We revisit a 2023 JAMA-published analysis of data from patients admitted for either COVID-19 or influenza to U.S. Veterans Administration (VA) hospitals in the fall/winter of 2022-2023. We note that baseline characteristics between the cohorts were similar, and the difference in vaccination rates was minimal, even after propensity score weighting. Had the vaccines been even minimally effective, significant differences in vaccination percentages would be expected. These findings call into question any efficacy claims associated with either the COVID-19 or influenza vaccines. Furthermore, the lack of efficacy of the influenza vaccines in reducing hospitalization is consistent with an earlier detailed study that found the same lack of efficacy, further validating the current result. This suggests that comparing vaccination rates between those hospitalized for different vaccine-preventable diseases can serve as a practical method for validating the vaccine efficacy claims of different vaccines.

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As DMs are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of DMs, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of DMs and provide guidelines for future researchers and practitioners across diverse disciplines.

The interplay between the human gut microbiota and the peripheral immune system significantly affects the efficacy of immune checkpoint inhibitors (ICIs) in cancer therapy. Despite this profound connection, the practical implications of microbiota dysbiosis in cancer immunotherapy ICIs & and CAR-T remain intricate and poorly understood in practical terms. This paper offers a comprehensive review of current literature on the role of gut microbiota dysbiosis in tumor development, and emerging microbiota modulation interventions, with a focus on fecal microbiota transplantation (FMT). FMT is a cost-effective therapeutic avenue, particularly in addressing diseases rooted in antibiotic-resistant bacteria, such as Clostridium difficile. Here we provided insights into the applications of FMT and other gut microbiota therapies in the landscape of cancer treatment, emphasizing their influence on the tumor microenvironment and the efficacy of ICIs with suggestions for future research directions and their implications for clinical practice.

In the realm of physical activity and sports science, motion tracking emerges as an important tool for specialists aiming to analyze movement data, whether for daily life activities or enhancing athletic performance. This study introduces an innovative approach to motion tracking through the development of a fully embedded smart shoe sensor system, which employs an inertial measurement unit (IMU) affixed to the shoe sole, coupled with six pressure sensors, to provide real-time calculation of individual stride lengths along with other vital movement statistics. Traditional motion tracking methods, which typically rely on various body-attached sensors, often necessitate substantial post-processing, delaying the feedback loop. In contrast, our system is designed to overcome these limitations by offering high-accuracy, real-time feedback with a minimal error rate of 1.88% from our experiments. Such prompt data acquisition facilitates immediate interventions by sports performance coaches or healthcare professionals, significantly contributing to injury prevention and performance enhancement. The experimental outcomes of this study underscore the system’s capability to deliver precise stride length measurements instantaneously, marking a significant advancement in motion tracking technology and its application in sports science and healthcare.

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems, and lays the foundation for guiding component repair and maintenance.

This study explores the role of chatbots in enhancing customer service, examining their benefits, limitations, and overall impact on modern support systems. Chatbots have emerged as a transformative technology in customer service, offering significant advantages such as increased efficiency through automation of routine tasks, reduced response times, and the ability to provide 24/7 support. By handling high volumes of inquiries and managing repetitive tasks, chatbots alleviate the workload of human agents, allowing them to focus on more complex issues. Additionally, chatbots employ advanced data analytics and machine learning to deliver personalized interactions, enhancing user engagement and satisfaction. Despite these benefits, the research identifies several challenges, including chatbots' difficulty in managing complex queries and their lack of emotional intelligence, which often necessitates escalation to human agents. The study also highlights the importance of well-designed chatbots and seamless integration with existing systems to ensure effective user experiences. Privacy and security concerns are critical, as chatbots handle sensitive customer data, requiring robust measures to protect against breaches and ensure compliance with data protection regulations. Looking ahead, advancements in technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) are expected to further enhance chatbot capabilities, addressing current limitations and expanding their role in customer service. The findings suggest that a hybrid approach, combining chatbots with human support, offers the most effective solution for delivering high-quality customer service. Overall, chatbots have proven to be valuable assets, and their continued evolution promises to significantly impact the future of customer service.

Introduction: Adolescence is a critical period for the development of affectivity and sexual-ity. Adolescents begin to explore their sexual identity, form intimate relationships, and learn to manage their emotions in new and complex contexts. This study aims to analyze the prevalence of habits and behaviors related to the affective-sexual health of adolescents in the 4th year of ESO, identifying risk factors, comparing behaviors and risks, monitoring these behaviors, analyzing pornography consumption, and evaluating the use of the in-ternet as a source of sexual information. Method: Descriptive cross-sectional study using questionnaires. Participants are 4th-year ESO students from a school in the Baix Llobregat province (Catalonia) aged between 14 and 18 years. Descriptive and bivariate statistical analyses were conducted using the Chi-square test and p-value calculations with R Project software. Frequency and percentage analyses were also used to describe the health behav-iors of the participants. Results: The study reveals that girls have better knowledge of the contraceptive pill and male condom than boys. Sexual initiation and condom use man-agement vary between genders, with girls being more capable of persuading their partners to use them. Pornography consumption also shows significant differences in age of initia-tion and frequency between boys and girls. Conclusions: The study on adolescent affectiv-ity and sexuality reveals knowledge about contraceptives, early sexual initiation, and high pornography consumption, highlighting the need for early and diverse affective-sexual education, youth empowerment, and media misinformation management to promote safer and healthier behaviors in municipal public health. Keywords: Adolescents, Sexuality, Sexual Health, Pornography Consumption, Contracep-tive Knowledge, Gender Differences

The last decade has seen a significant growth in low-cost air quality monitoring systems (LCAQMS) adoption, mostly driven by the spatial density limitations of traditional regulatory grade networks which restrict their effectiveness in several settings. The EU's regulatory framework advise for deploying one air quality monitoring unit per roughly 170,000 people or 15 square kilometers which is insufficient for capturing high spatio-temporal variance in complex urban scenarios. Often these settings are characterized by distributed emissions in small roads subjected to intense cars traffic, house heating driven emissions and frequently subjected to canyon effects. The Air Heritage project in Portici, funded by the EU's Urban Innovative Actions (UIA) program, addressed these challenges by integrating low-cost sensors with traditional monitoring systems and engaging the community in pervasive measurement strategies. The project highlights the importance of individualized sensor calibration and frequent recalibrations to maintain accuracy. It also showcases the benefits of citizen science approaches, IoT infrastructures, AI components, and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements. This paper reports on the lessons learned and experiences from the Air Heritage project, aiming to guide future implementations in similar contexts. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, public authorities, and citizens, about effective strategies for deploying and utilizing LCAQMS to enhance air quality monitoring and public awareness.

ATP is an essential molecule in many biocatalytic processes. The industrial implementation of these processes is hampered by the fact that they require stoichiometric quantities of this expen-sive co-factor. To avoid this problem, recycling systems are needed. This study characterizes a putative polyphosphate kinase enzyme from Burkholderia cenocepacia that can regenerate ATP from AMP. Sequence analysis shows that BcPPK2-III exhibits the characteristic structural motifs of known PPK2 enzymes, including conserved motifs Walker A and B, and the subclass-specific res-idue E137. Molecular docking simulations showed ADP had the highest binding affinity (-7.340 kcal/mol), followed by AMP (-7.284 kcal/mol), with ATP having the lowest affinity (-6.464 kcal/mol). The enzyme was overexpressed in E. coli. Protein expression and purification were monitored by SDS-PAGE and quantified by densitometric analysis. Enzymatic activity assays re-vealed that BcPPK2-III converts 70% ATP with a TTN of 334,000 after 24 hours and remains sta-ble over a pH range of 6-9 and temperatures up to 70°C. BcPPK2-III shows significant potential for industrial ATP regeneration and offers high conversion rates and stability. The enzyme's high conversion rate and stability make BcPPK2-III a promising candidate for the improvement of bio-catalytic processes that rely on ATP at industrial level.

The Riemann hypothesis is the assertion that all non-trivial zeros are complex numbers with real part $\frac{1}{2}$. It is considered by many to be the most important unsolved problem in pure mathematics. There are several statements equivalent to the famous Riemann hypothesis. Robin's criterion states that the Riemann hypothesis is true if and only if the inequality $\sigma(n) < e^{\gamma} \cdot n \cdot \log \log n$ holds for all natural numbers $n > 5040$, where $\sigma(n)$ is the sum-of-divisors function of $n$, $\gamma \approx 0.57721$ is the Euler-Mascheroni constant and $\log$ is the natural logarithm. We require the properties of superabundant numbers, that is to say left to right maxima of $n \mapsto \frac{\sigma(n)}{n}$. In this note, using Robin's criterion on superabundant numbers, we prove that the Riemann hypothesis is true.

OBJECTIVE The optimal surgical approach for thymoma resection is still an object of debate. The increasing experience in robotic-assisted thoracic surgery (RATS) has led to the progressive affirmation of this technique as a valid alternative to Video-Assisted Thoracic Surgery (VATS), sternotomy and thoracotomy in this setting. The present study aims to compare the post-operative and short-term results of RATS Thymectomy for thymoma with those of other main surgical approaches (sternotomy, thoracotomy, and VATS) from a high-volume single center. MATERIALS AND METHODS Between May 2021 - September 2023, 40 consecutive patients underwent RATS Thymectomy for I to limited-stage III thymoma in our center. Three homogenous groups of consecutive patients who received thymic surgery by main alternative approaches (sternotomy, thoracotomy, VATS) over the last 5 years, were identified in order to perform a comparative analysis. Data of operative technique, time of surgery, associated resections, conversion rate, overall morbidity, tumor size, radicality, postoperative pain, length of hospital stay, and cosmetic results were recorded and compared between each control group and the RATS group. RESULTS Tumor size was higher in the sternotomy group, but not significantly. The mean operative time of RATS interventions was significantly lower than that of sternotomy and VATS. It was significantly shorter compared to thoracotomy if excluding docking-undocking time. Higher rate of associated adjacent structures resection was reported in the sternotomy group (p=0.005). Conversion rate was significantly higher in VATS group (p= 0.026) compared to RATS. Postoperative pain at 24 and 48 hours was significantly lower in RATS group compared to the others. Improved cosmetics results were reported after RATS compared to sternotomy (p= 0.0001) and thoracotomy (p= 0.001) groups, with a trend towards better results compared to VATS (p=0.05). Length of hospital stay was shorter in the RATS group with a significant difference vs. the sternotomy group (p

This study investigates the microstructural evolution and mechanical properties of steel subjected to high-temperature heat treatment, with a focus on the formation and effects of dark phase precipitates. High Temperature Confocal Laser Scanning Microscopy (HTCLSM) was employed to observe the in-situ changes in microstructure as the steel was heated from room temperature to 900°C. The observations revealed that dark phase precipitates, identified as chromium carbides (Cr7C3), began forming at approximately 820°C and increased in quantity and size up to 900°C. Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX) confirmed that these precipitates significantly enhance the material's hardness, with microhardness values of 320 HV for the precipitates compared to 270 HV for the matrix. Tensile tests showed that the tensile strength of the steel increases with soaking time at 900°C, reaching a maximum after 3 minutes due to the strengthening effect of the precipitates. Beyond this point, strength decreases due to grain coarsening.

Introduction: Although aging is a natural phenomenon, in recent years it has accelerated. One key factor implicated in the aging process is oxidative stress. Oxidative stress play also role in frailty (frail) and metabolic syndrome (MetS). Methods: 66 elderly persons (65 years old and older) with no acute or severe chronic disorders were assessed: waist circumference (WC), arterial blood pressure, glycemia, glycated hemoglobin (HbA1c), plasma lipids and activity of erythrocyte superoxide dismutase (SOD-1). Patients were classified as NonMetS-Nonfrail (n=19), NonMetS-frail (n=20), MetS-Nonfrail (n=17), and MetS-frail (n=10). Results: There were no significant differences in superoxide dismutase activity among investigated elderly groups. However, the data suggests that MetS individuals, both frail and nonfrail, have higher risk factors for cardiovascular disease compared to NonMetS individuals. The correlations analyses of SOD-1 and other metabolic indices suggest that SOD-1 levels may be influenced by age, total cholesterol, HDL cholesterol, and fasting glucose levels in certain groups of seniors. Conclusions: Aging is associated with decreased antioxidant enzyme SOD-1 activity with glucose alteration in frailty syndrome as well as with lipids disturbances in metabolic syndrome.

