In response to the growing diverse needs of the ceramic industry, this paper presents a creative approach employing the ErgoLAB human–machine environment synchronization platform to identify key parameters for ceramic shape parametric design. By collecting and analyzing multimodal data, including eye movements, electroencephalographic signals, and skin conductance, the study systematically determines the core factors influencing the user experience. Based on this, a Python (IronPython 2.7.9 (2.7.9.0) on NET 4.0.30319.42000 (64-bit))-based parametric design process is developed, covering parameter selection, shape generation, and model visualization. A model library of classic ceramic shapes is rapidly constructed, and ergonomic experiments further investigate the human–computer interaction mechanisms involved. The designs are optimized using fuzzy comprehensive evaluation for aesthetic appeal. Combined with 3D printing technology, a complete closed loop from design to manufacturing is achieved, verifying the manufacturability of the designs. This study not only deepens the understanding of ceramic shape parametric design but also offers strong support for the diverse development of the ceramic industry, providing valuable references for parametric design applications in other industrial fields.

Fabric defect detection is a crucial step in ensuring product quality within the textile industry. However, current detection methods face challenges in processing efficiency for high-resolution images, detail recovery during upsampling, and the adaptability of loss functions for low-quality samples, which limit detection accuracy and real-time performance. To overcome these limitations, this paper introduces an improved YOLOv8-based model that optimizes both aspects for fabric defect detection. First, we introduce an efficient RG-C2f module to improve processing speed for high-resolution images. Second, the DySample upsampling operator is adopted to enhance edge and detail preservation, improving detail recovery within defect regions. Finally, an adaptive inner-WIoU loss function is designed to dynamically adjust focus on low-quality samples, thereby strengthening the model’s generalization capability. Experimental results validated on the TILDA and Tianchi datasets show that, compared with YOLOv8, the proposed model achieves mAP improvements of 6.4% and 1.5%, respectively, demonstrating significant enhancements in detection accuracy and speed. This advancement provides strong support for fabric defect detection.

Ensuring the reliability and efficiency of computer numerical control (CNC) machines is crucial for industrial production. Traditional anomaly detection methods often struggle with uncertainty in vibration data, leading to misclassifications and ineffective predictive maintenance. This study proposes rough long short-term memory (RoughLSTM), a novel hybrid model integrating rough set theory (RST) with LSTM to enhance anomaly detection in CNC machine vibration data. RoughLSTM classifies input data into lower, upper, and boundary regions using an adaptive threshold derived from RST, improving uncertainty handling. The proposed method is evaluated on real-world vibration data from CNC milling machines, achieving a classification accuracy of 94.3%, a false positive rate of 3.7%, and a false negative rate of 2.0%, outperforming conventional LSTM models. Moreover, the comparative performance analysis highlights RoughLSTM’s competitive or superior accuracy compared to CNN–LSTM and WaveletLSTMa across various operational scenarios. These findings highlight RoughLSTM’s potential to improve fault diagnosis and predictive maintenance, ultimately reducing machine downtime and maintenance costs in industrial settings.

Brain injury and cerebral inflammation are frequent complications following cardiopulmonary bypass (CPB) resulting in neurocognitive dysfunction, encephalopathy, or stroke. We compared cerebral inflammation induced by del Nido and histidine-tryptophan-α-ketoglutarate (HTK) cardioplegia in a porcine model. Pigs underwent 90 min cardiac arrest using HTK (n = 9) or Jonosteril®-based del Nido cardioplegia (n = 9), followed by a 120 min reperfusion. Brain biopsies were collected and analyzed for the mRNA and protein expression of hypoxia-inducible factor-1α (HIF-1α) and cytokines. HTK induced a decrease in blood sodium, chloride, and calcium concentration (cross-clamp aorta: psodium < 0.01, pchloride < 0.01, pcalcium < 0.01; 90 min ischemia: psodium < 0.01, pchloride < 0.01, pcalcium = 0.03) compared to the more stable physiological electrolyte concentrations during del Nido cardioplegia. Hyponatremia and hypochloremia persisted after a 120 min reperfusion in the HTK group (psodium < 0.01, pchloride = 0.04). Compared to del Nido, a higher mRNA expression of the proinflammatory cytokine IL-1β was detected in the frontal cortex (HTK: ∆Ct 6.5 ± 1.7; del Nido: ∆Ct 8.8 ± 1.5, p = 0.01) and the brain stem (HTK: ∆Ct 5.7 ± 1.5; del Nido: ∆Ct 7.5 ± 1.6, p = 0.02) of the HTK group. In conclusion, we showed comparability of HTK and del Nido for cerebral inflammation except for IL-1β expression. Based on our study results, we conclude that del Nido cardioplegia is a suitable and safe alternative to the conventional HTK solution.

