Air pollution has been a major environmental issue worldwide from the last few decades. It is a man-made issue that has a serious harmful impact on the environment and the health of humans. There are different sources of emission, but industrial processes and motor vehicles contribute to a chief part of air pollution. It has acute as well as chronic effects on the health of humans and affects different organs. It is thought the main environmental risk factor in the incidence and progression of several diseases such as lung cancer, asthma, Alzheimer's and Parkinson's diseases. Long-term exposure to air pollution has different toxicological impacts on humans such as respiratory and cardiovascular diseases, neuropsychiatric complications, eye irritation and skin diseases. As per the study by the World Health Organization (WHO), nitrogen oxides (NOx), carbon monoxide (CO), ozone (O₃), sulfur dioxide (SO₂), volatile organic compounds (VOCs) and heavy metals are the major air contaminants. These pollutants are different in their properties, composition and time of disintegration. The aim of this study is to review the different sources, types, impacts, challenges and mitigation of air pollution in detail.

Segregating one space from another is a rudimentary act of architecture and the basic principle of this act is creating differentiation between outside and inside. Thresholds, in their expanded or summed form, also produce spaces. They establish the staging for threshold areas in conjunction with space-defining elements. Threshold spaces are typically complicated spatial formations that serve as transition points. They pose a challenge to the interacting human senses in terms of perception. In this research threshold areas have been studied under a specific typology of architecture i.e the traditional housing in historic cities of India. The traditional dwelling settlements have evolved and grown over multiple layers of history, influenced by the culture, society, economy, climate and topography. The purpose of this research is to examine the characteristics of these transition spaces of the traditional dwelling units in relation to the symbolic reference and social meanings assigned to them that influence the architectural and urban spaces attached to them. It seeks to describe transition space in terms of its nature, character, morphology, and social significance. Transparency and material articulation, as well as hierarchy and enclosure, will be investigated. The importance and quality of the nature of "transition" will be examined from the inside to the outside. The research reveals that the space has great functional and socio-cultural importance. It allows multiple activities, provides protection from sun and rain, defines boundaries, connects people within the neighborhood, enhances security and gives an identity to the owner. The contributions made by this research have the potential to improve design considerations for transition areas between the dwelling units and the street.

In order to explore the effect of Wickerhamomyces on the production of flavor compounds in Xiaoqu Baijiu, this study examined the correlation between the fungal communities in Xiaoqu and ester compounds. It was hypothesized that Wickerhamomyces contributes to the aroma of Xiaoqu. Enhanced fermentation methods were used to validate the role of Wickerhamomyces in Xiaoqu Baijiu brewing. Compared to traditional Xiaoqu, enhanced fermentation significantly increased the acidity of fermented grains and starch utilization rate (P<0.05). The fungal communities in fermented grains were analyzed by high-throughput sequencing technology. The dominant fungi in both control and test groups were Saccharomyces, Cyberlindnera, Rhizopus, and Meyerozyma, with Wickerhamomyces replacing Saccharomymycopsis in the enhanced group. Flavor compounds in the fermentation experiments were analyzed, revealing that ethyl acetate content in Xiaoqu Baijiu increased by 949.94 mg/L in the test group, while isoamyl alcohol decreased by 23.62 mg/L and isobutanol decreased by 34.81 mg/L. Functional prediction analysis using PICRUSt2 confirmed a higher relative abundance of enzymes involved in ethyl acetate metabolism and a lower relative abundance of enzymes involved in higher alcohol metabolism in the test group. These findings demonstrate that Wickerhamomyces enhances ethyl acetate production and reduces higher alcohols during Xiaoqu Baijiu brewing, offering a theoretical foundation for enhancing the quality of Xiaoqu Baijiu.

Background: Symmetry is a special kind of balance. This study aims to systematically explore and apply the role of balanced composition in aesthetic judgments by focusing on balanced composition features and employing research methods from computational aesthetics and Neuroaesthetics. Methods: First, experimental materials were classified by quantifying balanced composition using several indices, including symmetry, center of gravity, and negative space. An EEG experiment was conducted with 18 participants, who were asked to respond dichotomously to the same stimuli under different judgment tasks (balance and aesthetics), with both behavioral and EEG data being recorded and analyzed. Subsequently, participants' data were combined with balanced composition indices to construct and analyze various SVM classification models. Results: Participants largely used balanced composition as a criterion for aesthetic evaluation. ERP data indicated that from 300-500ms post-stimulus, brain activation was more significant in the aesthetic task, with unbeautiful and imbalanced stimuli eliciting larger frontal negative waves and occipital positive waves. From 600-1000ms, beautiful stimuli caused smaller negative waves in the PZ channel. The results of the SVM models indicated that the model incorporating aesthetic subject data (ACC=0.9989) outperforms the model using only balanced composition parameters of the aesthetic object (ACC=0.7074). Conclusions: Balanced composition is a crucial indicator in aesthetics, with similar early processing stages in both balance and aesthetic judgments. Multimodal data models validated the advantage of including human factors in aesthetic evaluation systems. This interdisciplinary approach not only enhances our understanding of the cognitive and emotional processes involved in aesthetic judgments but also enables the construction of more reasonable machine learning models to simulate and predict human aesthetic preferences.

Introduction: Known for its persistent blood-feeding behavior from dawn to dusk, Aedes albopictus is recognized as one of the most dangerous mosquito species due to its role as a vector for diseases such as dengue. Studies have demonstrated that Aedes albopictus thrives in micro-breeding sites predominantly located on private properties, including abandoned containers and manholes used for rainwater and irrigation. This study investigates an innovative methodology, the TombiniNet, to mitigate the proliferation of Aedes albopictus by engaging citizens in the active management of micro-breeding sites on their properties. Materials and Methods: The study was conducted in Alessandria, a city with approximately 100,000 inhabitants, focusing on a sample of 200 houses and a population of about 5,000 people. Researchers conducted a comprehensive survey and analysis of micro-breeding sites for Aedes albopictus on both private and communal properties. The larval control measures were conducted from April to October, aligning with the peak mosquito breeding season. The study's sample included 51% of houses that allowed researchers access, and within these, 81% had Aedes albopictus larvae, often found in diverse water Containers holes of which basically manholes on villa garden floors. For monitoring the adult populations were used ovitraps that were not placed in private homes but in public areas to monitor and control the mosquito population. Results: The study identified that up to 70% of micro-breeding sites for Aedes albopictus in northern Alessandria were located on private properties. Citizen participation was critical to the success of the intervention, with a marked decrease in the number of adult tiger mosquitoes observed in areas where residents actively managed breeding sites using the TombiniNet. The TombiniNet was particularly effective in private areas, as it could be regularly maintained by residents, preventing debris accumulation that could hinder drainage. Discussion: The findings highlight the importance of engaging citizens in mosquito control programs to effectively manage Aedes albopictus populations in urban areas. The TombiniNet intervention demonstrated that a simple, cost-effective solution could significantly reduce mosquito breeding sites on private properties. By addressing both communal and private breeding sites, a comprehensive and sustainable approach to mosquito management can be achieved, thereby reducing the risk of disease transmission. Future studies should focus on long-term monitoring and the development of additional innovative solutions to enhance citizen participation and control measures. The study's success also pointed out the limitations of applying such interventions in public areas where debris could impede the net's effectiveness.

Pesticides are chemical compounds or mixtures of hazardous substances that are used by humans to increase and protect crops from pests and control disease-carrying insects. Because of its wide use, it may be poisonous to helpful and non-targeting plants and animals. Spraying or spreading pesticides over whole fields of crops can cause runoff to transfer the chemicals into aquatic ecosystems, and wind can carry the chemicals to nearby grazing regions, farms, and populated areas, where they can endanger other animals. This review summarises recent studies on pesticide combination toxicity to aquatic biota and approaches used to anticipate toxic effects. When toxicants harm economically significant organisms that humans consume, the negative effects become more severe and can cause stress conditions, vital organ damage, biochemical damage, or even the death of living things in both terrestrial and aquatic environments. Pesticides are highly toxic to aquatic organisms such as snails, fish, etc. Neurological dysfunction in freshwater organisms is also caused by pesticides. By producing excessive ROS, pesticides can cause severe biochemical and histological changes in freshwater organisms. So, it is therefore easy to understand the attention of the public on these substances. Because aquatic organisms play a major part in the food chain. However, through the biomagnification process, the quantity of pesticides increases in the higher organisms through the food chain. Which results play a terrific role in the higher organism’s body. So, It's essential to comprehend the way that insecticides work on aquatic organisms. In this review article, we are focusing on the way that pesticides work, which will help in the system of biomonitoring.

As the global population faces an increasing prevalence of physical disabilities, the challenge of motor rehabilitation has become increasingly urgent. Considering that approximately 15% of the global population lives with some form of disability, with a significant number having motor disa-bilities that greatly impact their quality of life, the integration of robotic technologies into rehabili-tation processes offers an opportunity for significant improvement in functional recovery and pa-tient autonomy. This analysis aims to evaluate the progress and challenges encountered in imple-menting robotic technology for the rehabilitation of patients with physical disabilities, highlighting the potential of emerging technologies such as exoskeletons, assisted training devices, and brain-computer interface systems in facilitating motor recovery. By examining relevant clinical studies, the analysis demonstrates the varied effectiveness of robotic therapies compared to traditional re-habilitation methods, while also highlighting the need for ongoing research to optimize care prac-tices and address challenges related to cost, accessibility, and treatment personalization. In con-clusion, robotic technologies represent a promising direction in motor rehabilitation, with the po-tential to revolutionize the care of patients with physical disabilities, significantly improving quali-ty of life and social integration.

In the main prickly pear (Opuntia ficus-indica) producing region in the State of Mexico, fruit production (tunas) occupies the largest cultivated area with 15,800 ha, while 900 ha are cultivated for fresh consumption (“nopal” or “nopalitos”). Two composite samples consisting of prickly pear cactus cladodes (n=6) and another of nopal vegetable (n=6) showing virus-like symptoms were collected. Both sample sets were subjected to high-throughput sequencing (HTS) to identify the viruses and viroids present. The HTS results were verified using RT-PCR and Sanger sequencing. Subsequently, 86 nopal samples (nopal vegetable, prickly pear cactus, xoconostle and wild) were analyzed via RT-PCR with specific primers for the viruses and viroids previously detected via HTS. Three viruses were discovered [Opuntia virus 2 (OV2), cactus carlavirus 1 (CCV-1), and Opuntia potexvirus A (OPV-A)], along with a previously reported viroid [Opuntia viroid 1 (OPVd-1)]. Additionally, two new viroid species were identified, provisionally named Mexican opuntia viroid (genus Pospiviroid) and Opuntia viroid 2 (OVd-2). Phylogenetic analysis, pairwise identity comparison, and conserved structural elements analysis confirmed the classification of these two viroids as new species within the Pospiviroidae family. This is the first report of a pospiviroid and two apscaviroids infecting prickly pear in the world. Overall, this study enhances our understanding of the virome associated with nopal in Mexico.