In this paper, the results of a high-precision control survey carried out in the underground mining of the Oyu Tolgoi project using terrestrial laser scanning technology are presented. The purpose of the measurements was to ensure that the direction, total length and volume of the penetration and excavation works were being implemented in accordance with the planned and designed dimensions. The coordinates of the geodetic traverse checkpoints were also assessed. In addition, we checked a traverse network of 18 checkpoints in the tunnel of the selected underground level. These checkpoints were repeatedly measured four times by two using readings of the Trimble S7 1” total station and were processed using the Compass/Bowditch method with Trimble Business Center software. All points except for one were within the tolerance limits. Based on these points, the excavation length and volumes were determined using the terrestrial laser scanners (TLSs) Trimble X7 and Trimble SX12 [25]. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured using laser scanner was calculated with the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed a length of 20285.72m and a volume of 441128.80m3 for the seven underground mine levels, while their project values were 16158.26m length and 422837.50m3 volume.

Rising obesity and associated multi-systemic complications amplify the health burden. Euphorbia kansui (EK) extract is clinically recognized for managing obesity. In human study, 240 obese individuals were categorized into two cohorts: those receiving solely herbal medicine (HM group), and those administered EK concomitantly with herbal medicine (EK group). An in vivo examination using C57BL/6-Lepob/Lepob mice elucidated mechanisms involving macrophages and gut microbiota, with associated metabolic advantages. The clinical study revealed a significant 7.22% body weight reduction during 91.55 average treatment days, and examined 16.71% weight loss at 300 days after treatment. In the whole subjects, 60.4%, 21.3% and 6.3% achieved weight reductions exceeding 5%, 10% and 15%, respectively. Impressively, the EK group exhibited superior weight loss compared to the HM group (EK: -7.73% vs. HM: -6.27%, p=0.012). The anti-obesity effect was positively associated with EK therapy frequency and herbal medicine duration. In vivo, EK significantly improved insulin sensitivity and mitigated infiltration of adipose tissue macrophages (ATMs) by modulating the CD11c+ and CD206+ subtypes. EK also correlated with increased Bacteroidetes and Firmicutes populations and reduced Proteobacteria and Verrucomicrobia. Consequently, EK is an effective adjunctive anti-obesity therapy offering metabolic benefits by modulating ATMs and gut microbiota profiles.

Ever derived...

Myelination, the process of forming a myelin sheath around nerve fibers, is critical not only during early development but also throughout adulthood. This article explores the multifaceted impacts of myelination on the adult brain, highlighting its importance in cognitive functions, neurological health, and overall brain efficiency. In adults, efficient myelination enhances information processing speed, supports memory consolidation, and contributes to cognitive reserve, potentially delaying the onset of neurodegenerative diseases. Myelination ensures synchronized neural activity and energy-efficient brain function, playing a key role in complex cognitive tasks and coordination. Despite a slower rate of plasticity compared to early development, the adult brain retains the ability to adapt and learn, with myelination facilitating these processes. This article also draws parallels between myelination in the brain and learning curves in deep learning Perceptrons, emphasizing how reduced stimulation and experience can significantly slow down cognitive and learning processes. Additionally, the clinical implications of myelination are discussed, including its role in multiple sclerosis and age-related cognitive decline, along with strategies to maintain and enhance myelin health through lifestyle choices and emerging pharmacological interventions. Understanding the ongoing significance of myelination in the adult brain, and its comparison to artificial neural networks, is crucial for developing interventions to support cognitive health and mitigate neurological disorders.

The review considers physics and some applications of a new approach that promises to be a breakthrough in efficiency and instrumentation of various methods for nondestructive testing (NDT) of composites. The approach is based on frequency-selective properties of defects which fall into resonance (Local Defect Resonance (LDR)) at certain frequencies. An activation of LDR enables to route the input acoustic energy directly to the defect that dramatically increases its vibration amplitude and thus facilitates defect-selective imaging, rises efficiencies of various derivative acoustic NDT methods (nonlinear, acousto-thermal) and enables to develop some new methodologies, like Resonant Air-Coupled Emission (RACE), machine and remote imaging.

The rapid proliferation of digital devices and connectivity has seen an unprecedented rise in the threats of cyber-attacks, making cybersecurity a high research priority worldwide. Among the various forms of cyberattacks, malicious software or malware, significantly disrupts the privacy and integrity of data on millions of computing devices globally. It is already a well-established notion that robust malware analysis underpins successful protection systems in the rapidly changing threat landscape. Traditional malware detection techniques, although effective against known malware types, are often found wanting against new and sophisticated varieties. The situation necessitates the exploration of advanced techniques like cryptographic hashing and fuzzy hashing that have proven instrumental in handling malware's dynamic nature. Fuzzy hashing, by allowing a score-based match rather than an exact digital signature, enables the identification of unknown malware that are derivatives of known malwares. However, the efficiency of the fuzzy hashing algorithms comes to the forefront concerning the encryption used. The research paper titled, "Using ASCON-based Fuzzy Hashing for Efficient Malware Analysis," seeks to explore leveraging the ASCON encryption, a lightweight but secure encryption standard, in the application of fuzzy hashing for malware detection.

Glioma is the most common primary malignant brain tumors. Despite advances in surgical techniques and treatment regimens, the therapeutic effects remain unsatisfactory. Immunotherapy has brought new hope to glioma patients, but the immunosuppressive nature of the tumor microenvironment (TME) has limited the therapeutic outcomes. This study aims to reveal the subpopulations and functional characteristics of tumor-associated macrophages (TAMs), to explore the regulatory effects of chlorogenic acid on the immune microenvironment, and investigate its potential for clinical application. In this study, Chlorogenic acid slowed tumor growth in the mouse model and extended the survival time of the mice. ScRNA-seq analysis elucidated the differentiation trajectories and functional characteristics of BMDMs and microglia, revealing the regulatory effects of chlorogenic acid on these two populations. It enhanced the antigen-presenting function of macrophages and the gene expression related to T-cell immune activation, activated microglia through the JAK-STAT pathway, and improved antitumor functions. Using the target prediction database, a PPI and molecular docking model were constructed, confirming the good affinity of chlorogenic acid with STAT1. A case of a patient treated with chlorogenic acid was reviewed, who survived for 5 years and 6 months, achieved partial remission (PR) after 9 months of treatment, and remains alive with no new symptoms or toxic side effects. Our study revealed the subtypes and differentiation trajectories of tumor-associated macrophages. Chlorogenic acid significantly improved the immune microenvironment of glioma by modulating the function of bone marrow-derived mono-macrophages (BMDMs) and microglia, demonstrating good antitumor effects. This study provides new insights for targeting the regulation of the TME and offers theoretical and practical support for the clinical application of chlorogenic acid.

This paper focuses on the concepts used in Quantum Cryptography and recent progress made. We look at the different categories of key distribution protocols that have been proposed and issues related to those. We then take an overview of two main approaches of communication using quantum cryptography concepts, i.e., ``Quantum Secure Direct Communication'' and ``Deterministic Secure Quantum Communication'', and limitations related to the usage of these schemes in a real-world system. At last, we list down the problems identified in the research and conclude our discussion by proposing areas of focus for future research.

Isolation of new plant-derived lactic acid bacteria (LAB) is highly prioritized in developing novel starter cultures for plant-based fermentation. This study explores the diversity of LAB in Danish flowers and their potential use for plant-based food fermentation. 46 flower samples under 34 genera were collected for LAB isolation. By introducing an enrichment step, in total of 61 LAB strains were isolated and identified by using MALDI-TOF and 16S rRNA sequencing. These strains represent 24 species across 9 genera, predominantly Leuconostoc mesenteroides, Fructobacillus fructosus, Apilactobacillus ozensis, and Apilactobacillus kunkeei. Phenotypic screening for exo-cellular polysaccharide production revealed that 40 strains exhibited sliminess or ropiness on sucrose containing agar plate. HPLC analysis confirmed that all isolates produced exo-cellular polysaccharides containing glucose, fructose or galactose as sugar monomer. Therefore, the strains were glucan, fructan and galactan producers. The suitability of these strains for plant-based fermentations was characterized by using almond, oat and soy milk. The results showed successful acidification in all three types of plant-based matrices but only observed texture development in soy by Leuconostoc, Weissella, Lactococcus, Apilactobacillus, and Fructobacillus. The findings highlight the potential of flower-derived LAB strains for texture development in soy-based dairy alternatives.

Extremely low-frequency electromagnetic fields (ELF-EMF) are ubiquitous in industrial-11 ized environments due to the continuous use of electrical devices. Our previous studies demon-12 strated ELF-EMF affects muscle cells by modulating oxidative stress and enhancing myo-genesis. 13 This pilot study investigated these effects on skeletal muscles of sedentary adult mice, assessing 14 physiological responses to ELF-EMF exposure and potential modulation by antioxidant supplemen-15 tation. Male C57BL/6 mice were exposed to ELF-EMF (0.1 or 1.0 mT) 1 hour/day up to 5 weeks, and 16 fed a standard diet without or with N-acetyl-cysteine (NAC). Results showed transient increases in 17 muscle strength (after 2-weeks’ exposure at 1.0 mT), potentially linked to muscle fiber recruitment 18 and activation, revealed by higher PAX7 and MyH expression levels. After ELF-EMF exposure, ox-19 idative status assessment revealed transient increases in expression levels of SOD1 and catalase en-20 zymes, in total antioxidant capacity and in protein carbonyl levels, marker of oxidative damage. 21 These effects were partially reduced by NAC. In conclusion, ELF-EMF exposure affects skeletal 22 muscle physiology and NAC supplementation partially mitigates these effects, highlighting the 23 complex interactions between ELF-EMF and antioxidant pathways in vivo. Further investigations 24 on ELF-EMF as a therapeutic modality for muscle health are necessary.

We apply the Canny Edge algorithm to the Utqiaġvik coastal sea ice radar system (CSIRS) to quantify open water and sea ice (landfast or drifting) concentration. The radar-derived sea ice concentration is compared against the 25-km resolution NSIDC Climate Data Record (CDR, one pixel closest to the radar field of view) and the 1-km merged MODIS-AMSR2 sea ice concentrations in the ∼11 km field of view for the year 2022-2023, when improved image contrast was first implemented. The algorithm was first optimized using sea ice concentration from 14 different images and 10 ice analysts (140 analysis in total) covering a wide range of ice condition with landfast ice, drifting ice and open water. The algorithm is also validated quantitatively against high-resolution MODIS-Terra in the visible range. Results show a correlation coefficient and mean bias error between the optimized algorithm, the CDR and MODIS-AMSR2 daily SIC of 0.18 and 0.54, and ∼ -1 and 0.9%, respectively, with an averaged inter-analyst error of ± 3%. In general, the CDR captures the melt period correctly and overestimates the SIC during the winter and freeze-up period, while the merged MODIS-AMSR2 better captures the punctual break-out events in winter including those during the freeze-up events (reduction of in SIC). Remnant issues with the detection algorithm include the false detection of sea ice in the presence, fog or precipitation (up to 20%), quantified from the summer reconstruction with known open water conditions. Coastal marine radars provide prove very useful in bridging the gap between (lower) resolution satellite-derived and in-situ (visual) sea ice conditions, and to quantify land contamination from lower resolution satellite-derived SIC.

