Colonoscopy, essential for diagnosing and treating colorectal cancer, relies on effective bowel preparation to inspect the colonic mucosa fully. Traditional education methods include printed instructions and verbal explanations, but these may not ensure clear patient understanding. Poor bowel preparation can obscure mucosal visibility, delaying cancer diagnosis and treatment. Using the outpatient’s Android mobile device, the mobile health care model is provided as a nursing guide to assist in bowel preparation for colonoscopy. This single-blinded, randomized trial en-rolled patients for colonoscopy at a Northern Taiwan gastroenterology center. Eligible patients were physically and mentally healthy and could communicate in Chinese. The study tested the ColonClean app during bowel preparation. After the procedure, endoscopists rated the prepara-tion quality using the Aronchick Scale. Data analysis with SPSS software (version 25.0) evaluated if the app significantly improved bowel preparation compared to those who did not use it. Forty patients were recruited for both the control (conventional method) and experimental (conven-tional method plus ColonClean app) groups. In the experimental group, endoscopists assessed bowel preparation quality; all achieved at least "Fair," with 75% receiving "Excellent" or "Good" ratings. The ColonClean App enhanced bowel preparation quality more effectively than tradi-tional nursing guidance.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Although composting has many advantages in the treatment of organic waste, there are still many problems and challenges associated with emissions, like NH3, VOCs, and H2S, as well as greenhouse gases such as CO2, CH4, and N2O. One promising approach to enhancing composting conditions is used of novel analytical methods bad on artificial intelligence. To predict and optimize the emissions (CO, CO2, H2S, NH3) during composting process kinetics thought mathematical models (MM) and machine learning (ML) models were utilized. Data about everyday emissions from laboratory composting with compost’s biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% d.m.) were used for MM and ML models selections and training. MM has not been very effective in predicting emissions, (R2 0.1 - 0.9), while ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R2 accuracy CO:0,71, CO2:0,81, NH3:0,95, H2S:0,72)) and decision tree (DT, RPART; R2 accuracy CO:0,693, CO2:0,80, NH3:0,93, H2S:0,65) have demonstrated satisfactory results. For the first time the ML models to predict CO and H2S during composting were demonstrated. Further research in a semi-scale and field study composting with biochar is needed to improve the accuracy of developments models.

Ménière's disease (MD) has an unclear cause. The microvascular dysregulation of the inner ear has been increasingly pointed as a potential contributor. This study investigates the prevalence of cardiovascular risk factors (CVRFs) in MD patients compared to those with posterior circulation cerebral infarction (POCI). CVRFs like hypertension, diabetes, dyslipidemia, obesity, coronary heart disease, and smoking were assessed in both MD and POCI patients. Brain MRI identified POCI etiology as "small vessel occlusion" (SVO) or "other etiology" (OE). The study included 64 MD and 84 POCI patients. Compared to MD, POCI OE showed a higher prevalence of CVRFs across various age groups, including hypertension, diabetes, dyslipidemia, and smoking. Notably, the odds of having POCI OE were significantly higher for individuals with hypertension and smoking. On the other hand, POCI SVO showed similar prevalence of CVRFs compared to MD. This study reveals no significant difference in CVRF prevalence between MD and smaller vessel POCI. However, a clear distinction emerges when comparing MD to POCI with involvement of larger blood vessels. Further research is needed to confirm these findings and explore potential shared risk factors between POCI (SVO) and MD.

Stress is an inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues like cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimate. However, in the case of more serious health issues such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable sensor computing platform with multiple channels. The platform aims to estimate stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor users' stress levels and integrate data on their blood pressure and blood glucose levels. Validation experiments were performed on patients with chronic diabetes, and the initial findings are presented in the study.

Amidst worsening climate change, drought stress imperils global agriculture, jeopardizing crop yields and food security, necessitating urgent exploration of sustainable methods like biopriming, harnessing beneficial microorganisms to bolster plant resilience. Recent research has revealed diverse biological compounds with versatile applications produced by Schizophyllum commune, rendering this fungus as a promising contender for biopriming applications. For the first time this study aimed to investigate,the potential of S. commune exo- (EPSH) and intra-PSH (IPSH), isolated from two strains: Italian (ITA) and Serbian (SRB) under submerged cultivation, in enhancing the resilience of Pisum sativum L. seeds through biopriming technique. Testing of seed quality of bioprimed, hydroprimed, and unprimed seeds was done using a germination test, under optimal and drought conditions. In optimal conditions, the IPSH SRB increased germination energy by 5.50% compared to the control, while the highest percentage of germination (94.70%) was shown after biopriming with PSH isolated from the ITA strain. Additionally, all assessed treatments resulted in a boost in seedling growth and biomass accumulation, where the ITA strain demonstrated greater effectiveness in optimal conditions, whereas the SRB strain showed superiority in drought conditions. The drought tolerance indices increased significantly in response to all examined treatments during the drought, with EPSH ITA (23.00%) and EPSH SRB (24.00%) demonstrating the greatest effects. Results demonstrate a significant positive influence of fungal PSH, indicating their potential as biopriming agents and offers insights into novel strategies for agricultural resilience in the face of changing environmental conditions.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

The study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

Continuous discovery and update of applications or their boundaries running in cloud environments in an automatic way is a highly required function of modern data center ops solutions. Prior attempts to address this problem within various products or projects were/are applying rule-driven approaches or machine learning techniques on specific types of data − network traffic as well as property/configuration data of infrastructure objects, which all have their drawbacks in effectively identifying roles of those resources. The current proposal (ADLog) leverages log data of sources, which contain incomparably richer contextual information, and native constructs of VMware Aria Operations for Logs in terms of Event Types and their distributions to group those entities, potentially automatically enrich with indicative tags, and recommend for further management tasks and policies. Our methods discriminate not only diverse kinds of applications, but also their specific deployments thus providing hierarchical representation of the applications in time and topology. For several applications under Aria Ops management in our experimental test bed, we discover those in terms of similarity behavior of their components with a high accuracy. The validation of the proposal paves the path for an AI-driven solution in the cloud management scenarios.

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. In this study, a series of 850 face-to-face interviews (650 with students and 200 with teachers) were conducted throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. The paper proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent's employment, academic achievement, living situation, paternal and maternal education levels, and engagement with social media collectively influence students' perspectives on this matter. Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers' viewpoints on school-based disaster risk reduction. Multivariate linear regression was used to explore predictors of students' and teachers' insights on school-based disaster risk reduction. Various statistical tests including Chi-square, t-tests, one-way ANOVA, and Pearson's correlation were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender and marital emerge as the primary predictors across various facets of students' and teachers' insights on school-based disaster risk reduction (awareness of disasters, disaster education activities, attitudes on disaster risk reduction education, enhancement of disaster information accessibility, etc). The results of this study provide insight into the key factors influencing students' and teachers' perceptions of disaster risk reduction in schools. As such, they can serve as a basis for improving educational programs, developing policies and strategies, normative educational frameworks, teacher training, and further research in disaster education.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

Nanotechnology sparks discussions and concerns about the impacts of new nanomaterials on health and the environment. It holds importance in various domains due to its unique struc-tures. Carbon dots (C-dots) are versatile nanomaterials with applications in bioimaging, sensing, catalysis, polymers, solar cells, and more. They offer desirable characteristics such as stability, cost-effectiveness, biocompatibility, and high photoluminescent quantum yield. C-dots have the potential to replace expensive fluorophores in solar cells. They are catego-rized as carbon quantum dots, graphene quantum dots, and carbonized polymer dots. C-dots have outstanding optical and photoelectric properties with low toxicity. Bottom-up and top-down approaches are used for the synthesis of carbon dots (CDs), with each method im-pacting their physicochemical characteristics. The choice of synthesis method depends on de-sired properties and application requirements. Researchers combine these methods and ex-plore new approaches to enhance synthesis efficiency and tailor CD properties. CDs have di-verse chemical structures with modified oxygen, polymer-based, or amino groups on their surface. Various characterization methods such as HRTEM, XPS, and optical analysis (PL, UV) are used to determine CD structure. Carbon dots (CDs) are cutting-edge fluorescent nano-materials with remarkable qualities such as biocompatibility, low toxicity, environmental friendliness, high water solubility, and photo-stability. They are easily adjustable in terms of their optical properties, making them highly versatile in various fields. CDs find applications in bio-imaging, nanomedicine, drug delivery, solar cells, LEDs, photo-catalysis, elec-tro-catalysis, and other related areas.

Nitrogen (N) is an essential macro nutrient for crop growth; therefore, N deficit can greatly limit crop growth and production. In the North China Plain (NCP), winter wheat is one of the main food crops, and its yield has increased from approximately 4000 kg ha-1 to 6000 kg ha-1 in the last two decades. Determining the proper N application rates at different growth stages and in all seasons is very important for the sustainable and high production of wheat in the NCP. A field experiment with five N application rates (250, 200, 150, 100, and 40 kgN·ha-1, designated as N250, N200, N150, N100, and N40, respectively) was conducted during the 2017-2018 and 2018-2019 winter wheat seasons to investigate the effects of the N application rate on water- and fertiliz-er-utilization efficiency, and on the crop growth and yield of winter wheat under sprinkler fer-tigation conditions. The results showed that, in the N application range of 40-200 kg ha-1, crop yield and water- and fertilizer-use efficiencies increased as the N application rate increased; however, further increases in the N application rate did not have additional benefits. The N up-take after regreening of winter wheat linearly increased with crop growth. Considering the wheat yield and N-use efficiency, the optimal N application range was 200 kg ha-1, and the best top-dressing strategy was equal amounts of N applied at the regreening, jointing, and grain-filling stages. The results of this study will be useful for optimizing field N management to achieve high wheat yield production in the NCP and in regions with similar climatic and soil environment conditions.

The lexical analysis of seminal policy-to-diplomacy documents from the socio-environmental discourse of the last fifty year agendas allowed examining the evolution of five themes: resources/waste, pollution, social and economic development, justice, and health. Contextual affinities were strongest between resources, pollution, and health and are linked to societal trends and pressures. On those grounds, the central role of resource stewardship according to nature’s physical limits is highlighted, and dedicated concepts, analytical frameworks, and methodologies with different degrees of sustainability are analyzed. To reframe the identified social and economic problems, the work proposes criteria and choices that (1) allow to compare the dynamics of socioecological states across the planet, (2) help matching the pace of socioecological change by addressing path dependencies through participation and engagement of communities, and (3) enable stakeholders to make trade-offs and take decisions in specific social, economic, political, and cultural contexts. The prioritization of resource justice and responsibility becomes a societal project : from Wellfare State to commonfare communities.