In recent years, the treatment of locally-advanced rectal cancer (LARC) has been spotlighted in a number of landmark, practice-changing trials, heralding in the age of so-termed “total neoadjuvant therapy” (TNT). However, the lack of a common control arm, standard inclusion criteria, or, indeed, definition of TNT necessitates further study in this field, particularly using real-world data. We conducted a retrospective analysis of all patients treated for LARC at a single Canadian academic cancer centre between July 1st 2019 and July 14th 2022. This includes the June 2020 incorporation of TNT. 290 patients were included in the final analysis, and 124 variables were collected on each patient, including demographics, treatment type, adverse events, response to treatment, surgery versus watchful waiting and survival. Our findings demonstrate that both TNT and non-TNT regimens are highly toxic, with regards to both overall toxicity and grade 2+ adverse effects. 62 (96.9%) of patients on TNT experienced toxicities, compared to 186 (82.3%) of the non-TNT patients. Grade 2+ toxicities were 46 (71.9%) and 143 (63.3%), respectively. TNT, however, was associated with improved rates of complete response, compared to non-TNT treatment regimens (44% vs 24%). These findings suggest that TNT harbours both benefit and risk in a real-world population, and both of these aspects should be considered when deciding on a treatment approach for an individual patient. More work in the area is needed to determine longer term survival, as well as impact of surgery versus organ preservation approach on patient outcomes.

This study delves into the complex interplay among attachment styles, hypersexuality, and sexual and couple satisfaction in emerging adults within the Mexican population. Despite established correlations between anxious and avoidant attachment styles and emotion dysregulation and hypersexuality, the roles of sexual and couple satisfaction in this context have remained relatively unexplored. This study aims to fill this gap by examining how these variables interact and contribute to psychological distress and sexual risk behaviors. Data analysis on 214 emerging adults, utilizing the Experiences in Close Relationships scale, Couple Satisfaction Index, Sexual Satisfaction Scale, SCL-90, and SAST-R, revealed a significant association between risky sexual behaviors—such as a higher number of sexual partners, drug and alcohol use during sexual activity, and lack of sexual protection—and heightened levels of hypersexuality. Notably, individuals with hypersexual tendencies reported diminished levels of sexual satisfaction but elevated levels of couple satisfaction. Avoidant attachment emerged as the attachment style most strongly correlated with early sexual experiences. Moreover, the abbreviated versions of the Couple Satisfaction Index (CSI-4) and the Experiences in Close Relationships scale (ECR-8) exhibited commendable psychometric properties, enhancing their applicability for research and clinical purposes. This research furnishes valuable insights into the complex dynamics among attachment styles, hypersexuality, and couple and sexual satisfaction, emphasizing the need for targeted interventions to foster healthier sexual and relationship behaviors among emerging adults.

Bacteriocins is a term given to products of the secondary metabolism of many bacterial genera described today with antimicrobial activity. Although there are several biomolecules with such activity, even today it has not been possible to reach a consensus on the method of classification for these biomolecules. In addition, many of them are not authorized for therapeutic use against multi-drug resistant microorganisms due to possible toxic effects. However, recent research has achieved significant progress in their understanding, classification, and elucidation of the mechanisms of action against microorganisms of medical and biotechnological interest. Therefore, in more recent times, protocols are already being carried out for their optimal use and applications to use them on the hopes of solving multiple health and food conservation problems.

In the most cases, invasive ductal carcinoma (IDC) of the breast is identifiable when they present with classic infiltrative growth pattern. However, subset of IDC can occur in a very sneaky way, significantly mimicking the appearance of ductal carcinoma in situ (DCIS). In this condition, it’s much easier to miss the invasive component without pulling ancillary staining when morphologic findings are extremely compatible with DCIS, especially the diagnosis of DCIS was made on the previous biopsy. Here, we reported a 55 year-old female who was noted to have microcalcification at the 11:00 o’clock of the right posterior breast on routine mammographic examination in 09/2023. Biopsy of the calcification area in 10/2023 reported high grade DCIS (ER+ PR-). Histologic examination of subsequent mastectomy specimen showed two separate DCIS-looking areas (Figure 1A-D and Figure 2). Immunohistochemical (IHC) staining showed that myoepithelial markers, smooth muscle myosin heavy chain (SMMHC), p63, CK5/6 and S100, were retained at the periphery of all the expanded acini in one area (Figure 1E-H). Unexpectedly and surprisingly, myoepithelial markers were completely lost at the periphery of part of the DCIS-looking acini in another area (Figure 3A-H), immunohistochemically compatible with the diagnosis of invasive ductal carcinoma admixed with DCIS. Knowing that invasive ductal carcinoma of the breast can present as DCIS-looking morphology, especially given the condition that the diagnosis of DCIS was rendered on the previous biopsy, will enhance awareness of pathologists to recognize DCIS-looking invasive ductal carcinoma. In turn, this will prevent misdiagnosis and under-treatment of patients with invasive ductal carcinoma of the breast.

This research investigates the perceptions of parents belonging to the Sidama ethnic group re-garding the significance of technology in early childhood education. The study aims to elucidate how parents perceive the role of technology, such as digital devices and educational applications, in the education and development of their young children. Through qualitative interviews con-ducted with 30 parents and thematic analysis, the study uncovers diverse perspectives, shedding light on cultural, socioeconomic, and educational factors influencing parental attitudes towards technology in early childhood education among the Sidama people. Findings indicate a spectrum of views ranging from enthusiastic embracement to cautious reservation, revealing nuanced considerations that inform parental decisions regarding technology integration in their children's learning experiences.

This study presents a comprehensive investigation into the spectrum and determinants of child abuse and neglect in Nkpor-Uno, a community in Anambra State, Nigeria. Through a descriptive surveys design, the study explores various forms of abuse and neglect experienced by children, as well as the underlying factors contributing to these phenomena. The population for the study consisted of 1000 parents/ caregivers who are residents of Nkpor-uno. The sample for the study consisted of 100 respondents, purposive sampling techniques was used for the study. The questionnaire was a self-designed structured questionnaire. Data collected were analyzed using frequency, mean and standard deviation. Findings reveal alarming prevalence rates of child maltreatment, including physical, emotional, sexual abuse, and neglect. Socioeconomic factors such as poverty, parental substance abuse, family dynamics, and inadequate sex education emerged as significant contributors to child maltreatment. The study underscores the urgent need for targeted interventions and support systems to address the root causes of abuse and neglect and safeguard the well-being and development of children in Nkpor-Uno. By raising awareness, enhancing access to resources, and promoting positive parenting practices, we can strive towards building a safer and healthier environment for children, ensuring they are protected from harm and given the opportunity to thrive.

Keywords: grape pomace extracts, encapsulation, freeze-drying, bioaccessibility, phenolic compounds

The Yb/Al multilayer films exhibit excellent theoretical reflectivity in the 54-90nm wavelength range. This study attempted to incorporate 1.5% wt.% of Si impurities into Al to suppress the crystallization of Al, reduce interfacial roughness, and enhance the actual reflectivity of the prepared Yb/Al multilayer films. The internal microstructure changes of the film layers before and after Si impurity doping were investigated using GIXRR, AFM, and XRD techniques. The reflectivity of two types of multilayer films, Yb/Al(1.5wt.%Si) and Yb/Al(pure), was tested to evaluate the effect of Si impurity on the film performance.

Enhancing the Efficiency of Natural Gas Liquid (NGL) Extraction and Fractionation Trains: An Integrated Approach of Simulation Analysis, Advanced Modifications, and Technological Advancements. Through comprehensive research, innovative solutions such as the heat pump aided distillation (HPAD) system and self-heat recuperation technology (SHRT) have been developed to significantly reduce the energy consumption associated with conventional distillation systems. To identify a practical system for NGL fractionation trains, this study extensively examined and compared various HPAD and SHRT options to retrofit a single column. The objective was to find the most suitable and efficient solution for the fractionation process of natural gas liquids (NGLs). The retrofit options analyzed in this study encompassed a range of techniques, including vapor compression (VC), mechanical vapor re-compression (MVR), thermal vapor re-compression (TVR), bottom flashing (BF), side heat exchanger (SHE), intermediate heating and cooling (IHC), self-heat recuperative (SHR), and modified self-heat recuperative (MSHR) distillation. These methods were carefully examined to determine their suitability and effectiveness in improving the performance of NGL fractionation trains.In this study, a depropanizer column, typically employed in conventional NGL plants, was selected as a case study. The simulation software Aspen HYSYS V7.3 was employed to model and analyze eight retrofit designs based on predefined criteria. The simulation data was carefully evaluated to identify the most efficient design for minimizing energy consumption. Among the retrofit options, the mechanical vapor re-compression (MVR) technique demonstrated the most significant energy cost reduction, with a remarkable 68.11 percent improvement compared to the base case conventional column. These findings highlight the potential of MVR as an effective solution for lowering energy costs in NGL fractionation trains.Following the MVR retrofit option, the study found that vapor compression (VC) achieved a considerable energy cost reduction of 66.65 percent, closely followed by modified self-heat recuperative (MSHR) at 64.02 percent, bottom flashing (BF) at 62.88 percent, self-heat recuperative (SHR) at 55.85 percent, side heat exchanger (SHE) at 54.23 percent, intermediate heating and cooling (IHC) at 39.54 percent, and thermal vapor re-compression (TVR) also at 39.54 percent. These findings highlight the significant potential for energy savings offered by these retrofit options, with VC being the most popular choice, closely followed by MSHR, BF, SHR, SHE, IHC, and TVR.

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.

(1) Background: Until December 2021, French Guiana (FG), located in South America, faced four consecutive COVID-19 epidemic waves. This study sought to analyze the mortality trend of severe COVID-19 patients admitted to the referral ICU of FG. (2) Methods: We conducted a prospective, observational, and non-interventional study in ICU at Cayenne Hospital. We included 383 patients older than 18 admitted with SARS-CoV-2-related pneumonia hospitalized from May 2020 to December 2021. The study covers three periods. Period 1 (Waves 1 and 2, original variant), period 2 (Wave 3, gamma variant), and period 3 (Wave 4, delta variant). (3) Results: The median age was 63 years (52-70). Frailty was diagnosed in 36 patients over 70 (32.4%). Only 4.8% of patients were vaccinated. The median ICU LOS was 10 days (6-19). Hospital mortality was 37.3%. It was 30.9% in period 1, 36.6% in period 2 (p=0.329 vs. period 1), and 47.1% in period 3 (0.015 vs. period 1). In multivariate analysis, independent factors associated with hospital mortality were age more than 40 years (]40-60 years] OR=5.2, 95%CI: 1.4-19.5; (]60-70 years] OR=8.5 95%CI: 2.2-32; (]70+ years] OR=17.9, 95%CI: 4.5-70.9), frailty (OR=5.6, 95%CI: 2.2-17.2), immunosuppression (OR=2.6, 95%CI: 1.05-6.7), and MV use (OR=11, 95%CI: 6.1-19.9). This model had an overall sensitivity of 72%, a specificity of 80.4%, a positive predictive value of 68.7%, and a negative predictive value of 82.8%. (4) Conclusions: The mortality of severe COVID-19 patients in French Amazonia was higher during the delta variant wave. This over-death is explained by the virulence of the responsible SARS-CoV-2 variant and the under-vaccination coverage of the studied population.

