Accounting information is a crucial management tool for Nigerian national development services. In every state or country, accounting is crucial to maintaining social peace, political stability, and economic sustainability. The explanation for this clear truth about the development of Nigeria is not plausible given the massive multi-sectoral scope of Nigerian economic activities in both the public and private sectors. For government national progress, reliable accounting information is essential. This research aims to explore the role of accounting on public sector accountability in Nigeria.

Plant breeding is central to agriculture, and shifts in plant breeding practices (e.g., hybrid development) and selection goals (e.g., response to synthetic fertilizer) have catalyzed monumental and persistent changes in agricultural production systems of all scales with social, political, economic, and environmental repercussions. While plant breeders are largely trained in the sciences of biology, genetics, and statistics, we posit an ethical imperative to examine the degree of equity with which the benefits of new research and plant varieties are distributed. In the United States, the history of plant breeding parallels the colonial history of agriculture, which compels reflection by current plant breeders about their role in shaping our agricultural system. In this perspective essay, we examine longstanding ideas about equitable food systems through the lens of public plant breeding in the United States. We propose a framework for equitable public plant breeding with respect to both its process and outcomes, and we intend for the ideas presented herein to catalyze reflection, discussions, and actions as the plant breeding community seeks greater equity in the food and seed systems our work supports.

The present study aims to analyze scientific production about technological cooperation networks in biotechnology sector, based on bibliometrics network analysis. We used the Gephi software to identify groups of partnerships, proving that the cooperation relationship is a practice used, re-sulting technological development. Findings identify that in the path for new resources that com-plement skills and competences universities are an important player in this cooperative ecosystem, and The United States is a large hub, with an extensive network of global cooperation. A strong role of its researchers in the publication of scientific articles in cooperation is highlighted. This study contributes to the advancement of the discussion about cooperative networks, as well as to the development of policies aimed at the biotechnology sector advancements

This brief communication considers and illustrates dark matter and dark energy within the Baryon Phase Transition (BPT) cosmological model as well as some experiments that may confirm (or deny) the validity of the model.

It is essential to consider the factors that affect early childhood development because doing so can help identify potential risks and window of opportunity for intervention to improve outcomes for children. However, identifying risk and protective factors in early childhood requires careful consideration of several challenges and an integrated approach that recognises the complex and dynamic nature of development. The purpose of this study was to identify and describe the prevalent psychosocial risk and protective factors in the early childhood development phase, using an ecological perspective. The study employed a quantitative cross-sectional study using a random sampling technique. The final sample consisted of 505 caregivers of children aged 5 years old and younger. This study revealed that the most prevalent protective factors include child self-regulation, parent-child warmth, family management and collective efficacy but more importantly social support. While risk factors may exist this is mitigated by social support as it moderates and buffers against the effects of risk factors, as indicated by the various research studies cited.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p < 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.

China is experiencing rapid urbanization. The focus on health issues has shifted from the treatment of diseases to passive or active prevention. Diseases caused by the deterioration of the urban environment, such as cardiovascular disease, diabetes, obesity and other chronic diseases and mental illness, have gradually become the main threat to public health. Studies have confirmed that urban green spaces (UGS) can help residents relieve mental stress, improve mood, reduce mental fatigue and restore attention, which has a positive effect on people's physical and mental health. In this study, the relationship between the quantity and quality factors of UGS and the physical and mental health of urban residents in Xihu District of Hangzhou is examined, and on this basis, UGS design strategies are proposed to promote the physical and mental health of residents. This study is of great significance for the future construction of UGS that are beneficial to the health of residents and the promotion of a healthy China and healthy city construction.

During the construction of buildings, dangerous chemicals such as benzene, formaldehyde and others are used, which accumulate during the operation of the building. High concentrations of these substances can harm human health. In this article, we have analyzed the possible impact of modular green wall systems on improving of the air. During the research, four different modular green wall systems and various plant species were considered. The result showed that a modular green wall system can improve the air quality by removing harmful pollutants from the air in the buildings. Urban design used to be turned greener and more sustainable using the modern technologies of modular green wall systems. This study focuses on the air-cleaning benefits provided by modular green wall systems and their assessment. This research contributes to fill existing literature gaps on the green wall systems on the buildings and biophilic architecture.

This paper investigates the relationship between public spaces and quality of life (QoL) for older adults, aiming to identify knowledge gaps within the context of population ageing and urbanization. Recognizing the growing importance of sustainable urban development, the research explores how cities can foster active ageing and improve QoL through accessible and inclusive public spaces. A scoping literature review identifies five key QoL domains for older adults: autonomy, meaningful activities, positive social relationships, leisure opportunities, and health. The review highlights areas requiring further investigation, including the interplay between environmental design and social connections, intergenerational perspectives on public space liveliness, the influence of context on QoL and the built environment, and the value of qualitative research in this field. By contributing to the understanding of QoL in relation to public spaces through the lens of Person-Environment fit theory, this study aims to inform urban design, landscape architecture, and policymaking in the creation of age-friendly communities with inclusive public spaces.

Abstract: Canine ehrlichiosis is a zoonotic disease transmitted by ticks, posing a formidable challenge to both veterinary and public health sectors worldwide. Across continents and regions, the prevalence rates of Ehrlichia canis exhibit significant variation, underlining the necessity for a nuanced and globally informed approach to understanding and combating this disease. The review navigates through this complexity, shedding light on the intricate pathogenesis of the illness. Central to this understanding is the bacterium's adept manipulation of the host's immune response, contributing to the diverse clinical manifestations observed in infected animals. Diagnostic methodologies, crucial for timely intervention and management, are subjected to critical assessment. From traditional microscopy to modern molecular techniques and serology, each approach is scrutinized for its strengths and limitations. By acknowledging these nuances, the review aims to equip practitioners with the knowledge necessary to make informed diagnostic decisions. A central tenet of this review is the advocacy for an integrated "One Health" approach. By leveraging advancements in genomics, proteomics, and artificial intelligence, there is a potential to enhance diagnostic accuracy and develop innovative therapeutic and preventive strategies globally. This collaborative approach recognizes the interconnectedness of human, animal, and environmental health, offering a holistic framework for tackling complex zoonotic diseases like canine ehrlichiosis. In conclusion, this review serves as a beacon of knowledge, illuminating the multifaceted landscape of canine ehrlichiosis. Through its synthesis of scientific literature and emphasis on methodological rigor, it provides a foundation upon which future research and interventions can be built. With a unified commitment to "One Health" principles and the integration of cutting-edge technologies, there exists the potential to mitigate the impact of this disease and safeguard both animal and human well-being worldwide

Phelipanche ramosa is a root parasitic plant fully dependent on host plants for nutrition and development. Once germinated, the parasitic seedling develops inside of the infected roots a specific organ, the haustorium, thanks to notably cell wall degrading enzymes of haustorial intrusive cells and induced modifications in host’s cell walls. The model plant Arabidopsis thaliana is susceptible to P. ramosa, thus mutants in cell wall metabolism, notably in pectin remodeling like Atpme3-1, are of interest in studying the involvement of cell wall degrading host’s enzymes in establishment of the plant-plant interaction. Host-parasite co-cultures in mini-rhizotrons systems revealed that parasite attachments are twice more numerous and tubercle growth is quicker on Atpme3-1 roots than on WT roots. When compared to the WT, increase in susceptibility in the mutant is associated to reduction in PME activity in roots and to lowering in the degree of pectin methylation at the host-parasite interface, as detected immunohistochemically in infected roots. In addition, both WT and Atpme3-1 responded to infestation by modulations in expression of PAE and PME encoding genes and in global related enzymes activities in roots, before and after parasite attachment. However, modulations differ in WT and Atpme3-1 and this may contribute to different pectin remodeling in roots and contrasted susceptibility to P. ramosa. By this integrative study, we could define a model of the cell wall response to this particular biotic stress and indicate for the first time the role of PME3 in this parasitic plant-plant interaction.

In this paper, a seamless connection between sensor technology and machine learning (ML) has been presented which has wide applicability in rehabilitation and physical medicine. The intent is to identify an ML algorithm which has a better performance, easy to deploy, and has less training time and consumes less computational resources. In short, we propose an algorithm which maximizes the synergy between a sensor and ML by maximizing classification performance and easy to do implementation, which is robust in computational power and lower in resource consumption. The premise of the work is to show the important considerations for evaluating ML algorithms for Upper-limb activity recognition for Physical medicine and rehabilitation field.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

Objective: The combination of docetaxel (DTX) with Laser-Activated NanoTherapy (LANT), as a treatment for head and neck cancer (HNC) may enhance the therapeutic efficacy of lower doses of DTX, thereby minimizing the effective dosage, side effects and treatment times. Material and methods: Three HNSCC cell lines, Detroit 562, FaDu, and CAL 27, were treated with four combinations of DTX + LANT to evaluate DTX dose reduction and cell viability. Results: The 1 nM DTX + 5 nM LANT combination was the most effective treatment, increasing cell death over its corresponding DTX monotreatment with approximately 86.6%, 80.7%, and 92.1% cell death for Detroit 562, FaDu, and CAL 27, respectively. In Detroit 562, the 1 nM DTX + 5 nM LANT combination treatment resulted in the highest percentage of DTX dose reduction at 84.6%; in FaDu and CAL 27, the 0.5 nM DTX + 5 nM LANT combination treatment resulted in the highest percentage of DTX dose reduction at 78.2% and 82.4%, respectively. Conclusion: LANT may increase the therapeutic efficacy of DTX at significantly lower doses, which could improve patient outcomes.

Internships can foster educational alliances between industry (business environment) and academic institutions, which can facilitate both flexibility and accelerated innovation. The purpose of this research is to search for the factors that determine HRD through the implementation of the Internship program among the students of Democritus University of Thrace and reveal the correlation between the factors. Also, this paper investigated the impact of the COVID-19 Eras on Human Resource Development (HRD) Factors and finally, investigated the correlation between the two variables "Recruitment in the specialization" and "Covid Eras". The sample of the research consisted of 1398 students of DUTh who implemented Internships during the academic years 2015-2016 to 2021-2022. A questionnaire with 60 variables was created for data collection. A random sampling method was applied to collect the sample. From the results of the survey, ten factors were extracted for human resource training that explained 75.512% of the total variance and for each one, Cronbach's Alpha reliability was tested, which was high. In detail, the factors revealed were Learning Outcomes, Efficacy, Emotional Commitment, Satisfaction, Participation, Empowerment, Role Ambiguity, Role Conflict, Supervisor and Collaboration. From the results of the study, it was evident that HR development - training is a multi-dimensional concept and is a necessary condition for the competitiveness of the firms. Training can help to improve the flexibility of the firm, but also to motivate the staff by implementing multidisciplinary training programs. In general, internship programs are valuable ways for students to acquire broad competences, where the practical knowledge gained supports and complements the theoretical studies acquired in educational institutions.

