The campuses of Nigerian universities especially the old ones, like the University of Ibadan, comprise old trees which can pose a threat to pedestrians, structures, and roads within the school environments. However, there is no adequate information on these avenue trees thereby impeding their sustainable management. Therefore, this study provides the basic information needed by developing a geodatabase for the avenue trees along Benue, Emotan and Oduduwa roads within the University of Ibadan, Ibadan, Nigeria. The coordinates of all the avenue trees, and some within the selected roads were obtained. Tree growth data collected include; diameter at the breast height (dbh), diameter at the base, middle and at the top, crown height, crown diameter, and total height of all the avenue trees. Tree visual evaluation for diseases (TVED) was carried out for root decay, and termite attack on any part of the tree (stem, leaves and visible part of the root). Risk rate was assessed as low, medium and high, depending on the potential target (human, vehicles, structures). The risk rate depended on the incidence of leaning towards a building or road, cracked branches, root attack by termites, basal decay or branch decay. The growth data were used to compute the tree volume while the avenue trees were georeferenced. The physical health status of the avenue trees was determined using TVED data. The spatial distribution of the avenue trees was developed in GIS while all the analysed data were pulled together to develop a geodatabase for the avenue tress using design templates of BOOTSTRAP, comprising of Hypertext Markup Language, Cascade Style Sheets, JavaScripts and Hypertext Preprocessor. A total of 121 individual avenue trees belonging to 14 species were identified in the study area. The analysis of the geospatial distribution shows that Emotan Road had a lesser spatial distribution of avenue trees compared to Benue and Oduduwa roads. Furthermore, the health risk assessment of the trees in the study area indicated that 17.35% of the individual avenue trees had defects. The database created is user-friendly, suitable and has the ability for easy data storage and quick information retrieval on the avenue trees to enhance their maintenance and risk management. This can be accessed using the link http://159.203.29.155/rebs/rebs.php.

The airline sector is critical in today's globalized society, supporting the efficient movement of people and products worldwide. Airlines are continuously looking for methods to enhance their operational performance to remain competitive in the face of increased competition. This study focuses on the application of Multi-Criteria Decision Making (MCDM) techniques to improve low-cost carrier (LCC) also known or low-cost (budget) airlines' ground operational performance. MCDM techniques have gained considerable attention in recent years to address complex decision problems with multiple conflicting goals. This research aims to bridge this gap by proposing a comprehensive framework that combines MCDM techniques to enhance airline operational strategies to increase performance. The study utilizes qualitative and quantitative data, drawing on previously published materials on MCDM techniques in the aviation sector. It utilizes fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. A case study is conducted to evaluate the ground operational performance of three airline companies based in Türkiye, considering five main criteria and 18 sub-criteria. The findings of this research will contribute to a comprehensive understanding of operational performance in the airline sector. It also highlights the implications and limitations of the research and suggests future research directions.

The energy production market based on hydrogen technologies is to be an innovative solution that will allow the industry to achieve climate neutrality in the future in Poland and in the world. The paper presents the idea of using hydrogen as a modern energy carrier, and devices that, in cooperation with renewable energy sources, produce the so-called green hydrogen and the applicable legal acts that allow for the implementation of the new technology were analysed. Energy transformation is inevitable, and according to reports on good practices in European Union countries, hydrogen and the hydrogen value chain (production, transport and transmission, storage, use in transport and energy) have a wide potential. Thanks to joint projects and subsidies from the EU, initiatives supporting hydrogen technologies are created, such as: hydrogen clusters, hydrogen valleys, and EU and national strategic programs set the main goals. As noted in this article, Poland is one of the leaders in hydrogen production both in the world and in Europe. Domestic tycoons from the energy, refining and chemical industries are involved in the projects. Eight hydrogen valleys that have recently been created in Poland successfully implement the assumptions of the "Polish Hydrogen Strategy until 2030 with a perspective until 2040", "Energy Policy of Poland until 2040", which are in line with the assumptions of the most important legal acts of the EU , including: the European Union's energy and climate policy, the Green Deal, the Fit for 55 Package.

The trillions of bacteria that live in and on our bodies make up the human microbiota, which is a very important part of keeping us healthy and protecting us from pathogenic infections. Dysbiosis is when there are changes in the composition and variety of the microbiota. This can upset the delicate balance between the host and its microbes, making the host more vulnerable to pathogens. Understanding the complicated link between microbiota and pathogens is important for coming up with effective ways to change microbiota and boost the body's defenses against infections. This study looks at how microbiota modulation affects the spread of pathogens and infections. It talks about how the makeup of microbiota affects how susceptible you are to pathogens. It shows how a healthy microbiota can protect you by making it hard for pathogens to grow and take over. The review also goes into detail about how the microbiota protects against pathogens, such as through competitive exclusion, the production of antimicrobial compounds, and the regulation of immune reactions. The study also looks at the idea of dysbiosis and how it is linked to pathogenic shifts. Disruptions in the microbiota can make it easier for pathogens to grow and make it harder for the host to fight off infections. It focuses on the need to recover microbial balance to improve the host's defenses and lower risks caused by pathogens. There is also talk about the use of probiotics, prebiotics, and fecal microbiota transplantation (FMT) as ways to change the microbiome. The goal of these interventions is to restore the diversity and function of microbes, improve the integrity of barriers, and boost the immune system's reaction to pathogens. The review shows how these methods could be used to change the microbiota and make it easier for the body to fight off infections. In conclusion, knowing how the microbiota and pathogens work together in a complicated way can help a lot when it comes to making specific interventions for microbiota modulation. Strategies that restore the balance of microbes and boost the host's defenses could stop pathogens from taking over and making people sick. More study needs to be done in this area to improve therapeutic approaches and use the microbiota to its fullest extent to fight infectious diseases.

Background: Wilms' tumor (or nephroblastoma) is a malignant and solid neoplasm that derives from the primitive renal bud. It represents the most frequent primary tumor of the urogenital tract in childhood, and treatment consists of surgery and chemo-radiotherapy. However, concerning the quality of life, the new therapeutic frontier is exploring other safer and potentially more effective options, such as minimally invasive surgery and biological drugs. Method: Literature (Pubmed) from January 2013 to March 2023 was reviewed. Results: One hundred five articles were included in the review. Conclusion: In addition to the therapeutic strategies already identified, such as classic surgery and pharmacological therapies, recent studies focus attention on the new frontiers of minimally invasive surgery, such as diagnostics using biomarkers and immunotherapy, which could represent a new therapeutic option and possibly less risky than in the past, contributing in fact to the current knowledge of the scientific panorama in terms of "tumor microenvironment" and systemic implications deriving from oncological disease.

Lipid nanoparticles (LNPs) are promising carriers for constructing drug delivery systems (DDSs) in disease treatments. Previous studies have suggested that lipid composition and biophysical properties of LNPs can significantly impact their interaction with cells and tissues, allowing for the development of suitable LNPs for precise drug delivery. Our previous study proposed the concept and facile preparation of reconstituted lipid nanoparticles (rLNPs), which not only have the advantages of traditional LNPs but also contain the lipids of mother cell/tissue. In this study, we have found that brain-derived rLNPs (B-rLNPs) can have much better accumulation to the ischemic area of the ischemic stroke (IS) model than liver-derived rLNPs (L-rLNPs). This homing effect hopefully makes rLNPs a useful tool for developing highly accessible devices with homologous targeting ability for precise drug delivery.

In this work, we investigated whether the Pseudo-Newtonian theory (PNP) is applicable to black hole solution in modified gravity. In this regard, as an example, a static spherically symmetric 4D AdS Schwarzschild Yang-Mills black hole in EinsteinGauss-Bonnet gravity is considered. Also, a brief discussion on the structure and horizons of this black hole spacetime is presented. We calculated angular momentum and energy in the framework of the general theory of relativity as well as in PNP theory. In both of these framework, a graphical comparison of angular momentum has been studied

Although 7055 aluminum alloy is a deformed aluminum alloy and will show excellent mechanical properties after recrystallization after large deformation, it is a method to enrich its application range if rare earth is added and the rare earth phase dispersion is promoted by heat treatment. This article used optical microscopy, scanning electron microscopy energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD), micro Vickers hardness and room temperature stretching methods to study the as-cast 7055-xEr (x=0wt.%, 0.2wt.%, 0.4wt.%, 0.6wt.%, 0.8wt.%) aluminum alloy after being subjected to 460 oC×3h homogenization and 410 oC×1h solid solution+150 oC×12h aging treatment the changes in microstructure and properties. The results indicated that: when 0.2wt.%Er was added to 7055 aluminum alloy after solution at 410 oC×1h and aging at 150 oC×12h, the dendrite structure was significantly reduced, the grain thinning was obvious and the distribution was uniform, Al8Cu4Er phase appeared in the lamellar eutectic η-Mg(Zn,Al,Cu)2 structure at grain boundaries, and the hardness reached 168.8HV. The yield strength, tensile strength and elongation were 542.12Mpa, 577.67MPa and 8.36%, respectively.

Event-related potentials (ERPs) are estimated by averaging time-locked single trial electroencephalography (EEG) signals in response to specific events or stimuli. Classifying ERPs accurately is a challenge because (a) single trials have poor signal-to-noise-ratios (SNRs) and (b) it is difficult to collect large single trial ensembles to generate high SNR ERPs for classifier training and testing. The m-subsample averaging (m-SA) strategy which generates small-sample ERPs by repeated averaging of a small number of single trials drawn without replacement, has been proposed as a solution to the two problems. An ERP formed by averaging m single trials is referred to as an m-ERP where m is referred to as the averaging parameter. In this study, we conduct thorough analyses of m-SA and focus on issues not addressed in previous studies to better understand the beneficial properties of m-SA and to further support its application for ERP classification. Specifically, we (a) analyze the improvement in SNR as a function of m using the mean-root-mean-square SNR and visual analyses of m-ERP plots with confidence intervals, (b) analyze the improvement in interclass separation as a function of m, (c) determine how the SNR and interclass separation analyses can help to select the averaging parameter m, (d) determine the number of distinct m-ERPs that can be drawn from a single-trial ensemble, and (e) determine several probabilities related to the generation of distinct m-ERPs. Furthermore, an extensive set of experiments are designed to analyze the performance of support vector machine and convolution neural network classifiers employing m-SA with various combinations of the averaging parameters used for generating the training and test sets. The results confirm that ERPs can be classified accurately using small subsample averaging. Most importantly, it is concluded that m-SA can be deployed in practice to accurately classify ERPs in brain activity research and in clinical applications without having to collect a prohibitively large number of single trials.

Probiotics have become a potential way to change the microbiota, which allows for more personalized ways to improve health results. This abstract gives an outline of the most important parts of personalized microbiota regulation through probiotics. It focuses on effectiveness and mechanisms, clinical applications, safety and tolerability, formulations and delivery methods, and customized approaches. Many studies have looked into how and why probiotics change the microbiome. These studies show that probiotics can change the makeup and function of microbial communities. Probiotics have been shown to help treat a number of health problems, such as digestive problems, immune system problems, metabolic problems, and mental health problems. It is important for the success of probiotics to find out if they are safe and can be tolerated. Studies have shown that probiotics are usually safe for most people, but there are some things that need to be taken into account to make sure they are safe, especially for people who are weak. Formulations and transport methods are very important for making sure that probiotics are alive, stable, and delivered to the right places in the GI tract. Probiotic strains work better in the gut because of improvements in how probiotic preparation technologies protect and control the release of probiotic strains. Personalized methods take into account how each person's microbiome and health are different. By using individual microbiota profiling, custom probiotic approaches can be made to address specific microbial imbalances and promote a healthier microbial environment, which could improve treatment results. Even though individual methods show promise, there are still problems, such as the need to standardize microbiota profiling techniques, figure out how to understand data, and keep track of people over time. For personalized probiotics to reach their full potential and change the way patients are cared for based on the microbiota-host relationship, more study, technological advances, and teamwork are needed. In conclusion, probiotics can be used to change your microbiome in a way that is specific to you and your health goals. By using the power of individualized microbiota profiling, personalized probiotic treatments might be able to help with certain health problems and make the microbial environment better. Putting personalized probiotics into clinical practice and changing the way patients are cared for will require more study and partnerships between different fields.

