The iron roughneck is an automated equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its structural characteristics and performance requirements. The study delves into the integration mode and working characteristics of the clamping mechanism and spin buckle mechanism for the integrated upper clamp body structure of the iron roughneck. Additionally, this paper conducts an in-depth theoretical study on the spin buckle mechanism. Firstly, it analyzes the actual working condition of the spin buckle roller from two perspectives: contact theory and rolling friction theory, determining the structural form of the spin buckle roller. Secondly, it investigates the relative displacement between the spin buckle mechanism and the drilling tool, proposing a design method for the floating device mounted on the spin buckle roller and establishing the kinematic equation of the spin buckle roller under the influence of the floating device. Furthermore, the kinematic equations of the spin buckle roller under the influence of the floating device are established. Finally, a dynamics simulation experiment is performed to simulate the working process of the spin buckle mechanism under actual working conditions, analyzing the dynamics and kinematics of the spin buckle mechanism and obtaining the relevant parameter curves of the spin buckle mechanism and drilling tools. Through data comparison and analysis, the correctness of the theoretical analysis results and the rationality of the performance and structure of the spin buckle mechanism are verified.

The emergence of antibiotic-resistant Campylobacter jejuni, a leading cause of gastroenteritis worldwide, presents a significant public health challenge requiring vigilant surveillance and disease control. This study aimed to characterize C. jejuni strains isolated in Romania from 2017-2020, focusing on genetic diversity, virulence, and antibiotic resistance determinants. The isolates underwent phenotypical testing, PCR, and antibiotic resistance assessment using Disc Diffusion Kirby–Bauer for ciprofloxacin, nalidixic acid, erythromycin, and tetracycline. Genetic analysis identified resistance and virulence genes, point mutations, and performed sequence typing (7-gene MLST) to determine genetic relatedness. Results indicated substitutions at position 86 in the amino acid sequence or position 257 in the nucleotide sequence of the gyrA gene in 47 fluoroquinolone-resistant isolates. Additionally, mutations in the rRNA23S gene at positions 2074 and 2075, associated with macrolide resistance, were found in 12 of the 66 isolates. Allelic profiles generated 38 sequence types (STs), including three new STs not present in the reference database. The sequence data analysis revealed a genetically diverse C. jejuni population with a weak clonal structure. This study provides crucial insights into the genetic diversity and antibiotic resistance of C. jejuni strains in Romania, highlighting the need for ongoing surveillance and control measures.

Idiopathic pulmonary fibrosis [IPF] is a chronic progressive disease characterised by the accumulation of scar tissue in the lung parenchyma. It primarily occurs in middle-aged and elderly adults and results in significant morbidity and mortality worldwide. The disease occurs due to repetitive lung epithelial injury, subsequent fibroblast activation and myofibroblast differentiation, resulting in excessive extracellular matrix deposition. This leads to scar formation and subsequent loss of lung function. Current treatment options for patients with IPF include the two anti-fibrotic drugs, pirfenidone and nintedanib, which can slow disease progression; however, there are currently no known cures for the disease. As such, novel methods and drug targets are warranted. In this review, we provide an up-to-date account of the importance of specific cytokines and the potential role of regulatory immune cells. We discuss their role in the pathogenesis of IPF and address some of the key gaps in knowledge.

Wicked problems, such as climate change, systemic poverty, and healthcare inequalities, rep- resent the most pressing and intractable challenges facing society today. We propose a novel approach that leverages complex systems theory as a comprehensive framework for understanding and tackling these challenges. By applying concepts such as state space analysis, attractors, and nonlinear dynamics, we offer a sophisticated toolkit for mapping the intricate web of interactions defining wicked problems and exploring potential interventions. Our approach moves beyond tradi- tional reductionist methods, embracing the inherent complexity and dynamism of these issues. We demonstrate how this framework can lead to more nuanced, adaptive, and effective strategies for addressing persistent societal problems. Despite the prevalence of wicked problems and the avail- ability of complex systems theory, practical applications of this approach remain uncommon. This paper aims to bridge this gap, illustrating the potential of complex systems theory to transform our engagement with intractable problems and pave the way for more robust, holistic solutions. By synthesizing insights from mathematics, physics, and social sciences, we present a transdisci- plinary approach that promises to revolutionize how we conceptualize and address society’s most challenging issues.

An efficacious treatment of H. pylori infection is cornerstone for the global control of the diseases caused by this bacterium. Continuous epidemiologic surveillance of susceptibility of H. pylori to the antibiotics recommended for its eradication is highly recommended. This study aimed to determine the antimicrobial susceptibility pattern of 651 isolates from five different collections isolated between 1996 to 2008 in México City. The susceptibility profile of H. pylori isolates was determined by the E-test strips for each antibiotic (amoxicillin, clarithromycin, metronidazole, and tetracycline). Antimicrobial susceptibility testing showed a very high proportion of H pylori strains susceptible to amoxicillin, clarithromycin, and tetracycline (94.5%, 95%, and 99.5 %, respectively). In contrast, a high proportion (95%) of isolates was resistant to metronidazole. Notably, the proportion of susceptible/resistant strains to the 4 antibiotics over 5 consecutive time periods remained stable over time. Despite a wide use of amoxicillin and clarithromycin in the general population, the H pylori high susceptibility to these 2 antibiotics has been preserved during more than a decade. The high prevalence of resistance to metronidazole in H. pylori strains in our population compromises its use in the empirical treatment of H. pylori infection.

The diel variations of viruses (< 0.2 μm), picoplankton (0.2—2 μm; heterotrophic bacteria, Syn-echococcus spp., Prochlorococcus spp. and picoeukaryotes) and nanoplankton (2—20 μm; hetero-trophic nanoflagellates and pigmented nanoflagellates) were investigated for 2 days with about 4 hours of temporal sampling in spring 2024 in coastal surface waters of the subtropical western Pacific. A period of wind change associated with rain and increased turbulence, disrupted diel patterns of the overall microbial communities during the second cycle. The abundance of bacteria did not follow a consistent diel pattern, while viral abundance positively correlated with bacterial abundance. Synechococcus spp. and Prochlorococcus spp. grew during the light period and with peak abundance at night, exhibited marked diel variability, however, opposite patterns were observed in picoeukaryotes. According to Synechococcus spp. and Prochlorococcus spp. diel changes, nano-flagellate grazing could control their abundances and may explain temporally varying picocyanobacteria abundances. As we observed in the culture experiments, the results showed a significant increase in picoeukaryotic abundance from noon to nighttime, and a decrease in bacterial abundance during nighttime, to show the prey-predator cycle. Our study suggests picoeukaryotes could serve as bacteria predators by being mixotrophs. Future studies aiming to understand the interactions between prokaryotes and picoeukaryotes within marine microbial communities should take these differences into account.