Abstract: Assessing infection by a parasite, such as Haemonchus contortus, requires time and experienced personnel. Field-based diagnostics using metagenomic species-specific targeting methods would be beneficial. Here, we examine two helminth control methods, zoledronic acid (ZA), an activator of γ δ T cells, and a neutralizing antibody (AB) against γ δ T cells. We conducted a randomized complete block designed investigation for the effects of Haemonchus contortus (Hc) infection on Alpine wethers (young castrated male goats) to identify if metagenomic methods will provide strong evidence for diagnostics. Different tissues were collected from wethers and the microbiome compositions were identified. The relative proportion of microbial community members in the environments; the richness of the microbial community; and the evenness of distribution in microbial community members were determined. It was hypothesized that numerous parameters that include many data points will provide a more precise means to identify infection or health based on similarity to uninfected control data parameters. We found that samples did not sequence well, when multiplexed with high concentration libraries, giving taxonomic numbers that had residual and unmeasured confounding bias related to analysis or selection that define significance of OTUs in the tissue samples at the phylum or species level.

Sugarcane mosaic virus (SCMV) and sugarcane streak mosaic virus (SCSMV) are the primary viruses responsible for mosaic diseases in sugarcane. In this study, the mosaic viruses SCMV and SCSMV with the involvement of sorghum mosaic virus (SrMV) were surveyed from eight commercial sugarcane cultivars grown in fields at four locations in East Java, Indonesia. Sugarcane leaves with mosaic symptoms were assessed for disease incidence and severity, and symptomatic leaves were collected for analysis. The incidence and severity of mosaic symptoms were the highest in the PS881 and NX04 cultivars grown in Kediri compared to the other cultivars. RT-PCR analysis showed that SCSMV was present in all sugarcane cultivars and locations. Interestingly, double mixed infections with SCMV were found in PS881, PS882, and Cening, whereas triple mixed infections with SrMV were found in the PS881 cultivar. Phylogenetic analysis grouped SCSMV, SCMV, and SrMV together with isolates from Thailand (KP987832.1), Florida, USA (MT725538.1), and Agentina (EU189037.1). The expression of ascorbate peroxidase gene (Apx) was increased in the virus-infected sugarcane cultivars and prominently elevated in the triple mixed infection of PS881 cultivars. However, the catalase gene (Cat) was slightly upregulated in the mixed infection group. The chlorophyll content was significantly reduced in the leaves of PS58, PS881, PS882, NX04, and HW Merah, in parallel with the expression of the photosystem I gene (PsaA). The expression of photosynthetic genes, such as ribulose-1,5 bisphosphate carboxylase small sub-unit (RbcS), phosphoenolpyruvate carboxylase (Pepc), sucrose-phosphate synthase (Sps1), and PsaA, was significantly reduced and almost depleted in the triple mixed infection of PS881, except for Pepc expression, which remained approximately half of that in the control healthy sugarcane. The decrease in photosynthetic activity was confirmed by the decrease in ribulose-1,5 bisphosphate carboxylase (Rubisco) and PEPC proteins, as detected by immunoblot analysis. The decrease in photosynthetic parameters indicated that the mosaic viral infection substantially reduced the growth and yield of sugarcane.

Teaching, learning, research, and development are all supported in a favorable atmosphere at the University of Lagos (UNILAG). The wetland systems made up of freshwater that are extremely biodiverse makeup about 10% of the total land area. Flooding, deforestation, excessive sedimentation, blockages to streams, abuse, overuse, and invasion of wetlands are some of the recent environmental challenges that the university faced. Urban heat island impacts, contaminants in the air, wetland degradation, social issues including stress, and a detachment from nature are all major concerns. With the green corridor behind the faculties of law and management science serving as a model supported by sustainability as a true foundation for UNILAG's campus landscapes, this article investigates the sustainable restoration of wetlands within the boundaries of the university. The use of qualitative techniques was employed, utilizing the Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis. Additionally, a geospatial mapping of the wetlands and tributaries that connect the Lagos Lagoon was conducted to evaluate the intrinsic ecological goods and services through a series of analytical steps, beginning with an analysis of the land area, current biodiversity, hydrology, and its potential through landscape mapping and analysis. The biophysical setup, landscape features, climatic data analysis, the ecological goods and services derived from the wetlands, and the records of flooding and rainfall are all included in the geospatial evaluation and analysis. The educational institution's site planning for the remediation of its wetlands takes into account the actual evolution of the area's drainage network. Throughout the wetlands, low carbon emissions are generated by off-road bike lanes, trails for walking, and raised hardwood platforms. Combining native plant species and park furniture, the academic landscape atmosphere will be improved in each of the wetlands and aesthetic values. the country's "University of First Choice" and its biophysically restored wetlands will help illuminate the landscape while the green corridors have an innate ability to attract human affinity within the academic community. All of these elements are implemented following an environmentally friendly landscape planning, management, design model, and enforcement process.

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

In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the Variational Quantum Classifier (VQC) and the Quantum Kernel Estimator (QKE). We evaluate the performance of these classifiers on six widely known and publicly available benchmark datasets and analyze how their performance varies with the number of samples on two artificially generated test classification datasets. As quantum machine learning is based on unitary transformations, this paper explores data structures and application fields that could be particularly suitable for quantum advantages. Hereby, we developed a data set based on concepts from quantum mechanics using the exponential map of a Lie algebra. This dataset will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy. We further compared the performance of VQC and QKE on six widely applicable datasets to contextualize our results.\\ Our results demonstrate that the VQC and QKE perform better than basic machine learning algorithms such as advanced linear regression models (Ridge and Lasso). They do not match the accuracy and runtime performance of sophisticated modern boosting classifiers like XGBoost, LightGBM, or CatBoost. Therefore, we conclude that while quantum machine learning algorithms have the potential to surpass classical machine learning methods in the future, especially when physical quantum infrastructure becomes widely available, they currently lag behind classical approaches. Our investigations also show that classical machine learning approaches have superior performance classifying datasets based on group structures, compared to quantum approaches that particularly use unitary processes.\\ Furthermore, our findings highlight the significant impact of different quantum simulators, feature maps, and quantum circuits on the performance of the employed quantum estimators. This observation emphasizes the need for researchers to provide detailed explanations of their hyperparameter choices for quantum machine learning algorithms, as this aspect is currently overlooked in many studies within the field.\\ To facilitate further research in this area and ensure the transparency of our study, we have made the complete code available in a linked GitHub repository.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

The health status of Hungary's population is unfavorable, with significant differences in health indicators not only compared to the EU15 but also to the Visegrad countries. Unfavorable health indicators can be disproportionate and particularly affect vulnerable groups, such as people with disabilities. The aim of the present study is to compare the health behaviour and indicators of adolescents with disabilities in Hungary with those of the HBSC survey in the Visegrad countries, and to compare the results of the Hungarian special needs adolescent population with those of the Visegrad countries. Eating habits are unfavourable for both the Visegrad countries and the young people we studied. Children with disabilities have significantly higher rates of schoolwork stress compared to other countries. Significantly higher prevalence of somatic complaints and worse mental well-being parameters higher in Hungary than in the other Visegrad countries. The results suggest that further interventions are needed in Hungary and that differentiated, professionalised health promotion is needed for young people with disabilities. The researchers propose to extend the research to adolescents with disabilities living in the Visegrad countries, on the basis of which a methodology for injury-specific health promotion could be developed, including through interna-tional interprofessional cooperation.

This study explores the geopolitical dynamics of water conflicts in Central Asia, focusing on Uzbekistan and its neighboring countries. Using a mixed methods approach within the framework of political geography, the research analyzes interactions among regional countries and the implications of population growth, agricultural industrialization, and economic competition, which heavily rely on shared irrigation systems and watersheds. It establishes research objectives, evaluates the water crisis, addresses challenges in water management, and examines contentious issues like hydropower, resource hegemony, and ethnic conflict. Also, the study uncovers historical and contemporary factors influencing water conflicts, regional power dynamics, and the complexities of water governance. The findings highlight the potential for conflict, regional instability, and disruptions to peace resulting from population growth and economic competition within shared watersheds. The study concludes with recommendations to enhance regional cooperation, sustainable water management, and peacebuilding, drawing on soft power theory and advocating for collaborative approaches. This research provides valuable insights for policymakers, researchers, and stakeholders involved in water management and regional cooperation amid evolving geopolitical challenges, contributing to informed decision-making, and promoting strategies for sustainable development in the region.

This article aims to create the nexus between sustainable development and the quality of the political regime. The current social and political context is characterized by a series of crises generated by COVID-19 pandemic, economic imbalances and regional conflicts. In this context, sustainable development is affected by the economic dynamics and the “democratic recession”. The study aims to respond to the following research questions: “how could influence the quality of the democracy the dynamics of the Sustainable Development Goals (SDGs 17)?” and “what are the premises for sustainable development in the new political context, characterized by democratic recession?” The purpose of the study is to underline the fact that democratic regimes are inclined to create both participative and deliberative frames for achieving the SDGs in accordance with UN 2030 Agenda. The research methodology used in this study is based on descriptive and inferential statistics. The research data are collected from secondary sources in the years between 2015 and 2021, from 193 countries covering all the geographical areas. The empirical results suggest two models of development: the Asian model of sustainable development characterized by economic growth and the Western democratic model based on democratic institutions, fair justice and mechanisms for preserving peace. We noticed that the key-variables for explaining the dynamics of sustainability in correlation with democratic index are represented by the functioning of the governments and the political participation. Through civic engagement and political ac-countability, democracy could be seen as a pre-requisite for achieving an optimal level of the SDGs. All these empirical results could prove valuable for the scholars interested in the relation between democracy and sustainability and for the political decision makers involved in shaping strategies for social, economic and environmental development.

The pyrolysis process has emerged as an attractive option for waste treatment. In this technology, inorganic compounds play catalytic effects in the pyrolytic reaction of organic materials, increa-sing the char's yield, porosity, and physicochemical properties. The present research investigates and compares the slow pyrolysis of spent coffee grounds (SCG) using potassium hydroxide (KOH) (weight ratio of 1:1) and landfill leachate membrane concentrate (LLMC) residue (weight ratio of 1:1) as pyrolitic additives. We hypothesized that the high content of alkali and alkaline earth metals in LLMC could enhance the yield or improve the quality of the carbon-based material produced in pyrolysis. This strategy could be a sustainable alternative for valorizing SCG and landfill wastes, replacing catalyzing agents such as steam, CO2, and chemical additives used on an industrial scale. The char yields in slow pyrolysis ranged from 18 to 24%. The SCG had a low specific surface area (4.5 m2 g-1), contrasting with the notable surface areas observed in both ac-tivated chars. In particular, the KOH-activated char displayed a higher surface area than the LLMC-activated char (1960 vs. 1130 m2 g-1). On the other hand, the combustion enthalpy of the LLMC-activated material was estimated at 22 MJ kg-1. This value was 22% higher than the energy enthalpy of SCG and KOH-activated char used for comparison. Our findings confirm that pyrolysis of SCG with KOH produces a microporous material with a high specific surface area. In contrast, the resulting LLMC-activated char demonstrates a higher calorific value. Our findings could contribute to establishing potential industrial applications for SCG and LLMC residues to produce energy and high-value materials.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Rates of involuntary childness are on the uprise which can bring high medical costs and emotional afflictions. This review explores the emerging roles of CoQ10 - including the ubiquinol form in fertility. PubMed, Science Direct and Google Scholar were searched for RCTs, retrospective or prospective controlled studies published between January 1, 2009, and April 31, 2024. The keywords CoQ10, ubiquinol, fer/sub/infertility, ovarian reserve, oocyte quality, ovulation, amenorrhea, polycystic ovary syndrome, sperm quality, seminal fluid, and IVF were used. A total of 15 publications were identified. From a mechanistic stance CoQ10 appears to improve mitochondrial energetics, attenuate oxidative stress and modifications to DNA, proteins, and lipids. Such mechanisms appear to underpin improvements in oocyte quality/fertilization, markers of sperm quality and more broadly PCOS symptoms and hormone levels which can impact on fertility. The role of the ubiquinol form looks particularly promising, which may be attributed to its bioavailability. Ongoing research is needed but there is scope to raise CoQ10 awareness amongst reproductive health experts.

