Search Results (37)

Background: Prostate cancer (PCa) imposes a substantial global health burden, with robot-assisted radical prostatectomy (RARP) established as the gold standard for localized disease. While da Vinci^® Xi maintains market dominance, Toumai^® MT-1000 offers a potentially cost-competitive alternative lacking prospective validation. Objective: To evaluate perioperative safety, oncologic quality (primary endpoint: positive surgical margins), early functional recovery (continence), and surgeon learning curve between Toumai^® MT-1000 (T-RARP) and da Vinci^® Xi RARP (DV-RARP) performed in high-volume European practice. Materials and Methods: This is a prospective single-center comparative study carried out at Policlinico Gemelli, Rome (May–November 2025), enrolling 80 patients with localized or locally advanced PCa, elected for radical prostatectomy and casually allocated to receive surgery with Toumai or the da Vinci robotic platform. The primary endpoint was the comparison of positive surgical margin (PSM) rates. Secondary endpoints included the comparison of operative time (skin-to-skin), estimated blood loss, length of hospital stay, 45-day postop outcomes, specifically Clavien–Dindo complications, urinary continence recovery (0–1 pad/day), and IIEF-5 scores. Learning curve was evaluated through the cumulative summation (CUSUM) analysis of operative times and linear regression of operative times (n = 80 cases). The analyses used STATA 19 with two-sided tests at p < 0.05 significance. Results: Baseline characteristics showed balance between cohorts (p > 0.05 for most covariates). Perioperative outcomes proved equivalent: median operative time (OT) was 192.5 min (IQR 165–230) for Toumai^® versus 183.5 min (IQR 147–225) for da Vinci^® Xi (p = 0.38); estimated blood loss (EBL) was 150 mL in both groups (p = 0.87); length of hospital stay (LOS) was 2 days in both groups (p = 0.92). PSM rates were identical at 17.5% (p = 0.79). Continence recovery reached 72.5% versus 80% (p = 0.43). Complications (Clavien–Dindo ≥ II) occurred in 7.5% versus 12.5% of cases (p = 0.45). The CUSUM analysis demonstrated operative time proficiency after only four procedures; operative time regression showed no significant trend (p = 0.38). Conclusions: Toumai^® MT-1000 demonstrates similar performance to da Vinci^® Xi across different RARP quality metrics, with no detectable learning curve for surgeons previously experienced with da Vinci. These findings support a safe integration of cost-effective platforms into clinical practice, pending multicenter randomized confirmation. Full article

This study presents a fully integrated process for the flexible conversion of biogenic waste into synthetic natural gas (bio-SNG) and electricity centred on a 100 kWth dual concentric bubbling fluidised bed steam gasifier. The raw syngas is processed in a high-temperature gas cleaning section, and the resulting clean, H₂-rich syngas is directed to three alternative downstream configurations: (i) conventional methanation, (ii) enhanced methanation with external H₂ supplied by a reversible solid oxide cell (rSOC), and (iii) electricity generation via the same rSOC operating in fuel cell mode. The overall process is modelled in Aspen Plus, in which the gasification section is constrained by experimentally derived syngas data, while downstream units are described through thermodynamic and kinetics-based models. Methanation is simulated using a plug-flow reactor model based on validated kinetic expressions, while the rSOC operating in electrolysis and fuel cell mode is modelled using performance parameters of commercial stacks. A plant-wide heat integration strategy based on composite curve analysis is implemented to maximise internal heat recovery and minimise external utilities. The enhanced methanation configuration enables the production of bio-SNG with high methane content (up to 93.3 vol.% dry, N₂-free), with a yield 0.72 kg/kg_Biomass and a fuel efficiency of 70.1%. In electricity production mode, the system reaches an electrical efficiency of 43.1% with complete elimination of auxiliary fuel through thermal integration. These results demonstrate the capability of a single integrated plant to flexibly switch between fuel synthesis and power generation, enhancing adaptability to fluctuating electricity and methane market conditions while maintaining high efficiency. Full article

