The temperature sensors play important roles in wide spreading human activities. Non-contact method has great advantages, but the detection precision encounters big challenge. In this work, a double-layer asymmetric terahertz (THz) metamaterial combined with phase transition oxide was proposed to realize non-contact temperature sensor with high sensitivity. The metamaterial exhibits band-stop filtering effects in the simulated transmission spectra. Temperature changes induce a reversible phase transition in VO2, resulting in altered conductivity. Numerical results indicate that the S21 parameter increases from -44.33 dB to -4.78 dB at a frequency of 1.22 THz as the conductivity of the VO₂ film increases from 10 to 5000 S/m, achieving a modulation depth of 89%. This work provide a great insights for the development of metamaterials based high-precision temperature measurement.

Background/Objectives: Post traumatic growth (PTG) has the potential to draw positive consequences from trauma. Hence, the interest of finding ways to promote PTG. Research has identified an attentional bias towards positive resilience-related words (e.g., “persistence”, “purpose”) in university students who report high PTG after experiencing adversities. Although people can respond to these experiences by showing low PTG, this bias seems to help with their struggle by making purposeful contents more accessible. Therefore, boosting attentional bias towards positive resilience-related words could help people with low PTG. Methods: In this study, the participants were thirty-six university students who had experienced bullying before entering university. Using a Stroop emotional task, they identified the color of resilience and neutral words, either positive or negative, before and after being submitted to transcranial direct current stimulation. Stimulation was targeted at the right temporal area involved in intentionality processing. Results: In the anodal condition, the results support a stimulation effect on the resilience attentional bias that could benefit participants with low PTG. A significant moderation of approach motivation for this effect was also found. Specifically, only when participants had medium or high approach motivation, did stimulation boost the attentional bias in students with low PTG. Conclusions: These results support that tDCS stimulation in this brain area is effective in enhancing resilience attentional bias in low PTG students. However, for this effect to occur it is necessary to have approach motivation, which is motivation related to goals.

Digitized heritage is regarded as an emerging field, with existing studies primarily focusing on one case study or technological aspect. Thus, there remains a notable gap in the literature regarding the understanding of how these technologies can be utilized in smart heritage management. Approaches that harness technological advancements to enhance smart management and aid the decision-making process regarding the use of appropriate technology at various heritage sites remain insufficiently explored. To address this gap, this study conducted a cross-case analysis to survey the use of heritage technologies across selected cases to find underlying trends and correlations related to the scale and technology used. The findings of this study provide insights into the nexus of technology integration into heritage management, highlighting the role of tangible heritage documentation technologies. The present study concludes that smart management framework necessitates an ongoing process involving the integration of cultural heritage data, documentation, and the Internet of Things (IoT). IoT facilitates the connectivity of physical items to the internet, thereby supporting the safeguarding and interpretation of cultural heritage knowledge. Such framework operates through a collaboration platform that engages experts, local communities, and tourists to ensure a meaningful and interactive interconnection.

The present study is reviewing leachate models useful for proactive and rehab actions to safeguard surface and subsurface soft water which become even more scarce. Integrated management plans of water basins are of crucial importance since intensively cultivated areas are adding huge quantities of fertilizers affecting surface water basins and groundwater. Aquifers progressively are being nitrified on account of the nitrogen-based fertilizers’ surplus, rendering water for human consumption not potable. Well-tested solute leaching models standalone or part of a model package provide rapid site-specific estimates of the leaching potential of chemical agents, mostly nitrates, below root zone of crops and the impact of leaching towards groundwater. Most of the models examined were process-based or conceptual approach. Nonetheless, empirical prediction models, though rather simplistic and therefore not preferrable, demonstrate certain advantages such as extensive calibration database information requirements, which in many cases is unavailable not to mention stochastic approach and the involving of Artificial Intelligence (AI). Models were categorized according to the porous medium and agents to be monitored. Integrated packages of nutrients’ models are irreplaceable element for extensive catchment to monitor the terrestrial nitrogen balanced cycle and to contribute to the policy making as regards the soft water management.

Prepregs, which are resin-impregnated and expensive composites, are mainly limited to high-end applications within the aeronautical, defence, automotive, and energy sectors. Prepreg technology is mainly protected by under trade secrets resulting in limited studies on recent advancements. A Scopus search revealed 861 articles and 16 reviews for the keywords ‘epoxy’ and ‘prepreg’. However, this was narrowed down to 123 articles and 4 reviews using keywords ‘formula development’ and ‘B-stage’. In this review, the control strategies of B-staging, resin tack, and viscosity and their measuring techniques are discussed. Based on resin impregnation techniques and B-stage control, two approaches are proposed to develop prepreg resin formulations: conventional resin impregnation and viscosity-controlled resin impregnation. The approaches would be extremely useful especially for advancing beyond the existing prepreg applications and developing smart materials and functional composites through advanced resin modification strategies.

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of its free form is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10-200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy.

Construction projects' unsatisfactory performance has been linked to factors influencing individuals' well-being and mental alertness on projects. Drowsiness is a significant indicator of sleep deprivation and fatigue, being able to identify the cognitive and physical preparedness of workers on site to engage in construction tasks. As a consequence of the strenuous nature of the work involved in construction, long work hours, and environmental conditions, drowsiness is commonplace and has received less attention despite being a leading cause of accidents occurring on-site. Detecting drowsiness is essential for determining the safety and well-being of site workers. This study presents a vision-based approach using an improved version of the You Only Look Once (YOLOv8) algorithm for real-time drowsiness exposure among construction workers. The proposed method leverages computer vision techniques to analyse facial and eye features, enabling early detection of drowsiness signs, effectively preventing accidents, and enhancing on-site safety. The model showed significant precision and efficiency in detecting drowsiness from the given dataset, accomplishing a drowsiness class with a mean average precision (mAP) of 92%. However, it also exhibited difficulties handling imbalanced classes, particularly the underrepresented 'Awake with PPE' class, which was detected with high precision but comparatively lower recall and mAP. This highlighted the necessity of balanced datasets for optimal deep learning performance. The YOLOv8 model's average mAP of 78% in drowsiness detection compared favourably with other studies employing different methodologies. The vision-based drowsiness detection system has broad applications in the construction industry. It can be integrated into existing safety protocols, enabling real-time alerts to supervisors or workers when drowsiness is detected. The system improves productivity and reduces costs by preventing accidents and enhancing worker safety. However, limitations, such as sensitivity to lighting conditions and occlusions, must be addressed in future iterations.

Background: Histological findings of infective endocarditis (IE) in mechanical valves present a complex diagnostic challenge owing to the lack of a precise definition. This ambiguity is further complicated by the natural degenerative processes that occur in the mechanical valves over time. Consequently, pathologists and clinicians face significant difficulties in distinguishing between genuine infective processes and normal wear and tear of mechanical valves. Methods: This retrospective study examined tissue samples from 93 patients who underwent surgical removal of mechanical heart valves, with 41 cases suspected of infective endocarditis and 52 cases of noninfective dysfunction. The researchers aimed to establish more precise histological criteria for distinguishing between these two conditions, focusing on three key features: vegetations, and inflammatory patterns. Results: IE in patients with prosthetic heart valves presents distinct histological features that aid in the diagnosis and differentiation of non-infective complications. Hallmark characteristics include vegetation and inflammatory infiltrates with neutrophils. Valve tissue specimens from patients whose mechanical valves were removed because of non-infectious complications showed a different histological profile. Inflammatory infiltrates were observed in approximately 26% of these cases; however, they were primarily composed of macrophages and lymphocytes rather than neutrophils. Conclusions: By emphasizing neutrophil-rich inflammation as a key indicator, clinicians and pathologists could more effectively distinguish between true infective endocarditis and non-IE that can occur in the mechanical valves. This distinction is crucial for appropriate patient management as the treatment strategies for infective and non-infective valve conditions differ significantly.

An elderly male, with recent COVID-19 and cardiovascular comorbidities, had a prolonged hospitalisation due to periprosthetic joint infection and bacteraemia, post hip hemiarthroplasty. Despite the initial clinical improvement while under targeted antimicrobial therapy, low-grade fever and signs of myelosuppression later occurred. In the May-Grünwald-Giemsa stain of peripheral blood smear (PBS), pseudohyphae among RBCs and phagocytosed blastospores in neutrophils and monocytes were detected, indicating candidaemia and not contamination of the stain. Echinocandin treatment was immediately initiated, and Candida albicans was identified, using multiplex PCR, from the obtained blood cultures. Despite the early initiation of antifungal therapy and the removal of the CVL (central venus line), the patient passed away within the next twenty-four hours. Candidaemia is a leading cause of nosocomial bloodstream infections with very high morbidity and mortality and is associated with multiple risk factors (surgery, CVLs, prolonged hospitalization, concomitant bacterial infection, broad-spectrum antibiotics, immunosuppression). The isolation from blood cultures is the gold standard for candidaemia diagnosis. Detection of candidaemia by PBS is extremely rare, requires an experienced microscopist, and is considered to be an emergency. Clinical suspicion, early laboratory identification, and immediate clinician notification can lead to prompt antifungal treatment.

A specific key point in liver cirrhosis is the decreased metabolic capacity for drugs. So medicines which are metabolized by oxidative biotransformation play a great role in liver pathology. Responsible for the drug metabolism that takes place in liver during illnes, are three cytochrome P450 (P450 or CYP) gene families in liver microsomes (CYP 1, CYP2 and CYP3). In attention of various studies, is cytochrome P450. Folowing the currently aim,we try to assess the effect of liver disease on multiple CYP enzymes by use of a validated cocktail composed of medicins. Liver diseases are associated with metabolic activity changes It is important to know and to tell a little bit about different directions in cirrhosis diagnostic, including laboratory tests or management ideas.

The evaluation of logging in a forest management area using only the volume variable is not sufficient to assess the effectiveness of the harvest. The objective was to evaluate harvested volume using different logging variables to point out possible divergences between what was planned and what was actually carried out, in order to indicate adjustments to next annual operating plans (AOPs). The database comes from a community forest management area in the Tapajós National Forest, considering three sources: forest inventory, AOP planning and the post-exploration report. The yield considering the number of trees felled and the effective volume were compared with the AOP planning. The volume estimated by the generic equation was compared with the cubed volume and the commercial height variable was evaluated. Of the 6,267 trees selected, 5,090 trees were harvested (3.2 trees ha^-1), indicating a yield of 85% of the estimated volume. Of the 25 commercial species logged, the most representative were: Manilkara huberi, Vochysia maxima, Lecythis lurida, Couratari guianensis and Hymenaea courbaril. It can be concluded that the number of trees, cubed volume and commercial height are post-exploration yield response variables and not just volume, as is currently used in Sustainable Forest Management Plans (SFMPs).

Bacterial vectors for biomolecule delivery to targeted organelles, facilitating temporary or continuous protein production, have emerged as a promising approach for treating acquired and inherited diseases. This method offers a selective cancer eradication and targeting strategy with minimal side effects. Bacterial vectors provide an alternative to viral gene delivery, given their capacity to deliver large genetic materials while inducing minimal immunogenicity and cytotoxicity. Bacteria such as Bifidobacterium, Salmonella, and Clostridium have demonstrated potential for tumor-targeted biomolecule delivery. These vectors have also been used to transfer and amplify genes encoding biomolecules such as prodrug-converting enzymes, toxins, angiogenesis inhibitors, and cytokines. The microenvironment of necrotic tumors offers a unique opportunity for targeted therapy with the non-pathogenic anaerobic bacterium. For example, Clostridium sporogenes can germinate selectively in the necrotic regions upon injection as endospores, which helps to enhance the specificity of Clostridium sporogenes, resulting in tumor-specific colonization. Further, some invasive bacteria vectors engineered with the invasin gene from Yersinia Pseudotuberculosis, which enables specific binding to integrin receptors abundantly expressed in cancer cells and escape the endosome with incorporation of the Listeriolysin O gene from Listeria monocytogenes, have shown potential for application in cancer therapy. This review provides an overview of the use of the bacteria vector in cancer therapy, highlighting strategies to maximize delivery efficiency and address potential challenges.

This paper presents a comprehensive simulation framework for the investigation of electromigration (EM) in nano-interconnects, with a primary focus on unravelling the influential role of microstructure. To this end, a novel approach to generate conductor metal’s microstructures is presented, whereby a predefined statistical distribution of grain sizes obtained from experimental texture analyses can be incorporated into the developed EM modelling framework. The framework considers the impact of diffusion heterogeneity through the metal texture and interfaces. As such, the intricate interplay between microstructural/interfacial properties, and the resulting atomic flux and stress distribution within nanointerconnects could be investigated. Additionally, the study advances beyond the state of the art by comprehensively simulating all stages of electromigration, including stress evolution, void nucleation, and void dynamics while considering the impact of microstructure. Specifically, the model was employed to study the impact of trench dimensions on dual damascene copper texture and its subsequent impact on electromigration aging, where the model findings were corroborated by comparing to available experimental findings. A nearly linear augmentation in normalized lifetime was detected for aspect ratios equal to or greater than 1. However, a saturation is detected for wider lines with no effective enhancement in lifetime. An aspect ratio of 1 seems to maximize EM lifetime for each cross-sectional area by fostering a bamboo-like structure, where about 2-fold of increase is estimated when going from aspect ratio 2 to 1.