Persistent Organic Pollutants (POP) such as dichlorodimethyltrichloroethane (DDT) are present and ubiquitous in the environment due to their resilient nature. DDT is a prevalent endocrine disruptor still found in detectable amounts in organisms and the environment even after its use was banned in the 1970s. Studies show that exposure to DDT can cause adverse health effects in humans and animals, impairing fertility and increasing the risk of developing cancer. The aim of this work is to provide a comprehensive overview of the available literature on the effect of DDT and its metabolites on female reproductive tract, leading to infertility and cancer. Medline and Google Scholar were systematically searched to detect all relevant animal and human studies published in the last 20 years (January 2003 to February 2023) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. In total, 38 studies were included for qualitative synthesis. This systematic search and review indicated that exposure to DDT is associated with female reproductive health decline and that DDT acts as a sufficient carcinogen instigating reproductive cancers.

Intersectional experiences, socio-cultural meanings, ethnic traditions and morals compound stigma-related stress (Jackson et al., 2020; Schmitz 2019). Sex workers are subject to various stigmatizing forces which can lead to secrecy, isolation and lack of social and cultural support (Koken 2012). Stigmatizing forces include structural humanitarian governance and aid interventions that conflate migration and sex work with insidious constraints and coercion. This study explored how migrant female sex workers from distinctive ethnic cultures manage their identity on a day to day basis in relation to the separation of work and home life. Methods: The perspectives of female sex workers were collected via a series of in-depth semi-structured interviews. The inclusion criteria were that the women had worked in sex work for over 18months, defined their involvement in sex work as voluntary, and were over 18yrs of age. The perspectives of seven women from South Asian (Pakistani), Brazilian, and British backgrounds were analyzed using Grounded Theory (Glaser and Strauss, 1967). Ethnicity was considered to explore how the women experienced stigma, how it impacted on the management of their identity, and how the process of change occurred. Results: The women used a variety of methods to maintain work and home life boundaries, processes they used switch into a role and all experienced stigma and tried to deal with it in ways such as concealment from friends and family. Two core categories and properties emerged from the data as participants felt guilt and/or shame but only the South Asian participants spoke of this with reference to their culture and religion. Conclusion: It was not migration per se but rather the relationship of migration to culture that was key to identity management. Participants reflected that as their country was considered collectivist country with interdependent thought, that any negativity felt could not only be reflected on the individual, but also the entire family. For these reasons Pakistani sex workers were subject to more complex stigmatizing forces, shame and guilt as regards risk and exposure. Discussion focusses on the processes and management strategies used to extend social and cultural support.

Abstract Background Fatigue Syndrome (FS) is described as extreme exhaustion following ordinary activities. Sleep deprivation, the use of antidepressants, autoimmune illnesses, and anemia are also risk factors for FS. Symptoms of FS include headache, dizziness, and dyspnea, among others. Objective: This study focused on assessing the prevalence of fatigue syndrome (FS) and its association with depression and anxiety, among the medical students in Saudi Arabia. Methodology In this cross-sectional investigation conducted in 2023, 740 medical students participated. Data were gathered using an online survey, which encompassed four key sections: Sociodemographic details, the 11-item Chalder Fatigue Questionnaire (CFQ), the 9-item Primary Health Questionnaire (PHQ) assessing depression, and the Generalized Anxiety Disorder 7-item (GAD-7) scale. Analyses were performed by the use of SPSS version 25. Results A total of 740 students were surveyed, 417 (56.4%) satisfied the diagnostic requirements for fatigue syndrome (FS), with most of them being females. Furthermore, 234 (31.2%) and 296 (40.8%) of the participants reported having significant anxiety symptoms and depressive symptoms, respectively. FS was positively correlated with symptoms of anxiety and depression (P

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

LPA3 receptors were expressed in TREx HEK 293 cells, and their signaling and phosphorylation were studied. The agonist, lysophosphatidic acid (LPA), increased intracellular calcium and ERK phosphorylation through pertussis toxin-insensitive processes. Phorbol myristate acetate, but not LPA, desensitizes LPA3-mediated calcium signaling. The agonists and the phorbol ester-induced LPA3 internalization. Pitstop 2 (clathrin heavy chain inhibitor) markedly reduced LPA-induced receptor internalization; in contrast, phorbol ester-induced internalization was only delayed. LPA induced rapid β-arrestin-LPA3 receptor association. The agonist and the phorbol ester induced marked LPA3 receptor phosphorylation, and phosphorylation sites were detected using mass spectrometry. Phosphorylated residues were detected in the intracellular loop 3 (S221, T224, S225, and S229) and in the carboxyl terminus (S321, S325, S331, T333, S335, Y337, and S343). Interestingly, phosphorylation sites are within sequences predicted to constitute β-arrestin binding sites. These data provide insight into LPA3 receptor signaling and regulation.

Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner.

Turkana County's fisheries sector, which is entirely dependent on Lake Turkana, is vital to the county's economy. The Fisheries Sector in the County supports 1,500 households directly and 1,100 households indirectly. Despite its economic and social importance, the sector faces numerous challenges such as high post-harvest fish losses, uncoordinated development approaches, over-exploitation of some lake areas and weak links between research, management, and other sector players. Furthermore, since the start of devolution in 2013, the sector has been operating without an overall policy and legal framework. The primary goal of the study was to identify important challenges in the management of the fisheries sector in Turkana County that would lead the successful and coordinated growth of the fisheries industry. To capture their perspectives on the problems and potential in the usage of Lake Turkana's fisheries resources, semi-structured questionnaires and Key Informants interviews were used to collect primary data from the sector's fisheries actors. A total of 60 people were interviewed, and their responses were recorded in the Kobo Collect toolbox for later analysis. Secondary data was also gathered from numerous national policy declarations, guidelines, and legislation that have been used to manage Lake Turkana's fishery resources. The findings indicate that a policy directing the exploitation and development of fisheries is required in order to fully realize the potential of fisheries and aquaculture in the County. The development of the policy will lay a solid foundation for the creation of a legal framework to strengthen the sector in Turkana County, which is a necessary step toward achieving sustainable development and management of fisheries in accordance with national, regional, and international frameworks. By leveraging the role of the County Directorate of Fisheries and Beach Management Units in the sectors' monitoring, management, and development, the policy will also promote effective coordination of stakeholders at all levels.

Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Despite several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint-CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth-throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing, and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, large shape deformations were explored to find the maximal possible morphing; both the maximal and intermediate deformations were simulated for morphology parametric studies. Results show that changing the mouth-tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. This study demonstrates that the hybrid adjoint-CFD approach can be a practical and efficient method to investigate morphology-associated variability in inhalation dosimetry of vapors and nanomedicines.

Software quantum simulators are the most accessible tools for designing and testing quantum algorithms. This paper presents a comprehensive approach to building a software-based quantum simulator based on classical computing architectures. We explore fundamental quantum computing concepts, including state vector representations, quantum gates, and memory management techniques. The simulator prototype implements various memory optimization strategies, such as full-state representation, dynamic state pruning, and shared memory parallelization with OpenMP and distributed memory models using MPI. Additionally, data compression techniques, like ZFP, are explored to enhance simulation performance by reducing memory footprint. The results are validated through performance comparisons with leading open-source quantum simulators, such as Intel-QS, QuEST, and qsim. Our findings highlight the trade-offs between computational overhead and memory efficiency. This demonstrates that a hybrid approach using distributed memory and compression offers the best scalability for simulating large quantum systems. This work provides a foundation for developing efficient quantum simulators supporting more complex quantum algorithms on classical hardware.

This study examines whether gas is emitted from the materials used in the fabrication of metal vacuum panels or not and if emitted, their degree as time goes by. As experimental materials, metal sheets, foamed concrete as a core material, and polymer materials as a sealing material between metal sheets were selected. Experiments on the type and the degree of bending of metal materials showed that aluminum’s vacuum reaching time of 0.001 torr was at least 40 sec to 90 sec in its flat plate, but its vacuum reaching time increased from 3 times to 4.5 times in case of 90 ° and 135 ° bending state. For this reason, it is judged that stainless steel or steel material is suitable because aluminum is inadequate in terms of processability at the time of fabricating the metal vacuum panel. Also, vacuum arrival times and weight changes with increasing foam content of inorganic foamed concrete increased from 22,000 sec to 42,000 sec with increasing foaming rate and also, the weight change increased from 1.7% to 8%. Also, the experimental results on the type of honeycomb materials, the PE (polyethylene) with a vacuum reaching time of 30,000 sec and with a weight change of 0.5% and the PTFE (Poly-tetrafluoro ethylene) with a vacuum reaching time of 29,000 sec and with a weight change of 2.2% showed the optimum value.

Product innovation is a crucial factor in enterprise survival. Even though there are sources from strategic theory that guides the clear comprehension towards appreciating the nexus between these two variables (product innovation and enterprise survival), there are still many lacunas that should be addressed and filled. Consequently, the need for additional empirical corroboration or support is pertinent. This study aimed at verifying the nexus between product innovation and enterprise survival, and how they are affected by the existence of antecedent variables such as competitive intensity and competitive advantage. In the methodology, this study adopts the conduct of explanatory and cross-sectional investigations through the use of structural equation modelling (SEM) to a sample of selected food and beverages enterprises in Lagos, Nigeria. Regarding the food and beverage enterprises in Lagos, Nigeria, this study discovered that competitive intensity has huge positive implication on product innovation at (0.39; t = 5.69, p < 0.05). This gives numerical evidence that, in the face of more market competition, enterprises will be pressured to adopt the model of costs reduction on products which will enhance the reduction of product prices, and will have significant impact on profit. However, the findings reveal that there is no significance between competitive advantage and product innovation at (0.002; t = 0.203, p > 0.05), and there is no significance between product innovation and enterprise survival at (-0.035; t = -1.583, p > .05). As a result, the food and beverage enterprises should concentrate more on product innovation so that they will be able to stand the intensity of competition. The results emanated from the study is germane as it make significant contribution to literature and the body of knowledge and on strategic management by enlightening that competitive intensity is a necessary inducement for product innovation.