The Hyper-Torus Universe Model (HTUM) proposes a novel framework to unify quantum mechanics, cosmology, and consciousness. This paper introduces HTUM's core concept: a higher-dimensional hyper-torus encompassing all possible states of existence. HTUM suggests that the universe functions as a quantum system where all outcomes are inherently connected, with consciousness playing a significant role in actualizing reality. The model introduces several key innovations, including the universe's 4-dimensional toroidal structure (4DTS), providing a new geometric interpretation of spacetime. HTUM presents dark matter and dark energy as nonlinear probabilistic phenomena within this toroidal framework, offering a unique perspective on their nature and interactions. The model proposes a mechanism for wave function collapse through universal self-observation, bridging quantum mechanics and gravity. It introduces a Topological Vacuum Energy Modulator (TVEM) function, offering a potential resolution to the cosmological constant problem. Furthermore, HTUM integrates consciousness as a fundamental aspect of the universe, influencing quantum state actualization and potentially resolving the hard problem of consciousness. The model explores the implications of a timeless singularity and the emergence of time as a product of causal relationships within the toroidal structure. This framework addresses critical challenges in modern physics, including the nature of quantum entanglement, the origin and fate of the universe, and the relationship between mind and matter. The paper discusses the mathematical formulation of HTUM, its implications for quantum mechanics and cosmology, and its potential to bridge the gap between science and philosophy. We present detailed predictions for the cosmic microwave background (CMB) power spectrum, gravitational wave signatures, and large-scale structure formation, demonstrating how HTUM differs from the standard ΛCDM model. Our analysis reveals distinctive oscillatory patterns in the relative difference between HTUM and ΛCDM predictions, with deviations reaching up to ±20% at certain scales. The paper also explores the unification of fundamental forces within the HTUM framework, proposing novel approaches to particle physics and quantum field theory. It introduces the concept of unified mathematical operations, suggesting a more holistic approach to mathematical thinking that aligns with the model's interconnected view of the universe. Additional testable predictions and proposed experimental approaches are discussed, including advanced space-based tests and laboratory experiments probing quantum gravity effects. These specific, quantifiable predictions offer multiple avenues for empirical validation, highlighting HTUM's potential to be distinguished from ΛCDM and other alternative cosmological models. HTUM represents a significant new perspective in our understanding of the universe, inviting further research and exploration into the nature of reality, consciousness, and the fundamental structure of the cosmos. By providing a comprehensive framework that unifies various aspects of physics, cosmology, and consciousness studies, HTUM offers a promising path toward a more complete understanding of our universe.

With the continuous advancement of digital transformation, digital payments are playing an increasingly important role in the financial industry. This study aims to utilize machine learning models to predict and analyze digital payment behavior. Initially, the background and significance of digital payments in the financial sector are introduced. Subsequently, the current status and trends of traditional digital payment distribution are reviewed, alongside related work on digital payment behavior prediction. Methodologically, principles and applications of machine learning models such as logistic regression, decision trees, and random forests are elaborated, along with experimental design and data preprocessing methods. The experimental results and discussion section illustrates the performance of each model in digital payment prediction and explores their impact on credit decisions. This exploration equips financial institutions with more effective user behavior analysis and risk management tools, thereby fostering future development and application of digital payment technologies.

The purpose of this narrative review is to provide an overview of the latest literature on bariatric and metabolic surgery for diabetes mellitus type II and obesity (diabesity) and to discuss its implications for future research and clinical practice.

Plant activators have emerged as promising alternatives to conventional pesticides for crop disease manage-ment due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce systemic acquired resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of developing pathogen resistance. Among the various defensive pathways tar-geted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful devel-opment of commercial activators like benzothiadiazole for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed across multiple levels, from whole-plant bioassays to molecular and transgenic approaches. We expound on the intricate components, biological relevance, and regulatory circuits governing the SA pathway, while criti-cally examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials as-sessing their practical applicability are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent en-deavors in China. Collectively, this comprehensive review aims to synthesize existing knowledge and provide a roadmap for future research toward developing more potent and mechanistically understood plant activa-tors that fortify crop immunity against disease.

In recent years, urbanization and industrialization have significantly increased the levels of outdoor noise pollution, affecting the quality of life in residential areas. In Uzbekistan, rapid development in urban centers has led to heightened concerns about the impact of noise on residents' health and well-being. Noise pollution can result from various sources, including traffic, construction, and industrial activities, causing disturbances and potential health issues such as stress, sleep disorders, and cardiovascular problems. Addressing this issue is crucial for creating a healthier living environment. This article explores effective strategies for mitigating the impact of outdoor noise on residential buildings in Uzbekistan.

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.

The enzyme acetylcholinesterase (AChE) plays a crucial role in the termination of nerve impulses by hydrolyzing the neurotransmitter acetylcholine (ACh). The inhibition of AChE has emerged as a promising therapeutic approach for the management of neurological disorders such as Lewy body dementia and Alzheimer's disease. The potential of various compounds as AChE inhibitors has been investigated. In this study, we evaluated the impact of natural compounds of interest on the intrinsic deformability of human AChE using computational biophysical analysis. Our approach incorporates classical dynamics, elastic networks, statistical potentials, energy frustration, and volumetric cavity analyses. The results revealed that cyanidin induced significant changes in the flexibility and rigidity of AChE, especially in the distribution and volume of internal cavities, compared to model inhibitors such as TZ2PA6, and through a distinct biophysical-molecular mechanism from the other inhibitors considered. These findings suggest that cyanidin could offer potential mechanistic pathways for future research and applications in the development of new treatments for neurodegenerative diseases.

Obstructive sleep apnea (OSA) often goes unrecognized despite common symptoms such as excessive daytime sleepiness, fatigue, and impaired quality of life (QoL). CPAP is the gold standard treatment for OSA, but optimal daily usage and time needed for observable effects remain unclear. This study aimed to investigate the short-term effects of CPAP on daytime sleepiness and QoL in patients with severe OSA. Medical records were collected from 87 patients with severe OSA who initiated CPAP therapy. Also, validated questionnaires were used before and after one month of CPAP to analyze QoL - the Calgary Sleep Apnea Quality of Life Index (SAQLI), the Cues to CPAP Use Questionnaire (CCUQ), and daytime sleepiness - the Epworth Sleepiness Scale (ESS). Multiple regression analysis was conducted to identify predictors of CPAP usage. Of the total participants aged 55.6±12.5, 77% were males, and 62% were CPAP adherent. Reductions in daytime sleepiness (ESS) were noted, as well as improvements in both overall QoL (SAQLI) and specifically in the domains of daily functioning, social interactions, emotional well-being, and symptom perception. Important cues for CPAP usage recognized by patients were physicians’ instructions and physicians’ worry regarding patients' condition. Furthermore, multiple regression revealed higher SAQLI scores and lower ESS scores as positive predictors of CPAP usage, along with lower AHI after one month of CPAP being associated with sufficient adherence.