The current study explores the problem of illiquidity in the corporate bond market and examines a broad set of liquidity proxies through empirical investigation. Purpose: The purpose of this paper is to give solution to the global issue of illiquidity in the corporate bond market. The problem is identified by many researchers and this paper attempts to find out the viable solution of the "fungibility route" as an alternative to the "liquidity route". Design/ Methodology: An Analysis of a sample size of 2,34,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is done to identify the problem. Findings / Solution proposed: A mathematical model based on credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be swapped by a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated risk adjusted discount rate using modified duration and credit risk differential. With our solution, it is possible to enhance the liquidity of the bond not directly but through fungibility route. Originality: the research is first of its kind in which solution is given to the global problem of corporate bond illiquidity. To execute the trade in the fungibility route, proposed parties involved are the Fungibility platform, the initiator, and the bidder. The derived fair value would facilitate trade and serve as an exit strategy for illiquid bond holders worldwide.

Life is supported by metabolism, a vital physiological activity. It guarantees enough energy available to power every cellular activity. The primary cellular substrate, glucose, is where the majority of the energy needed by the cell to support all other life activities and the growth of living creatures is derived. The quality of life is greatly impacted by any disease or flaw that alters the metabolism of glucose. There is no known cure for the metabolic illness known as diabetes. As diabetes is so varied, the only effective treatments would be based on precision medicine and a molecular strategy. This review covers a wide range of topics, including the ability to recognize the various forms of diabetes, the anthropogenic and genetic influence of the cause of diabetes, the severity of the various forms of diabetes, its potential existential risk, the disease burden, and clinical methods for diagnosing them. The most recent research goes on to recommend ways to ameliorate the disease, including diet-based management, biotherapies, and other individual lifestyle choices. Futuristic therapy using molecular biology approach was suggested. Recent research found a direct correlation between an individual's lifestyle and the rate of gene mutation in diabetes. There is an existential risk associated with the sickness as the rate of the spread of the disease among population globally is faster than the world's population growth rate. It is possible to think of diabetes as a silent pandemic.

Tobacco use is bad for your health in many ways, not just your lungs. It affects many other parts of your body, including your nervous system. This abstract looks at the link between smoking and the nervous system and shows how smoking affects the nerve system in specific ways. There are a lot of different diseases and risks that can come from smoking. By weakening blood vessels and making it easier for blood clots to form, smoking makes the chance of having a stroke much higher. Using tobacco is also linked to a drop in brain function and memory problems, as well as a higher chance of neurodegenerative diseases like Alzheimer's disease and dementia. Also, people who smoke have a higher chance of getting multiple sclerosis, which is a long-term autoimmune disease that affects the central nervous system. It can also cause peripheral neuropathy, which can damage nerves and cause tingling, pain, and stiffness in the limbs. Nicotine, the substance that makes tobacco addicting, changes the brain's reward circuits. This makes it hard to stop smoking because it leads to addiction. In addition to these effects on the body, smoking also has effects on the mind. It makes mood disorders like sadness and anxiety more likely, making the symptoms worse and hurting general health. Understanding the effects of smoking on the brain is important for improving overall health and getting people to stop smoking. Public health efforts, education, and targeted interventions are needed to make people aware of these risks and help them make choices about smoking that are based on accurate information. By looking at how smoking affects the brain, we can try to make people's lives better and protect their and their communities' well-being.

This manuscript presents a study on the spatial relationships between bike accidents, the built environment, land use, and transportation network characteristics in Budapest, Hungary using Geographic Weighted Regression (GWR). The sample period included bike crash data between 2017 and 2022. The findings provide insights into the spatial distribution of bike crashes and their severity, which can be useful for designing targeted interventions to improve bike safety in Budapest and be useful for policymakers and city planners in developing effective strategies to reduce the severity of bike crashes in urban areas. The study reveals that the built environment features, such as traffic signals, road crossings, and bus stops, are positively correlated with the bike crashes index, particularly in the inner areas of the city. However, traffic signals have a negative correlation with the bike crash index in the suburbs, where they may contribute to making roads safer for cyclists. The study also shows that commercial activity and PT stops have a higher impact on bike crashes in the northern and western districts. The GWR analysis further suggests that one-way roads and higher speed limits are associated with more severe bike crashes, while green and recreational areas are generally safer for cyclists. Future research should be focused on the traffic volume and bikes trips’ effects on the severity index.

Vectorial optical fields have sparked considerable interest with potential applications such as optical nano-fabrication, optical micromachining, quantum information processing, optical imaging, and so on. Traditional compact vectorial optical generators with amplitude modulation perform poorly in terms of diffraction effect reduction. In order to address this issue, the refractive 4f system in amplitude modulation is longitudinally aligned using an optimization approach presented in this research. The phase images used for longitudinal alignment are loaded into the liquid crystal spatial light modulator (SLM), and the distance between the lens and the mirror in the reflective 4f system is adjusted for longitudinal alignment by compensating for the neglected phase in the integrated module of the compact vectorial optical field generator. The spot images collected by the CCD are processed using the improved eight-direction Sobel operator, and the longitudinal alignment in the reflective 4f system is determined by the sharpness of the image. The sharpness of the lines' edges and the overall image are both enhanced after optimization compared to before optimization. The results show that the proposed method can significantly reduce the longitudinal alignment error of the reflective 4f system in the amplitude modulation of the compact vectorial optical field generator, lessen the diffraction effect, and improve the performance of the system.

For the first time VIS/NIR-SWIR (visible and near infrared – shortwave infrared) nighttime spectra of a satellite mission are analyzed, using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. The focus of this article is set on the spectral characteristics. First, the spectral calibration of EnMAP is checked based on sodium emissions of lighting. Here, applying a realized novel general method, shifts of +0.3nm for VIS/NIR and −0.2nm for SWIR are identified with uncertainties analyzed to be in the range of [−0.4nm,+0.2nm] for VIS/NIR and [−1.2nm,+1.0nm] for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Second, applying a realized simple general method, the dominant lighting types of Las Vegas, Nevada, USA, and thermal emissions are identified per pixel and the consistency of the outcomes is considered. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products and support the specification of, in particular, spectral characteristics of future nighttime missions.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions on them. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or generate water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modelling covers a wide range of parameters, such as varying wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modelling results were partially validated against direct measurements carried out by a test vehicle. The model outputs showed strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. Dimensional Analysis of the problem has highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface on the perceived precipitation level. The proposed model is used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

Coronary no-reflow (CNR) is a frequent phenomenon that develops in patients with ST-segment elevation myocardial infarction (STEMI) following reperfusion therapy. CNR is highly dynamic and develops gradually (over hours) and persists for days to weeks after reperfusion. Microvascular obstruction (MVO) developing as a consequence of myocardial ischemia, distal embolization and reperfusion-related injury is the main pathophysiological mechanism of CNR. The frequency of CNR or MVO after primary PCI differs widely depending on the sensitivity of the tools used for diagnosis and timing of examination. Coronary angiography is readily available and most convenient to diagnose CNR but it is highly conservative and underestimates the true frequency of CNR. Cardiac magnetic resonance (CMR) imaging is the most sensitive method to diagnose MVO and CNR that provides information on the presence, localization and extent of MVO. CMR imaging detects intramyocardial hemorrhage and accurately estimates the infract size. MVO and CNR markedly negate the benefits of reperfusion therapy and contribute to poor clinical outcomes including adverse remodeling of left ventricle, worsening or new congestive heart failure and reduced survival. Despite extensive research and the use of therapies that target almost all known pathophysiological mechanisms of CNR, no therapy has been found that prevents or reverses CNR and provides consistent clinical benefit in patients with STEMI undergoing reperfusion. Currently the prevention or alleviation of MVO and CNR remain unmet goals in the therapy of STEMI that continue to be under intense research.

With the rise of serious problems of urbanization and climate change, the call for intelligent and sustainable urban development is increasing nowadays. Therefore, smart cities have become a hot topic in academia, industry, and politics, resulting in the emergency of numerous smart city programs and projects. This study selected 26 evaluation frameworks proposed in recent years or large-scale research projects that have lasted for many years and analyzed and compared them from the 7 aspects of generalizability, comprehensiveness, availability, flexibility, scientific, transparency, and interpretability. The result indicates that the current smart city evaluation framework still needs to be upgraded and updated. These standards can not only guide the development direction and hotspot of the future smart city evaluation model but also serve as a guide for model demanders to test results and an effective tool to verify the framework designed by themselves.

The need for continuous research to refine the models used in accident reconstruction appears with the development of new car models that satisfy consumer complaints. The paper analyzed a sub-sequence of car and pedestrian accidents, from the perspective of the distance traveled by them in the contact phase, with the aim of improving the information regarding the reconstruction of road accidents. The research included the analysis of some accidents with pedestrian dummies, as well as the simulation of the impact between different classes of vehicles and pedestrians in two different walking positions. Specialized software was used with complex multibody models of pedestrians, modifying the speed and deceleration parameters at the time of the collision. The research results highlight the differences between bilinear models used in accident reconstruction and the proposed study. They can also be used to determine the distance traveled by the vehicle in the first phase of the collision with pedestrians.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Single parenting is a social phenomenon that has always been in existence and is as old as mankind, and today the number of single-parent families has been rampantly increasing. As a result, the researcher conducted a study in the Gambella region of Ethiopia to investigate the economic and educational implications of single parenting on female adolescent students. The study employed a qualitative research approach with a case study research design. The researchers used the purposive sampling technique to select the research area and research participants for in-depth interviews and focus group discussions, and eleven interviewees, seventeen participants in focus group discussions within two groups, and seven key informants participated. Interviews were audio-recorded, transcribed, translated, and thematically analyzed. Interviews with research participants revealed that adolescent girl students from single-parent homes experience financial difficulties. The study also explored that students with single parents are more likely to be absent from school, to come late to school, to score low marks, and to drop out of school. Therefore, having an understanding of the benefits of having intact parents for the well-being of adolescents and raising children together should be advocated.