Background: After the increasingly common anterior cruciate ligament reconstruction (ACLR) procedure in competitive athletes, rehabilitation is crucial for facilitating a timely return to sports (RTS) and preventing re-injury. This pilot study investigates the biomechanical and psychological outcomes of postoperative rehabilitation in competitive athletes, comparing supervised rehabilita-tion (SVR) and home-based rehabilitation (HBR). Methods: After ACLR, 60 (out of 74 screened) athletes were recruited and equally divided into 2 groups using non-probability convenience sam-pling: 30 HBR and 30 SVR (each group comprising 15 males and 15 females). Rehabilitation out-comes in respective groups were evaluated at 8 months using measures (Tegner-Activity-Scale [TAS], International Knee Documentation Committee subjective knee form [IKDC-SKF], ACL Return to Sport after Injury [ACL-RSI]) and objective parameters (isometric muscle strength, hamstring/quadriceps asymmetry). RTS was evaluated at 9 months; with ACL re-injury rates rec-orded approximately 6 months post-RTS. Results: Both groups exhibited decreased TAS scores (HBR: 8 to 6, SVR: 8 to 7), with the SVR group demonstrating superior post-operative IKDC-SKF scores (81.82 vs. 68.43) and lower ACL-RSI scores (49.46 vs. 55.25). Isometric and isokinetic mus-cle strength, along with asymmetry values, favored the SVR group 8 months post-ACLR (p

The oral mucosa represents a defense between the external environment and the rest of the body and encompasses several antimicrobial and antiviral mechanisms. Oral mucosal cells are constantly bathed in hypotonic saliva (normally one-third tonicity compared to plasma) and are repeatedly exposed to environmental stresses of tonicity, temperature and pH by the drinks we imbibe (e.g. hypotonic: water, tea, coffee; hypertonic: assorted fruit juices, red wines), as well as of diverse microbial and viral flora. In the mouth, the broad-spectrum antiviral mediator MxA (a dynamin-family large GTPase) is constitutively expressed in healthy periodontal tissues, and also induced by Type III interferons (e.g. IFN-λ1). Endogenously induced human MxA and exogenously expressed human GFP-MxA formed membraneless biomolecular condensates in the cytoplasm of oral carcinoma cells (OECM1 cell line). These condensates likely represent storage granules in equilibrium with antivirally active dispersed MxA. Remarkably, cytoplasmic MxA condensates were exquisitely sensitive sensors of hypotonicity – the condensates in oral epithelium disassembled within 1-2 min of exposure of cells to saliva-like one-third hypotonicity, and spontaneously reassembled in the next 4-7 min. OECM1 cells preloaded with calcein-AM (a reporter of cytosolic “macromolecular crowding”) confirmed unquenching and then requenching of calcein fluorescence during this process. Hypertonicity had little effect on MxA condensates. The spontaneous reassembly of GFP-MxA condensates in oral epithelial cells even under continuous saliva-like hypotonicity was slowed by the protein phosphatase inhibitor cyclosporin A (CsA) and by the K-channel blocker tetraethylammonium chloride (TEA) suggestive of the involvement of the volume-sensitive WNK kinase-protein phosphatase (PTP)-K-Cl cotransporter (KCC) pathway in the regulated volume decrease (RVD) during condensate reassembly in oral cells. Thus, physiologically, hypotonic drinks (water, tea, coffee) trigger a rapid deployment of antiviral MxA throughout the cytoplasm of oral epithelial cells, with subsequent salvage of MxA back into storage granules/condensates.

Virtual Reality (VR) can be useful in efforts that aim to improve wellbeing of older members of the society. Within this context, the work presented in this paper aims to provide to elderly a user-friendly and enjoyable Virtual Reality (VR) application incorporating memory recalling and storytelling activities that can promote mental awareness, so that negative side effects related to brain inactivity are prevented. An important aspect of the proposed VR application is the presence of a virtual audience that listens to the stories presented by elderly users, and interacts with the user. In an effort to maximize the impact of the VR application, research was conducted to study whether the elderly are willing to use the VR application and whether they believe it can help to improve well-being and reduce the effects of loneliness and social isolation. The results show that elderly users are positive towards the use of such an application in everyday life, as a means of improving their overall well-being.

The late diagnosis of Type 2 Diabetes Mellitus can occur due to lack of access to the primary health care system and the non-manifestation of symptoms. Faced with this problem, this work presents the creation of a mobile application, called DiabetesCheck, for predicting the risk of Type 2 Diabetes Mellitus. A mathematical model is used, which classifies the risk of Type 2 Diabetes Mellitus for a group of patients with intervention and for a comparative group, using Fuzzy Classification and Decision Trees. The proposed application allows the health professional to monitor patients through the history stored in a database, allowing a representation of the evolution of patients and indication of medications. Therefore, this proposal aims to carry out the diagnosis in the initial phase of the disease and help in the prescription of more effective treatments and in the proposition of public policy strategies in epidemiology and screening.

Indigenous peoples around the world are revitalizing their ancestral beliefs, practices, and languages, including traditional understandings of health and wellbeing. In the Aotearoa (New Zealand) context, a number of ground-breaking Māori health and wellbeing related models have emerged, each with their own unique applications and offerings. We sought in these qualitative studies to explore and identify some key sources of wellbeing for Māori individuals. Nine interviews were conducted with members of Māori communities to identify key themes of Māori wellbeing. A Reflexive Thematic Analysis was performed on these data, and then a further fifteen interviews were conducted to revise, refine, and reposition the previously generated themes. The Ngaruroro describes wellbeing as the embodied and active process of being in good relation with your 1) here tāngata (social and familial ties), 2) te taiao (the environment), and 3) taonga tuku iho (cultural treasures), while doing what you can to make lifestyle choices that are conducive to the health of your 4) tinana (body) and 5) wairua (spirit), while cultivating a balanced 6) ngākau (inner-system), fulfilling 7) matea (core needs), and exercising your 8) mana (authority). These themes illustrate and reiterate that Māori wellbeing is dynamic, interconnected, and holistic.

ACL instability poses a significant challenge in traumatology and orthopedic medicine, often requiring accurate diagnosis for appropriate treatment. While the pivot-shift test offers a crucial means of assessment, its reliance on subjective interpretation underscores the need for supplementary imaging studies. This study aims to address this limitation by developing a Bayesian classification algorithm tailored for integration into a mobile application. Using the built-in inertial sensors of smartphones, this new approach aims to dynamically evaluate rotational stability during knee examinations. Orthopedic specialists conducted knee evaluations on 52 subjects, with subsequent analysis revealing interesting insights. Intraobserver and interobserver analyses, as measured by ICC, demonstrated strong agreement both in terms of timing between maneuvers (ICC = 0.94) and signal amplitude (ICC = 0.71-0.66). Notably, the Bayesian algorithm successfully classified 95% of joint hypermobility cases, with an additional 7 cases of hyperlaxity identified by the Pivot-Shift Meter (PSM). These findings highlight the practicality and effectiveness of implementing a Bayesian classification algorithm within a mobile application for assessing and categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

Addressing within field spatial variability for nitrogen management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study we investigated the economic, environmental and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum). An online visible and near infrared (vis-NIR) spectroscopy sensor we utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop's normalized difference vegetation Index (NDVI) from Sentinel-2 satellite images was incorporated to online measured soil data to derive fertility management zones (MZs) maps, after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium-high fertile (VR-MH), medium-low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as: 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. Results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR -N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment 374.83 €/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific application of all farm resources through precision agricultural technologies.

The outstanding occurrences of red wood ant nests (RWA; Formica rufa-group) at two study sites in the Oberpfalz, NE Bavaria, Germany, could be confirmed by the 2023 re-inventory (a total of 5,393 nests and an increase by ≈8 %). RWA nests preferred mature and medium mature pine-dominated forests. Standing and downed dead wood do not negatively influence nests. Nest counts with woodpecker cavities and the diversity of the herb layer increased. The highly clustered nests in NW-SE direction suggests a strong interaction between nests distributions and the tectonic environment. Our developed (re-)inventory approach in combination with the comprehensive photo database is a valuable tool for a) monitoring the entire forest habitat including woodpecker cavities and dead wood in, at and around nests, and b) identifying GeoBio-Interactions. Such a comprehensive approach leads to more accurate and realistic counts of RWA nests during (re-)inventories and provides scientific evidence of the status quo of RWA occurrence. Our results disproved the unfounded and scientifically unsubstantiated blanket statement by laymen of the ASW Landesverband Bayern e.V. that RWA nest numbers in the Oberpfalz or even in the entire Bavaria are in sharp decline. We suggest seven to eight years as a suitable time interval for re-inventories.

Inadequately managed agricultural waste significantly impacts the environment, health, and economy. This form of pollution arises from the underutilization, poor awareness, and insufficient treatment of agricultural waste. Fruit and vegetable wastes are valuable sources of bioactive compounds. This study aims to revalorize discarded waste from red habanero chili peppers (Capsicum chinense) by extracting bioactive compounds through different extraction processes: maceration (ME), maceration assisted by ultrasound (US), Soxhlet extraction (SE), supercritical fluid extraction (SFE), and supercritical fluid extraction with a co-solvent (SFEC). Extraction processes have significant effects on extraction efficiency and phytochemical profile (capsaicinoids and carotenoids recovery). The results indicate that the highest efficiency process is obtained with SFEC, also a high phytochemicals recovery (14.9 mg of total capsaicinoids and total carotenoids 292.09 µg per gram of sample). Concerning to phytochemical profile in the extract, the maceration process gets the highest concentration of compound followed by US and SFEC. These data reveal that the use of SFE and SFEC extraction is recommended for extraction of phytochemicals with biologically activity from red habanero chili pepper waste for diverse industries application.

In the dynamic landscape of 6G and smart cities, Visible Light Communication (VLC) assumes critical significance for IoT (Internet of Things) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC’s robustness in outdoor settings, demonstrating effective communication up to 10 meters. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing an a freely available open-source simulator combined to an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC’s potential to improve wireless communication in the evolving landscape of 6G and smart cities.

Micro nano bubbles (MNBs) can generate ·OH in situ, which provides a new idea for the safe and efficient removal of pollutants in water supply systems. However, the difficulty in obtaining stable MNBs causes the low efficiency of ·OH generation, which makes the inability to guarantee the removal efficiency of pollutants. This paper reviews the application research of MNBs technology in water security from three aspects: the generation process of MNBs in water, the generation rule of ·OH during MNBs collapse, and the control mechanism of MNBs on pollutants and biofilms. We found that MNBs generation methods are divided into chemical and mechanical (about 10 kinds) categories, and the instability of bubble size restricts the application of MNBs technology. The generation of ·OH by MNBs is affected by pH, gas source, bubble size, temperature, external stimulation. And pH and external stimulus have more influence on ·OH generation in situ than other factors. Adjusting pH to alkaline or acidic conditions and selecting ozone or oxygen as the gas source can promote the ·OH generation. MNBs’ collapse also releases a large amount of energy, which the temperature and pressure can reach 3000K and 5Gpa respectively, making it efficient to remove ≈90 % of pollutants (i.e., trichloroethylene, benzene, and chlorobenzene). The biofilm can also be removed by physical, chemical, and thermal effect. MNBs technology also has great application potential in drinking water, which can be applied to improve water quality, optimize household water purifiers, and enhance the taste of bottled water. Under the premise of safety, let people of different ages taste, finding that compared with ordinary drinking water, 85.7 % of people think MNBs water is softer, 73.3 % of people think MNBs water is sweeter. This further proves that MNBs water has a great prospect in the drinking water application. This review provides innovative theoretical support for solving the problem of drinking water safety.

Indole is a versatile pharmacophore widely distributed in bioactive natural products. This privileged scaffold has been found in a variety of molecules isolated from marine organisms such as algae and sponges. Among these, indole alkaloids represent one of the biggest, most promising family of compounds, having shown a wide range of pharmacological properties, including anti-inflammatory, antiviral and anticancer activities. The aim of this review is to show the current scenario of marine indole alkaloid derivatives, covering not only the most common chemical structures, but also new synthetic routes developed during the last years as well as promising therapeutic applications.

Implementing digital technologies, such augmented reality and 3D models, to various cultural sectors is an ongoing procedure, especially favorable after COVID-19 pandemic. Even though such technologies are familiar to museums, galleries and archaeological places all over the world, little research or practical cases have been developed when it comes to theaters and theatrical costumes. Proposed research article describes the methodology of both reference material collection and applications development, while an analysis of functions and aesthetic results are described, especially when it comes to 3D models. The paper’s aim to reveal that theatrical operations (as part of cultural sector) as well as structured parts (such as acts, performances and costumes) can be preserved and served to larger audiences via technological means, such as an augmented reality application.

