Tennis is an individual sport characterised by high intensity action, including sprints and changes of direction (COD). There is a gap in the knowledge of limb asymmetries in adolescent tennis players and their association with performance. This study aimed to examine the relationship between asymmetry in vertical and horizontal jump tests (CMJ and HJ) and COD with lower limb performance variables in adolescent tennis players. Forty-five adolescent tennis players (age: 13.81 ± 1.08 years; height: 167.64 ± 9.9 cm; body mass: 57.48 ± 10.94 kg; body mass index: 20.27 ± 2.18 kg/m2) performed the CMJ test (unilateral and bilateral), horizontal jumps (unilateral and bilateral), 25 m sprint and 180º COD test. The single-leg countermovement jump showed the greatest asymmetries among the different tests (6.62 ± 9.35%). Significant negative relationships were found between CMJ asymmetry and COD asymmetry with unilateral horizontal jump variables (r = -0.30 to -0.53). In addition, CMJ asymmetry showed significant relationships with CMJR (r=0.49) and COD180R (r=0.29), whereas COD asymmetry showed a significant relationship with COD180L (r=0.40). On the other hand, HJ asymmetry showed no significant relationships with any variable. The main findings of this study indicate that greater imbalances in the CMJ and COD tests are associated with a reduced capacity to perform unilateral horizontal jumps. These results suggest the need to incorporate training protocols that reduce interlimb asymmetries in growing athletes to improve their performance.

Empowering women and promoting gender equality is crucial for accelerating sustainable development in fragile countries, including the Democratic Republic of Congo (DRC). However, there is scarce existing knowledge or understanding of the factors determining women’s empowerment in these contexts. We aimed to assess women's empowerment and determine its associated factors in Kinshasa, DRC. We analyzed data from the 2021 Performance Monitoring Assessment (PMA) survey. A sample of 1,365 women of childbearing age was retained for this study. Twenty empowerment items related to household decision-making, contraception use, and husband/partner influence were considered. We calculated the average women’s empowerment index (aWEI), identified the women empowerment variables using principal component analysis (PCA), and determined the associated factors for the first three principal components through the performance of multivariate binary logistic regression. In Kinshasa, the overall aWEI was estimated at 0.65. It was low for household decision-making (0.34) and high for husband/partner influence domains (0.93), respectively. Three principal components were identified and named, including the absence of threats, control of sexuality, and participation in decision-making. The factors associated with these components were having internet access, being in free union with a partner, being aged 40-49 years, and residing in a non-slum area. Increasing access to information would enable women in Kinshasa to make strategic decisions about their lives, benefiting themselves and others.

The mechanical properties of the materials stored inside agricultural silos influence the loads generated inside them. The different silo calculation methodologies allow predicting these loads under static and dynamic conditions if the mechanical properties of the stored materials are known. It has been known for many years that these properties are highly dependent on the moisture content of the material. However, to date there are not many studies focused on its determination. The goal of this research is the determination of the angle of internal friction, apparent cohesion, dilatancy angle and apparent specific weight of maize subjected to different moisture contents. Direct shear and oedometer assay devices were used for this purpose. In addition, the maize samples used were moistened using a climatic chamber. Moisture contents applied to maize sample ranged from 9.3% to 17.4%. The results obtained for the angle of internal friction and apparent cohesion, as well as those obtained for the apparent specific weight, were similar to those reported in the literature. However, the values obtained for the dilatancy angle of maize as a function of moisture content could not be compared because nothing has been published so far. The values obtained for this parameter overlap with those published for this material under ambient conditions. In addition, no direct relationship between the values obtained for this parameter and the moisture content of the sample could be found. Finally, the apparent specific weight increased with increasing moisture content of the samples, which differed from that published in the literature. The values provided here can be used for the optimization of storage and handling structures for granular agricultural materials using numerical methods.

Seed potato treatment is vital for plant protection, yield enhancement, and product quality. In the conducted research, the plant biostimulator Supporter was applied to evaluate its impact on potato yields. Field studies were conducted in Northeastern, Northwestern, Western, and Southwest Poland from 2015 to 2017, using a randomized complete block design with a split-plot arrangement. The use of seed treatment technology with the Supporter product resulted in increased overall and commercial potato yields and improved yield tuber structure compared to traditional cultivation methods without tuber treatment before planting. Conclusion: The application of the plant biostimulator Supporter in seed potato treatment technology brings significant benefits, including, increased potato yield and improved yield structure. The cultivation location significantly influences potato yield and tuber structure. Understanding these differences can aid in better adapting cultivation practices and selecting varieties to specific local conditions, potentially leading to increased potato crop productivity. In terms of efficiency and yield quality, further research into its application in agricultural practice is recommended, which may contribute to further increasing potato production efficiency and quality. Developing more ecological and efficient farming practices can help meet society's growing food needs.

Unmanned aerial vehicles (UAVs), commonly known as drones, present a cost-effective solution for the swift collection of data from vast and remote areas that are otherwise difficult to access. The Mediterranean Basin, known for being a hot spot for plant biodiversity, hosts several habitats and taxa of significant naturalistic value. However, many of these areas are often inaccessible to botanists, making exploration and research challenging. The aim of this paper is to involve the utilization of drone surveys and open-source software for botanical research. Our primary goal is to show the effectiveness of these tools in the field and demonstrate their practical application in Natura 2000 sites. The protected area chosen for this research is Rocca di Novara, situated in northeastern Sicily. Thanks to our drone investigations, we were able to capture images of the mountain’s side that is inaccessible to humans. This allowed us to observe the habitat of some species in detail. One of the most fascinating discoveries was the reappearance of Saxifraga callosa subsp. australis, which had not been confirmed in this area for over 140 years. Using drones for botanical research can boost field research, making monitoring easier and more cost-effective over time, especially in Natura 2000 sites.

Extracellular vesicles (EVs) carry diverse cargo of DNA, RNA, proteins, and lipids. They are crucial mediators for regulating various functions through interactions with the host. Recent investigations suggest that EVs or their constituent proteins derived from probiotics could improve the effectiveness of probiotic strains in disease management compared to probiotic strains themselves. This enhanced effec-tiveness arises from their innate antimicrobial properties, which can specifically target pathogenic mol-ecules or cells, in addition to their significant ability to regulate immune responses. Significantly, previous studies have highlighted the inhibitory potential of Lactobacillus crispatus PMC201 against Mycobacterium tuberculosis. In this study, we focused on optimizing isolation conditions for studying extracellular vesicles and proteins from anti-TB potent strains. We provide analysis results based on food-grade media (FGM) optimization and isolation methods. Nanoparticle tracking analysis (NTA) was used to determine se-cretion patterns. Proteomic analysis was performed by liquid chromatography and tandem mass spec-trometry (LC-MS/MS). A total of 64 proteins were identified in the L. crispatus PMC201 strain, particularly enriched in s-layer and splX. Questions remain about their relevance to anti-TB. This anti-tuber strain has potential as a therapeutic agent, given its significant role in intercellular killing. Further studies are needed to investigate its mechanisms of action.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article presents a comprehensive review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that heart rate variability (HRV) and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 (FIDIC 1999) framework. It aims to identify, categorize, and devise mitigation strategies for key types of Variations and Claims, aligning with the FIDIC Conditions of Contract. The research, drawing on inputs from construction industry professionals including Contract Administrators and Project Managers, focuses on the MENA re-gion. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts, predominantly through interviews, focusing on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the study uncovers the most prevalent and impactful causes of Variations and Claims, highlighting the critical need for managerial intervention. A key feature of this study is the integration of Scientometric Analysis for a quantitative review of current literature, providing a comprehensive academic context. A significant addition to the methodology is the implementation of a k-means clustering analysis. This advanced statistical technique further classifies the data into distinct clusters, each representing unique combinations of 'Frequency' and 'Impact' of Variations and Claims. The k-means analysis elucidates intricate patterns and potential solutions, offering profound insights into effectively managing these issues. These analytical advancements are crucial in identifying significant and manageable responses to reduce the frequency and impact of Variations and Claims in construction projects.

Poplars (Populus spp.) are of significant ecological and economic importance. Long-term breeding efforts were aimed mainly at obtaining fast-growing and productive plants and less considered resistance to pests. This study aimed to identify the patterns of susceptibility to Saperda carcharias (Linnaeus, 1758) (Coleoptera: Cerambycidae) among Populus hybrids and pure species, considering crossing combinations and some other traits. In our research, 35 clones of poplar species and hybrids of the Ukrainian and foreign selection from the Aigeiros, Tacamahaca, and Leucoides sections were tested in 2021 and 2023 in the plantation created in 2014 in Eastern Ukraine. S. carcharias preferred infesting clones with earlier foliage development, larger diameters, and height increments. Specifically, hybrids with American maternal lineage showed the highest susceptibility, whereas those with European maternal lineage or Asian origin displayed lower infestation levels or resistance, respectively. This variability suggests a complex relationship between genetic background and pest resistance, underlining the need for a deeper understanding of the factors influencing susceptibility to S. carcharias. Selecting native species clones or creating mixed clone plantations could enhance the resilience of poplar plantations to pest threats.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

Studies show that although female soccer players often have shorter change of direction (COD) deficits than males, indicating different biomechanical profiles, there is lack of research on the impact of physical metrics on COD performance in females. The aim of this study was to analyze whether performance metrics based on speed, and jumping could explain the variation in %CODD in young female soccer players. Thirty-three highly trained adolescent female soccer players (age: 16. ± 0.95 years; height: 160.4 ± 5.22 cm; body mass: 55.7 ± 7.22 kg) performed COD180 tests, 10-m and 30-m sprint test and single leg countermovement and horizontal jumps. Acceleration in the first 10 meters of a sprint was identified as a significant predictor of COD180 performance (R2 = 28%), (R2 = 50%), (p

Technological advancements in the Internet of Things (IoT), unmanned aerial vehicles (UAVs), and novel technological tools have transformed disaster management practices from localized approaches to a global framework. These innovations necessitate a unified understanding and application of diverse methodologies at an international level, ensuring they are accessible and operational across different sectors. This integration is crucial not only for technical personnel but also for medical teams, who play a pivotal role in disaster response and management. By fostering a comprehensive grasp of these methods, we can enhance coordination, efficiency, and effectiveness in addressing disasters on a global scale. The authors aimed to provide a comprehensive overview of emerging techniques that are driving shifts in strategic approaches and treatment technologies. Recognized as a domain of continuous research and development, this overview, by necessity, remains an incomplete snapshot of an evolving field. It highlights the dynamic nature of technological and strategic advancements, underscoring the importance of ongoing investigation and innovation to stay abreast of current trends and future directions. The article also highlights the importance of international collaboration beyond societal and national boundaries A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests developed. However, most of the previously developed testing procedures for fractional cointegration were primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test development is based on bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.

Inflammation is a cause of number of disorders. Many anti-inflammatory drugs are available to cure and prevent inflammation and its related disorders. Some of the drugs are from plant and animal origin. Among animals snake venom is also utilized as treatment option of certain disorders. In the current study the venom of N. oxiana from Northern areas of Pakistan is assayed to determine its anti-inflammatory activity. The Naja oxiana is also called central Asian cobra is endemic to Central Asia. Naja oxiana belong to family Elapidae and it is highly venomous snake. The venom has many enzymatic proteins as well as nonenzymatic proteins that is neurotoxins. The venom is lethal having very high mortality rate and the death may occur due to respiratory failure in 45 minutes to 24 hrs depending upon the nature and amount of venom. As the venom is highly toxic so initially after venom milking its protein estimation was done and toxicity profile and LD50 was determined to find out its therapeutic dose at which anti-inflammatory activity was observed. The toxicity and anti-inflammatory activity was determined on mice model. Inflammation was induced by carrageenan induced peritonitis method. The inflammatory markers ROS, TNF-α and IL-β were determined by ELIZA in peritoneal fluid. The LD50 of crude venom, fraction 1 and fraction 2 was found as 34 µg/kg, 7.4 µg/kg and 416 µg/kg respectively. Anti-inflammatory activity crude venom, fraction 1 and fraction 2 was found at dose 19 µg/kg, 1.8 µg/kg and 267 µg/kg respectively. The levels of inflammatory markers reduced after therapeutic dose administration of venom and its fractions that indicates that it can be utilized as anti-inflammatory agent and so can be effective in various disorders.