The control of environmental parameters in livestock farming is essential to achieve optimal ranges of temperature and humidity. HVAC systems for this purpose are characterized by high energy demands, causing significant GHG emissions when relying on fossil fuels. The aim of this study is the development and testing of a sustainable heating system for a nursery barn hosting 2500 weaners, as well as the assessment of the effectiveness and the performance of the new system. This work involved the implementation of a renewable energy source (RES) system incorporating a borehole thermal energy storage and photovoltaic thermal collectors, integrated with a Dual-Source Heat Pump. A smart control system was installed and the collected data were processed to define the optimal settings of the integrated plant for energy production and efficiency. The performance in terms of the control of the environmental conditions of the nursery barn was assessed on the basis of the environmental parameters analyzed, with particular reference to the animal-occupied zones. The results showed that a mix of RESs can be properly defined and integrated in an automated heating system to meet the specific requirements of a swine farm, thanks to a project specifically designed to exploit the renewable resources typically available in farming environments.

Over the years, the abuse of antibiotics has increased, leading to their presence in the environment. Therefore, a sustainable method for detecting these substances is crucial. Researchers have explored biomass-based carbon dots (CDs) to detect various contaminants, due to their low cost, environmental friendliness, and support of a circular economy. In our study, we reported the synthesis of CDs using pinecones (PCs) and pinebark (PB) through a sustainable microwave method. We characterized the PCCDs and PBCDs using X-ray diffraction, Raman spectroscopy, Transmission Electron Microscope, and Fourier transform infrared, Ultraviolet-visible, and photoluminescence (PL) spectroscopy. The PCCDs and PBCDs were tested for the detection of amoxicillin (AMX) and tetracycline (TC). The results indicated that the sizes of the PCCDs and PBCDs were 19.2 nm and 18.39 nm, respectively, and confirmed the presence of the 002 plane of the graphitic carbon structure. They exhibited excitation wavelength dependence, good stability, and quantum yields ranging from 6% to 11%. PCCDs and PBCDs demonstrated “turn-off” detection for TC and AMX. The limits of detection (LOD) for TC across a broader concentration range were found to be 0.062 µM for PCCDs and 0.2237 µM for PBCDs. For AMX detection, PBCDs presented an LOD of 0.49 µM.

Background/Objective: Recently, there has been an increasing need to implement the diagnosis of the presence of covert hepatic encephalopathy (CHE) in patients with cirrhosis. The aim of this study was to identify novel factors associated with CHE in clinical practice. Methods: This retrospective study enrolled a total of 402 patients with cirrhosis at 17 institutions. The Stroop test was performed to diagnose CHE at each center. Results: The patients comprised 233 males and 169 females, with a median age of 69 (IQR, 61–75) years. The median albumin and 25(OH)D3 levels were 3.9 (3.5–4.3) g/dL and 15.4 (11.0–21.0) ng/mL, respectively. This cohort included 181 patients with esophageal varices (EV). Multivariate analysis revealed that low 25(OH)D3 (p < 0.05) and EV (p < 0.05) were independent risk factors for CHE. When limited to only laboratory factors, low albumin (p < 0.01) and low 25(OH)D3 (p < 0.05) were independent factors for CHE. The optimal cut-off values of albumin and 25(OH)D3 for predicting CHE were 3.7 g/dL and 16.5 ng/mL, respectively. The prevalence of CHE was 59.2% for 25(OH)D3 < 16.5 ng/mL and EV, 53.8% for albumin < 3.7 g/dL and 25(OH)D3 < 16.5 ng/mL, and 66.7% for albumin < 3.7 g/dL, EV, and 25(OH)D3 < 16.5 ng/mL. Conclusions: Low 25(OH)D3 and albumin levels, and the EV were positively associated with CHE in patients with cirrhosis. Specifically, the prevalence of CHE increased with a decrease in 25(OH)D3 levels. Patients with such risk factors should be actively and carefully examined for the presence of CHE.