The space shuttle, a revolutionary spacecraft that has played a significant role in human space exploration, was composed of various advanced materials that were carefully selected to meet the extreme demands of spaceflight. This review paper provides a comprehensive examination of the materials historically employed in the construction of space shuttles and explores the latest trends shaping the field. The evolution of space shuttle materials is traced from the inception of the space program to contemporary missions, highlighting key milestones, challenges, and breakthroughs. Emphasis is placed on the critical role that materials play in the overall performance, safety, and sustainability of space shuttles. The paper begins by elucidating the diverse requirements that materials must fulfill in the harsh and complex environment of space, encompassing extreme temperatures, radiation exposure, and mechanical stresses. A detailed analysis of the materials utilized in the fabrication of various shuttle components, such as thermal protection systems, structural elements, and propulsion systems, is presented. Special attention is given to the challenges posed by re-entry and the strategies employed to mitigate heat-related issues. Furthermore, the review explores recent innovations and emerging materials that are reshaping the landscape of space shuttle design. Advancements in nanotechnology, composite materials, and additive manufacturing are discussed in the context of their potential applications for enhancing shuttle performance and reducing mission costs. The paper also addresses the importance of sustainability in space exploration, exploring materials with lower environmental impact and improved recyclability. The review concludes with a forward-looking perspective on the future of materials, considering ongoing research, development, and the potential incorporation of cutting-edge technologies. Insights are provided into how the evolving landscape of materials science may influence the design and manufacturing processes of the next generation of space vehicles. This paper provides an overview of the materials used in the construction of the space shuttle, including their properties, applications, and challenges. The materials used in the space shuttle are critical in ensuring the safety, performance, and longevity of the spacecraft, and understanding their characteristics and performance in space is crucial for the advancement of aerospace engineering.

The ongoing expansion in volume, velocity and variety of geospatial data has stimulated the development of artificial intelligence applications and associated online cartographic visualizations. In this context, the cartographic communication through peer-reviewed media evolved to incorporate Findability, Accessibility, Interoperability and Re-usability (FAIR) principles. In this paper, we review the functionalities proposed by current international peer-reviewed media for accessing, exploring and re-using geospatial data, and situate them on a diagram conceived for peer-reviewed cartography in the artificial intelligence era. We then present an overview of the cartographic infrastructure and workflow currently available in the Terra Digitalis journal for the online publication of massive spatiotemporal data. This free and opensource based solution allows a smooth integration, from data submission to final peer reviewed publication, of dynamic, interactive 2D and 3D maps. As a proof of concept, we present two maps derived from massive spatiotemporal data, related, respectively, to a climate numerical model (raster data) and a bike sharing system’s origin-destination dataset (vector data). We argue that the combination of technologies used in the journal provides an efficient and low-cost solution, unmatched by other publication media, to boost the accessibility and FAIR principles for geospatial big data.

The paper concerns with the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. In order to identify the relevant technologies and functionalities, both a descriptive and statistical literature research was carried out. As a result, both the currently most widespread technologies and eleven IT functionalities relevant to mechanical engineers were identified and described in terms of their application and implementation. In addition, a knowledge gap in the area of industry transfer in the field of Industry 4.0 / Digitalization was identified

The exceptional performance of graphene has driven the advancement of its preparation technique and application. Laser-induced graphene (LIG), as a novel graphene preparation technique, has been applied in various fields. Graphene periodic structures created by LIG technique exhibit superhydrophobic characteristics and can be used for deicing and anti-icing applications, which are significantly influenced by the laser parameters. The laser surface treatment process was simulated by a finite element software analysis (COMSOL Multiphysics) to optimize the scanning parameter range, and the linear array surface structure were subsequently fabricated by the LIG technique. The generation of graphene was confirmed by Raman spectroscopy, and energy-dispersive X-ray spectroscopy. The periodic linear array structure were observed by Scanning electron microscopy (SEM) and confocal laser imaging (CLSM). In addition, CLSM testings, contact angle measurements, and delayed icing experiments were systematically performed to investigate the effect of scanning speed on surface hydrophobicity. The results show that the high quality and uniform graphene can be achieved using the laser scanning speed of 125 mm/s. The periodic linear array structures can obviously increase the contact angle and suppress delayed icing. Furthermore, these structures have the enhanced ability of the electric heating deicing, which can reach 100°C and 240°C within 15 s and within 60 s under the DC voltage power supply ranging from 3 to 7 V, respectively. These results indicate that the LIG technique can be developed to provide an efficient, economical, and convenient approach for preparing graphene and the hydrophobic surface array structure based on LIG has considerable potential for deicing and anti-icing applications.

Nanotechnology is concerned with the creation of particles and materials at nanoscale levels. Nanocomposite is a combination or matrix, in which different materials combine to develop new properties of the materials ensuring that one of the materials have size in range of 1-100 nm. Nanocomposites are classified according to the types of reinforcement materials and matrix materials used in their construction. Nanocomposites are divided into three types based on the matrix material: ceramic matrix nanocomposites (CMNC), polymer matrix nanocomposites (PMNC), and metal matrix nanocomposites (MMNC) (MMNC). The synthetic methods of nanocomposites are frequently classified in to two classes. These are top-down and bottom up approaches. The type of synthetic technique used is determined by the nanocomposites' intended properties. There are different wet methods for synthesis of metal nanoparticles and nanocomposites among these co-precipitations, sol gel and hydrothermal methods are cost effective as compared to other methods. The synthesized nanocomposites can be characterized using different techniques to get insight into the morphology, particle size, phase, composition, thermal stability, optical, magnetic, electrical and thermal properties. Also, nanocomposites are used in medical, pharmaceutical industry, food packaging, to electronics and energy industry. Thus this review focuses on the main ideas about synthesis, characterization and application of nanocomposites.

To investigate the effect of nitrogen (N) application on carbon metabolism and yield of flag leaves and grains of spring wheat under drip irrigation in Xinjiang, a split - plot design was adopted from 2020 to 2021, with strong gluten wheat Xinchun 37 (XC37) and medium gluten wheat Xinchun 6 (XC 6) as the main plot factor and different nitrogen application rates as subplot factor. Four nitrogen application rates of 0, 210, 255 and 300 kg·hm-2 (CK2, B1, A1 and CK1, respectively) were set to analyze and compare the nitrogen response of key enzyme activity, soluble sugar, sucrose and starch content in flag leaves and grains to control yield formation. The results showed that with the increase of nitrogen application, Sucrose phosphate synthetase (SPS) and sucrose synthetase (SS) in flag leaves, SS in seeds, activities of adenosine diphosphate pyrophosphorylase (ADPG - PPase), soluble starch synthase (SSS), bound starch synthase (GBSS) and starch branching enzyme (SBE) in seeds, contents of soluble sugar and sucrose and yield in flag leaves increased first increasing and then decreasing. The A1 treatment (255 kg·hm-2) showed significant differences compared to other treatments, with increases of 7.95–158.33%, 8.69–155.12%, 7.65–53.25%, 5.41–62.83%, 2.85–85.52%, 3.08–57.30%, 8.87–79.16%, 9.33–196.55%, 9.43–112.78%, and higher levels of amylose, amylopectin, and total starch content than other treatments by 1.86–30.38%, 10.58–83.69%, and 7.96–63.09%, respectively. Correlation and stepwise regression analysis indicate highly significant positive correlation between yield and solube sugar and sucrose of flag leaves and grains, as well as their key emzymes and starch. Among them, soluble sugar in grains, amylopectin, and sucrose in grains have the greatest impact on the yield of XC37, determining 84.5% of its yield. SSS, soluble sugars in grains, amylopectin, and SBE have the greatest impact on the yield of XC 6, determining 80.4% of its yield. The starch shows a highly significant positive correlation with ADPG - PPase, SSS, GBSS, and SBE. There is a significant interaction effect between nitrogen application rate and variety, with better performance observed in Xinchun 37 compared to Xinchun 6. Under drip irrigation conditions in arid areas, nitrogen application of 255 kg·hm-2 can effectively regulate the metabolism of sucrose to starch in flag leaves and grains of spring wheat, which is conducive to the accumulation of starch in grains and the formation of yield.

I propose a new strategy to suppress human cancer completely with two entirely new drug compounds exploiting cancer's Warburg effect characterized by a defective mitochondrial aerobic respiration, substituted for by cytosolic aerobic fermentation/glycolysis of D-(+)-glucose into L-(+)-lactic acid. The two essentially new drugs compound 1 [P(op)T(est)162] and compound 3 (PT167) represent new highly symmetric, four-bladed propeller-shaped polyammonium cations. The in vitro antineoplastic highly efficacious drug compound 3 represents a covalent combination of compound 1 and compound 2 (PT166). The intermediate drug compound 2 is an entirely new colchic(in)oid derivative synthesized from colchicine. Compound 2's structure was determined by X-ray crystallography. Compound 1 and compound 3 were active in vitro versus 60 human cancer cell lines of the National Cancer Institute (NCI) Developmental Therapeutics Program (DTP) 60-cancer cell testing. Compound 1 and compound 3 both not only stop the growth of cancer cells to ±0% (cancerostatic effect), but completely kill nearly all 60 cancer cells to a level of almost −100% (tumoricidal effect). Compound 1 and compound 3 induce mitochondrial apoptosis (under cytochrome c release) in all cancer cells tested by (re)activating (in most cancers impaired) p53 function which results in a decrease of cancer's dysregulated cyclin D1 and an induction of the cell cycle-halting cyclin-dependent kinase inhibitor p21^Waf1/p21^Cip1.

The utilization of solar energy technology is crucial for generating hydrogen in sustainable energy systems. While traditional methods involving natural gas, coal, and oil have been employed for hydrogen production, this study aims to explore environmentally friendly techniques. Green hy-drogen, despite being a more expensive option compared to fossil fuel-derived hydrogen, has prompted engineers to develop affordable and low-carbon technologies. The microgrid model was developed to showcase a self-contained DC islanded system designed to supply power to an electrolyzer, capitalizing on both a solar array and an energy storage unit. This comprehensive model offers a versatile platform for evaluating the operational dynamics involved in the sustainable production of green hydrogen. Over a span of seven days, it permits the as-sessment of hydrogen generation characteristics through the utilization of solar energy, either di-rectly from a solar array or through an integrated system that incorporates energy storage. The model encompasses a multi-domain framework, encompassing electrical, thermal liquid, and thermal gas aspects, facilitating a holistic exploration of the intricate processes underlying the green hydrogen production system. Through this simulation, the feasibility and efficiency of harnessing renewable energy sources for hydrogen production in an off-grid setting can be comprehensively examined and optimized, contributing to the advancement of sustainable energy solutions.

Rapid haemostasis during surgery is essential when one wants to reduce the duration of opera-tions, reduce the need for transfusions, and above all when one wants to achieve better patient management. The use of haemostatic agents, sealants and adhesives improves the haemostatic process by offering several advantages, especially in vascular surgery. These agents vary widely in their mechanism of action, composition, ease of application, adhesion to wet or dry tissue, im-munogenicity and cost. The most used are cyanoacrylate-based glues (Glubran 2) or polysaccharide hydrogel-microsphere powder (AristaTMAH). This work is based on a retrospective study carried out on a sample of patients with different vascular diseases (FAV, pseudoaneurysm and PICC application) in which two different haemostatic sealants were used. The aim was to assess the safety, the advantages and the ability of both sealants to activate the haemostatic process at the affected site, also in relation to their chemical-physical characteristics. The obtained results showed that the application of Glubran 2 and AristaTMAH as surgical wound closure systems is effective and safe as the success achieved was ≥94% on anastomoses of FAV; 100% on stabilization of Picc catheters; ≤95% on pseudoaneurysms.