The global population is at risk of vitamin D deficiency due to low exposure to sunlight and low intake of the vitamin through diet. The aim was to investigate in women the association between vitamin D status and parathyroid hormone (PTH), ultraviolet radiation, lifestyle, ethnicity, social conditions, and residential greenness. A 1-year longitudinal study assessed vitamin D status in 309 women, latitude 51° 14' N. Blood samples were taken four times throughout the year for analysis of 25(OH)D and serum PTH concentration. After each seasonal visit, the individuals completed 4-day diet diaries and used two dosimeter badges for 1 week to estimate weekly UVR exposure. A questionnaire was applied to provide information about lifestyle and their ethnicity. Residential greenness was measured by Normalized Difference Vegetation Index (NDVI), within a 1000 m radius around each participant’s home address. Women living in greener spaces were more likely to have improved vitamin D status (RR: 1.51; 95%CI: 1.13 – 2.02), as well as those who were more exposed to UVR (RR: 2.05; 95%CI: 1.44 – 2.92). Our results provided a first insight into the connection between vitamin D, residential greenness, and lifestyle in a region of temperate climate and with a high degree of urbanization.

Background: Previous investigations have found a correlation between abnormal curvatures and a variety of patient complaints such as cervical pain and disability. However, no study has shown that loss of the cervical curve is a direct result of exposure to a motor vehicle collision (MVC). This investigation presents a retrospective consecutive case series of patients with both a pre-injury cervical lateral radiograph (CLR) and a post-injury CLR after exposure to a MVC. Computer analysis of digitized vertebral body corners on CLRs was performed to investigate the possible alterations in the geometric alignment of the sagittal cervical curve. Methods: Three spine clinic records were reviewed, over a 2-year period, looking for patients where both an initial lateral cervical x-ray and an examination were performed prior to the patient being exposed to a MVC; afterwards an additional exam and radiographic analysis were required. A total of 41 patients met the inclusion criteria. Examination records of pain intensity on numerical pain rating scores (NPRS) and neck disability index (NDI), if available, were analyzed. The CLRs were digitized and modeled in the sagittal plane using curve fitting and the least squares error approach. Radiographic variables included total cervical curve (ARA C2-C7), Chamberlain’s line to horizontal (skull flexion), horizontal translation of C2 relative to C7, segmental translations (retrolisthesis and anterolisthesis), and circular modelling radii. Results: There were 15 males and 26 females with an age range of 8-65 years. Most participants were drivers (28) involved in rear end impacts (30). The pre-injury NPRS was 2.7 while the post injury was 5.0; p < 0.001. The NDI was available on 24 /41 (58.5%) patients and was found to increase following exposure to the MVC: 15.7% to 32.8%, p < 0.001. An altered cervical curvature was identified following exposure to MVC, characterized by an increase in radius of curvature and an approximate 8° reduction of lordosis from C2-C7; p < 0.001. The mid cervical spine (C3-C5) showed the greatest curve reduction with localized mild kyphosis at these levels on average. Several participants developed segmental translations that approached the instability value of translation > 3.5mm. Conclusions: Abnormalities of cervical lordosis occurred following exposure to a MVC. The average loss of cervical curvature was 8° from C2-C7, with mild flexion of the mid cervical segments. Snap through type or dynamic buckling during the MVC may explain our findings. The primary contribution of this study to the motor vehicle collision literature is that altered cervical lordosis may result from MVC exposures and the consequences of an altered cervical alignment, in turn, may lead to future pain, disability and decreased health-related quality of life.

In recent years, the focus of disease resistance and susceptibility studies in cattle have been on determining patterns in the innate immune response of key proteins, such as Toll-like receptors (TLR). In the bovine genome, there are 10 TLR family members and, of these, TLR2, TLR4 and TLR6 are specialized in recognition of bacterial ligands. Indigenous cattle breeds of Anatolia have been reported to show fewer signs of clinical bacterial infections, such as bovine tuberculosis and mastitis, and it is hypothesized that this might be due to a less stringent genetic selection during breeding. In contrast, Holstein-Friesian cattle have been under strong selection for milk production, which may have resulted in greater susceptibility to diseases. To test this hypothesis, we have compared the TLR2, TLR4 and TLR6 genes of Anatolian Black (AB), East Anatolian Red (EAR), South Anatolian Red (SAR), Turkish Grey (TG), and Holstein (HOL) cattle using Next Generation Sequencing. The SAR breed had the most variations overall, followed by EAR, AB, TG and HOL. TG had the most variations for TLR2 whereas SAR had the most variations in TLR4 and TLR6. We compared these variants with those associated with disease and susceptibility traits. We used exon variants to construct haplotypes, investigated shared haplotypes within breeds and determined candidate haplotypes for disease resistance phenotype in Anatolian cattle breeds.

This study examines a case involving a 7-year-old child who developed dengue symptoms following Qdenga vaccination. Despite initial negative diagnostic results, molecular analysis confirmed an infection with DENV4. Next-generation sequencing detected viral RNA from both DENV2 and DENV4 serotypes, which were identified as vaccine-derived strains using specific primers. Phylogenetic analysis further confirmed that these sequences belonged to the Qdenga vaccine rather than circulating wild-type viruses. This case underscores the critical need for precise diagnostic interpretation in vaccinated individuals to avoid misdiagnosis and to strengthen public health surveillance. A comprehensive understanding of vaccine-induced viremia is essential for refining dengue surveillance, improving diagnostic accuracy, and informing public health strategies in endemic regions.

Background and objective: Chronic subdural hematoma (cSDH) is one of the most common neurosurgical entities affecting mostly elderly patients. In this study, we aimed to provide detailed characteristics of surgically treated patients with cSDH and evaluate outcomes between the two conventional treatment methods. Materials and methods: Patients with cSDH surgically treated with burr hole craniostomy (BHC) and craniotomy (CO) in tertiary centers in Croatia and Serbia during 2017-2019 were included in the study. Characteristics and outcomes of these patients were prospectively collected. Results: In total, 155 patients (mean age 72.3±13.4) were included in the study. BHC group consisted of 124 (80 %) and CO group of 31 (20 %) patients. 32 (20.6 %) patients presented with bilateral cSDH, head trauma was documented in 92 (59.4 %), and anticoagulant therapy in 37 (23.9 %) individuals. The most common symptom was hemiparesis (n=85, 54.8 %). The two groups did not differ by age. CO group had higher Glasgow Coma Scale (GCS) score at the admission, with a significant decrease at discharge compared to admission (12.6±4.8 vs 14.4±1.7, p

This paper discusses the application and advantages of artificial intelligence technology in digital media interactive product design. Firstly, the development background of artificial intelligence technology and its promoting effect on design innovation is introduced, and the application of new technologies, such as generative adversarial networks in art creation and design personalization, is definitely analyzed. It then explores in detail the ability of AI to break traditional constraints in interactive design, innovate design, and optimize user experience, especially in digital media. Finally, the case study of Microsoft Cortana shows the method of classifying and processing user queries by machine learning system and its experimental results. The research of this paper provides the theoretical basis and empirical support for the application of artificial intelligence technology in the future interactive design of digital media and has important academic and practical significance.

In this study, we aimed to explore the oral bacteria and fungi that can help discern oral squamous cell carcinoma (OSCC) and investigate the correlations between multiple key pathogens. Methods: Twelve participants (8 females and 4 males; mean age, 54.33 ± 20.65 years) were prospectively recruited into three groups: Group 1: healthy control, Group 2: patients with stomatitis, and Group 3: patients with OSCC, with 4 individuals in each group. Unstimulated whole saliva samples from these participants were analyzed using real-time PCR to assess the presence and abundance of 14 major oral bacterial species and Candida albicans. Results: The analysis revealed significant differences for certain microorganisms, namely Treponema denticola (T. denticola), Lactobacillus casei (L. casei), and Candida albicans. T. denticola was most abundant in the OSCC group (5,358,692.95 ± 3,540,767.33), compared to the stomatitis (123,355.54 ± 197,490.86) and healthy control (9,999.21 ± 11,998.40) groups. L. casei was undetectable in the healthy control group, but was significantly more abundant in the stomatitis group (1,653.94 ± 2,981.98) and even higher in the OSCC group (21,336.95 ± 9,258.79) (p = 0.001). A similar trend was observed for C. albicans, with DNA copy numbers rising from the healthy control (464.29 ± 716.76) to the stomatitis (1,861.30 ± 1,206.15) to the OSCC group (9,347.98 ± 5,128.54) (p = 0.006). The amount of T. denticola was positively correlated with L. casei (r=0.890, p<0.001) and C. albicans (r=0.724, p=0.008). L. casei's DNA copy number was strongly correlated with C. albicans (r=0.931, p<0.001). These three oral microbes exhibited strong positive correlations with each other and had various direct or indirect relationships with other species. Conclusions: In the OSCC group, T. denticola, L. casei, and C. albicans exhibited strong positive correlations with one another, further emphasizing the need for a deeper understanding of the complex microbial interactions in the OSCC environment.

During HIV-1 virus assembly the genomic RNA (vRNA) is selected for packaging by the viral protein Gag because it contains a specific packaging signal, Psi. While there have been numerous studies of Gag-Psi interactions, there is almost no information on kinetic aspects of this interaction. We have investigated the kinetics of Gag binding to different RNAs using switchSENSE DRX2 technology. We measured the association rate of Gag binding to monomeric Psi, to a “Multiple Binding Site Mutant” Psi that is inactive for genome packaging in vivo, and to a scrambled Psi. We discovered that Gag binds more rapidly to Psi RNA than to the mutant or scrambled RNAs. Furthermore, rapid Gag association kinetics are retained within sub-regions of Psi: Gag associates more rapidly with RNA containing only the 3’ two of the three Psi stem-loops than with monomeric RNA containing the 5’ two stem-loops or a scrambled RNA. No differences were detectable with individual Psi stem-loops. Interestingly, the rate of binding of Gag molecules to Psi increases with increasing Gag concentration, suggesting cooperativity in binding. The results are consistent with the hypothesis that selectivity in packaging derives from kinetic differences in initiation of particle assembly.

In recent decades, reports of deforestation, mass extinctions, biodiversity loss, and emergencies like floods and droughts have clearly highlighted the impact of human activities on these outcomes. This underscores the need for significant lifestyle changes to mitigate this impact. Pro-environmental behavior (PEB) change involves adopting actions that protect the environment and reducing environmentally harmful activities. While qualitative research may provide unique insights into PEB barriers that are challenging to capture through quantitative methods, reports of qualitative studies are scattered across different literatures with little integration. With the present review, we bring together insights from qualitative studies on PEB barriers and examine whether those studies reveal barriers compatible with existing barrier frameworks from environmental psychology. We identified 38 qualitative studies on barriers to PEB change. Our analysis revealed that most of the barriers to individual pro-environmental action identified in qualitative studies align with environmental psychology frameworks. These barriers were experienced not only by individuals in their role as consumer or citizen, but also by community-level actors (e.g., seeking to implement pro-environmental policies) and industrial decision-makers (e.g., aiming to reduce the ecological footprint of their business activities). Our findings underscore the multifaceted nature of barriers to PEB change and emphasize the importance of addressing structural and institutional factors alongside individual-level interventions.