Increasing interest has been devoted to the seeds of the amaranth, a plant that has garnered attention for its multifaceted uses in daily life. In this research, we focused on four genotypes of two amaranth species cultivated in two different sites in the southwest of France. Oil content, fatty acid composition, and unsaponifiable levels were carried out. The lipid composition was analyzed using Gas Chromatography with Flame Ionization Detection (GC-FID) analysis. The total polyphenol contents (TPC) of different seed extracts were measured by a Folin–Ciocalteu assay. Antioxidants and cytotoxic activities were additionally assessed for the methanol (70%), ethyl acetate, and cyclohexane extracts. Results showed that oil content varied greatly and ranged from 4.3 to 6.4%. Lera cultivated at Riscle had the highest squalene yield, reaching 7.7%. Linoleic acid and oleic acid were the most abundant fatty acids for the four genotypes in two sites, followed by palmitic acid. Triglycerides (TAGs) were the main glycerides in all samples growing in both sites. A total of 44 volatile compounds were identified in Amaranthus seed extracts. The chemical compositions of the amaranth have been discussed as influenced by genetic and environmental factors. These data highlight the bioactive potential of the amaranth seed.

The conversion of the waste of wet silica sand sludge (W3S) into useful products, such as bricks, glassware, and ceramics, is an alternative solid waste management method. The aim of this study is to determine the effect of silica sand wet sludge additive on brick quality. For this purpose, laboratory-scale brick manufacturing was implemented by using 10%, 30%, 50%, and 100% sludge in clay brick. For proper characterization to understand brick quality, the water absorption, shrinkage, bulk density, compressive strength, and SEM analysis of sintered samples were performed. At the end of the experimental procedure, no negative effects of sludge addition were determined in terms of mechanical strength, porosity, water absorption, or structural integrity. In addition, the incorporation of W3S contributed to sustainable waste management and helped mitigate its environmental impact. Experimental studies revealed that a product with the desired color could be obtained when 50% W3S was used in the mixture. In addition, the optimal composition for making bricks was found to be a mixture of 50% W3S and 50% brick clay, fired at 850 °C. With this mixture, not only is the preferred color achieved, but an optimum balance between mechanical strength, durability, and minimization of environmental damage is also attained. Such a formulation ensures high compressive strength, low porosity, and low water absorption, making it sustainable and a better choice in construction with industrial by-product use. The results obtained are useful in showing possibilities for the solution of environmental problems to utilize waste materials in useful products.

The proliferation of Internet of Things (IoT) devices presents significant challenges for cybersecurity and digital forensics, particularly as these devices have become increasingly weaponised for malicious activities. This research focuses on the forensic analysis capabilities of Raspberry Pi devices configured with Kali Linux, comparing their forensic capabilities to conventional PC-based forensic investigations. The study identifies key gaps in existing IoT forensic methodologies, including limited tool compatibility, constrained data retention, and difficulties in live memory analysis due to architectural differences. The research employs a testbed-based approach to simulate cyberattacks on both platforms, capturing and analysing forensic artefacts such as system logs, memory dumps, and network traffic. The research findings reveal that while traditional PCs offer extensive forensic capabilities due to superior storage, tool support, and system logging, Raspberry Pi devices present significant forensic challenges, primarily due to their ARM architecture and limited forensic readiness. The study emphasises the need for specialised forensic tools tailored to IoT environments and suggests best practices to enhance forensic investigation capabilities in weaponised IoT scenarios. This research contributes to the field by bridging the gap between theoretical frameworks and real-world forensic investigations, offering insights into the evolving landscape of IoT forensics and its implications for digital evidence collection, analysis, and forensic readiness.