Background: Student-specific stress is pervasive and impairs health and attainment. Experimentally induced self-affirmation can reduce stress, but little is known about the mechanisms underlying this effect, and the role of individual differences. The present study explores the relationship between spontaneous self-affirmation, student-specific stress, and overlapping constructs of positive self-regard during the COVID-19 pandemic. Methods: The study investigated inter-relationships between spontaneous self-affirmation, student-specific perceived stress and potential covariates including self-esteem, self-integrity and habitual positive self-thought. University students (N = 151) aged 18 to 50 responded to an online questionnaire including the Spontaneous Self-Affirmation Measure, College Student Stress Scale, Rosenberg Self-Esteem Scale, Habitual Index of Positive Thinking and Self-Integrity Scale. Results: Unexpectedly, regression analysis revealed that spontaneous self-affirmation was a positive predictor of student-specific stress, seemingly driven by relational affirmations. Self-esteem and habitual self-thought were negative predictors of student-specific stress, whilst self-integrity was not a predictor. Conclusions: Habitual positive self-thought may offer a sustainable solution to student-specific stress, but spontaneous self-affirmation does not. A reduction in defensive responses, elicitation of negative emotions, or use of conditional relationships in relational affirmation

Structural magnetic resonance imaging (sMRI) is widely used in the clinical diagnosis of diseases due to its advantages: high definition and noninvasive. Therefore, computer-aided diagnosis based on sMRI images is broadly applied in classifying Alzheimer’s disease (AD). Due to the excellent performance of Transformer in computer vision, Vision Transformer (ViT) has been employed for AD classification in recent years. ViT relies on access to large datasets, while the sample size of brain imaging datasets is relatively insufficient. Moreover, the pre-processing procedures of brain sMRI images are complex and labor-intensive. To overcome the limitations mentioned above, we propose Resizer Swin Transformer (RST), a deep learning model that can extract information from brain sMRI images that are only briefly processed to achieve multi-scale and cross-channel features. In addition, we pre-trained our RST on a natural image dataset and obtained better performance. The experimental results of ADNI and AIBL datasets prove that RST can achieve better classification performance in AD prediction compared with CNN-based and Transformer models.

Animal-assisted pedagogy (AAP) is well known in classroom practice, but scientific evidence is still lacking. At the same time, the biggest challenge in education systems worldwide is the social in-tegration of students. In a pre-post design, 30 heterogeneous students (16f/14m) from four different school classes (grades 5-7) were interviewed (problem-centred interview) about their social inclu-sion and their social climate in class before and after being taught selected subjects with a school dog (of different breeds) for one school term. At the second measurement point, they were also asked about their perception of AAP. The data was analysed both qualitatively (Kuckartz) and in terms of the number of quotes on the main topics using MAXQDA analysis software. The presence of a dog in the classroom leads to an improved social climate and more social integration. In ad-dition, the perception of the other students and the teacher changes to a more positive and friendly image. Through AAP, a new social role is added to the classroom, where caring and bonding are prioritised. Social interaction and norms are influenced and sterotypical and individual roles can be changed. Therefore, AAP can be a key to promoting social inclusion in the school environment.

The natural distribution of stress in the femur is altered when total hip arthroplasty (THA) is performed. In fact, when a stem is inserted inside the femur, there is a variation in stress due to the difference in rigidity between the material with which the stem is made and the femur. This generates the phenomenon of stress-shielding. The aim of this study is to design an optimized prosthesis that guarantees excellent rotational stability and re-duced stress shielding. Methods: through the finite element method (FEM) the mechanical behavior of the stem subjected to the loads described by ISO 7206-4: 2010 is studied. Re-sults: Through topological optimization, there is a reduction in stress shielding in the proximal zone of 31.46%. The addition of ridges on the dorsal side of the stem also im-proves rotational stability by 27.82%. Conclusions: The decrease in stiffness that is rec-orded with the optimized stem, guarantees a greater distribution of stress on the bone. The presence of dorsal ridges also favors the corticalization of the bone as it loads the bone near the dorsal ensuring further stability. The perforated prosthesis presented in this study, shows an increase in primary stability and an improvement in rotational stability. As there is also a bone regrowth inside the prosthesis.

Purpose – The paper aims to view how researchers have operationalized social impact assessment for construction projects over the last ten years. Design/methodology/approach – A systematic review was used to investigate case studies in the Social Life-Cycle Assessment (S-LCA) to analyze the application of the methodology. A qualitative study of 19 articles published between 2012 and 2022 was used to collect content on multiple categories impeding S-LCA through case studies in the construction industry. Findings – Our results showed the existence of limitations on the qualitative and quantitative aspects in measuring the social indicators. They were associated with the scoring method and the lack of data in some articles. Social implications – From this review, we understood that S-LCA has flaws in terms of the quality of the measurement, scoring method, and the lack of social data. Lack of social data means social impacts are being neglected and not assessed properly since there are several challenges pointed out throughout literature Originality/value – The originality of this research is that it focuses on case studies in the construction industry. It studies the operationalization of the S-LCA in this specific industry showing the different characteristics and challenges in the last 10 years.

In Germany, as in many developed countries, over 60% of buildings were constructed before 1978, where most are in critical condition, requiring either demolition with plans for redevelopment or renovation and rehabilitation. Given the urgency of climate action and relevant sustainable development goals set by the United Nations, more attention must be shifted toward the various sustainability aspects when deciding on the strategy for renovation or redevelopment of existing buildings. To this end, this study focused on developing a smart decision support framework for aging buildings based on lifecycle sustainability considerations. The framework integrated digital technological advancements, such as building information modeling (BIM), point clouds processing with field information modeling (FIM)®, and structural optimization, together with life-cycle assessment to evaluate and rate the environmental impact of different solutions. Three sustainability aspects, namely, cost, energy consumption, and carbon emissions, were quantitatively evaluated and compared in two scenarios, namely, renovation, and demolition or deconstruction combined with redevelopment. A real building constructed in 1961 was the subject of the experiments to validate the framework. The result outlined the limitations and advantages of each method in terms of economics and environmental impact. It was further observed that optimizing the building design with the goal to reduce embodied energy and carbon in compliance with modern energy standards, was crucial to improve overall energy performance. This work demonstrated that the developed BIM-based framework to assess the environmental impact of rehabilitation work in aging buildings can provide effective ratings to guide decision-making in real-world projects.

The petroleum industry produces thousands of barrels of oilfield waters from the initial stage driven by primary production mechanisms to the tertiary stage. These produced waters contain measurable amounts of oil in water emulsions, the exact amounts being determined by the chemistry of the crude oil. To meet strict environmental regulations governing the disposal of such produced waters, demulsification to regulatory permissible levels is required. Within the electric double layer theory, coupled with the analytical solutions to the Poisson Boltzmann Equation, continuum electrostatics approaches can be used to describe the static and electrokinetic properties of such emulsion systems. Therefore, theoretical understanding of the stability of oil in water emulsions within such fundamental concepts provides reliable approaches to demulsification. In this paper, we have used theoretical approaches, based on zeta potential and surface charge density models to determine the stability of oil in water emulsions for different crude oil samples. Accordingly, we have used literature based data on the chemistry of the crude oils, and have ranked the order of emulsion stability based on criterial established by zeta potential plots for the oil. Our theoretical calculations show that where the isoelectric points of crude oil samples are closer to each other, the degree of ionization plots versus pH indicate close tends for salinities at varying pH of oilfield waters. Based on the chemistry of crude oil samples, the most efficient and cost-effective means of demulsification is by reducing produced water pH to values closer to the average point of zero charge pH values

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions like “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but there has been no evaluation of their suitability for real-time applications, such as question answering. Here we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex black-box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find minimally contrastive and maximally probable high-precision counterfactual explanations, while limiting contrasted features to changes that are achievable. We demonstrate that using this method, it is possible to find such explanations quickly enough for use in real-time systems. High-precision achievable minimally contrastive explanations would be useful in applications where people seek remedial actions or question how effective a proposed remedy is likely to be.

Agricultural soils serve as crucial storage sites for soil organic carbon (SOC). Their appropriate management is pivotal for mitigating climate change. To evaluate spatial and temporal changes in SOC within agricultural fields, continuous monitoring is imperative. In-field data sets of Vis-NIR soil spectra were collected on a long-term experimental site using an on-the-go spectrophotometer. Data processing for continuous SOC prediction involves a two-steps modelling approach. In Step 1, a Partial Least Square (PLSR) regression model is trained to establish a relationship between the SOC content and the spectral information also including spectral preprocesisng. In Step 2, the predicted SOC content obtained from the PLSR models is interpolated using ordinary kriging. Among the tested spectral preprocessing techniques and semivariogram models, SG and gapDer preprocessing along with a Gaussian semivariogram model, yielded the best performance resulting in a root mean square error of of 1.24 and 1.26 g kg-1. A striping effect due to the transect-based data collection was addressed by testing the effectiveness of extending the spatial separation distance, employing data aggregation, and defining the distribution based on treatment plots using block kriging. Overall, the results highlight the immense potential of on-the-go spectral Vis-NIR data for field-scale spatial-temporal monitoring of SOC.

Diabetes and its complications are a global health burden, necessitating novel therapeutic approaches. Natural products and derivatives are gaining attention as potential agents for managing diabetes due to their diverse constituents and pharmacological activities. Algae, in particular, offer promising bioactive compounds with antidiabetic properties. This review provides an overview of natural products derived from algae in diabetes management, including advancements and future prospects. It highlights the significance of natural products in diabetes, explores algae's diversity, bioactive compounds, and mechanisms of action. The challenges, limitations, and strategies for optimizing algae-based interventions are discussed, alongside future research directions. Algae's multifaceted pharmacological activities and chemical constituents make them attractive for developing novel antidiabetic therapeutics. Further research and optimization of algae-based interventions have the potential to address the global burden of diabetes and improve patient outcomes.

On the analyzed section of 2.4 km of the national road No. 50 of the Młodzieszyn bypass, it was carried out by a consortium of OAT and SAT companies in the period April-September 2019, under constant traffic in the field of repairing the concrete pavement by replacing slabs, levelling them and sealing expansion joints, roughening and roadside reinforcements.

In this paper we have studied some properties for determinant-calculating for cubic-matrix of order 2 and order 3. These properties are analogous to some properties for determinants of square matrix we have proved and noted that these properties also are applicable (or not in some details) on this concept for cubic-matrix of orders 2 and 3. All results in this paper, are presented in detail during the theorem proofs.

A new pterygosomatid mite species, Neopterygosoma robertmertensi sp. n. (Acariformes: Pterygosomatidae) was collected from two specimens of Liolaemus robertmertensi (Liolaemidae) from Argentina. This new species is described based on active stages: adults (female and male) and juveniles (deutonymphs, protonymphs, larvae), and quiescent stages (nymphchrysalis, deutochrysalis and imagochrysalis). The changes in morphological characters during the ontogeny of N. robertmertensi have been analyzed in detail. A difference in larval sex morphology was observed for the first time in the family Pterygosomatidae (female larvae differ from male larvae by the shape and size of the idiosoma and position of the genital area). This new mite species is most similar to N. cyanogasteri but can be distinguished by the presence of different leg chaetotaxy patterns of genua IV and femora IV, four to six genital setae, three to five dorsomedial setae and two to three ventromedial setae. Phylogenetic analysis was conducted based on 120 morphological characters of all Neopterygosoma spp. and four outgroup species using the maximum parsimony approach. The results indicated that this species is nested within mites of the chilensis group of Neopterygosoma associated with host species of the section chiliensis of Liolaemus s. str. An updated diagnosis of the chilensis group of Neopterygosoma and an identification key for all species of this genus has been provided.

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

We generalize the concept of a fuzzy metric by introducing its approximating counterpart in order to make it more appropriate for the study of some problems related to words combinatorics. We establish close relations between fuzzy approximating metrics in case of special $t$-norms and approximating parametrized metrics, discuss some relations between fuzzy approximating metrics and fuzzy partial metrics as well as show some possible applications of approximating parametrized metrics in the problems of words combinatorics.