This systematic examines research into patients diagnosed with retinal vascular occlusive diseases (RVODs) secondary to coronavirus disease 2019 (COVID-19) infection or vaccination. RVODs include central retinal vein occlusion, branch retinal vein occlusion, central retinal artery occlusion, and branch retinal artery occlusion. Previous studies have posited a possible link between RVODs and COVID-19. RVODs are of two types: retinal vein occlusion and retinal artery occlusion. These disorders develop when retinal blood vessels become clogged by thrombi or fat deposition. The retina is an important component of the visual apparatus. Its photoreceptors are stimulated by the light hitting the eye and record its pattern. The retina then relays this visual information to the brain. When retinal blood vessels become clogged, the damage can range from slightly reduced vision to complete blindness. COVID-19 causes endothelial dysfunction and increased levels of von Willebrand factor antigens in the blood. Elevation of von Willebrand factor activates the coagulation process and platelet aggregation. The treatment methods and visual prognosis in COVID-19-related RVODs do not differ from conventional RVODs. However, ophthalmologists need to be aware of the possible relationship and should consider RVODs when they encounter patients with vision loss following COVID-19 infection or vaccination.

Warsaw is among European cities with the worst atmospheric air quality, mainly due to very high pollution emitted by the residential sector and road traffic. This results in high concentrations of particulate matter and nitrogen oxides, often exceeding WHO standards. The paper discusses the current and expected effects of actions taken by the Warsaw authorities, to significantly improve air quality in the city. The Clean Air Program currently implemented mainly involves the reduction, and ultimately the total elimination, of coal combustion in all heat sources of the municipal sector, which is identified in numerous publications as the main source of air quality degradation in cities. The program's goal, which timeframe covers the years 2018-2029, is a definite reduction in particulate pollution. The second part of the remedial activities, covering the years 2024-2034, mainly concerns the reduction of nitrogen oxide pollution, the main source of which is urban road traffic. In the latter case, the expected effects of introducing Low-Emission Zone (launched July 2024) in the city center are considered. According to the project, the zone's implementation will consist of five two-year stages, in which the car emission limits will gradually tighten. The paper compares the effectiveness of two being considered versions, differing in size zones.

This study developed a three-dimensional kinematic and dynamic model of a center-driven linkage system (CDLS). Force analysis was conducted to determine the required driving torque to ensure that the input link of the CDLS rotates at a constant speed with a certain angular velocity, even in the presence of frictional resistance torque. The primary objective was to optimize the maximum angular acceleration of the output links to reduce wiper noise. This optimization can enhance the stability of the linkage system while maintaining its operational functionality. In the adopted methodology, the frames of the fixed links are assumed to remain unchanged, and the input link is considered to operate at a constant angular speed and have a constant length. To meet standard vehicle windshield wiper requirements, 10 constraints were imposed on the optimization problem, which was solved using the differential evolution method. The lengths of both output links were reduced by over 10% after optimization.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, predominantly affecting older adults. Understanding the multifaceted factors contributing to AD is crucial for developing effective prevention and intervention strategies. This narrative review synthesizes current literature on the various factors influencing the development and progression of AD in older adult populations.Methods: A comprehensive search was conducted across electronic databases including PubMed, Web of Science, and PsycINFO using relevant keywords such as "Alzheimer's disease," "risk factors," "genetics," "environmental factors," and "lifestyle." Studies published between 2000 and 2023 were included, focusing on factors associated with AD in individuals aged 65 years and older.Results: The review identified several key factors contributing to AD in older adults. Genetic predisposition, particularly the presence of apolipoprotein E (APOE) ε4 allele, emerged as a significant risk factor. Environmental factors such as air pollution, pesticides, and heavy metals were also implicated. Furthermore, lifestyle factors including physical inactivity, poor diet, and smoking were associated with increased AD risk. Chronic conditions such as diabetes and cardiovascular diseases were found to exacerbate cognitive decline in older adults.Conclusion: This narrative review highlights the complex interplay of genetic, environmental, and lifestyle factors in the development and progression of Alzheimer's disease among older adults. Understanding these factors is crucial for targeted interventions aimed at reducing AD risk and improving cognitive health outcomes in aging populations. Future research should focus on longitudinal studies and personalized medicine approaches to elucidate specific mechanisms underlying AD pathogenesis and to develop effective preventive strategies.

Currently, there has been a tendency for companies to direct the development of their businesses following circular economy trends associated with sustainability. European Community guidelines have established deadlines for the implementation of management strategies that allow creating and maintaining an effective and resource-efficient economic system, reducing the amount of en-ergy and materials used in production. In this scenario, this study makes sense, which aims to an-alyze academic studies on these topics with the aim of understanding future trends. In this sense, the objective of this study is to understand the potential impacts on business development of the inte-gration of sustainability and the circular economy. This article uses a systematic literature review, which has the objective of gather and analyze materials from different authors, carrying out an analysis to answer a specific question. The findings indicate that CE and sustainability are con-nected and businesses should implement sustainability and CE appropriate strategies to increase their competitiveness and improve resource efficiency. These strategies may integrate innovative technologies, the use of sharing platforms, product life extension, resource recovery to minimize waste and the integration of sustainability concepts in business as well as their communication with their stakeholders to change trends and behaviors.

Boeing and Airbus developed a special testing procedure to investigate the compressive response of laminates that have been impacted (following standards ASTM D 7137 and DIN 65561). This study focuses on both experimental and numerical analysis of Kevlar plates subjected to compression after impact. To ensure high quality and appropriate mechanical properties, the composite plates were manufactured using autoclaving. The DIN 65561 protocol was followed for all three test systems. Initially, ultrasonic C-scanning was performed on all plates before testing to confirm they were free of any significant defects arising from the manufacturing process. Subsequently, low-energy impact testing was conducted at levels ranging from 0 to 8 Joules. Three specimens were tested at each energy level. After the impact, all specimens underwent ultrasonic C-scanning again to assess the internal delamination damage caused by the impactor. Finally, both pristine and impacted specimens were subjected to compressive testing using the special jig specified in DIN 65561. The compressive impact strength results obtained from these tests were plotted against the delamination area measured by C-scanning. This data was then compared to results obtained from specimens with artificial damage. Semi-empirical equations were used to fit both sets of curves. The same procedure (impact testing, C-scanning, and data analysis) was repeated to investigate the relationship between impact energy and total delamination area. Lastly, finite element modeling was employed to predict the buckling stresses that develop under compression in the impacted systems studied. These modelling approaches have demonstrated good accuracy in reproducing experimental results for CAI tests.

Background: The accelerated ageing of the world's population will lead to an increase in the number of older persons in the workforce. Computerized Cognitive Training (CCT) is effective in improving cognitive outcomes, but its benefits for older workers remain controversial. We investigate the real-world efficacy of CCT in the workplace, focusing on employees aged 50+ years from a public sector agency. Methods: Case managers (N = 82) were randomized to either Intervention group (24 40-minute CCT sessions two times per week) or waiting list passive Control group. Cognitive ability, wellbeing, job satisfaction, and productivity outcome measures were collected assessed before and after CCT or comparable Control wait time. Results: Participants undergoing CCT improved on a task of executive functioning (p = 0.04). There was a trend toward change in work productivity after CCT (p = 0.09) with the Control group showing a significant decrease (p = 0.02), while the Intervention group remained stable. Conclusions: CCT during office hours has a positive effect on cognition and well-being without affecting the productivity among white-collar office workers. CCT could be considered as an intervention to support the older workforce in managing the cognitive and behavioral challenges of changing workplace demands.

Extreme heat is a climate hazard that has been linked with high morbidity and mortality. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors, and other socio-demographic parameters. To quantify and locate vulnerabilities of populations to extreme heat, we analyze socio-economic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals and a methodology is developed for constructing this variable, based on socio-economic data available in public databases. Independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly, retired, unemployed, renting, living alone, residing in smaller houses, living in older houses, and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also allows comparison between vulnerability scores of different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health.

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed applying approximate and numerical approaches, the multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. The self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared.

Accurately predicting drug-drug interactions (DDIs) is crucial for preventing adverse drug events in clinical settings. However, existing methodologies often rely on complex models built from diverse data sources, posing challenges in computational drug discovery. To address the need for precise computational methods in predicting unknown DDIs, this study introduces a novel Deep Neural Network (DNN)-based approach. By leveraging a wide range of drug-related information, including substructure, targets, side effects, pathways, and indications, our method calculates multiple drug similarities. These similarities are then synthesized using a nonlinear fusion method to extract high-level features. Subsequently, a tailored neural network is deployed for interaction prediction. Comparative evaluation against three prominent machine learning classifiers—Extreme Gradient Boosting (XGBoost), Adaptive Gradient Boosting (AdaBoost), and Light Gradient-Boosting Machine (LGBM)—using three benchmark datasets demonstrates the superior performance of DNN. It achieves outstanding accuracy, precision, recall, and F1-score metrics, all reaching 94.6% in cross-validation. Additionally, case studies involving numerous drug pairs confirm the reliability of DNN in accurately predicting unknown DDIs. These findings underscore DNN as a potent and reliable method for DDI prediction, with promising implications for drug discovery and healthcare applications.

Asthma is a significant public health concern. This study identified the provinces with the highest morbidity and mortality rates due to asthma among the working-age population (15-69 years) in the Republic of Ecuador. The secondary objective was to explain the possible differences attribut-able to occupational exposure. This nationwide ecological and exploratory study was conducted in 24 provinces between 2016 and 2019. Government databases were used as sources of infor-mation. Standardized rates were calculated for codes the J45 and J46. The hospitalization mor-bidity rate for asthma decreased from 6.51 to 5.76 cases per 100,000 working-age population, and the mortality rate has consistently been low and stable from 0.14 to 0.15 deaths per 100,000 working-age population. Geographic differences between the provinces were evident. The risk of hospitalization and death due to asthma was higher in the Pacific coast (five provinces) and the Andean region (two provinces). The high rates observed in provinces with greater agricultural and industrial development could be the main determinants of national heterogeneity and could act as occupational risk factors. The contribution of occupational hazards in each province should be examined in depth through ad hoc studies. These findings provide valuable information for creating public policies aimed at promoting and safeguarding the respiratory health of the popu-lation, particularly that of workers.

This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.

This research aims to investigate psychological anxiety factors, particularly Music Performance Anxiety (MPA), that influence the sustainability of music learning systems. This study employs an innovative integration of qualitative and quantitative methodologies, marking the first use of a nonlinear system paired with a comprehensive data framework for analyzing questionnaire responses. This approach allows for a detailed examination of the effects of these factors on academic outcomes. Previous research has primarily focused on the fragmented music learning system from the perspective of teachers' instructional strategies, emphasizing phased teaching methods and learning objectives which focus often neglected a significant subset of music learners. Addressing this oversight, the current study specifically centers on the emotional aspects of anxiety within music learning systems, with a particular emphasis on students, who play a crucial role in music education. This perspective enables a deep exploration of how MPA and other related emotional factors affect music learning systems. A notable innovation of this research is the development of a stochastic nonlinear system designed to model the psychological factors impacting student music learning, complemented by a quantitative model that assesses actual learning outcomes. By analyzing this nonlinear system, the research identifies weight values for various negative emotions, particularly those related to MPA, that significantly influence the music learning process. The integration of MPA and other anxiety related emotional factors with practical learning outcomes provides a comprehensive understanding of their combined impact on the music learning system. These insights are invaluable for educators and policymakers aiming to enhance both the effectiveness and emotional well-being within music education. This comprehensive approach offers a novel perspective in understanding and improving the dynamics of music education through a more empathetic and scientifically grounded lens.