Background: Two explored EDA1 variants regulate the biological behaviors of human dental pulp stem cells (hDPSCs), such as proliferation and migration, as well as their effects on odontogenic differentiation, providing a theoretical basis for further exploration of the mechanisms underlying EDA1-induced tooth development. Methods: The experimental groups included a wild-type EDA1 group (Wt), non-syndromic tooth agenesis variants EDA1 group (NSTA-A259E, NSTA-S374R), syndrome type variant EDA1 group (STA-H252L), and an empty vector control group (PCR3/NC). CCK-8 method was used to investigate the proliferative activity of hDPSCs. The cell cycle distribution of hDPSCs was detected by flow cytometry; The cell migration ability was assessed using the cell scratch and Transwell tests; Odontogenic differentiation medium was used to induce mineralization of hDPSCs, followed by statistical analysis. RNA-Seq revealed that FOS, JUN and other genes were found to be enriched in MAPK signaling pathway and osteoclast differentiation pathway. qPCR was used to detect mRNA expression of hub genes (FOS and JUN). Results: Compared with the NSTA-EDA1 and STA-EDA1, Wt-EDA1 promotes the proliferation and transformation of hDPSCs from the G0/G1 phase to S phase (p<0.01), with no significant difference between NSTA-EDA1 and STA-EDA1 (p>0.05). Wt-EDA1 also upregulated hDPSCs cell migration compared to NSTA-EDA1 and STA-EDA variants, as demonstrated by migration assays (p<0.01). NSTA-EDA1 and STA-EDA1 variants inhibited the odontogenic differentiation of hDPSCs compared to the Wt group, as shown by Alizarin red staining and ALP staining (p<0.01). RNA sequencing revealed that the differentially expressed genes FOS and JUN were enriched in the MAPK signaling pathway and osteoclast differentiation. Compared with Wt-EDA1, EDA1 variants lost the ability to promote c-FOS mRNA expression, as confirmed by qPCR. Conclusions: EDA1 variants fail to the promote c-FOS mRNA expression in hDPSCs, thereby inhibiting odontogenic differentiation and losing the ability to enhance the proliferation of hDPSCs.

The impact of scientific articles is often measured using metrics such as the Journal Impact Factor (JIF) and CiteScore, which rely on citation counts and publication volume. In this study, we aimed to identify the predictors of article impact in the leading solid organ transplantation journals from 2019 to 2020. Article impact was determined using its citation count in 2021 compared to the JIF. Statistical analysis was performed using IBM SPSS Statistics to identify significant predictors of positive impact on the JIF. A total of 2,461 articles and reviews were included, with variations observed among the top transplantation journals. Factors such as the number of authors, open access publishing, and authors from top affiliations were found to significantly influence the impact of articles. Articles discussing kidney transplantation and COVID-19 were associated with a positive impact on the JIF, while those discussing lung transplantation had a negative impact. The findings suggest that collaborative research with more authors and involvement of top institutions enhances impact. Open access publishing also correlated with increased citations and a positive impact on the JIF. Further research incorporating a broader range of journals and years would provide a more comprehensive understanding of factors influencing article impact.

The two combined technologies WPT and AI still have huge prospects for several industries such as consumer electronics and industry. This paper looks at the recent developments in WPT, particularly concerning AI-improved efficiency, range, and reliability. AI-driven also works to control power, manage device alignment, and test when maintenance is necessary, this makes powering smarter and more efficient. Accuracy is given by deploying machine learning and computer vision, and on the other hand, the maintenance procedure is done through predictions. Furthermore, the ability of the management system to include the use of AI in power regulation enables the control of consumption based on the load. In this study, the positive future effect of AI is discussed concerning improvements in WPT technologies, which is paramount in subsequent applications. Based on the results, it is essential to continue the investigation of the topic within this interdisciplinary science to advance existing problems and discover novel possibilities for evolution. Data based on experiments shows a significant improvement in the efficiency of information transfer where AI is used with rates up to 25% higher than those using the traditional ways. Additionally, advanced maintenance forecasting algorithms improve system availability by cutting down on 30% of the downtimes when applied in real-life applications of AI technology. The study also looks at the possibilities of the outlook to the future including emergence of the distributed artificial intelligence for autonomous WPT network, and additional potential of quantum computing for the use to improve the algorithm. This indicates that there is a new era for the convergence of WPT and AI to enhance the progress of power transfer system in conditions of elegant, efficient, and robust.

Factor analysis, a staple of correlational psychology, faces challenges with ordinal variables like Likert scales. The validity of traditional methods, particularly maximum likelihood (ML), is debated. Newer approaches, like using polychoric correlation matrices with weighted least squares estimators (WLS), offer solutions. This paper compares ML with WLS for ordinal variables. While WLS generally outperforms ML, especially with non-bell-shaped distributions, it may yield artefactual estimates with severely skewed data. ML tends to underestimate true loadings, while WLS may overestimate them. Simulations and case studies highlight the importance of item psychometric distributions. Despite advancements, ML remains robust, underscoring the complexity of analyzing ordinal data in factor analysis. There's no one-size-fits-all approach, emphasizing the need for sensitivity analyses and careful consideration of data characteristics.

There is an increasing need for compact low-cost NMR apparatus that can be used on the laboratory bench and in the field. There are four main usage variants of usage: a) Time-Domain apparatus, particularly for physical measurements; b) Frequency Domain apparatus, particularly for chemical analysis, c) NMR Cryoporometry apparatus for measuring pore-size distributions, and d) MRI apparatus for imaging. For all of these, variable temperature capability may be vital. We have developed compact low-cost apparatus targeted at these applications. We discuss a hand-held size NMR spectrometer, and three different holdable NMR magnets, with sufficiently large internal bores for the Lab-Tools compact Peltier thermo-electric cooled variable-temperature probes. Currently the NMR Spectrometer is very suitable for a), NMR time-domain relaxation and c), NMR Cryoporometry. With a suitable high-homogeneity magnet it is also appropriate for simple use b), spectral analysis, or, with a suitable gradient set, use d), MRI. Together, the NMR Spectrometer, one of the NMR variable-temperature probes, and any of these NMR magnets make an excellent NMR Cryoporometers, as demonstrated by this paper, and previously published research. Equally, they make versatile general-purpose variable-temperature NMR systems for materials-science.

The use of green hydrogen as a high-energy fuel of the future may be an opportunity to balance the unstable energy system, which still remains renewable energy sources. This work is a comprehensive review of recent advancements in green hydrogen production. The review outlines the current energy consumption trends. It presents the tasks and challenges of the Hydrogen Economy towards green hydrogen, including production, purification, transmission, storage, and conversion into electricity. This work discusses the main types of water electrolyzers: alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, and anion exchange membrane electrolyzers. Despite the higher production costs of green hydrogen compared to grey hydrogen, the review suggests that as renewable energy technologies become cheaper and more efficient, the cost of green hydrogen is expected to decrease. The review highlights the need for cost-effective and efficient electrode materials for large-scale applications. It concludes by comparing the operating parameters and cost considerations of the different electrolyzer technologies. It sets targets for 2050 to improve the efficiency, durability, and scalability of electrolyzers. The review underscores the importance of ongoing research and development to address the limitations of current electrolyzer technology and to make green hydrogen production more competitive with fossil fuels.

Hip dysplasia is a prevalent hereditary condition affecting dogs and cats, characterized by inadequate development of the hip joint. This condition leads to pain due to capsular distension, microfractures, and joint incongruity in young animals, ultimately progressing to osteoarthritis in adult and elderly animals. The pursuit of pain relief and improved quality of life for individuals with osteoarthritis has been a focal point in several studies. In this context, veterinary physiotherapy has gained prominence in small animal practice, serving as both palliative care and a supportive modality in the clinical or post-surgical management of degenerative joint diseases. Laser therapy and hydrotherapy, recognized for their analgesic effects and capacity to promote muscle mass gain, have garnered attention for their potential efficacy in treating joint diseases. This study aims to evaluate the therapeutic effects of super pulsed laser therapy and controlled low-impact exercises (water treadmill) on a patient recovering from surgery involving bilateral femoral head and forehead resection. We observed positive outcomes, including pain resolution, improved muscle mass, and enhanced range of motion, contributing to the patient's overall recovery.

Asthma is a chronic inflammatory disease for which the main anti-inflammatory drugs for better disease control are steroids or corticosteroids. The use of steroids in asthma patients, in particular in uncontrolled asthma patients, is associated with an increased risk of osteoporosis and fragility fractures. A single oral corticosteroids course increases the risk of osteoporosis and continual use of inhaled corticosteroids is correlated over time to an increased risk for both bone conditions. With the use of new, available biologic therapies for asthma, perhaps even anticipating the times of their use in what are the steps of therapeutic management, in the current guidelines and with targeted strategies of prevention it may be possible to improve asthma management, preventing some comorbidities, such as osteoporosis.

The real-time input of ground motions can effectively assess earthquake disasters in building structures. In this study, we proposed a nearby real-time simulation method that can be applied to assess regional earthquake disasters using structural monitoring recordings based on an urban earthquake simulation system. Real-time accelerations recorded during the 5.1 magnitude Tangshan earthquake were used to update and modify the computing models. El Centro waves were calibrated to 400 cm/s2 for structural seismic resilience analyses. The results indicated that approximately 70% of the structural functions were lost during rare earthquakes. Regional numerical models of 216 buildings were constructed and timeously updated using the Geographic Information System and measured data. The inputting ground motions recorded in the 5.1 magnitude Tangshan earthquake and typical El Centro waves were selected to analyze the structural seismic response, in which the damage indexes were computed, and the damage predictions of regional 216 buildings were also simulated in different levels of earthquakes by being combined with the simplified principles of structural damage estimation. Finally, the evaluation results were visualized in three dimensions using ParaView software. The simulated results of earthquake disasters at an urban scale will promote the prediction abilities of local earthquake administration agencies and have considerable potential to provide essential information for emergency responses and regional disaster mitigation.

During the last few years, the world witnessed one of the worst calamities to humankind- the COVID-19 pandemic. We also witnessed the development of vaccines at an unprecedented pace against the SARS-CoV-2- the causative agent. India was among the hard-hit countries by COVID-19, developed its indigenous vaccine and implemented vaccines developed in other countries as well. Initially, the antibody level and the neutralizing antibody titer against the pathogen were considered as the correlates of immune protection from the disease. However, the long-term protection from the pathogen was provided by the immune memory cells viz. T and B memory cells and their ability to efficiently make recall responses. In this regard, globally the research studies focused mainly on the mRNA-based vaccines. The studies on immune memory response, particularly- the B cell memory response bestowed by the vaccines given to Indians is relatively obscure. We studied the level of RBD specific memory B cells in the peripheral circulation of the study participants and the ability of B cells to secrete antigen-specific antibodies among Indians vaccinated with Covaxin (BBV152), Covishield (AZD1222), Corbevax (BECOV2D) and Sputnik Light (Gam-COVID-Vac) as well as unvaccinated individuals. Our study revealed that Corbevax and Sputnik Light conferred better antibody-secreting cell (ASC) responses.

This literature review provides a comprehensive analysis of the functional morphology and paleoecology of Pteranodon, a prominent genus of Pterosaur from the Late Cretaceous period. By synthesizing recent research findings with classic studies, this paper elucidates the aerodynamic and ecological adaptations of Pteranodon, which was one of the largest pterosaurs to ever soar the skies. The review examines the distinctive cranial features, including its elongated cranial crest and its implications for sexual dimorphism and species identification. Detailed analysis of wing morphology and limb proportions reveals insights into its flight mechanics, suggesting that Pteranodon was adapted for dynamic soaring, leveraging its expansive wingspan for efficient long-distance travel. Additionally, the paper explores Pteranodon's feeding habits and dietary preferences based on dental and jaw structure, indicating a diet primarily consisting of fish and other aquatic prey. The review also integrates fossil evidence with ecological models to reconstruct the paleoenvironment of the Late Cretaceous, highlighting the role of Pteranodon in its ecosystem as both a predator and a competitor. This synthesis not only enhances our understanding of Pteranodon’s physiological and ecological roles but also contributes to broader discussions on pterosaur evolution and behavior.

The cyclamen mite, Phytonemus pallidus, is one of the important pests in strawberry cultivation in Canada. This insect pest hides on young strawberry leaves that have not yet opened, which limits their control. In addition, the fight against the latter involves the use of synthetic chemicals that are not without effect on the environment, human health and beneficial organisms. We hypothesized that vectorization by bees could reduce the number of applications, product runoff and provide targeted control of these pathogens. To do this, field trials were conducted to evaluate the effectiveness of bumblebees Bombus terrestris in propagating Beauveria bassiana to control this pest. Our results showed that bumblebees were able to disseminate the inoculum on strawberry plants. Also, all the products used (B. bassiana and neem oil) were effective on P. pallidus. It should be noted that high mortality has been noted on this pest thanks to the application of this entomopathogen by spraying. In addition, neither of the two products tested had a significant effect on lady beetles C. septempunctata. These results therefore suggest the use of bee vectoring and neem essential oils in the sustainable management of strawberry mites.