The signs and symptoms of Long COVID can be explained by a shortage of blood in the body and a resulting deficient blood flow through nearly all organs. This shortage arose during the acute phase of COVID19 by an increased breakdown of haematocytes, to which the liver responds by reducing the production of plasma. In order to prevent a too large decrease in haematocrit. In order to ensure the perfusion of organs that are directly necessary for survival, the body takes the emergency measure of diverting blood from other organs and tissues. The perfusion of the blood-producing organs is also affected by this distribution measure, which hinders the smooth recovery of the total blood volume. The body is stuck in this vicious circle: a shortage of circulating blood hinders the recovery of blood production. This explains the long duration of Long COVID. My proposed treatment of Long COVID focuses on the recovery of the correct volume of blood in the body of the right composition by the intravenous administration of donor blood products, starting with albumin concentrate. A trial treatment can be performed in any hospital without much additional preparations, and has a lower associated risk for the patient than analysing the total blood. A diagnosis ex juvantibus, by therapeutic response, is therefore preferable. I suspect that we will find a haematocrit value at the high extreme of the reference range, and an albumin value at the low extreme of the reference range in most Long COVID patients before the start of the treatment, because the liver can not keep up with the recovery of the red bone marrow.

Substance abuse is a significant public health issue both globally and nationally. Substance abuse involves the detrimental or risky consumption of psychoactive substances, posing a significant global issue, particularly among the youth. In India, cannabis abuse is a major concern. Cannabis is consumed legally in many states as Bhang, also known as marijuana or weed, while Charas and Ganja are illegal under both international drug conventions and the Indian NDPS Act of 1985. The Cannabis users are at great risk of developing Cannabis use disorder which is a pattern of problematic cannabis use leading to clinically significant impairment or distress. It is the most widely used illegal substance worldwide, showing a high prevalence in India.

Conclusions: CGP do not substantially differ between initial tumor vs tumor recurrence and recurrent thymomas vs non-recurrent thymomas. Cell-cycle control genes alterations are associated with an early recurrence after thymectomy. Multiple target therapies are potentially available by performing a comprehensive CGP, suggesting that a precision medicine approach on these patients could be further explored.

Background: Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians. Objective: This study aimed to assess the accuracy level of arti-ficial intelligence as a Clinical Decision Support System (CDSS) called Therapa compared to MRI in predicting lumbar disc herniated patients. Methods: One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in 3 stages, at first, a case se-ries was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Lastly, to determine the ac-curacy of the newly developed software, a cross-sectional study was undertaken, involving 100 patients. Results: The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%. Conclusion: The study’s findings revealed that CDSS using Therapha has a reasonable level of ef-ficacy, and this can be utilized clinically to acquire faster and more accurate screening of patients with lumbar disc herniation.

This paper presents a Cartesian model of sustainability by a novel integration of Circular Economy principles and ESG (Environmental, Social, and Governance) criteria to explicitly establish a clear path for closing the loops of negative social and environmental externalities. As global sustainability transformations accelerate, they bring both opportunities and inherent financial risks and social uncertainties. Historically, economic development has often been achieved at the expense of environmental balance. The contemporary challenge is to attain a dynamic equilibrium between natural and socio-economic systems. We introduce an integrated, multidisciplinary, and dynamic framework for studying and managing sustainability transitions. Our innovative 4-quadrant framework utilizes a Cartesian coordinate system to map the impacts of various business models on natural and socio-economic systems, spanning from linear open-loop to circular closed-loop ESG models. The framework is data-driven, employing normalized coordinates (-1 to 1) to facilitate empirical analysis and the establishment of temporal Key Performance Indicators. By integrating the Human Development Index within ecological limits, our model targets regenerative development that harmonizes human progress with planetary boundaries. This versatile and robust framework serves as a practical tool for businesses, households, organizations, and policymakers to navigate the complexities of sustainable development, promoting innovation and fostering a just transition towards regenerative practices. Our approach provides a comprehensive roadmap for achieving high human development within the planet's ecological limits, contributing significantly to the broader discourse on sustainability science and management.

While there are millions of reptile images available online, they are not well organized and not easily findable, accessible, interoperable, or reproducible (FAIR). More importantly, they are not standardized and thus hardly comparable. Here we present a reference database of more than 14,000 standardized images of 1,045 reptile species (969 lizard and 76 snake species), that are based on preserved specimens in 20 different collections, including 533 type species of genera and type specimens of 72 species. All images were taken with standardized views, including dorsal and ventral body shots as well as dorsal, ventral, and lateral views of the heads and other body parts. Although only 11 out of the 20 collections are cross-referenced in Vertnet, some others are indexed in GBIF, and this fraction will certainly grow in the near future. The utility of this and similar image collections will further grow with additional material and further cross-referencing, e.g., to DNA sequence databases or citizen science projects. The images are searchable and freely available on Morphobank.org (Project 5121) and on Figshare.com.

This article raise a topic of critical examination of polypropylene, a key polymeric material, and its extensive application within the automotive industry, particularly focusing on the manufacturing of brake fluid reservoirs. The study aims to enhance the understanding of polypropylene's behavior under mechanical stresses through a series of laboratory destruction tests and numerical simulations, emphasizing the Finite Element Method (FEM). A novel aspect of this research is the introduction of the PEAK parameter, a groundbreaking approach designed to assess the material's resilience against varying states of strain, known as triaxiality. This parameter facilitates the identification of critical areas prone to crack initiation, thereby enabling the optimization of component design with a minimized safety margin, which is crucial for cost-effective production. The methodology involves conducting burst tests to locate crack initiation sites, followed by FEM simulations to determine the PEAK threshold value for the Sabic 83MF10 polypropylene material. The study successfully validates the predictive capability of the PEAK parameter, demonstrating a high correlation between simulated results and actual laboratory tests. This validation underscores the potential of the PEAK parameter as a predictive tool for enhancing the reliability and safety of polypropylene automotive components. The research presented in this article contributes significantly to the field of material science and engineering by providing a deeper insight into the mechanical behavior of polypropylene and introducing an effective tool for predicting crack initiation in automotive components. The findings hold promise for advancing the design and manufacturing processes in the automotive industry, with potential applications extending to other sectors.