This work presents the results of optimizing the physicochemical properties of composite briquettes based on microsilica for metallurgical-grade silicon production. Microsilica, a waste product of silicon production, contains a sufficiently high amount of silicon dioxide (96-97%). This fact makes microsilica a promising composite material for metallurgical processing. However, its granulometric composition limits its direct use. The method of briquetting on an industrial briquetting press was used. The influence of the granulometric composition of the composite mixture on the heat tolerance and strength of the finished briquettes was studied. The methods of obtaining and testing the briquettes, their composition, and the briquetting and drying process are described. The obtained briquettes were tested for the smelting of metallurgical silicon. The silicon recovery rate in the metal with the standard charge was 71%, while the recovery rate with the briquetted monocharge was 85%. The results of silicon smelting from the briquettes demonstrated the high quality of the obtained metal, corresponding to the standards of metallurgical-grade silicon. The implementation of these technologies not only improves the quality of the final product but also addresses environmental issues related to the disposal of microsilica, reducing its accumulation and minimizing its impact on the environment.

High temperature Ni-superalloys are limited by reliance on critical elements and complex processing steps. High entropy alloys (HEAs) have demonstrated stable single/dual phase microstructures with strong ordering tendencies. A dual phase (FCC+BCC) Al0.5Co0.5CrFeNi1.5Ti0.25 alloy (developed using CALPHAD) was tested for room and high temperature compression behavior. The microstructure mainly comprises of coarse ordered FCC(L12) surrounding decomposed lamellae of ordered FCC(L12) and BCC(L21) phases, which coarsened upon annealing at high temperature (1100 °C) into a bi-continuous structure of disordered FCC+BCC(B2). Additionally, cold rollability of the cast alloy increased from &lt;5% to ~40% after annealing, with a corresponding softening observed in room temperature compression mainly due to disordering of FCC and BCC phases. Peak flow stresses of 793.45MPa, 535.12MPa, and 324.40MPa were achieved at 800oC, 900oC, and 1000oC respectively for the high temperature annealed alloy.

The leather industry is one of the most polluting industries in the world due to the large amounts of waste following the raw hides processing, but also due to the high content of chemicals present in their composition. The main problem of chromium-tanned leather solid waste is related to their storage, because of the ability of chromium to leach in soil or water, and also owing to the high ability of trivalent chromium to oxidize to its toxic form, hexavalent chromium. The purpose of this study is to present the latest methods for extracting trivalent chromium from the composition of solid leather wastes. The extraction methods identified in the present study are based on acid/basic/enzymatic hydrolysis and substitution with the help of organic chelators (organic acids and organic acid salts). In addition, the study includes a comparative analysis between the advantages and disadvantages of each identified extraction method. At the same time, the study also presentes alternative chromium extraction methods based on the combination of conventional extraction methods and ultrasound-assisted extraction.

Rice (Oryza sativa L.) is a primary energy food for Asian population. One of the most constraints in rice production is soil salinity because of the rice is very susceptible to salt. Meanwhile, many agricultural land in Asia are in the saline area. It is important to identify and develop salt-tolerant rice varieties that can adapt in Asian climate. By combining morphological, physiological, and biochemical assessments for screening the salt tolerant of 116 Asian rice cultivars was able to classify into tolerant, moderate, and sensitive rice cultivars under salinity stress condition, and also for understanding the salt tolerance mechanisms. The rice cultivars which belong to salt-tolerant including Pokkali from India, TCCP 266 from Philippines, IR 45427 also from Philippines, and Namyang 7 from Korea. Whereas, salt-sensitive rice varieties like IR29 from Philippines, IR58 also from Philippines, Daegudo from Korea, and Guweoldo also from Korea. The salt-tolerant varieties showed signs of tolerance, including lower percent reduction of percentage germination, root length, root fresh weight, shoot length, plant biomass, and chlorophyll content. In order to maintain the cellular osmotic balance under saline condition, the salt-tolerant ones exhibited less membrane damage, lower Na/K ratio, high proline and sugar accumulation, lower malondialdehyde (MDA) and hydrogen peroxide (H2O2). Pokkali from India, TCCP 266 from Philippines, IR 45427 also from Philippines, and Namyang 7 from Korea are recommended as valuable germplasm resource for Asian rice breeding program in saline agricultural area.

To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

The infrared (IR) satellite remote sensing of sea surface skin temperature (SSTskin) is challenging in the northern high latitude region, especially in the Arctic because of its extreme environmental conditions, and thus the accuracy of SSTskin retrievals is questionable. Several Saildrone uncrewed surface vehicles were deployed at the Pacific side of the Arctic in 2019, and two of them, SD-1036 and SD-1037, were equipped with a pair of IR pyrometers on the deck whose measurements have been shown to be useful in the derivation of SSTskin with sufficient accuracy for scientific applications, providing an opportunity to validate satellite SSTskin retrievals. This study aims to assess the accuracy of MODIS-retrieved SSTskin from both Aqua and Terra satellites by comparisons with collocated Saildrone-derived SSTskin data. The mean differences in SSTskin from the SD-1036 and SD-1037 measurements are different by ~0.4 K, largely resulting from differences in the atmospheric conditions experienced by the two Saildrones. The performance of MODIS on Aqua and Terra in retrieving SSTskin is comparable. Negative brightness temperature (BT) differences between 11 μm and 12 μm channels are identified as being physically based, but are removed from the analyses as they present anomalous conditions for which the atmospheric correction algorithm is not suited. Overall, the MODIS SSTskin retrievals show negative mean biases, -0.234 K for Aqua and -0.295 K for Terra. The variations in the retrieval inaccuracies show an association with diurnal warming events in the upper ocean from long periods of sunlight in the Arctic. Also contributing to inaccuracies in the retrieval is the surface emissivity effect in BT differences characterized by the Emissivity-introduced BT difference (EΔBT) index. This study demonstrates the characteristics of MODIS-retrieved SSTskin in the Arctic, at least at the Pacific side, and underscores that more in situ SSTskin data at high latitudes are needed for further error identification and algorithm development of IR SSTskin.

Due to global climate change, cold temperatures have significantly impacted rice production, resulting in reduced yield and quality. In this study, we investigated two traits related to cold tolerance (CT) of 1992 diverse rice accessions at the seedling stage. Geng accessions exhibited higher levels of CT compared to xian accessions, with the GJ-tmp subgroup displaying the strongest CT. However, extreme CT accessions were also identified within the xian subspecies. Through GWAS analysis based on survival rate (SR) and leaf score of cold tolerance (SCT), a total of 30 QTLs associated with CT at the seedling stage were identified, among which four QTLs (qSR3.1a, qSR4.1a, qSR11.1x and qSR12.1a) were found to be important. Furthermore, five candidate genes (LOC_Os03g44760, LOC_Os04g06900, LOC_Os04g07260, LOC_Os11g40610 and LOC_Os12g10710) along with their favorable haplotypes were identified through gene function annotation and haplotype analysis. Pyramiding multiple favorable haplotypes resulted in a significant improvement in CT performance. Subsequently, three selected accessions (CX534, B236, and IRIS_313-8565), carrying different superior alleles for CT, were selected and recommended for molecular breeding for CT using marker-assisted selection (MAS). The findings from this study provide valuable resources for enhancing rice's ability for CT while laying a foundation for future cloning of novel genes involved in conferring CT.

The utilization of artificial intelligence in education can contribute to sustainable development goals in various ways. AI in education can effectively convey sustainability issues to students. Through simulations and virtual environments, learners can experience sustainable practices firsthand. On the other hand artificial intelligence can assist administrators and educators in making data-informed decisions. This allows for more effective shaping of educational policies. This study explores how teachers and artificial intelligence (AI) address ethical dilemmas in edu-cation, comparing their decisions and examining the influence of variables such as gender, years of service, and education level. A total of 141 teachers from public schools in Turkey participated, and their responses were compared to AI-generated decisions using Yin's nested multiple-case de-sign. The scenarios were analyzed through five ethical frameworks: virtue ethics, deontological ethics, utilitarianism, social justice ethics, and situation ethics. Results showed that AI provided the same answer as the majority of teachers in six out of eight dilemmas but differed in two. AI favored a more analytical, result-oriented approach, while teachers emphasized empathy and rule adherence. Gender differences revealed that males leaned towards utilitarianism, while females favored social justice and situation ethics. Less experienced teachers preferred deontological and social justice ethics, while more experienced teachers leaned towards virtue ethics and utilitarian-ism. Elementary teachers emphasized virtue and social justice ethics, while middle and high school teachers favored deontological ethics and utilitarianism. The findings provide insights into ethical decision-making in education by highlighting the differences between solutions proposed by artificial intelligence and those offered by teachers. Additionally, they emphasize the im-portance of adapting artificial intelligence to everyday life to foster a sustainable world.

The global steel industry is a major contributor to greenhouse gas emissions, and the adoption of green hydrogen in Direct Reduced Iron (DRI) production has the potential to significantly reduce CO2 emissions and support climate targets. This review explores the environmental, economic, technological, and social implications of using green hydrogen in DRI processes. The findings suggest that green hydrogen can reduce emissions by up to 90% compared to traditional methods, enhance energy security, and stimulate local economic development. However, high initial costs, energy efficiency concerns, and social acceptance issues remain significant barriers. The review also highlights the importance of integrating carbon capture and storage technologies, addressing logistical challenges, and considering the geopolitical implications of a shift towards green hydrogen. Green hydrogen presents a promising solution for decarbonising the steel industry but continued research, investment in infrastructure, and strategic planning are essential to overcome the challenges and drive widespread adoption.

This study investigated the magnitude and direction of inter-limb asymmetry in 21 professional female sepak takraw players across several task-specific tests. Five inter-limb asymmetry assessments were employed: unilateral countermovement jump (Uni-CMJ), bilateral countermovement jump (Bi-CMJ), single leg hop (SLH), triple hop test (THOP), and isokinetic concentric peak torque of the knee flexors and extensors at 60 deg/sec-1, 120 deg/sec-1 and 180 deg/sec-1 angular velocities. A “true” inter-limb asymmetry was only observed for Uni-CMJ jump height (16.62%) and THOP distance (6.09%). Kappa coefficients demonstrated fair agreement in the direction of asymmetry between the Uni-CMJ and Bi-CMJ tests for jump height (Kappa = 26.67), but only slight agreement for peak force (Kappa = 0.11), propulsive impulse (Kappa = -0.12) and eccentric impulse (Kappa = -0.14), respectively. Fair agreement was observed between the SLH and THOP (Kappa = 0.32). Slight to moderate agreement was found for concentric peak torque across angular velocities for the knee extensors (Kappa = 0.08 to 0.48) while fair to nearly perfect agreement was noted for the knee flexors (Kappa = 0.31 to 1). The Uni-CMJ and THOP are most sensitive to detect between-limb asymmetries in female sepak takraw players. However, given the inconsistency in asymmetry direction across tests, monitoring asymmetry direction is crucial for conditioning.