Culture signifies the customs, language, ceremonies, Indigenous Medical Knowledge (IMK) and other material objects of a tribe. These and many other features are a representation of identity for many Zambian communities. IMK practices are skills inherent by certain individuals in a tribal grouping who are chosen by the spirits (gods) or trained by those who practice. This intrinsic skill is in danger of diminishing as a result of modern medication. This article attempts to understand the role of traditional healers (also known as asing’anga in local language) in preservation of IMK in Chibombo District of Zambia. In so doing, the paper examined methods of preservation used by asing’anga and the challenges faced. Using hermeneutic phenomenology and linear snowball sampling, findings revealed that preservation was done through training. The study found unwillingness, expiration, and spirituality as challenges in the process of preserving IMK. The study recommended that the Lenje Cultural Association consider documenting and disseminating information to the local people by showcasing what is available in the Mukuni Culture Village Museum and Library. This was seen as a way through which many youths would be encouraged to appreciate the indigenous values in their culture.

Tourism Geographers in Anglo-American geographical departments migrate to teach and research tourism in applied schools and departments such as business, resource and environment, and even cultural and creative industry. Under national research assessment and funding pressures, tourism geographers are not encouraged to keep on undertaking serious theoretical researches related to their mother discipline and transferred to research applied researches and publish in hybrid journals. Such changes in tourism geographers` research behavior have not been tested and reported in Asian academic contexts, especially Taiwan while frequently being discussed internationally. This research aims to investigate the differences in the research behavior of tourism geographers between those who are staffed in geography departments and those who migrate to applies departments such as tourism and recreation, resource and environment, and others in terms of publishing journals, paradigmatic approaches, research types(qualitative, quantitative, and mixed), research methods and geographical scales conducted based on content analysis of articles published by tourism geographers in geography and tourism and recreation departments. This study revealed that there are significant differences in the research behavior of tourism geographers between different department. Finally, this paper explains the differences in terms of Taiwan changing social and economic conditions, academic traditions and politics of geography and applied departments, research habits and preferences of tourism geographers among departments and other aspects. This study can help international tourism geography academic circles to understand the complexity and diversity of tourism geography academic landscape of the Chinese language.

Detecting IgE sensitizations in the serum of allergic dogs is commonly done using allergen extracts, but these are difficult to standardize. This article details the development and validation of the Pet Allergy Xplorer (PAX; Nextmune, Stockholm, Sweden), the first multiplex macroarray for the detection of IgE sensitization in dogs using allergen extracts and molecular components; the PAX is derived from the Allergy Xplorer (ALEX2; MacroArray Diagnostics, Vienna, Austria). The selection of allergens, cartridge processing, strategy for identifying and blocking IgE directed against cross-reactive carbohydrate determinants (CCD), and the method used for determining the positivity threshold are described. The validation of the PAX included evaluations of the specificity of its anti-IgE monoclonal antibody, specificity of IgE binding to target allergens, assay precision, and internal consistency. Additionally, the influence of possible confounding factors, such as sample type, the influence of hemolysis, lipemia, bilirubinemia, and elevated CCD-IgE, was tested. Finally, the sensitization rates of 23,858 European dogs to 145 environmental and Hymenoptera venom allergens were summarized. The PAX is accurate and reproducible and has a unique CCD-detection and blocking strategy; its molecular allergens offer a unique window on allergen cross-reactivity.

while composting of organic wastes is increasingly adopted in sustainable agriculture, continued interest on biochar application, dark soils, and compost, showed morphological aspects and functionality of biochar as a challenge to solve the CO2 aspects related to climatic change, and carbon stability in sustainable soils, as Black carbon presents long chemical permanency due to incomplete combustion of biomass. The objective of this work was to study the characteristics of soil amendments and to review the state of the art of biochar, showing differences in composition and main microorganisms with relevant presence in the soils. Arbuscular mycorrhizal spores (Scutellospora sp., Glomus like, Funneliformis geosporus, Glomus crenatum, and Acaulospora excavata, obtained from TPI soil samples are shown. New strategies are highlighted such as nutrient supplementation of compost, which can constitute the basis for improved compost-based biofertilizers in the future. Thus, the most important strategies for C sequestration are underlined to diminish substantial organic agro- and municipal wastes, increasingly disposed in the fields, landfills or either released by burning or discarding.

Diffusion magnetic resonance imaging (dMRI) is an imaging technique that provides information on the microstructural organization of biological tissue. dMRI allows for non-invasive visualization and quantitative assessment of white matter architecture in the brain by characterizing restrictions on the random motion of water molecules. Ultra-high field MRI scanners, such as those operating at 7 Tesla (7T) or higher, can boost signal-to-noise ratio (SNR) to improve dMRI compared with what is attainable at conventional field strengths such as 3T or 1.5T. However, at 7T wavelength effects cause lower transmit magnetic field efficiency in the human brain, mainly in the posterior fossa region, manifesting as signal dropouts in this region. Recently, we reported a simple approach of using a wireless radiofrequency (RF) surface array to improve transmit efficiency and signal sensitivity at 7T. Here we demonstrate the feasibility and effectiveness of using this RF enhancer for in-vivo dMRI at 7T. The use of the RF enhancer can effectively recover signal dropouts in the regions with intrinsically lower SNR such as cerebellum, leading to a better depiction of principal fiber orientations and enhanced visualization of extended tracts to the brainstem and pons enabling more comprehensive delineation of the full white matter architecture.

Blueberries are rich in a variety of functional substances and have high nutritional and health values, but they are not resistant to decline during fresh storage. Here, the effects of six Bacillus species on storage quality of, and antioxidant levels in, the southern highbush blueberry ‘O'Neal’ fruit were investigated. Bacillus treatments reduced the fruit decay rate, slowed fruit quality decline, inhibited malondialdehyde accumulation, and increased superoxide dismutase and peroxidase activity levels. Bacillus altitudinis Y-14 had the best effect overall. Further, the effects of 1-methylcyclopropene (1-MCP), B. altitudinis Y-14, and 1-MCP+B. altitudinis Y-14 treatments on the storage quality and antioxidant of rabbiteye blueberry ‘Brightwell’ fruit were investigated, and each treatment effectively reduced the decay rate and weight loss of fruit. When stored for 25 days, the decay rate of the 1-MCP+B. altitudinis Y-14 group was only 8.33%, significantly lower than that of the control. The three treatments delayed the fruit quality decline, inhibited malondialdehyde accumulation, and increased superoxide dismutase and peroxidase activity levels. The 1-MCP+B. altitudinis Y-14 treatment had the best effect. Thus, the combined 1-MCP and B. altitudinis Y-14 treatment may be an effective way to improve the storage quality and extend the storage period of blueberries.

New strategies for enhancing drug delivery to the blood–brain barrier (BBB) represents a major challenge in treating cerebral diseases. Nano-emulsion based nanocarriers represent an ideal candidate to improve drug delivery thanks to their versatility in functionalization and cargo protection. In this work, a paclitaxel loaded nano-emulsion has been firstly functionalized and stabilized with two layers constituted of chitosan and hyaluronic acid, and, secondly, the latter has been conjugated to the CRT peptide. CRT is a bioactive peptide that selectively recognizes bEnd.3 cells, model of the BBB, thanks to its interactions with transferrin (Tf) and its receptor (TfR). Cytotoxic results showed a higher uptake of CRT functionalized nano-emulsion 41.5 % more than the negative control, demonstrating the ability of this novel tool to be accumulated on the brain endothelium tissue. Thanks to these results, our approach can be fully generalizable to the design of multifunctional nanocarriers for delivery of therapeutic agents to the central nervous systems.

Objective To explore the endoscopic anatomical structures of lumbar surgery. Methods A total of 290 patients who underwent lumbar endoscopic surgery due to lumbar intervertebral disc (IVD) herniation from September 2020 to September 2022 were retrospectively reviewed in our hospital,179 patients had complete surgical video recordings, including 108 males and 71 females, aged from 19 to 65 years, with an average age of 41±16 years. The endoscopic structures through the intervertebral foramen and interlaminar space were independently identified by 3 doctors with more than 3 years of related surgery experience. When there were differences of opinions, the three doctors would discuss and come to a consensus. The probability, location, and neighboring structures of each specific structures were statistically analyzed. Results The anatomical structures observed through the lumbar foramen under endoscopy include muscles and ligaments attached to the superior articular process (SAP), main blood vessels inside the intervertebral foramen, blood vessels on the surface of IVD, and blood vessels on the surface of the dural sac and nerve root. The major anatomical structures observed through interlaminar space are multifidus, the conjugated ligament of ligament flavum and capsule ligament (capsule part of flavum ligaments), edges of the lamina, specific vessel bundle to facet with exit nerve root. Conclusion The anatomical structures under spinal endoscopy have certain characteristics, which are grossly different from those under open surgery. Familiarity with these anatomical structures has important clinical significance.

Background: The safety and effectiveness of factor-Xa (FXa) inhibitors for nonvalvular atrial fibrillation (NVAF) remains inconclusive. We investigated the current dosing of rivaroxaban, apixaban and edoxaban by monitoring drug plasma concentration (PC) and coagulation activity. Methods and Results: This multicenter clinical study monitored the drug PC levels and two coagulation biomarkers (fibrinogen: FIB, and fibrin monomer complex: FMC) at peak and trough timing in 268 outpatients receiving rivaroxaban (n = 72), apixaban (n = 71), and edoxaban (n = 125) for NVAF. Doses were adjusted according to the dose adjustment criteria of each drug. Based on our previous study, each peak drug PC remained below the cut-off level for predicting bleeding events except in eight patients (three on rivaroxaban, two on apixaban and three on edoxaban) in whom bleeding events occurred; among them, two (one each on rivaroxaban and edoxaban) had a peak drug PC below the cut-off level. Drug PCs widely varied from peak to trough, whereas FMC levels remained within the normal range (< 6.1 µg/ml) regardless of PC variation. These finding suggested that the anticoagulant effects of these drugs persisted throughout the day regardless of the drug PC levels, dosage, and frequency of dosing. Regarding the change in peak drug PC levels over time, rivaroxaban tended to accumulate more than edoxaban (p

The growing deforestation of tropical forests requires the implementation of restoration actions capable of assisting the recovery of biodiversity and functioning of these ecosystems. This research aimed to identify the environmental factors that influence woody plant recruitment abundance and diversity in an Andean forest restoration project in Medellin (Colombia). Data from all individuals taller than 80 cm was collected in 22 plots of 200 m2. The environmental factors selected were edaphic variables, plantation structure, slope, elevation, prior land use and landscape forest cover. A generalized linear model was adjusted for recruitment density, and linear mixed models were built for recruited species richness, diversity and dominance. Woody plant recruitment attributes in our study area were similar to a typical secondary succession in an Andean forest, but planted trees contributed little to these attributes. While recruitment density was affected by slope, canopy closure and landscape forest cover, recruitment diversity was influenced by physical (bulk density) and chemical (pH, aluminum, CEC) edaphic factors, planted trees diversity (species richness and composition), canopy closure and mortality rate of planted trees. We conclude that a good development of planted trees enhances both recruitment attributes, indicating a synergy between active restoration and passive regeneration processes.

Plastics, once regarded as a revolutionary material shaping modern society, now poses an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to the cement factories that burn it together with other fuels. However, with some processing techniques such catalytic pyrolysis this fraction could be valorized. In this paper the experiments which were carried out at a laboratory scale installation, in batches with a processing capacity of 1-3 kg of waste. The pyrolysis reactor is connected to a distillation column giving the possibility to separate the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5-10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions C1-C6, C6-C10 and C11-C13) in relation to the heavy fractions C13-C20 and C20+. Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments, was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much easier exploited by burning in an engine that drives an electric generator.