The Ethnographic Museum in Dubrovnik holds an extensive textile collection that includes a range of dress and costume from Croatia and neighbouring countries. Until recently, the costumes were displayed on mannequins created by the academic artist Zvonimir Lončarić (1927–2004). His sculptures are expressive forms constructed from terracotta, fibreglass, wood, metal, acrylic paint and other materials selected for their visual appeal. Due to their importance, the museum considered the mannequins as protected artworks in the permanent exhibition, however, they had a harmful effect on the costumes displayed. In 2018, after completing a series of conservation-restoration projects within the collection, it was decided to replace the existing mounts with new, museum–quality mannequins. Two project–based learning workshops were organised, led by a team of textile conservators from the University of Dubrovnik and the Croatian Conservation Institute, in cooperation with the curator of the Ethnographic Museum. This case study highlights the challenges of exhibiting ethnographic costumes by discussing past presentation methods and how new solutions have been implemented conforming with the modern conservation-restoration profession. The new solutions consider ethics, decision–making processes, and the understanding of contemporary materials and methodology.

Laser Powder Bed Fusion (L-PBF)-based manufacturing is a growing area of research, with a particular focus on evaluating constraints in printed products. This research emphasises the importance of considering various factors—including mechanical properties and support structures—that influence the final product during the design phase. Specifically, the design of microchannel heat exchangers is highlighted, where all constraints are critical. This article will present a method for analysing channel pressure limitations and report findings about mechanical properties loss from examining tube porosity. This includes a summary of results and the establishment of an experimental device system.

Hyperspectral imaging data holds great potential for various stages of the mining life cycle in active and abandoned mines. The technology, however, has yet to achieve large-scale industrial implementation and acceptance. While hyperspectral satellite im-agery yields high spectral resolution, high signal-to-noise ratio (SNR), and global availability with breakthrough satellite sys-tems like EnMAP, EMIT, and PRISMA, limited spatial resolution poses challenges for mining sectors, which require decimeter- to centimeter-scale spatial resolution for applications such as reconciliation, ore/waste estimates, geotechnical assessments, and en-vironmental monitoring. Hyperspectral imaging from drones (Uncrewed Aerial Systems; UASs) offers high spatial resolution data relevant to the pit/ mine scale, with the capability for frequent, user-defined re-visit times. Collecting data in the visible to near and shortwave infrared (VNIR-SWIR) wavelength regions offers to detect different minerals and surface alteration patterns, potentially revealing crucial information for exploration, extraction, re-mining, waste remediation, and rehabilitation. In this paper, we review of applicable instrumentation, software components, and relevant studies deploying hyperspectral imaging in or appropriate to the mining sector, especially for hyperspectral VNIR-SWIR UASs. If directly applicable, draw on previous insights derived from airborne, satellite, and ground-based imaging systems. We also discuss common practices for UAS survey planning and sampling considerations for interpretation.

Complicated grief (CG) predicts decreased mental health over time. Furthermore, feeling connected to nature (CN) is positively associated with beneficial mental health outcomes, such as psychological wellbeing and perceived psychological resilience. Thus, we hypothesized that CN moderates the association between general grief or CG and negative mental health for bereaved people. Further, we hypothesized that one’s physical exposure to nature—that is, estimated time spent in nature and greenness (i.e., vegetation) surrounding one’s residential area—might moderate the association between general grief or CG and negative mental health for bereaved people. To test these hypotheses, we sampled 153 participants who experienced the death of a close other by COVID-19 infection. Participants reported CG, general grief, anxiety symptoms, depression symptoms, CN, estimated time spent in nature, and residential area postal code via a single online survey. We estimated greenness surrounding participants’ residential areas using their self-reported 5-digit U.S. postal code. Cross-sectional analyses indicated that, as predicted, CN attenuated the association between CG and depression, but only trended toward moderating the association between CG and anxiety and did not moderate the associations between general grief and depression or anxiety. Other variables related to the experience of nature—the estimated time an individual spends in nature and the greenness surrounding one’s residential area—did not moderate the association between general grief or CG and depression or anxiety. We thus conclude that a sense of feeling connected to nature—not simply spending more time in nature or being surrounded by nature—may serve an important role in the mental health status of people experiencing complicated grief, perhaps because CN replenishes general belonging when someone significant has passed away.

Background/Objectives: Encouraging results reported of Platelet-Rich Plasma (PRP) treatment for knee osteoarthritis (KOA). The present retrospective study reports the efficacy and safety of a high dose of neutrophile and red blood cell-depleted PRP to treat 212 patients with KOA. Methods: 212 consecutive patients diagnosed with Kellgren-Lawrence (KL) grading 1-3 KOA chronic knee pain for at least 1 year were treated with 3 injections at 15-day intervals with a high dose of neutrophil-depleted PRP (4 billion platelets). Clinical outcomes were retrospectively recorded as a percentage of responders at 3,6, and 12-month follow-up, following the OMERACT-OARSI criteria. Pain through VAS score and WOMAC score have also been recorded. Results: 4ml of PRP containing 4X109 platelets were obtained by single spin centrifugation and injected intra-articular into each patient with no pre-activation. The overall responder rate of patients responding to OMERACT-OARSI criteria at 3,6,12 months was 68.9%, 72,7% and 70,6%. There was a significant improvement in VAS and WOMAC scores at 3-, 6- and 12-month follow-up compared to the pre-treatment value (p&lt; 0.01). The lowest VAS score was observed at 6 months overall and in all three-grade KL groups. KL2 groups showed the best results regarding pain reduction and WOMAC score at 6 months (p&lt; 0.01). Conclusions: a high dose of neutrophil-depleted PRP is an effective treatment for KL1-3 KOA. It provides symptomatic relief, can potentially reduce disease progression, and has sustained effects for up to 12 months.

Both, in developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need of extensive sampling. Furthermore, these particles are often enriched in potentially toxic trace elements and organics pollutants. This study evaluates both, the composition of atmospheric dust accumulated during a certain time-span on leaves of Hedera helix and Senecio cineraria, as well as the potential to use them as bio-monitors. The test plants were positioned nearby automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. Gravimetric deposition of PM10 and PM2.5 on leaves was consistent with data recorded by the monitoring stations and related to the weather conditions reported by Argentina´s National Meteorological service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied, XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2), PM10 and PM2.5 concentrations in the air exceeded WHO guidelines. However, several trace elements were found enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated to construction, traffic and unpaved street sources. Furthermore, based on its high capability to sequester PM and trace elements Senecio cineraria can be take into consideration to be adopted as a bio-monitor or even for PM mitigation.