Introduction: Caries is a public health problem in Kosovo among children, Caries is a multifactorial microbial disease and connection of this disease with soft drinks and food habits between children and their poor oral health is very strong. Aim: The aim of present study is to evaluate prevalence of caries among 12 to 16 years old students in municipality of Suhareka and to analyze related risk factors that contribute in occurrence of caries. Material and methods: To observe parameters of soft drink we used experimental method and a cross-sectional study was carried out among 120 adolescents in “Sadri Duhla” elementary school in Duhel and “7 Marsi” elementary school in city of Suhareka. Oral clinical examination and self administered questionnaire were used to collect information about dental caries and its correlation to soft drinks and food habits. Examination was done between May and October in the year 2023. Impact of soft drink in caries was assessed through DMFT index and frequency of consumption of soft drinks. The data were collected statistically using SPSS version 26 and presented in tables and graphs. Results: The highest result of DMFT (4.36) with standard deviation 1.23 was noted in the group consuming more than 16 cans per week also the highest score in adolescent compared to frequency of consumed cans per week. Soft drinks found in Kosovo’s mar-kets are acidic with low pH, 93.75% of them had a pH below 4 which are considered erosive and only 6.25% are minimally erosive with a pH > 4, in terms of buffer capacity soft drinks can be ordered like this energy drinks >soda >100% fruit juice > fruit drinks. Improper tooth-brushing, dental visits only in case of pain and not preventive visits were significantly related to dental caries. Conclusion: The results of the research awarned us for high frequency consumption of soft drinks related to dental caries. It is recommended to be aware of the use of soft drinks, regular visits to the doctor, higher maintenance of oral health by using paste with sufficient fluoride content also brushing teeth 1 hour after consuming acidic food.

Preschoolers with disabilities and their family caregivers are missed out in many public health initiatives especially in low-income countries. Yet they can benefit from early intervention to promote their development from soon after birth and to provide a better quality of life for their families. In this paper, we describe how a community-based approach has been implemented with minimal funding in two areas in Zimbabwe: a township in Harare and in rural areas of Manicaland Province. Our aim in sharing this information – allied with references to research studies recently undertaken in Africa – is that it will enable similar supports to be replicated in other communities by local personnel. A logic model is used to describe the situation in which the two projects work, the various inputs they have provided to their community and the different forms of support they have offered to the children and their caregivers. The project outputs are listed in terms of the number of beneficiaries helped and activities undertaken. The outcomes achieved for the children, the family and communities are reported. The sustainability and extensions of community-based projects to address unmet needs are discussed. The main conclusion is that disadvantaged communities can be energized to address the needs of their most marginalized residents.

Duchenne muscular dystrophy (DMD) is caused by genetic mutations in the cytoskeletal-sarcolemmal anchor protein dystrophin. Repeated cycles of sarcolemmal tearing and repair lead to a variety of secondary cellular and physiological stressors that are thought to contribute to weakness, atrophy, and fibrosis. Collectively, these stressors can contribute to a pro-inflammatory milieu in locomotor, cardiac, and respiratory muscles. Given the many unwanted side effects that accompany current anti-inflammatory steroid-based approaches for treating DMD (e.g., glucocorticoids), there is a need to develop new therapies that address inflammation and other cellular dysfunctions. Adiponectin receptor (AdipoR) agonists, which stimulate AdipoR1 and R2 isoforms on various cell types, have emerged as therapeutic candidates for DMD due to their anti-inflammatory, anti-fibrotic, and pro-myogenic properties in pre-clinical human and rodent DMD models. Although these molecules represent a new direction for therapeutic intervention, the mechanisms through which they elicit their beneficial effects are not yet fully understood, and DMD-specific data is limited. The overarching goal of this review is to investigate how adiponectin signaling may ameliorate pathology associated with dystrophin deficiency through inflammatory-dependent and -independent mechanisms, and to determine if current data supports their future progression to clinical trials.

Some optimal control problems need metaheuristics in searching for the optimal solution, especially when the process has certain characteristics like the Photobioreactor for Algae Growth (distributed parameter, nonlinearities, etc.). Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations, there is a big computational effort that is reflected in a large controller execution time. The authors propose to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO, PM); the training data is obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple Linear Regression and the Regression Neural Networks were considered as predicting models. The paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models, implementing the controllers, and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior such that the process evolves almost identically. The resulting controllers’ execution time drastically decreased while keeping their optimality.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

Factors responsible of species distribution were intensively investigated in the past, including response at different spatial scales. The aim of the present research is to review the most relevant factors explaining chironomid species distribution focusing to factors operating at different spatial scales, such as latitude, longitude, altitude, water temperature, dissolved oxygen, nutrients acting at regional, country, water basin extension. Data including chironomid species abundances from different lentic and lotic waters from Italy and other surrounding countries were analyzed with different multivariate methods: partial canonical correspondence analysis and multiple discriminant analysis. Spatial analysis included univariate Moran’I correlograms, multivariate Mantel correlograms and Moran’s Eigenvectors Maps (MEM) calculation. The results confirmed previous conclusions, that is habitat type including different running waters reaches (kryal, krenal, rhthral, potamal) and different lake types is the most significant factor in separating chironomid assemblages and spatial factors act only as indirect influencers..

This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery.

The main sources of intrinsic noise in excitable cells at the microcircuit and network levels are the stochastic characteristics of ion channel gating and activation of the synaptic conductance. Studies using in vivo, in vitro, and in silico methods to examine the effects of synaptic background activity were not adequately investigated in non-neuronal excitatory cells, where neurotransmitter-based innervation also occurs. We created a mathematical model to replicate the background synaptic noise dynamics in a non-neuronal cell. We utilized the stochastic Ornstein-Uhlenbeck process to represent excitatory synaptic conductance, which was incorporated into a whole-cell model to produce spontaneous and evoked cellular electrical activities. The single-cell model includes many biophysically detailed ion channels represented by a set of ordinary differential equations in Hodgkin-Huxley and Markov formalisms. This paradigm effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potential (sAP) resembling in vitro-like electrical activity in the cells. The input resistance decreased by multiple factors, and the spontaneous action potential firing rate elevated. The potential to reach the action potential threshold is altered. Background synaptic activity can alter the input/output characteristics of non-neuronal excitatory cells. Suppressing these baseline activities would facilitate the discovery of new pharmaceutical targets for different clinical diseases.

The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

The anthropometric features offer an overall image of the nutritional status. The investigated groups were aged 19 and 21. The anthropometric measurements of height and weight were recorded following standard procedures. The objectives of this study were to evaluate the harmonious body development, the nutrition status and the body composition. The evolution of the stature increases until 20 years after which stagnation follows. The body mass index of subjects was found to be normal. The body fat may vary over time in subjects depending on age and gender. Body water and body muscle mass content increased with age. The harmony of body development was assessed by means of the Amar index. Regarding the constitutional index, the women subjects belong to the leptosome type and the male subjects belong to the picnic type. The status of the investigated subjects is within the average stature.

The soil organic matter (SOM) content and stability in natural gypsum soils and soils from mining/industrial areas were compared, in terms of their physical, chemical and micromorphological properties, to determine whether the old brownfield soils were acting as carbon (C) sinks, counteracting increasing carbon dioxide concentrations in the atmosphere. The mining/industrial soils were 50–400 years old, so had been left sufficiently long for full self-restoration. Despite having very different origins, both natural and brownfield soils stored similar amounts of SOM, and had similar pH values, calcium carbonate contents and textures. They were also fully covered with similar grass plant communities. The mining/industrial soils exhibited similar microstructures to the gypsum soils and were often quite porous. The most striking difference between them and the natural gypsum soils was that the mining/industrial soils were strongly polluted with zinc, lead and cadmium. The 50-year-old brownfield soils differed from the others in terms of having a lower state of SOM transformation, with a much higher C/N ratio, lower aggregate water resistance index, lower dehydrogenase activity and greater areas of undecomposed or slightly decomposed plant residues. These parameters in the older (150–400 years) brownfield soils were similar to those of the natural soils.

School-aged children may benefit from education interventions focused on healthy eating and physical activity to improve their quality of life. This article aims to review scientific evidence on food and nutrition education (FNE) in Spanish-speaking countries for the prevention of overweight and obesity in school-aged children (6-12 y). Articles were searched between April and August 2021. The searches were conducted using MEDLINE, Google Scholar, and SciELO. The primary search for articles focused on the experience of FNE interventions in Spanish-speaking countries. Overall, 518 articles were found and 33 studies were eligible for data extraction. Forty-two percent of the studies had a focus on FNE and 64% were focused on describing the eating habits of schoolchildren. Nutritional assessments (anthropometric) were found frequently with 91% and 30% of the studies focused on physical activity. All interventions focused on preventing or reducing overweight and obesity in school-age children. Educational, cognitive, dietary, and physical activity practices were described, whit being educational the most frequent intervention. Interventions reported positive effects of FNE interventions to improve knowledge and practices of healthy lifestyles in school-age children.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

This study investigates the effect of perceived discrimination on the mental health of Afghan refugees, and secondly, tests the distress moderating effects of pre-migration traumatic experiences and post-resettlement adjustment factors. In a cross-sectional design, 259 Afghans completed surveys assessing perceived discrimination and a number of other factors using scales developed through inductive techniques. Multivariate analyses consisted of a series of hierarchical regressions testing the effect of perceived discrimination on distress, followed by a sequential analysis of moderator variables. Perceived discrimination was significantly associated with higher distress, and this relationship was stronger among those with a strong intra-ethnic identity, high civic engagement, and high pre-resettlement traumatic experiences. Discrimination is a significant source of stress for Afghan refugees, which may exacerbate stresses associated with other post-migration stressors. Future research is needed to tailor interventions that can help mitigate the stress associated with discrimination among this highly vulnerable group.

We interviewed 48 people including local communities, ex hunters and protected area management professionals. The purpose of the interviews was to understand the motivations for, and the nature of, illegal hunting of prey species of iconic predators - tigers and leopards - in the northern section of Bardia National Park. Participants reported that hunting of prey species occurs mostly in spring and autumn and is less common during the summer. In the past, hunting was primarily for the purposes of obtaining meat for household consumption. Since the introduction of a road network in the region, opportunities to sell wild meat at ad-hoc ‘highway markets’ have developed. The purported medicinal properties of wild meat was also cited as a driver for illegal hunting. Mostly, locally hand-made guns are used for hunting and the use of dogs in hunting was often reported. Protected area managers informed that illegal hunting problems in the study area are associated with a lack of presence of park authorities, remoteness and underdevelopment and poverty of the community. Our study suggested that skills development training for local community members might reduce dependency on wild meat for household consumption and earning thereby reducing illegal hunting.