There are laws and rights aimed at people with disabilities, especially those with Mild Intellectual Disability (LDD), who actively participate in society but face barriers in learning and participating in various activities. In Colombia, where 1.3 million people with disabilities are registered, 16.8% correspond to the category of DIL, some of whom are part of the economically active population. Despite this, significant employment gaps persist.In order to address this gap, this research focuses on the analysis of the specific problems related to the labor inclusion of people with IDL in logistics environments, particularly in cold rooms. The aim is to prevent occupational hazards through the application of Industry 4.0 technologies.Through the collaboration of the Analytics and Design Research Seedbed, with entrepreneurs and experts in the field, significant challenges in labor inclusion in low-temperature environments were identified. The agile Scrum methodology was used to develop a prototype of an application that addresses these issues. This prototype meets the functional and non-functional requirements necessary to ensure the protection and prevention of risks for workers with DIL who perform functions in refrigerated rooms.This prototype manages to provide an inclusive alternative for people with disability Intellectual Light, challenging business cultural paradigms, by integrating mobile devices with Industry 4.0 technologies, encouraging greater interaction between machines and human beings, thus prioritizing the health and safety of employees in their daily activities.

Plant growth and productivity are hindered by abiotic stresses. Crop productivity and the ecosystem are suffering due to climate change and harmful farming practices. We urgently need to use environment-friendly techniques like Arbuscular Mychorriza Fungi (AMF) to improve crop productivity. It is a well-established fact that AMF are effective bio-fertilizers. It is believed that AMF injections can increase plant resilience to stressors like heat,metals, salt, drought, and extreme temperatures. AMF aids in enhancing tolerance mechanisms and safeguarding essential metabolic pathways. AMF, as natural root symbionts, give plants important inorganic nutrients, helping them grow better, produce more, and acclimatize even under stress. AMF acts as a bio-fertilizer, boosting plants’ ability to adjust to different environments. Thus, researchers need to conduct further studies on how AMF promotes crop quality and productivity. This review describes the current understanding of AMF’s impact on host plants at their different growth stage, their benefits, applications, and the significance of plant nutrient relationships with AMF.

Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.

The impact of long-term soil and crop management practices on soil biological and chemical properties in oilseed brassica-based production systems were deliberated for holistic soil health improvement in the present study. Different tillage, crop residue re-cycling and cropping systems were studied for 5-years in 0-15 and 15-30 cm soil profiles. No-till permanent beds with crop residue (PB+R) noticeably improved soil organic carbon (SOC), microbial biomass carbon (MBC), enzymes [dehydrogenase (DHA) and alkaline phosphatase (AlP)], nitrogen fractions (available, nitrate and total), and available phosphorus and potassium content in both the soil layers compared to conventional tillage without crop residues. Though, the plow soil layer (0-15 cm) showed higher concentration of soil carbon, enzymes, N fractions and available P than in the sub soil (15-30 cm). The dynamic soil biological and chemical properties were also varied with the crop stage, and recorded higher MBC at 30 days, SOC and enzymatic activities at 60 days, and N fractions and available P and K at harvest of mustard crop. Green gram-mustard rotation showed higher values of biological and chemical parameters. Thus, the legume-based mustard crop rotation following no-till permanent beds and residue re-cycling found holistic in improving soil health, and nutrient cycling.

Computational Fluid Dynamics (CFD) is revolutionizing the oil and gas industry by offering powerful insights and innovative solutions. This paper explores the cutting-edge applications of CFD in enhancing and innovating within the sector. CFD simulations provide engineers with invaluable insights into fluid behavior, enabling optimized designs and informed decision-making. The software facilitates foresight by predicting system performance, optimizing drilling techniques, and maximizing reservoir management strategies. Moreover, CFD optimizes equipment design, increases operational efficiency, and reduces costs through virtual testing, eliminating the need for costly physical prototypes. The application of CFD aligns with the United Nations Sustainable Development Goals (SDGs), promoting energy efficiency, climate action, responsible production, and sustainable consumption. It addresses flow assurance challenges, enhances safety protocols, and mitigates environmental impacts. CFD is a powerful tool driving innovation, resilience, and sustainability within the oil and gas industry, ensuring a more efficient, safe, and environmentally conscious future. As advancements in CFD methodologies continue, the potential for further enhancements and breakthroughs within the sector becomes even more promising, driving the industry towards a sustainable and prosperous future.

In the present study, the effect of melatonin and sodium nitroprusside treatments on the improvement of high temperature tolerance in California Wonder green bell pepper plant was studied. For this purpose, a factorial experiment was conducted in form of a completely randomized design in three replications at Zabol University's Faculty of Agriculture. In this research, green bell pepper plants of Wonder cultivar were exposed to different temperature treatments (25°C, 35°C and 40°C) for 24 hours after being sprayed (foliar application) with 0μM, 50μM and 100μM concentrations of melatonin and Sodium nitroprusside. Results showed that Instantaneous leaf water consumption efficiency in 40°C temperature treatment with 50μM and 100μM melatonin increased. Also in 40°C temperature with 100µM melatonin, fruit dry weight in compared to control (0uM) 13.15% increased. Also, melatonin and sodium nitroprusside treatments in compared to control treatment improved fruit characteristics and reduced Appearance Characteristics of Fruit Marketability (ACFM) number. Under 40°C temperature treatment in compared to control temperature (25°C), fruit total soluble solids (TSS) 18.50% decreased also under 100μM melatonin treatment, fruit TSS 20.69% was increased compared to control treatment (0μM). Also fruit fructose at 40°C temperature in compared to 25°C temperature treatment 5.52% reduced. Fruit calcium at 35°C and 40°C temperature in compared to control temperature 6.96% and 15.65% decreased respectively. At temperature 35°C under foliar application of 100uM sodium nitroprusside, fruit copper in compared to control 28.93% increased.

The cycling of soil phosphorus (P) is inherently linked with soil organic carbon-iron (C-Fe) cycling, yet empirical integration of these processes within paddy soils remains scarce. In this study, we conducted a microcosm experiment using paddy soils subjected to six distinct fertilization regimes involving varying P inputs for five years. In addition to evaluating P activation under reflooding conditions, we assessed the Fe reduction process and characterized the properties of dissolved organic matter (DOM) at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), alongside profiling the composition of soil microbial communities with high-throughput sequencing. Our findings revealed that after 25 days of reflooding, soil Olsen-P content increased by an average of 73% compared to its initial state, showing a strong correlation with the Fe reduction process. Specifically, treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation, highlighting the role of organic matter in facilitating Fe reduction. Investigations on the relative abundance of typical iron-reducing microbes further supported their importance in P activation, but the rate of iron reduction is limited by soil organic matter content. Delving deeper into DOM properties, soil DOM composition profiling and network analysis suggested that high-molecular-weight DOM, particularly lignins, served as the primary resources driving Fe reduction by iron-reducing microbes, consequently promoting Fe reduction and P release. Taken together, our study assembled the C-Fe-P cycling dynamics in paddy soils, emphasizing the pivotal role of microbial-driven Fe reduction facilitated by soil DOM in P availability and subsequently sustainable agricultural practices.

The aim of this study was to study the effect of leisure identity, flow, satisfaction, and re-participate intention among outdoor leisure activity participants in South Korea. Due to the recent COVID-19 pandemic, there has been a tendency to more frequently participate in outdoor leisure activities as opposed to indoor leisure activities. Leisure activities bestow various benefits. Therefore, this study was used to analyze the relationships amongst leisure identity, flow, leisure satisfaction, and re-participate intention among various theories about leisure. The participants in this study were collected from amongst 369 people who were frequent participants in outdoor leisure activities. For the data analysis, the researchers used frequency, confirmatory analysis, reliability, correlation and structural equaling modeling. The findings were as follows: First, leisure identity had wielded an influence on leisure flow. Second, leisure identity had influence leisure satisfaction. did not have any significant influence on recreation specialization. Third, leisure identity had influence on re-participate intention. Fourth, leisure flow did not have any significant influence on leisure satisfaction. Fifth, leisure flow did not have any significant influence on leisure satisfaction. And finally, leisure satisfaction had an influence on re-participate intention. In the era of the COVID-19 pandemic, a new leisure identity has been formed, and it is believed to be a new study on leisure flow, leisure satisfaction, and re-participation intention. This study aims to provide basic data for constructing infrastructure to enable continued participation in outdoor sports in Korea.

In this paper, a buck DC-DC converter with the proposed twin frequency control scheme (TFCS) and accurate load current sensing (ALCS) have been designed and implemented with 0.18µm CMOS technology for a supply voltage ranging from 2.0-3.0V which is compatible with state-of-the-art batteries (NiCd/NiMH: 1.1-2V, Li-Ion: 2.5-4.2V). The proposed converter yields a peak efficiency of about 92.7% with a load current of 30mA. Furthermore, it only occupies a silicon area of 1.3mm2. The proposed buck converter is dedicated for smartphone applications whereby it spends most of its time in idle, low load conditions.

In this research, the concept of the instability of tension in Microgrid still becomes the focus of study and purpose. Scrutinously, First-Swing Instability occurs in the system in noted changes with properties of making the system unstable or restore to a state of stability. The latter calls itself as a phenomenon of re-synchronization. Allied to aspects of system instability, it is evident that by connecting fast electric chargers for battery-electric vehicles to the distribution system from microgrids, they increase the necessary basic electrical charges and reduce the stability of the electrical energy system. Transient electrical currents have the characteristic of severely impacting microgrids not prepared in connected states or island of the main system. The occurrences of the electromagnetic incompatibility of the inrush current, due to their unpredictability and intermittent characteristic, have complex identification studies. With the elevation of electricity demand by battery-electric vehicle chargers, concerns about the effects of the integration of the electricity grid increase proportionally. The research described in this article emphasizes the influence of fast chargers’ initialization on the power variation related to the re-synchronization phenomenon and its mitigation of the stability of electricity microgrid. An optimization algorithm based on particle swarm optimization (PSO) implements the development of virtual impedances. The PSO optimization algorithm analyzes all possible operating points and aims to maximize the stability index of microgrid, maintaining reactive energy incompatibilities at the minimum level supported. The fractional objective function facilitates the service to these goals simultaneously. The optimization algorithm is implemented in a case study and the corresponding virtual impedance is incorporated into the microgrid. Voltage levels are verified as a condition in the optimization process, comparing system effectiveness.

The paper covers the development of formulations and technology for low energy building materials based on the alkaline alumino-silicate binder and appropriate for special purposes. The microstructure of the binder was investigated using scanning electron microscopy and atomic force microscopy. The identification of the phase compositions was done by means of X-ray diffraction. By leach rate measurement and chemical analysis of pore liquid, the linkage of the alkali metal ions in water-insoluble composites was researched. The dependence of structure formation on mix design and curing conditions was investigated. Specific examples of manufacture and application of binder-based glues and adhesives have been considered, incl. ones for low-temperature insulating composite materials, heat insulation perlite-based materials, heat insulation of industrial boilers, and the fire resistant heat insulating system. The advantages of the binder-based temperature insulating composites compared to traditionally used ones are shown.

Optical wireless communication is a promising emerging technology that addresses the limitations of radio-frequency-based wireless technologies. This study presents a novel hybrid modulation for optical camera communication (OCC); it integrates two waveforms transmitted from a single transmitter light-emitting diode (LED) and received by two rolling-shutter camera devices. Then, a smart camera with a high-resolution image sensor captures the high-frequency signal, and a low-resolution image sensor from a smartphone camera captures the low-frequency signal. Through this hybrid scheme, two data streams are transmitted from a single LED lamp; this reduces the cost of the indoor OCC device compared to one that transmits two signals from two different LEDs. In proposed scheme, rolling-shutter orthogonal frequency-division multiplexing is used for the high-frequency signals, and M-ary frequency-shift keying is used for the low-frequency signals in the time domain. This proposed scheme is compatible with smart phone camera and USB camera. By controlling the OCC parameters, the hybrid scheme can be implemented with high performance for a communication distance of 10 m.