There are currently no effective treatments for retinal pigment epithelial (RPE) cell loss in atrophic AMD (aAMD). However, our research on Prom1, a known structural protein in photoreceptors (PRs), has revealed its distinct role in RPE and offers promising insights. While pathogenic Prom1 mutations have been linked to macular diseases with RPE atrophy, the broader physiological impact of dysfunctional Prom1 in RPE loss is unclear. We have shown that Prom1 plays a crucial role in regulating autophagy and cellular homeostasis in human and mouse RPE (mRPE) cells in vitro. Nevertheless, a comprehensive understanding of its in vivo expression and function in mRPE remains to be elucidated. To characterize Prom1 expression in RPE in situ, we used RNAscope assays and immunogold electron microscopy. Our use of chromogenic and fluorescent RNAscope assays in albino and C57BL/6J mice retinal sections has revealed Prom1 mRNA expression in perinuclear regions in mRPE in situ. Immunogold imaging showed Prom1 expression in RPE mitochondria. To confirm Prom1 expression in RPE, we interrogated human RPE single-cell RNA-sequencing datasets using an online resource, Spectacle. Our analysis showed Prom1 expression in human RPE. To investigate Prom1's function in RPE homeostasis, we performed RPE-specific Prom1-knockdown (KD) using subretinal injections of AAV2/1.CMV.saCas9.U6.Prom1gRNA in male and female mice. Our data show that RPE-specific Prom1-KD in vivo resulted in abnormal RPE morphology, sub-retinal fluid accumulation, and secondary PR loss. These changes were associated with patchy RPE cell death and reduced a-wave amplitude, indicating retinal degeneration. Our findings underscore the central role of Prom1 in cell-autonomous mRPE homeostasis. The implications of Prom1-KD in causing aAMD-like RPE defects and retinal degeneration in a mouse model are significant and could lead to novel treatments for aAMD.

More than 5% of the global population suffers from disabling hearing loss, primarily sensorineural hearing loss (SNHL). SNHL is often caused by factors such as vascular disorders, viral infections, ototoxic drugs, systemic inflammation, age-related labyrinthine membrane degeneration, and noise-induced hearing loss (NIHL). NIHL, in particular, leads to changes in blood-labyrinth-barrier (BLB) physiology, increased permeability, and various cardiovascular health issues, including hypertension, diabetes, neurological disorders, and adverse reproductive outcomes: Research has connected an elevated risk of hypertension with loud noise. The endocrine and autonomic nervous systems are also linked to arousal following an acute exposure to loud sounds. As a preventive option for both NIHL and hypertension as sequela of noise trauma, we present the transcutaneous vagus nerve stimulation (taVNS). taVNS may be appropriate to prevent NIHL-associated hypertension, as it typically reduces blood pressure and improves heart rate variability. On the other hand, normalizing cerebral blood flow preserves cochlear homeostasis, which is maintained by the BLB.The paper provides a comprehensive overview of NIHL pathophysiology and the impact of noise on auditory structures and function. Our research focuses on the auditory structures and functions affected by noise, such as the strial BLB, as well as the autonomic changes caused by NIHL, specifically the emergence of NIHL-associated hypertension as a primary comorbidity. We describe the clear link between taVNS use and increased HRV and slower progression of chronic hypertension, and we propose it as a cutting-edge preventive option for both conditions.

Keywords: traffic safety, children pedestrians, elderly people pedestrians, conflict zone reconstruction, incoming speed, traffic microsimulation

Green Infrastructure (GI) is increasingly prioritised in landscape policy and planning, due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. To help address this challenge, we present proposals to integrate GI elements for the province of Almeria, southeast Spain. We delimit and define key GI elements, and apply GIS-based connectivity tools to generate ecological corridors connecting the province’s 35 PAs. Currently, the PAs are threatened by the advance of intensive greenhouse agriculture and tourism-related urban growth of the municipalities to which they belong. Common patterns in the location of the most suitable areas for the development of GI can be identified. New GI networks would be located along the coast, specifically in the western part of the study area. They would also occupy the valleys of the main rivers of the province. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas.

The pathophysiologic substrate of pediatric hydrocephalus has not been thoroughly explained. Because of that, the available treatment options and protocols will be largely based on expert opinions or the results of single-center databases, until a more comprehensive knowledge of the various subcategories of hydrocephalus is undertaken. In retrospect, we mention that valve-based shunt systems have been constituting the mainstay of therapy since the late 1950s. The initially used shunt systems were concerning the ventricular system and the atrium. In the 1970s, VA shunts were not the main stay of treatment, as the primary location for the distal end was substituted by the peritoneum, due to the emergence of the ventriculoperitoneal shunts. Several types of one-way valve-based shunt systems have been developed. The ultimate goal was the drainage of the appropriate amount of CSF, in order to avoid either overdrainage or underdrainage, without interfering with normal brain development or cognition. Our review briefly analyzes these valve types and attempts a comparison, based on their functional characteristics. In addition to the peritoneum, other distal locations for shunt placement have been explored, including the pleural space and gallbladder. A separate subcategory includes the shunt systems which incorporate the lumbar subarachnoid space as the proximal location for the central catheter. Nowadays, the only available surgical alternative for the treatment of hydrocephalus is ETV. Since 2000, approximately 50% of children suffering hydrocephalus have been considered to be candidates for the execution of ETV. The potential benefits are that it closely resembles to the normal, physiological pathway of CSF absorption, thus circumventing the need for an artificial shunt system. Moreover, this technique is associated with lower infection rates, as well as on average, with a lower reoperation rate. Attempts of future progression are largely based on the development of improved shunt valve systems that could be affordable worldwide, as well as with the constant availability of ETV in developing countries. Another term that deserves special mention is related with the outcome of ETV in children who had a medical history of previously incorporated shunts and who were subsequently experiencing shunt dysfunction. This subset of patients is discussed in terms of efficacy (success rate from 40% to 80%) and safety (0%–32.5% of complications). Based on literature reviews, well-recognized predictive factors associated with secondary ETV failure include age, early onset of hydrocephalus, and prematurity. Currently, there is no consensus when the optimum surgical strategy is concerned in the different subgroups of patients, who are suffering from shunt dysfunction. Therefore, another issue that this review attempts to elucidate is the identification of subgroups of patients in whom shunt treatment is associated with a favorable outcome, compared to ETV, and vice versa. The objective of our study is to evaluate the comparative safety and efficacy, as well as the outcomes, of shunting and ETV in pediatric hydrocephalus cases. Anatomic and molecular substrate of hydrocephalus need to be better elucidated, so that medications or genetic modifications become more effective in order to offer a potential cure for hydrocephalus. Advancements in the treatment of hydrocephalus will depend on research into the shared molecular mechanism underlying various forms of the condition, paving the way for new therapeutic strategies.

Traditional methods for crawling and parsing web applications predominantly rely on extracting hyperlinks from initial pages and recursively following linked resources. This approach constructs a graph where nodes represent unstructured data from web pages, and edges signify transitions between them. However, these techniques are limited in capturing the dynamic and interactive behaviors inherent to modern web applications. In contrast, the proposed method models each node as a structured representation of the application’s current state, with edges reflecting user-initiated actions or transitions. This structured representation enables a more comprehensive and functional understanding of web applications, offering valuable insights for downstream tasks such as automated testing and behavior analysis.

Background. Obesity is an important threat to national and global public health in terms of prevalence and economic burden due to the obesity-related complications (ORCs) and morbidities. Aim of this study is to calculate the obesity related public expenditures in Hungary, to estimate the expenses and contribution of patients in 2019 (before) and in 2022 (after) the covid-pandemic.Method. The National Health Insurance Fund is the only health-care financing agency in a single-payer social-insurance system in Hungary, managing all of the public payments. Expenditures related to the patients with obesity and more frequent co-morbidities were requested from the national database. Financing of the inpatient (hospital) and the outpatient (secondary or ambulatory care) providers, reimbursement for prescription of medicines and healing aids were collected. Lower and upper thresholds were estimated, representing the prevalence of the respective morbidities among obese persons. The upper values were used in the final estimation, because many of other obesity related morbidities and treatments were not included in the calculation. Results. Obesity-related morbidities represent 10-12 % of all cases in the public health care system. National public health care expenses could be about 144 Billion HUF (444 Million EUR) for 2019 and 133 Billion HUF (393 Million EUR). These sums represent 7-11% of the national spending for health care provision, without sick leave expenditures. The co-payment of patients was estimated 34 Billion HUF (104 Million EUR) and 43 Billion HUF (109 Million EUR), respectively. Public expenditures and co-payments related to diabetes Type2, and hypertension were the highest. The public and individual expenditures together represents 0.3-0.4% of the Hungarian GDP.This study presents whole national database which is appropriate for reliable calculation. Reducing of high expenses could be possible by weight maintenance, preferable as early prevention, and weight reduction among persons living with obesity at individual level. Beside these, more efforts are needed in the education and public health policy. To calculate the cost of obesity, to improve homogeneity and comparability an international consensus is required on standardized methods.

A review of different modelling techniques, specifically in the framework of carbon-based nanomaterials (CNMs, including nanoparticles as graphene and carbon nanotubes - CNTs) and the composites and devices that can be derived from them, is presented. The article highlights the multiscale nature of these types of materials and systems, which require different approaches depending on the type, size, internal structure/configuration of the material and properties of interest. Far from attempting to cover the entire spectrum of models, this review examines a wide range of analysis and simulation techniques, highlighting their potential use, some of their weaknesses and strengths, and presenting the latest developments and some application examples. Specifically, this paper shows how electronic, atomistic, mesoscopic, continuum and system models can be used to study and design CNMs, to predict different properties and responses of materials, or even to study devices such as sensors. This review has been performed within the framework of COST Action EsSENce (High-performance Carbon-based composites with Smart properties for Advanced Sensing Applications – CA19118).

Turandot (Tot) family proteins, which are induced via the JAK/STAT pathway after infection, also suppress lymph gland tumors in Drosophila mxcmbn1 mutant larvae. We investigated the potential role of hemocytes in Tot induction in tumor-bearing mutants by immunostaining and RNAi experiments. Normal hemocytes transplanted into mutant larvae were recruited to the tumor and fat body (FB), suggesting that these cells transmit tumor-related information. The transplanted hemocytes ectopically expressed Unpaired3 (Upd3), which is necessary to activate JAK/STAT. Eiger, a Drosophila Tumor necrosis factor (TNF) ortholog, was highly expressed in tumors. Depletion of the Eiger receptor in hemocytes reduced Tot levels and eventually enhanced tumor growth. The c-Jun N-terminal kinase (JNK) pathway, acting downstream of the receptor, was also activated in the hemocytes of mutants. Downregulation of the JNK pathway in hemocytes inhibited Tot induction, leading to enhanced tumor growth. These results suggest that the upd3 expression in hemocytes depends on the Eiger-JNK pathway. We propose that after Eiger activates the JNK pathway in hemocytes on the tumor, cells expressing Upd3 are recruited to the FB. Upd3 then activates JAK/STAT to induce the expression of antitumor proteins. This study highlights the intricate communication between tissues via blood cells during tumor suppression.

The concept and practice of ’livelihood’ has undergone various stages in the development of Western countries. Its core has always focused on eradicating rural poverty and promoting rural development. This study utilizes the entropy value method to measure the comprehensive development level of the livelihood sustainability of farm households. Additionally, the Theil index is used to measure the presentation income gap, and multiple linear regression is utilized to test the impact of the livelihood capital sustainability of farm households on the presentation income gap. Results showed that the the sustainability of farm households’ livelihood capital has a significant negative impact on the urban-rural income gap. In other words, improving the livelihood capital of farm households can significantly reduce the urban-rural income gap. Accordingly, it is proposed that efforts be made to enhance the sustainability of farm households’ livelihoods, and to increase the contribution of the sustainable enhancement of farm households’ livelihood capital to narrowing the rural-urban income gap.

It is difficult to automatically recognize defects using digital image processing method in X-ray radiograph that tested from lap joint of unequal thickness plates. The continuous change of the wall thickness for the lap joint workpiece brings about the very different gray levels in X-ray background image. Besides, due to the shape and fixturing of the workpiece, the distribution of the weld seam in the radiograph is not vertical and results in an existence of a certain angle with the vertical direction. This makes it difficult for automatic defect detection and localization. In this paper, a method of X-ray image correction based on invariant moments is presented to solve the problem. In addition, an automatic defect detection method combined of image noise suppression, background removal, image segmentation and mathematical morphology is introduced. The results show that the proposed method can effectively recognize the gas pores in lap joint of unequal thickness and it is suitable for automatic detection.