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry with numerous applications which guide engineers in better decision-making. The most powerful tool that most production development decisions rely on is reservoir simulation with applications in numerous modeling procedures, such as individual simulation runs, history matching and production forecast and optimization. However, all these applications lead to considerable computational time and computer resources associated costs, rendering reservoir simulators as not fast and robust enough, thus introducing the need for more time-efficient and smart tools, like ML models which are able to adapt and provide fast and competent results that mimic the simulator’s performance within an acceptable error margin. The first part of the present study (Part I) offers a detailed review of ML techniques in the petroleum industry, specifically in subsurface reservoir simulation, for the cases of individual simulation runs and history matching, whereas the ML-based Production Forecast Optimization applications will be presented in Part II. This review can assist engineers as a complete source for applied ML techniques since, with the generation of large-scale data in everyday activities, ML is becoming a necessity for future and more efficient applications.

Wind energy is a rapidly expanding renewable energy technique. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind-farm performance. In this research, we investigate the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind-turbine array on various rough topographies. First, we evaluated our LES method through mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyze the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results demonstrate that the ground roughness element’s flow resistance boosts large wind-farm power production by almost 68% in fully aerodynamic rough surface compared to flat terrain. Dispersive stress analysis revealed that the primary degree of spatial heterogeneity in the wind farm is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress is almost 45% to the overall drag. We also observed that the power performance of the wind farm in complex terrain outperforms the drag. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

Conventional methods of climate change (CC) mitigation have not ‘bent the curve’ of steadily rising annual anthropic CO2 emissions or atmospheric concentrations of greenhouse gases. This study reviews the present position and likely future of such methods, using recently published literature with a global context. It particularly looks at how fast they could be implemented, given that the limited time available for avoiding catastrophic CC (CCC). The study then examines solar geoengineering, an approach often viewed as complementary to conventional mitigation. The review next introduces equity considerations, and shows how this will shorten even further the time available for effective action for CC mitigation. The main findings are as follows. Conventional mitigation approaches will be implemented too slowly to be of much help in avoiding CCC, partly because some suggested technologies are infeasible, while others are either of limited technical potential, or, like wind and solar energy, cannot be introduced fast enough. Because of these problems, solar geoengineering is increasingly advocated as a quick-acting and effective solution. However, it could have serious side effects, and given that there will be winners and losers at the international as well as the more regional level, political opposition may make it difficult to implement. The conclusion is that global energy consumption itself must be rapidly reduced to avoid catastrophic climate change.

The magnetic properties of RCo3B2 compounds, with heavy rare earths, are investigated by using magnetic measurements and electronic structure calculations. The cobalt sublattice magnetizations are very small and antiparallel oriented to rare earths moments. Contributions of cobalt moments to the Curie constants were also shown. The cobalt magnetic behavior has been analyzed in spin fluctuations model. The presence of short-range order above the Curie temperatures in RCo3B2 compounds with R=Tb, Dy, and Ho has been attributed to reminiscent R5d-Co3d interactions.

As an important means of controlling the spread of cities, large-scale urban green belts have been widely used in the planning and construction of green space systems in various mega-size cities around the world. In previous studies, there has been more research on the overall isolation effect of green belts, while there has been less research on changes in their internal structure. There have been more perspectives on the land-use change (LUC) caused by urban expansion, and less systematic research on the dual driving factors of LUC. In this paper, we constructed a multi-dimensional index system to measure the "contraction-expansion" mechanism of land use in the overlapping vegetation areas of large green belts under the dual influence of urban expansion and urban greening, from the perspectives of quantity, space, and connectivity during different construction periods. Taking the boundary of two green belts in Beijing as an example, we hope to provide scientific support for the construction and ecological protection of Beijing's green belts.

Smallholder dairying engages around 70 to 80 million rural households who are mostly landless, small and marginal farmers. These farmers face challenges in terms of adoption of balanced feeding practices including inclusion of compound feed in animal feeding. This necessitates to understand the socio-economic factors that are influencing the adoption of compound feed. The study found that 51% of dairying households adopted compound feed in animal feeding. The adopters and non-adopters of compound feed differed (P< 0.01) in terms of land holding, family educational status, breeds owned, membership status in farmer’s collectives, fodder adoption, area under fodder, access to grazing resources, availability of crop residues, milk production and milk marketing opportunities. Furthermore, users and non-users of compound feed had significant difference (P< 0.05) in extension agency contact, mass media contact, attitude towards dairying and gender participation in farming. The logit regression model explained the variance in adoption to 30%. Further it indicated the role of selective socio-economic characters, farming situations, feeding and marketing practices in adoption of compound feed. In specific membership in farmer’s collectives, marketing opportunity, milk production, farm men participation, family education status, grazing resources and attitude towards dairying has greatly influenced the adoption behaviour of compound feed in positive direction. Added, households with high experience, cultivating fodder and having access to crop residues tend to influence the adoption decision negatively. The research observed that farmers tend to use compound feed for increasing productivity in areas of better markets and there exists trade-offs between milk market opportunities, access to cheaper roughages and usage of compound feed.

Patient value is an important factor in clinical decision-making, but conventionally, it is not incorporated in the decision processes. In this paper, a value-based therapy recommendation comprehensive model is proposed. A literature analysis is conducted to collect value-based evidence. Categorized values and candidate therapies are used in combination as filtering keywords to build this evidence. Then a formalism model is put forward to integrate the value-based evidence with clinical evidence. The relative importance of two evidence can be adjusted dynamically by decision-makers during the decision-making processes. A prototype system was implemented using a case study for breast cancer and validated for feasibility and effectiveness.

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other there models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

We attempt to re-express Nernst law in terms of a suitable information measure (IM). This is done on the basis of adapting the idea of purity to a thermal Gibbs environment, which yields what we might call a ``purity'' (and symbolize it by the symbol $D$). We further apply the properly generalization of this $D$-IM to a classical scenario. This generalization turns our to have interesting consequences, when used in conjunction with the classical Shannon entropy $S$. These consequences are related to Nernst law.

Antifragility, a concept pioneered by Nassim Nicholas Taleb, has undergone significant development over the past decades. In his work, Taleb describes antifragility as the opposite of fragility—a system that not only withstands stress and volatility but actually thrives and improves as a result. However, many existing systems, including those in Information Technology (IT) and complex economic models, tend to fail under stress. This paper aims to explore the potential implementation of antifragility principles into software architecture. By embracing the concept of an- tifragility, software systems could be designed to not only withstand stressors but also harness them to enhance their robustness and performance. The authors recognize that traditional approaches to software architecture often focus on minimizing failure points and ensuring stability. While these approaches are valuable, they often neglect the dynamic nature of real-world systems and fail to adapt to unforeseen challenges. The paper proposes an alternative perspective that considers stress as an opportunity for improvement. By introducing antifragile elements into software architec- ture, such as decentralized decision-making, self-healing mechanisms, and adaptive resource allocation, the authors argue that software systems can become more resilient, responsive, and capable of capitalizing on stress-induced dis- ruptions. To validate their ideas, the authors present case studies and practical examples of how antifragile software architectures could operate in various domains. They also discuss potential challenges and trade-offs associated with implementing antifragility, such as increased complexity and resource requirements. By shedding light on the possi- bility of embracing antifragility in software architecture, this paper seeks to inspire further research and innovation in creating more adaptive and robust software systems that thrive in the face of stress and uncertainty.

Increasing photon absorption by capturing light is an important way to increase the efficiency of photovoltaic devices. In this regard, the small optical band gap (Eg) and high absorption coefficient of Se-containing thin nanofilms make them ideal for next generation photovoltaic devices based on selenides. This study reports on chemical synthesis processes in a bath for the insertion of amorphous selenium into polyamide-6 (PA 6) matrix and the effect of incorporation of Cd2+ and Ag+ on the structural, morphological and optical properties. The resulting films had a pyramidal structure of single crystals, nanosized particles of nonspecific shape, agglomerated and indistinct grains. ATR-FTIR spectroscopy revealed the interaction between PA 6 and inorganic components, which causes obvious conformational changes in PA 6 chains. The data of X-ray diffraction analysis, depending on the stage of synthesis, showed a mixture of various compounds: Se8, CdSe, Ag2Se and Ag. This may explain the observed optical properties. The optical properties of the composites indicate a shift in the band gap from 4.46 eV for PA 6 to 2.23-1.64 eV upon stepwise insertion of amorphous Se, Cd2+ and Ag+ ions. Eg is conveniently located in the visible region of solar energy, making the obtained nanofilms ideal for solar energy harvesting.

This manuscript explores the significance of continuous improvement in software development and digital product management, highlighting its role in addressing challenges and achieving superior results in today's dynamic business landscape. Drawing on Lean Thinking and Agile principles, the paper emphasizes the importance of customer focus, multidisciplinary collaboration, respect for people, inspection and adaptation, experimentation, incremental delivery, and continuous feedback in driving continuous improvement. It identifies key problems associated with the lack of continuous improvement, including quality issues, stagnation, inefficiency, lack of learning, lack of participation and collaboration, and lack of adaptability. To overcome these challenges, the paper proposes a solution that leverages agile events and interactions, such as sprint retrospectives and daily scrum meetings, and introduces a "kaizen board" as a visual tool to manage and monitor the continuous improvement process. The manuscript highlights the benefits of continuous improvement in terms of quality enhancement, efficiency gains, improved customer satisfaction, and adaptability to change. It stresses the need for fostering a culture of learning and constant adaptation, and provides insights on applying continuous improvement across different dimensions: product, process, team, and culture. The paper concludes by emphasizing the significance of commitment and active participation from all team members for achieving sustainable progress and success in digital transformation efforts.

Preclinical evidence suggests that voltage gradients can act as a kind of top-down master regulator during embryogenesis and orchestrate downstream molecular-genetic pathways during organ regeneration or repair. Moreover, electrical stimulation shifts response to injury towards regeneration instead of healing or scarring. Cancer and embryogenesis not only share common phenotypical features, but also commonly upregulated molecular pathways. Ion channel activity is directly or indirectly linked to the pathogenesis of cancer hallmarks, while experimental and clinical studies suggest that their modulation may exert antitumor effects. A large recent clinical trial served as a proof-of-principle for the benefit of preoperative use of topical sodium channel blockade as a potential anticancer strategy against early human breast cancers. Apart from the obvious importance in perioperative medicine, it calls for the planning of further carefully designed clinical trials to expand on the concept of ion channels as tumor drivers.

This systematic review examines the potential of digital language learning in contributing to students' cognitive gains. The study reviews existing research on the relationship between digital language learning and cognitive benefits, with a focus on enhanced problem-solving skills, memory, and multitasking ability. The findings indicate that higher levels of digital competences correlate positively with language proficiency outcomes, suggesting that participants with greater digital skills achieve better language learning outcomes. Digital technologies offer interactive and engaging methods of learning, which can positively impact retention and understanding. However, the study also acknowledges the limitations of digital foreign language learning, such as the lack of face-to-face interaction and the need for high motivation and discipline. The research questions explored in this study are: (1) Does digital language learning contribute to cognitive gains in foreign language education? and (2) What are the pedagogical implications for cognitive improvement in digital foreign language education? The study employs the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) methodology to identify and analyze relevant research articles. The results of the review suggest that working with printed texts may be more effective for cognitive gains compared to electronic texts. Additionally, implementing more senses through digital language education appears to be beneficial for cognitive gains. The article concludes by discussing the implications of these findings and the need for further research in this area.