Proxy re-encryption (PRE) is a momentous and widely used cryptographic technique. It enables a proxy to forward ciphertext without the need of decryption. PRE has received significant interest in applications like cloud computing, blockchain, and the Internet of Things. Despite its wide range of uses, PRE has also been subject to new security and privacy regulations. In PKC’19, Cohen $et~al.$~first drew attention to the weakness in PRE's security against chosen-plaintext attacks (CPA) and put up a more stringent security concept known as security against honest re-encryption attacks (HRA). Notably, Cohen provides a beneficial conclusion as well. It is proveded that PRE schemes with re-encryption simulatability property can be elevated from CPA to HRA security. It is also proved that CPA-secure PRE schemes with re-encryption simulatability property can be directly elevated to those satisfying HRA security. However, those PRE schemes with re-encryption simulatability are almost always based on pairings. In this study, to the best of our knowledge, we directly construct HRA-secure PRE with re-encryption simulatability for the first time based on the learning with errors (LWE) assumption, which was widely believed to be quantum-resistant. Based on the re-encryption key generation algorithm and the re-encryption algorithm construction method of the above scheme, we can modify attribute-based conditional proxy re-encryption (AB-CPRE) as well as the corresponding attribute-based proxy re-encryption (AB-PRE) algorithm to make them have re-encryption simulatability properties. Finally, by using this property, We boost the security of AB-CPRE scheme of ESORICS'21 from CPA to HRA and simplify the HRA-security proof for the AB-PRE of ESORICS'21.

Astronauts in space are subject to a continuous bombardment of ionizing radiation. The Earth's magnetic field and the ISS shield some biologically damaging particles traveling through. Still, travel beyond low-Earth orbit and extravehicular activities are exceedingly more dangerous and there is a concern for the acute and late-occurring adverse health effects befalling astronauts. So, it is vital to consider the current tools and models used to describe and study the organic consequences of ionizing radiation exposure. It is equally important to see where these models may improve. This article reviews the historical development and current state of knowledge of radiation effects impacting astronauts in orbit. We explain the space radiation environment, cellular microenvironment, and how these may be incorporated in radiobiological models to aid in our understanding of the influence space travel may have on astronaut health. The topics discussed in this paper include a review of DNA damage and repair mechanisms and the numerical models that aim to explain the biological effects resulting from ionizing radiation damage. Historically, radiobiological models focused on how radiation damages nuclear DNA, built upon the hypotheses of Crowther and Lea in the 1940s and 1960s, and neglected other sub-cellular targets outside of nuclear DNA.

During a multi-detector computed tomography (MDCT) examination, it is crucial to efficiently organize, store, and transmit medical images in DICOM standard, which requires significant hardware resources and memory. Our project processed large amounts of DICOM images by classifying them based on cross-section views that may carry important information about a possible diagnosis. We ensured that images were retained and saved in PNG format to optimize hardware resources while preserving patient confidentiality. Furthermore, we have developed a graphical, user-friendly interface that allows physicians to visualize specific regions of interest in a patient's brain where changes may indicate disease. Our proposed method enables quick classification of medical images into predefined classes of confirmed diseases of brain parenchyma, contributing to swift decision-making for further diagnosis for more precisely evaluating and characterizing brain changes, and it can lead to the rapid application of adequate therapy, which may result in better outcomes.

In this study, an optimized multi-material system was designed and developed to print samples in various applications, including biomedical fields (e.g., mandibular bone loss). To improve the mechanical and biological properties of scaffolds utilized for dental bone loss applications, a multi-material setup was devised, which employs digital light processing technology. This setup consists of a linear system comprising two resin vats and one ultrasonic cleaning tank, enabling the integration of diverse materials and structures to optimize the composition of the scaffold. This approach was used to print multi-material PLLA scaffolds containing 20 wt.%. HA on the interior side, and PLLA containing 1 wt.% GO on the exterior surface of the scaffold, which were evaluated mechanically and biologically after printing. The scaffold was designed using a triply periodic minimal surface (TPMS) lattice structure, which is known to possess favorable mechanical and biological properties. Various multi-material samples were successfully printed and evaluated to illustrate the multiple-material setup's potential for ensuring proper function, cleaning, and adequate interface bonding. By numerically evaluating several TMPS structures, a novel Gyroid TPMS scaffold with a nominal porosity of 50% was developed and validated experimentally. The biological properties of the scaffolds were also evaluated, including surface morphology, (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) MTT assay, and cell adhesion. Based on the results, multi-material components with the least contaminations with suitable mechanical and biological properties were successfully printed. By combining PLLA-HA and PLLA-GO, this innovative technique holds tremendous potential for enhancing the effectiveness of regenerative procedures in the field of dentistry.

X-linked myotubular myopathy (XLMTM) is a rare congenital myopathy resulting from dysfunction of the protein myotubularin encoded by the MTM1 gene. XLMTM has a high neonatal and infantile mortality rate due to a severe myopathic phenotype and respiratory failure. However, in a minority of XLMTM cases, patients present with milder phenotypes and achieve ambulation and adulthood. Regarding wide phenotypic variability, we investigated the genotype-phenotype correlations in newly diagnosed XLMTM patients in a patients' cohort (previously published data plus three novel variants, n=414). We found a significant association between severe phenotype and truncating variants (p < 0.001), frameshift variants (p < 0.001), nonsense variants (p = 0.006), and in/del variants (p = 0.036). On the contrary, missense variants were significantly associated with the mild and moderate phenotype (p < 0.001). We found no significant association between phenotype and the MTM1-specific functional domain, contrasting with previously published data. Based on the significant facial gestalt difference between XLMTM patients and unaffected controls (p = 0.001), we consider the Face2Gene application as an effective diagnostic tool in XLMTM when using the correct algorithm. The Face2Gene application seems to be a practical, non-invasive diagnostic approach in XLMTM.

This study assessed the impact of mineral fertilization on tuber yield and food quality of yam genotypes, specifically Dioscorea alata and Dioscorea rotundata. Four genotypes, each from D. alata and D. rotundata, were used in field experiments conducted in Ibadan, Nigeria, in the 2017 and 2018 cropping seasons. Experiments were conducted under low soil NPK fertility conditions with and without fertiliser treatments. A recommended NPK rate for the area (90, 50, and 75 Kg N, P, K ha-1) was applied to plots with fertiliser treatment. Tuber yield, functional and pasting properties of yam flour, and sensory attributes of pounded yams were collected. The results showed differential responses of the genotypes to tuber yield and food quality with fertiliser application. Fertiliser application has generally increased fresh tuber yield, but it has affected some genotypes' flour quality and textural attributes of the pounded yams. As regards genotypes Danacha and TDa0200012, fertiliser application has increased tuber yield without causing a decrease in tuber quality hence, suggesting Danacha and TDa0200012 as suitable genotypes for maximizing fertiliser productivity. However, in another genotype Ojuiyawo, fertiliser application effectively increased tuber yield but deteriorated tuber quality. Our results suggested that it is crucial to determine the impact of fertilisation on tuber yield and quality for each genotype and select the appropriate genotype to improve productivity through mineral fertilisation. The results of this study can be widely applied to enhance yam productivity in sub-Saharan Africa and useful for sustainable agriculture in yam-growing areas.

(1) Background: Prevention of acute cardiovascular events in patients with cardiovascular diseases (CVDs) requires promoting health-protective behaviors (e.g., physical activity) and preventing health-compromising behaviors (sitting). Digital health interventions provide much potential. Based on multiple behavioral change theory, an intervention (reCardial app) was evaluated, and the following hypotheses were tested: H1: Health behaviors (physical activity, sitting) and disease self-management (self-care maintenance, self-care confidence) are closely interrelated. H2: Change in health behaviors and disease self-management indicators over time is more pronounced in reCardial app-users. H3: In the intervention group, changes in systolic and diastolic blood pressure indicate a positive trajectory. (2) Methods: A study with the design of a randomized controlled trial over 12 weeks was conducted. 40 CVD-patients were randomized equally to the Intervention Group (35% women; Mage=60.6years) and Control Group (45% women; Mage=61.5years). (3) Results: Findings support H1 with significant correlations between health behaviors (r=0.45-0.63**), and disease self-management (r=-0.32-0.54**), H2 with Eta²=0.21 (not statistically significant) and H3 partially with d=0.101 for systolic blood pressure but not regarding diastolic blood pressure. (4) Conclusions: Replications are needed with larger samples and more objective measures. The app can help prevent and manage CVD by means of promoting health-protective behaviors and preventing health-compromising behaviors. Taking different behaviors into account can increase intervention effects and with that support of users.

Keywords: Fatigue crack propagation; Welding residual stress; Plastic re-distribution; Stress ratio effect

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

Amyotrophic Lateral Sclerosis (ALS) stands as a debilitating neurodegenerative condition characterized by the progressive degeneration of motor neurons. Despite extensive research in various model animals, the cellular signal mechanisms of ALS remain elusive, impeding the development of efficacious treatments. Among these models, a well-characterized and diminutive organism, Caenorhabditis elegans (C. elegans), has arisen as a potent tool for investigating the molecular and cellular dimensions of ALS pathogenesis. This review summarizes the contributions of C. elegans models to our comprehension of ALS, emphasizing pivotal findings pertaining to genetics, protein aggregation, cellular pathways, and potential therapeutic strategies. We analyze both the merits and constraints of C. elegans system in the realm of ALS research, and point towards future investigations that could bridge the chasm between C. elegans foundational discoveries and clinical applications.

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.

Intracranial compliance (ICC) plays a pivotal role in understanding the underlying mechanisms of various brain disorders and is of great clinical importance. Therefore, addressing the challenges in practical application of ICC is crucial for neurosurgeons. This study explored the significance of ICC assessment considering the time-dependency of specific brain disorders through two distinct approaches: short and large time elapsed (TE) in measuring volume or intracranial pressure (ICP) changes in the ICC equation (∆Volume/∆ICP). Variations in ICC values were observed across various ICC assessment methods and different TE values. Notably, the compensatory response of the brain exhibited non-monotonic and variable changes in a large TE for certain brain disorders, diverging from patterns observed in short TE assessments. Furthermore, the recovery behavior of the brain changed under different brain disorders when exposed to short and long TE conditions. These findings emphasized the dynamic nature of ICC and provided valuable insights into the correct practical assessment of ICC by selecting the appropriate TE, as well as considering the differences in strain rates and loading durations on the brain in different brain disorders. These insights also shed light on the reasons why, despite its clinical significance, ICC monitoring has not yet become a standard component of clinical care, unlike ICP monitoring.