Purpose: Despite significant improvements in early detection of breast cancer, some patients still require mastectomy. One clinical problem currently being analyzed is the possibility of preserving the areola-nipple complex (NAC). The aim of this study was to evaluate the treatment results of breast cancer patients who underwent mastectomy with immediate breast reconstruction with sparing of the NAC. Methods: 335 patients who underwent mastectomy with breast reconstruction – with sparing (group I: 300 patients) or removal of NAC (group II: 35 patients), treated in the period 07.2014-06.2020. In the study groups, the length of overall survival (OS) and recurrence-free survival (RFS) were determined – up to 24 months after the end of treatment (short-term results) and up to 80 months (long-term results). The mean follow-up time of patients was 55.6 ± 19.7 months. Results: In 50 patients (14.9%), recurrence of neoplastic disease was observed (in group I: 14.7%, in group II: 17.1%; p=0.6972). It was most often a recurrence of cancer in the scar after mastectomy – in 25 patients (respectively: 7.7% vs 5.7%; p=0.6775) or distant metastases – in 16 patients (4.0% vs 11.4%; p=0.051). A total of 10 deaths were observed (6 – 2.0% vs 4 – 11.4%; p=0.0019). A total of 37 patients (10.7% vs 14.3%) required removal of their implant, mainly due to symptoms of infection – in 24 cases (7.3% vs 5.7%). Conclusion: Preserving the NAC is a safe therapeutic procedure in patients undergoing mastectomy with the option of reconstructive treatment. In both groups of patients, similar early and late treatment results were obtained.

Mesenchymal stem/stromal cells (MSCs) are becoming increasingly important for biomedical applications such as cell therapy, disease modeling, and drug screening. At the same time, long-term cultivation, which is necessary to prepare a sufficient amount of cellular material for therapeutic and research purposes, is accompanied by the development of replicative senescence. Partial reprogramming emerged as a novel method that shows promising results in rejuvenation of cells in vitro and in vivo, however, it has not yet been applied for human MSCs under-went replicative senescence in culture. In the present study, we subjected senescent human endometrial MSCs to partial reprogramming using Sendai virus vectors containing genes encoding Yamanaka transcription factors Oct4, Sox2, Klf4 and c-Myc. Characterization of MSCs 5 days after transduction showed the loss of key markers of senescence: the youthful morphology was restored, the expression of senescent-associated β-galactosidase and the number of double-strand DNA breaks decreased, proliferation was activated, and DNA damage response was enhanced. Further, using in vitro wound-healing assay, we demonstrated that conditioned medium from partially reprogrammed MSCs showed higher therapeutic activity than that from senescent cells. However, biosafety test revealed the presence of viral components in conditioned medium, which caused the agglutination of erythrocytes. Collectively, our data suggest that partial re-programming is a potentially effective strategy for rejuvenation of cultured MSCs in late passages, but requires the use of virus-free protocols such as chemical reprogramming.

A little-known thermomechanical relation between entropy and action, originally discovered by Boltzmann in the classical domain, was later reconsidered by de Broglie in relation to the wave-particle duality in the free propagation of single particles. In this paper we present a version adapted to the phenomenological description of the hadronization process. The substantial difference with respect to the original de Broglie scheme is represented by the universality of the temperature at which the process occurs; this in fact coincides with the Hagedorn temperature. The main results are: 1) a clear connection between the universality of the temperature and the existence of a confinement radius of the color forces; 2) a lower bound on the hadronic mass, represented by the universal temperature, in agreement with experimental data; 3) a scale invariance which allows the reproduction of the well-known hadronic mass spectrum solution of the statistical bootstrap model. The approach therefore presents a heuristic interest connected to the study of the strong interaction.

The intrinsic quality of meat is directly related to muscle and fat tissues. Factors such as the rate and extent of anaerobic glycolysis affect muscle pH, influencing the meat's color, water retention, and texture. Postmortem anomalies can result in deviations from this intrinsic quality. The ani-mals' diet plays a crucial role in meat quality. Specific nutrients, such as proteins, vitamins, and minerals, affect meat's texture, flavor, and juiciness. Feeding rich in omega-3 fatty acids can im-prove the sensorial quality of meat. Meat processing and methods such as aging, marinating, and cooking affect texture, flavor, and juiciness, which can be evaluated by specific equipment or trained or untrained consumers. This comprehensive review investigates the relationship be-tween animal feeding practices and meat processing techniques and their combined impact on meat quality and sensory perception. By synthesizing recent research, we explore how various feeding protocols (including diet composition and feed additives) and processing methods shape meat products' nutritional value, texture, flavor profile, and overall consumer appeal. Under-standing this nexus is crucial for optimizing meat quality while ensuring sustainability and safety in the food supply chain.

Since the use of antioxidants can potentially reduce the risk of chronic diseases related to oxidative stress, such as metabolic dysfunction-associated steatotic liver disease (MASLD), we analyzed the effects of aqueous infusions from natural sources with a high antioxidant capacity on a model of MASLD. We applied a central composite rotatable design to establish the extraction conditions for Citrus sinensis (orange) leaves with maximum antioxidant capacity. These conditions were then applied to Baccharis articulate (carqueja) leaves. This approach resulted in antioxidant capacities of 71.6 ± 2.2 % and 51.5 ± 1.3 % for orange and carqueja leaves, respectively. Only flavonoids were significantly higher in Baccharis articulata (0.44 ± 0.08 mg/mL) than in Citrus sinensis (0.14 ± 0.04 mg/mL), as confirmed by HPLC. We developed an in vitro MASLD model with human hepatic cells, specifically Hep-G2 cells. MASLD cells were treated with both infusions for 48 h. Our results demonstrate that only Baccharis articulata could inhibit lipid accumulation in the MASLD cells (1.11 ± 0.21 AU [infusion-treated MASLD cells] vs. 2.19 ± 0.32 AU [MASLD cells], p < 0.05). We suggest that despite the different components of the infusions, the high concentration of flavonoids could be responsible for such activity.

The sound power LW of wind turbines (WTs) over the past decades has developed in relation to their electric power P as LW ∝ logP. The spectral distribution of the sound did not change significantly over time. This applies to the average performance of wind turbines, with differences between individual WT types. Because of the expected growth of onshore wind energy, a greater number of people will be living close to wind farms. This sustains the need for sound reduction. Sound reduction measures, such as serrations, reduced tip speed and low noise modes, may counteract the development of higher sound power from ever bigger WTs. To investigate this, the sound production of WT types over the last decades is analysed in relation to their size and electric power and the application of sound reduction measures. The analysis includes the broad band A-weighted and low frequency sound power levels as well as more detailed spectral distributions. Results show that the sound power level of wind turbines above 3 MW on average increases less with size than smaller turbines did. This is due to a lower blade tip speed and the use of sound reducing serrations. The spectral content of wind turbine sound, including the lower frequencies, has not changed significantly.

Data on antimicrobial resistance in Burkina Faso’s agricultural sector is still limited. The study assessed the occurrence of extended spectrum β-lactamase -producing Escherichia coli (ESBL-Ec) and Klebsiella spp. (ESBL-K) in lettuce, environment, and gardeners’ stools in market gardens in Ouagadougou, Burkina Faso. A total of 356 samples were collected from three vegetable gardens (76 lettuce, 76 soil, 62 manure, 63 irrigation water and 79 human stools). ESBL-selective medium was used for initial selection of ESBL-producing bacteria, and the isolates were further identified using biochemical tests. Antibiotic susceptibility test was performed using the disk diffusion method. The overall prevalence of ESBL-Ec and/or ESBL-K in the samples was 232/356 (65.2%). Of the lettuce samples, the prevalence of ESBL-Ec was 19/76 (25.0%) and ESBL-K 33/76 (43.4%). In the market gardens environment, the prevalence of ESBL-Ec was 32/201 (15.9%) and ESBL-K 124/201 (61.7%). In the gardeners’ stools the prevalence of ESBL-Ec was 42/79 (53.2%) and ESBL-K 24/79 (30.4%). Two ESBL-K isolates were found to produce NDM carbapenemase. Due to the high prevalence of ESBL-producing bacteria, which may also be carbapenemase producers, it is necessary to monitor pathogens in agricultural products with a "One Health" approach to limit and prevent infections in the population.

Transactivation Response Element RNA-binding Protein (TRBP) is a double-stranded RNA-binding protein widely known for its critical contribution to RNA interference (RNAi), a conserved mechanism of gene-expression regulation mediated through small non-coding RNA moieties (ncRNAs). Nevertheless, TRBP has proved to be also involved in other molecular pathways and biological processes, such as cell growth, organism development, spermatogenesis and stress response. Mutations or aberrant expression of TRBP have been previously associated with diverse human pathologies, including Alzheimer’s disease, Cardiomyopathy and Cancer, with TRBP playing essential role(s) in proliferation, invasion and metastasis of tumor cells. Hence, the present study aims to investigate new, still elusive, functions and properties of TRBP, particularly regarding its cell cycle-dependent regulation during cancer-cell division. Our results unveil, for the first time, the tight and remarkable, mitosis-dependent, control of TRBP protein expression, as clearly evidenced by the lack of its immunofluorescence-facilitated detection, during mitotic phases, in several human cancer-cell lines of different tissue origin. Notably, the obtained TRBP-downregulation patterns seem to derive from molecular mechanisms that act independently of oncogenic activities (e.g., malignancy grade), metastatic capacities (e.g., low versus high) and mutational signatures (e.g., p53-/- or p53ΔΥ126) of cancer cells. Taken together, we, herein, propose that TRBP serves as a novel cell cycle-dependent regulator, likely exerting mitosis-suppression functions, and, thus, its mitosis-specific downregulation can hold strong promise to be exploited for the efficient and successful prognosis, diagnosis and (radio-/chemo-)therapy of diverse human malignancies, in the clinic.

Atherosclerosis remains a major driver for cardiovascular disease (CVD), despite advancements in traditional risk factor management therapies. Recent evidence emphasizes the crucial role of the gut microbiome in the progression of atherosclerosis and plaque rupture, highlighting a promising therapeutic avenue. This review focuses on the intertwined relationship between the gut microbiome, its metabolites, and atherosclerosis and CVD, also highlighting the potential therapeutic role of probiotics and prebiotics. Given the diverse and unique gut microbiota signatures among individuals, a one-size-fits-all therapeutic approach is unlikely to be effective. Personalized treatment strategies are therefore necessary. Here we discussed how Artificial Intelligence (AI) can be leveraged to analyze individual gut microbiome profiles from microbiome sequencing, predict treatment response, and optimize therapeutic strategies based on individual patients which would significantly improve outcomes of the treatment for atherosclerosis/CVD patients.

Some mathematical problems reach such complexity that their solution and engineering interpretation is no longer possible, or at least extremely difficult for researchers without the use of artificial intelligence tools. Mathematical relations for such problems are very difficult to interpret. In connection with the development of means for mathematical calculations, such problems have partially lost their relevance. However, new problems can be posed in mathematics, for which the existing means of mathematical calculations may still be insufficient. Presumably, such problems include the problem of differentiation and integration to a complex degree. Derivation of various functions is widely used in many branches of mathematics, technology, and science. Historically, derivation was known for cases where the exponent index of the degree of derivation was a positive integer value, which meant the multiplicity of taking the derivation operation. Later, this operation was extended with the notion that the exponent index can also be negative, which means multiple integration. Derivation to a negative power is defined as integration, and integration to a negative power is defined as derivation. Later, the concept of the possibility of fractional (non-integer) derivation and, accordingly, fractional integration was developed and widely used. This extension of the mathematical tool proved to be of considerable practical value, since it permits the design and implementation of more efficient controllers for systems having negative feedback, for instance. Publications about taking the derivative to a purely imaginary degree have already appeared, but, apparently, the question of differentiation was also discussed in the literature, in which the degree of taking the derivative would be expressed by a complex number. The article suggests an approach to solving this problem, which may not have been discussed yet. If this complex number, denoting the degree of differentiation, has a positive real part, the operation is better called a special form of differentiation, but if the real part of the degree of differentiation is negative, then the operation is more consistent with the concept of integration. Formally, inverting the exponent of the degree of differentiation turns the operation into integration and vice versa. Throughout history, it has been repeatedly confirmed that mathematics occasionally solves problems that at the time of their discovery have no obvious applied value; however, the development of the theory is valuable in and of itself, even if there is currently no obvious applied value for such development. In addition, experience has shown that every new mathematical tool will ultimately be applied to a significant practical issue.

This paper analyses the evolution of the prices of intercity high-speed rail (HSR) services operated by the Spanish public company Renfe on the Madrid-Barcelona route between April 2019 and June 2022. This period marks one of the most important events in the recent history of railways in Spain: the end of Renfe's monopoly and the opening of this and other corridors to private competitors. Our main objective is to study the impact of the entry of the first competitor, Ouigo, on the incumbent's pricing strategy. Our analysis confirms that Renfe perfectly anticipated Ouigo's entry and adjusted its prices about four months before the market opened. Interestingly, the incumbent also modified its tariff structure in advance according to the willingness to pay of the target customer. These results may be of interest for the forthcoming liberalization of other inter-city corridors.