We consider new insights into the origin and nature of pointer states and their role in wave function collapse in macroscopic quantum coherence. The work includes new theory of quantum coherence underpinned by turbulence, generated by a field of pointer states in the form of recirculating vortices (toroids), interconnected via a vortex cascade. Decoherence occurs when the interconnected field of vortices between pointer states is disrupted by external forces, leading to their localisation. The applicability of this work is considered in addressing unresolved questions in high temperature superconductivity and macroscopic quantum processes in biological systems. We also consider its implications for our understanding of intrinsic spin and pointer states within the standard "point source" representation of a quantum particle, which intuitively requires a more complexed description.

Cigarette butts (CBs) are the most diffuse waste in the world, often abandoned in the environment without proper disposal. They are dangerous because of the numerous harmful chemicals poten-tially released in the environment. There are, in literature, several technological options for CB recycling, but some critical concerns could affect their effectiveness due to the quality and quantity of CB litter collected in the proper way. The present paper focuses on policy framework social behavior, waste collection and transport and technological processes. The Extended Producer Responsibility scheme for CBs is proposed at European level as an action to tackle CB litter and encourage sustainable product development. The CB waste collection and transport is a key step for bringing CB to the recycling process. The main concern is the small quantity of CBs collected: 0.06 % of the municipal waste and 0.18 % of the unsorted waste in the administrative area of Pe-rugia. Another crucial issue is the need for behavioral interventions to increase education and awareness of smoker citizens, addressing the discrepancy between smokers’ behaviors and beliefs.

During the production of furniture, large amounts of waste of these materials are generated, which are most often stored in warehouses without a specific purpose for their subsequent use. In highly developed countries, as many as 25 million tons of textile waste are produced annually, of which approximately 40% is non-clothing waste, such as carpets, furniture and car upholstery. The aim of the research was to produce and evaluate the dry-formed high-density fiberboards (HDF) with various shares of recycled particles of natural leather used in upholstery furniture production at different contents (1, 5 and 10% by weight). Mechanical properties (modulus of rupture, modulus of elasticity and screw withdrawal resistance) and physical properties (density profile, thickness swelling after water immersion, water absorption, surface absorption) were tested. The density profile and contact angle of natural leather have been also characterized. The results show that increasing the content of leather particles in HDF mostly has a positive effect on mechanical properties, especially screw withdrawal resistance and water absorption. It can be concluded, that, depending on the further use of HDF, it is possible to use recovered upholstery leather particles as a reasonable addition to wood fibers in HDF technology.

Neuropathic pain (NP) affects about the 8% of the general population. Current analgesic therapies have limited efficacy, making NP one of the most difficult to treat pain conditions. Evidence indicates that excessive oxidative stress can contribute to the onset of chronic NP and several natural antioxidant compounds have showed promising efficacy in NP models. Thus, the aim of the present study was to investigate the pain-relieving activity of honokiol (HNK)-rich standardized extract of Magnolia officinalis Rehder &amp; E. Wilson bark (MOE), well known for its antioxidant and anti-inflammatory properties, in the spared nerve injury (SNI) model. The molecular mechanisms were investigated in spinal cord samples from SNI mice and in LPS-stimulated BV2. MOE and HNK showed antioxidant activity. MOE (30 mg/kg p.o.), produced an antiallodynic effect in the absence of locomotor impairment. MOE treatment reduced spinal p-p38, p-JNK1, iNOS, p-p65, IL-1ß and Nrf2 overexpression, increased IL-10 and MBP levels and attenuated the Notch signaling pathway by reducing Jagged1 and NEXT. All these effects were prevented by the CB1 antagonist AM251. HNK reduced the proinflammatory state of LPS-stimulated BV2 cells and reduced Jagged1 overexpression. MOE and HNK, by modulating oxidative and proinflammatory responses, might represent interesting candidates for NP management.

In this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

Natural ecosystem of Islands and coastal region are vulnerable to climate change phenomena such as increasing temperature, fluctuating rainfalls, ocean acidification and tsunami. Andaman and Nicobar group of islands lies in Bay of Bangal facing such extreme climate phenomena. A spatial-temporal analysis of forest cover of middle Andaman region of the Andaman and Nicobar group of islands was done from 1990 to 2019 with an interval of 5-10 years. Support vector machine classifier, spectral indices such as Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index were used for the analysis of greenery, water resources, and urban land. Land surface temperature was estimated using split window algorithm for Landsat 8 and mono window algorithm for Landsat 5. The data showed relative contribution of forest region toward rising temperature in the island region. The research also showed that subsurface hydrology linked to interconnected lineaments provides a stable zone for forest cover. The open forest showed maximum fluctuation while minimum change was observed in Evergreen Forest. The spectral characteristics analysis using indices showed significant change except in 2005 due to Tsunami occurred in 2005. The land surface temperature showed fluctuation near to 30° C from 1990 to 2019.

The quadratic polynomial differential systems in the plane are the easiest nonlinear differential systems. They have been studied intensively due to their nonlinearity and to their big number of applications. These systems can be classified in ten classes. Here we provide all the topologically different phase portraits in the Poincaré disc of two of these classes.

Our aim is to provide an insight of the procedures and the dynamics that lead the spread of contagious diseases through populations. Our simulation tool can increase our understanding on the spatial parameters which affect the diffusion of a virus. SIR models are based on the hypothesis that populations are “well mixed”. Our model constitutes an attempt to focus on the effects of the specific distribution of the initially infected individuals through the population and provide insights, considering the stochasticity of the transmission process. For this purpose, we represent the population using a square lattice of nodes. Each node represents an individual that may or may not carry the virus. Nodes that carry the virus can only transfer it to susceptible neighboring nodes. This important revision of the common SIR model provides a very realistic property: the same number of initially infected individuals can lead to multiple paths, depending on their initial distribution in the lattice. This property creates better predictions and probable scenarios to construct a probability function and appropriate confidence intervals. Finally, this structure permits realistic visualizations of the results to understand the procedure of contagion and spread of a disease and the effects of any measures applied.

We present evidence of the anchoring bias and disposition effect (DE) in investor trading decisions across different asset markets (stable and volatile assets) and market scenarios (stable and volatile markets), as well as how these biases affect investors' portfolio performance. The study employs a quasi-experimental design, allowing subjects to engage in interactive trading with four securities, two with potential negative returns and two with potential positive returns, within a simulated asset market. Our findings reveal the presence of the disposition effect and anchoring bias among individual investors in India. Second, market scenarios or volatility can influence the investors’ behavioral biases. The disposition effect was more prominent in volatile markets and the total market, while the anchoring bias was significant in stable and total markets. Additionally, investors exhibiting the disposition effect exhibit lower portfolio performance, while those demonstrating anchoring bias perform relatively better. The study's findings can help individual investors understand their behavioral biases, avoid them in future trading decisions, and improve portfolio performance. Financial institutions and regulatory agencies could identify investment products and supportive investor interactions towards better market trade decisions.

Various theoretical studies have shown that highly diluted plutonium solutions could have a positive temperature effect, but up to now, no experimental program has confirmed this effect. The French Plutonium Temperature Effect Experimental Program (or PU+ in short) aims to effectively show that such a positive temperature effect exists for diluted plutonium solutions. The PU+ experiments were conducted in the “Apparatus B” facility at the CEA VALDUC research center in France. It involved several sub-critical approach type experiments using plutonium nitrate solutions with concentrations of 14.3, 15, and 20 g/l at temperatures ranging from 20 to 40 °C. Fourteen (five at 20 g/l, four at 15 g/l, and five at 14.3 g/l) phase-I experiments (consisting in independent subcritical approaches) were performed between 2006 and 2007. The impact of the uncertainties on solution acidity and plutonium concentration made it difficult to demonstrate the positive temperature effect, requiring an additional phase-II experiment (with a unique plutonium solution) from 22 to 28 °C, that were performed in July 2007. This phase-II experiment has shown the existence of a positive temperature effect of ~ +2.2 pcm/°C (from 22 to 28 °C for a plutonium concentration of 14.3 g/l). It has been recently possible to confirm the results of this program with MORET 5 calculations by generating thermal scattering data S(α,β) at the correct experimental temperatures.

This study assesses the dynamic relationship between macroeconomic factors and bank asset quality based on changes in the condition of stock market returns. A Generalized Method of Moments (GMM) model is employed, using panel data from 18 universal banks spanning the period 2007 to 2021. The analysis revealed that the real GDP growth rate, the average lending rate, and the real exchange rate represent a set of macroeconomic factors with a marked influence on banks' asset quality. In addition, a high inflation rate was found to exert an adverse effect on asset quality, as it affects borrowers' financial ability to meet loan repayment obligations. Furthermore, the study verified the existence of a positive relationship between market condition and asset quality, which implies that bank performance adapts to changes in market conditions as posited under the Adaptive Market Hypothesis (AMH). Bank managers should consolidate banks' asset bases during conditions of market stability to withstand periodic market fluctuations to boost trading momentum. Policy recommendations are suggested to foster a conducive business environment for bank stability.

This paper explores the challenges faced by blue-collared international migrants in adapting to new and unfamiliar climates while pursuing job opportunities, with a focus on poverty alleviation. Drawing on existing research and empirical evidence, the study investigates the trials encountered by these migrants as they navigate climate shocks and assesses the potential impacts on poverty reduction efforts. The findings underscore the significant difficulties blue-collared migrants experience when confronted with abrupt changes in climate conditions, adversely affecting their physical and mental well-being, as well as their employment prospects. The process of adjustment involves not only acclimating to new climate patterns but also navigating cultural nuances and social dynamics, exacerbating stress and feelings of isolation. Consequently, these challenges can perpetuate poverty by disrupting employment stability and limiting opportunities for career advancement. To address these issues, the paper proposes comprehensive support measures, including targeted pre-departure training programs, access to retraining opportunities, and assistance in securing sustainable livelihoods. By prioritizing climate resilience and empowering blue-collared migrants to adapt effectively to new climates, it becomes possible to enhance their livelihood prospects, reduce poverty levels, and foster sustainable development. The paper concludes by highlighting the significance of integrating climate adaptation strategies with poverty alleviation efforts to create a more inclusive and resilient future for blue-collared migrants in their pursuit of sustainable livelihoods.

Work on callings has burgeoned in the past 20 years, yet recent reviews exposed a lack of conceptual clarity and disagreements around its definition, components and measures. One lingering point of contention revolves around the element of prosociality: Is a calling orientation primarily motivated by self-interest, prosocially orientated, or a mix of both? This conceptual paper reviews and examines the pro-self and prosocial component of a calling outlook, by examining and comparing the ways in which they feature in different calling subtypes: Classic, neoclassic, and modern callings. Our analysis suggests that these subtypes vary in where they are located on a pro-self – prosocial continuum: Classic callings are located on the prosocial side of the axis, modern callings are located on pro-self side of the axis, and neoclassic callings can be situated in the middle of the con-tinuum, integrating self-orientated and other-orientated motivations. Our analysis further suggests that these calling subtypes draw on divergent value systems: Classic callings are propelled by self-transcendent values, modern callings are driven by self-actualization motivations, and neoclassic callings integrate both value systems. We therefore argue that the subjective experiences of pursuing a calling within each subtype pathway differ, alt-hough they may culminate in similar outcomes. The paper offers a novel framework for analyzing people’s calling that draws on their values.