Gold nanoparticles (AuNPs) have been used in a broad range of applications conferring to bio-molecules diverse proprieties such as deliver, stabilization and reduction of adverse effects of drugs or plant ex-tracts. Polyphenolic compounds from Bacopa procumbens (BP) are able to modulate proliferation, adhesion, migration and cell differentiation, reducing artificial scratch area in fibroblast cultures and promoting wound healing in in vivo model. Here, chemically synthetized AuNPs conjugated with BP (AuNP-BP) were characterized by Uv-Vis, ATR-FTIR, DLS and zeta-potential. Results showed over-lapping FTIR spectra of polyphenolic compounds from B. procumbens capping the AuNPs, stable size by the DLS and zeta potential, AuNP-BP was 44.58 nm, the zeta-potential of AuNPs was -36.3±12.3 mV after conjugation with the BP; - (AuNP-BP), the zeta-potential was reduced to -18-2±7.02 mV. Enhancement of wound healing effect were evaluated by morphometric, histochemical and FTIR changes in rat wound excision model. Results showed that the nanoconjugation process reduced 100 folds BP concentrations to have the same wound healing effect of BP alone. Besides histological and FTIR spec-troscopy analyses, demonstrated that AuNP-BP treatment displayed better macroscopical performance; the reduction on inflammatory cells, and the increased synthesis and better organization of collagen fibers.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, partic-ularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Genera-tive Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer, by incorporating segmentations of spinal vertebrae in CT scans for the region of interest focussing. The approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of Mean Squared Error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n=440 CT data were processed. We evaluated the performance of our model using multiple metrics, in-cluding Mean Absolute Error (MAE) (for each slice of the 3D volume [MAE0] and for the entire 3D volume [MAE]), Cosine Similarity, Peak Signal-to-Noise Ratio (PSNR), 3D Peak Sig-nal-to-Noise Ratio (PSNR-3D), and Structural Similarity Index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of the approach in reconstruct-ing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

After reviewing the history of the struggle between human beings and plant diseases, insects and weeds, especially reviewing the thoughts of plant protection in ancient Chinese agricultural books, the target recognition of plant pests and the six types of plant protection methods and 36 subdi-vision measures were summarized. Then the development overview of pesticide application technology was focused and reviewed systematically by combining the development timeline of pesticide application and the key technologies including the performance measurement and the simulation and modeling of pesticide spraying system. And three suggestions for further research were proposed from the perspectives of human-being and environmental health, the sustainable and eco-friendly application medium, and the efficiently application equipment system in plant protection.

The information technology has brought in a revolution in the area of digital agriculture and hydrological modeling . With the advent of IOT and AI such as machine learning is now capable of Predicating flood forecast , drought forecast and farms based water predications. In this article various machine learning algorithms , multiple sensors for environmental and agricultural has been proposed and used . The purpose is to acquire data of soil moisture , temperature , crop stages, irrigation and precipitation on a region constitute of two farms and then performed machine learning predications for total discharge predications at farms outlets so that in case of excessive rainfall or an irrigation event the water is adjusted in the second nearby farm or reroute to a reservoir for future use to avoid flooding. The focus is mostly to work on the concept and building of an andriod -ardiuno based mobile application for the endusers (agricultural system analyst, farmers) to provide an ease. The whole system of smart agricultural based on two farms and reservoir will provide an efficient ,fully automatic, proactive and decision support system to save water waste and reuse. In future the work is also in progress for developing a desktop based application .

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

This study aimed to assess the diagnostic accuracy of a mobile application by comparing its diagnoses to those of Orofacial Pain and Oral Medicine specialists and further imaging results (TMJ CBCT and MRI) in 500 patients with temporomandibular disorder (TMD). The research focused on three diagnostic categories: initial specialist diagnoses, final diagnoses after imaging, and the mobile app's diagnoses. The concordance rate, sensitivity, specificity, and positive predictive value of the diagnoses were examined, with further imaging serving as the gold standard. The mobile app demonstrated a high concordance rate compared to both final (0.93) and initial specialist diagnoses (0.86). Sensitivity, specificity, and positive predictive values also indicated strong reliability, affirming the app's diagnostic validity. Although the concordance rate was slightly lower when comparing the app's diagnoses to imaging results (CBCT and MRI), specialist diagnoses yielded similar results. The study suggests that user-friendly diagnostic mobile applications, based on the Diagnostic Criteria for Temporomandibular Disorders, could enhance the clinical management of TMD. Given the reliability of mobile applications for diagnostic purposes, their wider implementation could facilitate the provision of appropriate and timely treatments for patients with TMD.

The utilization of dual-potential mode of integrated array (IDA) electrodes and other two-electrode systems has revolutionized electrochemical detection by enabling the simultaneous and independent detection of two species, accompanied by the exhibition of unique characteristics. In contrast to conventional dual-potential electrodes, the rotating ring disk electrodes (RRDE), IDA electrodes demonstrate analogous yet vastly improved performance metrics, characterized by remarkable collection efficiency and sensitivity. Notably, due to the distinctive microscale structure of IDA electrode, the special “feedback” effect makes IDA a unique signal amplifier. In recent decades, the research surrounding IDA electrodes has garnered escalating interest due to their attractive attributes. This review centers its focus on the fabrication and applications of IDA electrodes. In the realm of fabrication, two critical breakthroughs are poised for realization: the achievement of reduced dimensions and the diversification of materials. Established fabrication methods for IDA electrodes encompass photolithography, inkjet printing, and direct laser writing, each affording distinct advantages in terms of size and precision in IDA construction. Predominantly employed materials for IDA electrodes include gold, platinum, and carbon, with the selection of fabrication methods guided by considerations such as material properties, desired dimensions, cost-efficiency, and specific application requisites. Within the domain of applications, IDA electrodes, capitalizing on their distinctive attributes, have found utility in diverse fields. This review summarizes the applications of IDA electrodes based on their fundamental working principles, encompassing redox cycling, resistance modulation, capacitance variations, and more. The potential for further development of this specialized tool to exhibit enhanced properties holds substantial promise.

Based on the high-precision quantitative measurement ability, the S-band subsystem of China's high- resolution aviation system carried out the agricultural application test for the first time in Jilin elm, China. The experiment is mainly carried out in combination with vegetation growth, soil moisture content inversion, wheat straw returning and ground feature classification. Through the design of quantitative measurement scheme, the results of different straw coverage and observation at different times are compared, which shows the prospect of S-band full polarization synthetic aperture radar system in the field of agricultural application.

Sous-vide is a process in which food is vacuum-sealed and prepared in a water bath, heated to a precise temperature, and circulated in a sous-vide machine. This cooking technique is increasingly common in homes and catering establishments due to its simplicity and affordability. However, manufacturers' and chefs' recommendations for low-temperature and long-term sous-vide cooking in media raise food safety concerns, particularly when preparing beef tenderloin. In this study, Salmonella enterica was found to be inactivated by heat and sage essential oil (EO) in beef tenderloin from musculus psoas major that had undergone sous-vide processing. To determine whether heat treatment was likely to increase the sous-vide efficiency, S. enterica and sage EO were mixed. After being vacuum-packed and injected with S. enterica, the samples were cooked sous-vide for the prescribed time at 50, 55, 60, or 65 °C. On days 1, 3, and 6, the amounts of S. enterica, total bacteria, and coliform bacteria were measured in both groups of sous vide beef tenderloin. Mass spectrometry was used to identify bacterial strains on various days and categories. Each day that was measured, the test group exposed to a temperature of 50 °C for 5 minutes had a higher number of all microbiota. The most isolated microorganisms from the control and treated groups were Pseudomonas fragi, and in the treated group also S. enterica. It has been shown that adding sage essence oil (EO) in combination with the sous-vide gastronomic method leads to the stabilization and safety of beef tenderloin.

Tuberculosis (TB) continues to pose a global health challenge, exacerbated by the rise of drug-resistant strains. The development of new TB therapies is an arduous and time-consuming process. To expedite the discovery of effective treatments, computational structure-based drug re-purposing has emerged as a promising strategy. From this perspective, conditionally essential targets present a valuable opportunity, and the mycobactin biosynthesis pathway stands out as a prime example highlighting the intricate response of Mycobacterium tuberculosis (Mtb) to changes in iron availability. This study focuses on the re-purposing and revival of FDA-approved drugs (library) as potential inhibitors of MbtA, a crucial enzyme in mycobactin biosynthesis in Mtb conserved among all species of mycobacteria. Literature suggests this pathway to be associated with drug efflux pumps, which potentially contribute to drug resistance. This makes it a potential target for antitubercular drug discovery. Herein we utilized cheminformatics and structure-based drug repurposing approaches, viz., molecular docking, dynamics, and PCA analysis, to decode the intermolecular interactions and binding affinity of the FDA-reported molecules against MbtA. The virtual screening revealed ten molecules with significant binding affinities and interactions with MbtA. These drugs, originally designed for different therapeutic indications (4: antiviral, 3: anticancer, 1: CYP450 inhibitor, 1: ACE inhibitor, and 1: leukotriene antagonist), are repurposed as potential MbtA inhibitors. Furthermore, our study explores the binding modes and interactions between these drugs and MbtA, shedding light on the structural basis of their inhibitory potential. Principal component analysis highlighted significant motions in MbtA-bound ligands, emphasizing the stability of the top Protein-Ligand Complexes (PLCs). This computational approach provides a swift and cost-effective method for identifying new MbtA inhibitors, which can subsequently undergo validation through experimental assays. This streamlined process is facilitated by the fact that these compounds are already FDA-approved and have established safety and efficacy profiles. This study has the potential to lay the groundwork for addressing the urgent global health challenge at hand, specifically in the context of combating Antimicrobial Resistance (AMR) and Tuberculosis (TB).

Treatment of tendinopathies with multipotent mesenchymal stromal cells (MSCs) is a promising option in equine and human medicine. However, conclusive clinical evidence is lacking. The purpose of this study was to gain insight into clinical treatment efficacy and to identify suitable outcome measures for larger clinical studies. Fifteen horses with early naturally occurring tendon disease were assigned to intralesional treatment with allogeneic adipose-derived MSCs suspended in serum or with serum alone by block randomization (dosage adapted to lesion size). Clinicians and horse owners remained blinded to the treatment during 12 months (7 horses per group) and 18 months (7 MSC- and 5 control-group horses) of follow-up including clinical examinations and diagnostic imaging. Clinical inflammation-, lameness- and ultrasonography scores improved more over time in the MSC-group. The lameness score difference significantly improved in the MSC- compared to the control-group after 6 months. In the MSC-group, 5 out of 7 horses were free of re-injuries and back to training until 12 and 18 months. In the control-group, 3 out of 7 horses were free of re-injuries until 12 months. These results suggest that MSCs are effective for treatment of early phase tendon disease and provide a basis for a larger controlled study.

Prostate cancer (PCa) is a major cause of cancer-related mortality worldwide, with a rising incidence observed over the years. The androgen receptor (AR) signaling pathway plays a pivotal role in male development and maintaining masculine characteristics. Dysregulation of AR signaling in prostate cancer can lead to disease progression and resistance to standard therapies. Understanding the intricate regulation and function of AR in both healthy and diseased states is crucial for developing effective treatment strategies. This review comprehensively explores the role of androgen receptors in prostate cancer susceptibility, disease progression, and treatment response by analyzing recent literature. An extensive search of peer-reviewed publications in major databases, including PubMed, Scopus, and Web of Science, was conducted using specific keywords related to androgen receptor, prostate cancer, disease progression, and treatment resistance. Relevant conference abstracts and clinical trial reports were also included. The review presents an overview of the role of androgen receptors in prostate cancer initiation, progression, and treatment resistance. It highlights emerging biomarkers associated with AR signaling dysregulation and their potential utility for early detection and personalized treatment approaches. Additionally, recent advances in targeting the AR pathway for novel therapeutic strategies to improve patient outcomes and overcome treatment resistance in advanced prostate cancer are discussed. The findings contribute to a comprehensive understanding of the AR signaling pathway in prostate cancer and offer insights into its multifaceted role in disease development and treatment response. They may pave the way for innovative therapeutic interventions and precision medicine approaches based on specific AR signaling profiles, enhancing patient care and reducing the burden of this lethal disease.