In recent decades, there has been a rising demand for anti-aging interventions aimed at postponing or potentially reversing indicators of skin aging. The use of collagen-based nutraceutical supplements has gained popularity as they have shown promise in enhancing skin health and reducing signs of aging. The aim of this randomized, placebo-controlled, blinded study was to investigate the effects of 2.5 g COLLinstant® LMW, a novel cosmeceutical containing low molecular weight (≤ 1000 Da) collagen peptides, on skin aging and health. The trial was conducted in 80 healthy women aged 30 years and older. They received a daily oral dose of either the food supplement (n = 40) or placebo (n = 40) for six weeks. Skin assessment was performed based on validated objective methods, such as Visioface 1000D (skin wrinkling), cutometry (elasticity and fatigue) and corneometry (skin hydration) at baseline (T0), and at week 6 (T6). After 6 weeks, participants that received collagen had significant improvements in biometric skin wrinkle parameters from baseline, with a reduction in volume by 46%, in area by 44%, and in depth by 9%, along with a greater increase in skin moisturization (by 34%) than those in the placebo group (p &lt; 0.001). The food supplement did not significantly modify skin firmness or fatigue and had only slight beneficial effects on skin elasticity. The investigational product was well tolerated. The observed effects aligned closely with the subjective assessments reported by study participants. The study provides substantiated evidence supporting the efficacy of low molecular weight collagen peptides in restoring altered skin biometric parameters, as objectively assessed. Thus, regular supplementation with this nutraceutical may contribute to achieving smoother and more radiant skin.

This paper presents the development of an analytical model of Microelectromechanical Systems (MEMS) internal vibrating ring gyroscope. The internal ring structure consists of eight semicircular beams that are attached to the externally placed anchors. The research work analyses the vibrating ring gyroscope inplane displacement behavior and the resulting elliptical vibrational modes. The elliptical vibrational modes appear as pairs with the same resonance frequency due to the symmetric structure of the design. The analysis commences by conceptualizing the ring as a geometric structure with a circular shape, possessing specific dimensions such as thickness, height, and radius. We construct a linear model that characterizes the vibrational dynamics of the internal vibrating ring. The analysis develops a comprehensive mathematical formulation for the radial and tangential displacements in local polar coordinates, considering the inextensional displacement of the ring structure. By utilizing the derived motion equations, we highlight the underlying relationships driving the vibrational characteristics of the MEMS vibrating ring gyroscope. These dynamic vibrational relationships are essential in enabling the vibrating ring gyroscope's future utilization in accurate navigation and motion sensing technologies.

This study aimed to develop a carbon yield model for a Makino bamboo plantation and addressed a monoculture Makino bamboo plantation in central Taiwan. We established a long-term trial to monitor the stand dynamic following thinning. A total of 16 plots were installed based on four thinning treatments, each sharing four plots. After thinning, data were collected from two investigations, resulting in 32 records. We calculated the number of culms (N), mean diameter at breast height (MDBH), and basal area (BA) for each record. An allometric function developed by previous research was used to predict aboveground biomass and to obtain aboveground carbon storage (AGCS). The model used the N, MDBH, and BA with various types (reciprocal and natural logarithm types) as independent variables to predict ln(AGCS) by the stepwise method. According to the Radj2 value, the best predictive models were: ln(ABCS) = -0.004 + 0.820 ln(MDBH) + 1.008 ln(BA), and ln(ABCS) = 7.550 - 0.820 ln(N) + 1.828 ln(BA). From these two models, we found the factors, either BA and MDBH or BA and N, can effectively predict AGCS. It indicated that stand density was critical in affecting AGCS, especially the variable BA.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

This study delves into the dynamics of 'Variations' and 'Claims' within construction projects, specifically under the FIDIC-Red Book 1999 framework. The study aims to identify, categorize, and devise mitigation strategies for key types of variations and claims aligning with the FIDIC conditions of contract. The research is drawing on inputs from construction industry professionals including contract administrators and project managers, focusing on the MENA region. This choice is driven by the region's extensive adoption of FIDIC standards and its rapidly growing construction sector. Data collection encompassed a questionnaire distributed to 80 industry experts predominantly through interviews that focused on countries like Saudi Arabia, UAE, Kuwait, and Egypt. These locations were chosen to reflect diverse construction practices and the involvement of international firms. Utilizing SPSS-V.25 for statistical analysis, the study uncovers the most prevalent and impactful causes of variations and claims, highlighting the critical need for managerial intervention. A key feature is the integration of scientometric analysis for a quantitative finding. A significant addition to the methodology is the implementation of a k-means clustering analysis. The survey had high internal consistency with a Cronbach's alpha of 0.97, and respondents reported frequent and significant claims like delayed drawings, ambiguous documents, and client changes. The results showed that effective claims management requires clear communication and balanced contracts, while poor design and contract documentation cause variations and claims. Spearman's correlation showed strong positive relationships between certain claim types and causes, emphasizing the importance of dealing with these factors to reduce claims. Most respondents said the survey could predict and reduce claims.

International cooperation and food diplomacy play a pivotal role in addressing global challenges to food security. This study explores the effectiveness of Indonesia's international partnerships and diplomatic strategies in bolstering its food security amidst global uncertainties. The research employs a mixed-methods approach, combining qualitative analysis of policy documents and interviews with key stakeholders, alongside quantitative data on food production and trade. Our findings indicate that Indonesia has benefited significantly from technical and financial assistance through collaboration with organizations such as FAO, ASEAN, and bilateral agreements with countries like Japan and China. These partnerships have facilitated the adoption of advanced agricultural technologies, improved infrastructure, and enhanced human resource capacities. However, challenges remain, including bureaucratic inefficiencies and dependency on food imports, which pose risks to sustainable food security. The study concludes that while international cooperation has provided critical support, Indonesia must further streamline its policy implementation processes and reduce its reliance on imports by fostering local production. Strengthening research and development collaborations and actively participating in global forums are also recommended to ensure long-term resilience and food sovereignty. This research provides valuable insights for policymakers and stakeholders aiming to develop effective strategies to navigate the complexities of global food diplomacy and cooperation.

Advanced persistent threat (APT) attacks are sophisticated and organized attacks commonly motivated by political, financial, and strategic objectives. In order to comprehend their tactics, techniques, and procedures (TTP) and indicators, APT reports are valuable sources. While blue teams typically rely on server logs, firewall rules and user authorizations managed in database tables, attackers have a graph-based mindset. In this work, we propose a framework for discovering and evaluating APTs using graph-based algorithms. Cyber threat intelligence (CTI) was extracted from 40,358 pages of APT reports and transformed into a graph. Centrality, community, and similarity analyses were executed on the graph. As a result, critical and influential APT groups and indicators of compromise (IoC) were discovered. Similar attacks and APT groups were revealed. Analysis results were interpreted to create new strategic CTI that can be utilized in future security operations.