Landfills pollute air, soil, surface and groundwater worldwide. Present work aims to assessment environmental Risks three landfills in southern Riyadh using GIS, soil quality guidelines, and contamination indices. GIS tools indicated an increase in the area of the landfill sites with time. The concentration of heavy metals (HMs) in the investigated landfills had the following descending order: Fe (11532 mg/kg) ˃ Al (5405 mg/kg) ˃ Pb (561.7 mg/kg) ˃ Zn (356.8 mg/kg) ˃ Mn (165 mg/kg) ˃ Cr (74.8 mg/kg) ˃ Cu (42.7 mg/kg) ˃ Ni (22.4 mg/kg) ˃ V (21.8 mg/kg) ˃ As (5.16 mg/kg) ˃ Co (4.08 mg/kg). The highest values of Al, As, Co, Ni, Pb, V, and Zn were recorded in Riyadh landfill 3, at the third industrial city. However, average values of all HMs decrease those from most worldwide soils and background, except Zn, Cu, Cr, and Pb. Results of enrichment factor and statistical analysis indicated deficiency to minimal enrichment and geogenic sources for Al, Co, Mn, and V, while those of As, Cr, Pb, Zn, and Ni showed EF ˃ 2 implied anthropogenic activities, especially in in Riyadh landfill 3. Additionally, very high contamination and high effects range-median were reported in individual samples, especially for Pb, As, and Zn, indicated frequent adverse effects for these HMs. The difference in contamination for the HMs in the studied landfill sites might be attributed to the difference in the magnitude of input for each metal into the landfill site and/or the difference in the removal rate of each metal from it.

There is an increasing understanding that some mitral valve pathologies have developmental origins. The time course of valvulogenesis varies by animal model, where in cattle, the branched chordae tendineae architecture becomes fully developed at full term. The mechanism by which chordae tendineae bifurcate during fetal development remains unknown. The current study presents a detailed description of bovine chordae tendineae formation and bifurcation during fetal development. Analysis of Movat Pentachrome-stained histological sections of the developing mitral valve apparatus were accompanied by micro-CT imaging. TEM imaging of chordae branches and common trunks allowed measurement of collagen fibril diameter distributions. We observed a proteoglycan-rich “transition zone” at the junction between the fetal mitral valve anterior leaflet and chordae tendineae with “perforations” lined by MMP1/2 and Ki-67 expressing endothelial cells. This region also contained clusters of proliferating endothelial within the bulk of the tissue. We hypothesize this zone marks a region where chordae tendineae bifurcate during fetal development. In particular, perforations, created by localized MMP activity serve as a site for the initiation of a “split” of a single chordae attachment into two. This is supported by TEM results that suggest a similar population of collagen fibrils runs from the branches into a common trunk. A clear understanding of normal mitral valvulogenesis and it’s signaling mechanisms will be crucial in developing therapeutics and/or tissue-engineered valve replacements.

Multiple myeloma (MM) is a plasma cell malignancy that resides within the bone marrow microenvironment, relying heavily on interactions with its cellular components. Among these, endothelial cells (ECs) play a pivotal role in MM progression and the development of therapeutic resistance. In this study, we analyzed publicly available single-cell RNA sequencing data to identify unique pathway activations distinguishing ECs from MM patients and healthy donors. We developed a novel protocol to isolate and culture endothelial progenitor cells (EPCs) and ECs directly from MM patient bone marrow, demonstrating their ability to promote myeloma cell proliferation. Validation studies confirmed that these MM-derived ECs exhibit angiogenic potential as well as expression of characteristic endothelial lineage markers. These findings underscore the critical role of bone marrow ECs in the MM tumor microenvironment and highlight potential new therapeutic targets to disrupt MM progression.

Objective: We evaluated the possibility of diagnosing pleural mesothelioma (PM) through pleural effusion cytology in a multicentric prospective clinical trial (MesoCyto study). Methods: We included patients with pleural effusion and suspected PM scheduled for thoracentesis and pleural biopsy. Both pleural effusion cytology including cell block and pleural biopsy were independently performed and compared to assess the diagnostic reliability of pleural effusion cytology. The primary endpoint aimed to demonstrate that the specificity of PM diagnosis by pleural effusion cytology reached 100%. Secondary endpoints included the frequency of adverse events during thoracentesis and pleural biopsy and assessment of diagnostic accuracy of pleural effusion cytology for each histological type. Results: Of the 50 enrolled patients, histological examination confirmed PM in 42 patients; 29 patients met the diagnostic criteria of PM in pleural effusion cytology and received a cytological diagnosis. Of the remaining 21 patients, 13 displayed atypical cells suggestive of PM based on pleural effusion cytology but did not meet the diagnostic criteria, and in 8, atypical cells were not identified and PM diagnostic criteria were not met. Thus, pleural effusion cytology specificity was 100%. The diagnostic concordance rate by pleural effusion cytology for each histological type was 72.2% for epithelioid, 75% for biphasic, and 0% for sarcomatoid. No adverse events were associated with thoracentesis, but the incidence of adverse events during pleural biopsy was 14%. Conclusions: PM can potentially be diagnosed through only pleural effusion cytology including the cell block method using immunohistochemical staining.

Amyotrophic lateral sclerosis (ALS) is a devastating disease whose etiology is currently unknown. This paper presents a novel theory in which ALS is caused by an impairment of the alpha2de;ta subunit of voltage-gated calcium channels (VGCCs), most likely due to autoimmune targeting. I show how the impairment mechanistically induces chronic calcium, which in turn explains the major clinical, epidemiological, and pathological evidence known about the disease. The same theory explains the major variant of frontotemporal dementia (FTD) that greatly overlaps with ALS.

This paper introduces $\mathbb{NS}_4$, a novel number system that extends traditional binary arithmetic by incorporating algorithmic mappings for addition, carry-over, and multiplication operations. We establish a mathematical framework demonstrating that $\mathbb{NS}_4$ forms a commutative ring, thereby extending beyond conventional number systems like complex numbers. The proposed system offers efficient representation and manipulation of numerical structures, with potential applications in computational mathematics and digital logic design.

Objetives/Background: Vitamin D Binding Protein (VDBP) and free vitamin D are new markers that are being studied as a possible markers of vitamin D status. The main aim of our study was to analyse the VDBP genotype and quantify the levels of VDBP and free vitamin D in a sample of cystic fibrosis (CF) patients. Methods: Multicentre, cross-sectional and prospective study including patients with CF, exocrine pancreatic insufficiency who were clinically stable. We investigated vitamin D levels (total and free) and the different VDBP haplotypes. Free vitamin D levels were measured using an electro-chemiluminescence assay. Results: A sample of 48 patients was obtained (52% male; median age 13.8 years). The most common allele of VDBP was Gc1s (72%)>Gc2 (52%)>Gc1f (27%). Median calcidiol was 21.2 ng/ml (IR 15.3 - 26.9), 81% had levels in the insufficiency range: 23 patients (48%) below 20 ng/ml, and 16 (33%) between 20 and 30 ng/ml. The median free vitamin D level was 4.2 pg/ml (IR 3.9 – 5.6). A positive correlation was observed between calcidiol and free vitamin D levels (r=0.871; p<0.0001). After adjustment for season, vitamin D supplementation, sex and CFrelated diabetes, patients with Gc1f polymorphism had a lower risk of vitamin D deficiency, OR 0.22 (95% CI 0.05 – 0.99), p=0.027. A negative linear trend was observed between the polymorphisms grouped into 3 categories (Gc1/Gc1, Gc1/Gc2, and Gc2/Gc2, in that order) and vitamin D and free vitamin D levels (p=0.025 and p=0.033, respectively). Conclusion: In CF, as in the general population, the most common VDBP haplotype in Caucasian race is Gc1s. VDBP polymorphisms influence serum vitamin D and free vitamin D levels in CF patients. There is a good correlation between free vitamin D and calcidiol levels, suggesting that measuring the latter in CF does not seem to provide any additional benefit.

(1) Background: The study aimed to analyze and rank the benefits of adopting Artificial Intelligence (AI) in the logistics area, considering the opinion of professionals working in the Brazilian logistics sector through the implementation of ISO/IEC 42001; (2) Methods: The procedures adopted included a literature review to identify the benefits of the use and the application of a survey aimed at professionals in the logistics sector. The analysis used the TOPSIS method to identify and rank the most decisive benefits; (3) Results: Of the 15 benefits mapped in the literature, the professionals attributed the greatest importance to customer satisfaction, operational efficiency, incentives for innovation, and improving the company's image and competitive advantage. The results indicate that adopting ISO/IEC 42001 not only promotes process optimization and cost reduction, but also encourages innovation and strengthens competitiveness in the market; (4) Conclusions: the implementation of this standard is vital for companies in the logistics sector, especially in a Brazilian context of major infrastructural and regulatory challenges. The findings highlight the need for a strategic approach to the adoption of AI, emphasizing the importance of efficient logistics management and ethical practices in the application of this technology.

Solid-state batteries (SSBs) hold the potential to revolutionize energy storage systems by offering enhanced safety, higher energy density, and longer life cycles compared with conventional lithium-ion batteries. However, the widespread adoption of SSBs faces significant challenges, including low charge mobility, high internal resistance, mechanical degradation, and the use of unsustainable materials. These technical and manufacturing hurdles have hindered the large-scale commercialization of SSBs, which are crucial for applications such as electric vehicles, portable electronics, and renewable energy storage. This paper provides a systematic review and cross-sectional bibliometric and thematic analysis of the global patent landscape for SSB technologies. The study classifies innovations into key problem and solution areas by meticulously examining 244 patents across multiple dimensions, including geographic distribution, inventor engagement, award latency, and technological focus. Key findings reveal progress in addressing performance, safety, material sustainability, and manufacturing scalability, with significant innovations in electrolyte, electrode, architectural, and process engineering. This research contributes a comprehensive analysis of the technological landscape, offering valuable insights into ongoing advancements and providing a roadmap for future research and development. This work will benefit researchers, industry professionals, and policymakers by highlighting the most promising areas for innovation, thereby accelerating the commercialization of SSBs, and supporting the transition toward more sustainable and efficient energy storage solutions.

This study looks at how basic research capabilities develop within enterprise clusters, focusing on the complex and adaptive nature of these systems. It builds a conceptual model using systems theory and applies information entropy to measure how much these capabilities have emerged. By using information entropy, the study creates a model to gauge how research abilities are forming in enterprise clusters. To dive deeper, the China Pingmei Shenma Group was used as a case study. Data came from interviews, surveys, and text analysis. This case—focused on a state-owned enterprise cluster in China’s coal-based energy and chemical industries—highlights the key factors that influence research capability growth. These factors include support from external systems, how internal resources are used, and their renewal over time. From 2017 to 2022, changes in entropy were tracked, revealing the process of research development driven by both internal and external forces. The study shows that when a system can bring in external resources while reducing internal disorder, its research capability is greatly improved. In real-world terms, the research proves that measuring entropy is a valuable way to evaluate and enhance research abilities within enterprise clusters, offering a clear method to manage innovation systems in tech-driven industries.

Curd, a traditional fermented dairy product, is integral to various culinary traditions, cultural practices, and nutritional systems globally. This review offers an in-depth exploration of curd, encompassing its production methods, nutritional profile, health benefits, and cultural significance. We begin with a detailed examination of the fermentation process, focusing on the role of lactic acid bacteria and the factors affecting curd’s quality and sensory characteristics. The paper evaluates the microbial composition of curd and its potential health benefits, including a comparative analysis of its probiotic effects relative to yogurt. Additionally, we delve into curd-based beverages, assessing their production techniques, nutritional value, and health-promoting properties. The review also explores curd’s cultural and dietary importance, tracing its historical roots in Indian cuisine and traditional medicine, and contrasts its global culinary use with yogurt. By providing a comprehensive overview, this review aims to enhance the understanding of curd and curd-based beverages as functional foods, cultural symbols, and valuable sources of nourishment across diverse populations.

This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. Traditional financial risk assessment methods are often unable to effectively cope with the dynamic changes and complexities of the financial market, but RL technology provides innovative solutions for modern financial risk management by means of real-time adjustment and adaptation mechanism. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.

COP21 represented a starting point for several nations to develop and implement energy transition strategies to face and mitigate climate change, making the electrical power sector crucial in achieving the established goals and commitments. This research presents a 7-step methodology to improve decision-making in energy transition strategies for the power sector by integrating an economic dispatch optimization model based on linear programming to determine vulnerable aspects of power generation and transmission in strategic planning scenarios that could jeopardize the country’s energy transition. The application of the methodology is implemented under the open-source platform Python Optimization Modelling (PYOMO) and illustrated through a case study of the Mexican Electrical Power System (SEN) during the year 2025. The case study shows that the reserve margin fluctuated due to the variable renewable energy installed despite having a vast installed capacity to supply the country’s total demand. In addition, the results showed that most of the transmission lines had a congestion frequency higher than 90% of their capacity during most of the year. Two regions were identified as the best options for reducing greenhouse gas emissions by installing new power plants. Finally, most technologies reflected an under-generation, suggesting a high dependence on some fuels to supply the Mexican demand. The model’s programming is freely available in GitHub.