Aggressive angiomyxoma (AAM) is a rare and benign mesenchymal tumor, localized predominantly in the female pelvis. Its invasive growth and local recurrence are challenging for professionals. Histologically, AAM is a tumor with a myxoid, highly vascularized stroma. The diagnosis is made relatively late due to nonspecific symptoms. Treatment of AAM is extensive local surgical excision. Gonadotropin-releasing hormone agonists are added postoperatively. The prognosis is good and metastasis is rare. We present a case of an AAM located in the left greater labial area in a 50-year-old woman initially diagnosed as left inguinal hernia. The tumor was extirpated under epidural anaesthesia on the date of the patient's informed consent. The patient was prescribed postoperative therapy with Zoladex 3,6 mg according to the schedule. The patient was subjected to monthly gynecological check-ups for one year after the surgical intervention. To date, no recurrence of the underlying disease has been detected. Long-term results show a good trend. In conclusion, we can say that the optimal treatment of AAM is wide local excision. Рadicality is not recommended because of the risk of postoperative complications. Postoperative administration of GnRH-a is advisable to avoid recurrences. All patients undergo regular follow-up examinations for long-term.

Gout is at least 3 times more prevalent in men than in women. However, concurrent with rising total gout prevalence, complex factors, including comorbidities, diet and lifestyle, and aging, have promoted higher gout prevalence in females. This narrative review focuses on summarizing recent developments in the landscape of gout in women, and mechanisms involved. New knowledge on sex hormone effects on both urate-excreting and urate-reabsorbing transporters, and higher hypertension and chronic kidney disease prevalence in women compared to men, may help explain why gout incidence rises robustly after menopause in women to approach that in men. Racial and ethnic factors, risk profiles based on heritable genetic polymorphisms of urate transporters, and diet, body mass index, and lifestyle factors differ according to sex. In addition, sex differences in clinical phenotypes and outcomes of gout and non-gout illnesses include more frequent comorbidities, more pain and disability during gout flare, different perceptions of disease burden, and more frequent severe cutaneous hypersensitivity reaction to allopurinol in women. Significantly, women with gout also have poorer COVID-19 outcomes than do men. Collectively, such findings support the potential clinical benefits of tailoring gout and hyperuricemia treatment according to sex.

This case study features an observation of an obese female (body mass index ≥ 30 kgm-2) who undertook a 10-day water and electrolyte fast with the aim of reducing body fat and improving physiological markers of health. A comprehensive range of physiological measures (body mass, girths, blood pressure, body fluid balance, urine osmolarity, and resting oxygen consumption), mood indicators, metabolic markers (blood ketones, blood glucose, breath ketones, urine ketones), and measurement of intake and expenditure (fluid intake, food intake, nutritional macros, steps taken, and estimated movement caloric expenditure) were measured before, during and after the fasting period to determine the effectiveness of undertaking extended therapeutic fasting for improving markers of health and weight loss. Body mass on the first day of the fast was 95.1 kg and on the tenth and final day of the fast it was 88.1 kg representing a loss of 7 kg at a rate of 0.7 kg·day1. Mean body mass in the 15 days after the fast was 88.3 ± 0.4 kg. This case study is novel in reporting key physiological and psychological markers over a 10-day fast, and in pre- and post-fast periods. A 10-day fast appears to be a safe and effective method of weight loss, mood is positively affected by fasting and weight loss from fasting can be sustained providing care is taken in the approach to post-fasting nutrition.

Alcohol use disorder (AUD) is a chronically relapsing disease characterized by loss of control in seeking and consuming alcohol (ethanol) driven by recruitment of brain stress systems. However, AUD differs among the sexes: men are more likely to develop AUD, but women progress from casual to binge drinking and heavy alcohol use more quickly. The central amygdala (CeA) is a hub of stress and anxiety, with corticotropin releasing factor (CRF)-CRF1 receptor and GABAergic signaling dysregulation occurring in alcohol dependent male rodents. However, we recently showed that GABAergic synapses in female rats are less sensitive to the acute effects of ethanol. Here, we used patch clamp electrophysiology to examine the effects of alcohol dependence on the CRF-modulation of rat CeA GABAergic transmission of both sexes. We found that GABAergic synapses of naïve female rats were unresponsive to CRF application compared males, although alcohol dependence induced a similar CRF responsivity in both sexes. In situ hybridization revealed that females had less CeA neurons containing mRNA for the CRF1 receptor (Crhr1) than males, but in dependence, the percentage of Crhr1-expressing neurons in females increased, unlike males. Overall, our data provide evidence for sexually dimorphic CeA CRF system effects on GABAergic synapses in dependence.

Diabetics or Diabetic Mellitus is a metabolic disorder of blood sugar levels in the human body. It is a major non-communicable disease and involved many serious health risk issues. This disease is rapidly increasing in India. It is a chronic condition and it occurs when a body doesn't produce enough insulin hormone to control the blood sugar level. In this study, different variables have been analyzed that cause the diabetics, and different machine learning algorithms are used to predict whether an unknown sample is diabetes or not. For this purpose, PIMA diabetic detection for Female patients was used. Here 10 different classification model is used for prediction. Finally, the detailed performance analysis of the different variables of the PIMA dataset and also the classification model are discussed.

The world is moving to the next phase of the energy transition with high penetrations of renewable energy. Flexible and scalable redox flow battery (RFB) technology is expected to play an important role in ensuring electricity network security and reliability. Continuous performance improvements will further enhance their value by reducing parasitic losses and maximizing available energy conversion over broader operating conditions. Concentration overpotentials from poor internal reactant distribution at high and low states of charge (SOC) limit power densities and are thus an important area of investigation. However, efforts to address these coupled electrochemical phenomena can compromise mechanical performance. Modelling and simulation of cell design innovations have shown it is possible to reduce losses from pump energy while increasing the availability of active species where required. The combination of wedge-shaped cells with static mixers investigated in this paper can reduce pressure drop and improve energy efficiency. Toroidal vanadium redox flow battery (VRB/VRFB) designs incorporating this innovation are presented for further development to improve community engagement with the technology.

The Global Carbon Budget is the cumulative carbon emissions that human activities can generate while limiting the global temperature increase to less than 2°C. On this basis, most countries ratified the Paris Agreement 2015, pledging to reduce national emissions and the impacts of climate change. The European Union has planned to reduce emissions by 80% of their 1990 value by 2050 but such a target needs to be coupled with a further constraint on the cumulative greenhouse gases released along the path to 2050. The aim and the novelty of this study are to propose, for the first time, a carbon budget for the European Union, which represents the most significant physical characteristic to assess the feasibility of current EU-28 greenhouse gas reduction objectives under the goals of the 2015 Paris treaty

Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.

Aim: To analyze a single center's experience with Short Bowel Syndrome/ Chronic Intestinal Failure (SBS/CIF) in adult patients treated with Home Parenteral Nutrition (HPN). Materials and methods: In the last thirty years, 13 patients with SBS/CIF were included in an HPN program. Results: Between 1992 and 2023, 13 patients were included in an HPN program. The primary underlying pathology was acute mesenteric ischemia. The median age of the starting HPN was 44 years old. Most are submitted to several surgeries of extensive intestinal resection with posterior intestinal reconstruction. Five of the 13 patients have died while on HPN for a median of 42 months. The causes of death HPN-related were catheter sepsis, endocarditis with cardiac failure, or hepatic failure. One patient died due to underlying pathology: pelvic abscesses and bleeding related to radiotherapy. Eight patients remain alive with a median time of HPN of 173 months. During the HPN support, the most frequent complications were venous catheter infection and venous territory thrombosis. None of the eight patients alive have hepatic failure. Two patients recently started teduglutide, with good tolerance, and need reduction of HPN support. All these eight patients have a satisfactory quality of life (parenteral support needs range between five to two nutrition bags per week). Conclusion: HPN remains the gold standard of SBS/CIF treatment, although Teduglutide may reduce HPN needs and HPN complications and provide a better quality of life. An HPN multidisciplinary healthcare group support is essential to ensure these patients' survival and quality of life. Keywords Short bowel Syndrome, Chronic Intestinal failure, Home Parenteral Nutrition, Teduglutide, Complications of Parenteral Support.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

The AUC-ROC is a widespread measure to assess the performance of a binary classification model across various discrimination thresholds. Yet, it is not apt for unbalanced class problems and is misleading for early retrieval. For instance, link prediction in complex networks is an unbalanced early retrieval problem, whose goal is to offer a ranking of the nonobserved network links, prioritizing a small cohort of positive links (for which we are sure the labels are reliable) on top of a list largely populated by unlabeled links (for which the labels are uncertain). Differently from binary classification, here the evaluation focuses on how the predictor prioritizes the positive class, because the negative class does not exist and is replaced by an unlabeled class. For decades, scholars have been investigating the necessity to correct and to generalize the AUC-ROC to work for both unbalanced classification and early retrieval evaluation, this is requested in many applied domains of computational science. Here we propose the area under the magnified ROC (AUC-mROC), a new measure that adjusts the standard AUC-ROC to work for unbalanced early retrieval problems such as link prediction. Finally, we introduce the area under the generalized ROC (AUC-gROC), which unifies both AUC-mROC and AUC-ROC to provide a universal and reliable evaluation measure that can work for both classification and early retrieval problems, regardless of class proportion.This discovery represents a critical achievement, culminating in success after decades of contentious research. Its solution promises to significantly impact various domains within applied computational science.

Based on a literature review, a framework for digital organizational culture with a focus on innovation is proposed. This digital organizational culture becomes relevant today since digital transformation requires an appetite for risk and a willingness to undertake, aspects that are strengthened by an environment and atmosphere that motivate individuals to generate new ideas. The proposal begins with a diagnosis of the existing organizational culture, which allows identifying the differences between the components of the current organizational culture and the innovation-oriented digital organizational culture. Through this diagnosis, the gaps that need to be addressed to achieve a digital organizational culture with innovative characteristics are identified. In addition, specific recommendations are proposed to carry out this transformation. In the process, the assumptions underlying the current typology of the company's organizational culture are highlighted. The importance of integrating creativity, risk readiness, and entrepreneurial willingness into the digital organizational culture aimed at innovation is emphasized. These elements are essential to create an environment

This study proposes a structure for companies to use when implementing human resource practices in open innovation. Despite the fact that open innovation has received a lot of attention in the innovation management field as companies open their doors to information exchange in an effort to spur creative thinking, there are very few empirical articles that connect this trend to the human resource management literature. Our findings are the result of an extensive qualitative investigation into Julphar Gulf Pharmaceutical Industries Manufacturers in the United Arab Emirates (UAE) and its open innovation program. Internal, external, and combined are the three primary pillars of human resource management. We also demonstrate how the evolution of the open innovation initiative is linked to the state of the art in HRM and open innovation literature. The framework identifies HRM practices for both internal and external participants in the open innovation effort. Much of this HRM is done off the books, in a setting separate from the host company. By providing actual evidence of how firms use HRM to manage open innovation projects, our research adds to the scant and mostly theoretical literature linking open innovation and HRM.

Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from January 1, 2013, to December 22, 2023. Articles were collected using key search terms such as “artificial intelligence” and “machine learning.” Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. 8 studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.

This study aims to investigate the influence of various economic, environmental, and social factors on sustainable development, with a specific focus on the impact of green technology and climate change adaptation. A panel dataset comprising 38 OECD member countries from 1990 to 2020 is employed, and a series of dynamic panel data models are estimated using the system generalized method of moments (GMM) approach. Numerous covariates, such as globalization, socio-economic conditions, economic and political values, climate and technological progress, and environmental determinants, are examined. The empirical findings highlight the significant role played by macroeconomic, institutional, social, and government policy-related factors in sustainable development. More importantly, our results provide novel and robust evidence that the diffusion of green technology and climate change adaptation positively affect green growth. The study's empirical outcomes are demonstrated to be robust to misspecification errors and statistically significant at traditional significance levels. These findings carry substantial policy implications for the development and implementation of strategies that promote climate change adaptation and green innovation. Consequently, policymakers should prioritize the integration of green technology and adaptive measures in their sustainable development agendas to foster a greener, more resilient future.

Modern organic synthesis is majorly focused on developing environmentally benign synthetic protocols by employing green chemistry principles. Accordingly, in our recent research work, we herein report the use of modified supramolecular host cyclodextrin as an effective solid based green catalyst for accessing structurally diverse and medicinally relevant pyrimidone architectures. The catalyst and the synthesized compounds 4 (a-r) were characterized using FT-IR, NMR and GC-Mass spectroscopy. Major highlights of the reported work include: atom economical process, extremely milder reaction conditions, operational simplicity, high isolated yields, and excellent catalyst turnover number. The molecular docking studies suggest that the compound 4n has the hydrogen bonding, hydrophobic and π-pair interactions with the active site of active sites of CXT M 15 receptor.

With LiDAR (Light Detection and Ranging) time series being used for various applications, optimal realisation of a common geodetic datum over many epochs is a highly important prerequisite with direct impact on accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches ranging from pragmatic to more rigorous solutions are applied to an 8 year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, we obtain relative datum discrepancies in the range of 1-2 cm for the complete time series. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method.

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

Hydrogen is the lightest atom and composes roughly half of the atomic content in macromolecules, yet their location can only be inferred or predicted in most macromolecular structures. This is because hydrogen can rarely be directly observed by the most common structure determination techniques (such as X-ray crystallography and electron cryo-microscopy). However, knowledge of hydrogen atom positions, especially for enzymes, can reveal protonation states of titratable active site residues, hydrogen bonding patterns, and the orientation of water molecules. Though we know they are present, this vital layer of information, which can inform a myriad of biological processes, is frustratingly invisible to us. The good news is that, even at modest resolution, neutron crystallography (NC) can reveal this layer and has emerged this century as a powerful tool for elucidating enzyme catalytic mechanisms. Due to its strong and coherent scattering of neutrons, incorporation of deuterium into the protein crystal amplifies the power of NC. This is especially true when solvation and the specific participation of key water molecules are crucial for catalysis. Neutron data allow the modeling of all three atoms in water molecules and have even revealed previously unobserved and unique species such as hydronium (D3O+) and deuteroxide (OD-) ions as well as lone deuterons (D+). Herein, we briefly review why neutrons are ideal probes for identifying catalytically important water molecules and these unique water-like species, limitations in interpretation, and four vignettes of enzyme success stories from disparate research groups. One of these groups was that of Dr. Chris G. Dealwis, who died unexpectedly in 2022. As a memorial appreciation of his scientific career, we will also highlight his interest and contributions to the neutron crystallography field. As both the authors were mentored by Chris, we feel we have a unique perspective on his love of molecular structure and admiration for neutrons as a tool to query those structures.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

Cleomaceae is an important group that is medicinally and economically important. However, its systematic position has been controversial. In this study, we have obtained three chloroplast genomes sequencing results of Cleomaceae species. The genome sizes ranged from 157,073 bp (Sieruela rutidosperma) to 158,152 bp (Gynandropsis gynandra). These chloroplast genomes have a typical quadripartite formation with a large single-copy (LSC) region (86,422-87,242bp), a small single-copy (SSC) region (18,485-18,548bp), and a pair of inverted repeat (IR) regions (26,083-26,184bp). The chloroplast genomes encoded 131-132 genes, comprising 8 rRNA genes, 37 tRNA genes, and 79-84 protein-coding genes. The overall GC contents of the three chloroplast genomes are almost identical (35.81%-36.02%). The phylogenetic trees concluding 8 Cleomaceae plants were constructed based on ML and BI methods. The results showed that Gynandropsis gynandra and Sieruela rutidosperma were in the same branch, with Corynandra viscosa as their sister. Six hypervariable areas were identified, containing clpP, matK, ndhF, atpH-atpI, atpF-atpH and petN-psbM which could be used in species identification and population genetics studies. This study provides valuable information for further species identification, evolutionary and genetic studies of Cleomaceae in the future.

In recent years, with the development of the Internet of Things and distributed computing, the "server-edge device" architecture has been widely deployed. This study focuses on leveraging autoencoder technology to address the binary classification problem in network intrusion detection, aiming to develop a lightweight model suitable for edge devices. Traditional intrusion detection models face two main challenges when directly ported to edge devices: inadequate computational resources to support large-scale models and the need to improve the accuracy of simpler models. To tackle these issues, this research utilizes Extreme Learning Machine for its efficient training speed and compact model size to implement autoencoders. Two improvements over the latest related work are proposed: First, to improve data purity and ultimately enhance detection performance, this study partitions the data into multiple regions based on the prediction results of these autoencoders. Second, leveraging autoencoder characteristics to investigate further the data within each region. We used the public dataset NSL-KDD to test the behavior of the proposed mechanism. The experimental results show that when dealing with multi-class attacks, the model's performance was significantly improved, the accuracy and the F1-score are improved by 3.5% and 2.9%, respectively maintaining its lightweight nature.

The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are crucial for disease management and understanding viral evolution. In this study, a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data is presented. By integrating state-of-the-art bioinformatics tools with artificial intelligence, the pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity. Before aligning the reads to the reference genomes, quality control measures and adapter trimming are performed to ensure the integrity of the data. Unmapped reads are subjected to de novo assembly to reveal novel viral sequences and genetic elements. The effectiveness of the pipeline is demonstrated by the identification of virus sequences, illustrating its potential for the detection of known and emerging pathogens. SNP discovery is performed using a custom Python script that compares the entire population of sequenced viral reads to a reference genome. This approach provides a comprehensive overview of viral genetic diversity and identifies dominant variants and a spectrum of genetic variations. The robustness of the pipeline is confirmed by the recovery of complete viral sequences, which improves our understanding of viral genomics. This study highlights the synergy between traditional bioinformatics techniques and modern approaches, providing a robust tool for analyzing viral genomes and contributing to the broader field of viral genomics.

This study introduces an innovative seeding method for cells on large scaffolds (1-100 sqcm) with an accuracy of 0.1mm, comparing the conventional drop-wise seeding technique with an automated seeding approach using an open sourced robotic liquid handling system (OpenLH). This automated system employs an algorithm in which the user inputs the desired droplet volume and seeding pattern dimensions such in number of rows, columns and distance between droplets, thus optimizing the distribution of cells on the scaffold. By adjusting these parameters, the method achieves greater accuracy and consistency over manual seeding and requires less operation space volume than dynamic seeding. Additionally, the system’s tip replacement feature can support the precise seeding of multiple cell types on a single scaffold and the dispensing of growth factors to foster patterns of cell differentiation and proliferation on the large 3D scaffold. This seeding method not only enhances the precision and efficiency of cell distribution on scaffolds but also shows significant potential for scalability in the cultured meat industry, where large scaffolds are essential. The adoption of such automated systems could promote efficient scaffold-based tissue engineering by promoting uniform cell distribution and enabling the creation of complex tissue constructs, thereby advancing both biomedical research and cultured meat production.

A hemostatic sponge prototype was successfully synthesized from fish gelatin as an alternative to mammalian gelatin, which was mixed with alginate in certain combinations and double cross-linking of calcium ions and gamma irradiation at a dose of 20 kGy to improve the characteristics and effectiveness of its function as a local hemostatic agent. There was an improvement in the physicochemical and mechanical properties, porosity index, absorption capacity, biodegradation properties, biocompatibility, and hemocompatibility of the FGAS prototypes compared with the pure fish gelatin sponge. Hemostatic activity tests showed that the means of clotting time, prothrombin time, and activated partial thromboplastin time were shorter in the FGAS prototype than in the negative control, and there was no significant difference compared with the commercial gelatin sponge. The hemostatic mechanism of the FGAS prototype combined a passive mechanism as a concentrator factor and an active mechanism through the release of calcium ions as a coagulation factor in the coagulation cascade process.

COVID-19, caused by SARS-CoV-2, was first identified in China in 2019. The exact origin of the disease remains uncertain, with theories suggesting it may have emerged from animals or accidentally leaked from a research laboratory. If the hypothesis that COVID-19 originated from a research laboratory is accurate, it is crucial to investigate potential pathways for pathogens to escape from biocontainment research animal facilities (BRAFs). We examine key biosafety issues associated with BRAFs, including inadequate decontamination procedures for water and experimental samples, handling high biosafety level pathogens in lower level laboratories, risks of animal bites and sharps injuries, contamination of bedding and enrichment materials, and improper management and transportation of biohazard samples. Additionally, we discuss the role of veterinarians in African research animal facilities and the challenges they encounter in maintaining biocontainment standards. We emphasize the importance of routine monitoring of effluent water to detect possible disease outbreaks. We recommend a thorough investigation of the COVID-19 pandemic to identify potential sources of pathogen release from BRAFs, which could serve as hotspots for future disease outbreaks. Findings from such investigations will inform the development of policies aimed at safeguarding human populations from future pandemics and preventing BRAFs from becoming sources of infectious disease outbreaks.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly within social media platforms like X (formerly Twitter) and in addressing cyberbullying present on them. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of Transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Leveraging state-of-the-art LLMs such as ChatGPT-4o, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and quantized for multilabel classification (detecting both biases and cyberbullying). The study's outcomes include a prototype of a hybrid application that combines a Bias Data Detector and a Bias Data Generator, validated through extensive testing.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Species of the genus Drosophila have served as favorite models in speciation studies; however, genetic factors of interspecific reproductive incompatibility are underinvestigated. Here we performed the analysis of hybrid female sterility by crossing Drosophila melanogaster females and Drosophila simulans males. Using transcriptomic data analysis, molecular, cellular, and genetic approaches, we analyzed differential gene expression, transposable element (TE) activity, piRNA biogenesis, and functional defects of oogenesis in hybrids. Premature germline stem cell loss was the most prominent defect of oogenesis in hybrid ovaries. Due to the differential expression of genes encoding piRNA pathway components, rhino and deadlock, the functional RDCmel complex in hybrid ovaries was not assembled. However, the activity of the RDCsim complex was maintained in hybrids, independent of the genomic origin of piRNA clusters. Despite the identification of a cohort of overexpressed TEs in hybrid ovaries, we found no evidence that their activity can be considered the main cause of hybrid sterility. We revealed a complicated pattern of Vasa protein expression in the hybrid germline, including partial AT-chX piRNA targeting of the vasasim allele and a significant zygotic delay in vasamel expression. We arrived at the conclusion that the hybrid sterility phenotype was caused by intricate multi-locus differences between the species.