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used in ophthalmology for visualizing retinal layers, aiding in early detection and monitoring of retinal diseases. OCT is useful for detecting diseases such as age-related macular degeneration (AMD) and diabetic macular edema (DME), which affect millions of people globally. Over the past decade, the area of application of Artificial Intelligence (AI), particularly Deep Learning (DL), has significantly increased. The number of medical applications is also rising, with solutions from other domains being increasingly applied to OCT. The segmentation of biomarkers is an essential problem that can enhance the quality of retinal disease diagnostics. For 3D OCT scans, AI is beneficial since manual segmentation is very labor-intensive. In this paper, we employ the new SAM 2 and MedSAM 2 for the segmentation of OCT volumes for two open-source datasets, comparing their performance with the traditional U-Net. The model achieved an overall Dice score of 0.913 and 0.902 for macular holes (MH) and intraretinal cysts (IRC) on OIMHS and 0.888 and 0.909 for intraretinal fluid (IRF) and pigment epithelial detachment (PED) on the AROI dataset, respectively.

Megaselia scalaris and Senotainia tricuspis are two parasitoid flies of the honeybee Apis mellifera and are known to infest apiaries of different European and Mediterranean countries. However, their prevalence is still little known and no systematic control of their presence and countermeasures are currently adopted. To achieve a better knowledge about their distribution in apiaries, quantitative PCR (qPCR) based methods for their rapid detection directly in hive matrices were developed in this study. These were applied to samples of adult honeybees and hive debris collected in the summer season in Central Italy and Kosovo. In total 16 samples per type per region were analyzed. Both flies were detected in the two geographical contexts but more frequently in Central Italy. The positive apiaries in Central Italy were re-sampled one year later by capturing viable honeybees and isolating emerging flies after differentiation into adults. These were genotyped by sequencing regions of the mitochondrial cytochrome oxidase subunit I gene COI for M. scalaris and cytochrome B gene cytB for S. tricuspis amplified with specific primers designed in this study. Different genotypes of M. scalaris were detected and these were related to biotypes from distant nations. Some fly isolates were shown to harbor DNA or RNA of honeybee microbial pathogens. Results suggested to implement efficient control against these parasites in apiaries to limit their detrimental effects. The qPCR tests that have been developed can facilitate larger screening studies and early detection.

The utilisation of non-Saccharomyces yeasts in co-inoculation and non-thermal technologies for must sterilisation is becoming increasingly prevalent due to its notable utility and potential. This approach optimises the fermentation process, facilitates the production of wines with distinctive characteristics and enhances their stability. Two trials were conducted concurrently, designated as A and B. The same red must was utilized in both trials, with one undergoing UHPH treatment and the other serving as the control. The non-Saccharomyces yeasts were identical in both trials. In Trial A, different percentages of initial inoculum were employed with respect to the total volume of must to be fermented, with the objective of evaluating the influence and competitiveness between yeasts. Trial B was designed to investigate the impact of two nutrients that provide vitamins, energy and protection from oxidative stress on the development of these yeasts and their metabolic expression. A microbiological analysis was conducted, along with oenological parameter measurements, acidification assessments, volatile compound analyses, and spectrophotometric evaluations. It was observed in both trials that the prevalence of Lachancea thermotolerans (Lt) was longer than that of Metschnikowia pulcherrima (Mp), and that the use of quercetin + thiamine had a positive effect on yeast growth. Furthermore, the combination of Lt and Mp yeasts demonstrated remarkable synergy, resulting in the production of a substantial quantity of lactic acid (&gt;5 g/L). With regard to aroma compounds, the UHPH must exhibited a nearly twofold increase in ethyl lactate. Additionally, the TPI was observed to be 8-10% higher in wines derived from UHPH musts, indicating that this technology may potentially safeguard against oxidation.

Bloody stools in the newborn may be a clue for several clinical entities of varying severity ranging from a transient hematochezia to food protein induced allergic proctocolitis (FPIAP) or necrotizing enterocolitis (NEC). To distinguish among them at an early stage is challenging, but crucial, as the treatment and prognosis are different. We conducted a monocentric retrospective study including all preterm infants admitted to the NICU of the Vittore Buzzi Children’s Hospital (Milan) with bloody stools from December 2022 to May. Patients diagnosed with NEC exhibited a significantly lower eosinophil count and higher procalcitonin levels than both patients with CMPA and patients with transient hematochezia, as well as a statistically significant increase in pathological features at abdomen ultrasounds and abdominal X-rays. In contrast, no Lab markers or imaging have been demonstrated to be useful in distinguishing between transient hematochezia and FPIAIP. Thus, after excluding a diagnosis of NEC the only way to confirm the FPIAP is the oral food challenge, which can be performed even in premature newborns presenting blood stools, otherwise healthy, under medical supervision, in order to identify infants who may benefit from a cow’s milk free diet.

Background: Depression presents with altered energy regulation and neural plasticity. Previous electroencephalography (EEG) studies showed that practice increases power in beta range (13-30 Hz) in healthy subjects but not in those with impaired plasticity. Here, we ascertain whether depression presents with alterations of spectral activity and connectivity before and after practice. Methods: We used publicly available resting state EEG (64 electrodes) of 122 subjects. Based on Beck Depression Inventory (BDI) score, they were assigned to either high BDI (hBDI, BDI&gt;13, N=46) or control (CTL, BDI&lt;7, N=75) group. We analyzed spectral activity, theta-beta and theta-gamma phase-amplitude coupling (PAC) of EEG recorded before and after a learning task. Results: At baseline, compared to CTL, hBDI group exhibited greater beta power over fronto-parietal regions and gamma over right parieto-occipital area. After practice, increases in all frequency ranges were observed only in CTL. Theta-beta and theta-gamma PAC were greater in hBDI before the task but not after the task. Conclusions: The lack of substantial post-task growth of beta power in depressed subjects likely represents power saturation due to greater baseline values. We speculate that inhibitory/excitatory imbalance, altered plasticity mechanisms, and energy dysregulation present in depression may contribute to this phenomenon.

Hyperspectral imaging data holds great potential for various stages of the mining life cycle in active and abandoned mines. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite im-agery yields high spectral resolution, high signal-to-noise ratio (SNR), and global availability with breakthrough satellite sys-tems like EnMAP, EMIT, and PRISMA, limited spatial resolution poses challenges for mining sectors, which require decimeter- to centimeter-scale spatial resolution for applications such as reconciliation, ore/waste estimates, geotechnical assessments, and en-vironmental monitoring. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the pit/ mine scale, with the capability for frequent, user-defined re-visit times. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers to detect different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we review of applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging in or appropriate to the mining sector, especially for hyperspectral VNIR-SWIR UASs. If directly applicable, draw on previous insights derived from airborne, satellite, and ground-based imaging systems. We also discuss common practices for UAS survey planning and sampling considerations for interpretation.

This paper examines the use of multisensory virtual reality (VR) as a novel approach in psychological therapy for autobiographical memory retrieval with benefits for cognitive enhancement, stress reduction etc. The research in this paper was done under Romanian Arut Research Grant no. 27/09/10/2023 ”Intelligent VR system for treating autobiographical/episodic memory deficits".  By simulating realistic multisensory experiences, VR technology offers an immersive environment that surpasses traditional therapeutic methods in effectiveness. Previous studies demonstrate improved outcomes in treating various psychological conditions (affective disorders and PTSD, specific phobias and age-related cognitive decline). Technological advancements in VR, such as olfactory integration cand contribute to the realism and therapeutic potential of these environments, the integration of various physical stimuli with VR holds promising potential psychological therapies and highlighting the need for further interdisciplinary research. In this pilot study we tested the efficacy of a new system for triggering autobiographical memory retrieval. For this we used images combined with odors in a congruent manner and offering participants the chance to interact with the VR environment by using 2 virtual hands. We evaluated the efficacy of this system using qualitative methods with emphasis on the evaluation of the emotions associated with memory recollection and the ease of triggering memories. All participants in our pilot study experienced intense emotions related to childhood or adolescence and the pleasant feelings they had during the experiment persisted even after it ended. This is an advancement to what exists currently and original research elements for our paper.

The increasing emergence of multidrug resistant (MDR) pathogens, due to antibiotics misuse, translates in obstinate infections, with high morbidity and high-cost hospitalizations. To oppose these MDR superbugs, new antimicrobial options are necessary. Although both quaternary ammonium (QASs) and phosphonium salts (QPSs) possess antimicrobial effects, QPSs have been studied at lesser extent. Recently, we have successfully reported on the bacteriostatic and cytotoxic effects of a triphenyl phosphonium salt against MDR isolates of Enterococcus and Staphylococcus genus. Here, aiming at finding new antibacterial devices possibly bactericidal towards a broader spectrum of clinically relevant bacteria, responsible for severe human infections, we synthesized a water-soluble, sterically hindered quaternary phosphonium salt (BPPB). It encompasses two triphenyl phosphonium groups linked by a C12 alkyl chain, thus embodying the characteristics of molecules known as bola-amphiphiles. BPPB was characterized by ATR-FTIR, NMR and UV spectroscopy, FIA-MS (ESI), elemental analysis and potentiometric titrations. Optical and DLS analyses evidenced BPPB tendency to self-forming spherical vesicles in solution (45 nm by DLS), having a positive zeta potential (+18 mV). The antibacterial effects of BPPB were for the first time assessed against fifty clinical isolates of both Gram-positive and Gram-negative species. Excellent antibacterial effects were observed on all strains tested, involving all the most worrying species included in ESKAPE bacteria. The lowest MICs were 0.250 µg/mL, while the highest ones (32 µg/mL) were observed on MDR Gram-negative metallo-β-lactamase-producing bacteria and/or species resistant also to colistin, carbapenems, cefiderocol, so that intractable with current available antibiotics. Moreover, when administered to HepG2 human hepatic and Cos-7 monkey kidney cell lines, BPPB showed selectivity indices &gt; 10 for all Gram-positive isolated and for clinically relevant Gram-negative superbugs such as those of E. coli species, thus being very promising to be clinically developed.

This article provides a detailed analysis of non-invasive techniques for prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state-of-the-art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to enhance fault diagnosis, as well as underscore the crucial role of non-invasive techniques in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

(1) Background: LUS has emerged as a simple, rapid and non-invasive method for the dynamic assessment of pulmonary congestion, a major prognostic factor and a therapeutic target in AHF. (2) Methods: In the single-center prospective observational study, 42 patients hospitalized for AHF in the post-emergency polyvalent medicine department of CHU Grenoble were successively included between May 2021 and July 2022. Clinical examination, LUS and electrolyte panel results were collected daily. Vital status was assessed 30 days after the last LUS. The primary endpoint was the evolution of the number of B-lines according to the dose of diuretic administered ; (3) Results: We performed 188 LUS. Our patients were elderly (85.8 [SD 8.1]) and comorbid. The median number of B-lines decreased from 17 at admission to 7 in the middle of the hospital stay, then stagnated until discharge. Simultaneously, the median dose of diuretics, initially stable at a maximum of 40 mg/d IV, decreased to 20mg/d IV by the end of hospital stay. Patients with CKD had a higher mean number of B-lines at entry (24.2 [SD 11.6]) compared to those without CKD (8.2 [SD 8.8]). However, the evolution of B-lines number was independent of creatinine levels. 30-day all-causes mortality was significantly related to the number of B-lines at discharge (15.2 [SD 10.1] in deceased patients versus 3.9 [SD 5.4] in living patients, p

There is limited real-world data from India examining the treatment characteristics, safety, and efficacy of ceftazidime-avibactam against Gram-negative organisms especially multidrug-resistant pathogens including Carbapenem-Resistant Enterobacterales and Carbapenem-Resistant Pseudomonas. In this retrospective study, the real-world treatment patterns, effectiveness, and safety of ceftazidime-avibactam in treating Gram- negative infections were assessed. Data was extracted from electronic health records of adult patients admitted to the hospital with documented Gram-negative infection who had received treatment for at least 48 hours with ceftazidime-avibactam as a part of routine clinical management. Among the 189 patients, on day 3, clinical symptoms improvement was recorded in 79.6% of patients. Clinical success was achieved in 79.5% and 76.3% of assessed patients on day 7 and day 14/End-of-treatment, respectively. Microbiological success was reported in 76% of patients on day 7 and in 60.3% of patients on day 14 or EOT. The mean treatment duration of ceftazidime-avibactam therapy was 6.92 (± 4.1) days. No new safety concerns were identified. In conclusion, this study provides real-world evidence on treatment patterns and clinical outcomes associated with ceftazidime-avibactam in India, complementing the previously reported literature. The results suggest ceftazidime-avibactam is an effective and tolerable option for the management of Gram-negative infections in critically ill patients.