With the widespread use of the Internet of Things, underwater control and monitoring systems for purposes such as ocean data sampling, natural disaster prevention, underwater surveillance, submarine exploration, and the like have become a popular and challenging topic in computers. So far, various topology control and routing solutions have been proposed for these networks. However, as technology expands and applications grow, so does the need for a stable underwater communication platform. On the other hand, underwater communication is associated with challenges such as node mobility, long propagation delays, low bandwidth, limited resources, and high error rates. In this research, for the first time, a topology control platform based on the RPL tree is modelled by applying its structural changes underwater. The proposed RPLUW methods in the case of RPLUWM fixed nodes are introduced to support the mobility of nodes underwater. Flexible objective functions, utilisation of decision-making systems, and application of control schedules in these methods have increased network life, reduced overhead, and increased node efficiency. The simulation results of the proposed method, in comparison with recent methods in this field, show an increase in network efficiency.

The issue of antibiotic resistance represents a significant threat that extends beyond human health, encompassing animal welfare and the sustainability of the natural environment. The inappropriate use of antibiotics, poor sanitation and inadequate control measures, have contributed to the emergence of drug-resistant bacteria, which can spread at the human-animal-environment interface. The documented presence of antimicrobials in agriculture, veterinary medicine and human medicine has convinced all stakeholders of the need to contribute to the development of a "One Health" approach, which recognises the interrelationships between animals, humans and their environment and the need to adopt a common strategy encompassing these sectors to limit the spread of antimicrobial resistance (AMR). Although a number of countries have implemented integrated surveillance systems to monitor antibiotic use and antimicrobial resistance, available data suggest that the use of antibiotics in humans, animals and the environment is increasing worldwide. Consequently, the emergence of antibiotic-resistant pathogens is becoming a recurring concern. This review provides a comprehensive overview of the measures that are necessary to be taken and implemented in order to guarantee global health security.

This study explores the potential of digital technologies for preserving cultural heritage through the case study of the Raya Gopura, an incomplete monument in Melukote, India. Employing a combination of terrestrial photogrammetry, drone-based photogrammetry and terrestrial laser scanning, the research team captured highly detailed 3D models of the structure. This non-destructive approach offers a comprehensive digital record for future research and preservation. Furthermore, for the conjecture for the Raya Gopura two possible purposes: an entrance to a planned fort or a gateway to the temple are perceived through the collective intelligence (CI) approach. These insights inform a conjectural architectural drawing of the potentially completed monument. The 3D models hold significant potential for various applications, including tourism, education, and architectural research, with the potential for immersive experiences through VR and AR technologies. The study demonstrated the effectiveness of digital tools in heritage preservation, not only safeguarding the Raya Gopura but also offering valuable in-sights for future research and public engagements.

In a trip planning service, travelers first set a starting point, a destination, and a sequential list of specific points of interest types (e.g., museums, restaurants, and parks). Based on this information, the service searches the spatial database to customize the best travel itinerary for the tourist. However, in previous studies, planners only considered the time factor when designing the optimal route, and failed to fully consider the quality of each point of interest. In this study, we specifically leveraged the capabilities of large language model to parse and respond to complex travel-related user queries. To apply large language model to route planning, we fine-tuned the model to understand geotagging and user travel preferences. We have introduced a novel graph search algorithm combined with large language model output, which optimizes the route search process to provide optimal travel recommendations by taking into account various factors such as distance length, budget constraints, and popularity of tourist attractions. In addition, we have integrated real-time traffic data and historical travel data to further improve the prediction accuracy and application usefulness of the model. In the experimental validation phase, we designed a series of benchmarks to compare the performance of the system with traditional algorithms and other machine learning-based route planning methods. Experimental results show that our model has a significant improvement compared with traditional methods in improving the speed and accuracy of path selection.

The study examines the risk and protective factors in childhood for boys and girls who engage in unlawful behavior, through the lens of developmental criminology. This field explores the elements contributing to the emergence of antisocial and prosocial behaviors in childhood, which are linked to the increase or desistance of delinquency in later years. To enhance understanding in this domain, the study analyzes risk and protective factors associated with criminality in children across various levels: individual, family, and community. Utilizing a systematic review grounded in the PRISMA method, the study identifies and categorizes a range of factors, including conduct problems, socio-emotional competencies, biological determinants, social exclusion, interpersonal relationships, institutional governance, family adversity, caregiver characteristics, and family composition. This review underscores the significance of recognizing risk and protective factors in child development to design interventions at multiple levels, aiming to prevent antisocial behavior proactively.

Purpose To compare and adjust activated clotting time (ACT) between two systems and to optimize ACT during atrial fibrillation (AF) ablation. Methods Electromagnetic detection using a rotation tube (EM system; Hemochron Response, Soma Tech Intl, Bloomfield, CT, USA) and photo-optical detection using a cartridge immersed in blood (PO system; ACT CA-300TM, APEL) were compared. Results The EM system showed an ACT of 320 ± 44 s (average ± 1 standard deviation), with a range of 156–487 s, from 3363 measurements in 1161 patients during uncomplicated procedures. An ACT >400 s was observed in 3.7% (124/3364) of patients. The pre-ablation ACT measured by the EM method was 143 ± 28 s. Cardiac tamponade was observed in 4/2085 ablations (0.19%) over 5 years. The ACT associated with cardiac tamponade ranged from 330 to 391 in the EM system. The regression equation was ACT-EM system = 1.19 × ACT-PO system + 9.03 (p

Due to the adverse effect of prolonged drought stress on plants, accurate detection is essential for water use efficiency and maintaining productivity. Hyperspectral imaging is frequently used for non-invasive plant phenotyping, allowing for the long-term monitoring of crop health due to its sensitivity to subtle changes in leaf constituents. The broad spectrum of hyperspectral data enables the development of multiple vegetation indices (Vis) derived from the different spectral regions to estimate plant biophysical and biochemical traits. However, the known VIs often do not generalize well and perform poorly for multiple plant stresses. This study proposes new VIs combined with machine learning models to identify drought stress in wheat species under different nitrogen (N) levels. A wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high N and drought-stress low-N. In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) were taken regularly, serving as the ground truth data for this study. Sensitive spectral features were selected using a custom-designed ensemble modelling technique. New drought VIs are proposed using different combinations of the selected features. Three classification models (support vector machines, random forest and deep neural network) were developed and trained using four sets of data: known VIs, proposed VIs, combined VIs (from the known and proposed VIs) and PCA-transformed features (over the whole spectral data). From the results, the proposed VIs outperformed the known VIs, yielding &gt; 0.94 accuracies for all three models, and the performance improved when they were trained with the combined VIs. The combined VIs were used to train three regression models to predict the stomatal conductances and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat.