Academic procrastination, more than merely postponing tasks, represents a significant failure in the self-regulation process of learning. Research on study skills highlights academic time management as one of the crucial elements of learning strategies and sustainable education. These abilities will help achieve the Sustainable Development Goals fourth goal. Therefore, this study aimed to understand how students plan time management in school activities and its influence on study procrastination, analyzing differences between genders and the hours students spend studying. The Time Management Planning Inventory, the Study Procrastination Questionnaire, and a personal and school data sheet were used. A sample of 506 students from elementary schools in northern Portugal was utilized. The results revealed that gender and study hours significantly influence how students plan time management for school tasks in the short and long term. This variable also showed a significant impact on the procrastination of study activities. The practical implications of this study are substantial, as they provide educators and researchers insights into the factors influencing academic procrastination and the role of time management planning and study hours. These insights can be applied to develop effective strategies to reduce academic procrastination and promote sustainable education.

This study investigates anomaly detection of IoT device traffic using Convolutional Neural Networks (CNN) and Variational Autoencoders (VAE) to enhance the detection capability of security threats in IoT environments. A series of hardware configurations, software environments, and hyperparameters were utilized to optimize the training and testing processes of the models. The CNN model demonstrates robust classification performance, achieving an accuracy rate of 95.85% on the test dataset, effectively distinguishing between different types of IoT device traffic. Meanwhile, the VAE model exhibits proficient anomaly detection capabilities by effectively capturing abnormal patterns in the data using reconstruction loss and KL divergence. The combined use of CNN and VAE models offers a comprehensive solution to cybersecurity challenges in IoT environments. Future research directions include exploring diverse IoT traffic data, practical deployment for validation, and further optimization of model structures and parameters to improve performance and applicability.

Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is a growing momentum towards the adoption of intelligent transportation systems (ITS) and sustainable transportation solutions. This research aims to explore the potential of renewable energy sources to power electric vehicles (EVs) and flying automobiles, thereby accelerating the transition towards a low-carbon future. Objectives: The primary objectives of this research are as follows: - Investigate the role of renewable fuels in powering both EVs and flying cars to reduce reliance on fossil fuels and mitigate carbon emissions. - Analyze the dynamic growth and sustainability implications of advancements in battery technology and alternative renewable energy sources, such as Hydrogen Fuel Cells. - Evaluate the challenges and opportunities associated with the integration of flying vehicles into existing transportation infrastructure, focusing on safety, airspace control, and infrastructure development. - Review insights and guidance to stakeholders on promoting sustainable transportation solutions for EVs and flying cars through the utilization of renewable energy sources and the reduction of carbon emissions from the literature. Methodology: This research employs a mixed-method approach, combining literature review, and causal loop diagram analysis. The literature review provides a comprehensive understanding of current trends, challenges, and opportunities in sustainable transportation and renewable energy technologies. A causal loop diagram is utilized to visualize the dynamic interactions between various factors influencing the adoption of EVs and flying cars, including technological advancements, policy interventions, and market dynamics. Results and Discussion: The findings of this research highlight the significant potential of renewable energy sources, such as solar, wind, and hydrogen, in powering both EVs and flying cars. Advances in battery technology and hydrogen fuel cells are identified as key drivers in accelerating the transition towards sustainable transportation systems. However, challenges remain in terms of infrastructure development, safety regulations, and airspace management for flying vehicles. Nevertheless, there is a noticeable advancement in reducing carbon emissions and reliance on fossil fuels with the widespread adoption of EVs. By harnessing renewable energy and implementing effective policy measures, stakeholders might able to promote the widespread adoption of sustainable transportation solutions, thereby contributing to environmental protection and societal well-being. Conclusion: Literally, this research underscores the importance of sustainable transportation solutions in addressing urban congestion, environmental pollution, and energy security concerns. By leveraging renewable energy sources and embracing technological advancements, such as EVs and flying cars, stakeholders pave the way for a more sustainable and resilient transportation system. However, concerted efforts are needed to overcome existing challenges and ensure the successful integration of these technologies into existing infrastructure. Through collaborative partnerships and innovative initiatives, a low-carbon future for transportation logistics can be achieved, benefiting both the environment and society as a whole.

(1) Background: The lateral arm flap has been the very useful choice in the reconstruction of small- to medium-sized defects like hand, extremity, oral head and neck area reconstructions. Its versatile characteristics and surgical feasibility allow this flap to be widely applied, but the reconstructive potential in facial subunit after tumor ablative procedures has never been reported. In this study, we aimed to utilize the advantages of the flap to treat facial temple subunit defect reconstruction. (2) Methods: Between 2020 and 2023, 12 patients underwent temple reconstruction with lateral arm free flaps after malignant tumor wide excision. There were 7 women and 5 men, and the mean patient age was 60.6 years old. Among the patients with cancer, 6 had squamous cell carcinoma, 5 had basal cell carcinoma and 1 had myxofibrosarcoma. All the flaps were elevated under general anesthesia. Aloprostadil(PGE1, Eglandin®) were administered postoperatively. (3) Results: All the flap types were fasciocutaneous type with varied sizes from 3 × 4 to 5 × 7 cm (Average size of flap 22.7cm2) were utilized. The average pedicle length was 6.1cm. The versatility of the lateral arm flap enabled successful coverage of all cases with no specific complications. Good functional outcome and range of motion in the recipient arm were obtained after surgery. (4) Conclusion: The authors successfully verified the advantages of lateral arm flaps in the treatment of medium sized facial subunt temple defect reconstruction.

Background: Self-harm (SH) refers to self-injury or intentional self-poisoning with varying degrees of suicidal intent and is a major public health issue for children and adolescents (C&A) worldwide. Given significant gaps exist in our understanding of the associated psychopathology in C&A, this paper outlines the protocol for a prospective clinical study examining risk factors of SH and additional candidate parameters (not routinely assessed) during an acute SH episode. Methods: The EXPAAND (EXploring Psychiatric and Attentional risk-factors in Adolescents Needing intervention for Deliberate self-harm) is a randomised control trial to assess additional novel candidate risk factors for SH in C&A and to evaluate the effectiveness of SH-targeted brief psychological intervention (Therapeutic Assessment (TA)), within a real-world acute clinical setting, as we include neurodiversity and gender diversity in this study. EXPAAND is conducted in 4 phases: (i) recruitment & randomisation; (ii) extended assessment; (iii) randomised intervention; and (iv) follow-up observational period to ascertain clinical outcomes for comparison. Expected Results: We envisage that by diagnosing and treating attentional disorders when detected will mitigate potential confounding effects of these conditions. We also expect that SH-focused TA will be effective in reducing subsequent SH and in improving engagement in follow-up and subsequent treatments.

This study provides a comprehensive comparison of the Peter Chew Sum method and Newton's Sum in Quadratic Root Function Computations. Both methods offer significant advancements by alleviating the need for memorization of multiple formulas. However, when confronted with higher-order quadratic root functions such as the calculation of power 33. Newton's Sum method proves cumbersome due to its requirement of all previous answers for final computation, leading to increased steps and error risks. In contrast, the Peter Chew Sum method simplifies the computational process by requiring only a few previous answers, resulting in a more straightforward series of operations. We contextualize our analysis within the historical progression of mathematical techniques, highlighting the transformative leap represented by the Peter Chew Sum method in quadratic root function computations. Peter Chew Sum addressing the limitations of Newton's Sum , especially concerning higher-order functions such as the calculation of power 256. Furthermore, study demonstrates that the innovative approach of the Peter Chew Sum method not only simplifies complex computations but also fosters enhanced conceptual understanding among students. In conclusion, study findings underscore the profound impact of the Peter Chew Sum method on quadratic root function computations.

This research follows the publication of recent scoping and systematic reviews of existing literature on vaccine literacy (VL), its relationship to vaccine hesitancy (VH), and associated variables, such as vaccination beliefs, attitudes, and behaviors towards immunization. In this review, 17 articles were selected, published between December 2022 and November 2023, in which VL tools were used, most of which support the notion of a negative association between VL and VH, although not confirmed by others. Moreover, the definitions of the variables under consideration were not homogeneous between studies, some aimed to evaluate the association of VL with vaccine confidence, while others were more focused on outcomes such as willingness to be vaccinated or vaccine uptake. The complexity of the factors underlying VL, and the heterogeneity of the methods applied in the studies, in particular the limitations of cross-sectional surveys, still make it difficult to have a comprehensive understanding of the role of VL. Therefore, it is important to develop future assessment tools that consider not just the measurement of psychological factors connected to the motivational aspect of VL, but also those related to knowledge and competencies. For this purpose, a theoretical framework is proposed, where the positioning of VL at the intersection between antecedents and beliefs/attitudes leading to behaviors explains why and how VL serves as a tool and a critical, direct, or mediating driver of vaccination choices, overcoming VH and increasing vaccination rates, operating at personal, as well as at organizational and community level.

The Yellow-legged Hornet (Vespa velutina nigrithorax) (YLH) is an invasive insect that arrived in Europe in 2004 and is now spread across nine countries. It is a threat to the native entomofauna and harmful to beekeeping and agriculture, as it is a ravenous predator of the European Honey Bee (Apis mellifera) and other pollinating species. Its expansion has been unstoppable and all resources are needed to fight against it, including native vertebrate predators. Among these, the European Bee-eater (Merops apiaster) (EBE) is a potential one, but little is known about its predation on YLH. In a study carried out in Portugal, remains of YHL were detected in EBE nesting sites, which to the best of our knowledge is the first such report. This means that this bird could be one more agent in the biological control of this pest (although research on predation intensity is still needed), in conjunction with other natural predators and other strategies. In the Iberian Peninsula both species are allopatric in vast regions, so the role of EBE may be more limited. However, in the rest of Europe, at a country or continent scale, the scenario may be different and sympatry may occur to a greater extent.

This study uses the social impact theory to analyse consumer behaviour amidst Covid-19. Fear of scarcity has changed consumer buying behaviour in crisis times such as the now abating global health crisis – i.e., Covid-19. The study asserts that the existing five-stage model of consumer purchasing decisions does not fit neatly in turbulent times. It proposes a shortened four-stage process of need recognition, purchase, ownership and satisfaction as a better and more adaptable fit. Cutting out information search and evaluation of alternatives in the old model, and replacing these with ownership, is where the main contribution of the study lies. The study has implications for managing the marketing mix as we know it. It asserts that not all the 4Ps of the marketing mix are important in crisis times.

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. The study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 keypoints using an open-source tool called DeepLabCut, which helped accurately mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the Long Short-Term Memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior were over 90%.

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. This work's significance lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.

Audio classification using deep learning models, essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle in optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel Spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model compression techniques, notably magnitude pruning, Taylor Pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy.

Accurate forecast of tail water level (TWL) is of great importance for the safe and economic operation and management of hydropower stations. The predictive performance is significantly influenced by the backwater effect of downstream hydropower stations and tributaries, but the explicit quantification method of the backwater effect is lacked. In this study, a deep learning model based forecasting framework for TWL predictions is established and applied to forecast TWL of Xiangjiaba (XJB) hydropower stations, which is influenced by the backwater effect of downstream tributaries including Hengjiang River (HJR) and Minjiang River (MJR). Firstly, the lag time of the backwater effect of HJR and MJR is analyzed based on the permutation importance. The results demonstrate that the lag time of backwater effect on the TWL of XJB is 5-7 hours for the HJR and 1-2 hours for the MJR. Then, the runoff thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained by scenario comparison, and the results show that the thresholds of HJR and MJR are 700 m3/s and 7000 m3/s respectively. Finally, the deep learning methods based TWL forecasting model is established based on the lag time and threshold analysis. The model is used to forecast the TWL in future 48 hours. The results show that the forecasting model has a good predictive performance with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm and the maximum absolute error is 63.35 cm.