Solvent accessible surface area (SASA) of amino acid residues plays a significant role in inter-residue, protein-ligand, and protein-protein interactions. Affecting the exposure and interactions of hydrophobic and hydrophilic regions, driving protein folding. While previous SASA calculations are accurate with respect to the chosen parameters of simulation, they do not account for the dynamic behavior of peptides in solution under physiological conditions and cannot be used to compare populations within phi,psi dihedral angle space (phi,psi space). Molecular dynamics trajectories obtained at 310 K, 1 atm, and 150 mM NaCl for the Ac-Ala-Xaa-Ala-NH2 model peptides simulated using the CHARMM36m force field and TIP3Pm water as implemented in GROMACS 2022 were used to study SASA. The more balanced parametrization of CHARMM36m, resulted in increased sampling of beta region compared to alpha, alphaL, and epsilon regions were observed. There are statistically significant differences in SASA for the backbone and side chain comparing the beta, alpha, alphaL, epsilon and contiguous regions of phi,psi space. Differences occur in sampled phi,psi space particularly in the alphaL and epsilon regions of phi,psi space as a function of side chain size and chemical properties. The epsilon region of phi,psi space is not significantly sampled for multiple amino acids while the alphaL region is.

Rice yield underperformance coupled with production instability in Odisha state of India is primarily due to low irrigation potential, the spatiotemporal disparity in rainfall pattern and relentless confrontation with biotic and abiotic stresses. System of Rice Intensification (SRI) was introduced in the state with the promises of higher production horizon. However, the present scenario of patchy adoption pattern has necessitated comprehensive study on dynamics and determinants of adoption of SRI. We found that, 57 percent farmers continued SRI, 28 percent discontinued and rest were practicing conventional rice farming. The focused group discussion among the farmers helped us to triangulate the determinants of SRI adoption.The increased SRI area allocation was observed at the expense of reduced number of adopting farmers. Economic scarcity of skilled labour, difficulties in transplantation and mechanical weeding, low irrigation potential and poor on-farm water management were major constraints in SRI practice. Farmers’ compliance in following different resilient SRI components that also varied spatiotemporally has resulted in realized incremental yield. SRI was proved to be resilient to severe drought during 2015-16 and yielded 27 percentage higher than conventional one with considerable decline in hazardous chemicals usage. Farmers’ decision on SRI adoption/discontinuation was analysed using probit regression analysis. The results indicated that, active social involvements of NGOs, on-farm training and demonstrations, and realized incremental rice income influenced SRI adoption. On the other hand, SRI area expansion hindered mainly because of infeasible land topography, area saturation and lack of farmers’ interest. The sustainability index was developed to analyse SRI’s performance to other conventional methods at farm level. Comparatively, SRI adopters benefitted in terms of incremental net return by enhancing ecological balance and sustained productivity of soil, use of eco-friendly and own farm inputs. Nonetheless, higher women labour displacement observed under SRI can be curtailed by providing training to young farm women.

Background: Neuroprotective and immunomodulatory microRNAs derived from mesenchymal stem cells (MSCs), neural growth factors, and anti-inflammatory cytokines are contained in nano-sized extracellular vesicles called mesenchymal stem cell-derived exosomes (MSC-Exo). These vesicles attenuate neuro-inflammation, promote neo-vascularization, induce neurogenesis, and decrease the apoptotic loss of neural cells. Methods: Systematic retrieval of information was performed on PubMed. 60 articles were found in a search on Mesenchymal Stem Cell, Exosomes, and Alzheimer's Disease. These 2 articles were Meta-analyses, Randomized Controlled Trials, clinical trials, and Systematic Reviews. The rest were Literature review articles. These articles were till the year 2024. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. The clinical trials on Mesenchymal Stem Cell Exosomes for Alzheimer's Disease were searched on clinicaltrials.gov. Results: Several experimental investigations have shown that MSC-Exo improves cognitive im-pairment in rats. In this review paper, we summarized existing understanding regarding the molecular and cellular pathways behind MSC-Exo-based cognitive function restoration, with a focus on MSC-Exos therapeutic potential in the treatment of Alzheimer's disease. Conclusion: AD is a significant health issue in our culture and is linked to several important neuropathological characteristics. Exosomes generated from stem cells, such as MSCs or NSCs, have been examined more and more in a variety of AD models, indicating that they may be viable therapeutic agents for the treatment of diverse disorders. Exosome yields may be increased and their therapeutic efficacy can be improved using a range of tailored techniques and culture conditions. It is necessary to provide standardized guidelines for exosome manufacture to carry out excellent preclinical and clinical research.

Background and Objectives. To examine trends in the links between induced abortion and subsequent mental health problems for U.S. women approaching completed fertility. Materials and Methods. Panel data on a nationally-representative cohort of 3,842 ever-pregnant women assessed at mean ages of 15, 22, 28 and 37 years from the National Longitudinal Study of Adolescent to Adult Health (Add Health) were examined for pregnancy outcome-related mental health disorders, with extensive adjustment for confounders including sociodemographic dif-ferences, mental health history, and unwanted pregnancies. Outcomes were defined using pub-lished diagnostic criteria or validated index for depression, anxiety disorder, post-traumatic stress disorder (PTSD), and suicide ideation (affective problems); alcohol abuse, drug abuse, opioid abuse, and cannabis abuse (substance abuse problems); and summary counts of affective problems, substance abuse problems and total disorders. Results were adjusted for abortion under-reporting using cross-wave analysis. Results. The relative risk of mental health disorder with exposure to induced abortion followed an inverted “V” pattern as women aged to midlife, increasing from Wave III (age 22; IRR 1.41) to Wave IV (age 28; IRR 1.67), then dropping sharply by Wave V (age 37; IRR 1.20). The population at-tributable fraction of mental health disorder due to abortion dropped from 18.1% at Wave IV to 8.0% at Wave V. Unlike previous Waves, at Wave V mental health risk was more strongly reduced by exposure to childbirth (IRR 0.70, 95% CI .63, .78, p < .001). Reduced relative metrics at Wave V were not due to reduced prevalence of mental health disorders for ever-aborting women, which increased from Wave IV to Wave V by 1.7%, but increased much more, by 20.5%, for never-aborting women. Conclusion. At midlife approaching completed fertility, differential negative psychological out-comes for ever-aborting U.S. women remained elevated but reduced from younger ages.

Based on the detailed exploration of AI techniques for the early identification of learning difficulties in education, this study underscores the pivotal role of educational data analysis in enhancing student outcomes. Educators can proactively identify and address learning challenges through systematic data collection and analysis, thereby minimizing long-term academic obstacles and emotional stress. The integration of AI, particularly through natural language processing and computer vision technologies, facilitates personalized learning paths and real-time interventions, offering tailored support that maximizes each student's learning potential. These advancements optimize educational resource allocation and foster a more inclusive and effective learning environment, paving the way for future advancements in personalized education and intelligent teaching practices.

This paper is concerned about the novel exact solitons to the truncated M-fractional (1+1)-dimensional non-linear generalized Bretherton model with arbitrary constants . This model is used to explain the resonant nonlinear interaction between the waves in different phenomenon, including fluid dynamics, plasma physics, ocean waves, and many others. A series of exact solitons, including bright, dark, periodic, singular, singular-bright, singular-dark, and other solitons are obtained by applying the extended sinh-Gordon equation expansion (EShGEE), and the modified (G′/G2)-expansion tech- niques. A novel definition of Fractional derivative provides the solutions distinct from the present solutions. Mathematica software is used to obtain, and verify the solutions. The solutions are shown through 2-D, 3-D, and density plots. The stability process is performed to verify that the solutions are exact and accurate. The modulation instability is used to determine the steady-state stable results to the corresponding equation.

CD44 regulates cell adhesion, proliferation, survival, and stemness and has been considered a tumor therapy target. CD44 possesses the shortest CD44 standard (CD44s) and a variety of CD44 variant (CD44v) isoforms. Since the expression of CD44v is restricted in epithelial cells and carcinomas compared to CD44s, CD44v has been considered a promising target for monoclonal antibody (mAb) therapy. We previously developed an anti‐CD44v10 mAb, C44Mab-18 (IgM, kappa) to recognize the variant exon 10-encoded region. In the present study, a mouse IgG2a version of C44Mab-18 (C44Mab-18-mG2a) was generated to evaluate the antitumor activities against CD44-positive cells compared with previously established anti-pan CD44 mAb, C44Mab-46-mG2a. C44Mab-18-mG2a exhibited higher reactivity compared with C44Mab-46-mG2a to CD44v3–10-overexpressed CHO-K1 (CHO/CD44v3–10) and oral squamous cell carcinoma cell lines (HSC-2 and SAS) in flow cytometry. C44Mab-18-mG2a exerted a superior antibody‐dependent cellular cytotoxicity (ADCC) against CD44v3–10. In contrast, C44Mab-46-mG2a showed a superior complement‐dependent cytotoxicity (CDC) against CD44v3–10. A similar tendency was observed in ADCC and CDC against HSC-2 and SAS. Furthermore, administering C44Mab-18-mG2a or C44Mab-46-mG2a significantly suppressed CD44v3–10, HSC-2, and SAS xenograft tumor growth compared with the control mouse IgG2a. These results indicate that C44Mab-18-mG2a could be a promising therapeutic regimen for CD44v10-positive tumors.

Rock materials failures are accompanied by the co-existence of various failure mechanisms, in-cluding rock fracturing, shearing, and compaction yield. These mechanisms manifest macro-scopically as multiple failure modes and nonlinear strength characteristics related to stress levels. Considering the limitations of current rock mechanics strength theories, which are primarily derived from single failure mechanisms, this study evaluates the applicability of alternative strength theories. Based on the extensional-strain criterion and the PMC (Paul-Mohr-Coulomb) model, a piecewise linear strength model was proposed that is suitable for analyzing multiple failure mechanisms in rocks and revealing the intrinsic mechanisms of multi-mechanism rock material failure. A multiple failure mechanism strength model in the form of inequalities was proposed, using the generalized shear stress, mean stress, and stress Lode angle as parameters. Strength tests conducted on sandstone and granite rock material samples under different stress conditions revealed distinct piecewise linear strength characteristics for both rock types, vali-dating the rationality and applicability of the multiple failure mechanism model. The findings construct a multi-mechanism failure model for rocks, providing enhanced predictive capabilities and aiding in the prevention of rock structural failures.

A sustainable low cost activated carbon substitute was produced based on pretreated lignocellulosic biomass, especially spruce sawdust. A harmful liquid waste, desalination brine was used for the treatment of a solid wood industry waste, spruce sawdust. This approach is in the circular economy theory and aims to the decarbonization of the economy. The pretreated sawdust was tested as an adsorbent appropriate for the removal of a commonly used pollutant, methylene blue, from industrial wastewater. The adsorption capacity of the pretreated material was found to increased four times comparing to the untreated one in the case that the Freundlich equation was fitted to the isotherms’ data. The treatment experimental conditions with desalination brine that gave maximum adsorption capacity correspond to 1.97 combined severity factor in logarithmic form value. The same adsorption capacity was found to increase five to six times comparing to the untreated spruce sawdust in the case that the Langmuir isotherm model was applied at the same data. Moreover, kinetic experiment was cried out as regards the methylene blue adsorption process. The desalination brine pretreated sawdust adsorption capacity increased 1.4 times comparing to the untreated one, in the case of the Lagergren kinetic equation fitting. Furthermore, the adsorption capacity of the same material was increased 1.6 times when the second order kinetic model was applied to the same data. Industrial scale applications can be based on the kinetic data findings as they have to do with a very shorter adsorption period comparing to the isotherm data.