This empirical study establishes a seamless integrated project-based learning framework mediated by an evidence-based project-based learning system. Combining theoretical model construction and practical application, this research aims to bridge gaps in learning objectives, processes, resources, and assessments. The model employs conceptual clusters, problem chains, and evidence-driven task design to foster the development of interdisciplinary competency. A 40 h case study, centered on the “Interior Space Design of Teachers’ Apartments” project, demonstrated quantifiable improvements; students’ design proposal scores increased by 45.5%, problem-solving efficiency improved by 62.5%, and team collaboration scores rose from 60 to 85. Additionally, innovative applications per design surged from 2–3 to 6–8, while student engagement time doubled. These results validate the effectiveness of the seamless integrated project-based learning framework in enhancing interdisciplinary knowledge integration, core competencies, and intrinsic motivation, providing a data-supported framework for technology-enhanced educational reform.

This study investigates the relationship between sustainable leadership styles and conflict management strategies within the context of Greek Public Sector. Specifically, it examines how collaborative, transformational, and authoritarian leadership styles impact workplace conflict resolution. The research adopts a case study methodology, focusing on Departments of Public Works in Greece, where data were collected through questionnaires. The analysis involved quantitative methods, including exploratory factor analysis (EFA), to examine the relationship between leadership styles and conflict management techniques. Results indicate that collaborative leadership is strongly associated with higher employee satisfaction and more effective conflict resolution, particularly in organizations with flat hierarchical structures. Transformational leadership fosters trust and open communication, which further enhance conflict resolution. On the other hand, authoritarian leadership styles correlate with increased workplace tension, lower satisfaction, and less effective conflict management, especially in high power-distance environments. The study also highlights cultural factors, such as the Greek emphasis on interpersonal relationships, as critical influences on leadership effectiveness. These findings underline the need for culturally adaptive and sustainable leadership strategies and provide practical recommendations for promoting harmony and productivity in Greek organizations.

Ovarian cancer survival depends strongly on the time of diagnosis. Detection at stage 1 must be the goal of liquid biopsies for ovarian cancer detection. We report the development and validation of graphene-based optical nanobiosensors (G-NBSs) that quantify the activities of a panel of proteases, which were selected to provide a crowd response that is specific for ovarian cancer. These G-NBSs consist of few-layer explosion graphene featuring a hydrophilic coating, which is linked to fluorescently labeled highly selective consensus sequences for the proteases of interest, as well as a fluorescent dye. The panel of G-NBSs showed statistically significant differences in protease activities when comparing localized (early-stage) ovarian cancer with both metastatic (late-stage) and healthy control groups. A hierarchical framework integrated with active learning (AL) as a prediction and analysis tool for early-stage detection of ovarian cancer was implemented, which obtained an overall accuracy score of 94.5%, with both a sensitivity and specificity of 0.94.

With the proliferation of artificial intelligence in education, AI-generated digital educational resources are increasingly being employed as supplements for university teaching and learning. However, this raises concerns about the quality of the content produced. To conduct a comprehensive quality assessment, this paper presents an evaluation index system for AI-generated digital educational resources by combining the Delphi method and the Analytic Hierarchy Process. The initial quality indicators across the dimensions of content, expression, and user and technical aspects are identified through a systematic literature review of the recent research. Then, the Delphi method is utilized to modify the quality indicators according to experts’ opinions through two rounds of questionnaire surveys. Subsequently, the weight coefficients of the quality indicators are calculated using the Analytic Hierarchy Process. Finally, a quality indicator system for evaluating AI-generated digital educational resources is developed, which comprises four dimensions and twenty indicators. The findings reveal that content characteristics are of critical importance in assessing the quality of AI-generated educational resources, followed by expression characteristics as the second most significant factor, with user and technical characteristics also being recognized. Among the second-level indicators, “authenticity”, “accuracy”, “legitimacy”, and “relevance” are accorded greater importance relative to other indicators. The proposed system equips relevant stakeholders with a framework for selecting high-quality AIGDERs and steering AI tools in line with educational standards. Finally, some implications are provided to support the selection of high-quality AI-generated resources and guidance on aligning these resources with educational standards.