One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a data set. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (Convolutional Block Attention Module, CBAM) is added for extracting spatial information in different remote sensing bands to improve the efficiency of the model. Long Short-Term Memory (LSTM) neural networks is also added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. The proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

In recent years, with the popularity of smart mobile devices, the interaction between devices and users, especially in the form of voice interaction, has become increasingly important. If smart devices can understand more users' emotional states through voice data, more customized services can be provided for users. This paper proposes a novel machine learning model for speech emotion recognition, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN), called CLDNN. To make the designed system can recognize the audio signal closer to the human auditory system does, this article uses the Mel frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal is extracted as the input of the model, and the feature values of the data are calculated using several local feature learning blocks (LFLB) composed of one-dimensional CNN. Because the audio signals are time-series data, the feature values obtained from LFLBs then input into LSTM layer to enhance the learning on time-series level. Finally, fully connected layers are used for classification and prediction. Three databases RAVDESS, EMO-DB and IEMOCAP are used for the experiments in this paper. The experimental results show that the proposed method can improve the accuracy compared to other related researches in speech emotion recognition.

To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

This paper introduces a multi-class hand gesture recognition model developed to identify a set of defined hand gesture sequences in two-dimensional RGB video recordings. The work presents an action detection classifier that looks at both appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model uses an available dataset to then adopt a technique known as transfer learning to fine-tune the model on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (± 0.37) with a mean Jaccard index of 0.812 (± 0.105) for 22 participants. The presented model illustrates the possibility of training a model with a small set of data (113,410 fully labelled frames). The proposed pipeline embraces a small-sized architecture that could facilitate its adoption.

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

In this paper we propose a novel self-supervised approach of keywords and keyphrases retrieval and extraction by an end-to-end deep learning approach, which is trained by contextually self-labelled corpus. Our proposed approach is novel to use contextual and semantic features to extract the keywords and has outperformed the state of the art. Through the experiment the proposed approach has been proved to be better in both semantic meaning and quality than the existing popular algorithms of keyword extraction. In addition, we propose to use contextual features from bidirectional transformers to automatically label short-sentence corpus with keywords and keyphrases to build the ground truth. This process avoids the human time to label the keywords and do not need any prior knowledge. To the best of our knowledge, our published dataset in this paper is a fine domain-independent corpus of short sentences with labelled keywords and keyphrases in the NLP community.

The aim of this study was to analyze the relationship between healthy vascular aging (HVA) and the Mediterranean diet alongside other lifestyles in a Spanish population aged 35 to 75 years without previous cardiovascular diseases. Methods: Cross-sectional descriptive study with 501 individuals aged 35 to 75 years recruited from five health centers by random sampling stratified by age and sex (55.90±14.24 years, 49.70% men). HVA was determined in two steps. Step 1: Subjects with vascular damage to the carotid arteries or peripheral arterial disease were classified as non-HVA. Step 2: The study population was classified by age and sex using the percentiles of the vascular aging index (VAI), with VAI ≤p25 considered HVA and >p25 considered non-HVA. The VAI was estimated using the following formula (VAI = (log (1.09) × 10 cIMT + log (1.14) cfPWV) × 39.1 + 4.76. Carotid-femoral pulse wave velocity (cfPWV) was measured with the SphygmoCor® device and carotid intima-media thickness using Sonosite Micromax® ultrasound. Mediterranean diet (MD) adherence, alcohol and tobacco use were recorded through validated questionnaires. Physical activity was assessed with the ActiGraph-GT3X® accelerometer. Results: The mean VAI value was 61.23±12.86; (Men 63.47±13.75 and women 59.04±11.54; p

The increasing adoption of student-centred learning environs by the institutions of higher education in business, has led to new mechanisms and methodologies such as the Simulation-Based Learning (SBL), Problem-Based Learning (PBL) and Challenge-Based Learning (CBL). Literature has shown evidence of raising interest in these methodologies towards a more experiential paradigm with regard to upcoming events from the surrounding real-world context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the integration and application of these innovative and collaborative methodologies in higher education. It aimed to identify its relevant advantages, challenges, and consequences for the future of business education. Bibliographic databases were searched for documents published up to May 2024 to categorize key topics discussed in the literature on SBL, PBL and CBL. The review process identified 89 empirical and non-empirical papers on SBL, PBL and CBL, business related paperwork, which resulted from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method. PRISMA is a framework with evidence-based data designed to help authors perform a systematic literature review. Data analysis revealed diverse subthemes for those methodologies uses in business higher education institutions. The research questions “What are the advantages, challenges and implications of SBL, PBL and CBL altogether, in the training of business students?” guided the study. The central output of the research is the discovery of the varying uses of SBL, PBL and CBL overall, to cope with diverse demands arising within environmental context. Future research directions are suggested.

Introduction: The escalating morbidity, mortality, and healthcare costs associated with invasive candidiasis pose significant challenges in pediatric intensive care units (PICUs). Conventional diagnostic methods fall short in effectiveness, while various risk factors exacerbate the prevalence of these infections. This study introduces a novel model that amalgamates the Candida Score with key predictors to enhance the specificity and applicability of mortality risk assessments for pediatric patients with fungemia. Methods: This retrospective cohort study aimed to develop an integrated model for estimating mortality risk in children with fungemia in PICUs. We constructed and evaluated multiple predictive models using the Candida Score and other relevant clinical and laboratory variables. The discriminative abilities of the models were assessed using the area under the receiver operating characteristic curve (AUC). Results: The study cohort comprised 85 pediatric patients admitted between 2016 and 2020, with a median age of 6 months and a male predominance of 62.4%. The mortality rate was 45.9%. Candida albicans was the most prevalent causative agent, accounting for 37.55% of fungemia cases. Multivariable Logistic regression revealed the duration of mechanical ventilation, and the Candida Score were significant predictors of mortality. Each additional day of ventilation increased mortality odds by 11.5% (OR = 1.115, 95% CI: 1.025-1.212, p = 0.011), while a higher Candida Score more than doubled the odds (OR = 2.205, 95% CI: 1.359-3.578, p = 0.001).The Random Forest predictive model demonstrated superior discriminative performance, with an AUC of 0.838 (95% CI: 0.738-0.917), outperforming Gradient Boosting and Logistic Regression models, which had AUCs of 0.7759 (95% CI: 0.667-0.877) and 0.7085 (95% CI: 0.589-0.813), respectively. Although the internal validation yielded satisfactory results, external validation is warranted. Conclusion: This study presents an innovative approach for accurately assessing the risk of mortality due to fungemia in PICU patients by integrating the Candida Score with critical predictors. The Random Forest model emerged as the most effective predictive tool due to its exceptional discriminative ability.

Habenaria, a member of Orchidaceae family is the cosmopolitan distributions, which has significant medicinal and ornamental values. Regardless of morphology and molecular data that have been studied in recent times, the phylogenetic relationship is still under debate. Here, we sequenced, assembled, and annotated the whole chloroplast (cp) genome of two species (Habenaria aitchisonii Rchb.f. and Habenaria tibetica Schltr.ex Limpricht) of Habenaria grown on the Qinghai-Tibetan Plateau (QTP), and combined with seven already published cp genomes which may assist to uncover their genomic profiling. The two genomes ranged from 155259-155269 bp in length and both encoded 132 genes, including 86 protein, 38 tRNA and 8 rRNA. In the cp genomes, the tandem repeats (797), SSRs (2195) and diverse loci (3214) were identified. Comparative analyses of codon usage, amino frequency, microsatellite, oligo repeats and transition and transversion substitutions showed similarities among the species. Moreover, we identified 16 highly polymorphic regions with nucleotide diversity above 0.02, which may be suitable for robust authentic barcoding and inferring in the phylogeny of Habenaria species. Among the polymorphic regions, positive selection was significantly exerted on the several genes such as cemA, petA, and ycf1. This may suggest that the important adaptation stratagem for two Habenaria species on the QTP. The phylogenetic relationship displayed that H.aitchisonii and H. tibetica have closer relationship than others and the rest seven species clustered in the other three groups. Our findings also supported the idea that Habenaria could be divided into different sections. This study enriched the genomics resources of Habenaria, which may be helpful for the conservation efforts of these endangered species.

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and the third leading cause of cancer-related death worldwide. Liver transplantation (LT) is the best therapy for most patients with non-metastatic HCC. In recent years, the management of patients with HCC has changed considerably, thanks to the improvement of molecular biology knowledge and the introduction of immunotherapy. To date, systemic therapy is authorized in the Western world only in patients with advanced HCC. However, this therapy could lead not only to stabilize the tumour disease or improve survival but could display excellent response and lead to downstaging of the tumor that finally permits liver transplantation. There are increasing reports of patients that have performed liver transplantation after pretreatment with immune checkpoint inhibitors (ICIs). However, due to the intrinsic mechanism of ICIs to stimulate the immune response against tumors, they could also cause graft rejection. This review aims to evaluate the readiness and outcomes of liver transplantation in patients with advanced HCC who have previously undergone treatment with ICIs. It seeks to identify the challenges, risks, and benefits associated with this conversion therapy. The integration of ICIs into the treatment paradigm for advanced HCC necessitates a nuanced approach to liver transplantation. While early evidence supports the feasibility of LT following ICI therapy, there is an urgent need for standardized guidelines and more extensive longitudinal studies to optimize patient selection, timing, and post-transplant management.

Surface water plays a crucial role in climate change, human production, and life, making accurate monitoring and observation of surface water particularly important. However, due to the significant diversity and complexity of water distribution in surface space, accurate mapping of surface water faces considerable challenges. When extracting water bodies from medium-resolution satellite remote sensing images, CNN methods may suffer from limitations in receptive fields and insufficient context modeling capabilities, leading to the loss of water body boundary details and poor fusion of multiscale features. Currently, there is relatively little research on this issue; therefore, it is necessary to explore new combinations of deep learning networks to address these challenges. The purpose of this study is to address the above issues. We propose a new combination of deep learning networks that fully utilize multiscale information to enhance water features. Specifically, we first combine deformable convolutions with the Swin Transformer to increase effective receptive fields while better integrating global semantic information. This combination can capture features of water bodies at different scales, improve the accuracy and integrity of water extraction, and provide reliable technical support for detailed water body extraction. We tested the newly constructed model using Sentinel-2 satellite images. Our model achieved results of over 90%, with an average accuracy of 97.89%, average precision of 94.98%, average recall of 90.05%, and an average F1 score of 92.33%. In addition, our model achieved an accuracy of 98.03% in mountainous areas. Our experiments and results validate the potential of combining the Swin Transformer and deformable convolutions in detailed water body extraction.