This paper proposes an experimental design to publish on the Internet the measurements resulting from the use of four sensors: a sensor inside a cistern to measure the level of drinking water, a sensor inside a water tank to measure the water level, a sensor to measure the water flow or pressure brought by the pipes, and another sensor to measure the water quality. Using these sensors, data was collected and published in real-time on the Internet, and at the same time, it was recorded in a database. Several tests of filling and emptying the cistern were performed. A system analyzes, makes decisions, and acts on the data collected by the sensors. Finally, experimental results demonstrated that if the cistern perceived that there was no water in the tank, it turned on the water pump to fill the tank to 100\% of its storage capacity; while this was happening, the water level in the cistern and tank, the flow of water from the piped water, and the quality of the water could be visualized on a dashboard. In short, this proposal monitors water levels and flows through the Internet of Things.

Breast implant surgery has evolved significantly, yet challenges such as capsular contracture remain a persistent concern. This review presents an in-depth analysis of recent advancements in understanding the immune mechanisms and clinical implications associated with breast implants. The article systematically examines the complex interplay between immune responses and capsular fibrosis, emphasizing the pathophysiological mechanisms of inflammation in the etiology of this fibrotic response. It discusses innovations in biomaterial science, including the development of novel anti-biofilm coatings and immunomodulatory surfaces designed to enhance implant integration and minimize complications. Emphasis is placed on personalized risk assessment strategies, leveraging molecular insights to tailor interventions and improve patient outcomes. Emerging therapeutic targets, advancements in surgical techniques, and the refinement of post-operative care are also explored. Despite notable progress, challenges such as variability in immune responses, long-term efficacy of new interventions, and ethical considerations remain. Future research directions are identified, focusing on personalized medicine, advanced biomaterials, and bridging preclinical findings with clinical applications. As we advance from bench to bedside, this review illuminates the path forward, where interdisciplinary collaboration and continued inquiry weave together to enhance the art and science of breast implant surgery, transforming patient care into a realm of precision and excellence.

Controlling an object that is extremely prone to oscillations is a complex and highly relevant task. The tendency to oscillate arises due to the presence of an oscillatory element and a delay element, and control is often complicated by the presence of an integrator in the object. For such objects, a very effective method of numerical optimization of the controller is not effective enough without choosing the most efficient structure, more efficient than the PID controller and even than the PIDD controller. The method of using the Smith predictor, which is switched on parallel to the plant, allows solving a similar problem, but the resulting quality of control remains not good enough, since a large overshoot remains in the system. The article recommends, after using the Smith predictor, to additionally use an external control loop, the sequential controller of which should also be designed using the numerical optimization method. But the Smith predictor is not the best solution either. The article recommends the use of pseudo-local feedback. Such a connection provides more opportunities, since its structure and parameters can be optimized, while the Smith predictor in its parameters is completely determined by the object model and its modification is not allowed. The article shows that even the best sequential controller in the case of using the Smith predictor on a test example still cannot provide the required quality of control; in such a system, significant overshoot remains, more than 10%. The proposed method allows reducing overshoot to less than 0.5%. It is shown that the resulting system is robust, since the quality of control remains within specified limits even when the gain changes by 30% in both directions.

The research for this paper began several years ago in a graduate medical statistics class where I was told that “certain ethnic and cultural groups just have worse medical outcomes than others”. This paper looks at the different major divisions of our population and then; 1) develops a framework for understanding which groups are affected by the differences in medical access and outcomes, 2) attempts to document some of the reasons for these differences, and 3) offers some conclusions and possible first steps to improve the health of these groups and our society’s ability to provide care for the entire population.

The human brain, a highly complex dynamical system, exhibits various states of consciousness—such as wakefulness, sleep, and altered states—each characterized by distinct patterns of neural activity. To capture the dynamical properties of these states, a range of complexity measures is utilized, with a primary focus on Statistical Complexity (SC) and Lempel-Ziv complexity (LZc), and supplemented by Approximate Entropy (ApEn) and Kolmogorov Complexity (KC). These measures are applied to both simulated data, generated through logistic maps and Multivariate Autoregressive (MVAR) models, and intracranial depth electrode recordings from patients. The results demonstrate that these complexity measures effectively capture intricate dynamics of the brain across different states. Specifically, SC captures the structural complexity and information processing within the system, reflecting organized and predictive neural behavior by accounting for temporal correlations in the data. In contrast, LZc is more sensitive to randomness, measuring the diversity and unpredictability of patterns within the data. This distinction allows SC to serve as a more reliable indicator of organized information processing, while LZc highlights the variability in neural signals. Notably, the study reveals that states of higher consciousness are associated with greater complexity, supporting the entropic brain hypothesis. This research contributes to the ongoing efforts to quantify consciousness through mathematical frameworks and offers insights into the neural correlates of different states of awareness.

In order to discover how formed the multiple oxides observed in the final external scales after long exposure of a low–Mn high–Cr Cantor’s alloy to hot air, oxidation tests in furnace were performed for seven different durations. Metallographic characterization was carried out concerning the oxidation products obtained after each test duration. The different oxides did not appear one after the other, but simultaneously, at the early beginning of the exposure to hot air and after. They thickened all progressively and the chemical composition of each also evolved with time, more or less. Globally the innermost oxide is almost chromia much richer in Cr than in Mn while the outermost one contains Mn principally. The interrupted tests also allowed specifying the mass gain kinetic, which is parabolic, twice faster than a chromia–forming alloy. Despite the lowered content in Mn, manganese still plays an important role in the oxidation phenomenon, starting very early.

In this study, we consider the valuation of rainbow options using unsupervised machine learning methods. In particular, we consider the pricing of multi-asset (rainbow) European and American options, using Physics Informed Neural Net- works (PINNS). After developing the PINNS architecture, we benchmark the method by using it to price vanilla and exotic options with one and two un- derlying assets. We then use the methodology to price a multi-asset European option, followed by the pricing of the multi-asset American option by solving the linear complementarity problem. We compare our results to those obtained using preexisting numerical methods and note excellent agreement. Unlike con- ventional numerical methods, we note that this methodology does not suffer from the ’curse of dimensionality’. The time complexity of our method is con- siderably less than that of the conventional techniques. Thus PINNS may offer a faster more efficient solution to the pricing of rainbow options.

The issue of organizing efficient interaction between V2X system elements has become increasingly critical in recent years. Utilizing vehicle-to-everything (V2X) technology enables achieving the necessary balance of safety, reducing system load, and ensuring a high degree of vehicle automation. This study aims to develop a simulation system for V2X applications in various element placement configurations and conduct a numerical analysis of several V2X system interaction schemes. The research analyzes various methods, including clustering, edge computing, and fog computing, aimed at minimizing system losses. The results demonstrate that each proposed model can be effectively implemented on mobile nodes. Results also provide insights into the average expected request processing times, thereby enhancing V2X system organization. The authors propose a comprehensive model that enables the distribution and prediction of system parameters and resources for diverse computational tasks, essential for the successful implementation and utilization of V2X technology.

Design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element (FE) and machine learning (ML) methodologies to evaluate blastworthiness performance. Optimization is carried out using the non-dominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis (GSA) using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels (AFSPs). This study concludes that ASPs have potential applications in broader engineering fields.

Diagnostics often require specialized equipment and trained personnel in laboratory settings, creating a growing need for point-of-care tests (POCT). Among genetic testing methods, Loop-mediated Isothermal Amplification (LAMP) offers a viable solution for developing genetic POCT due to its compatibility with simplified devices. This study aimed to create a genetic test that integrates all steps from sample processing to results while minimizing the complexity, handling, equipment, and time required. Several challenges were addressed to achieve this goal: 1) Developing a buffer for bacterial DNA extraction that is compatible with both LAMP and immunochromatographic tests; 2) Adapting the LAMP protocol for its use with the device; and 3) Optimizing the detection protocol for specific test conditions, with a lateral flow immunoassay format selected for its POCT compatibility. Following these developments, the test was validated using Escherichia coli and non-E. coli strains. A portable heating station was also developed to enable amplification without costly equipment. The resulting genetic POCT achieved 100% sensitivity and 85% specificity, with results available in 60 to 75 minutes. This study demonstrated that our closed-method POCT efficiently performs DNA extraction, amplification, and detection for bacterial identification. The test’s simplicity and cost-effectiveness will support its implementation in various settings.

Background/Objectives: Prospective cohort studies are useful for studying how biomolecular status affects risk of adverse health outcomes. Less well known is that the longer the follow-up time, the lower the apparent effect due to “regression dilution.” Here we evaluate how follow-up time affects the relationship between serum 25-hydroxyvitamin D [25(OH)D] concen-tration and incidence of stroke and major cardiovascular events (MCEs). Methods: Findings re-garding the relative risk (RR) of stroke and MCEs with respect to serum 25(OH)D concentrations at baseline from prospective cohort studies were plotted against mean follow-up time. Fifteen studies from mainly European countries and the United States were used for stroke, with nine studies for MCEs. Linear regression analyses were performed for follow-up periods of up to 10 years. Results: For stroke, the linear regression fit for 1–10 years is RR = 0.34 + (0.065 × follow-up [years]), r = 0.84, adjusted r2 = 0.67, p <0.001. No significant change was evident for studies with follow-up periods of 10–20 years. For MCEs, the linear fir for 1–8.1 years is RR = 1.61 ‒ (0.074 × follow-up [years]), r = 0.75, adjusted r2 = 0.49, p = 0.03. Discussion: The shorter the follow-up period, the greater the apparent effect of vitamin D in reducing risk of stroke and MCEs. In addition, the apparent effect of higher 25(OH)D concentration found for the shortest follow-up time is more than twice as high as the estimate based on averaging the results for all studies without considering follow-up time. Mechanisms have been found to explain how higher serum 25(OH)D concentrations could reduce risk of stroke and MCEs. Randomized controlled trials have not shown that vitamin D supplementation significantly reduces risk of either stroke or MCE, probably because risk of both outcomes increases rapidly below 15 ng/mL (38 nmol/L) and it is difficult in Western developed countries to enroll enough participants with concentrations that low. Nonetheless, vitamin D’s role in reducing risk of stroke and MCE can be considered causal on the basis of an evaluation of the evidence with respect to Hill’s criteria for causality in a biological system. Conclusions: Serum 25(OH)D concentrations above 20 ng/mL are associated with significantly reduced risk of stroke and MCEs in an apparent causal manner. Raising serum 25(OH)D concentrations to >20 ng/mL should be recommended for everyone likely to be at risk for stroke or MCE and indeed in the general population.

The analysis focuses on the intensity of energy consumption in the transportation sector, of the oil-rich country, Iran. The study emphasizes the importance of reducing energy consumption in transportation and identifies factors that affect energy efficiency. The research explores the impact of population growth, clean fuels, energy prices, and investment in energy efficiency in the transport sector. Using data from 1979 to 2011, the study estimates a model with the Autoregressive Distributed Lag model and emphasizes the significance of clean and renewable energies in reducing energy intensity. It also discusses the theoretical background, energy intensity factors, production technology, and the role of clean energy sources. The model framework includes variables like energy intensity index, relative energy price, number of cars, investment ratio, and clean energy production. The findings suggest an increasing trend in energy consumption in the transportation sector, highlighting the importance of identifying drivers that affect energy consumption in the sector.

The Association of Mutual Funds of India (AMFI) under the direction of the Securities and Exchange Board of India (SEBI) provided open access to various risk parameters with respect to MidCap and SmallCap funds on its website in February, 2024. This disclosure of month wise cross-sectional data depicting stress-test and liquidity analysis was hailed as a milestone as it provided a glimpse into inherent risk of mutual funds. The AMFI dataset consisted of 14 variables including stress test results, ownership concentration on both liability & asset sides, standard deviation on portfolio’s returns, portfolio beta, trailing 12-month PE ratio, portfolio turnover etc. Among these, the primary variable identified in grading mutual fund is the stress test parameter, expressed as number of days required to liquidate 50% and 25% of the portfolio respectively on a pro-rata basis under stress condition. A higher number of days to liquidate the portfolio under stress conditions represented higher risk for the investor. The objective of our paper is to test various neural network models which can help in predicting stress levels with the highest accuracy and specificity in MidCap and SmallCap mutual funds based on AMFI’s 14 parameters as predictors.Our study utilizes AMFI datasets available for MidCap and SmallCap mutual funds from February 2024 (inception period) to May 2024. To operationalize the analysis, the derived response variable was binned to represent high and low risk based on the time required for pro-rata liquidation of the portfolio. The study employed feed-forward neural networks, with the logistic regression as the step function, to model the relationships. Results suggest that the simpler neural network models with one hidden layer and two to three nodes show higher accuracy and specificity. Analysis also shows that more complex architectures do not always guarantee better performance as these models might suffer from higher bias. By leveraging advanced computational techniques, this study contributes to the ongoing discourse surrounding risk management and decision-making in the realm of mutual fund investments.