(1) Background: Second Victims are health care workers negatively affected by adverse events and are prone to developing dysfunctional coping strategies compromising not only their own, but also patient safety. An effective way to address this issue is the implementation of peer support programs. This paper reports on the effectiveness of a peer support program at the Austrian Hietzing Clinic (KHI). (2) Methods: Telephone interviews were carried out after peer support sessions using a questionnaire. Descriptive analysis was carried out reporting frequencies and proportions for nominal variables, means with standard deviations for ordinal and interval-scaled variables. (3) Results: So far, 45 peer supporters have completed telephone interviews after a peer support session. The main reasons for the session were bad news from the private sphere and death of a patient. The peer supporters rated the helpfulness of the sessions for their colleagues as very high while not feeling very burdened themselves afterwards. (4) Conclusions: Underutilization of the program is probable. However, the high rating of helpfulness of the carried-out sessions by the peer supporters in combination with them not feeling very burdened, shows that the program is safe for the peer supporters and an upscaling is warranted.

Introduction: Type 1 diabetes mellitus is a chronic disease of autoimmune etiology that is highly prevalent in childhood. In the last decade, the possibility of using the Bacillus Calmette-Guérin vaccine for the treatment of this disease has been studied thanks to its immunomodulatory action . Objectives: The objective of this work is to carry out a systematic review of the available scientific evidence on the relationship between the tuberculosis vaccine and the treatment of diabetes in childhood. Secondarily, the aim is to study the incidence of Type 1 Diabetes Mellitus in a cohort of children from the Basque Country according to whether or not they have received the vaccine. Methods: An exhaustive bibliographic search has been carried out on the evidence available to date. For the epidemiological study in Euskadi, a comparison of accumulated incidences between vaccinated and unvaccinated population groups was made. Results: According to the literature analyzed, There is more and more evidence about the possible use of the tuberculosis vaccine in Type 1 Diabetes Mellitus. The results of our study were not conclusive, but we believe that it is necessary to continue with long-term studies to eliminate biases. Conclusions: The Bacillus Calmette-Guérin vaccine could be used for the treatment of autoimmune, allergic, infectious or oncological processes, although more studies are still necessary in this regard.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

This study utilized phytochemical screening to conduct qualitative analysis of plant extracts, aiming to identify various classes of secondary metabolites. Moreover, the antibacterial activity of different types of O. vulgare and S. triloba extracts was determined. To achieve the aim of the study, aqueous, ethanolic, and enzymatic extracts were prepared and screened for phytochemical capacity and antioxidant activities. Determination of the antibacterial activity included phenotypic screening of antibiotic susceptibility pattern of oral and food pathogenic bacterial strains, determination of Minimum inhibitory concentration and minimum bactericidal concentration - Microdilution broth test and in vitro valuation of antibacterial efficacies - anti-biofilm properties of the studied herbal extractions. Results: Our study evaluated the phytochemical composition and the antioxidant, antibacterial, and anti-biofilm properties of O. vulgare and S. triloba extracts. The analyzed samples contained bioactive compounds, such as phenolics and flavonoids, contributing to the observed strong antioxidant effect. Furthermore, they exhibited notable activity against oral biofilm formation and demonstrated significant antibacterial efficacy against dental caries microorganisms as well as food pathogens. Despite methodological variations, all extracts showed significant antioxidant capacity and promising antibacterial activity against various pathogens, including resistant strains, while also inhibiting biofilm formation. Although limited to two plant species and facing methodological constraints, the study lays a groundwork for future research, indicating the therapeutic potential of O. vulgare and S. triloba extracts. Further exploration is needed to report on underlying mechanisms and validate efficacy through clinical trials.

The difficulties faced by the society in its goal of reducing mortality and improving the health status during the period of the COVID-19 pandemic showed that it is necessary to develop new approaches in the therapy of infectious diseases, leading to higher treatment efficiency and few side effects. The subject of the present study is the formation and characterization of the proper-ties of stable liposomes loaded with extracts from Bulgarian medicinal plants and monitoring their antiviral action against the human strain of coronavirus OC43 (HCoV-OC43) in vitro. The hydrodynamic diameter and -potential of the liposomes containing each type of payload were determined with DLS and electrokinetics. The encapsulated amount of extracts was evaluated against quercetin and gallic acid by using colourimetric methods. The BALB 3T3 Neutral Red Uptake (NRU) phototoxicity/cytotoxicity assay was used to estimate the safety of the compounds. For assessment of the antiviral activity against HCoV-OC43 of liposomes (Lip) containing medicinal plant extracts, the cytopathic effect inhibition test was used. Measurements of the virucidal activity and the effect on the virus adsorption stage was carried out using the endpoint dilution method, and ∆lgs were calculated by comparison with untreated controls. The estimated photo irritation factors (PIF) for the Lip containing extracts were < 2 which assigns them to the category of non-phototoxic substances. When the extracts were introduced into the liposomes as their carriers, the antiviral activity of the vesicles against the replication of HCoV-OC43 in vitro in-creased by several times compared to their individual activity described in our previous study. The determination of the effect of structures loaded with extracts on the extracellular virions and the stage of their adsorption to sensitive HCT-8 cells proved that the liposomes do not affect directly the viral surface or cell membrane, but only perform a function of carriers of the active substances and the observed result is due solely to the intracellular action of the extracts.

Understanding the interplay among organophosphorus flame-retardants (OPFRs), microplastics, and freshwater organisms is crucial for unravelling the dynamics within freshwater environments and foreseeing the potential impacts of organic pollutants and plastic contamination. For that purpose, the present research assessed the exposure impact of 10 mg L–1 flame-retardant alumin-ium diethylphosphinate (ALPI), 10 μg mg–1liver microplastics polyurethane (PU), and the combina-tion of ALPI and PU on the freshwater planarian Girardia tigrina. The exposure of G. tigrina to both ALPI and PU revealed a sequential effect, i.e., a decrease in locomotor activity, while other molecular biomarkers (oxidative stress biomarkers and metabolic response) remained unaffected. Despite this fact, it was possible to observe that the range (min-max) of responses of exposed or-ganisms were altered, in the particular case of electron transport system, cholinesterase activity, glutathione S-transferase, catalase and levels of total glutathione and proteins, showing that the energetic costs for detoxification and antioxidant capacity might be causing less energy allocated for the planarian activity. By examining the physiological, behavioural, and ecological responses of planarians to these pollutants, insights can be gained into broader ecosystem-level effects and inform strategies for mitigating environmental risks associated with OPFRs and microplastic pol-lution in freshwater environments.

Succinic semialdehyde dehydrogenase (SSADH) is a mitochondrial enzyme involved in the catabolism of the neurotransmitter γ-amino butyric acid. Pathogenic variants in the gene encoding this enzyme cause SSADH deficiency, a developmental disease manifesting as hypotonia, autism and epilepsy. SSADH deficiency patients usually have family-specific gene variants. Here, we describe a family exhibiting four different SSADH variants, Val90Ala, Cys93Phe, and His180Tyr/Asn255Asp (a double variant). We provide a structural and functional characterization of these variants and show that Cys93Phe and Asn255Asp are pathogenic variants that affect the stability of the SSADH protein. Due to impairment of the cofactor NAD+ binding, these variants show a highly reduced enzyme activity. However, Val90Ala and His180Tyr exhibit normal activity and expression. The His180Tyr/Asn255Asp variant exhibits a highly reduced activity as a recombinant species, is inactive and shows a very low expression in eukaryotic cells. A treatment with substances that support protein folding by either increasing chaperone protein expression or by chemical means was not capable of increasing the expression of the pathogenic variants of the SSADH deficiency patient. However, stabilization of the folding of pathogenic SSADH variants by other substances may provide a treatment option for this disease.

Hyperspectral imaging data holds great potential for the different stages of the mining life cycle in active and post-mining environments. However, the technology has yet to reach the stage of large-scale industrial implementation and acceptance. While hyperspectral satellite imagery can achieve high spectral resolution, signal-to-noise ratio (SNR) and global availability with break-through satellite systems like EnMAP, EMIT and PRISMA, limited spatial resolution poses chal-lenges for sectors like mining, which require decimetre to centimetre scale resolution for applica-tions such as reconciliation, ore/waste estimates, geotechnical assessments and environmental monitoring. Hyperspectral imaging from drones (referred to herein as Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the camp/ mine scale, with the capability for frequent, user-defined re-visit times. This has been made possible by the miniaturization of hy-perspectral imaging systems. Collection of data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions enables the detection of different minerals and surface altera-tion patterns potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we provide a review of relevant studies de-ploying hyperspectral imaging in or applicable to the mining sector, especially for the use of hy-perspectral VNIR-SWIR Uncrewed Aerial Systems. Where required, we draw on previous in-sights derived from satellite or ground-based systems. We also discuss UAS survey planning, and sampling considerations for validation and interpretation.

This study aimed to investigate the knowledge of patients with hypertension about their condition, adherence to antihypertensive medication, and the factors influencing it. A cross-sectional study was conducted in two cardiology outpatient clinics of two tertiary hospitals, in Greece. The study included 188 patients diagnosed with hypertension. The patient’s knowledge about their disease and adherence to medication were assessed by using the HK-LS and A-14 scales, respectively. Patients had sufficient knowledge levels about their disease, but significantly low levels of adherence to medication. Patients with higher knowledge levels were more adherent to medications [r(188) = 0.885, p &lt; 0.001]. By using multivariate analysis, higher age (p = 0.018), residence in a more populous area (p = 0.041), more years with the disease (p = 0.012), and a lower number of medications (p = 0.03) were associated with higher levels of knowledge. Conversely, younger age (p = 0.036), lower educational levels (p = 0.048), fewer years with the disease (p = 0.001), and a higher number of medications (p = 0.003) were associated with lower adherence to medication. The Greek patients' hypertension knowledge was sufficient, however, adherence to medication was significantly low. Healthcare managers could utilize our findings to design targeted interventions for improving adherence to medication for these patients.

Atherosclerosis, a complex metabolic-immune disease characterized by a chronic inflammation driven by the buildup of lipid-rich plaques within arterial walls, has emerged as a pivotal factor in the intricate interplay between cancer and cardiovascular disease. This bidirectional relationship, marked by shared risk factors and pathophysiological mechanisms, underscores the need for a comprehensive understanding of how these two formidable health challenges intersect and influence each other. Cancer and its treatments can contribute to the progression of atherosclerosis while atherosclerosis with its inflammatory microenvironment can exert profound effects on cancer development and outcomes. Both cancer and cardiovascular disease involve intricate interactions between general and personal exposomes. In this review we want to summarize the state-of-the-art of translational data and try to show how oncologic studies on cardiotoxicity can broaden our knowledge of crucial pathways in cardiovascular biology with a positive impact on precision cardiology and cardioncology.