Hydrogels have gained significant attention as vehicles for transdermal delivery systems. These polymeric-structured substances have been implemented in pharmaceutical and cosmetic applications. Many factors are employed in their preparation. Light, pH, temperature and specific molecules ligands are commonly used in the production of hydrogels. The Transdermal delivery system (TDD) is a widely used route, used to deliver actives/drugs via the skin. Hydrogels as a TDD have been shown to be efficient in enhancing the efficacy in delivering the actives and drugs. The aim of this review is to discuss the hydrogels application in both dermatological therapeutic and cosmetic applications, covering the types, and properties of hydrogel polymer networks. Furthermore, this review presents one of the most extensively used polymers (Hyaluronic acid HA) in this field and discusses the properties, preparation methods and applications of HA hydrogel patches as a delivery system, including methods of loading the actives and the releasing of these actives from them.

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.

Immunotherapy, particularly Immune Checkpoint Blockade (ICB), has demonstrated significant efficacy in treating melanoma in recent years. However, accurately predicting treatment success and avoiding ineffective therapies remains an unresolved challenge. Therefore, this study aims to develop statistical models utilizing neural networks to forecast the effectiveness of immune checkpoint therapies for melanoma patients. Our models primarily rely on Artificial Neural Networks (ANN) to anticipate both Overall Survival (OS) and Progression-Free Survival (PFS) among melanoma patients undergoing anti-CTLA4 and anti-PD1/anti-PDL1 therapy. We incorporate gene expression data, measured in Transcripts per Million (TPM), derived from bulk tumor RNA-sequencing datasets. Additionally, clinical variables such as gender, age, and treatment type are factored into our analysis. The ANN underwent optimization to attain the highest feasible precision in anticipating the predetermined survival outcome. Issues stemming from high-dimensional data, such as overfitting, were tackled through regularization and feature selection methods. Consequently, the ANN-based model incorporating feature selection exhibited the capacity to forecast survival (PFS) in response to ICB therapy with a maximal precision of 86%. Conversely, the ANN lacking feature selection but incorporating regularization achieved accuracies of up to 72% for PFS and 71% for OS, correspondingly. In order to confront the challenge posed by limited patient samples and to assess replicability, the model underwent training and validation based on the amalgamation of all five datasets. However, this amalgamation failed to enhance predictive performance, necessitating further investigations.

The institutionalization of people with intellectual disabilities has been a common international response across cultures and generations that persists to this day. This chapter recounts its inheritance and manifestation in modern service provision. An institutional mind-set persists among clinicians and managers despite policies that emphasise the rights of persons with intellectual disability and anti-discriminatory practice. Alternative provision is rooted in person-centred practice; with a growing body of evidence for the improved quality of life that results from it. A reflective activity encourages readers to examine a recent case when they were associated with an institutional placement and what alternatives might have been sought. Yet alliances with other professionals, self-advocates and family carers will be needed to effect real and lasting changes in our service cultures and even then, the long shadows of institutional care may still persist.

Objective To investigate nailfold capillary parameters in community-dwelling individuals aged over 60 years who have hypertension and do not exercise regularly. Furthermore, the study examined the correlations between capillary function and other health-related indicators. Design A single- center pilot trial Setting Faculty of Health, Tsukuba University of Technology, Japan Participants hypertensive community-dwelling elderly people Intervention Microcirculation was observed before and 1 min after arm-curl exercise by means microscopy of capillary with non-exercised limb. Additionally, we examined other health-related indicators. Outcome measures: Acute effects of reperfusion on nailfold density, flow, and diameters. Secondary outcomes included the correlations between microvascular parameters and other health-related indicators. We hypothesized that brief exercise could enhance microcirculation reperfusion and correlate with other health-related parameters.　 Results: There were 20 participants with a mean (SD) age of 67.1 (5.8) years. The capillary flow rate changed from 2.3±6.7 to 2.7±0.2 log µm/s (P

The main proteinase (Mpro), or 3CLpro, is a critical enzyme in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lifecycle and is responsible for breaking down and releasing vital functional viral proteins crucial for virus development and transmission. As a catalytically active dimer, its dimerization interface has become an attractive target for antiviral drug development. Recent research has extensively investigated the enzymatic activity of Mpro, focusing on its role in regulating the coronavirus replication complex and its significance in virus maturation and infectivity. Computational investigations have identified four druggable pockets, suggesting potential allosteric sites beyond the substrate-binding region. Empirical validation through site-directed alanine mutagenesis targeted residues in both the active and allosteric regions corroborated these predictions. Structural studies of drug target proteins can inform therapeutic approaches, with metadynamics simulations shedding light on the role of H163 in regulating Mpro function and providing insights into its dynamic equilibrium to the wild-type enzyme. Despite the efficacy of vaccines and drugs in mitigating SARS-CoV-2 spread, ongoing viral evolution, selective pressures, and continued transmission pose challenges, potentially leading to resistance mutations. Phylogenetic analyses indicated the existence of several resistance variations predating drug introduction to the human population, emphasizing the likelihood of drug spread. Hydrogen/deuterium-exchange mass spectrometry reveals the structural influence of the mutation, while clinical trials on 3CLPro inhibitors underscore the clinical significance of reduced enzymatic activity and offer avenues for future therapeutic exploration. Understanding the implications of 3CLPro mutations holds promise for shaping forthcoming therapeutic strategies against COVID-19. This review delves into factors influencing mutation rates and identifies areas warranting further investigation, providing a comprehensive overview of Mpro mutations, their categorization, and terminology. Moreover, we examined their associations with clinical outcomes, illness severity, unresolved issues, and future research prospects, including their impact on vaccine efficacy and potential therapeutic targeting.

Limited research has been conducted on the metabolites present in the essential oils (EOs) of Phlomoides rotata (Benth. ex Hook. f.) Mathiesen (PR, syn. Lamiophlomis rotata (Benth.) Kudô), and their potential pharmacological activities, such as antioxidant properties, are often overlooked. We conducted a metabolomics-based screening to identify the characteristic metabolites (CMs) in the EOs extracted from PR. Meanwhile, we evaluated their in vitro antioxidant activities (AOAs). The EOs obtained through hydro-distillation are characterized by a light yellow color and a fresh, elegant fragrance. Cryoprecipitation was used to separate the crystals (Crs) from the EOs, resulting in the crystal-free EOs (CFs). A total of 121 components are identified and quantified in the EOs, Crs, and CFs. Among them, 84 were reported for the first time from such EOs. The major compounds include long-chain fatty acids (LCFAs) and their esters, such as palmitic acid (PA), myristic acid (MA), linoleic acid (LA), oleic acid (OA), stearic acid, and methyl palmitate (MP). Furthermore, 11 kinds of apocarotenoids (also called norisoprenoids) were first systematically investigated. Among them, eight C13-norisoprenoids, with trans-β-damascenone (DN) being particularly notable for its flavor, were identified. A sum of 11 CMs are screened , including PA, MA, LA, OA, MP, hexahydrofarnesyl acetone, phytol (PT), DN, stearic acid, linalool, and hexanal. These CMs are biosynthesized through FAs and 2-C-Methyl-D-erythritol 4-phosphate/1-Deoxy-D-xylulose 5-phosphate pathways. The in vitro AOAs of EOs, Crs, CFs, and seven selected CMs (PA, MA, LA, OA, MP, PT, and DN) were evaluated. Generally, PA and MA exhibited pro-oxidant activities (POAs) or weak AOAs. LA and OA demonstrated POAs or weak AOAs at lower concentrations, but at higher concentrations, they displayed medium to strong AOAs. Importantly, the CFs typically exhibited stronger AOAs compared to the corresponding EOs and Crs, potentially due to variations in their PA content. Overall, this study provides valuable insights into the potential utilization of EOs from PR.

Fluorinated cytokinins have emerged as promising alternatives to traditional cytokinins in plant tissue culture, offering enhanced stability and bioactivity. However, their metabolic fate and impact on endogenous cytokinin profiles remain largely unexplored. This study investigated the cytokinin metabolism of Phalaenopsis orchids under varying exogenous cytokinin treatments (BA, FmT, FmTR) and culture conditions (open vs. closed containers). The results reveal distinct metabolic profiles associated with each treatment, highlighting the complex interplay between exogenous and endogenous cytokinin levels. Additionally, we proposed an alternative inactivation pathway involving the conversion of FmTR and BA to pT and pTR. These findings provide valuable insights into the intricate relationship between cytokinin metabolism and plant growth under in vitro conditions, with implications for optimizing micropropagation protocols

The increasing focus on sustainability and the adoption of green practices across industries underscore the necessity of equipping future professionals with competencies in implementing sustainable solutions. This study explores the perceptions of former students from two departments within the technological sector towards sustainability practices. A cross-sectional online survey was carried out among alumni from two programs at the University of Thessaly, a university recognized for its commitment to sustainability and green initiatives as a member of the INVEST European University alliance. The survey questionnaire included background inquiries and 16 statements designed to gauge alumni perspectives towards sustainability practices. Specifically, it aims to assess their views through self-assessment and compare differences between the two alumni groups. Responses were collected on a 5-point Likert scale ranging from strongly disagree to strongly agree. A total of 300 graduates from the Computer Science and Telecommunications as well as Electrical Engineering undergraduate programs were encouraged to take part in the survey, achieving a response rate of 69%. Of those who responded, 61% (n = 126) represented the Computer Science and Telecommunications group, while 39% (n = 81) were from the Electrical Engineering program. Our findings indicate a generally positive attitude towards sustainability, with statistically significant differences in perspectives on the implementation of sustainable technologies and stakeholder engagement. These results underscore the necessity of integrating sustainability education deeply within the curricula of technological programs to cultivate a workforce capable of addressing future environmental challenges. This study illuminates the critical role of educational frameworks in shaping the sustainability competencies of technology professionals.

Introduction: Glomerulopathy is a term used to describe a broad spectrum of renal diseases, character-ized by dysfunction of glomerular filtration barrier, especially of podocytes. Several podocyte-associated proteins have been found and proved their usefulness as urine markers of podocyte dysfunction. Two of them are nephrin (NEP) and prodocalyxin (PDC). Purpose: This study aims to evaluate the association of podocyte damage, as it is demonstrated via the concentrations of urinary proteins, with clinical and histological data from patients with several types of glomerulonephritis. Methods: We measured urine levels of two podocyte-specific markers, NEP and PDC (corrected for urine creatinine levels), in patients with a wide range of glomerulopathies. Serum and urine parameters as well as histological parameters from renal biopsy were recorded. Results: In total, data from 37 patients with glomerulonephritis and 5 healthy controls were analyzed. PDC and NEP concentrations correlated between them and with serum creatinine levels (p=0.001 and p=0.013 respectively), and with histological lesions associated with chronicity index of renal cortex, such as severe interstitial fibrosis, severe tubular atrophy and hyalinosis (for PDC/NEP, all p3gr/24h with diffuse fusion of podocyte foot processes (p=0.016) was identified. Conclusions: Podocalyxin and nephrin concentrations in urine represent markers of podocyte dysfunction and in our study they were associated both with serum creatinine and histological chronicity indices.

The rise of the Internet has enabled people to express their opinions on various subjects through social media, blogs, and website comments. As a result, there has been a significant increase in research on sentiment analysis. However, most of the research efforts have focused on analyzing sentiment in English-language data, neglecting the wealth of information available in other languages. In this paper, we provide a comprehensive review of the current state-of-the-art in multilingual sentiment analysis. The survey investigates techniques for data preprocessing, representation learning, and feature extraction in multilingual sentiment analysis. It explores cross-lingual transfer learning, domain adaptation, and data augmentation methods that enhance the performance of sentiment analysis models, particularly in low-resource languages and domains. It provides insights into the state-of-the-art approaches, challenges, and opportunities in this evolving field and encourages further advancements in multilingual sentiment analysis research.