Sour waters are one of the main aqueous byproducts generated during petroleum refining and require processing in Sour Water Treatment Units (SWTUs) to remove contaminants such as H₂S and NH₃ in compliance with environmental legislations. Therefore, monitoring the composition of SWTU effluents, including acid gas, ammoniacal gas, and treated water, is essential. This study aims to present an AI (artificial intelligence) hybrid-based methodology to develop soft sensors capable of real-time prediction of H₂S and NH₃ mass fractions in the effluents of SWTUs and validate them using real data from industrial units. Initially, a new database based on the dynamic simulation of a two stripping columns SWTU phenomenological model, developed in Aspen Plus Dynamics® V10, was generated, aiming at non-faulty runs, unlike our previous work [1]. Ensemble methods (Decision Trees), such as Gradient Boosting and Random Forest, and Support Vector Machines were compared for soft sensor creation using these simulated data. The best outcome was the development of six soft sensors based on Random Forest with R² greater than 0.87, MAE less than 0.12, MSE less than 0.17, and RMSE less than 0.41. Variable importance analysis revealed that the temperature of the second stage of Column 1 significantly influences the thermodynamic equilibrium of H₂S and NH₃ separation from sour waters, being critical for five of the six soft sensors. After this initial stage using data from the phenomenological model, data from an industrial-scale SWTU were used to develop real soft sensors. The results proved the effectiveness of the conjugated use of physical model and industrial data approach in the development of soft sensor for two-column SWTUs.

The familiar stirred tank reactor poses an inherent challenge of variability. Some inflowing chemicals spend too little time in the reactor, and others stay too long, harming the uniformity of the end product. This issue can be addressed by replacing the continuous operation with a flow of individually treated fluid quanta. Ideally one would devise a universal fluid handling apparatus that can be adjusted to take care of all the needed chemical engineering unit operations. Proposing a cylinder shape capsule containing a rotating piston that moves back and forth between the edges of the capsule. The piston is cut through with certain fluid pass ways (holes) which may be shifting between being open, being closed, and anything in between. The combined movement of holes-drilled rotating piston will allow this universal unit to adjust for pumping, mixing and reacting fluid, and fluid components. By lining up the inner sides of the piston holes with fluid impact materials (FIM) the same generic unit can be used for catalytic reactions and for separation challenges. These generic units are linked "Lego like" to form a compound process, replacing the familiar continuous operation. The multi-mode piston (MMP) unit is completely specified by a digital representation of the shift position , the rotational position and the open/closed positions of the fluid pass ways. This allows for an efficient imposition of control strategy and for optimized manufacturing. The resultant digital representation renders the control strategy to become AI friendly, consistent with supervised learning tools for improving the efficiency of many chemical processes that transform from continuous mode to quantum fluid mode. While appearing promising, the concept is in its preliminary stages and its prospective impact is not yet well determined.

This study examines the integration of Environmental, Social, and Governance (ESG) factors into global banking practices and their impact on financial stability, measured by the Bank to Capital Asset Ratio. Through a comprehensive literature review and data analysis, the paper highlights the dual role of banks as catalysts for ESG investment and risk managers. It discusses the challenges and opportunities in transitioning to ESG-aligned business models, emphasizing the importance of regulatory frameworks and risk management approaches. The findings suggest that ESG integration enhances bank stability and competitiveness, contributing to sustainable economic development. The paper concludes with policy implications and recommendations for further integration of ESG factors into banking governance.

This paper explores the critical role of the banking sector in facilitating the ecological transition through the integration of ESG (Environmental, Social, and Governance) factors. By analyzing the intersection of ESG considerations and access to credit globally, the study highlights how banks can catalyze sustainable investments while balancing financial risks. Using a systematic literature review and k-Means clustering analysis, we assess the global landscape of credit access, emphasizing the implications of ESG adoption on financial stability and economic growth. The findings suggest that while ESG integration presents challenges, it offers significant opportunities for banks to enhance their competitiveness and foster resilient financial systems. The paper concludes by proposing policy recommendations aimed at improving the incorporation of ESG factors within credit risk management and promoting sustainable finance.

Gastric cancer has become a serious worldwide health concern, emphasizing the cru-cial importance of early diagnosis measures to improve patient outcomes. While tradi-tional histological image analysis is regarded as the clinical gold standard, it is la-bor-intensive and manual. Recognizing this problem, there has been a rise in interest in using computer-aided diagnostics tools to help pathologists with their diagnostic ef-forts. In particular, deep learning (DL) has emerged as a promising solution in this sector. However, current DL models are still restricted in their ability to extract exten-sive visual characteristics for correct categorization. To tackle this limitation, this study proposes the use of ensemble models, which incorporate the capabilities of sev-eral deep learning architectures and use aggregate knowledge of many models to im-prove classification performance, allowing for more accurate and efficient gastric cancer detection. To see how well these proposed models performed, this study com-pared them to other works, all of which were based on the Gastric Histopathology Sub-size Images Database, a publicly available dataset for gastric cancer. This research demonstrated that the ensemble models achieved high detection accuracy across all sub-databases, with an average accuracy exceeding 99%. Specifically, ResNet50, VGGNet, and ResNet34 performed better than EfficientNet and VitNet. For the 80 × 80 pixel sub-database, ResNet34 exhibited an accuracy of approximately 93%, VGGNet achieved 94%, and the ensemble model excelled with 99%. In the 120 × 120 pixel sub-database, the ensemble model showed 99% accuracy, VGGNet 97%, and ResNet50 approximately 97%. For the 160 × 160 pixel sub-database, the ensemble model again achieved 99% accuracy, VGGNet 98%, ResNet50 98%, and EfficientNet 92%, high-lighting the ensemble model's superior performance across all resolutions. Overall, the ensemble model consistently provided an accuracy of 99% across the three sub-pixel categories. These findings showed that ensemble models may successfully detect criti-cal characteristics from smaller patches and achieve high performance. The findings will help pathologists diagnose gastric cancer using histopathological images, leading to earlier identification and higher patient survival rates.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

This study proposes a prototype (Business Intelligence) for monitoring the continuing education of indigenous health professionals at the Secretariat of Indigenous Health (SESAI) of Brazil’s Ministry of Health (MS). The prototype builds on SESAI/MS staff knowledge and takes into account the char-acteristics and demands of the 34 Special Indigenous Health Districts (DSEIs). The prototype provides information for managing SESAI/MS continuing education in order to meet strategic goals. By im-proving the effectiveness and efficiency of the continuing education of professionals who serve Bra-zil’s indigenous population, we will be acting directly on access issues and improving the quality of life of the indigenous population.

Introduction Pedestrian road injuries are a significant public health issue in Georgia, Syria, and Tajikistan. This study investigates the trends in mortality, Years of Life Lost (YLLs), and incidence rates of pedestrian road injuries from 1990 to 2021, with a focus on sex-specific differences, using age-standardized data from the Global Burden of Disease (GBD) database. Methods The data for this analysis were obtained from the GBD database, emphasizing age-standardized rates of mortality, YLLs, and incidence of pedestrian-related road injuries in Georgia, Syria, and Tajikistan. The population was divided into male and female cohorts for comparison. Statistical methods included descriptive statistics, independent samples t-tests, and effect size calculations to evaluate the differences between sexes. Temporal trends were analyzed to observe changes over the study period. Results Mortality The analysis revealed that males had a significantly higher mean mortality rate (7.18, SD = 2.94) compared to females (2.26, SD = 0.78). The independent samples t-test confirmed a significant difference (t(62) = 9.141, p < 0.001). The effect sizes were substantial, with Cohen's d at 2.285, Hedges' g at 2.257, and Glass's Δ at 6.281. Over time, the mortality rates exhibited peaks in the early 1990s and mid-2000s, with a general decline afterward. Males consistently showed higher mortality rates than females throughout the period. YLLs (Years of Life Lost) For YLLs, males had a significantly higher mean rate (332.59, SD = 132.31) compared to females (103.35, SD = 32.57), with the t-test indicating a significant difference (t(62) = 9.515, p < 0.001). Effect sizes supported the significance of this difference, with Cohen's d at 2.379, Hedges' g at 2.350, and Glass's Δ at 7.308. The YLL rates showed notable peaks in the early 1990s and mid-2000s, followed by a decline, with males maintaining higher rates than females. Incidence Regarding incidence, males had a higher mean rate (235.90, SD = 36.09) compared to females (89.53, SD = 15.29). The t-test results (t(62) = 21.125, p < 0.001) and effect sizes (Cohen's d = 5.281, Hedges' g = 5.217, and Glass's Δ = 9.574) confirmed a significant difference between the sexes. Incidence rates demonstrated a significant decline from 1990 to 2000, followed by fluctuations, with males consistently experiencing higher rates than females. Conclusion This study underscores significant sex differences in mortality, YLLs, and incidence rates due to pedestrian road injuries in Georgia from 1990 to 2021, with males consistently showing higher rates. These findings emphasize the need for targeted interventions to address the specific vulnerabilities of male pedestrians in Georgia. The data and analysis were facilitated through collaboration with the Global Burden of Disease Study, whose contributions were invaluable.

Balandra, one of the most popular beaches in La Paz, Baja California Sur, was declared a flora and fauna protection area in 2012 and, in 2021, was declared the best beach in Mexico. More recently, it was included among the ten most beautiful beaches in the world. Currently, this beach faces overcrowding. Formulating effective management policies depends, to a certain extent, on the knowledge of their recreational value and visitor characteristics. Recreational value allows us to know the benefits of the tradeoffs among the ecosystem services and society and exhibit the value of possible damages to marine ecosystems. Like the one caused in 2022 by the fire of a tourist boat inside Balandra. Using the individual travel cost method and applying 159 questionnaires to site visitors, four models were estimated to calculate the individual willingness to pay for accessing Balandra, which ranges between 3.86 and 10.38 US$/day/visitor. Recreational economic value (REV) for Balandra was estimated using two essential criteria. One, total of visitors registered in 2021. Two, daily maximum carrying capacity. We also calculated the REV as a proxy of the welfare loss derived from the site's closure. Finally, possible environmental and economic policy instruments that could be implemented are discussed.

Costa Rica, renowned for its environmental sustainability, faces a significant waste management crisis. Despite its commitment to renewable energy and biodiversity conservation, waste management inefficiencies, particularly in urban areas such as San José, pose severe environmental and public health risks. This study investigated the current state of waste management in Costa Rica, highlighting critical issues of landfill capacity, regional practices, and public participation in recycling. Using data from 2015 to 2023, linear regression models predict trends in waste minimization practices, providing insights into the potential impacts of ongoing government initiatives. The key findings indicate that Costa Rica's recycling rate is approximately 9%, which is significantly lower than the global average of 32% and is well below that of the EU at 47% and that of Germany at 67%. This underscores the need for improved infrastructure and public education. The Environmental Health Route policy aims to increase the recycling rate to 25% by 2033, while the National Circular Economy Strategy targets comprehensive waste management improvements.Effective implementation, increased funding, and enhanced enforcement are essential, particularly in rural areas where recycling participation is less than 10%. This research offers a comprehensive analysis of Costa Rica's waste management challenges and proposes actionable recommendations to improve practices and increase recycling rates.

Pharmaceutical companies use crisis management as a vital technique to lessen the effects of catastrophes. In this study, the author highlighted the significance of supply chain management for crisis management in the pharmaceutical industry. The study concludes by discussing the role that supply chain management plays in crisis management and offering pharmaceutical businesses advice on how to run their supply chains in an emergency. We conclude after conducting a thorough analysis of the literature review and regress research. These are actions that pharmaceutical organizations can take to strengthen their supply chain management skills and enhance their crisis readiness: Identifying possible supply chain risks through routine risk evaluations; Creating backup strategies in case the supply chain is disrupted; Establishing procedures to guarantee a smooth and efficient supply of medications in an emergency; and Collaborating with vendors and other stakeholders to increase the resilience of the supply chain. The pharmaceutical industry is critical to healthcare, providing safe and effective medicines to patients. Supply chain management is essential for maintaining the flow of medicines during crises. Proactive risk assessment, backup plans, teamwork, and efficient communication are all critical for crisis management in the pharmaceutical industry, particularly in supply chain management. In India, inadequate transportation infrastructure leads to a significant expense for pharmaceutical businesses. To mitigate negative effects, such as reduced healthcare access and insurance coverage, the study suggests implementing multi-echelon, closed-loop green supply chain models. These strategies aim to minimize total costs and ensure efficient resource management. To improve the resilience of pharmaceutical and consumable medical equipment during crises, policymakers should integrate modern technologies and consider business complexity, economic development, intense competition, and changing customer needs. The relationship between manufacturers, distributors, prescribers, and insurance organizations as purchasers should also be considered.