In the context of highly interconnected global markets and the rapid dissemination of multilingual information, traditional risk prediction methods that rely on single numerical sequences or monolingual text are insufficient for achieving early perception of cross-market risks. To address this issue, a cross-market risk early warning framework based on multilingual large language models and multimodal sensing fusion is proposed. The proposed approach is centered on a unified risk semantic space, where cross-lingual semantic alignment is employed to reduce semantic discrepancies across languages. Furthermore, a semantic–volatility coupling attention mechanism is introduced to capture the dynamic relationship between textual semantic evolution and market fluctuations. In addition, cross-market knowledge transfer and low-resource enhancement strategies are incorporated to improve the model’s generalization capability across multilingual and multi-market environments, thereby establishing an intelligent perception and early warning system for complex sensing scenarios. Experimental results demonstrate that the proposed method significantly outperforms multiple baseline models in multilingual cross-market risk prediction tasks. In the main experiment, the model achieves a root mean squared error (RMSE) of $0.1127$, an mean absolute error (MAE) of $0.0846$, and an area under the curve (AUC) of $0.8879$, while the early warning gain is improved to $5.2$ days, which is substantially better than the Transformer model (RMSE $0.1365$, AUC $0.8042$) and the multilingual BERT-based fusion model (AUC $0.8395$). In terms of classification performance, higher accuracy, precision, and recall are consistently achieved, with overall accuracy exceeding $0.88$, and both precision and recall are maintained above $0.85$, indicating strong discriminative capability in risk identification tasks. Cross-lingual generalization experiments further verify the robustness of the proposed framework. When trained solely on the English market, the model achieves AUC values of $0.8624$ and $0.8471$ on the Chinese and European markets, respectively, with RMSE reduced to $0.1185$, significantly outperforming competing methods. Overall, the proposed approach achieves substantial improvements in prediction accuracy, cross-lingual generalization, and early warning performance, providing an effective solution for artificial intelligence-driven sensing and risk early warning. Full article

Nai plum (Prunus salicina Lindl. var. cordata) is a high-value fruit crop in southern China, yet its post-harvest quality is often compromised by fruit browning, a major constraint to storage and marketability. Addressing this challenge requires a deeper understanding of the species’ reproductive biology, which underpins both fruit set and cultivar improvement. In this study, we characterized the flowering biological characteristics of Nai plum accessions introduced from Yanling and Liuyang (Hunan Province) and Shaoguan and Lechang (Guangdong Province). Using field observations combined with microscopic and submicroscopic techniques, we documented flowering phenology, flowering dynamics, floral organ traits, pollen viability and stigma receptivity. The flowering period was in March, lasting 26–28 d, and the group blooming period was divided into three stages: Initial opening stage, Full blooming stage, and Final flowering stage. The single-flower opening process was divided into eight stages. Pollen viability followed a unimodal curve, peaking at the petal flattening stage (PF) across all accessions, though peak values varied by provenances. Stigmas were of the wet type, with receptivity following a weak–strong–weak pattern; peak receptivity occurred at early flowering (EF) and PF in most accessions. The EF of Nai plum from Yangling (S1) lasted for 7 h, and PF lasts for 28 h. The EF of Nai plum from Yangling (S2) lasted for 3 h, and the PF lasted for 11 h. Both the EF and the PF of Nai plum from Shaoguan (S3) lasted for 14 h. The bud white stage (BW) of Nai plum from Lechang (S4) lasted for 6 h and the EF lasted for 7 h. The EF of Nai plum from Liuyang (S5) lasts for 7 h, and the PF lasted for 28 h. These findings clarify the reproductive phenology and floral biology of Nai plum, providing foundational knowledge that can inform breeding strategies and cultivation practices aimed at improving fruit set and, ultimately, post-harvest quality. Full article