The paper presents the observational, remote sensing and model simulation to analyze Southern Brazil Antarctic Ozone Hole influence (SBAOHI) events occurred between 2005 and 2014. To an-alyze it we use Total Ozone Column (TOC) data provide by Brewer Spectrophotometer (BS) and OMI (Ozone Monitoring Instrument) instrument satellite, besides the AURA/MLS (Microwave Limb Sounder) instrument satellite ozone profiles was utilized with DYBAL (Dynamical Barrier Localization) code in MIMOSA (Modélisation Isentrope du transport Mésoéchelle de l’Ozone Stratosphérique par Advection) model Potential Vorticity (PV) fields. TOC has 7.0 ± 2.9 DU re-ductions average in 62 events. October have more events (30.7%). Polar Tongue events are 19.3% of total, being in October more frequently observed (50% of cases), with medium intensity (58.2%) and in stratosphere medium levels (55.0%). Already Polar Filament events (80.7%) are more frequent in September (32.0%), medium intensity (42.0%) and in stratosphere medium lev-els (40.7%). ENSO index positive phase influenced 61.3% of events, having dominance in all in-tensity categories (minor 57.9%, medium 51.6%, major 58.3%), while the equilibrium dominate the QBO influence with 50% of total cases in positive phase and 50 % in negative phase.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Optical motion capture is a mature contemporary technique for the acquisition of motion data, alas it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied for gap filling problem in motion capture sequences within FBM framework providing the representation for body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out, that for longer sequences simple linear feedforward neural networks can outperform the other, sophisticated architectures. We were also able to identify, that acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results.

Neural decoding from spiking activity is an essential tool for understanding the information encoded in population neurons, especially in applications like brain-computer interface (BCI). Various quantitative methods have been proposed and have shown superiorities under different scenarios respectively. From the machine learning perspective, the decoding task is to map the high-dimensional spatial &amp; temporal neuronal activity to the low-dimensional physical quantities (e.g., velocity, position). Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. Recently, the Transformer-based models are widely used in processing natural languages and images due to its superior performances in handling long-range and global dependencies. Hence, in this work we examine the potential applications of Transformers in neural decoding and introduce two Transformer-based models. Besides adapting the Transformer to neuronal data, we also propose a data augmentation method for overcoming the data shortage issue. We test our models on three experimental datasets and their performances are comparable to the previous state-of-the-art (SOTA) RNN-based methods. In addition, Transformer-based models show increased decoding performances when the input sequences are longer, while LSTM-based models deteriorate quickly. Our research suggests that Transformer-based models are important additions to the existing neural decoding solutions, especially for large datasets with long temporal dependencies.

An article's tone and framing not only influence an audience's perception of a story but may also reveal attributes of author identity and bias. Building upon prior media, psychological, and machine learning research, this neural network-based system detects those writing characteristics in ten news agencies' reporting, discovering patterns that, intentional or not, may reveal an agency's topical perspectives or common contextualization patterns. Specifically, learning linguistic markers of different organizations through a newly released open database, this probabilistic classifier predicts an article's publishing agency with 74% hidden test set accuracy given only a short snippet of text. The resulting model demonstrates how unintentional 'filter bubbles' can emerge in machine learning systems and, by comparing agencies' patterns and highlighting outlets' prototypical articles through an open source exemplar search engine, this paper offers new insight into news media bias.

Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.

In diabetic foot injury prevention, no studies relate physical activity with the inflammatory response of the plantar tissues, evaluated by thermography and pressure distribution. These measurements are easy to be implemented in diabetic foot follow-up. The objective of this study was to analyze whether the plantar skin response, evaluated with thermography, after physical activity in healthy or diabetic patients, is a foot ulcer prognostic tool and to asses if there is a correlation between exercise intensity, plantar pressure and plantar thermographic response. A systematic review was carried out following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format in the PubMed, Scopus and Web of Science databases, with no year limit during the second quarter of 2024. A total of 14 studies have been included in this review. The use of infrared thermography for early inflammatory tissue detection, plantar pressures and the monitoring of controlled physical activity, make possible to monitor changes that allow the identification of patients at risk of developing foot ulcers.

A major problem of the technological production process of the metallurgical-grade silicon is formation of a large amount of microsilica. However, this material can be a significant source of raw materials for its own production. Hence, this study is aimed to solve the problem of micro-silica utilization, i.e. the technogenic dust-like waste generated during the melting of the technical-grade silicon. Within this study, the technology of the joint briquetting of microsilica was investigated with the carbonaceous reducing agents, i.e. by obtaining of mono-charge and by melting of the technical-grade silicon from the obtained mono-charge in the 200kVA ore-thermal furnace. Results of the study show that the briquettes in the melting process of the technical-grade silicon can be used in the range from 0 to 50%. The using of briquettes may significantly improve the technological parameters. When briquetted charge is applied only, the technological parameters will deteriorate significantly. The material balance of silicon melting showed the recovery rates of the main elements (Si, Al, Ca and Fe). It recorded a decrease in content of aluminum from 1.9-2.1% to 0.5-0.7%, calcium from 0.2-0.3% to 0.07-0.1% and titanium from 0.2-0.23% to 0.11-0.13% during the transition to briquetted monocharge. Phosphorus content increased from 0.004-0.005 to 0.011-0.013%.

In the evolving landscape of digital marketing, cross-channel attribution modeling plays a crucial role in understanding and optimizing the customer journey across various touchpoints. As consumers interact with brands through multiple channels—such as social media, email, search engines, and display ads—accurately attributing conversions to the right channels is vital for optimizing marketing strategies and budgets. However, the advent of stringent privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), has significantly impacted the data collection and analysis processes in digital marketing. This paper explores the challenges and opportunities presented by cross-channel attribution modeling in the age of heightened privacy awareness. It examines how traditional data-driven attribution models, which often rely on tracking individual user behavior, are being adapted or replaced in response to privacy concerns and regulatory requirements. The paper discusses the emergence of privacy-preserving techniques, such as aggregated data analysis, differential privacy, and the use of anonymized data, which aim to balance the need for accurate attribution with the protection of consumer privacy. Furthermore, the paper highlights the role of first-party data and the growing importance of consent management in the collection and utilization of consumer information. It also investigates how marketers are leveraging advancements in artificial intelligence and machine learning to enhance attribution models in a privacy-conscious world. The study concludes by offering best practices for businesses seeking to navigate the complexities of cross-channel attribution in the context of evolving privacy regulations, emphasizing the need for transparency, compliance, and ethical data handling. This research provides valuable insights for marketers, data scientists, and policymakers on how to effectively manage the interplay between accurate attribution and privacy, ensuring that marketing efforts remain effective while respecting consumer rights.

Treating central giant cell granuloma (CGCG) is challenging due to high recurrence rates and variable therapy responses. This study examines the efficacy of various treatments in reducing CGCG recurrence. A literature review explored outcomes of surgical excision, curettage, intralesional corticosteroid injection, and adjuvant therapy, considering factors like lesion location, size, and histological features. Aggressive surgical techniques such as en bloc resection were found to potentially lower recurrence rates compared to conservative approaches. However, treatment should be tailored to individual patient needs. Further research is needed to confirm these findings and improve treatment strategies. A concise literature review was conducted using PubMed, MEDLINE, and Google Scholar, focusing on papers published from 1986 to 2024. Search terms included "central giant cell granuloma," "recurrence," "treatment modalities," and "surgical excision". Studies reporting recurrence rates and treatment outcomes for CGCG were analyzed. Twenty-nine studies were reviewed, including six studies on surgical excision and curettage, eight studies on intralesional corticosteroid injections, six studies on calcitonin therapy, five studies on interferon alpha therapy and four studies on the therapy with denosumab. Analysis indicated that aggressive surgical treatments like en bloc resection were associated with lower recurrence rates compared to conservative methods. Predictors of recurrence included lesion size (&gt;3 cm), location (mandible), and aggressive histopathological features. Aggressive surgical excision combined with nonsurgical methods may lower recurrence rates, while conservative techniques remain viable in some cases. Further prospective research is needed to validate these findings and enhance CGCG treatment options.

The clothing industry is a hotbed for exploitative working conditions and environmental damages, of which affluent global North countries remain a substantial driver. The circular economy is a potential solution to these issues, but its social implications remain unclear. Using the UK as a case study, this article analyses the global social impacts of a transition to circular clothing economies in the global North, finding these to be (almost) entirely dependent upon the intentions and design of the associated politics. Some aspects of a transition will be unambiguously good for some people; others will only be beneficial with careful governance. Moreover, entrenched global economic inequalities leave trade-offs borne largely by the global South, highlighting the need for circular economy research – its drive for localisation notwithstanding – to look globally to consider how a fair transition can be achieved.

This research aims to explore the adoption and impact of green supply chain practices (GSCP) on sustainable development in Bangladesh. The study seeks to understand the current state of GSCP adoption across various industries, identify key drivers and barriers, assess the perceived benefits and challenges, and evaluate the overall impact on environmental sustainability, economic performance, and social well-being. The research employs a qualitative approach, utilizing interviews with key stakeholders, document analysis, and literature review to gather data and insights. Interviews were conducted with representatives from government agencies, businesses, civil society organizations, and academia to obtain diverse perspectives on GSCP adoption and its implications for sustainable development in Bangladesh. The findings reveal a mixed scenario of GSCP adoption across different industries in Bangladesh, with sectors such as textiles leading the way. Key drivers of GSCP adoption include regulatory pressures, market forces, corporate social responsibility initiatives, and financial incentives. However, organizations face significant barriers such as high upfront costs, lack of awareness and expertise, regulatory uncertainty, and supply chain complexity. Despite these challenges, GSCP offer various benefits, including environmental sustainability, economic performance, and social well-being. Successful initiatives demonstrate tangible impacts, highlighting the potential of GSCP to contribute to sustainable development. The research is limited by its qualitative nature and focus on selected industries and stakeholders. Future studies could employ quantitative methods and broader sampling to provide a more comprehensive understanding of GSCP adoption and its implications in Bangladesh. The findings provide actionable insights for policymakers, industry leaders, and other stakeholders to promote GSCP adoption and enhance sustainable development in Bangladesh. Strategies for overcoming barriers and maximizing benefits are outlined, emphasizing the importance of collaboration and capacity-building initiatives. GSCP adoption can have positive social implications by promoting ethical labor practices, improving working conditions, and fostering community engagement. By prioritizing sustainability, businesses can contribute to social well-being and inclusive growth. This research contributes to the literature on GSCP adoption and sustainable development in Bangladesh by providing empirical insights and practical recommendations. The study adds to the understanding of the drivers, barriers, benefits, and challenges associated with GSCP adoption, highlighting the importance of integrating environmental, economic, and social considerations in supply chain management.