Abstract: Like other plants, every PV power plant will one day reach the end of its service life. Calculations show that 20,400 tons of PV waste will be generated worldwide by 2030 and 60.2 million tons by 2050, not including the mass of the support structure. Such large amounts of waste pose a potential threat to the environment and people if not properly managed. The paper analysed the options for dealing with PV waste, namely reuse, recycling and landfilling. For recycling as the best option in terms of environmental protection and circular economy, an overview of recycling technologies and the percentage of achievable recycling for the materials contained in each PV system component is given. In addition, the current situation of legislation and recycling of PV modules in Europe was examined with special reference to the Balkan countries. There are a small number of factories for recycling PV modules in Europe, but none in the Balkan countries. The main reason for this is the small amount of PV waste in these countries and consequently the economic unprofitability. For this reason, PV modules (after dismantling the aluminum frame and cables) are mostly disposed of as non-hazardous waste in landfills in these countries. Finally, the main barriers to faster implementation of PV module reuse and recycling are listed, along with guidelines for their removal. The cost of recycling Si modules is about ten times higher than the cost of disposal. To change this ratio in favor of recycling, cheaper recycling methods need to be developed and taxes on landfill disposal need to be increased.

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.

The escalating environmental challenges that stem from urban residential waste in densely populated areas have become increasingly prominent in recent times. As a result, the waste management sector has experienced significant growth nationwide. However, due to the predominantly manual nature of these processes, their effectiveness falls short of meeting the current demand. Addressing these issues requires process enhancement. In this context, Industry 4.0, particularly the concept of Digital Twins, emerges as a potential avenue for refining processes. To tackle both current and future industry challenges, it is imperative to create a comprehensive methodology capable of generating effective solutions within the waste management sector. This paper provides an explanation of digital twins, outlines the methodological framework comprising an architectural structure and layered model, and details a series of sequential methodical stages for implementing digital twins based on the specific scope determined by the end-user. A noteworthy aspect is the transformative potential of digital twins in enhancing efficiency and precision within waste management practices. By providing a virtual real-time representation of the waste management system, digital twins empower simulations and experimentation to fine-tune processes. Furthermore, they facilitate informed decision-making by offering a detailed visualization of the complete system, simplifying the identification of challenges and opportunities for improvement. that this article derives from the presentation titled "Digital Twin application methodology for the improvement of production and service systems. Application to waste management processes" at the "Sustainable Smart Cities and Territories International Conference" held in the city of Manizales, Colombia, from 21st to 23rd June 2023.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Cattle slurry is an important nitrogen source for maize on dairy farms. Slurry injection is an effective measure to reduce ammonia emissions after field application, but with higher risk of nitrous oxide emission than surface application. This study compared soil mineral nitrogen dynamics and nitrous oxide emissions with two ways of application. First, traditional injection at 25 cm spacing between rows followed by ploughing (called “non-placed slurry”), and second, injection using a new so-called goosefoot slurry injector that placed the slurry in ploughed soil as a c. 30 cm broad band at 10 cm depth below maize crop rows with 75 cm spacing (named “placed slurry”). Furthermore, the effect of treating slurry with the nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) in Vizura® was tested with both application methods. The field experiment was conducted on a sandy loam soil in a temperate climate. Both nitrous oxide emissions, and the dynamics of soil mineral nitrogen, were monitored for eight weeks after slurry application and seeding of maize using static chambers. The level of nitrous oxide emissions was higher with non-placed compared to placed slurry, mainly due to higher emissions during the first four weeks. That might be due to higher rates of nitrification rate and in turn stimulation of denitrification process. In both placed and non-placed slurry treatments, Vizura® caused higher soil ammonium concentrations and lower nitrate concentrations, particularly from 3 to 8 weeks after slurry application. The final level of soil nitrate was similar with and without the nitrification inhibitor, but higher with placed compared to non-placed slurry. Adding Vizura® to non-placed slurry reduced nitrous oxide emissions by 70 % when compared to placed slurry. Surprisingly, there was a non-significant trend towards higher cumulative emissions from placed slurry with the nitrification inhibitor compared to untreated slurry, which were due to higher emissions in the last part of the monitoring period (5-7 weeks after slurry application). Possibly degradation of the nitrification inhibitor, and nitrification activity inside the slurry band as the soil dried, promoted nitrous oxide emissions by this time. In summary, placement of untreated slurry in a broad band under maize seeds reduced nitrous oxide emissions compared to non-placed slurry with more soil contact. A comparable reduction was achieved by adding a nitrification inhibitor to non-placed slurry. The pattern of nitrous oxide emissions from placed slurry treated with the inhibitor was complex and requires more investigation.

Integrating systems acquired from third parties and legacies has become of great necessity in companies. This is mainly due to the need to exchange information between entities, such as banks, suppliers, customers, allies, etc. Therefore, it is essential to guarantee the integrity of the data and keep the integration up to date with the changes that can occur. Furthermore, reducing the transaction risk and avoiding losing information is necessary. Implementing this complex task involving technological and business challenges requires appropriate architecture implementation. This article presents an architecture named Dynamic Canonical Data Model through agnostic messages. The contribution involves treating the low link in integrating software units, also called the integration of loosely coupled software units. In particular, it focuses on internal data and external data integration. The proposal reduces their integration, maintenance time, and cost, maximizing their scalability and promoting reuse. To illustrate their components, a case study undertaken at the Mexican Logistics Company Paquetexpress is presented. The company has evaluated different enterprise application integration systems over 26 years to achieve a standardized integration. The article describes an architecture designed and implemented for this delivery company in a real-world case. The implementation’s source code was registered in the National Registry of Copyrights of Mexico.

Demonstrating biosimilarity entails comprehensive analytical evaluations, clinical pharmacolo-gy profiling, and efficacy testing for at least one medical indication in patients. These require-ments are stipulated by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The costliest element—efficacy testing—can be waived if other compliance benchmarks are satisfied, including comparing functional pharmacodynamic (PD) biomarkers, even when they do not di-rectly correlate with clinical outcomes. Most biological drugs, such as monoclonal antibodies (mAbs), lack identifiable PD biomarkers. The FDA has employed various 'omics' technologies to identify potential PD biomarkers, including proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. Although these efforts provide a robust scientific basis for estab-lishing biosimilarity, they are neither practical nor necessarily superior to existing functional biomarkers, such as receptor binding and mode-of-action outcomes. As we report for the first time, these functional biomarkers can effectively serve as PD indicators for all FDA-licensed bio-logical drugs. We recommend that the FDA consider officially listing these functional biomarkers to expedite and reduce the cost of biosimilar development, thereby increasing the accessibility of biological drugs. PD surrogates, like the receptor binding and pharmacokinetic profiles, are more robust and offer a rational solution to finding PD markers to compare for establishing biosimi-larity.

This study focuses on the aerodynamic optimization of the re-entry capsule (REC) Orion Crew Exploration Vehicle (CEV) module, evaluating its performance using computational fluid dynamics (CFD). Three different models, namely Baseline, 2-Duct, and 4-Duct, are investigated under a wide range of flow speeds, from supersonic (Ma=1.6) to hypersonic (Ma=6) regimes. Thermofluids aspects, drag coefficient, surface heat flux, and the effectiveness of the modified geometry are assessed using aerodynamic and temperature-based models. The results show that the introduction of ducts in the 2-Duct and 4-Duct models leads to an average decrease in the drag coefficient of approximately 2.1% and 2.9%, respectively, compared to the baseline model, across all Mach numbers. The maximum reduction in drag coefficient is 3.55%, observed at Ma=1.4 using the 4-Duct capsule. However, it is found that the addition of extra ducts to the model increases the maximum heat transfer rate. Specifically, the heat flux is increased by up to 69% for the 4-Duct model at Ma=6, compared to the baseline model. On average, the heat flux is increased by approximately 28.6% for all conditions. The findings indicate that the introduction of ducts in the REC model improves its aerothermodynamic performance by reducing drag but also increases the heat transfer rate on the surface.

With the rapid increase in diabetes worldwide, the number of patients with diabetic kidney disease (DKD), a complication of diabetes mellitus, is also on the rise. DKD is a major cause of chronic kidney disease progressing to end-stage renal failure; however, current medical treatments for DKD do not achieve satisfactory outcomes. Molecular hydrogen (H2) is an antioxidant that selectively reduces hydroxyl radicals, a reactive oxygen species with a very potent oxidative capacity. H2 was recently shown to exert not only antioxidant, but also anti-inflammatory, cell lethality-regulating, and signal transduction-regulating effects, and is now being applied clinically. Many factors contribute to the onset and progression of DKD, with mitochondrial dysfunction, oxidative stress, and inflammation being strongly implicated. Recent preclinical and clinical trials reported that substances with antioxidant properties may slow the progression of DKD. Therefore, we conducted a literature review on animal models and human clinical trials in which H2 showed efficacy against various renal diseases. This literature review and our previous findings collectively suggest that H2 exerts therapeutic effects in patients with DKD by improving mitochondrial function. Future large-scale clinical studies are needed to confirm these findings.

The issue of suitability of industrial hemp (IH) for large-scale European pulp mills, referred to in this study as EKPM mills, was discussed, taking into account the causal, cultivation, technological, and application aspects of this issue. The work showed that there are premises for the use of non-wood fibrous raw materials (NFRM) in these mills. As for the IH, it was found that IH stalks are the best IH FRM for EKMP. There are few cultivation factors (CF) favouring the use of IH stalks in them and few, though important (e.g., small cultivation areas), factors not conducive to this use. Most of the technological factors (TF) favour the use of IH in EKPM, apart from the large differences in the length of the IH bast and woody-core fibres. The analysis of application factors (AF) indicates lower usefulness of IH than wheat/rye/triticale straws, Miscanthus giganteus, Virginia mallow, and kenaf due to the much greater availability of these cereal straws than IH and less variation in fibre length of cereal straw, Miscanthus giganteus, Virginia mallow, and kenaf than in IH stalks. The presence of IH variety/ies with fibre lengths more similar to wood would reduce the number of TF and AF factors unfavourable to their use in EKPM.

: Broad-spectrum antifungal drug deployed for topical therapy, specific for onychomycosis, Itraconazole was used as the active pharmaceutical ingredient in a nail lacquer formulation1. The objective of the study was to optimize an antifungal nail lacquer, which when applied topically onto the dorsal surface of the nail plate would facilitate the transungual penetration of the drug. Itraconazole hydrochloride was synthesized to aid the formulation of the lacquers. The optimization involved comparing four different formulations of 1%w/w Itraconazole nail lacquers containing combinations of penetration enhancers (Urea, Salicylic acid, and Papain) based on their ability to facilitate diffusion of the drug through the nail plate over five days. The drug uptake was quantified by extracting the drug from the nail clippings and analyzing it. The tests conducted to account for the quality of the optimized formulation were drug content, spreadability, check for precipitation, and accelerated stability. The results demonstrated that the optimized 1%w/w Itraconazole nail lacquer contained 5%w/w Salicylic acid, 5 %w/w Papain, and 2.8% w/w Urea. The drug uptake from nail clippings was higher than the MIC90 for T. Rubrum strain, and the quality of the optimized lacquer was not compromised throughout three weeks2. The hypothesis stated that the inclusion of Salicylic acid, Papain, and Urea in an Itraconazole (1%w/w) nail lacquer would have the highest transungual penetration on application to the nail plate. In conclusion, the hypothesis was accepted, and the optimized lacquer passed the drug uptake evaluation and all quality control tests.

Distributions with bounded support show considerable sparsity over those with unbounded support, despite the fact that there are a number of real-world contexts where observations take values from a bounded range (proportions, percentages, and fractions are typical examples). For proportion modelling, a flexible family of two-parameter distribution functions associated with the exponential distribution is proposed here. Mathematical and statistical properties of the novel distribution are examined, including quantiles, mode, moments, hazard rate function, and its characterization. The parameter estimation procedure using the maximum likelihood method was carried out, and applications to environmental and engineering data were also considered. To this end, various statistical tests are used, along with some other information criterion indicators to determine how well the model fits the data. The proposed model is found to be the most efficient plan in most cases for the datasets considered.