The beneficial effects of Lanthanide incorporation into 0.94Na0.5Bi0.5TiO3-0.06BaTiO3 (BNT-BT) matrix on its functional properties were investigated. The conventional solid-state method was used for synthesizing samples. The structural refinement revealed that all samples crystallized in R3c rhombohedral symmetry. Raman spectroscopy study was carried out using green laser excitation and revealed that no clear perceptible variation in frequency is observed. Dielectric measurements unveiled that the introduction of rare earth obstructed the depolarization temperature promoted in BNT-BT with decreasing the diffusive phase transition with increasing the lanthanide size. Only Dysprosium addition showed comparable diffusion constant and dielectric behavior as the unmodified composition. Further, the comparison of the obtained ferroelectric hysteresis and strain-electric field loops revealed that only Dy-phase exhibited interesting properties comparing parent composition. In addition, the incorporation of lanthanides Ln3+ into the BNT-BT matrix led to the development of luminescence characteristics in the visible and near infrared regions, depending on the excitation wavelengths. The simultaneous occurrence of photoluminescence and ferroelectric/piezoelectric properties opens up possibilities for BNT-BT-Ln to exhibit multifunctionality in a wide range of applications.Keywords: Sodium Bismuth Titanate; structural refinement; dielectric properties; ferroelectric properties; piezoelectric properties; Photoluminescence.

An 8-months old male American Staffordshire terrier was referred for a no weightbearing lameness of the right pelvic limb, hyperthermia, lethargy and inappetence caused by hematogenous osteomyelitis. Orthopedic, radiographic and tomographic examinations revealed a bone sequestrum of 4 x 1.4 cm and active periosteal reaction of the caudo-lateral cortical in the metaphysis and the distal third of the right femoral diaphysis, medullary osteolysis and interruption of the cranio-medial cortical profile, with involvement of the femoral trochlea leading to a secondary medial patella luxation. Once skeletally mature, after 4 months from surgical debridement and aggressive antibiotic therapy against Klebsiella oxytoca revealed by bacteriological exam, the patient underwent prosthetic surgery for the application of a custom-made patellar groove replacement (PGR) to fill the bone defect and restore the femoral trochlea surface. Despite the serious injury that afflicted the right pelvic limb, the surgery had satisfactory outcomes until the last 18-months postoperative follow up.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Adenoviruses (Ads) have been extensively manipulated for the development of cancer selective replication, leading to cancer cell death or oncolysis. Clinical studies using E1-modified oncolytic Ads have shown that this therapeutic platform was safe, but with limited efficacy, indicating the necessity of targeting other viral genes for manipulation. To improve the therapeutic efficacy of oncolytic Ads, we treated the entire Ad genome repeatedly with UV-light and have isolated AdUV which efficiently lyses cancer cells as reported previously [1]. In this report, we show that no mutations were observed in the early genes (E1 or E4) of AdUV while several mutations were observed within the Ad late genes which have structural or viral DNA packaging functions. This study also reported the increased release of AdUV from cancer cells. In this study, we found that AdUV inhibits tumor growth following intratumoral injection. These results indicate the potentially significant role of the viral late genes, in particular the DNA packaging genes, to enhance Ad oncolysis.

Objective: With just ten questions, the Berlin questionnaire (BQ) stands out as one of the simplest and most widely implemented non-invasive screening tools for detecting subjects at high risk for Obstructive Sleep Apnea (OSA), a still underdiagnosed syndrome characterized by partial or complete obstruction of the upper airways during sleep. The main aim of this study was to enhance the diagnostic accuracy of the BQ through Machine Learning (ML) techniques. Methods: A ML classifier (hereafter, ML-10) was trained using the ten questions of the standard BQ. A simplified variant of the BQ, BQ-2, which comprises only two questions out of the total of ten, was also assessed in a ML context. A 10-fold cross validation scheme was used. Ground truth was provided by the Apnea-Hypopnea Index (AHI) measured by Home Sleep Apnea Testing. Model performance was determined comparing ML-10 and BQ-2 with the standard BQ by the Receiver Operating Characteristic Curve (ROC), Area Under the Curve (AUC), sensitivity, specificity, and accuracy. Results: ML-10 demonstrated superior performance in predicting the risk for OSA compared to the standard BQ and was also capable in classifying OSA with two different AHI thresholds (AHI>=15, AHI>=30), typically used in clinical practice. Remarkably, BQ-2 was also better in sensibility to assess moderate to severe OSA (AHI≥ 15) compared to the ML-10. Conclusions: The study underscores the importance of integrating ML techniques for early OSA detection, suggesting a direction for future research to improve diagnostic processes and patient outcomes in sleep medicine.

Keywords: gut microbiota; small intestinal bacteria overgrowth; SIBO; diet, dysbiosis; microbial ecology.

Background: Schistosomiasis is the third most important Neglected Tropical Disease, with more than 4 million South Africans being infected. Cases of re-infection with schistosomiasis after being treated using modern medication has opened ways to seeking alternative treatment modalities using medicinal plants. This study aims to explore and understand the way traditional healers diagnose schistosomiasis, the types of traditional medicines they use to treat it in communities with a high prevalence of infection, and to establish whether the unavailability of prescribed anti-schistosomal medicines leads to the use of traditional medicine. Methods: A qualitative exploratory study was conducted using a questionnaire from December 2022 to March 2023 in the iLembe District, KwaZulu-Natal Province, South Africa. In-depth individual interviews were carried out among traditional health practitioners (THPs) involved in managing female urogenital schistosomiasis (FUS). Recorded narrative data was transcribed verbatim, read through thoroughly and coded, and analysed using thematic or content analysis Results: Traditional Health Practitioners provide services to many women who did not want to be treated in the mainstream health care system for their FUS. The disease was perceived and understood the same way by most of the respondents, that being caused by use of fresh water. Various plants species, either alone or in combination, were used to manage bilharzia in women. Senecio serratuloides (Asteraceae) and Hypoxis hemerocallidea (Hypoxidaceae) were prominently used by THPs in FUS management. Conclusion: Traditional Health Practitioners are playing a key role in managing bilharzia in women in rural areas of the iLembe District, this study reported that THPs can diagnose schistosomiasis symptoms based on their knowledge and experience. Respondents indicated that modern conventional treatment was readily available and free in public sector healthcare facilities; however, they consulted patients suffering from FUS. Various traditional herbal medicines are reportedly used by THPs to manage bilharzia in women. Further studies are needed to investigate the views and experiences of clients regarding the success of the treatments used and why they seek care from THPs.

Patients with metastatic epidural spinal cord compression (MESCC) and favorable survival prognoses may benefit from radiation doses exceeding 10 x 3.0 Gy. In a multi-center phase 2 trial, patients receiving 15 x 2.633 Gy (41.6 Gy10) or 18 x 2.333 Gy (43.2 Gy10) were evaluated for local progression-free survival (LPFS), motor/sensory functions, ambulatory status, pain, distress, toxicity, and overall survival (OS). They were compared (propensity score adjusted Cox regression) to a historical control group (N=266) receiving 10 x 3.0 Gy (32.5 Gy10). In the phase 2-cohort, 50 (of 62 planned) patients were evaluable for LPFS. Twelve-month rates of LPFS and OS were 96.8% and 69.9%, respectively. Motor and sensory functions improved in 56% and 57.1% of patients, and 94.0% were ambulatory following radiotherapy. Pain and distress decreased in 84.4% and 78.0% of patients. Ten and two patients experienced grade 2 and 3 toxicities, respectively. Phase 2 patients showed significantly better LPFS than the control group (p = 0.039) and a trend for improved motor function (p = 0.057). Ambulatory and OS rates were not significantly different. Radiotherapy with 15 x 2.633 Gy or 18 x 2.333 Gy was well tolerated and appeared superior to 10 x 3.0 Gy.

Crigler-Najjar Syndrome (CNS) is a rare genetic disorder caused by mutations in the UGT1A1 gene, leading to impaired bilirubin conjugation and severe unconjugated hyperbilirubinemia. CNS presents in two forms: CNS type 1 (CNS1), the more severe form with a complete absence of UGT1A1 activity, and CNS type 2 (CNS2), with partial enzyme activity. CNS1 requires aggressive management, including phototherapy and plasmapheresis, but liver transplantation (LT) remains the only definitive cure. The timing of LT is critical, as it must be performed before the onset of irreversible brain damage (kernicterus), making early intervention essential. However, LT poses risks such as graft rejection and lifelong immunosuppression. CNS2 is milder, with patients re-sponding well to phenobarbital and having a lower risk of kernicterus. Recent advancements in gene therapy and autologous hepatocyte transplantation offer promising alternatives to LT. Gene therapy using adeno-associated virus (AAV) vectors has shown potential in preclinical studies, though challenges remain in pediatric applications due to liver growth and pre-existing immunity. Autologous hepatocyte transplantation avoids the risks of rejection but requires further research. These emerging therapies provide hope for more effective and less invasive treatment options, aiming to improve the quality of life for CNS patients and reduce reliance on lifelong interven-tions.

Endometrial cancer (EC) poses a significant global health challenge, with increasing prevalence in 26 of 43 countries and over 13,000 deaths projected in the United States by 2024. This rise correlates with aging populations, the obesity epidemic, and changing reproductive patterns, including delayed childbearing. Despite the early diagnosis in 67% of cases, approximately 30% of cases present with regional or distant spread, leading to nearly 20% mortality rates. Unlike many cancers, EC mortality rates are escalating, outpacing therapeutic advancements until recently. One of the reasons for this was the lack of effective therapeutic options for advanced disease until recently. The introduction of immunotherapy has marked a turning point in EC treatment, particularly benefiting patients with defects in mismatch repair proteins (dMMRs). However, dMMR status alone does not ensure a favorable response, underscoring the need for precise patient selection. This review explores the pivotal role of mismatch repair proteins in EC, emphasizing their heterogeneity, the challenges in their assessment, and their potential as predictive biomarkers.

Background: Regular moderate-vigorous intensity exercise is recommended for adults as it can improve longevity and reduce health risks associated with a sedentary lifestyle. However, there are barriers to achieving intense exercise that may be addressed using virtual reality (VR) as a tool to promote exercise intensity and adherence, particularly through visual feedback and competition. The purpose of this work is to compare visual feedback and competition within fully-immersive VR to enhance exercise intensity and user experience of exercise for young adults; and to describe and compare visual attention during each of the conditions. Methods: Young adults (21- 34 years old) bicycled in three 5-minute VR conditions (visual feedback, self-competition and competition against others). Exercise intensity (cycling cadence and % of maximum heart rate) and visual attention (derived from a wearable eye tracking sensor) were measured continuously. User experience was measured by an intrinsic motivation questionnaire, perceived effort and participant preference. A repeated measures ANOVA with paired t-test post-hoc tests was conducted to detect differences between conditions. Results: Participants exercised at a higher intensity and had higher intrinsic motivation in the two competitive conditions compared to visual feedback. Further, participants preferred the competitive conditions and only reached a vigorous exercise intensity during self-competition. Visual exploration was higher in visual feedback compared to self-competition. Conclusion: For young adults bicycling in VR, competition promoted higher exercise intensity and motivation compared to visual feedback.

