardness at levels of 4 and 6 mm. Data were analysed using one-way ANOVA and Tukey’s post hoc test. Results. Significant reduction in VMH was observed for all tested materials when comparing top surface and bottom (p < 0.01). The highest VMH was obtained for GSX and AFX, and the lowest for TEC. The results show that the degree of polymerization was adequate for all tested materials at a depth of 6 mm, since the hardness ratio exceeded 0.80 in all cases. The hardness ratio at 4 mm was high for all tested composites ranging from 0.91 for TEC to 0.98 for GSX. All composites showed adequate DOC at the bottom of the 6 mm bulk samples. However, the hardness ratio was the highest for Admira Fusion X-tra (0.96) and GrandioSO X-tra (0.97). Conclusions. All tested materials showed a significant decrease in microhardness from the top surface to the bottom. The DOC was adequate for all bulk-fill composites at a depth of 6 mm cured under standard mode for 20 s. All bulk-fill resin composites evaluated in this study can be used in bulk, up to 6 mm. Full article

In recent years, various devices utilizing surface acoustic waves (SAW) have emerged as powerful tools for manipulating particles and fluids in microchannels. Although they demonstrate a wide range of functionalities across diverse applications, existing devices still face limitations in flexibility, manipulation efficiency, and spatial resolution. In this study, we developed a dual-sided standing surface acoustic wave (SSAW) device that simultaneously excites acoustic waves through two piezoelectric substrates positioned at the top and bottom of a microchannel. By fully exploiting the degrees of freedom offered by two pairs of interdigital transducers (IDTs) on each substrate, the system enables highly flexible control of microparticles. To explore its capability on particle aggregation, we developed a two-dimensional numerical model to investigate the influence of the SAW phase modulation on the established acoustic fields within the microchannel. Single-particle motion was first examined under the influence of the phase-modulated acoustic fields to form a reference for identifying effective phase modulation strategies. Key parameters, such as the phase changes and the duration of each phase modulation step, were determined to maximize the lateral motion while minimizing undesired vertical motion of the particle. Our dual-sided SSAW configuration, combined with novel dynamic phase modulation strategy, leads to rapid and precise aggregation of microparticles towards a single focal point. This study sheds new light on the design of acoustofluidic devices for efficient spatiotemporal particle concentration. Full article

In lace textile manufacturing, the dyeing process in parallel machine environments faces challenges from sequence-dependent setup times due to color family transitions, machine eligibility constraints based on weight capacities, and probabilistic re-dyeing operations arising from quality inspection failures, which often lead to increased tardiness. To tackle this multi-constrained problem, a stochastic integer programming model is formulated to minimize total estimated tardiness. A novel symmetry-driven two-population collaborative differential evolution (TCDE) algorithm is then proposed. It features two symmetrically complementary subpopulations that achieve a balance between global exploration and local exploitation. One subpopulation employs chaotic parameter adaptation through a logistic map for symmetrically enhanced exploration, while the other adjusts parameters based on population diversity and convergence speed to facilitate symmetry-aware exploitation. Moreover, it also incorporates a symmetrical collaborative mechanism that includes the periodic migration of top individuals between subpopulations, along with elite-set guidance, to enhance both population diversity and convergence efficiency. Extensive computational experiments were conducted on 21 small-scale (optimally validated via CVX) and 15 large-scale synthetic datasets, as well as 21 small-scale (similarly validated) and 20 large-scale industrial datasets. These experiments demonstrate that TCDE significantly outperforms state-of-the-art comparative methods. Ablation studies also further verify the critical role of its symmetry-based components, with computational results confirming its superiority in solving the considered problem. Full article