Abstract: Malnutrition in children aged 0-5 years is a significant public health concern in many societies and is influenced by various sociodemographic factors that, when understood, may improve intervention planning and results. Background. This study aimed to identify the sociodemographic determinants of malnutrition in rural and urban areas of Abia State, Nigeria. Methods. A cross-sectional observational study involving 842 children (aged 6-59 months) and their caregivers was conducted across selected rural and urban areas in Abia State, Nigeria. Data collected through semistructured questionnaires focused on sociodemographic information as well as anthropometric measurements such as weight, height, and mid-upper arm circumference (MUAC). The dietary diversity score (DDS) was also calculated for each child via a questionnaire that recorded the consumption of various food groups over a 7-day period. Chi-square tests were adopted for categorical variables, whereas binomial logistic regression was used to identify determinants of malnutrition and weight classification. Results. Marked sociodemographic differences were found between rural and urban caregivers, with urban caregivers being more likely to have higher knowledge of nutrition (p < 0.01) and education (p < 0.01). Malnutrition, indicated by global acute malnutrition (GAM), was significantly higher in rural areas (53.5%) than in urban areas (46.5%) (χ2 = 5.353, p = 0.021). Logistic regression revealed that children of caregivers in rural areas were more likely to have an abnormal BMI-for-age (AOR: 1.407, 95% CI: 1.001–1.978, p = 0.049), whereas children of caregivers in urban areas were less likely to be malnourished (COR: 0.565, 95% CI: 0.347–0.921, p = 0.022). Conclusion. Implementation scientists should focus on enhancing caregiver education, particularly in rural areas, to promote awareness of diets, thereby reducing malnutrition.

Compassion fatigue and burnout are two distinct forms of mental health distress faced by educational workers. Researchers have shown a high level of both phenomena across the field of education, however, a better understanding of what educational workers already do for positive mental and emotional health is needed. This research study examined three years of data, collected via survey, inquiring into the various interventions, namely supports and resources, that educational workers, specifically teachers, educational assistants, school leaders, and support administrators , use to support positive mental health. Quantitative data analysis via descriptive and inferential statistics revealed that educational workers relied heavily on their personal support network followed distantly by medical professionals and other interventions revealing a gap that needs to be addressed by employers. Qualitative thematic analysis revealed a trend towards increased use of environmental interventions to promote positive mental health. Suggestions for other interventions that might be helpful are provided.

The study focuses of analysis the impact of corporate social responsibility (CSR) on economic growth and reducing inequality, highlighting the importance of CSR in achieving sustainable development and social justice. The main aim is to analyze how different CSR initiatives contribute to economic development, social prosperity and the reduction of inequality, reviewing the methods used to assess their impact. The research methodology includes a detailed literature review, bibliometric analysis and scientific mapping, surveys of various business organizations and a Gap Analysis regarding the identification of gaps between the current state of CSR activities and the expected outcomes. The research shows that companies perceive CSR as a key tool for improving corporate image, responding to stakeholder expectations and investing in social justice. Despite positive intentions, challenges include the lack of clearly defined methodologies for measuring impact on economic inequality, as well as difficulties in assessing the long-term effects of CSR initiatives. Key conclusions highlight the need for more structured approaches to assessing the social and economic effects of CSR, recommending that companies improve their transparency and accountability, and implement clear indicators of success to achieve sustainable economic and social outcomes.

ChatGPT, developed by OpenAI, is an advanced large language model (LLM) that leverages artificial intelligence (AI) and deep learning to generate human-like responses. This paper explores its applications in digital health and healthcare, focusing on enhancing patient engagement through medical history collection, symptom assessment, and decision support to improve diagnostic accuracy. We also examine its role in promoting health literacy and clinical decision-making across various organ systems. Despite its advantages, the integration of ChatGPT in healthcare poses limitations and risks, including the potential for inaccurate information, overreliance by users, and ethical concerns related to informed consent. We discuss the challenges of keeping pace with rapidly evolving medical knowledge and highlight the need for continuous updates. Additionally, the paper addresses intellectual property and regulatory issues associated with AI deployment in medicine. Through this analysis, we provide insights into the benefits and potential risks of employing ChatGPT in healthcare.

Animal venoms are complex mixtures of bioactive peptides and enzymes that have garnered significant interest for their therapeutic potential, particularly in drug delivery systems. Among these peptides, cell-penetrating peptides (CPPs) have emerged as promising candidates due to their ability to traverse cellular membranes and deliver a variety of molecular cargos, including drugs, imaging agents, and nanoparticles. Recent advancements in omics technologies have enhanced our understanding of the structural and functional properties of CPPs, leading to their classification based on conformation, origin, and physicochemical characteristics. This review discusses the diverse types of CPPs, including three-finger toxins, disintegrins, and bradykinin-potentiating peptides, highlighting their roles in targeted drug delivery and therapeutic applications. The potential of venom-derived peptides in developing novel strategies for drug delivery and disease treatment is explored, emphasizing their impact on biotechnological and biomedical research.

Most of the management in retail and sports facilities about tennis balls is subjective and leads to early disposal, further contributing to environmental waste. This paper discusses a machine learning-based framework to optimize tennis ball usage by providing an accurate forecast of their lifetime, using data-driven insights. We develop and test several predictive models using Random Forest, Support Vector Machines, and Neural Networks based on holistic datasets that include data on bounce dynamics, material composition, usage frequency, and environmental conditions. The framework also allows real-time monitoring and decision-making for the replacement of the tennis balls, in addition to identification of the key factors that influence performance degradation. Our results show that this could significantly extend the life of tennis balls, which again can help reduce the overall waste and support sports industry sustainability efforts. The proposed open-access framework is scaling up a solution for tennis clubs/stores in order to improve operational efficiency with a low environmental impact while contributing to the circular economy at the same time.

Kiwifruit Vine Decline Syndrome (KVDS) has recently become a concern in Italy, impacting plant health and production. This study aims to investigate the influence of KVDS on soil health indi-cators and the composition of soil microbial communities by comparing symptomatic and asymptomatic kiwifruit orchards located in two areas in Latium, Italy. Soil samples were collected in spring and autumn to evaluate the seasonal variations in soil physicochemical properties, enzyme activities, and microbial biomass. The results revealed that KVDS impacted soil properties such as pH, electrical conductivity, and water-soluble carbon and nitrogen contents, though these effects varied by orchard and season. Moreover, KVDS significantly affected soil enzyme activities and microbial biomass assessed through PLFAs analysis, particularly showing an increase in fungal biomass in symptomatic areas. In addition, metabarcoding revealed differences in the microbial communities between symptomatic and asymptomatic soils, with notable shifts in their diversity and relative abundance. Our findings highlight the complex interactions between plant-soil-microbiome in KVDS, suggesting that KVDS is multifactorial and is likely associated with an imbalance of soil microbial communities at the rhizosphere level, which can negatively affect soil health.

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross-mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of low-cost optical particle counters, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader application in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology's Aerospace Systems Design Laboratory. Unlike general NER models, this system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to enhance its precision in identifying and classifying named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirrors the complexity of real-world aviation safety reports. This method significantly improves the model's generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Fine-tuning of the Aviation-BERT model demonstrated strong performance in precision, recall, and F1 scores, showing its efficacy in accurately identifying a wide range of entity types within aviation safety reports. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted the model's robustness, with performance metrics indicating its potential to significantly enhance the automation of aviation safety report analysis. This work addresses a critical gap in English language NER models for aviation safety and establishes a new benchmark for domain-specific NER systems, promising substantial improvements in the analysis and understanding of aviation safety reports.

This paper explores the burgeoning field of mindfulness in organisational contexts, examining its evolution from Buddhist practices to contemporary corporate applications. It critically analyses the implementation of mindfulness interventions in Fortune 500 companies, elucidating both the potential benefits and challenges. The research highlights the role of mindful leadership in fostering positive organisational cultures and addresses the complexities of integrating these practices within diverse corporate settings. Drawing on case studies, the paper illuminates the variability in mindfulness approaches and their impacts on employee well-being, productivity, and organisational resilience. Whilst acknowledging the promising outcomes, the study also considers critiques regarding the commodification of mindfulness and its potential to blur work-life boundaries. The concept of Microdosed Mindfulness is introduced as a nuanced strategy for overcoming resistance and facilitating gradual adoption. This comprehensive analysis provides valuable insights for organisations navigating the implementation of mindfulness practices in the contemporary world.

Exposure to endocrine-disrupting chemicals (EDCs) is inevitable, and growing scientific evidence indicates that even very low doses can negatively impact human health, particularly during pregnancy and the neonatal period. As part of the European project LIFE18 ENV/IT/00460, this study aims to identify the presence of EDCs in 20 infant formulas (both powdered and liquid) and the release from baby bottles and teats. Particularly, sensitization of young people and future parents towards the potential harmful effects of EDCs could significantly help to reduce exposure. Seven different UPLC-MS/MS methodologies and one ICP-AES were set-up to quantify already assessed and suspected EDCs among 85 different chemicals (bisphenols, parabens, phthalates, pesticides, herbicides, and their main metabolites, PFAS, and metals). Results showed that in 2 out of 14 baby bottles only anthracene and phenanthrene of the group of PAHs were released (10.68-10.81 ng/mL). Phthalates such as mono-ethyl phthalate (MEP) were found in 9 of 14 samples (0.054–0.140 ng/mL), while mono(2-ethyl-5-oxohexyl) phthalate (MeOHP) appeared in 2 samples (0.870–0.930 ng/mL). In accordance with current EU regulations, other chemicals were not detected in baby bottles and teats. However, Bisphenols, Parabens, PAHs, Phthalates, PFAS and Metals were detected in infant formula, emphasizing the need for continued monitoring and public health interventions.

This review discusses the key factors influencing the exceptional thermal resistance and surface properties of silicone-containing composites. Silicone polymers, known for their excellent chemical and physical proper-ties, are widely used in a number of innovative products. In order to make silicone composites suitable for innovative applications, it is essential to ensure that they have both very good thermal resistance and su-perhydrophobic properties. Identification of the key factors influencing these properties enables the use of these composites in coatings, electronics and photovoltaic panels. The discussion includes the role of or-ganosilicon polymer structures and the incorporation of micro- and nano-additives to enhance the perfor-mance of these materials. Different methods for the modification and production of silicone composites are analyzed, with an emphasis on achieving thermal stability and surface superhydrophobicity simultaneously. The review highlights the growing demand for silicone-based coatings, driven by technological advances and environmental concerns. Furthermore, the role of surface modification and hierarchical surface structures in achieving these unique properties is discussed, as well as the potential applications and challenges in the development of next-generation silicone-containing materials.

Supplementing essential amino acids (EAA), without considering non-EAA (NEAA) and energy contents in reduced crude protein (CP) diets may alter essential-to-total AA or true protein (E:T) and energy-to-protein ratios, potentially compromising growth. This study aimed to evaluate the effects of CP, E:T and net energy (NE) on broiler performance. Treatments were: T1— reduced CP (16%, RP), low NE (9.9 MJ/kg, LNE), low E:T (0.56, LE:T); T2—RP, LNE, high E:T (0.60, HE:T), with imbalanced EAA (excess Met and deficient Thr); T3—RP, high NE (10.4 MJ/kg, HNE), LE:T; T4—RP, HNE, HE:T; T5— normal CP (18%, NP), LNE, LE:T; T6—NP, LNE, HE:T; T7—NP, HNE, LE:T; and T8—NP, HNE, HE:T. The study employed as-hatched Cobb 500 broilers in two experiments. Exp.1 studied performance from d19 to 35, with 8 replicates per treatment and 16 birds per replicate (n=1024). Exp.2 measured NE values in respiration chambers from d25 to 28, with 6 replicates per treatment, and 2 birds (a male and a female) per replicate (n=96). The measured NE values were used to calculate NE intake (NEi) in Exp.1. The results showed that T4 improved (P &lt; 0.001) weight gain (WG), feed conversion ratio (FCR), and NEi relative to T1, T2 and T3, and protein efficiency (WG/CP intake) relative to all treatments. The live performance (feed intake, WG, FCR) and NEi of birds fed T4 reached a level equal to those of birds fed NP-diets (T5 to T8). These results suggest that a dietary E:T ratio of 0.60 is necessary to maximize nutrient utilization and to restore growth rate in broilers fed RP-diets.