The epistemology of the traditional risk assessment examined has a taxonomy based upon deterministic, behaviouristic and compatibilist methodologies which are likely driven by neo-liberal market related requirements for bigger, faster cheaper wind-turbines. Analysis of available risk assessments suggested that there is no conscious effort to deceive the reader although the intended audience is unclear. The language, and scoring mechanisms utilized indicates the presence of conformity bias, confirmation bias, and numerical inconsistency leading to data ossification. By reverse engineering risk assessment content, Pragmatic and Social Psychological aspects can be investigated, and the adequacy of the document can be evaluated using evidence-based models.

Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as scanning tunneling microscopy (STM), nanotechnology, microstrip cavity resonance measurement, dielectric impedance measurement (DEPIM), etc. This article reviews the single cell isolation and sorting techniques, summarizes the common single cell operations such as impedance flow cytometry, cell electrical impedance, electrical impedance sensing, and summarizes the current single cell analysis techniques and their applications.

Indole-3-acetic acid (IAA) is a key vegetative hormone that plays crucial roles in plant growth and development. In this study, the effect of the exogenous application of IAA as a biostimulant in the cultivation of onions (Allium cepa) was investigated. Different IAA concentrations were evaluated to determine their effects on onion growth and chemical parameters. Various physical and chemical parameters such as total fresh weight, plant height, chlorophyll content, nitrates, total phenols, and antioxidant capacity (DPPH) were analyzed. The results revealed that exogenous application of IAA had a significant impact on onion growth and quality. Specifically, it was found that certain concentrations of IAA fostered a greater fresh weight of the bulb, an increase in the levels of phenolic compounds and a greater DPPH. However, the onion response to IAA was dependent on the concentration. In conclusion, the present study provides evidence that the exogenous application of IAA as a biostimulant can improve onion growth and quality. These results are relevant for the development of sustainable agricultural practices and could be oriented towards crop improvement.

The depletion of the ore reserves in the world necessitates the search for secondary sources such as waste products (tailings and slag). A smelter in Namibia has historic slag which accumulated over decades of its operating life. Analytical and mineralogical characterization of the slag was performed using the X-ray fluorescence (XRF) analysis, Atomic Absorption Spectrometer (AAS), ICP-OES, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS) analysis, and optical microscopy analysis. The chemical analyses showed that the metal values contained in the slag were mainly copper, lead, and zinc whose average contents were approximately 0.3%Cu, 2.4%Pb and 3.2%Zn. About 10.5%Fe was also contained in the slag. Germanium was detected by scanning electron microscopy, but was however below detection limits of the chemical analysis equipment used. Based on the results, approximate conditions under which the different slag phases were formed were estimated and the recovery routes for the various metals were proposed. Analysis by both optical and scanning electron microscopy revealed that Zn and Fe occurred mainly in association with O as oxides, while Cu and Pb were mainly associated with S as sulphides. The slag consisted of three different phases; namely the silicate phase (slag), metallic phase and the sulphide phases. The slag phases were mainly glass, silicate phases and metallic and sulphide phases. It was observed that the metallic and sulphide phases were dominant in the finer size fractions (-75 µm) whereas the sulphide phase was also present in the coarser size fractions (+300 µm). An important finding from the microscopy examination was that the sulphide phases were interstitial and could be liberated from the slag. This finding meant that liberation and subsequent concentration of the sulphide phases was feasible using conventional processing techniques.

Objective: To explore the application effect of predictive psychological nursing in the nursing of outpatients with indwelling needle intravenous infusion .Methods: Select 80 patients who received intravenous indwelling needle infusion therapy in the outpatient department of our hospital from January 2022 to December 2022 as the research objects , and divided them into observation group and experimental group according to the different nursing plans received by the patients . 40 cases in each group. The observation group received routine nursing intervention, and the experimental group received predictive psychological nursing intervention. The puncture situation, psychological state level, self-care ability, adverse reactions and nursing satisfaction were compared between the two groups of patients .Results: The puncture success rate of the observation group was 72.5% (29/40), and that of the experimental group was 95.0% (38/40). The puncture success rate of the experimental group was significantly higher than that of the observation group (P<0.05) ; The SAS scores and SDS scores of the treatment group after nursing were significantly lower than those of the observation group (all, P<0.05) ; the self-care skills, self-concept, self-responsibility, health knowledge and ESCA total scores of the experimental group after nursing were significantly higher than those of the observation group. group (all, P<0.05); the incidence of adverse reactions in the observation group was 25.0% (10/40), the incidence of adverse reactions in the experimental group was 7.5% (3/40), and the incidence of adverse reactions in the experimental group was significantly lower than that in the observation group group (P<0.05); the nursing satisfaction of the observation group was 80.0% (32/40), and the nursing satisfaction of the experimental group was 100.0% (40/40), and the nursing satisfaction of the experimental group was significantly higher than that of the observation group (P<0.05 ).Conclusion: The application effect of the predictive psychological nursing intervention model in the outpatient department for intravenous indwelling needle infusion therapy is significant. The implementation of this model can effectively improve the success rate of puncture, improve the patient's psychological state, improve the self-care ability of patients, and reduce the adverse effects of patients. Risk of reaction, so as to further obtain the satisfaction and recognition of patients and their families.

A commercial phage biocontrol for reducing Listeria monocytogenes has been described as an effective tool for improving fresh produce safety. Critical challenges in the phage application must be overcome for the industrial application. The validation studies were performed in two processing lines of two industry collaborators in Spain and Denmark, selecting shredded iceberg lettuce as the ready-to-eat (RTE) product of higher process volume. The biocontrol treatment optimized in lab-scale trials for the application of PhageGuard ListexTM was confirmed in industrial settings by four tests, two in Spain and two in Denmark. Results showed that the method of application that included the device and the processing operation step were appropriate for the correct application. The proper dose of Phage Guard ListexTM was reached in shredded iceberg lettuce and the surface was adequately covered for the successful application of phages. There was no impact on the headspace gas composition (CO2 and O2 levels), nor on the color when untreated and treated samples were compared. The post-process treatment PhageGuard ListexTM did not cause any detrimental impact on the sensory quality, including flavor, texture, browning, spoilage and visual appearance of over the shelf-life as the phage solution was applied as a fine, mist solution.

Choosing a college major is a crucial issue for twelfth-grade students. Many students feel confused and influenced by their surroundings when making this decision. This can have negative consequences if it does not align with the students interests and talents. This research proposes a web application as a solution, providing introductory information for each major. We employed a design and developement approach to create an application that assists prospective students in identifying majors based on their interests. The primary objective is to alleviate students discomfort during their studies, which often leads to decreased performance. With this application, students can avoid selecting majors solely due to peer pressure or parental coercion.

Waste management is a significant issue that Indonesia must face. The presence of waste will always exist as long as life continues. One of the main challenges in waste management is the proper separation of organic and inorganic waste. As a solution to address this issue, the Ministry of Environment has made efforts to develop Waste Banks. However, it also creates new problems related to manual data collection, lack of monitoring by the community in implementing waste management activities, and a lack of specific reports regarding waste management conducted by officers. We propose the creation of a website using the Agile Development SDLC method. This website involve several steps and incorporate functions for data digitalization and expedited data collection, such as the addition of QR Code functionality, Monthly Reporting Function, and enhanced data security. The method used in this research is Agile Development. The application has been developed with a user-friendly interface and the latest web development technologies to ensure smooth performance. Overall, the web-based application for organic and inorganic waste using Laravel is an important step to promoting sustainable waste management practices.

This article presents the graphical user interface of the ACUX-R mobile recommendation system, tailored for the cultural tourism domain. ACUX-R offers personalized recommendations based on visiting preferences, augmenting the overall user experience. Building upon a comprehensive methodology and recommendation algorithms from previous work, this contribution focuses on the user interface aspects of the ACUX-R by highlighting key design considerations, user interface elements and functionalities that contribute to an effective and engaging user experience. In an effort to evaluate the proposed interface, an initial case study with a dataset consisting of points of interest from the City of Athens, Greece has been performed. In the framework of the aforementioned study, the proposed user interface attained high ratings with respect to inspiring, exciting, interesting, and enthusiastic user experiences.

One of the issues facing modern society, whatever the socio-economic level of the communities involved, is the development of sustainable strategies in the management of sludge/biosolids. Today, it is imperative to replace solutions aimed at simply “disposing of” with those oriented towards “maximizing recovery benefits”. It is desirable that agricultural use remains the main option in sludge/biosolids management, but to ensure effective and safe agronomic benefits, correctly fulfill the legal requirements, and build stakeholder and public confidence, rigorous and sustainable procedures need to be estab-lished. The development of realistic and enforceable regulation is crucial as it represents the right bal-ance between the different aspects of a coordinated and effective management. Furthermore, it is to recognize that regulation needs to be supported by standardized character-ization procedures and guidelines of good practices, because well-defined procedures allow le-gal requirements to be correctly and uniformly met, thus ensuring reliable comparison of re-sults obtained under different conditions. In this article, main aspects to consider for a sustainable application of this management practice are discussed, together with the parameters that need to be evaluated for the characterization of sludge/biosolids, according to the various aspects related to the agricultural use.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

World health is increasingly threatened by the growing number of spice-related food hazards. Further development of reliable methods for rapid, non-targeted identification of counterfeit ingredients within the supply chain is needed. ENEA has developed a portable, user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser and multivariate calibration. Following a study on the authenticity of saffron, the instrument was challenged with a more elusive adulterant, olive leaves in oregano. The results show that the reported method of laser sensing and chemometric analysis was able to detect adulterants at mass ratios of at least 20% in less than five minutes.

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By analyzing data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's influence in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations and innovations to enhance ChatGPT's capabilities and impact across domains.

For Convolutional Neural Network (CNN), Depthwise Separable CNN (DSCNN) is a preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It can benefit from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses two 4-bit multiplication operations to implement a 12-bit multiplication operation by reusing the same multiplier array. With this approximate multiplier, sequential multiplication operations are pipelined in a modified DSCNN to fully utilize the PE array in the convolutional layer. This Approximate (A-DSCNN) was implemented on TSMC 40-nm CMOS process with a supply voltage of 0.9 V. At the clock frequency of 200 MHz, the design achieves 4.78 GOPs/mW while occupying 1.24 mm x 1.24 mm silicon area. Compared to conventional DSCNN implemented in a similar process node, the chip area and power consumption were reduced by 53% and 25%, while the throughput was improved by 17%.

Chronic pulmonary diseases such as asthma, COPD, and Idiopathic pulmonary fibrosis are significant causes of mortality and morbidity worldwide. Currently, there is no radical treatment for many chronic pulmonary diseases, and the treatment options focus on relieving the symptoms and improving lung function. Therefore, efficient therapeutic agents are highly needed. Bronchial epithelial cells and airway smooth muscle cells and their crosstalk play a significant role in the pathogenesis of these diseases. Thus, targeting the interactions of these two cell types could open the door to a new generation of effective therapeutic options. However, the studies on how these two cell types interact and how their crosstalk adds up to respiratory diseases are not well established. With the rise of modern research tools and technology, such as lab-on-chip, organoids, co-culture techniques, and advanced immunofluorescence imaging, a substantial degree of evidence about these cell interactions emerged. Hence, this contribution aims to summarize the growing evidence of bronchial epithelial cells and airway smooth muscle cells crosstalk under normal and pathophysiological conditions. The review first deliberates the effects of both healthy and stressed epithelial cells on airway smooth muscle cells, taking into account three themes; contraction, migration, and proliferation. Then, it discusses the impact of airway smooth muscle cells on the epithelium in inflammatory settings. Later, it examines the role of airway smooth muscle cells in the early development of bronchial epithelial cells and their recovery after injury.