The rapidly evolving domain of multimodal sentiment analysis is crucial for unraveling the intricate layers of emotional expression in social media content, customer service interactions, and personal vlogs. This research introduces a cutting-edge Advanced Cross-Modal Gating (ACMG) framework that significantly enhances the precision of sentiment analysis by refining the interplay among textual, auditory, and visual modalities. Our approach addresses three foundational aspects of sentiment analysis: (1) Advanced learning of cross-modal interactions, which focuses on extracting and synthesizing sentiment from varied modal inputs, thus providing a holistic view of expressed emotions; (2) Mastery over the temporal dynamics of multimodal data, enabling the model to maintain context and sentiment continuity over extended interactions; and (3) Deployment of a novel fusion strategy that not only integrates unimodal and cross-modal cues but also dynamically adjusts the influence of each modality based on its contextual relevance to the sentiment being expressed. The exploration of these dimensions reveals that the nuanced modeling of cross-modal interactions is crucial for enhancing model responsiveness and accuracy. By applying the ACMG model to two highly regarded datasets—CMU Multimodal Opinion Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI)—we achieve groundbreaking accuracies of 83.9\% and 81.1\%, respectively. These results represent significant improvements of 1.6\% and 1.34\% over the current state-of-the-art, showcasing the superior performance and potential of our approach in navigating the complexities of multimodal sentiment analysis.

This article aims to analyze the problem of the identity and persistence of objects based on the hylomorphic doctrine, that is, the doctrine according to which objects are mereological compositions of the elements of form and matter. After introducing the contemporary discussion on the problem of identity and persistence and identity of objects, we propose a brief formalism of the logic of the relationship between part and whole or mereology to define the solution to the problem under study that defines a possible solution for it.

Sedimentation is an undesirable phenomenon that complicates the design of microsystems that exploit dense microparticles as delivery tools, especially in biotechnological applications. It often informs the integration of continuous mixing modules, consequently impacting system footprint, cost, and complexity. The impact of sedimentation is significantly worse in systems designed with the intent of particle metering or binary encapsulation in droplets. Circumventing this problem involves the unsatisfactory adoption of gel microparticles as an alternative. This paper presents two solutions – a hydrodynamic solution that changes the particle sedimentation trajectory relative to a flow-rate dependent resultant force; and induced hindered settling (i-HS), which exploits Richardson-Zaki (RZ) corrections of Stokes’ law. The hydrodynamic solution was validated using a multi-well fluidic multiplexing and particle metering manifold. Computational image analysis of multiplex metering efficiency using this method showed an average reduction in well-to-well variation in particle concentration from 45% (Q = 1 mL/min, n = 32 total wells) to 17% (Q = 10 mL/ min, n = 48 total wells). By exploiting a physical property (cloud point) of surfactants in the bead suspension in vials, the i-HS achieved 58% reduction in the sedimentation rate. This effect results from surfactant phase change, which increases the turbidity (transient increase in particle concentration), thereby exploitingthe RZ theories. Both methods can be used independently or synergistically to eliminate bead settling in microsystems or minimize particle sedimentation.

To date, the development of unmanned technologies using autonomous underwater vehicles (AUV) has become an urgent demand for solving the problem of inspection of industrial sub-sea structures. A key issue here is precise localization of AUVs relative to underwater objects. However, the well-known challenges of recognizing and coordinating underwater objects (impossibility to use GPS, undercurrents, etc.) do not allow high-precision navigation if based solely on a standard suite of AUV navigation tools (sonars, etc.). An alternative is a technology of processing of optical images that, at short distances, can provide higher accuracy of AUV navigation compared to a technology of acoustic measurement processing. In this study, in the context of the problem of subsea production system inspection, we propose a method to recognize underwater objects and provide coordinate reference of AUV to them, based on stereo-image processing. Its distinctive features are the use of a non-standard technique for generating a geometric model of an object from its views (foreshortening) taken from positions of a pre-made overview trajectory, the use of various characteristic geometric elements when recognizing objects, and the original algorithms for comparing visual data of the inspection trajectory with an a priori model of the object.

(1) Background: This report aims to illustrate the development, progression, diagnosis and treatment of chronically present articular surface lesions; (2) Methods: In this report, two patients are described from the point of initial presentation of symptoms to surgical consultation based on radiologic findings. These patients underwent corrective surgery in the form of mosaicplasty to repair the lesion present at the articular surface and the underlying subchondral bone. (3) Discussion: Diagnosing juvenile OCD remains challenging due to variable clinical presentation and minute radiologic discoveries. X-rays are useful, however, the gold standard remains to be arthroscopy which can be both diagnostic and therapeutic. Future prospects include the use of novel sonographic methods and the use of artificial intelligence within the given modalities. (4) Conclusions: The detailed imaging provided by MRI, combined with the insights from X-rays and potentially other modalities, allows for a nuanced understanding of the disease. This comprehensive approach ensures that treatment decisions are well-informed, optimizing outcomes for young patients with this condition.

In this work, non-ordered and ordered CeO2-based catalysts are proposed for the CO2 conversion to dimethyl carbonate (DMC). Particularly, a non-ordered mesoporous CeO2, consisting of small nanoparticles of about 8 nm, is compared with two highly porous (635-722 m2/g) ordered CeO2@SBA-15 nanocomposites obtained by two different impregnation strategies (a two-solvent impregnation (TS) and a self-combustion (SC) method), with a final CeO2 loading of 10 wt%. The Rietveld analyses on XRD data combined with TEM imaging evidence the influence of the impregnation strategy on the dispersion of the active phase: nanoparticles of 8 nm for the TS composite vs. 3 nm for the SC composite. The catalytic results show comparable activity for the mesoporous ceria and the CeO2@SBA-15_SC nanocomposite, while a lower DMC yield was found for the CeO2@SBA-15_TS nanocomposite. This finding is presumably ascribed to a partial obstruction of the pores by the CeO2 nanoparticles in the case of the TS composite, which leads to a reduced accessibility of the active phase. On the other hand, in the case of the SC composite, where the CeO2 particle size is much lower than the pore size, there is an improved accessibility of the active phase to the molecules of the reactants.

A new heterocyclic compound, 2-(2,6-diisopropylphenyl)-1-methylimidazo[1,5-a]quinolin-2-ium tetrafluoroborate 1 was obtained from its precursor, N-(2,6-diisopropylphenyl)-N-(quinolin-2-ylmethyl)acetamide 2, by POCl3-mediated cyclization. For the first time – the tertiary acetamide 2, precursor of 1, was synthesized by using commercially available starting materials. The structure of 1 was unquestionably confirmed by 1H-, 13C-, 2D-NMR-, IR- spectroscopy and mass-spectrometry. Its optical properties were also studied.

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar el-bow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corre-sponding forearm by hook and loop velcro and thus, helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation and thus, the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm, but through a prismatic pair which allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, that is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mecha-nism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage, by giving a first mechanical de-sign and a 3D printed prototype.

Capulus danieli, a distinct member of Capulidae, exhibits a unique ecological behaviour by attaching and drilling onto the pecten, distinguishing itself from other gastropods. Herein, we present the first characterisation of the mitochondrial genome of Capulus danieli. The results showed that the genome spans 15600 base pairs, with an A + T content of 71.12% and a G + C content of 28.88%. Within this genetic makeup, we identified 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis revealed a close evolutionary relationship between Capulus danieli and members of the Ficoidea superfamily. The divergence time estimation suggested that Capulus danieli diverged approximately 52.29 million years ago. These findings significantly contribute to our understanding of the evolutionary history and genetic architecture of Capulus danieli, shedding light on its unique ecological adaptations to the marine environment.

Efficient beam steerable high-gain antennas enable high-speed data rates over long-distance networks, including wireless backhaul, satellite communications (SATCOM), and SATCOM On-the-Move. These characteristics are essential for advancing contemporary wireless communication networks, particularly within 5G and beyond. Various beam-steering solutions have been proposed in the literature, with passive beam-steering mechanisms employing planar metasurfaces emerging as cost-effective, power-efficient, and compact options. These attributes make them well-suited for use in confined spaces, large-scale production and widespread distribution to meet the demands of the mass market. Utilizing a dual-band antenna terminal setup is often advantageous for full duplex communication in wireless systems. Therefore, this article presents a comprehensive review of the dual-band beam steering techniques for enabling full-duplex communication in modern wireless systems, highlighting their design methodologies, scanning mechanisms, physical characteristics, and constraints. Despite the advantages of planar metasurface-based beam steering solutions, literature on dual-band beam steering antennas supporting full duplex communication is limited. This review article identifies research gaps and outlines future directions for developing economically feasible passive dual-band beam-steering solutions for mass deployment.

In nature and engineering applications, flexible fiber beds covering biological surfaces can play a role in reducing resistance. These fibers deform under the action of fluids, and this deformation affects the fluid flow state, forming a complex fluid-solid interaction phenomenon. To quantitatively analyze such issues, the physical model is simplified to the deformation problem of an soft hair bed caused by Stokes flow, and the deformation problem of a single fiber caused by Stokes flow is further studied. The deformation problem of an elastic single fiber in a channel caused by Stokes flow can be described by a nonlinear integral equation. We have obtained a new series solution, which has been compared with the previous perturbation method to verify the accuracy and effectiveness of the series solution. Meanwhile, we have further provided an extended form of flexible fiber deformation through experimental fitting. This fluid-solid interaction problem involves multiple fields and is very important in many natural and engineering systems. The research in this paper can not only help us better understand complex phenomena in nature but also delve into the interaction mechanism between fluids and solids, providing a theoretical basis for future scientific research and engineering applications.

As shown previously, Rhodamine 6G (R6G) and 4-nitro-2,1,3-benzoxadiazole (NBD) linked through a spacer molecule spermidine (spd), R6G-spd-NBD, produce a fluorescent probe with pH-sensitive FRET effect, that can be useful in a variety of diagnostic applications. Specifically, cancer cells can be spotted due to a local decrease in pH (Warburg effect). Here we extend this approach to infectious diseases – namely, leishmaniasis, brucellosis, and tuberculosis, are difficult to treat largely because of their localization inside macrophages. R6G-spd-NBD offers an opportunity to detect such bacteria and potentially deliver therapeutic target to treat them. The micellar form of the R6G-spd-NBD probe (chitosan or heparin grafted with lipoic acid residues, Chit-LA and Hep-LA, respectively) was obtained to improve the pH sensitivity in the desired range (5.5–7.5) and enable the selective targeting of bacterial cells, thus improving CLSM imaging. According to AFM (atomic force microscopy) data, the micelles containing NBD-spd-R6G have a compact and highly spherical shape, with a diameter ranging from 70 to 110 nm. The micellar form of the R6G-spd-NBD further improves absorption and penetration into bacteria, including those located inside macrophages. Due to negative charge of the surface, bacteria absorb positively charged R6G-spd-NBD, and even more so in the micellar form, when chitosan derivatives are used to form the polymeric micelle. Additionally, macrophages’ lysosomes can be easily distinguished due to their acidic pH. Confocal laser scanning microscopy was used to visualize samples of macrophages cells containing absorbed bacteria. The micellar formulation showed a significant selectivity to model E. coli vs Lactobacillus bacterial cells, and R6G-spd-NBD, being a mild bactericide, clears over 50% E.coli in conditions where Lactobacillus remains almost unaffected. Taken together, our data indicates that R6G-spd-NBD, as well as similar compounds, can have value not only for diagnostic, but also for theranostic applications.