This study describes a single-laboratory validation of an ultra-high-performance liquid chromatographic (UHPLC) method for the determination of key compounds like hericenones, hericenes, erinacines, and ergosterol in Hericium erinaceus (H. erinaceus, Lion’s Mane) raw materials and finished products. The expanding market for Hericium erinaceus (Lion’s Mane) has increased the need for practical, routine-ready analytical methods that can quantify characteristic marker compounds and strengthen quality control across both raw materials and finished products. In this study, an ultra-high-performance liquid chromatographic (UHPLC) separation method was developed for the determination of hericenones, hericenes, erinacines, and ergosterol in Hericium erinaceus raw materials and finished products. Under the optimized conditions, the major target analytes—hericenones, hericenes, erinacine A, and ergosterol—were fully resolved (R_s > 1.5) within 38 min using an HSS T3 column at 30 °C. All the peaks in the LC chromatogram of Hericium erinaceus samples and standard solutions were structurally confirmed by LC–UV-MS/MS based on the possible mass spectra. The quantitative calibration curves were linear, covering a range of 10–300 μg/mL for hericenone C, D and E, and hericene A, D and C; 3–100 μg/mL for deacylhericenone and deacylhericene; 1–50 μg/mL for erinacine A, and 5–200 μg/mL for ergosterol. Limits of quantification (LOQs) for hericenone C, D, and E and for hericene A, D, and C were approximately 9.263, 4.545, 4.650, 1.854, 10.72, and 11.18 µg/mL, respectively, while LOQs for deacylhericenone and deacylhericene were 1.083 and 2.109 µg/mL. Erinacine A and ergosterol showed LOQs of 0.642 and 8.352 µg/mL, respectively. The recovery of ergosterol was evaluated for the method at two different levels: 91.6~93.9% for 0.2% spiking and 93.0~102.6% for 0.08% spiking. The method was successfully validated, demonstrating inter-day Relative Standard Deviation (RSD) values between 1.1% and 5.7% for detected analytes across diverse matrices. This validated method provides a consistent quantification of hericenones, hericenes, erinacine A, and ergosterol across a range of commercial products and raw Hericium erinaceus materials, providing a sensitive and reliable tool for product characterization and quality control. This method provides QC laboratories with a robust, UV-based tool for standardized product characterization without requiring mass spectrometry. Full article

Anomalous transaction behaviors in cryptocurrency markets exhibit high concealment, substantial diversity, and strong cross-modal coupling, making traditional rule-based or single-feature analytical methods insufficient for reliable detection in real-world environments. To address the research focus, a data-centric multimodal anomaly detection framework integrating generative augmentation, latent distribution modeling, and dual-branch real-time detection is proposed. The method employs a generative adversarial network with feature-consistency constraints to mitigate the scarcity of fraudulent samples, and adopts a multi-domain variational modeling strategy to learn the latent distribution of normal behaviors, enabling stable anomaly scoring. By combining the long-range temporal modeling capability of Transformer architectures with the sensitivity of online clustering to local structural deviations, the system dynamically integrates global and local information through an adaptive risk fusion mechanism, thereby enhancing robustness and real-time detection capability. Experimental results demonstrate that the generative augmentation module yields substantial improvements, increasing the recall from $0.421$ to $0.671$ and the F1-score to $0.692$. In anomaly distribution modeling, the multi-domain VAE achieves an area under the curve (AUC) of $0.854$ and an F1-score of $0.660$, significantly outperforming traditional One-Class SVM and autoencoder baselines. Multimodal fusion experiments further verify the complementarity of the dual-branch detection structure, with the adaptive fusion model achieving an AUC of $0.884$, an F1-score of $0.713$, and reducing the false positive rate to $0.087$. Ablation studies show that the complete model surpasses any individual module in terms of precision, recall, and F1-score, confirming the synergistic benefits of its integrated components. Overall, the proposed framework achieves high accuracy and high recall in data-scarce, structurally complex, and latency-sensitive cryptocurrency scenarios, providing a scalable and efficient solution for deploying data-centric artificial intelligence in financial security applications. Full article

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article

The transition to low-carbon energy systems demands robust strategies that leverage existing fossil resources while integrating renewable technologies. In this work, a single-cycle Gaussian-based producibility model is developed to forecast natural gas production profiles, domestic consumption, export potential, hydrogen production and revenues, adaptive for untapped natural gas discoveries. Annual natural gas production is represented by a bell curve defined by peak year and maximum capacity, allowing flexible adaptation to different reserve sizes. The model integrates renewable energy adoption and steam–methane reforming to produce hydrogen, while tracking revenue streams from domestic sales, exports and hydrogen markets alongside carbon taxation. Applicability is demonstrated through a case study of Eastern Mediterranean gas discoveries, where combined reserves of 2399 bcm generate a production peak of 100 bcm/year in 2035 and deliver 40.71 billion kg of hydrogen by 2050, leaving 411.87 bcm of reserves. A focused Cyprus scenario with 411 bcm of reserves peaks at 10 bcm/year, produces 4.07 billion kg of hydrogen and retains 212.29 bcm of reserves. Cumulative revenues span from USD 84.37 billion under low hydrogen pricing to USD 247.29 billion regionally, while the Cyprus-focused case yields USD 1.79 billion to USD 18.08 billion. These results validate the model’s versatility for energy transition planning, enabling strategic insights into infrastructure deployment, market dynamics and resource management in gas-rich regions. Full article