The Diaphorina citri Kuwayama (D. citri) is one of the major pests in the citrus industry, which spreads Citrus Huanglongbing disease. It has developed resistance to chemical insecticides. Therefore, searching for greener solutions for pest management is critically important. The main aim of this study was to evaluate the repellent and insecticidal efficacy of essential oils (EOs) from four species of Myrtaceae plants: Psidium guajava (PG), Eucalyptus robusta (ER), Eucalyptus tereticornis (ET), and Baeckea frutescens (BF) against D. citri and to analyze their chemical composition. GC-MS analysis was performed, and the results indicated that the EOs of PG, ER, ET, and BF were rich in terpenoids, ketones, esters, and alcohol compounds. The repellent rate of all four EOs showed that it decreased with exposure time but increased with concentrations of EOs. Repellent rates ranged from 80.50% to 100.00% after treating D. citri for 6 hours with four EOs at 100% concentration and decreased to 67.71% to 85.49% after 24 hours of exposure. Among the compounds from the EOs tested, eucalyptol had the strongest repellent activity, with a 24-hour repellency rate of 100%. The contact toxicity bioassay results showed that all EOs have insecticidal toxicity to D. citri; the LC50 for nymphs was 36.47-93.15 mL/L, and for adults, it was 60.72-111.00 mL/L. These results show that when PG is used as the reference material, both ER, ET, and BF EOs have strong biological activity against D. citri, which provides a scientific basis for the further development of plant-derived agrochemicals.

The purpose of this research is to examine the benefits and limitations of implementation of novel digital academic advising systems using automated collection and reporting processes for ABET student outcomes data based on principles of authentic OBE for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools® - Advising Module based on assessment methodology incorporating the Faculty Course Assessment Report, Performance Indicators and hybrid rubrics classified according to the affective, cognitive and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and Performance Indicators information; and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the Faculty Course Assessment Report using Performance Indicators and hybrid rubrics can provide comprehensive and realistic outcomes data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better performing outcomes to reinforce natural affinity for learning specialized competencies and pursue related and successful career paths.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

This study presents a groundbreaking approach in the field of gaming AI, focusing on the classic game” Pacman” through the lens of DRL integrated with advanced optimization techniques. The core of the research involves adapting DRL models for” Pacman”, utilizing the ESO for hyper- parameter tuning. These novel adaptations significantly enhance the AI agent’s performance, demonstrating a remarkable improvement in adaptability, responsiveness, and efficiency within the game environment. A pivotal aspect of this research is the innovative integration of metaheuristic optimization techniques into the DRL framework, a first in the domain of Atari gaming AI. This integration has proven essential in fine-tuning the DRL models, leading to a more effective and dynamic gaming experience. The study thoroughly evaluates and compares the performance of these algorithms, providing empirical support for their effectiveness and setting a new benchmark in AI-driven game development. The implications of this research extend beyond gaming AI, opening up possibilities for future exploration in several directions. Expanding DRL models to other complex gaming environments, continuous algorithmic enhancements, real-time learning adaptations, and applying these principles to robotics and autonomous vehicles are examples. The research also emphasizes ethical and responsible AI use in gaming to address fairness and addiction issues.

Epilepsy is one of the most prevalent and serious brain disorders, affecting 70 million people worldwide. Antiseizure medications (ASMs) though relieve symptoms and prevent the occurrence of future seizures in epileptic patients have limited effect on epileptogenesis. Addressing the multifaceted nature of epileptogenesis and its association with Nod-like receptor family pyrin domain containing 3 (NLRP3) inflammasome-mediated neuroinflammation requires a comprehensive understanding of the underlying mechanisms for development of targeted therapeutic strategies beyond conventional antiseizure treatments. Several types of NLRP3 inhibitors have been developed and their effect has been validated both in vitro and in vivo models of epileptogenesis. In this review, we discuss the advances in understanding the regulatory mechanisms of NLRP3 activation as well as progress made and challenges facing in the development of NLRP3 inhibitors for the treatment of epilepsy.

The present study explored whether exposure to complex childhood trauma (CCT) is reflected in the resting-state electroencephalography (EEG) temporal alpha power of complex trauma-exposed adolescents, given the association of temporal alpha with fear circuitry (learning and conditioning) in a sample of 25 adolescents and similar controls, ages 12-17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded, following a preregistered protocol for data collection. Temporal alpha power, corresponding to T5 and T6 electrode location (10-20 international system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, right, but not left, temporal alpha was significantly higher in adolescents exposed to CCT, relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns, specifically in the right temporal region. Our study highlights the importance of considering neural markers, such as temporal alpha power, in understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

Verification of inclusivity of molecular detection methods gives indications on the reliability of viral infections diagnosis because of the tendency of viral pathogens to undergo sequence variation. This study was aimed to select inclusive probe based reverse transcription quantitative PCR (RT-qPCR) assays for the diagnosis of the most widespread and detrimental viruses infecting honeybees, namely the acute bee paralysis virus (ABPV), the black queen cell virus (BQCV), the chronic paralysis bee virus (CBPV), the deformed wing virus variants A (DWVA) and B (DWVB), and the sacbrood virus (SBV). Therefore, detection methods previously described were re-evaluated in silico for specificity and inclusivity. Based on this evaluation, selected methods were modified or new ones were designed and tested in duplex RT-qPCR reactions. The limits of detection (LODs), effect of multiplexing on sensitivity and viral RNA quantification potential in bees and hive debris were assessed. This study made available diagnostic assays able to detect an increased number of virus variants compared to previously described tests and two viral pathogens in a single PCR reaction.

Background This study aimed to identify the prominent risk factors that may trigger danger signals of early onset of vascular deregulation in T1D. The risk factors we have identified may serve as dual-purpose biomarkers of rapid diagnosis that offer prospective tailor-made resolution therapeutics. Methods The healthy control (HC) was compared with individuals diagnosed with T1D for the risk factors of vascular deregulation in published research studies from year 2013 to 2023. The PubMed, Web of Science and Google Scholar databases were searched from 1/1/2013 to 1/9/2023. The risk of bias was assessed with the Cochrane (ROBINS-I ) tool, which is relevant to clinical subjects. A random effect model for continuous data was followed and analysed by RevMan 5.4 and GraphPad Prism software. Results 80 relevant case-control studies having 5293 HC and 7492 T1D patients were included. The age and sex-matched HC consisted of persons free of disease and not under any medication while clinical subjects of all age groups were included. 28 risk factors were grouped into six primary outcome models, all of which favoured the T1D synonymous with a high risk of CVD. Conclusions Our findings have strong implications for improving the quality of life and health economics related to vascular disease in T1D. HbA1c% is the most effective biomarker, followed by FBG, LDL-C, and AI% which the clinicians could evaluate using a simple blood test or non-invasive techniques. (Protocol registered at https://www.crd.york.ac.uk/prospero/ CRD42022384636).

Coal bottom ash (CBA) is a byproduct of coal-burning power plants. This material can be used as a fine aggregate replacement in concrete to reduce global natural material depletion. Concrete usage is increasing rapidly; subsequently, the industry’s carbon footprint is increasing. Using CBA in the construction industry will reduce the technical and economic problems associated with power plants by reducing solid waste. This project aimed to determine if adding new sustainable materials, such as CBA, to concrete will reduce natural raw material usage, energy consumption, and greenhouse gas emissions while improving performance. This study included two projects: comparing increased CBA content to the two-control project and determining the optimum content based on the compressive strength. The optimum CBA content is 50%, which reduces fine aggregate usage in a concrete mix by 50% while maintaining equivalent or better concrete strength than a pure Portland concrete mix without CBA. The air content of the concrete mix containing % CBA decreased with increased CBA content and compressive strength, possibly due to the spacing factor of the voids in the mix and CBA’s porous and fineness behavior. The CBA optimum content had a unit weight lower than the controls for both projects, which makes the CBA mix a lightweight concrete. Using this material can significantly reduce natural material usage and environmental harm by reducing CBA waste disposal and improving concrete performance.

To facilitate the promotion and application of the whole process engineering consulting (WPEC) model, the transmission mechanism of influencing factors in WPEC's promotion and application is investigated. It identifies and categorizes the influencing factors within the framework of "external environment-project factors-stakeholders", systematically organizing and summarizing ten primary categories and thirty-eight secondary influencing factors of WPEC's promotion and ap-plication. Utilizing the complex network analysis method, a network of influencing factors in the promotion and application of WPEC is constructed. The network is then converted into three binary networks based on weight, which are subjected to structural analysis and motif analysis. On this basis, the transmission mechanism of influencing factors in WPEC's promotion and applica-tion and its causations are explored.

Thrombosis after severe acute respiratory syndrome coronavirus 2 vaccination is a serious complication in patients who have a thrombophilic predisposition. In this case, a female patient with no family history of thrombophilia who had pediatric antiphospholipid syndrome (APS) developed deep vein thrombosis (DVT) when she was 6 years old; 6 months after receiving a second dose of the BNT162b2 vaccine, DVT reoccurred. The original thrombi were found in the right common iliac vein and the inferior vena cava, with concomitant left pulmonary infarction. The patient was treated with anticoagulants for 6 years after DVT onset, with subsequent treatment cessation for 5 years without recurrence. She received the BNT162b2 vaccine at 17 years of age, 1 week before a routine outpatient visit. Platelet factor 4 elevation was detected 14 days after the first vaccination, persisting for 5 months without thrombotic symptoms. Six months after the second vaccine dose, the DVT recurred and was treated with a direct oral anticoagulant. The vaccine exacerbated the patient’s APS by activating coagulation, causing thrombosis recurrence. Platelet factor 4 levels may indicate coagulation status. When patients predisposed to thrombosis are vaccinated, coagulation status and platelet activation markers should be monitored to prevent DVT development.

Physical exercise is the greatest stress on the cardiovascular system, not only in humans but also in all vertebrates. Consequently, the cardiovascular response cannot be thought of as having only one "anthropocentric" interpretation. The modulation of cardiac output (Q) in response to exercise in the five major groups of vertebrates exhibits considerable variability. Moreover, the response of the heart rate (HR) and the stroke volume (SV), the fundamental factors that cause the rise in Q, is also highly variable. The difference in the range of HR is difficult to determine in many vertebrates, both at rest and at maximal effort. Additionally, the increase in SV varies among vertebrates. For example, while rainbow trout and leopards increase their SV by increasing end diastolic volume (EDV), humans do so at the expense of both increasing EDV and reducing end systolic volume (ESV). The variation in arterial pressure that arises during exercise varies notably among vertebrates, such as amphibians, whose blood pressure values do not increase as significantly as in mammals. Such differences in cardiovascular response among vertebrates may be linked to their habitat or living environment. This review aims to analyze the cardiovascular response of vertebrates to physical exercise as the most common stressor.

HIV incidence remains alarmingly high among female sex workers (FSWs) in low- and middle-income countries, with Uganda being no exception. The elevated risk of HIV transmission among this vulnerable population can be attributed to a complex interplay of factors, including engaging in sexual activities with multiple partners, inconsistent condom usage, substance use, and structural barriers, such as legal criminalization and pervasive stigmatization by healthcare providers. To combat this epidemic, various HIV prevention interventions have been devised, targeting these multi-faceted challenges. This study involved conducting focus groups among FSWs and healthcare providers in Uganda, with the overarching objective of informing the development of a tailored HIV prevention intervention aimed squarely at FSWs. Through these discussions, valuable insights were gained into FSWs’ perceptions of their HIV risk, their attitudes regarding HIV prevention strategies, and their recommendations regarding the content and delivery of future interventions.

Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.

This article presents a comprehensive study on enhancing grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, Long Short-Term Memory (LSTM) networks for forecasting solar irradiance and load profiles, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of Battery State of Charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The results highlight the potential of the proposed approach in strengthening grid resilience, reducing downtime, and fostering sustainable energy utilization.