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

Froth flotation, a widely used mineral beneficiation technique, generates substantial volumes of data, offering the opportunity to extract valuable insights from these data for production line analysis. The quality of flotation data is critical to designing accurate prediction models and process optimisation. Unfortunately, industrial flotation data are often compromised by quality issues such as outliers that can produce misleading or erroneous analytical results. A general approach is to preprocess the data by replacing or imputing outliers with data values that have no connection with the real state of the process. However, this does not resolve the effect of outliers, especially those that deviate from normal trends. Outliers often occur across multiple variables and their values may occur in normal observation ranges, making their detection challenging. An unresolved challenge in outlier detection is how far is far enough for an observation to be considered an outlier. Existing methods rely on domain experts’ knowledge which is difficult to apply when experts encounter large volumes of data with complex relationships. In this paper, we propose an approach to conduct outlier analysis on a flotation dataset. The approach uses a 2σ rule as the threshold to find quasi-outliers and multiple machine learning (ML) algorithms including k-Nearest Neighbour (kNN), Local Outlier Factor (LOF), and Isolation Forest (ISF) to identify true outliers. The approach then analyses the mutual coverage between quasi-outliers and outliers from the ML algorithms to identify the most effective outlier detection algorithm. We found that the outliers by kNN cover outliers of other methods. We use the experimental results to show that outliers affect model prediction accuracy and excluding outliers from training data can reduce the average prediction errors.

In the global fight against malaria, standard vector control methods such as indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are intended to protect inside residential structures and sleeping spaces. However, these methods can still leave individuals vulnerable to residual transmission from vectors that they may be exposed to outdoors. Nchelenge District in northern Zambia experiences high persistent malaria transmission even with ITNs and IRS in place. However, very few studies have examined outdoor vector activity. To assess diversity and abundance of outdoor foraging female anopheline mosquitoes, CDC light traps were used as proxy measures for mosquito host-seeking, set in three outdoor trapping schemes randomly assigned on different nights: 1) locations where people congregate at night outside of the house within the peri-domestic space, 2) animal pens or shelters, and 3) high human-trafficked areas, such as paths to latrines, where traps were baited with BG-Lure®. 1,087 total female anophelines were collected over a total of 74 trap nights. Anopheles funestus s.s. comprised the majority of the collection (86%), with An. gambiae s.s. (2%) and a highly diverse sampling of other anophelines (12%) making up the remainder. Plasmodium falciparum parasites were only detected in An. funestus (1%). No significant difference in species diversity or female anopheline abundance was detected between trapping schemes. Outdoor foraging anopheline mosquitoes, including a number of infectious An. funestus, may partially explain the difficulty of controlling malaria transmission in Nchelenge District where vector control is only targeted indoors. BG-Lure® shows some promise as an alternative to human-baited landing catch collections in this resource-poor setting.

Antimicrobial peptides (AMPs) are regarded as a promising alternative to traditional antibiotics in the face of ever-increasing resistance. Our research has focused on crotalicidin (Ctn), an AMP from a snake venom, and a fragment thereof, Ctn[15-34], with improved in vitro antimicrobial and anticancer activities and remarkable serum stability. As the retroenantio versions of both AMPs maintained favorable profiles, in this work we evaluate the in vivo efficacy of both the na-tive-sequence AMPs and their retroenantio counterparts in a murine infection model with Aci-netobacter baumannii. A significant reduction in bacterial levels is found in the mice treated with Ctn[15-34]. However, contrary to expectations, the retroenantio analogues either exhibit toxicity or lack efficacy when administered to mice.

This study investigates the influence of digital marketing on consumer purchasing decisions, focusing on how various strategies impact consumer behavior. By analyzing data collected from in-depth interviews and thematic analysis, the research highlights several key factors driving consumer engagement and purchases. Personalized content emerged as a significant factor, with tailored recommendations and customized email campaigns creating a sense of individual attention and enhancing consumer involvement. Social media platforms played a pivotal role, with advertisements, influencer endorsements, and user-generated content significantly affecting purchasing decisions. The impact of visual content, including high-quality images and videos, was also notable, as these elements helped consumers better understand products and increased their confidence in making purchases. Email marketing proved effective, particularly when offering exclusive discounts and creating a sense of urgency. Mobile marketing demonstrated its growing influence, with consumers valuing the convenience of mobile-friendly websites and app notifications. The integration of artificial intelligence further improved the shopping experience by providing instant assistance and personalized recommendations. Ethical considerations, such as data privacy and honest advertising, were essential for maintaining consumer trust and loyalty. User-generated content provided authentic insights that influenced purchasing decisions, while omnichannel strategies offered a seamless and flexible shopping experience. Gamification elements, including reward programs and interactive challenges, enhanced consumer engagement and motivation. The study concludes that a multifaceted approach to digital marketing, incorporating these strategies, is crucial for effectively engaging consumers and driving sales. The findings offer valuable insights for marketers aiming to optimize their digital marketing strategies in the evolving digital landscape.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

Access to travel information is crucial for blind and partially sighted (BPS) people in travel planning. However, finding relevant information can be complex and challenging due to the unavailability of appropriate information and accessible information sources. To investigate this issue, a mixed-method study consisting of semi-structured interviews and co-design workshops was conducted to identify the needs of BPS people and the barriers BPS people experience due to the lack of access to such information. The findings provide insights into the information-seeking process and highlight the role of experience-sharing in cultivating a sense of agency, contribution, and interdependence. We contribute an accessible tourism ecosystem based on our findings to motivate research on technologies to support inclusive leisure travel for BPS people and to inform the design of inclusive tourism services.

Long COVID is a persistent symptom that appears in many COVID-19 patients, regardless of whether they are asymptomatic, mild, or severe. At least 10% worldwide are suffering from long COVID. The anti-inflammatory effect was evaluated in LPS-treated RAW264.7 cells using six Korean national insurance-covered herbal prescriptions with therapeutic potential for mitigating inflammation in the respiratory tract, and additional network pharmacology analysis was performed. Soshihotang (SST) significantly downregulated iNOS and COX2 expression, reduced the production of NO (nitric oxide) and inflammatory cytokines, such as TNFα, IL-1β, and IL-6, and inhibited IκBα phosphorylation and reduced the nuclear translocation of NF-κB in LPS-treated RAW264.7 cells. Network pharmacological analysis showed that SST was closely related to the NF-κB signaling pathway. Further study on the potential of herbal prescriptions will be needed before they can be used clinically by long COVID patients.

Drug repositioning is an efficient strategy to search for new treatment alternatives that is especially valuable for neglected parasitic diseases such as leishmaniasis. Tamoxifen and raloxifene are selective estrogen receptor modulators (SERM) that have shown antileishmanial activity. Clomiphene is a SERM structurally similar to tamoxifen whose antileishmanial potential is unknown. That is why the objective of the present work was to evaluate its antileishmanial activity in vitro and in vivo, in comparison with tamoxifen. The inhibitory effect against promastigotes of L. amazonensis, L. major and L. mexicana was evaluated for both compounds; as well as the cytotoxicity against mouse peritoneal macrophages, the growth inhibitory activity in intracellular amastigotes of L. mexicana, and the in vivo activity in mice experimentally infected with L. mexicana. Clomiphene was about twice as active as tamoxifen against both promastigotes and intracellular amastigotes, with IC50 values of 1.7-3.3 µM for clomiphene and 2.9-6.4 µM for tamoxifen against all three species promastigotes; and 2.8 ± 0.2 µM and 3.7 ± 0.3 µM, respectively, against L. mexicana amastigotes. Clomiphene structurally affected several parasite organelles in a concentration-dependent fashion, leading to the death of both promastigotes and intracellular amastigotes. Interestingly, the macrophage host cell did not appear damaged with any of the clomiphene concentrations tested. By oral administration at 20 mg/kg for 14 days, both compounds showed similar effects in terms of reducing the growth of the lesions, as well as the weight of the lesions and the parasite load at the end of the follow-up period. The results showed the potential of SERM as antileishmanial drugs and support further testing of clomiphene and other compounds of this pharmacological group.

Magnesium monofluoride is a polar molecule amenable to laser cooling which has caused renewed interest in its spectroscopy. In this work we consider the case of three low-lying electronic excitations, namely $\rm X \, {}^{2}\Sigma \to A \, {}^{2}\Pi, \ \to B \, {}^{2}\Sigma, \ \to C \,{}^{2}\Sigma$, using well developed quantum chemistry approaches, i.e., without reference to the spin-orbit splitting of the $\rm A \, {}^{2}\Pi$ states. Accurate experimental data for these transitions have been available for over 50 years. Here we explore the linear response method at the level of CC2 theory, as well as equation of motion methods at the level of CCSD and CC3, using two families of basis sets. Excellent agreement is obtained for the first three transitions when using the correlation-consistent basis sets and extrapolation to the complete basis limit within EOM-CC3 (at a relative precision of $10^{-4}$), and qualitative agreement for the other two methods. The purpose of this paper is to serve as a guide in how to approach the accurate calculation of excitations in polar diatomic molecules.

Information theoretic quantities such as entropy, entropy rate, information gain, and relative entropy are often used to understand the performance of intelligent agents in learning applications. Mean squared error has not played a role in these analyses, primarily because it is not felt to be a viable performance indicator in these scenarios. We build on a new quantity, the log ratio of entropy powers, to establish that minimum mean squared error (MMSE) estimation, prediction, and smoothing are directly connected to Mutual Information Gain or Loss in an agent learning system modeled by a Markov chain for many probability distributions of interest. Expressions for mutual information gain or loss are developed for MMSE estimation, prediction, and smoothing and an example for fixed lag smoothing is presented.

In the last years, different biotechnological approaches were developed to reduce the indiscriminate use of chemical fertilizers and pesticides and enhance plant growth and health. The most attractive, safe, and environmentally mild alternatives include those based on plant beneficial microorganisms. After a long period of studies on isolation, selection, and characterisation of plant-beneficial microorganisms, the main lines of research have been oriented to optimising the fermentation processes to produce high-quality and large volumes of biomass/spores and their further formulation. However, further well-structured schemes for improvement of all main steps of the production of bio-formulates should be developed following the ideas based on “healthy soil-healthy plants-healthy humans”.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.

Indoor farming systems enable plant production in precisely controlled environments, managing factors such as lighting, photoperiod, and nutrient supply, to enhance crop yield and quality. However, the implementation of stable growth conditions and the absence of stress stimulants can weaken plants' defense responses and limit the accumulation of bioactive, health beneficial phytochemicals. A potential solution is the controlled application of stressors, such as supplemental ultraviolet (UV) light, which stimulates secondary metabolites (SM) synthesis and increase overall antioxidant potential. To this end, we analyzed the efficiency of short-term pre-harvest supplementation of red-green-blue (RGB, LED) spectrum with ultraviolet B (UV-B, 311 nm) or C (UV-C, 254 nm) light to boost phytochemicals synthesis. Additionally, given the biological harm of UV radiation due to high-energy photons, we monitored plants’ photosynthetic activity during treatment. Our analyses showed that UV-B radiation, applied with a cumulative dose of 15.622 kJ m–2, did not negatively impact photosynthetic activity while significantly increasing the overall antioxidant potential of lettuce through enhanced levels of polyphenolic SM (total phenolics, flavonoids, anthocyanins) and ascorbic acid. Conversely, UV-C radiation, applied with a lower cumulative dose of 6.008 kJ m–2, induced anthocyanin accumulation in the green leaf cultivar but significantly harmed the photosynthetic apparatus and limited plant growth. These results are crucial for understanding the potential of UV light supplementation in producing functional plants.

Hydroxyurea (Hu) has been a front-line therapeutic agent for myeloproliferative neoplasms (MPN) for many years and still enjoys the endorsement of experts in the field. However, several publications have reported sub-optimal response, the need for treatment interruption because of cytopenias and lack of sustained response. In all these studies, Hu was used as continuous therapy at a daily dose ranging from 500mg to 3000mg. At our Centre we have used Hu as intermittent therapy (akin to schedules used in patients with solid tumours) at 20-30mg/Kg doses, given as a single dose, twice or thrice weekly. We have consistently observed sustained responses without troublesome cytopenias. In this report we present our experience in 118 patients treated with intermittent Hu during the past 30 years (median follow-up 8.5 yrs): polycythemia vera – 29; essential thrombocythemia – 84; primary myelofibrosis – 5. Based on the pharmacokinetics of Hu and our experience, we speculate that the efficacy of intermittent Hu therapy without troublesome myelotoxicity over long periods of time is attributable to the following: i) higher plasma level from intake of Hu as a single dose; ii) higher uptake of Hu by cells with higher mitotic activity (i.e. the abnormal clone); and iii) unhindered, normal haemopoiesis on the drug free days each week. We hope that this article will generate interest and contemplation, leading to randomized clinical studies to compare the two dosage schedules (Continuous Vs Intermittent) in MPN patients.