Cropland area is closely related to food production. More previous focuses were paid on impacts of extreme events on food production, but less on cropland dynamics. This study took the Farming-Pastoral Ecotone of Northern China (FPEN) as a case area, to investigate its cropland area dynamics and driving factors in view of perspectives of extreme events, environmental conditions, socioeconomic development, urban expansion, and ecological construction. We used ridge regression approach to quantify contributions of these drivers to cropland area dynamics. Results showed that cropland area increased significantly at a rate of 333.5km2/a during 1992−2020 and were spatially clustered in east of the FPEN. Impact extent and size each driving factor on cropland trend presented large spatiotemporal differences, but ecological construction (EC) had overall the greatest impact on cropland area changes, followed by urban expansion (UE). In comparison, TL10p has the smallest. UE-dominated areas increased 41.9% since 2010s, but still less than EC. Furthermore, we found that extreme events effects on cropland area trend evidently increased. Particularly, TH90p displayed the most increase (~99.4%). Cropland area changes dominated by extreme temperature events in 2010−2020 increased nearly six times than that in 1992−2010. These findings suggest that increasing impacts of extreme weather events on cropland area changes should be cautioned.

At 2.3 megabases in length, the dystrophin gene is enormous: transcription of a single mRNA requires approximately 16 hours. Principally expressed in skeletal muscle, the dystrophin protein product protects the muscle sarcolemma against contraction-induced injury, and dystrophin deficiency results in the fatal muscle-wasting disease, Duchenne muscular dystrophy. This gene is thus of key clinical interest, and therapeutic strategies aimed at eliciting dystrophin restoration require quantitative analysis of its expression. Approaches for quantifying dystrophin at the protein level are well established, however study at the mRNA level warrants closer scrutiny: measured expression values differ in a sequence-dependent fashion, with significant consequences for data interpretation. In this manuscript we discuss these nuances of expression and present evidence to support a transcriptional model whereby the long transcription time is coupled to a short mature mRNA half-life, with dystrophin transcripts being predominantly nascent as consequence. We explore the effects of such a model on cellular transcriptional dynamics, and then discuss key implications for the study of dystrophin gene expression, focussing both on conventional (qPCR) and next-gen (RNAseq) approaches.

Respiratory infections are usually characterized by mucus hypersecretion. This condition may worsen and prolong symptoms and signs. Mucoactive agents include different molecules with different mechanisms of action. Sobrerol is a monoterpene able to fluidify mucus, increase mucociliary clearance, and exert antioxidant activity. Sobrerol is available in various formulations (granules, syrup, nebulized, and suppository). Sobrerol has been on the market for over 50 years. Several studies investigated its efficacy and safety in acute and chronic respiratory diseases characterized by mucus hyperproduction. Seven pediatric studies have been conducted with favorable outcomes. Recently, regulatory agencies reduced the treatment duration to three days. Therefore, a future study will test the hypothesis that a combination of oral and topical sobrerol could benefit children and adults with frequent respiratory infections. The rationale considers that mucus accumulation could be a risk factor for increased susceptibility to have infections.

In order to ensure food security worldwide in the face of current climatic changes, a higher quality and quantity of crops is necessary to sustain the growing human population. By developing a sustainable circular economy and biorefinery approaches, we can move from a petroleum-based to a bio-based economy. Plant biostimulants have long been considered an important source of plant growth stimulants in agronomy and agro-industries with both macroalgae (seaweeds) and microalgae (microalgae). There has been extensive exploration of macroalgae biostimulants. A lack of research and high production costs have constrained the commercial implementation of microalgal biostimulants, despite their positive impact on crop growth, development and yields. The current knowledge on potential biostimulatory compounds, key sources and their quantitative information from algae is summarized in the present review. Our goal is to provide a brief overview of the potential for microalgal biostimulants to improve crop production and quality. A number of key aspects are discussed, including the biostimulant effects caused by microalgae extracts, as well as the feasibility and potential for cocultures and coapplication with other biostimulants and biofertilizers. This article also discusses the current knowledge, recent developments and achievements in extraction techniques, types of applications, timings of applications. Ultimately, this review highlights the potential for microalgal biostimulants for sustainable agricultural practices, the algal biochemical components contributing to these traits, and finally bottlenecks and involved prospects in commercializing microalgal biostimulants.

Solar energy is a rapidly growing sector, and solar farms are playing an increasingly important role in meeting the world's energy needs. However, as the size and complexity of these farms increase, so do the challenges associated with managing them efficiently. This article presents a comprehensive analysis of the fundamental parameters that underpin solar energy systems. Focusing on the latest research, we examine the challenges and opportunities intrinsic to the implementation of solar energy systems, paying particular attention to the various parameters that contribute to their performance. These parameters encompass a range of factors such as thermal performance, atmospheric boundary layer, solar energy meteorology, heat islands, cloud influences, agrivoltaic systems, shading factors, and surface energy budget. The review underscores the importance of considering a diverse array of parameters when developing solar energy systems to optimize their efficiency and effectiveness. Ultimately, by unraveling these challenges and opportunities, we can work towards creating more sustainable and reliable renewable energy sources and reducing our dependence on non-renewable alternatives.

In the context of partial autonomy, where autonomous vehicles and humans share control of the vehicle, bringing out-of-the-loop drivers back into the loop is a significant challenge. While warning signal design guidelines are commonly used to provide alerts, few studies have examined each signal in depth with an emphasis on the autonomous environment. This study aims to fill this gap by investigating visual, auditory, and tactile stimuli and modifying their sub-attributes to explore variations related to age, gender, and other individual backgrounds. A driving simulator was utilized to create a realistic driving environment and measure participants' reaction times in takeover request situations. Analysis of the data revealed correlations between age and reaction times for auditory and tactile signals, with interaction effects observed between age and sub-attribute intensity. Additionally, participants exhibited varying reaction time patterns in response to different sub-attribute intensities. By evaluating individual differences in perception based on modality characteristics, often overlooked in prior research, this study serves as a foundational contribution to future research in the field.

Feedback trading theory is one of the most primitive theories about financial market. But for a long time，researches and modelings on this topic are rarely seen. The model in this paper shows the effects of sentiment shocks on asset prices in a market characterized by feedback trading in the long run. We find that，generally，feedback trading will lead to cognitive bias effect and trading inducement effect. Cognitive bias effect increases with the feedback trading parameter ( FTP) . In our model，the abnormal volatility of asset prices is captured by cognitive bias effect，sentiment shock effect and trading inducement effect．

The climate and its changes are major risk factors for agricultural activities because agricultural systems are highly affected by climatic factors such as temperature, rain, humidity, wind, and solar radiation. While some agricultural areas of the world are irrigated, the vast majority rely on natural rainfall patterns. Farmers will consume more water as a result of climate change and experience less water availability, resulting in a decrease in production capacity. As one of the most important natural phenomena affecting agricultural productivity, drought is a limiting environment for plant growth. The response of plants to water stress is complex and involves coordination between gene expression and its integration with hormones. It has been shown that bacteria have mechanisms to reduce water stress in these plant species and promote greater growth. The underlined mechanism involves root-to-shoot phenotypic changes in growth rate, architecture, hydraulic conductivity, water conservation, plant cell protection, and damage restoration through integrating phytohormones modulation, stress-induced enzymatic apparatus, and metabolites. This review demonstrates how plant growth-promoting bacteria can mitigate negative effects associated with water stress in plants and provide examples of technology conversion in agroecosystems.

During the dry season, glaciers contribute significantly to the water supply downstream, especially in areas with seasonal rainfall. It is important to have physical-based glacier models, which are more advanced in quantifying the future dynamics of glaciers and melt distribution. Toward this purpose, an investigation of different numerical modeling tools and their approach is conducted and planned further to incorporate a glacier module into a spatially distributed model called mHM (Mesoscale Hydrological Model, mhm-ufz.org). An open-source tool called MATILDA (Modeling Water Resources in Glacierized Catchments) is used beforehand to implement the glacier entities to the application catchment Maipo Basin in Chile. Due to limited time availability and source of data, the glacier melt of the MATILDA model is integrated into the mHM model to see what the implications would be if the glacier module was implemented from scratch in mHM. The analysis of the glacier hydrology resulting from the Maipo-Glacier model was carried out by comparing the modeled glacier flows with the total observed flows. To assess the credibility of the models, the results are compared based on three goodness-fit measures, specifically r^2Coefficient of Determination), NSE (Nash-Sutcliffe Efficiency), and KGE (Kling-Gupta Efficiency). Application of the mHM model in the Maipo basin in Central Chile is performed by using prepared input datasets, showing that the approach is viable as evidenced by a Kling-Gupta Efficiency of 0.80 and Nash-Sutcliffe Efficiency of 0.70 from the starting periods of 1950 to 2020. Overestimation by the mHM model occurs during the late summer season, particularly when temperature decreases. The MATILDA model is used on glacier melt distribution, forcing data is prepared from regional datasets, and a glacier profile is constructed from ice thickness datasets. According to the model, the average KGE result for the years 2012–14 and 2015–18 was respectively 0.44 and 0.41. Glacier melt produced by Matilda is in the range of 58-68 % of the total runoff and the glacier area decreased by 22-23% during the simulation period from 2012-14 and 2016-2018. A pilot sub-basin Olivares is used in the coupling mHM model with initializing MATILDA glacier melt. The new model is underestimated during the seasonal transition in the initial days of the simulation. A considerable portion of the ablation period might be missed as a result of the initial storage quality, true glacier extent, and height distribution being understated. As a conclusion, the mHM Model was capable of predicting runoff well in a mountainous region and could also serve as a monitoring tool for watershed management. There are differences in results between the two models because of the different spatial resolutions and methodologies. MATILDA have a glacier retreat routine that gives better result for the glacier characteristics such as. A new glacier dynamics model needs to be implemented in the future to develop intermediate complexity, bridge catchment, and glacier scales after using the mHM, MATILDA, and mHM with MATIDLA model for analysis of glacier and highlighting the model's deficiencies in the initial period of simulative assessments.

The inability to efficiently metabolize homocysteine (Hcy), which occurs in nutritional and genetic deficiencies, leads to hyperhomocysteinemia (HHcy) thereby causing endothelial dysfunction, a hallmark of atherosclerosis which underpins cardiovascular disease (CVD). The dysregulation of mammalian target of rapamycin (mTOR) signaling, and impaired autophagy play important roles in CVD. Biochemically, HHcy is characterized by elevated levels of Hcy and its metabolites, Hcy-thiolactone (HTL) and N-Hcy-protein (N-Hcy). However, whether these metabolites can dysregulate mTOR signaling and autophagy in endothelial cells is not known. Here, we examined the influence of HTL, N-Hcy, and Hcy on the PHF8/H4K20me1/mTOR/autophagy pathway in human umbilical vein endothelial cells (HUVEC). We found that treatments with HTL, N-Hcy, or Hcy significantly reduced PHF8 protein and mRNA expression, increased H4K20me1, and upregulated mTOR signaling. Autophagy was also impaired (significantly downregulated BECN1, ATG5, ATG7, and LC3 protein and mRNA levels). We also found that these changes were mediated by PHF8-targeting microRNA (miR): miR-22-3p and miR-1229-3p. The effects of HTL, N-Hcy, or Hcy on the miR expression and on the PHF8/H4K20me1/mTOR/autophagy pathway were abrogated by treatments with an inhibitor of miR-22 or miR-1229. Taken together, these findings show that Hcy metabolites can upregulate miR-22-3p and miR-1229-3p expression, which then dysregulate the PHF8/H4K20me1/mTOR/autophagy pathway, important for vascular homeostasis.