The implementation of scientific cultivation practices on soda saline–alkali land plays a pivotal role in safeguarding food security and promoting sustainable agro-economic development at the regional scale. However, there exists a critical knowledge gap regarding the optimization of tillage strategies for rain-fed maize (Zea mays L.) cultivation across heterogeneous saline–alkali soil matrices. This study selected meadow alkaline soil, saline meadow soil, and mild saline–alkali soil under the typical micro-landscape morphological characteristics of soda saline–alkali soil in the Songnen Plain as experimental plots. Under three tillage methods, namely no tillage (NT), rotary tillage + no tillage (RT), and subsoiling + rotary tillage + no tillage (SRT), the effects of the tillage methods on the soil physical properties at the seedling stage, root development at the V6 stage, and yield at the R6 stage during the process of cultivating maize in different types of soils were analyzed. The research results showed that compared with NT and RT, the SRT treatment better improved the physical properties, such as penetration resistance and the bulk density in micro-spaces (0–40 cm), of different soil types. The SRT treatment had a positive impact on the root development of maize seedlings in saline meadow soil and meadow alkaline soil. In terms of yield, compared with the NT treatment, the SRT treatment in meadow alkaline soil and saline meadow soil had a positive effect on the plant height, root dry weight, 1000–grain weight, and grain yield of maize. The increases in maize grain yield were 27.94% and 13.24%, respectively. Compared with NT, the differences in the effects of the SRT and RT treatments on maize yield in mild saline-alkali soil were the smallest, being 6.98% and 4.77%, respectively. The relevant results provide guidance on tillage methods and a theoretical basis for improving the properties of different types of soda saline–alkali soils and increasing maize yield.

The Mediterranean region is one of the 36 hotspots of the world that will be most affected by climate change, with river ecosystems being among the most sensitive to these effects. Therefore, it is necessary to understand and monitor the effects that are occurring through the use of aquatic macroinvertebrates as bioindicators of climate change. To study the use of macroinvertebrates as bioindicators, a systematic literature review was conducted using the PRISMA method. The obtained bibliography was analyzed alongside other known studies to determine the response of these organisms to temperature increases and decreases and alterations in precipitation, as well as their reaction to extreme drought and flood events. The results show that different taxa of macroinvertebrates respond differently to the effects of climate change, always leading to a community alteration with changes in the abundance, richness, phenology, and composition. Therefore, aquatic macroinvertebrates are good bioindicators of the changes caused by climate change, as they respond clearly to the alterations induced by climate change.

Background: Magnesium (Mg) deficiency is associated with many common chronic conditions and potentially severe health care outcomes, including cardiovascular disease, cardiovascular risk factors, and diabetes. However, Mg deficiency is underdiagnosed and often underrecognized in the ambulatory health care setting, and nutrition education and training are often limited for health care providers (HCPs). Methods: A clinical guideline for detecting and treating Mg deficiency in the ambulatory care setting was developed. A pilot study was conducted in which HCPs received education on Mg and completed pre-test and post-test questionnaires to assess the intervention efficacy of the guideline. Results: Ten HCPs participated in the pilot study via telephone or face-to-face session. In general, there was a statistically significant increase in Mg knowledge among HCPs, due to the intervention of presentation of the guideline, with a nonsignificant increase in clinical practice application. However, the 1-month follow-up survey results showed that HCPs were likely to incorporate Mg assessment and treatment tools from the guideline in their future practice. Conclusions: These findings suggest that the use of the proposed clinical guideline may increase HCP knowledge and improve the diagnosis and treatment of Mg deficiency. Further use, development, and evaluation of this guideline is warranted.