Background: Determining the status of DNA mismatch repair (MMR) proteins is crucial for guiding treatment decisions and predicting prognosis in patients with colorectal cancer. This study evaluates a streamlined panel utilizing two antibodies for MMR protein assessment, aiming to enhance cost-effectiveness, reduce workload, and expedite result delivery. Methods: We re-classified patients with adenocarcinoma requiring MMR determination by evaluating only PMS2 and MSH6 proteins. The diagnostic performance of the proposed two-antibody panel was compared to the traditional four-antibody panel against a polymerase chain reaction (PCR) assay used as the reference standard. Results: A total of 404 cases were analyzed, with the predominant histological type being adenocarcinoma not otherwise specified. The most common histological grade was 2, and 60.9% of cases were in clinical stage II. Comparative analysis showed no statistically significant differences between the two-antibody and four-antibody panels. Sensitivity was 95.35% for the four-antibody panel versus 90.7% for the two-antibody panel; specificity was 98.74% versus 98.11%, respectively. Additionally, the positive predictive value was 95.35% for the four-antibody panel compared to 92.86% for the two-antibody panel, and negative predictive values were 98.74% versus 97.50%. The area under the curve was 0.970 for the four-antibody panel and 0.944 for the two-antibody panel (p=0.419). Conclusion: The analysis shows that determining MMR status using only two antibodies is as effective as employing a traditional four-antibody panel, providing a viable, cost-effective alternative for clinical practice.

The purpose of this study was to examine the relationship between demyelination and glial cell responses in the cerebellum of CDV-infected dogs. We subdivided the disease staging by adding the degree of demyelination determined by LFB staining to the previously reported disease staging from the acute stage to the chronic stage, and investigated the relationship between demyelination in cerebellum and the number and histological changes of astroglia, microglia and Purkinje cells in each stage. Reactions of astrocytes and microglia were observed at an early stage when demyelination was not evident. Changes progressed with demyelination. Demyelination initially began in the medulla adjoining the fourth ventricle and gradually spread to the entire cerebellum, including lobes. CDV immune-positive granules were seen from the early stage, and inclusion bodies also appeared at the same time. CDV immune-positive reaction and inclusion bodies were observed in astrocytes, microglia, neurons, ependymal cells, and even leptomenin-geal mononuclear cells. On the other hand, infiltration of CDV immunoreactive particles from the pia mater to the gray matter and further into the white matter through the granular layer was observed from an early stage. Purkinje cells decreased from the intermediate stage, and a decrease in cells in the granular layer was also observed. There was no clear association between age and each stage, and the stages did not progress with age.

Crouch gait is one of the most common gait abnormalities. In the literature, there are few works related to the crouch gait recognition. In this work, we aim at the integration of a framework for multiclass crouch gait recognition using synthetic eight-DoF model gait data in anatomical space. The main contribution of our study is the joint function generator algorithm, which could be used to generate other physiological signals; and the gait data validation stage. In addition, we use a feature selection method and a body region segmentation approach to improve classification performance. We generated a gait dataset containing 25 join functions of each DoF for each gait class (4 crouched and normal). The algorithms that we evaluated for classification were: K-nearest neighbors (kNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artificial neural network (ANN). We calculated the metrics: accuracy (Acc), recall (R), specificity (SP), precision (P) and the F-measure (FM) to asses the classification performance. In general, for this work, the best algorithms were discriminant analysis and artificial neural networks. The main results of the proposed recognition framework provide evidence that can be used in real clinical settings to diagnose and make decisions about treatments related to crouch gait diseases.

Effective change management, corporate sustainability, and employee resilience are crucial factors influencing organizational success. This study explores how demographic factors impact these dimensions, specifically focusing on the role of leaders during change, sustainability, sustainable competitiveness, and employee resilience within a diverse workforce. Statistical analyses, including ANOVA and regression, were performed to examine the associations between demographic factors (gender, age, salary range, sector, job position, and total job experience) and the measured scales. The study found significant variations in employee perceptions of sustainable competitiveness based on gender and sector, with males and employees in the education sector reporting higher scores. Age and salary range significantly affected employee resilience, with younger employees and those in lower salary brackets showing higher resilience scores. The findings highlight the importance of considering demographic factors in assessing organizational performance, sustainability, and employee resilience. Gender and sector differences suggest targeted strategies may be necessary to address specific perceptions and experiences. Additionally, younger employees and those with lower salaries demonstrate higher resilience, which could inform organizational support programs. These insights can guide future research and organizational practices aimed at enhancing employee and organizational outcomes.

Malignant melanoma and urological cancers originate from different tissues and organs, yet several studies highlight connections between these malignancies, including common risk factors, genetic predispositions, and immunological pathways. Research indicates that individuals with a history of melanoma have an elevated risk of developing renal cell carcinoma (RCC), while those with RCC are more likely to develop melanoma. Shared factors such as a personal or family history of cancer, UV radiation exposure, smoking, and obesity have all been linked to an increased incidence of various cancer types. A major link between malignant melanoma and urological cancers is the presence of shared genetic mutations and familial cancer syndromes. Key mutations, including germline mutations in BRCA1, MITF, CDKN2A, TP53, and alterations in the PI3K/AKT pathway, significantly contribute to the risk of both types of malignancies. Personalized medicine, which tailors prevention and treatment strategies to an individual’s genetic, environmental, and lifestyle factors, has significantly improved cancer care. The primary aim is to select the most effective treatment for each patient, maximizing therapeutic outcomes, reducing side effects, and minimizing the risk of drug resistance. Advances in genomics and immunology are driving the development of personalized therapies that target specific molecular pathways and immune responses common to both melanoma and urological cancers. Angiogenesis inhibitors and checkpoint inhibitors have demonstrated notable success in treating these cancers, with tumor mutational burden serving as a valuable biomarker for predicting the efficacy of immune checkpoint inhibitors.

Here, using the theory of electrical modelling of the neutron and proton as a three-phase alternating current electrical generator, we will begin the path to demonstrate that the interactions of electromagnetic forces, weak force and strong force that are part of the standard model, can be described by a single electromagnetic force interaction, in other words: SU(3) x SU(2) --> U(1). We will also hypothesize two particles that would give rise to gravitons. It is the interactions of the quarks (U, Û, D, ^D) that dominate the dynamics; They are responsible for the origin of bosons (photons, gluons, W⁺, W⁻, Z⁰, gravitons and Higgs boson).

Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as Random Forest, for categorizing defoliation levels in R7-stage soybean plants. The research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The Random Forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97 and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the RedEdge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

This paper explores Burger King's sustainability efforts in the face of growing environmental and social challenges. It begins by defining sustainability and its importance in corporate operations, then transitions to analyze Burger King's specific practices, particularly its efforts to reduce methane emissions from beef production and tackle deforestation linked to its supply chain. While the company has made some strides in addressing environmental concerns, such as introducing low-emission beef burgers, these initiatives have been deemed inadequate by experts. The paper highlights the gaps in Burger King's approach to sustainability and compares it to industry leaders like McDonald's, emphasizing the need for more robust and transparent measures. The conclusion suggests that Burger King must take more aggressive steps in aligning its operations with sustainable practices to meet the demands of modern consumers and secure its long-term viability.

Given the complexity and variability of gastrointestinal polyps, there is a growing need for more accurate machine-learning algorithms that can assist medical professionals in decision-making processes. In our study, we employed machine learning (ML) techniques to classify gastrointestinal polyps seen in colonoscopy images, that present a risk of colon cancer, affecting a large portion of the population. The dataset used in the research comprised 152 instances and included three types of lesions, totaling 76 polyps. The study consisted of applying Logistic Regression (LR) to classify gastrointestinal images. The principal motivation for choosing this algorithm consists in the fact that is preferred by many medical researchers. Another motivation consisted in the fact to compare the polyps classification accuracy of physicians and LR with optimized hyperparameters. The approached model performance was evaluated with the following metrics: Accuracy, Precision, Recall, F1_Score and Macro-average. We used the Multiclass Logistic Regression (mLR) classifier to classify polyps into hyperplastic (27.63%), serrated (19.74%), and adenoma lesions (52.63%). It should be noted that serrated polyps are hard to diagnose, even by physicians, because they have characteristics of both hyperplastic and adenoma ones. One of the objectives consisted in the study of the hyperparameters tunning of the Logistic Regression to find the best-fitted model. To check the influence of the data mix on the results obtained, for the two best models (LR with liblinear solver, L1 penalty and C = 0.01), it was performed a comprehensive statistical analysis. Based on an algorithm that admits obtaining accurate results. Such analyses are mistakenly applied in the scientific literature. The model with optimized hyperparameters achieved an accuracy score of 70.39%, indicating a reasonable level of prediction, surpassing both beginner and expert physicians and obtaining results similar to those obtained by Random Subspace and Random Forest. The model successfully classifies the hyperplastic and adenoma polyps but showed to have a more limited ability to predict the serrated ones.

The kidneys contribute to the overall health of the organism by maintaining systemic homeostasis. This process involves the regulation of various biological mechanisms, in which the Krüppel-like factor (KLF) transcription factors are essential for regulating development, differentiation, proliferation, and cellular apoptosis. They also play a role in the metabolic regulation of essential nutrients, such as glucose and lipids. The dysregulation of these transcription factors is associated with the development of various pathologies, which can ultimately lead to renal fibrosis, a process that severely compromises the function of these organs. In this context, the present article provides a comprehensive review of the existing literature, offering an enriching analysis of the findings related to the role of KLFs in the renal field, while also highlighting their potential therapeutic role in the treatment of renal diseases.

Background: Urinary tract infections (UTI) are a significant health concern, particularly in women, with almost half of them experiencing at least one UTI in their lifetime. This problem is further compounded by the high recurrence rate within 6-12 months. Postmenopausal women are especially vulnerable to recurrent UTI due to the natural decline in estrogen levels, which alters the urogenital epithelium and subsequently affects the urogenital microbiome. Methods: This study included 674 female patients that were admited at "Dr. Carol Davila" Central Military Emergency University Hospital in Bucharest over a period of 3 years. Results: Of the 674 women with urinary tract infection, 435 (79.3%) had more than one positive culture, and 239 (35.4%) experienced at least one recurrent urinary tract infection 6-12 months after the initial diagnosis. The mean age of women with urinary tract infection was 63 (± 15.61 years). Recurrent urinary tract infections were most prevalent in those aged 65-80 years (52%). Escherichia coli was detected in 71% of the positive cultures. Conclusions: The management of recurrent urinary tract infections in women presents a complex challenge that necessitates a multifaceted approach. The emerging understanding of the urobiome and its role in bladder health offers promising avenues for developing targeted interventions. As research in this field advances, it is imperative to integrate these new insights into clinical practice to balance traditional antibiotic treatments with novel strategies that consider the intricate microbial ecosystem of the urinary tract.

The objective is to describe a new technique for subtrochanteric hip nonunions. Three patients with a nonunion of a previously nailed subtrochanteric hip fracture were selected. The nail was withdrawn, and a simple lateral approach was performed. A hip screw was introduced through the neck, and a dynamic plate (135-150º) was applied, to increase the femoral-neck angle (valgus) and reduce the head offset at the hip. The correction was achieved just by mobilizing the nonunion site. No osteotomy was done. Fixation promoted dynamic mechanical compression and fracture healing. The mean age of patients was 57 (34-85). 2 women, and one man. Duration of surgery was 102 min (63-123). Femoral neck angle before surgery was 125º (122-128), and after surgery 147º (142-150). Offset was 50 mm (42-59) before surgery and 20 mm (19-24) after surgery. Leg length was increased by 8,6 mm (6-12). Harris hip score was improved from 37,33 (30-42) to 93,33 (88-96). All patients healed and were asymptomatic after 12 months follow-up. In conclusion, valgus correction with a dynamic hip screw is a useful technique for a nonunion of a subtrochanteric hip fracture. This zone is well vascularized, so just dynamic mechanical compression at the fracture site is needed.

With the gradual development and integration of artificial intelligence into various industries, there is also a great range of integration in the financial industry. Therefore, this article focuses on the trend prediction model and financial risk management problems of deep reinforcement learning (DRL), one of the largest branches of artificial intelligence, in the cryptocurrency market. In addition, in the experimental part of this paper, the artificial intelligence machine learning Long short-term memory network (LSTM) model is used to make effective time series prediction and analysis on the relevant data of the cryptocurrency market, so as to make a large-scale analysis to improve the accuracy of market trend prediction and the effectiveness of risk management. In addition, in this experiment, technology-related indicators, emotional states of financial market customers and other content related to large language models are combined. While optimizing investment strategy by using deep reinforcement learning algorithm, machine learning prediction model is also used to capture the time dependence of financial market. The experimental results also show that the predicted results are consistent with the actual value. Therefore, the model has high practical application value in predicting the time series price trend of cryptocurrency in the financial market and indicates that the integrated DRL model framework can further optimize and manage the price and trading strategy of the financial market. Future research should focus on improving the LSTM model and incorporating more features to improve prediction accuracy and adapt to market changes.