Today, the digital economy has pursued the required social and economic development, and enterprises can achieve long-term sustainability by relying on the digital platform ecosystem. However, the development of the digital economy and the rapid growth of platform ecosystems have also generated some problems. To study effective digital platform ecosystem governance, this study used the tripartite evolutionary game method and analyzed the changes and effects of platform regulation under different development stages from the perspectives of government, platforms, and enterprises. The results indicate that in the early stages, digital platforms primarily relied on the government’s obligation to regulate. With the digital platform ecosystem’s evolution, the platform organization and settled enterprises’ governance effects gradually appeared, establishing the three common constraints of governance mode. Finally, when the digital platform ecosystem is in its self-renewal period, the government effect is reduced, with the platform itself and settled enterprises taking primary supervisory roles. This study provides reasonable methods for effective platform governance at different stages and enriches the theoretical basis and management practices of platform ecosystems.

Shape of supracondylar fracture of the humeral of pediatric patients is analysed with Procrustes method. XR–images of fractures are considered both in anterio-posterior (AP) view (66 images) and in a lateral (L) view (24 images). Applying Procrustes method for both views mean images are constructed and compared. Variability of shapes is quantified with a shape principal component analysis. Non-parametric tests (permutational and bootstrap) do not indicate statistical difference between Procrustes mean shapes in anterio-posterior and lateral projections. It is shown, however, that AP and L shapes of humeral fractures differ in their variability quantified by shape principal components.

Meningiomas are cerebral cancers that arise from abnormal cell development in the meninges and defensive layers covering the mind and spinal line. They can attack areas close to the cerebrum tissue and apply tension to neighboring designs, prompting different neurological side effects. Recent advances in atomic science and genomics have revealed insights into the fundamental subatomic adjustments and flagging pathways involved in the improvement of meningoma. Cabozantinib, a chemotherapeutic agent, has shown promising results in preclinical and clinical studies in various malignancies, including meningia. It inhibits angiogenesis, reduces cancer development, and prompts cell passage, providing areas of strength for clinical examination. Inhibition of VEGFR2 signaling has shown promise results in clinical trials in various cancer types, including a variety of cancers. This review summarizes the current knowledge on the pathophysiology and potential therapeutic effects of cabozanteb as a therapeutic agent for intracranial meninga.

The (numerical) solution of Ordinary Differential Equations (ODEs) problems is of paramount relevance, ODEs system being an ubiquitous mathematical formulation of many physical phenomena (such as those involved in fluid dynamics, chemistry, biology, evolutionary-anthropology, ...): almost every dynamici phenomenon can be modeled by means of an ODEs system. The present paper is the first manifesto of FOODIE, a library aimed to numerically solve ODEs problems by means of a clear, concise and efficient abstract interface. FOODIE, meaning Fortran Object oriented Ordinary Differential Equations integration library, has manifolds aims: to provide a set to built-in numerical schemes that are accurate, robust, validated and efficient and to allow easy application of these schemes to (almost) all ODEs problems by means of an effective Abstract Calculus Pattern. The key idea is to allow the same solver-implementation to be applied to all ODEs problems thus avoiding the re-implementation of the ODEs solver for each different ODEs problem: code re-usability is consequently maximized, FOODIE being a general robust framework. Besides, the same framework also allows rapid development of new ODEs solvers due to the high abstraction level of the library itself. The present paper is the first announcement of FOODIE project: the current implementation is extensively discussed and its capabilities are proved by means of tests and examples.

Corn is the main agricultural product and has been produced in Kazakhstan as important nutrition in human and animal food. The research results shown in the article, the fact of increasing the sustainability of corn production is formulated, methodological approaches to assessing the level of sustainability are developed, and criteria are defined for the main indicators of sustainability and efficiency of corn production. The world's corn production was 1,223 million tons in the 2021/22 season. Kazakhstan's corn production average was 1,1 million tons and the production area are 206,870 in the 2021/22 season. The yield per hectare was 5,985 kg in the 2021/22 season. Corn consumption was 1170.5 million tons and exported was 180.1 m.tons of corn in the 2021/22 season in Kazakhstan. The purpose of this study is a determination of sustainable developments of corn consumption and production in Kazakhstan and worldwide between the 2011/12 and 2021/22 seasons. Statistical data have been obtained from the International Grain Council, FAOSTAT, USDA, PhD diploma thesis of the article's author, Bureau of National Statistics. Data obtained have been shown in the tables issued by the use of percentage and index calculations. The study analyzed a major factor affecting supply and demand, sustainable development of corn production and market prices.

Battery Management System helps to improve the transient performance and life of electro-chemical rechargeable batteries like a Li-ion, lead–acid, etc. The investigation here was focused on the crucial part of the Battery Management System that is, Cell balancing, State of charge of the battery, etc. By analyzing the different control strategies a new optimal control strategy is developed to improve the battery performance according to application. The Control strategy is developed using an active and passive cell balancing model with the help of AVL Cruise and MATLAB/SIMULINK software. For the active cell balancing technique, a switched capacitor technique is chosen. To have longer battery life, the time required for the cell balancing must be as low as possible for which many control logics have been defined. The effect of these cell balancing techniques on the performance of battery packs is investigated and it is observed to produce a promising result.

Chiral interactions play a crucial role in both chemistry and biology. Understanding the behavior of chiral molecules and their interactions with other molecules is essential, and chiral interactions in solutions are particularly important for studying chiral compounds. Chirality influences the physical and chemical properties of molecules, including solubility, reactivity, and biological activity. In this work, we used Isothermal Titration Calorimetry (ITC), a powerful technique for studying molecular interactions, including chiral interactions in solutions. We conducted a series of ITC measurements to investigate the heat of dilution and the heat of racemization of several amino acids (Asn, His, Ser, Ala, Met, and Phe). We also performed ITC measurements under different solute concentrations and temperatures to examine the effects of these parameters on chiral interactions, as well as the heat of dilution and racemization. The results of our measurements indicated that the heat of dilution, specifically the interactions between the solvent (water) and solute (chiral molecules), had a significant impact compared to the chiral interactions in the solution, which were found to be negligible. This suggests that the interactions between chiral molecules and the solvent play a more dominant role in determining the overall behavior and properties of the system. By studying chiral interactions in solutions, we can gain valuable insights into the behavior of chiral compounds, which can have implications in various fields, including drug design, chemical synthesis, and biological processes.

Weeds that infest crops are a primary factor limiting agricultural productivity world-wide. Weedy rice, also called red rice, has experienced independent evolutionary events through gene flow from wild rice relatives and de-domestication from cultivated rice. Each evolutionary event supplied/equipped weedy rice with competitive abilities that allowed it to thrive with cultivated rice and severely reduce yields in rice fields. Under-standing how competitiveness evolves is important not only for noxious agricultural weed management but also for the transfer of weedy rice traits to cultivated rice. Molec-ular studies of weedy rice using simple sequence repeat (SSR), restriction fragment length polymorphism (RFLP) and whole genome sequence have shown great genetic variations in weedy rice population globally. These variations are evident both at whole genome and at single allele level, including Sh4 (shattering), Hd1 (heading and flower-ing), and Rc (pericarp pigmentation). The goal of this review is to describe the genetic diversity of current weedy rice germplasm and the significance of weedy rice germplasm as a novel source of disease resistance. Understanding these variations, es-pecially at an allelic level, is also crucial as individual locus that control important traits can be of great target to rice breeders.

According to the wide variety of internet of things (IoT) devices within smart environments, many challenges face conventional digital forensic investigation (DFI) in smart environments. Challenges in this environment include heterogeneity, distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, it significantly slows down or even incapacitates the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine learning (ML) techniques might be a viable option in certain situations, this paper presents the integration of ML into DF. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.

One of the ramifications of the COVID-19 pandemic is that it has lent urgency to ongoing discussions on mental well-being. While standard techniques are available to diagnose mental health disorders such as depression, anxiety, and stress, ambiguity persists regarding the emotional aspect of well-being. Emotional well-being (EWB) is a recently developed concept that seeks to understand the contribution of emotions to one’s well-being. Interactive approaches for such investigations are recommended to understand people’s contextual experiences. In this study, we use a qualitative approach to examine the psychological impact of the built environment on its residents to formulate the desiderata for an ideal place based on their perceptions. We use personal construct theory and the associated qualitative repertory grid technique. We recruited 15 students from the United Arab Emirates University and obtained information on their perceptions of three built environments they experienced. The findings revealed information on the students’ emotional constructs that were associated with a set of design-related factors, and the way the ideal hostel should be characterized in response to these associations. These findings enrich our knowledge of EWB within built environments and can inform their future design by considering the emotional aspect of the well-being of residents.

Peptidases of the papain family play a key role in protein degradation, regulated proteolysis, and the host-pathogen arms race. Although the papain family has been the subject of many studies, knowledge about its diversity, origin, and evolution in Eukaryota, Bacteria, and Archaea is limited; thus, we aimed to answer these long-standing questions. We traced the origin and expansion of the papain family through phylogenomic analysis, using sequence data from numerous prokaryotic and eukaryotic proteomes, transcriptomes, and genomes. We identified the full complement of the papain family in all prokaryotic and eukaryotic lineages. Analysis of the papain family provided strong evidence for its early diversification in the ancestor of eukaryotes. We found that the papain family has undergone complex and dynamic evolution through numerous gene duplications, which produced eight eukaryotic ancestral paralogous C1A lineages during eukaryogenesis. Different evolutionary forces operated on C1A peptidases, including gene duplication, horizontal gene transfer, and gene loss. This study challenges the current understanding of the origin and evolution of the papain family and provides valuable insights into their early diversification. The findings of this comprehensive study provide guidelines for future structural and functional studies of the papain family.

E-commerce platforms created to meet the needs of digital consumers, who are the leading actors of digital-oriented consumption, are increasing rapidly. Revolutionizing our daily lives by offering the digital equivalent of real-world experiences, e-commerce now offers a comfortable shopping experience that transcends the limitations of time and space. This phenomenon has been embraced by individuals spanning all age groups, from young children to the elderly. Structural equation modeling was used in this study, in which the factors affecting online shopping addiction were determined. In this direction, the causal and correlational relationships between the variables of fear of missing out (FoMO), cognitive load, personality types, some socio-demographic characteristics (gender and monthly shopping amount) and online shopping addiction in online shopping were revealed structurally. Since the purposive sampling was used, the study was carried out on 488 people. The goodness-of-fit indices included in the created model were found at an “acceptable” level (χ2/df= 2.248, p=0.000; RMSEA=0.051; GFI=0.878; AGFI=0.856; CFI=0.950; NFI=0.914). These results show that the final version of the model meets the necessary criteria for a good fit, its validity and accuracy. The study revealed the causal and correlational relationship between online shopping addiction and cognitive load, personality types, FoMO, gender, and monthly online shopping amount.