Introduction: Breast cancer affects worldwide almost 1.5 million women below the age of 45 years each year. Many of these women will be advised to undergo adjuvant chemotherapy to minimize the risk of death or recurrence of the tumor. For these patients, chemotherapy is a known cause of infertility, as it can damage primordial follicles which can lead to early menopause or premature ovarian insufficiency. This systematic review aims to synthesize the current evidence of the most suitable treatments for fertility preservation. Methodology: This review is performed following the PRISMA guidelines. The authors conducted an extensive search from the last 15 years. Relevant studies were pursued in PubMed, Embase and the Cochrane Library up until the July 31, 2023. A total of 7 eligible studies were identified. Discussion: The most suitable treatments for fertility preservation in young patients, is the temporary suppression with luteinizing hormone-releasing analogs, while the patient undergoes chemotherapy and cryopreservation. For cryopreservation, the physicians might deem it necessary to either cryopreserve ovarian tissue taken from the patient before any treatment or cryopreserve embryos/oocytes. Cryopreservation of oocytes and/or embryos is the most effective solution for fertility preservation in women of reproductive age, who have sufficient ovarian reserve and are diagnosed with breast cancer, regardless of the histological type of the tumor. Since approximately 50% of young breast cancer patients are interested in becoming pregnant right after completion of therapy, the evolution and development of fertility preservation techniques promise to be very exciting.

Background. TRPA1-related drugs alter sensation, particularly in conditions of inflammation. To further characterize the role of these drugs in bladder sensation, the TRPA1 agonist cinnamaldehyde (CMA) and oral true cinnamon spice were examined in preclinical models of bladder pain. Methods. Female, adult rats with and without acute zymosan-induced cystitis, were anesthetized and visceromotor (VMR) and cystometric responses to urinary bladder distension (UBD) determined following either the intravesical administration of CMA/vehicle solutions or the oral administration of true cinnamon/vehicle. ELISA measures of bladder TRPA1 content were also determined. Results. Acute cystitis resulted in increases in bladder TRPA1 content and produced an increased vigor of the VMRs to UBD and a lowering of micturition volume thresholds for activation of a micturition response. Intravesical CMA produced a robust inhibition of VMRs to UBD in rats with cystitis, but not in those without. Micturition volume thresholds were lowered by CMA in rats without cystitis, but had no additional effect in rats with cystitis. Oral cinnamon also produced a robust inhibition of VMRs to UBD in rats with cystitis and a mild augmentation of VMRs to UBD in rats without cystitis. Conclusions. A potentially analgesic effect of the spice, true cinnamon, in the treatment of the pain of acute cystitis was suggested by these preclinical studies. Human studies are indicated.

Mastectomy and hysterectomy with bilateral salpingo-oophorectomy are the first steps in surgical female-to-male (FtM) gender affirmation. We aimed to critically review our experience with the combined approach of hysterectomy/salpingo-oophorectomy and bilateral mastectomy and to focus on intra- and postoperative complications. In a retrospective cohort study, 108 consecutive patients were included who underwent combined hysterectomy, bilateral salpingo-oophorectomy, and bilateral mastectomy in a single operating session, between November 1998 and December 2017. Main outcome measures were operating times and intra- and postoperative courses, including major and minor adverse events. Patients were 28.9 ± 6.7 years of age and revealed a mean BMI of 24.4 ± 4.1 kg/m2. The median total operating time for patients without additional procedures was 237.6 ± 110.3 minutes. Taking all intra- and postoperative complications together, there were eight (7.4%) and 21 (19.4%) patients with major and minor complications, respectively. The most frequent complication was breast hematoma (18/108, 16.7%). There was a completely uneventful intra- and postoperative course in 82 cases (75.9%). In conclusion, for FtM reassignment surgery, the combined hysterectomy / salpingo-oophorectomy and bilateral mastectomy in a single operating session seems to be feasible and safe.

The DEAD-box RNA helicase DDX3X is a multifunctional protein involved in RNA metabolism and stress responses. In this study, we investigated the role of RG/RGG motifs in the dynamic process of liquid-liquid phase separation (LLPS) of DDX3X using cell-free assays and explored their potential link to cancer development through bioinformatic analysis. Our results demonstrate that the number, location, and composition of RG/RGG motifs significantly influence the ability of DDX3X to undergo phase separation and form self-aggregates. Mutational analysis revealed that the spacing between RG/RGG motifs and the number of glycine residues within each motif are critical factors in determining the extent of phase separation. Bioinformatic analysis of cancer genomic datasets uncovered a significant enrichment of DDX3X mutations in RG/RGG motifs across multiple cancer types, particularly in the N-terminal region of the protein. Furthermore, we found that DDX3X is co-expressed with the stress granule protein G3BP1 in several cancer types and can undergo co-phase separation with G3BP1 in a cell-free system, suggesting a potential functional interaction between these proteins in phase-separated structures. DDX3X and G3BP1 may interact through their RG/RGG domains and subsequently exert important cellular functions under stress situation. Collectively, our findings provide novel insights into the role of RG/RGG motifs in modulating DDX3X phase separation and their potential contribution to cancer pathogenesis.

Green investments help to create less harmful alternatives and adequate funds that contribute to economic growth, sustainable development, and social well-being. The applied methods are empirical and analytical based on the study of literature, multi-criteria modelling, determination of weights, and ranking of criteria in deciding green investments mapping of indicators, and monitoring of progress towards green growth through official OECD data. The analysis uses official sources on indicators related to the green economy and the environment. Multi-criteria decision-models (MCDM) were used to select adequate green investments, where instead of indicators from the official OECD-database, investors use the areas in which the indicators are distributed. According to the findings of AHP, half of the investments in the green economy come from public sources (0.51), and the other half are private (0.25), and institutional investors (0.24). While, BWM reveals that the best criterion for the decision to invest in the green economy is the environmental and resource productivity of the economy, and the worst is the base of natural assets. This paper aims to enable decision-makers to use these results as weights for the overall assessment of green investments in ESG and to simplify the decision-making approach in future analyses.

Atopic dermatitis (AD) has been extensively investigated for genetic associations utilizing both candidate gene approaches and genome-wide scans. Here, we comprehensively evaluated the available literature to determine the association of candidate genes in AD to gain additional insight in the etiopathogenesis of the disease. We systematically screened all studies that explored the association between polymorphisms and AD risks in cases of European and Asian ancestry and synthesized the available evidence through random-effects meta-analysis. We identified 97 studies that met our inclusion/exclusion criteria that examined 17 candidate loci in Europeans and 14 candidate genes in Asians. We confirmed the significant associations between FLG variants in both European and Asian populations and AD risk, while additional synthesis of available data revealed novel loci mapped to IL18 and TGFB1 genes in Europeans and IL12RB1 and MIF in Asians, that have not yet been identified by genome-wide association studies. Our findings provide comprehensive evidence for AD risk loci in cases of both European and Asian ancestries, validating previous associations as well as revealing novel loci that could imply previously unexplored biological pathways.

The reduction of biodiversity loss is one of the targets of the 2030 Agenda for Sustainable Development. The protection of endangered species is critical for conserving global biodiversity. Asian elephants，as one of the last few mega-herbivores on Earth, are currently threatened by climate changes and anthropogenic modifications. The modelling of their living habitats is of top priority to the conservation of Asian elephant. In this study, we used the maximum entropy model (MaxEnt) to identify the current and potential future habitats of Asian elephants in South and Southeast Asia. We performed analyses for future projections with 17 scenarios by using the present results as baseline. To optimise the modelling results, we delineated the core habitats by using the Core Mapper Tool and compared them with existing protected areas (PAs) through gap analysis. The results showed that the current total area of core habitats is 491,455 km2 in size and will be reduced to 332,544 km2 by 2090 under SSP585 (the shared socioeconomic pathway). The projection analysis under differential scenarios suggested that most of the core habitats in the current protected areas would remain stable and suitable for elephants in the future. However, the remaining 75.17% of the core habitats lay outside the current PAs, and finally we mapped approximately 219,545 km2 of suitable habitats as priority protected areas in the future. Although our model did not perform well in some regions, our analyses and findings still could provide useful references to the planning of protected areas and conservation of Asian elephant.

Preventing childhood obesity and associated comorbidities is often hampered by disproportionate disparity in healthcare provision in minority ethnic populations. This study contextualised lifestyle factors influencing childhood obesity and related comorbidity from the perspectives and experiences of parents of ethnic minority children with obesity comorbidity. Families of Black, Asian and Minority Ethnicities (BAME) in the Northeast of England (n=180) were contacted, and families with a child diagnosed with obesity and comorbidity (non-alcoholic fatty liver disease, musculoskeletal problems, respiratory disorder, or polycystic ovarian disease) were interviewed (n=11). Semi-structured interviews and qualitative thematic analysis included parents’ perspective and experience on how their children were impacted by obesity and comorbidities, healthcare preventative interventions including lifestyle physical activity and nutrition, and views on tackling obesity. Parents’ perspectives and experience revealed eleven themes centred around living with a child with obesity, risks and impact of obesity diseases; access to lifestyle nutritional and physical activity, and ethnicity specific barriers. Parents revealed social disadvantage, fear of victimisation by social services, perceptions on their cultural and religious traditions when dealing with nutrition and exercise, and child’s weight racial stigmatisation. Parents reported closer bonding with their children than health authorities to protect from obesity consequences. Work pressure, time, and lack of professionals’ guidance were seen as barriers to healthy lifestyle, while support from friends and family bond in adopting healthy lifestyle behaviours were facilitators. Parents reported no awareness of health service provision of nutritional and physical activity obesity prevention programmes. Minority ethnic communities’ perspective on childhood obesity prevention does not match the healthcare system preventative offerings. Community and family-oriented obesity preventative approaches, especially lifestyle nutrition and physical activity interventions are needed beyond those administered by the primary healthcare system.