The integration of virtual and augmented reality is transforming processes in the field of product design. This study evaluates the usability of immersive digital tools applied to industrial design through a combined market research and empirical case study, using the software ‘Gravity Sketch’ and the immersive headset ‘Meta Quest 3’. An embedded single case study was conducted based on the international standard ISO 9241-11, considering the dimensions of effectiveness, efficiency, and satisfaction, analysed through nine indicators: tasks completed, time to complete tasks, dimensional accuracy, interoperability, interactivity, fatigue, human error, learning curve, and perceived creativity. The results show a progressive improvement in user–system interaction across the seven Design Units, as users become more familiar with immersive technologies. Effectiveness improves as users gain experience, though it remains sensitive to design complexity. Efficiency shows favourable values even in early stages, reflecting operational fluency despite learning demands. Satisfaction records the greatest improvement, driven by smoother interaction and greater creative freedom. These findings highlight the potential of immersive tools to support design processes while also underlining the need for future research on sustained usability, interface ergonomics, and collaborative workflows in extended reality environments. Full article

Driven by the path of ecological transition, municipal solid waste management is now more than ever at the center of debates on the most efficient delivery methods. Although competition policy advocates subdivision into lots to facilitate medium-sized enterprise participation, in some cases—notably when substantial investments are required to achieve circular economy and sustainable development goals—a single-operator model may prove more efficient. Using a mixed research approach that integrates empirical evidence and market analysis, this study examines the relevance of cost curves, transaction costs, and market structure in determining the optimal service delivery model. The findings indicate that for large cities, consolidating MSW management services under a single contract yields significant cost advantages due to economies of scale and scope and is better suited to supporting the investments necessary for circular economy objectives. Practical implications for local policymakers highlight the need to assess utility sector policies carefully. Decisions at the local level should account for the interplay between the economic environment and the role of industrialization and economies of scale in fostering sustainable development. We suggest policymakers design policies that balance market efficiency with equitable access to services while also considering the scale of service provision, as it influences sustainability and economic resilience. Full article

This study evaluates the performance of China Taipei firms in the global business environment, focusing on the role of firm-level factors, the geographical setting context, internationalization, and product diversification. These variables are chosen for their potential to enhance resilience and bring firms into global competitiveness. This study performs a generalized least squares (GLS) and curved relationship analysis of 2160 observation samples in the panel analysis, based on a sample of 360 firms across eight industries. The analysis reveals positive correlations between non-labor-intensive operations and effective supply chain management and firms’ overall performance, while dependency on China negatively impacts performance. Notably, the degree of internationalization and product diversification significantly influences the correlations between the key predictors and geographical diversification. A highlight of this study is the application of a curvilinear relationship analysis (non-linear analysis) to assess the real assumptions, providing insight into how these factors interact to affect firm performance. This study stresses the importance of diversifying supply chains, reducing reliance on single markets like China, and enhancing supply chain efficiency through non-labor-intensive operations. This study highlights the need for supportive policies that encourage global expansion, product diversification, and competitiveness in the global business environment. Full article

Roe deer meat is a prized game product in many European countries. However, concerns exist regarding the accuracy of the amount of declared roe deer in processed game meat foods. This study aimed to develop a reliable method for the detection and quantification of roe deer in commercialized game meat products. A TaqMan probe-based quantitative real-time PCR (qPCR) assay was designed, targeting a single-copy 120-bp region of the roe deer agouti signaling protein (ASIP) encoding gene. The method employed the normalized ∆Cq approach to establish a calibration curve for roe deer detection and quantification within 0.05–50% (w/w) in complex raw and processed matrices. The method proved to be specific for roe deer identification, achieving limits of detection and quantification of 0.04 ng of roe deer DNA and 0.05% (w/w) of roe deer in simulated pâté. Following validation with blind samples, highlighting the precision and trueness of the approach, the assay was applied to 46 market samples from four European origins (Poland, Portugal, France, and Spain). The analysis revealed significant discrepancies between declared roe deer content and actual levels in all roe deer labeled products. The global analysis of results, combining the previous survey on red deer species with present roe deer data, identified 61% of mislabeled/adulterated samples due to the absence of deer species, substitution of roe deer with red deer, substitution of fallow deer with other deer species and red deer with pork, and undeclared addition of roe deer. This study demonstrates the effectiveness of the developed qPCR method for accurate roe deer meat authentication in foods, showing its usefulness as a tool for routine food inspection to ensure labeling compliance. Full article