This study introduces a cascade nonlinear proportional integral retarded (CNPIR) controller for the first time in the literature, aiming to achieve real-time precise control of a double-tank liquid level system. It begins by outlining the characteristics of de-lay-based systems and detailing the dynamic behavior of the liquid-level system. The study evaluates the system's performance by developing and analyzing the mathe-matical models of both the feedforward proportional-integral (FF-PI) controller and the CNPIR controller. Two different control techniques were employed, with real-time tests conducted to assess the system's axis movement performance. The FF-PI controller was used as the initial control technique, while the CNPIR controller was introduced to enhance system performance during trajectory tracking. Experimental results demon-strated that the CNPIR method significantly improved trajectory tracking response, reduced overshoot, and achieved better steady-state error under external disturbances compared to the FF-PI controller. Thus, the experimental study, highlights the effec-tiveness of the proposed control design and its superiority over some existing classical methods.

In surgically treated individuals with surgical stomas, the return to sports activity is an indicator of quality of life that reflects their well-being. With the aim of synthesizing the available evidence regarding the return to physical activity and sports practice in individuals with surgical stomas, a scoping review was developed following the methodological approach of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews criteria. Searches were conducted in Medline (PubMed), Scopus, Web of Science, Cinahl, and Lilacs, as well as the meta-search engines TripDatabase and Epistemonikos, using the MeSH terms "exercise", "return to sports", and "surgical stomas"; including studies in Spanish, English, Portuguese, and German, without any limitation on the year of publication. A total of n = 15 studies with different designs were included (n = 2 qualitative; n = 1 cohort; n = 8 cross-sectional; n = 2 case reports; n = 1 case series; and n = 1 Randomized Clinical Trial), which showed variability in the quality of the designs. The studies revealed the complex relationship between physical activity and having surgical stomas, associated with the individuals' characteristics. There is a need to increase the number and rigor of research to understand the phenomenon of return to physical activity and sport, thus providing evidence-based clinical recommendations for sports practice in people with stomas.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Backyard chickens often suffer from foot pad dermatitis (FPD), a condition exacerbated by poor husbandry, nutritional deficiencies, and obesity. Pressure-relieving bandages, commonly used in treatment of FPD, are impractical for outdoor chickens as they quickly become wet and dirty, necessitating daily changes which are often unfeasible. This retrospective study explores the use of custom-fit silicone shoes, created via 3D-printed molds, as an alternative to traditional bandages. CT scans were used to design shoes, adapted from a design used for birds of prey. Over three years, 16 chickens with varying degrees of FPD were treated. The results demonstrated that sili-cone shoes were an effective treatment for FPD, allowing outdoor activity, and facilitated daily lesion care without frequent bandage changes. The median shoe-wearing period was 14 days, and most owners provided positive feedback. Despite some attachment issues, the occurrence of sec-ondary pressure marks and feather regrowth challenges, the silicone shoes offered better ventila-tion, reusability, and consistent pressure relief compared to traditional bandages. This study con-cludes that silicone shoes are a viable solution for managing FPD in backyard chickens, promoting faster healing and improved owner compliance.

Choline and essential fatty acids (EFA) are vital for fetal brain development, supporting pregnancy, and maintaining hormonal balance. They also promote overall health. The childbearing years present a window of opportunity to increase the intake of these key nutrients and develop healthy dietary habits. Survey data were collected for the Estudio Latinoamericano de Nutrición y Salud (ELANS), including participants from Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Peru, and Venezuela (n=9,218; 15-65 years old). Women of childbearing age were extracted from the largest database (n=3,704). The aims of this study were to evaluate the intake of choline and essential fatty acids in women of childbearing age, identify their food sources, and determine if supplements containing choline and EFA were available across the ELANS countries. In general, choline intake was inadequate in all countries, while essential fatty acids intake was normal or above requirements. Chile had the lowest intake of choline and Colombia the highest. The results showed that some countries had more inadequate choline intake than others. Consuming a larger quantity of eggs helped reduce choline inadequacy, as did including eggs and fish in the diet. The intake of essential fatty acids, including ALA, EPA, and DHA, showed variability. The contributions of EPA and DHA were lower than that of ALA, and the results differed by age group. In conclusion, choline intake is inadequate and EFA intake is variable among women of childbearing age in the ELANS study. More awareness and education are needed to achieve better intake of these nutrients.

This article titled "AI and IoT in Automation and Security: A Vision for Next Generation Applications," explored the implications of marrying IoT with AI in the realms of automation and security; this piece goes further into what that entails. In this paper, we present innovative application descriptions arising from this technology convergence and its future-generation applications. We found that AI brings strong cyber security, predictive maintenance, and decision-making capabilities to the table due to its advanced algorithms and machine learning models. IoT has the potential to allow businesses to extract and generate big data, automate processes, and monitor in real-time thousands and even millions of businesses at once. For now, we believe some of the possible main conclusions will be that the smart city, stand-alone systems, and user experience will be quite consumer-friendly. Importantly, the study also assessed facets such as data protection, system interoperability, and a few others. Our vision underscores the essential of a strong structure to connect the full latent of AI and IoT, finally aiming to create more capable, protected, and approachable environments. Significant results include achieving 95.8% accuracy in anomaly detection and 93.4% accuracy in predictive maintenance. The showcasing results efficacy of AI models to enhance system reliability as well as operational efficiency. Vigorous encryption protocols performance ensures data security hence addressing issues of privacy. These culminations underscore the transformative potential of AI-IoT integration in next-generation automation and security applications.

This study explores the optimization of the admission process at the Escuela Colombiana de Ingeniería Julio Garavito through data anal- ysis. By integrating advanced tools like Power BI, Python Flask, and data mining techniques, a model has been developed to improve student selection and retention, enabling more informed and effective decisions. The results indicate that applying data science not only facilitates efficient data management but also promotes more inclusive and equitable educational poli- cies. This pioneering approach in the field of higher education in Colombia offers a frame- work for future research and technological de- velopments, highlighting the vital importance of data science in improving educational and administrative processes.

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, for this type of earthquake. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

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

This research explores the impact of 5G technology on supply chain efficiency and marketing innovations, highlighting its transformative potential and associated challenges. The study delves into how 5G enhances real-time tracking and monitoring within supply chains, significantly improving inventory management, reducing stockouts and overstocking, and ensuring optimal conditions during product transit. The advanced communication capabilities of 5G enable seamless collaboration among supply chain partners, facilitating quicker decision-making and improved risk management. Additionally, the integration of 5G technology with data analytics has optimized processes, improved forecasting accuracy, and led to cost reductions, showcasing the value of data-driven decision-making. In the marketing domain, 5G technology has driven the development of immersive augmented reality (AR) and virtual reality (VR) applications, enhancing customer engagement through interactive and experiential content. Personalized and targeted marketing strategies, supported by real-time data and deeper customer insights, have led to higher customer satisfaction and increased sales. The proliferation of connected devices, such as smart speakers and wearables, has further enabled personalized interactions and convenience, fostering brand loyalty and driving growth. Despite these advancements, the implementation of 5G presents challenges, including significant infrastructure investment, cybersecurity risks, and privacy concerns. Companies must navigate these issues by investing in robust security measures and developing comprehensive policies. The research concludes with an optimistic outlook on the future impact of 5G technology, emphasizing its potential to drive innovation, enhance operational efficiency, and transform customer experiences, ultimately shaping the future of business operations.

Familial pancreatic cancer (FPC) represents a significant yet underexplored area in pancreatic cancer research. Basic research efforts are notably limited, and when present, they are predominantly centered on BRCA1 and BRCA2 mutations due to the scarcity of other genetic variants associated with FPC, leading to a limited understanding of the broader genetic landscape of FPC. This review examines the current state of FPC research, focusing on the molecular mechanisms driving pancreatic ductal adenocarcinoma (PDAC) progression. It highlights the role of homologous recombination (HR) and its therapeutic exploitation via synthetic lethality with PARP inhibitors in BRCA1/2-deficient tumors. The review discusses various pre-clinical models of FPC, including conventional two-dimensional (2D) cell lines, patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and genetically engineered mouse models (GEMMs) as well as new advancements in FPC research.

Academic engagement provides opportunities and resources for students to engage in socio-educational interactions and academic learning. Our study provides an overview of high-impact research in academic engagement and the potential causes of its high valuation in the scientific community based on a bibliometric analysis of 4667 articles indexed in Web of Science, processed mainly by VOSviewer software. In terms of results, the publication of selected articles grows exponentially year by year, presenting concentration levels of 1% in authorship, 49% in a single country, and 5% of the journals. As well as 6% in highly cited articles, which in this case is associated with authors with high levels of publication. The most cited current topics are those relating academic engagement with motivation and emotional aspects.

This study explores the transformative impact of machine learning on predictive analytics within supply chain management. As supply chains become increasingly complex and dynamic, traditional methods of forecasting and management are proving inadequate. Machine learning offers advanced algorithms capable of analyzing vast datasets to uncover patterns and trends that enhance decision-making processes. This research investigates how machine learning improves demand forecasting accuracy, optimizes inventory management, strengthens supplier relationships, and enhances risk management. The study employs a qualitative approach, analyzing data from industry case studies and expert interviews to identify key themes and challenges associated with the adoption of machine learning. Findings reveal that machine learning significantly improves forecasting precision, reduces inventory costs, and enables proactive supplier and risk management. However, challenges such as ensuring data quality, acquiring specialized skills, integrating new technologies with existing systems, and addressing ethical considerations are critical. The study also highlights the potential of emerging technologies like blockchain, IoT, and 5G to further enhance machine learning applications in supply chain management. This research contributes to a deeper understanding of how machine learning can drive innovation and efficiency in supply chains, while also addressing the hurdles that organizations must overcome. The results underscore the need for a balanced approach that incorporates technological advancements and ethical practices to fully leverage the benefits of machine learning.

Insulin resistance is a condition characterized by a reduced biological response to insulin. It is one of the most common metabolic diseases of modern civilization. Numerous naturally occurring substances have shown to have a positive effect in restoring the proper sensitivity to insulin. Mature 3T3-L1 adipocytes with artificially induced insulin resistance by palmitic acid (16:0) were used for the study. Cinnamic acid and 1,2-dicinnamoyl-sn-glycero-3-phosphocholin (1,2-diCA-PC) were tested at three concentrations: 25 μM, 50 μM, and 125 μM. The number of mitochondria and the expression of genes encoded by mtDNA were elucidated in control and experimental cells. In experimental cells treated with 1,2-diCA-PC, we displayed increased insulin-stimulated glucose uptake in a dose-dependent manner, accompanied by an increase in mtDNA copy number. Moreover, in experimental cells treated with 1,2-diCA-PC at the concentration of 125 μM, we observed a significant increase in the expression level of all analyzed genes encoded by mtDNA compared to control cells. Our study showed a relationship between improved cellular sensitivity to insulin by 1,2-diCA-PC and an increase in the number of mitochondria and expression levels of genes encoded by mtDNA. Summarizing, the results suggest a therapeutic potential of cinnamic acid derivative 1,2-diCA-PC in enhancing the insulin sensitivity of adipocytes.

This case report elucidates the clinical trajectory of a 60-year-old male with a history of mild cytopenias, subsequently diagnosed with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) characterized by a complex karyotype and a TP53 mutation. The initial diagnostic evaluation included a bone marrow biopsy revealing 34% myeloid blasts with aberrant immunophenotyping and complex cytogenetic abnormalities, including losses and gains of chromosomal regions. Genetic analysis confirmed a high Variant Allele Frequency of 80% for the TP53 mutation. The patient’s management strategy commenced with a combination of Inqovi (decitabine and cedazuridine) and Venetoclax, supported by prophylactic measures to mitigate tumor lysis syndrome and vigilant infection control. While offering a glimmer of hope for improving outcomes in this high-risk cohort, this treatment approach underscores the inherent challenges posed by the aggressive nature of the disease and the TP53 mutation’s contribution to therapeutic resistance. The case highlights the critical need for personalized treatment regimens and rigorous follow-up to optimize patient care and advance our understanding of this complex malignancy.