To investigate the bioactivities of fresh garlic and its processed product, black garlic, we conducted comparative analyses of antioxidant, anti-inflammatory, innate immune activation and anti-cancer activities, in addition to chemical composition (sugar, amino acid, polyphenol contents) of these materials. Simultaneous assay using neutrophil-like cells showed that fresh garlic exhibited antioxidant and innate immunostimulatory activities, whereas black garlic displayed a potent anti-inflammatory effect. The antioxidant activity index was correlated with phenol and flavonoid contents, while the innate immunostimulatory activity was correlated with fructan content. Furthermore, some black garlics with low fructose content were found to inhibit the proliferation of UM-UC-3 cancer cells, while other black garlics rich in fructose increased UM-UC-3 cell proliferation. It was shown that the thermal processing steps of fresh garlic could change the composition of sugars, antioxidants, and amino acids, which have different effects on neutrophil-like cells and UM-UC-3 cells, as well as on bioactivities.

In the rapidly evolving field of data management, numerous terminologies, such as data warehouse, data lake, data lakehouse, and data mesh , have emerged, each representing a unique analytical data architecture. However, the distinctions and similarities among these paradigms often remain unclear. The present paper aimed to navigate the data architecture landscape by conducting a comparative analysis of these paradigms. The analysis a identified and elucidated the differences and similari- ties in features, capabilities, and limitations of these architectural constructs. The study outcome serves as a comprehensive guide, assisting practitioners in selecting the most suitable analytical data architecture for their specific applications.

This qualitative study explores the dynamics of Supplier Relationship Management (SRM) and its impact on innovation within the e-commerce sector. Through in-depth interviews and documentary analysis of three case study firms known for their innovative practices, the study examines how collaborative SRM practices influence innovation outcomes, organizational resilience, and competitive advantage in dynamic market environments. Key themes include the pivotal role of trust, transparency, and joint problem-solving in fostering effective SRM relationships, enabling knowledge sharing and co-development of innovative products and services. Innovation outcomes observed encompass product innovation, process improvements, and technological advancements facilitated by close collaboration between e-commerce firms and their suppliers. Challenges such as managing supplier diversity, navigating regulatory complexities, and mitigating geopolitical risks highlight the complexities and strategic imperatives of SRM implementation in global supply chains. The study also emphasizes the transformative role of digital technologies, including real-time data exchange, predictive analytics, and agile supply chain management, in enhancing SRM effectiveness and innovation capabilities. Strategic alignment with organizational goals and market strategies further enhances the impact of SRM, enabling firms to prioritize innovation initiatives, allocate resources effectively, and sustain growth amidst economic uncertainties. The findings contribute to theoretical understanding and practical insights into SRM strategies that promote innovation excellence and competitive positioning within e-commerce. The implications for practice underscore the importance of fostering collaborative supplier relationships, investing in digital transformation, and adopting proactive strategies to manage risks and regulatory compliance.

: The objective of this work is to elucidate the role of partnerships and the number of partners necessary for lead user innovation outcomes. Lead user theory identifies these users as being ahead of market trends and experiencing significant benefits from innovating. So far, most lead user research has concentrated on firms and not individual lead users’ perspective. Data were obtained using a quantitative design during which 321 respondents participated. The SPSS software was used to analyze the data and determine significance. These findings indicate a connection between number of partners and lead user innovation success. This demonstrates that number of partners is significant in determining the success or failure of an innovation. This study provides empirical evidence that the number of partners is an essential factor that influences the success of innovation, and establishes the absolute numbers for achievement of successful innovation. Future studies must investigate the possible implications of integrating the partner variables with other characteristics of the lead user.

From the merge of arts and crafts towards a practice for mass production of desirability, consumption and product development in a capitalist economy of scale, design has lost its natural ability of problem comprehension and tension alleviation. The modern world needs the creativity, flexibility, and responsiveness embedded into design practices, mostly when a behavioural change, either individual or organizational, is intended. Still, the informality nature of the field is creating a gap between the study, research, and industrial design practice. Here it is presented the Biology of Creativity Model (BoC) which is a design-by-analogy method that promotes an empowered design practice through analysis of mostly biology reference texts for enhanced creativity and innovation performance in a diverse array of contexts.

There have been relatively few researches that determine the role of bricolage in mediating the empirical relationship between strategic flexibility and product innovation. However, most of the research has studied strategic flexibility to engage the absorptive capacity and facilitate product innovation. The Bricolage approach emphasizes utilizing existing resources in product innovation and explores the mechanisms behind bricolage. A resource-based approach to product innovation involves combining resources in the development process in a concrete manner. This is a groundbreaking study since it is the first to examine empirically how absorptive capacity affects product innovation through bricolage and the serial mediation of strategic flexibility. As a consequence of presenting our results, we concluded that absorptive capacity positively and significantly influences product innovation through the serial mediation of strategic flexibility and bricolage. For strategic flexibility to play a role in supporting product innovation, bricolage is one of the mechanisms that can be utilized. This study contributes to the literature on strategic flexibility by examining the effect of strategic flexibility on bricolage and product innovation from the standpoint that absorptive capacity enhances the strategic flexibility of high-tech SMEs in China. Furthermore, this research offers new insights into the relationship between absorptive capacity and product innovation. In addition, it also provides insight into the economic opportunities that may result from product innovation in transition economies such as China.

This paper uses the spatial analysis software GeoDa as a tool, takes GRP (Gross regional product) of Sichuan Province in 2012 and 2018 as the dependent variable, and takes the city (autonomous prefecture) factor-driven, investment-driven and innovation-driven indicators as the dependent variable to explore the impact of innovation activities on regional economic development and the spatial distribution characteristics of regional economy. Through the comparison of the global correlation and local correlation, this paper explores the crux of the regional economic polarization and unbalanced development, and puts forward some measures to solve the existing economic development problems, such as cultivating and improving the regional industrial dependence, accelerating the regional transportation accessibility and convenience, and constructing the regional collaborative innovation system, So as to achieve the strategic goal of the construction of innovative Province in Sichuan Province.

Because of the significant enabling role smart meters can play in securing the transition towards sustainable energy distribution, the paper provides insights to support smart meters implementation projects. Energy utilities must propose adequate solutions to manage grid-upgrading projects and, in the meantime, increase efficiency levels. Based on empirical data analysis the paper provides insights aimed at maximize probability of success of smart meters projects. Results show common patterns of variables that may support project undertakers, policymakers and scholar when it comes to analyze projects to predict to maximize opportunities. For smart meters projects to succeed, regulatory stability is essential as long-period investments grids produce benefits for energy utilities, and for society.

Sustainable innovation in the agro-food system has become a strategy increasingly used by companies as a means to increase their competitiveness and position themselves in the market. In this context, the objective of this work is to identify the attitudes and sensory perceptions of consumers towards sustainable food technology through two scales (Food Technology Neophobia and Domain Specific Innovativeness) and hedonic tastings. For this, a new product was selected in the market: powder to prepare rice with milk. Most consumers have attitudes toward low neophobia to products with food technology, but there is also some caution, situation that is corroborated by the moderate predisposition towards innovations. Color and flavor attributes can make the difference in positive perceptions. It should not be forgotten that there is a segment of innovative sustainable consumers that represent a key market.

This study explores the industrial property market in Hong Kong, a sector characterized by its unique features and atypical market behaviour. Particularly, this research employs machine learning techniques to predict property prices based on a set of features, including location, square footage, floor level, accessibility to mass transit railway station and the like. To ensure transparency and understanding, the Shapley value is employed to quantify the relative importance of each feature in predicting property prices. Our analysis reveals the existence of non–linear relationships among these features, as demonstrated by the wide distribution of SHAP values for most features, which are illustrated in a beeswarm plot that span both sides of the baseline. This finding indicates a complex interaction among such features as square footage, age, floor level, carpark, proximity to mass transit railway stations, location, and property prices. The results contribute valuable insights into the relationships between industrial property characteristics and their corresponding values, thereby equipping stakeholders with enhanced understanding of the market to support informed decision–making.

Hydrogen is increasingly recognized as a crucial element in the shift toward sustainable energy systems due to its high energy density and minimal environmental impact. Biomass, a renewable material derived from plants and animals, presents a promising feedstock for hydrogen production, offering both environmental and economic benefits. This paper examines diverse biomass resources for hydrogen production, including agricultural and forest residues, dedicated energy crops, and organic waste. It reviews key technologies for converting biomass to hydrogen, categorized into thermochemical, biochemical, and hybrid processes. Thermochemical methods like gasification and pyrolysis are discussed for their ability to handle varied feedstocks and produce high-purity hydrogen. Biochemical processes, such as anaerobic digestion and dark fermentation, are analyzed for their efficiency and lower operational costs. Hybrid approaches, which integrate multiple technologies, are explored for their potential to enhance system performance. Industrial applications of biomass-derived hydrogen are highlighted, including its use in transportation, power generation, the chemical industry, and heating. Future perspectives emphasize the need for advanced catalysts, process integration, sustainable feedstock sourcing, and supportive policies to drive commercial viability. The paper concludes by highlighting biomass’s significant promise in advancing sustainable energy systems and achieving global energy security and environmental goals.

The characteristics of powders on a bulk scale are heavily influenced by both the material properties and the size of their primary particles. Throughout the stages of storage and transportation in the powder processing industry, various forms of deformation and stress, such as pressure and shear, impact these materials. Recognizing the point at which a powder undergoes yielding becomes particularly significant in numerous applications. There are also times when you need to understand the level of stress needed to maintain it. Measurement of powder yield and flow properties remains a challenge and is addressed in this study. As part of the CALIPER collaborative project, a number of shear experiments were performed using two shearing devices: Schulze ring shearing device and Anton Paar Powder Cell (APCC). These experiments have three purposes: i) Test reproducibility/consistency between two shear devices and test protocols. ii) To relate bulk behavior to microscopic particle properties, focusing on bulk density and thus the effect of cohesion between particles; iii) Investigate the influence of the temperature of heated powders on the powder flow properties, which is important for the industrial reactors. Interestingly, for samples with small particle size, bulk cohesion increases slightly, but bulk friction increases significantly, because of particle interaction effects. The experimental data not only provide useful insight into the role of microscopically attractive van der Waals gravitational and/or compressive forces on the macroscopic flow behavior of bulk powders, but also have industrial relevance. We also provide a robust datas of cohesive and attritional fine powder for silo design used for calibration and validation of silos, models and computer simulations.

Today major environmental issue facing the universe is Industrial wastewater. They are present in the form of various pollutants like organic/inorganic, heavy metals, and non-disintegrating materials at an enormous amount. As of now, eliminating these pollutants from wastewater from industries in a viable manner has become a significant issue. Productive refinement methodologies are required to eliminate those toxins before removal. Furthermore, nanomaterials are innovationally powerful to purification of water by utilizing inexpensive nanofiltration and nano adsorbent. Heavy metal ions removal in an efficient way from the environment is the first and foremost problem from a biological and ecological perspective, and numerous research practices have been dedicated to the removal of harmful metal particles, involved both in the underground and surface wastewater. This article mainly focuses on the nanomaterials utilization of various contaminating materials removal from industrial wastewater with an exceptional spotlight on rare earth components and nanofiber and nanocomposite films. The objective is to offer references an outline in the field of developing nanomaterials utilization for harmful pollutants removal from industrial wastewater for industrializers and analysts.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (&lt;50%), compared to those presented in the related literature where the whole set of channels is used.