Loneliness is a significant challenge for millions worldwide, with chronic loneliness having harmful effects on physical health, mental well-being, and relationships. Cognitive distortions play an important role in perpetuating loneliness. Psychological interventions targeting such distortions have been effective at alleviating feelings of loneliness. However, less is known about which cognitive distortions are most prevalent among lonely individuals and how these distortions relate to loneliness and mental well-being. This exploratory study prescreened a Census-matched sample of 1000 U.S. adults for loneliness, then asked those in the top quartile (N = 237) to rate multiple patterns of cognitive distortion related to loneliness. Factor analyses identified six common and influential patterns of cognitive distortion (mindreading, future reward, catastrophizing, essentializing, deservedness, and externalizing). Essentializing was the most strongly endorsed factor, followed by mindreading and catastrophizing. Essentializing also evidenced the strongest correlation with loneliness. Additionally, the relationship between loneliness and participants’ stress was completely mediated by mindreading, catastrophizing, and essentializing. These findings highlight the importance of targeting specific cognitive distortions in loneliness interventions to effectively improve the mental well-being of lonely individuals. Full article

Understanding the utilization patterns of nuclear medicine (NM) services is essential for optimizing resource allocation and service provision. This study aimed to address the regional evidence gap by reporting the demand for NM services by sex and age at a public hospital in Jamaica. This was a non-experimental, retrospective study of NM scans that were completed at the University Hospital of the West Indies from 1 June 2022 to 31 May 2024. While all scans were reported in the descriptive totals, for patients with multiple scans during the study period, only the data from the first visit was used in the inferential statistical analysis. This was performed with the IBM SPSS (version 29.0) software and involved the use of chi-square goodness of fit and multinomial logistic regression. A total of 1135 NM scans for 1098 patients were completed (37 patients had more than one scan); 596 (54.3%) were female and 502 (45.7%) were male, with the ages ranging from 3 days to 94 years old. Among the female patients, there was a greater demand in the ≥60 years age group for cardiac amyloid scans (χ² = 6.40, p < 0.05), while females 18–59 years had a greater demand for thyroid scans (χ² = 7.714, p < 0.05) and bone scans (χ² = 3.904, p < 0.05). On the other hand, significantly more males in the ≥60 age group presented for cardiac amyloid (χ² = 4.167; p < 0.05) and bone scans (χ² = 145.79, p < 0.01). Males were significantly less likely to undergo a thyroid scan than females (p < 0.01, OR = 0.072, 95% CI: 0.021, 0.243) while individuals aged 18–59 years were more likely to undergo this scan than patients aged 60 or older (p = 0.02, OR = 3.565, 95% CI: 1.258, 10.104). Males were more likely to do a cardiac amyloid scan (p < 0.05, OR = 2.237, 95% CI: 1.023, 4.891) but less likely to undergo a cardiac rest/stress test than females (p = 0.02, OR = 0.307, 95% CI: 0.114, 0.828). Prolonged life expectancy and an aging population have the potential to impact NM utilization, thus requiring planning for infrastructure, equipment, work force, and supplies. Cancer-related and cardiovascular indications are a top priority at this facility; hence, age- and sex-specific analysis are useful in establishing models for policy makers with regard to the allocation of economic and human resources for the sustainability of this specialized service. Full article

In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple frequency levels, which increases the complexity of extracting important information from them. To address this problem, this paper proposes a Masked and Cascaded Multi-Branch Attention Network (MCMBAN), which combines the Noise Mask Filter Block (NMFB) with the Multi-Branch Cascade Attention Block (MBCAB), and significantly improves the noise immunity of the fault diagnostic model and the efficiency of fault feature extraction. NMFB novelly combines a wide convolutional layer and a top k neighbor self-attention masking mechanism, so as to efficiently filter unnecessary high-frequency noise in the vibration signal. On the other hand, MBCAB strengthens the interaction between different layers by cascading the convolutional layers of different scales, thus improving the recognition of periodic fault signals and greatly enhancing the diagnosis accuracy of the model when processing complex signals. Finally, the time–frequency analysis technique is employed to explore the internal mechanisms of the model in depth, aiming to validate the effectiveness of NMFB and MBCAB in fault feature recognition and to improve the feature interpretability of the proposed modes in fault diagnosis applications. We validate the superior performance of the network model in dealing with high-noise backgrounds by testing it on a standard bearing dataset from Case Western Reserve University and a self-constructed composite bearing fault dataset, and the experimental results show that its performance exceeded six of the top current fault diagnosis techniques. Full article