Heparin resistance (HR) on ECMO exacerbates bleeding and thrombogenesis. So far, there is no universal definition of HR on ECMO and no unified strategy for the assessment of heparin efficacy in ECMO patients. The most frequent discrepancy in defining HR is the difference in reported doses: units per day (U/d) or per kilogram per hour (U/kg/h). Another disagreement occurs ow-ing to different ways of measuring UFH efficacy, contributing to further disagreement in a com-mon definition of HR. Due to numerous processes that start with ECMO initiation, including protein layer formation on the surface of the circuits, recruitment of immune cells, activation of complement and contact activation systems, and platelets, the assessment of pure antithrombin consumption is complicated. Moreover, there is an alternative anticoagulation performed by a serine protease inhibitor named Heparin Cofactor II, the contribution of which could also be measured on heparin consumption. Considering simultaneously launched processes of inflam-mation and thrombogenesis in response to contact with artificial surfaces on ECMO, we listed possible mechanisms contributing to additional antithrombin consumption. The effect of the flow on the platelets activation and vWF assembly was also described. We examined the literature from Pubmed and Embase to find possible definitions of heparin resistance on ECMO among pediatric and adult cohorts. We extracted 13 records with different HR assessment approaches and sug-gested our vision of delineating HR on ECMO.

Microorganisms play a pivotal role in ecology, medicine, agriculture, and biotechnology. To comprehend their diversity, physiology, and functions, isolation and characterization are imperative. This review provides a concise overview of prevalent molecular biotechniques for microbial isolation. It describes the advantages, constraints, and practical applications of both culture-dependent and culture-independent approaches. Discussed methods encompass selective/differential media, 16S rRNA gene sequencing, metagenomics, fluorescence in situ hybridization (FISH), polymerase chain reaction (PCR)-based techniques, and single-cell genomics. Emerging high-throughput sequencing and single-cell isolation methods exemplify cutting-edge procedures explored herein. Integration of these molecular tools offers comprehensive insights into microbial-diversity, community structure, metabolic capabilities, and functional interplays.

Background/Objectives: Body surface area is one of the most important anthropometric parameters in medicine. The study's primary objective is to compare the consistency of the BSA estimation results through applying available formulas. Other objectives include determining the ability of these formulas to discriminate between the deaths and living patients, comparing the formulas’ diagnostic features as well as investigating whether the risk associated with a low BSA is independent of BMI. Methods: The study included 1029 patients (median age: 54 years, female: 13.7%, NYHA I/II/III/IV: 6.3% / 36.5% / 47.7% / 9.5%) diagnosed with heart failure. For each patient, BSA was calculated using the 25 formulas. Over the 3-year observation period, 31.2% of the patients died. Results: The average BSA value of the optimal discrimination thresholds was 1.79 m2 ± 0.084 m2 and the BSA difference between the estimators with the lowest (BSAMeeh1879) and the highest (BSANwoye1989) optimal discrimination thresholds was 0.42 m2. The lowest mortality rate was 35.2% and occurred in the subgroup of individuals with BSA values below the optimal discrimination threshold using the BSASchlich2010 estimator. The highest mortality was observed when estimators BSAMeeh1879 or BSALivingston&Lee2001 were used. Conclusions: Our study has shown a relatively good concordance of 25 BSA estimators in BSA assessment in patients, without extremes of weight or height known to disrupt it. All BSA estimators presented a significant, although weak, ability to discriminate death from alive in 3-year follow-up. The higher risk of death in smaller patients as shown by BSA was independent of BMI in all but 2 BSA estimators.

Trueperella pyogenes is a widely distributed opportunistic pathogenic bacterium which can infect livestock, wildlife, community animals, and humans, resulting in suppurative infection of tissue and organ mucosa, including pneumonia, liver abscessation, mastitis, metritis, endocarditis, and osteoarthritis. TP1804 and TP1808 were isolated from the uterine lavage fluid of cows with endometritis. Several virulence factors are significant contributors to the pathogenicity expressed by T. pyogenes, including purulent pyolysin (PLO), fimbriae (Fim), neuraminidase (NanH and NanP), and collagen-binding protein (CbpA). These virulence factors are contributed to promote adhesion, including fimbriae, extracellular matrix-binding proteins, and neuraminidases. This study analyzed the prokaryotic transcriptomics and proteomics of two strains of T. pyogenes with similar growth curves but different cytotoxicity. Studying the metabolic mechanisms of these differentially expressed genes and proteins can greatly promote the discovery of new biomarkers and improve the accuracy of biomarker identification, which is of great value for molecular mechanisms, biomarkers, early diagnosis of diseases, molecular typing, prognosis, and clinical diagnosis.

Recent studies have shown that Lipoprotein(a) (Lp(a)) is an important risk factor for a plethora of different cardiovascular diseases. It has been proven that Lp(a) levels are genetically determined, and correlate with risk of cardiovascular disease, independent of lifestyle factors. As of yet, treatment options to reduce Lp(a) levels are limited, but new research into Lp(a) reduction yields promising results. This review delves into Lp(a) biochemistry, mechanism of effect, association between Lp(a) and cardiovascular diseases, and possible therapies to minimize cardiovascular disease.

Fusobacterium nucleatum (F. nucleatum, Fn), an oral commensal that can become pathogenic, is associated with periodontitis (PD), adverse pregnancy outcomes, and colorectal cancer (CRC). MicroRNAs (miRNAs) are conserved, non-coding RNAs that play a regulatory role in gene expression and are detected in microbial infections. The aim of this study was to characterize the global microRNA expression kinetics in the mandibles of C57BL/6J mice infected with F. nucleatum for 8 and 16 weeks, and to identify miRNAs as biomarkers of the PD process using high-throughput NanoString nCounter® miRNA panels. Mice were divided into four groups: 8 weeks of Fn infection, 16 weeks of Fn infection, and their respective sham infection. Fn-infected mice at both 8- and 16 weeks showed 100% bacterial colonization on the gingival surface, along with a significant increase in alveolar bone resorption (p<0.0001) and intravascular dissemination to heart, indicating its invasive potential. Out of 577 miRNAs analyzed, seven miRNAs (miR-361-5p, miR-99b-5p) were upregulated, and two miRNAs (miR-362-3p and miR-720) were downregulated in the 8-week Fn infection group. In the 16-week Fn infection group, seven miRNAs (miR-361-5p, miR-99b-5p) were upregulated miRNAs and 13 miRNAs (miR-323-3p, miR-488) were downregulated. Notably, miRNAs such as miR-205, miR-210, miR-199a-3p which were differentially expressed (DE) in the Fn-infected mice at 8 weeks and miR-28 at 16 weeks, have been previously reported in human periodontitis. The 13 miRNAs induced by F. nucleatum (miR-361-5p) are linked to multiple malignancies, including esophageal, gastric, pancreatic, and colorectal cancers. Additionally, the DE miR-126-5p in the 8-week infection group has been identified as a potential biomarker for patients with PD and cardiovascular disease. The results indicate that F. nucleatum acts as a potent oncomicrobe, inducing several miRNAs associated with PD and linking it to systemic comorbidities. Furthermore, the study revealed similar expressions between PD-associated miRNAs and nine CRC tumor-expressed miRNAs.

The aim of this study is to explore the influence of Environmental, Social, and Governance (ESG) factor on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted Principal Component Analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of 7 variables that predict business failure. The results indicate that ESG factor significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

This article presents a mathematical model that captures the temporal dynamics of efficiencies in the responses to pleasure and pain, represented by $k_P(t)$ and $k_D(t)$, as well as the interaction factor $k_{PD}(t)$ between cortisol and dopamine. The model allows for the analysis of how these parameters evolve over time as a function of an individual's activities and physiological states, incorporating processes of tolerance, sensitization, and homeostasis. We present the updated differential equations, qualitative analyses, potential simulation scenarios, implications, and potential applications of the model.

Parental willingness is a prerequisite for childhood immunization. This study evaluated whether parental vaccination readiness and willingness, measured using the modified Japanese 7C Vaccination Readiness Scale, predicted vaccination behavior. In December 2021, 2 months before the approval of COVID-19 vaccination for children aged 5–11 years in Japan, we asked 1,190 parents of children in this age groups about their intention regarding vaccinating their children against COVID-19 using an online modified 7C Vaccination Readiness Scale, consisting of seven components, namely, Confidence, Complacency, Constraints, Calculation, Collective responsibility, Compliance, and Conspiracy. The modified 7C components were analyzed for model fit and association with parental intention using structural equation modeling. The model fit of the modified 7C scale was good (goodness-of-fit index: 0.98), and the factor loadings and model fit for parental readiness were high (standardized factor loadings: 0.84). In January 2024, we asked the same parents about their children's COVID-19 vaccination status (valid responses: 536). The components of the modified 7C scale were compared with actual vaccination behavior. Parents with a low readiness to vaccinate their children against COVID-19 had a high actual non-vaccination rate. The use of the modified 7C scale may assist in the development of childhood vaccination interventions.

This paper introduces an innovative theoretical framework for quantum-inspired data embeddings, grounded in foundational concepts of quantum mechanics such as superposition and entanglement. This approach aims to advance semi-supervised learning in contexts characterized by limited labeled data by enabling more intricate and expressive embeddings that capture the underlying structure of the data effectively. Grounded in foundational quantum mechanics concepts such as superposition and entanglement, this approach redefines data representation by enabling more intricate and expressive embeddings. Emulating quantum superposition encodes each data point as a probabilistic amalgamation of multiple feature states, facilitating a richer, multidimensional representation of underlying structures and patterns. Additionally, quantum-inspired entanglement mechanisms are harnessed to model intricate dependencies between labeled and unlabeled data, promoting enhanced knowledge transfer and structural inference within the learning paradigm. In contrast to conventional quantum machine learning methodologies that often rely on quantum hardware, this framework is fully realizable within classical computational architectures, thus bypassing the practical limitations of quantum hardware. The versatility of this model is illustrated through its application to critical domains such as medical diagnosis, resource-constrained natural language processing, and financial forecasting—areas where data scarcity impedes the efficacy of traditional models. Experimental evaluations reveal that quantum-inspired embeddings substantially outperform standard approaches, enhancing model resilience and generalization in high-dimensional, low-sample scenarios. This research marks a significant stride in integrating quantum theoretical principles with classical machine learning, broadening the scope of data representation and semi-supervised learning while circumventing the technological barriers of quantum computing infrastructure.

It is expected that two-dimensional (2D) metal nitrides (MNs), consisting of the elements of the 13th group of the periodic table and nitrogen, namely aluminium nitride (AlN), gallium nitride (GaN), indium nitride (InN) and thallium nitride (TlN) have enhanced physical and mechanical properties, due to a honeycomb graphene-like atomic arrangement, characteristic to these compounds. The basis for the correct design and improved performance of nanodevices and complex structures based on 2D MNs of the 13th group is the understanding of the mechanical response of their components. In this context, a comparative study to determine the elastic properties of metal nitride nanosheets was carried out making use of the nanoscale continuum modelling (or molecular structural mechanics) method. The differences in the elastic properties (surface Young´s and shear moduli, and Poisson´s ratio) found for 2D 13th group MNs are attributed to the bond length of the respective diatomic hexagonal lattice. The results obtained contribute to a benchmark in the evaluation of mechanical properties of AlN, GaN, InN and TlN monolayers by analytical and numerical approaches.

Mergers and acquisitions (M&A) represent pivotal moments for businesses, with the potential to unlock growth, synergies, and market power. However, the success of such transactions is heavily dependent on the robustness of the contractual agreements that define them. Poorly designed contracts introduce numerous risks that can derail the benefits of the deal, leading to financial losses, legal disputes, or operational inefficiencies. This work examines the core risks associated with inadequate contract design in M&A, supported by academic research.

Programming languages drive most if not all of modern problem- solving using computational methods and power. Research into new programming languages and methods is essential to the furthering or improvement of computational problem-solving by making the design, implementation, and application of automation to general or particu- lar problem-solving ever easier, more accessible, and more performant. General-purpose Programming Languages (GPLs) typically are designed to be purely domain agnostic—meaning they can be applied in any field, for any kind of problem. However, this typically also makes them hard and difficult to apply in problems where non-programmers or even ex- perts with little or no GPL programming skills are required to leverage programmatic problem solving capabilities, which is why Domain Specific Languages (DSLs) come into play; they are generally more fine-tuned to- wards improving human productivity and performance than that of the machine, while making solving particular, domain-oriented problems sim- pler. In this paper, we review the literature concerning how to design and then fully implement a new DSL, with special focus on a DSL for generic problem-solving leveraging Text Processing methods—essentially, a Text Processing Language (TPL). We consider leveraging the design research paradigm and philosophy as a systematic framework for guiding research into the development of new TPLs. This work presents for the first time, new unifying theory concerning general, but also TPL-specific language engineering theory and guiding frameworks—UPLT, PLEF & PLEf. We consider quantitative but also qualitative evaluation of programming lan- guages. The SOE framework is re-introduced for this purpose too. Finally, we set the pace for future theoretical and practical research into the field of programming language engineering especially with focus on TPLs.