This review highlights the key molecular and cellular mechanisms contributing to aging, such as DNA damage, mitochondrial dysfunction, telomere shortening, protein dysfunction, and defective autophagy. These biological mechanisms are involved in various oral health conditions prevalent in the elderly, including periodontal disease, oral cancer, xerostomia, dental caries, and temporomandibular joint disorders. Exosomes generated by mesenchymal stem cells possess substantial therapeutic potential. These exosomes are nanosized extracellular vesicles derived from cells and are involved in essential intercellular communication and tissue homeostasis. The exosome-based therapies proved superior to traditional cell-based approaches, due to lower immunogenicity, ease of storage, and avoidance of complications associated with cell transplantation. Furthermore, the diagnostic potential of exosomes as non-invasive biomarkers for aging processes and age-related oral diseases offers insights into disease diagnosis, staging, and monitoring. Among the challenges and future perspectives of translating exosome research from preclinical studies to clinical applications is the need for standardized procedures to fully harness the therapeutic and diagnostic capabilities of exosomes.

Genetic variants related to susceptibility to chronic respiratory conditions such as interstitial lung disease (ILD) could share critical pathways in the pathogenesis of COVID-19 and be implicated in COVID-19 outcomes and post-COVID-19. We aimed to identify the participation of genetic variants in lung function and ILD genes in severe COVID-19 outcomes and post-COVID-19 condition. We studied 936 hospitalized patients with COVID-19. The requirement of invasive mechanical ventilation (IMV) and the acute respiratory distress syndrome (ARDS) classification were considered. The mortality was assessed as the in-hospital death. The post-COVID-19 group included 102 patients evaluated for pulmonary function tests four times during the year after discharge. Five variants (FAM13A rs2609255, DSP rs2076295, TOLLIP rs111521887, TERT rs2736100, and THSD4 rs872471) were genotyped using TaqMan assays. A multifactor dimensionality reduction method (MDR) was performed for epistasis estimation. The TERT rs2736100 and THSD4 rs872471 variants were associated with differential risk for ARDS severity (moderate vs. severe, CC + CA, p = 0.044, OR = 0.66, 95% CI = 0.44–0.99; and GG p = 0.034, OR = 2.22, 95% CI = 1.04–4.72, respectively). These variants and FAM13A rs2609255 were also related to pulmonary function post-COVID-19. The MDR analysis showed differential epistasis and correlation of the genetic variants included in this study. The well-known variants in recognized genes related to pulmonary function worsening and interstitial disorders are related to the severity and mortality of COVID-19 and lung performance in the post-COVID-19 condition.

Many specialized metabolites found in plants have significant potential for developing environmentally friendly weed management solutions. This review focuses on the phytotoxic effects of volatile terpenes and phenolic compounds, particularly nepetalactone, an iridoid monoterpenoid from Nepeta species, and phloretin, a dihydrochalcone predominantly found in the genus Malus. We highlight current findings on their herbicidal effects, including morphological, physiological, and biochemical responses in target plants. These results underscore their potential for developing sustainable herbicides that could control weeds with minimal environmental impact. We also discuss their soil persistence and methods to enhance their solubility, chemical stability, and bioavailability. Additionally, the possible effects on non-target organisms, such as pollinators, non-pollinating insects, and soil microbiota, are considered. However, further research and a deeper understanding of their long-term ecological impact, along with a resistance development risk assessment, is essential for the potential development of bioherbicides that could be applied in sustainable weed management practices.

A significant number of people with disabilities rely on assistive devices, yet these technologies often face limitations, including restricted functionality, inadequate user-centered design, and a lack of standardized evaluation metrics. While upper-limb prosthetics remain a key research focus, existing commercial solutions still fall short of meeting daily reliability and usability needs, leading to high abandonment rates. CYBATHLON integrates assistive technologies into daily living tasks, driving innovation and prioritizing user needs. In CYBATHLON 2024, the HANDSON hand secured first place in the arm prosthesis race, showcasing breakthroughs in human–robot integration. This paper presents the HANDSON hand’s design, core technologies, training strategies, and competition performance, offering insights for advancing multifunctional prosthetic hands to tackle real-world challenges.