It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

The impact of COVID-19’s unexpected outbreak forced the scientific world to thoroughly seek alternative ways of treatment in order to overcome the hindrance of traditional medicine in alleviating the symptoms of this virus and possibly be an option for a potential cure in the near future. The antibiotic erythromycin, which was introduced in 1952, has been reported to pose an effective substitute medication for various ailments such as skin, respiratory, bone, female reproductive conditions, and cancer, as well as the newly added COVID-19’s aftermath. Countries, World Health Organization and the health industry have recruited and cooperated with numerous universities and institutions, so as to tackle the demand for efficient antibiotics, with USA and China leading this effort. Consequently, the catalytic mechanism of erythromycin’s synthesis and industrial production was discussed and analyzed further in order to gain a better understanding and extrapolate innovative and environmentally friendly methods of producing this antibiotic. Therefore, the demand for an increase and improvement in the production of erythromycin should be globally promoted, in order to deliver more effective results against infectious diseases such as COVID – 19.

Preoperative assessment is crucial in surgical care, traditionally relying on physical examinations and paper-based questionnaires. The emergence of digital tools like Pathpoint® ePOA represents a significant advancement, aiming to enhance the efficiency, accuracy, and standardisation of preoperative evaluations. Pathpoint® ePOA is a cloud-based platform designed to integrate seamlessly with Electronic Health Records (EHRs), providing a comprehensive, data-driven approach to preoperative assessments. It supports improved risk stratification, optimised resource allocation, and enhanced patient engagement through features such as customisable templates and real-time data integration.Current research highlights that digital preoperative tools like ePOA offer notable advantages, including increased clinical capacity, better complexity stratification, and improved patient follow-up. Despite these benefits, gaps remain in comparative studies, detailed clinical outcomes, and patient engagement strategies, indicating areas for further exploration. Pathpoint® ePOA adheres to rigorous safety standards and risk management protocols, ensuring reliability and data security while contributing to improved surgical outcomes. The benefit-risk ratio for ePOA is favourable, underscoring its potential to advance preoperative care by optimising clinical workflows and enhancing patient safety.

Hydraulic fracturing has enabled production from unconventional reservoirs in the U.S., but production rates often decline sharply, limiting recovery factors to under 10%. This study optimizes the CO2 huff-n-puff process for multistage-fractured horizontal wells in Wolfcamp A formation in the Delaware Basin. The potential for enhanced oil recovery and CO2 sequestration simultaneously was addressed using a coupled geomechanics-reservoir simulation. Geomechanical properties were derived from a 1D mechanical earth model and integrated into reservoir simulation to replicate hydraulic fracture geometries. The fracture model was validated using a robust production history matching. Fluid phase behavior analysis refined the equation of state, and 1D slim tube simulations determined a minimum miscibility pressure of 4300 psi for CO2 injection. After the primary production phase, various CO2 injection rates were tested from 1 to 25 MMSCFD/well, resulting in incremental oil recovery ranging from 6.3% to 69.3%. Different injection, soaking, and production cycles were analyzed to determine the ideal operating condition. The optimal scenario improved cumulative oil recovery by 68.8% while keeping the highest CO2 storage efficiency. The simulation approach proposed by this study provides a systematic workflow for evaluating and optimizing CO2 huff-n-puff in hydraulically fractured wells, enhancing the recovery factor of unconventional reservoirs.

The world is struggling with the increasing rate at which water bodies are constantly polluted due to rapid modernization and industrialization. The different types of water contaminants include both anthropogenic and natural contaminants. Environmentalists are mainly concerned about heavy metals such as cadmium ions, as they are considered poisonous and carcinogenic even if they are present at a minimum concentration, and they are considered an immediate concern, according to the World Health Organization. Recently, many methods, such as solvent extraction, ion exchange, electrochemical precipitation, and reverse osmosis, have been developed to remove heavy metals from wastewater. These methods have been reported to be expensive and inefficient; hence, more cost-effective and eco-friendly alternative strategies are needed. Amidst these challenges, a ray of hope emerges in the form of biosorbents. These abundant, eco-friendly, and cost-efficient materials have shown promise as an alternative method for removing heavy metals from wastewater. They are equipped with functional groups that can effectively absorb metal ions onto their surfaces, offering a potential solution to our water contamination woes. This review is significant as it delves into the potential use of several biosorbents to decontaminate cadmium ions from wastewater. This review is significant as it delves into the potential use of several biosorbents to decontaminate cadmium ions from wastewater

No matter where we reside, the issue of greenhouse gas emissions impacts us all. Their influence has a disastrous effect on the earth's climate, producing global warming and many other irreversible environmental impacts, even though it is occasionally invisible to the independent eye. Phase change materials (PCMs) can store and release heat when it is abundant during the day (e.g., from solar radiation), for use at night, or on chilly days when the building needs to be heated. As a consequence, buildings use less energy to heat and cool, which lowers greenhouse gas emissions. Consequently, research on thermally active medium-density fiberboard (MDF) with PCMs is presented in this work. MDF is useful for interior design and furniture manufacturing. The boards were created using pine (Pinus sylvestris L.) and spruce (Picea abies L.) fibers, urea-formaldehyde resin, and PCM powder, with a phase transition temperature of 22 oC, density of 785 kg m−3, the latent heat capacity of 160 kJ kg−1, the volumetric heat capacity of 126 MJ m−3, the specific heat capacity of 2.2 kJ kgK−1, the thermal conductivity of 0.18 W mK−1, and a maximum operating temperature of 200 °C. Five variants of MDF with PCM additive were developed: 0%, 5%, 10%, 30%, and 50%. According to the study, scores at the MOR, MOE, IB, and Screw Withdrawal Resistance (SWR) tests decreased when PCM content was added. However, the results of the thickness swelling and water absorption tests indicate that the PCM particles do not exhibit a substantial capacity to absorb water, retaining the dimensional stability of the MDF boards. The panel's thermal characteristics improved with increasing PCM concentration, according to the data. The density profiles of all the variations under consideration had a somewhat U-shaped appearance; however, the version with a 50% PCM content had a flatter form and no obvious layer compaction on the panel surface. Therefore, certain of the mechanical and physical characteristics of the manufactured panels can be enhanced by a well-chosen PCM addition.