The optimal combination of five components to develop a concrete for high-temperature applications was studied through a response surface methodology known as mixture design of experiment. The selected components were water, an alkali-activated binder based on ferronickel slag activated with potassium silicate and potassium hydroxide, and three aggregate sizes corresponding to 0-4 mm, 4-8 mm, and 8-16 mm. Selected response parameters were slump and compressive strength before and after heat exposure. Six cubes of side 150 mm were cast for each concrete mix, three for ambient and three for residual (post-heat exposure) compressive strength. All 20 concrete mixes were exposed to a thermal load of 600 °C for two hours. The optimal concrete mix was adapted for large-scale applications, by increasing slump and replacing limestone with olivine aggregates for enhanced residual compressive strength. The adapted mix resulted in concrete with 76 MPa and 32 MPa compressive strength before and after high-temperature exposure. The CO2 emissions were found to be 77 % lower when compared to traditional OPC concrete.

Perimenopause, the transitional phase leading up to menopause, affects millions of women worldwide, yet remains poorly understood and under-addressed in healthcare. This report investigates the significant impact of perimenopause symptoms on women's lives, emphasizing the often debilitating effects such as anxiety, depression, weight gain, and hot flashes, which collectively cost an estimated $1.8 billion annually in lost work time. Despite the availability of treatment options like Hormone Replacement Therapy (HRT) and non-hormonal alternatives, awareness and utilization of these options vary significantly among women. The report highlights the historical neglect of women's health issues and the need for improved communication between patients and healthcare providers. A cross-sectional survey conducted with 1,000 adults from the United States and Canada reveals widespread dissatisfaction with the quality of healthcare communication, underscoring the importance of personalized and comprehensive insights for women's health. The findings advocate for more at-home solutions and resources to empower women in managing perimenopause and menopause, promoting informed decision-making and reducing stigma. This study aims to enhance awareness and support for women during this critical life stage.

Fission-track modelling rests on etching, counting and measuring the lattice damage trails from uranium fission. The tools for interpreting fission-track data are advanced but the results are never better than the data. Confined-track samples must be an adequate size for statistical analysis, representative of the track population, consistent with the model assumptions and with the calibration data. Geometrical and measurement biases are understood and can be dealt with up to a point. The interrelated issues of etching protocol and track selection are however more difficult to untangle. Our investigation favours a two-step protocol. The duration of the first step is inversely proportional to the apatite etch rate, so that different apatites etch to the same Dpar. A long immersion reveals many more confined tracks, terminated by basal and prism faces. This allows consistent length measurements and permits to orient each track relative to the c-axis. Long immersion times combined with deep ion irradiation reveal confined tracks deep inside the grains. Provided it is long enough, the precise immersion time is not important if the effective etch times of the selected tracks are calculated from their measured widths. Then, whether the sample is mono- or multi-compositional, we can, post hoc, select tracks with the desired properties. The second part of the protocol has to do with the fact that fossil tracks in geological samples appear to be under-etched compared to induced tracks etched under the same conditions. This should be assumed if the semi-axes (l_C, l_A) of a fitted ellipse plot above the induced-track line. In that case an additional etch can increase the track lengths to a point where they are consistent with the model based on lab-annealing of induced tracks, a condition for valid thermal histories. Here too, it is possible to select a subset with effective etch times consistent with the model if the widths of confined tracks are measured along with their lengths and orientations.

Background: Since 2020, when the Covid-19 outbreak began, researchers worldwide have been investigating its biological processes, transmission patterns, and the mathematical expression of this infectious disease. Multiple mathematical models have been presented thus far, with the simplest being the SIR model. The question now is whether the SIR model can provide a comprehensive representation of Covid-19 or if a more intricate model is necessary. With this in mind, the SEPAIVRD model has been developed. Objective: This study focuses on understanding the third wave of Covid-19 that began in mid-May 2021 in Bangladesh. To achieve this, we introduced three essential compartments in our model: Presymptomatic (P), Asymptomatic (A), and Vaccinated (V) classes. The Asymptomatic class will help us determine how close the population is to achieving herd immunity. The Presymptomatic class will provide insight into whether we can effectively control Covid-19 by identifying infected individuals over an extended period. Lastly, the Vaccinated class will allow us to explore how the Presymptomatic class affects those who have received the vaccine. The results of this study aim to address these questions. Findings: According to the study findings, an increase in vaccination rates could result in a decrease in the number of people getting infected every day, lessening the strain on healthcare resources and lowering the risk of transmission to others. Moreover, the research highlights the significance of using highly effective vaccines to combat COVID-19 cases and fatalities, particularly in areas with low vaccination rates. Additionally, implementing social distancing measures can be an effective approach to contain the spread of the virus by reducing the effective contact rate. Conclusion: The model presented in this study provides a comprehensive overview of Covid-19 transmission in Bangladesh. Furthermore, this model can be applied to other countries in South Asia and is therefore a valuable mathematical model for understanding Covid-19.

Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Under the new pattern of "Double-Cycle" economy, how to restructure the domestic economic structure to cope with the continuous softening of the global economic structure is an urgent issue. This paper empirically examines the impact of digitalization level on the division of labor in National Value Chain (NVC) within cities using panel data from 263 cities between 2001 and 2016, employing a spatial Durbin model.The study demonstrates that the digitization level can enhance division of labor in NVC in neighboring regions through spatial spillovers. An increase in digitization not only positively deepens the construction of NVC but also contributes to the development of NVC in adjacent areas.The digitization level can deepen the division of labor in NVC through the impact of cost reduction, consumption upgrading, and market integration. This effect exhibits regional variations, with consumption upgrading playing a crucial role in expanding the division of labor in NVC in the eastern region through improving digitization levels, while cost reduction is a key factor in the central region. The research results of this paper provide some empirical evidence for further progress and transition of China's NVC developing mode from the perspective of digital level.

Portland cement emits large amounts of CO2 in production. Given proposals for "carbon peaking and carbon neutralization" studying alternative low-carbon cementitious materials to reduce emissions is extremely important. Alkali-activated slag (AAS) cement, a new green cementitious material, has high application potential. The chemical corrosion resistance of AAS concrete is important for ensuring durability and prolonging service life. Studying the chemical corrosion mechanism of AAS concrete and understanding its influence on performance provides important theoretical foundations for application in different environments.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

The construction industry is an important part of the national economic market of various countries; since 2013, the construction industry's added value in the gross domestic product has been more than 6%, reaching 6.89% in 2022, and is a pillar industry of the national economy. Intelligent construction is the realistic demand to promote the high-quality development of China's construction industry. It is the key focus of transforming and upgrading the traditional construction industry to information, digital, and intelligent. As a new production factor, construction robots have become the key to promoting intelligent construction. Under the guidance of various national and industry policies, thanks to China's huge construction market volume and rich application scenarios, many innovative and entrepreneurial entities have entered the field of construction robots. Architectural visualization is a crucial aspect of architectural design and communication. With the development of science and technology, artificial intelligence (AI) technology is increasingly becoming an essential tool for architectural visualization and communication. The emergence of AI technology has provided architects with more flexible and creative ways to present design ideas. All along, designers who love architecture have been passionate about exploring better solutions for architecture, but there is never an optimal solution for architectural design; AI is the fire of the future era; it brings us more opportunities but also forces us to face new challenges from the future.

Measuring host communities' perceptions and attitudes towards tourism development has been increasingly common since the mid-1970s. The greater the growth of the tourism industry worldwide, the more relevant it has become to assess the impact of tourism activity on the local residents. In this paper we present the results of a study carried out in two EU countries highly dependent on tourism act though at a different stage of development. Our findings are based on qualitative and quantitative data obtained via a questionnaire survey designed in pre-covid 19 times to analyze the perceptions and attitudes of tourism college students from Croatia and Spain, today’s tourism employers and employees, towards tourism development in sun and sea desti-nations. Our aim is to contrast the hypothesis that the nationality of our sample is a significant variable when evaluating the socioeconomic and environmental impact of the development of the tourism industry. Croatia’s workforce, the most highly dependent on the EU travel and tourism industry show to be much more aware of the negative impacts of tourism development than their Spaniards counterparts; showing a tourism workforce more reluctant to further tourism development.

Upholstery nonwoven fabric is a waste product mainly generated during upholstered furniture production. The polyester composition makes it problematic to recycle and reuse this product. This study examined the manufacturing process of nonwoven fabric-reinforced plywood composites and their selected mechanical and physical properties. Nonwoven fabric was integrated between veneers bound with urea-formaldehyde resin to improve standard layered composites' mechanical and physical properties. Several board variants were produced, differing in the position of the nonwoven layers in the composite structure. The composites were evaluated for modulus of rupture (MOR), modulus of elasticity (MOE), and screw withdrawal resistance, among others. The results showed that the addition of nonwoven fabric significantly improved some properties. Variants with strategically placed nonwoven layers showed the highest performance increases. The results underscore the potential of nonwoven fabric as an effective reinforcing material, offering a path to developing high-performance plywood composites suitable for demanding applications. Another, environmental profit is that the nonwoven fabric waste, that was used in tested plywood production, has not been subjected burning nor landfilling, but, by incorporation to plywood structure, have positively contributed to Carbon Capture and Storage (CCS) policy. The findings advocate for a circular economy approach, where industrial waste is effectively repurposed, contributing to the development of green materials in the wood-based composite industry.

Recycling upholstery textiles is challenging due to the complexity of materials, which often include a mix of fabrics, foams, and adhesives that are difficult to separate. The intricate designs and layers in upholstered furniture make it labor-intensive and costly to dismantle for recycling. Additionally, contaminants like stains, finishes, and flame retardants complicate recycling. Despite these difficulties, recycling upholstery textiles is crucial to reducing landfill waste and conserving resources by reusing valuable materials. It also helps mitigate environmental pollution and carbon emissions associated with producing new textiles from virgin resources. The presented research aimed to establish the feasibility of incorporating textile fibers from waste artificial leather fibers from the upholstery furniture industry into the structure of high-density fiberboards. This approach positively contributes to carbon capture and storage (CCS) policy and mitigates the problem of such waste being sent to landfills. The research shows that while selected mechanical and physical parameters of the panels decrease with a rising content of recycled textile fibers, it is possible to meet proper European standard requirements by adjusting technological parameters such as nominal density.

In this paper we introduce the Adomian decomposition method for a class of first order fuzzy dynamic equations on arbitrary time scales for existence of solutions. The results are provided with suitable numerical examples.

Based on the nonlinear failure criterion and modified tangential technique, the upper bound solutions of supporting pressure on the deep tunnel face were obtained under pore water pressure conditions. The influence of parameters on the supporting pressure and collapse range was investigated according to the unlimited block failure mechanism. It is found that the upper bound solutions of supporting pressure increase with the growth of the nonlinear coefficient and pore water pressure coefficient. The collapse range of the tunnel face scales out with the increase of the nonlinear coefficient and shrinks when increasing the pore water pressure coefficient. Moreover, with the increase of the nonlinear coefficient, the impact strength on supporting pressure and collapse range declines gradually. According to the calculating results, it is manifest that both the pore water pressure and nonlinear criterion factors have negative impacts on the stability of the tunnel face. Thus, more attention should be paid to these parameters to ensure face stability in deep tunnel construction.

This study presents a strategic approach to developing applications focused on implementing Large Language Models using the Langchain framework in Python. Three language models are highlighted: GPT-3.5 (turbo mode), LLaMA 2, and Vicuna 7B, each with their distinctive features and capabilities. The methodology used is described in detail, including data extraction from medical reports using zero-shot prompting data extraction techniques, interaction with language models, and structured storage of results. The performance of the models in data extraction is evaluated, presenting metrics such as precision, recall, and F1 score. The results demonstrate high model capability in extracting information, although areas for improvement are identified, particularly in data extraction precision. In conclusion, the efficacy of the models in extracting information from medical histories is not considerably acknowledged, with an emphasis on the importance of improving precision and increasing the volume of trained data for future research in healthcare digitalization.