Remittances are essential to economies in mountainous regions that face massive labour migration due to limited income generation and employment opportunities. In 2021, the share of remittances to GDP in the top 10 mountainous economies in the world was over 20%. Nonetheless, most are characterized by relatively lower GDP per capita and high poverty levels. Drawing a comparison with other mountainous areas, Phuthadjthaba, an emerging mountainous and border city of South Africa with Lesotho, faces similar out-migration and inferior socio-economic parameters. A global systematic literature review on the impact of remittances on livelihoods specifically targeting mountainous areas is missing. We, therefore, interrogate the role remittances can play in Phuthadijthaba. To inform our intended research, we seek to draw lessons from evidence on how migration and remittances affect mountainous communities globally. Accordingly, we carry out a systematic literature review (SLR) based on an updated Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) 2020 statement supported by Bibliometric (co-word) Analysis (BA) in VOSViewer. We collected data from Scopus and Dimensions websites and drew 165 publications, of which only 88 were included after exclusion and inclusion assessments. PRISMA results show that Mountain Research and Development, King Russell, and Nepal is the most productive and cited journal, the most productive and cited author, and the most researched country, respectively. Bibliometric analysis on keyword co-occurrences reveals women, agriculture, labour migration, land management, forest, and poverty, to be the research hotspots. In light of these findings, we proffer important recommendations to future researchers and policymakers.

Coal is an important resource that is closely related to people’s lives and has an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. To this end, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, the support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and then the simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model was established using the improved algorithm to classify and predict the safety of the coal mine environment into four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved, and the performance of the IAHA-SVM model has a significant improvement.

Time series (TS)-based predictions are made by examining the behaviour of historical data to forecast future values. Multiple industries; such as stock market trading, power load forecasting, medical monitoring, and intrusion detection; frequently use it. Several variables; such as the performance of other markets as well as the economic situation of a country which affect its market performance; significantly affect the prediction of stock market prices. Therefore, this present study uses numerous variables; such as the opening, lowest, highest, and closing prices; to predict the indices of the stock market of the Kingdom of Saudi Arabia (KSA). Successfully accomplishing an investment goal largely depends on choosing the right stocks to buy, sell, or maintain. The project's output is the projected closing prices (regression) over the next seven days, which helps investors make the best decisions. This present study used exponential smoothing (ES) to remove noise from the input data obtained from the Saudi Stock Exchange, or Tadawul, before using a multivariate long short-term memory (LSTM) deep learning (DL) algorithm to forecast stock market prices. The proposed multivariate LSTMDL model had satisfactory prediction rates of 97.49% and 92.19% for the univariate model. Therefore, it can be used to effectively predict stock market prices. The results also indicate that DL as well as multiple sources of information can be used to accurately predict stock markets.

Researchers achieved high torque volume ratios in magnetorheological (MR) fluid braking devices by introducing multiple layers and multiple poles concepts. The number of magnetic poles, pole geometry and placement have a significant effect on these multi-pole MR brake performances. This research work focused on the effect of the pole head geometry in a multi-pole dual-disc MR brake for the enhancement of braking torque. The magnetic field generated by the electromagnetic coil has to penetrate through pole heads and travel orthogonally through the rotor and MR fluid layers to achieve maximum brake torque. This flow depends on the profiles of the magnetic pole head area and corresponding core dimensions. This study considered pentagon, square, hexagon, trapezoid and ellipse pole head profiles in place of the conventional circular pole and the best possible core dimensions were used for each geometry. A three-dimensional electromagnetic analysis of the MR brake is conducted to simulate the brake performance. Results showed that the hexagonal and elliptical pole heads are having better magnetic field area, magnetic intensity distribution and higher magnetic flux lines piercing through the MR fluid perpendicularly than the circular and square pole heads. An increment of 3.88% for braking torque performance is obtained, and the braking torque is significantly enhanced.

Bollworm poses a significant threat to cotton crops worldwide, including in the Fergana province of Uzbekistan. This study aims to explore innovative approaches to bollworm control and contribute to the global understanding of integrated pest management strategies (IPM). Using an extensive literature review and field observations, the study evaluates the effectiveness of an integrated protection system in reducing reliance on chemical inputs while increasing overall efficiency. The results highlight the multigenerational life cycle of bollworm, its impact on various crops, and the need for artificial biological control methods and targeted insecticide applications. In addition, the study highlights the need to adopt globally accepted IPM practices to ensure sustainable pest management. The findings underscore the importance of collaboration among researchers, farmers, policymakers, and industry representatives in developing and implementing advanced control measures. Through integrated approaches and the incorporation of scientific advances, bollworm infestations can be contained while safeguarding agricultural productivity and minimizing environmental risks.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

The study follows the interaction between the individual and the social context regarding the development of adolescents protected in residential houses from the child protection system in Iasi County, Romania. Starting from the evidence that in situations of providing relatively identical material and social resources, the results obtained by these children vary greatly, we examine how the meaning (understood mainly as purpose and mattering) can constitute a developmental resource. Considering theoretical models and previous research, the study aims to describe, through a qualitative approach, how these young people see themselves and the external environment (family and residential care), how they project their future (their purpose and objectives), and how these constructions and perceptions can influence their quality of life and social integration. We conducted three focus groups with 35 adolescents and young people (the ages 13-21) protected in and for different periods in residential houses (period 1-20 years). The results highlighted that a specific meaning is reconfigured as compensatory when affected by a trauma or adverse external event and thus can be a resource for resilience. On the other hand, the research highlighted that only the existence of goals alone is insufficient to generate action, and the unrealistic meanings given to own persons, experiences, and resources can be dangerous.

This study examines the activities conducted at the archaeological site of Aptera in Crete, Greece. The research was part of the DIACHRONIC LANDSCAPES International Design Workshop, organized by the CAM (Center for Mediterranean Architecture), TUC (Technological University of Crete School of Architecture), and UNIFE (University of Ferrara Department of Architecture). This article outlines the methods used for data acquisition and processing on a territorial scale, which generated digital outputs necessary for the analysis and design phases of the workshop, as well as for further examination of the results. The collected data, obtained through low-cost aerial photogrammetric surveying and GNSS terrestrial coordinate detection, were integrated in a Structure from Motion workflow that led to the creation and exportation of various digital outputs, such as point clouds, DTM, DSM, orthophotos, and contour lines. An accuracy analysis was performed to evaluate the effectiveness and efficiency of the digital models compared to the implemented surveying strategies, including the Ground Control Point and Quality Check Point marker positioning strategy. The resulting digital models proved to be valuable assets for analysis and design within the workshop and provided insightful prospects for future research and territorial-scale projects.

The fractal dimension of geomagnetic variations at the Baigazan magnetic station at Russian Altay for 2011-2013 was calculated using the Higuchi method. The daily variation of Higuchi Fractal Dimension (HFD) for the D,H,Z-components of the geomagnetic field has been investigated, its contribution to the variability of HFD is from 30 to 40 percent of the total variance. A correlation analysis of the fractal dimension of the variations of the D,H,Z-components with Auroral Electrojet (AE) index and solar wind characteristics was carried out. A negative correlations with logarithms of AE-index, interplanetary magnetic field (IMF) strength and solar wind velocity were found. About 25 percent of the fractal dimension variance is controlled by the variability of these characteristics. Pair and partial correlation coefficients for these parameters were calculated for every month of 2011-2013.

The free energy principle (FEP) is a mathematical and scientific principle that describes the relationship between the physical behavior of a dynamical system and the interpretation of such behavior as carrying out inference. It blurs the boundary between the ontological mode of the system of interest and the epistemological significance it acquires through its dynamic interaction with the environment. New materialism (or neo-materialism), despite lacking a single definition, refers to a range of emerging perspectives that attempt to dismantle the long-held divisions between ontology, epistemology, and even ethics, with the aim of achieving a comprehensive transformation of naturalized thinking. In this context, new materialism provides powerful tools to reframe some of the conceptual foundations of the FEP. This paper is the first in a series that aims to systematically construct a neo-materialistic perspective for the FEP, exploring its wide-ranging implications. It serves as an introduction to the series, arguing for the justification of deploying new materialism to reframe the FEP and introducing some essential concepts to be utilized in developing a neo-materialistic account of the FEP.

The study was carried out to evaluate the microbiological quality of raw milk produced in Supare-Akoko of Ondo State, Nigeria. Two study areas (Supare and Akungba Villages) both in the Akoko South-West L.G.A. of Ondo State, in Nigeria's South-West geopolitical zone, were selected for the study. The study area served as the raw milk collection points. Raw milk samples were collected with the aid sterile container into the milking bucket and then transported to the laboratory of the Adekunle Ajasin University's department of microbiology in Akungba-Akoko, Ondo state, Nigeria, for further analysis. Several tests such as test for serial dilution, P_H measurement, gram staining, and biochemical analysis, to identify and evaluate the microbe species in the raw milk selected for the study.The findings showed that numerous microbe species, including Bacillus, Staphylococcus, Pseudomonas, Alcaligens, Actinomycetes, Enterobacteria, Licheniformis, Listeria, and Streptococcus species, were present in all of the raw milk samples. Therefore, the study recommended that there should be adequate awareness & education on handling of milk in the most hygienic way possible, as well as the establishment of bulk milk tanks and milk processing plants in key locations that could enable appropriate test and production of quality milk before the product is being made available to the general public for consumption.

The behaviour of steel frame buildings under progressive collapse conditions depends on a combination of several parameters including the interplay between different collapse resistance mechanisms mobilized in different structural components. Previous studies have shown that the extent to which these mechanisms may contribute to progressive collapse resistance depends on the ability of the beam-column connections to undergo large inelastic deformations prior to reaching their deformation capacity limits. For this reason, and due to the important role of their flexural strength and tying capacity in the development of essential collapse resistance mechanisms, the response of beam-column connections is one of the most important features of progressive collapse performance. Based on the knowledge gained through previous studies on the mechanics of the problem, the role of the connections is critically reviewed by examining the results of several experimental studies conducted during the past decade. The factors that may adversely affect progressive collapse resistance – such as failure modes of certain connection types – are evaluated, and novel approaches of limiting these factors which are currently under development are reviewed. The assessment of these parameters leads to useful conclusions of practical significance, while highlighting the aspects of the problem that need further study and understanding.

The aim of this paper is to optimize thickness variation function of simply supported and cantilever beams, in terms of maximizing gaps between chosen neighboring frequencies and analyze obtained results. The optimization results are examined in terms of achieving the objective function (related to eigenproblem), but also in terms of their dynamics stiffness (forced vibrations excited by point harmonic load). In the optimization process, a Genetic Algorithm was used. Problems related to structural dynamics were solved by FEM implementation into the algorithm. Sample results were presented, and the developed algorithm was analyzed in terms of result convergence by examining several variable parameters. The authors demonstrated the validity of applying the described optimization tool to the presented problems. Conclusions were drawn regarding the correlation between stiffness and mass distribution in the optimized beams and the natural frequency modes in terms of which they were optimized.

The mammalian or mechanistic target of rapamycin (mTOR) integrates multiple intracellular and extra-cellular upstream signals involved in the regulation of anabolic and catabolic processes in cells, and plays a key regulatory role in cell growth and metabolism. Activation of the mTOR signaling pathway has been reported to be associated with a wide range of human diseases. A growing number of in vivo and in vitro studies have demonstrated that the gut microbes and its complex metabolites can regulate host metabolic and immune responses through the mTOR pathway, and result in disorders of host phys-iological functions. In this review, we summarize the regulatory mechanisms of gut microbes and mTOR in different diseases, and discuss the crosstalk between gut microbes and their metabolites and mTOR in the disorders in gastrointestinal tract, liver, heart and other organs. We also discuss the promising appli-cation of multiple potential drugs that can adjust the gut microbiota and mTOR signal pathways. Despite the limited findings between gut microbes and mTOR, elucidating their relationship may provide new clues for the prevention and treatment of various diseases.