Milk is the most consumed liquid food in the world due to its high nutritional value and relatively low cost, characteristics that make it vulnerable to adulteration. One of the most common types of milk adulteration involves the undeclared addition of cow’s milk to milk from other mammalian species, such as goats, sheep, buffalo or donkeys. The incidence of such adulteration not only causes a crisis in terms of commercial market and consumer uncertainty but also poses a risk to public health, as allergies can be triggered by proteins in undeclared cow’s milk. In this study, a specific qualitative touchdown (TD) PCR method was developed to detect the undeclared addition of cow’s milk in goat and sheep milk based on the discrimination of the peak areas of the melting curves after the modification of bovine-specific primers. The developed methodology has high specificity for the DNA templates of other species, such as buffalos and donkeys, and is able to identify the presence of cow’s milk down to 1%. Repeatability was tested at low bovine concentrations of 5% and 1% and resulted in %RSD values of 1.53–2.04 for the goat–cow assay and 2.49–7.16 for the sheep–cow assay, respectively. The application of this method to commercial goat milk samples indicated a high percentage of noncompliance in terms of labeling (50%), while a comparison of the results to rapid immunochromatographic and ELISA kits validated the excellent sensitivity and applicability of the proposed PCR methodology that was able to trace more adulterated samples. The developed assays offer the advantage of multiple detection in a single run, resulting in a cost- and time-efficient method. Future studies will focus on the applicability of these assays in dairy products such as cheese and yogurt. Full article

This article compares different production, economic, and selected environmental aspects of agroforestry systems in a standard (alley cropping) and a newly proposed design with fast-growing trees grown in short-rotation coppice. Our models of agroforestry systems (AFSs) are as follows: (i) alley cropping AFS with cherry and walnut trees in single rows (tree strips) with 28 m-wide arable fields between them (crop strips), and (ii) coppiced tree belt AFS with poplars and willows and 25 m-wide arable fields between them (crop strips). To evaluate the production characteristics of trees, we used yield curves from experimental plantations in conditions of the Czech Republic from previous research projects. Cost data were collected from long-term experimental plantations and combined with current operation and energy prices. The article presents an economic methodology for assessing the competitiveness of biomass production in AFSs under the current identified market conditions. Our results show that AFSs with short-rotation coppice can have similar economic and production results as annual crops if grown on suitable sites and with appropriate quality of agronomy. In comparison, alley cropping AFSs with fruit trees would not be economically viable for farmers without a significant subsidy for establishment and maintenance in the first years after establishment. Concerning the latest economic and political developments, the product from SRC (energy woodchips) can be evaluated as strategic, increasing the producer’s independence from purchased energy fuels. Full article

In a supply chain composed of multiple members, supply chain coordination plays a crucial role in achieving overall optimization and efficiency. Various supply contract forms have been studied in the existing literature to facilitate supply chain coordination. However, most existing literature has established coordination models assuming constant production costs. In reality, per-unit production costs often decrease as production quantity increases, which is called the learning effect. This paper underscores the significance of considering this learning effect in decision-making processes for coordinated supply contracts. We propose a supply contract scheme for channel coordination that incorporates the learning effect within a supply chain comprising a single manufacturer and a single retailer. In this framework, the manufacturer acts as a Stackelberg leader, initiating the process by designing and presenting the contract. The supply contract scheme is designed to ensure that the retailer’s order quantity aligns with the global solution. We also demonstrate how the contract parameters are determined when the relative bargaining powers of the supply chain members are given exogenously in the market. Our findings reveal that contracts with a learning curve can generate additional profits for both the manufacturer and the retailer compared to the existing coordinated contracts with static production costs. This study provides valuable insights into the impact of the learning effect on supply chain efficiency and offers practical implications for supply chain practitioners. Full article