Polyethylene terephthalate (PET) is one of the most produced plastic materials in the world. The emer-gence of microplastics and nanoplastics (MPs/NPs) as a significant environmental contaminant has become a matter of increasing concern. While the toxicological effects of PET NPs have been widely re-searched, there is a lack of methodologies for studying their accumulation. The present study introduces a novel method to monitor the distribution of PET NPs in germinating wheat (Triticum aestivum L.) seeds. This involves the functionalization of superparamagnetic iron oxide nanoparticles (SPIONs) with PET NPs (PET-fSPION) coupled with magnetic resonance microimaging (µMRI) to provide insight into their distribution within the seed. The present study has demonstrated that PET-fSPION accumulates in specific regions of germinating wheat seeds, including the shoot apical meristem, the radicle, the cole-optile, the plumule, and the scutellum. Furthermore, the accumulation of PET-fSPION has been shown to exert a discernible effect on spin-spin relaxation, as observed via MRI and quantitative T2 relaxation time analysis. The accumulation of PET NPs in embryo regions was also confirmed by SEM. Diffu-sion-weighted magnetic resonance imaging (DW-MRI) and non-invasive chemical shift imaging analyses demonstrated that PET NPs resulted in restricted diffusion within the highlighted areas, as well as an impact on lipid content. Our study reveals that using µMRI with fSPION provides a non-invasive method to monitor the biodistribution of PET nanoparticles in wheat seeds. Additionally, it offers valuable in-sights into the microstructural interactions of PET.

(1) Background: The rapid development of information technology has changed the traditional concept of space, gradually shifting the "container space" to the "relational space" . The constraints of geographical spatial elements are diminishing, and the theory of virtual agglomeration is gaining attention. (2) Methods: In this study, we constructed an evaluation system for virtual agglomeration, comprising four primary indicators: virtual infrastructure level, virtual output level, virtual input level, and virtual service level. The TOPSIS method was used to calculate the virtual agglomeration level for each province, and the DEA-Malmquist method was employed to measure provincial total factor productivity (TFP) in China. Using panel data from 30 provincial regions in China from 2013 to 2021, we examined the mechanism and impact path of virtual agglomeration on TFP. (3) Result: There is an inverted U-shaped relationship between virtual agglomeration and TFP in China. The overall development level of virtual agglomeration is relatively low, within the range where the marginal effect of virtual agglomeration on TFP is positive but diminishing, indicating significant potential for further development. The impact of virtual agglomeration on TFP is stronger in the Yangtze River Economic Belt compared to non-Yangtze River areas, and the highest stable point of TFP is higher in central provinces than in eastern and western provinces. (4) Conclusions: This study provides a theoretical basis and practical reference for improving the level of virtual agglomeration .

Keywords: knowledge; behaviour; ergonomics; posture; musculoskeletal; pain; university; students; survey.

Understanding HCPs COVID-19 vaccine concerns can provide critical insights to optimize success of the vaccine rollout program as they are a trusted source of health information to patients and the general population. We sought to understand reasons for health care providers’ COVID-19 vaccine hesitancy in Kenyatta National teaching and referral Hospital. From April to July 2021, we interviewed 60 HCPs purposively sampled from the Infectious Disease Unit (IDU) (n=20) and other departments (n=40). We analyzed data using inductive and deductive approaches to understand reasons for COVID-19 vaccine hesitancy. The HCPs had a median age of 37 years, (Interquartile range: [IQR 20.0-58.0]), 57% were female, 30% nurses among other cadres. Most HCPs cited low confidence in the vaccine such as mistrust in healthcare system and government, vaccine safety and efficacy concerns. Constraints such as uncertainty about the second vaccine dose availability, and misinformation caused hesitancy. Further, few were reluctant receiving the vaccine because of perceived herd immunity from a recent COVID-19 infection, and the need for more information before deciding. The study highlights the need to address any misinformation about vaccines safety and efficacy, and ensure accurate information is easily accessible to HCPs which can translate to higher vaccine uptake.

Acute myeloid leukemias are characterized by genetic alterations that lead to a complete or partial block at various stages of myeloid differentiation, leading to continual proliferation of myeloid progenitor cells. These alterations can include a well-documented chromosomal translocation that leads to the synthesis of the PML-retinoic acid receptor fusion protein that leads to a block in differentiation and the ability to induce differentiation with all-trans retinoic acid and other small molecules. In essence, in large part, the disease is due to stem cell-like cells that are blocked in their ability to undergo terminal differentiation to more mature, non-proliferative cell types. We discovered a long non-coding RNA (lncRNA), lncRNAp53int1, encoded from the first intron of the p53 tumor suppressor gene. Interestingly, lncRNAp53int1 appears to play an important regulatory role in maintaining the undifferentiated and proliferative state in a manner independent from p53 gene expression. lncRNAp53int1 is expressed at high levels in proliferating, undifferentiated promyelocytes and its levels fall significantly during their terminal differentiation to non-proliferating granulocytes or macrophages. Furthermore, forced overexpression of lncRNAp53int1 blocks terminal differentiation. The observation that the expression of this lncRNA is repressed during terminal differentiation of human leukemic cells and its expression blocks differentiation of leukemic myeloid progenitor cells, is likely to have clinical implications with regards to differentiation therapy for myeloid leukemia. Ongoing research aims to identify the molecular partners that it associates with and the signaling pathways that this lncRNA participates in with the long-term goal of identifying molecules that inhibit lncRNAp53int1 activity and serve as potential therapeutic drugs. The goal of this mini-review is to disseminate these novel and interesting findings to readers and to describe where we think this research is headed and the impact it can have on myeloid leukemias.

Rising antimicrobial resistance (AMR) in Salmonella serotypes host-adapted to cattle is of increasing concern to the beef and dairy industry. The bulk of the existing literature focuses on AMR post-slaughter. In comparison, the understanding of AMR in Salmonella among pre-harvest cattle is still limited, particularly in Texas, which ranks top five in beef and dairy exports in the United States; inherently, the health of Texas cattle has nationwide implications for the health of the United States beef and dairy industry. In this study, long-read whole genome sequencing and bioinformatic methods were utilized to analyze antimicrobial resistance genes (ARGs) in 98 isolates from beef and dairy cattle in the Texas Panhandle. Fisher exact tests and elastic net models accounting for population structure were used to infer associations between ARGs and antimicrobial phenotypic profiles and metadata. Gene mapping was also performed to assess the role of mobile genetic elements in harboring ARGs. ARG were found to be statistically different between the type of cattle operation and Salmonella serotypes. Beef operations were statistically significantly associated with more resistance genes compared to dairy operations. Salmonella Heidelberg, followed by Salmonella Dublin isolates, were associated with the most ARGs. Additionally, specific classes of ARGs were only present within mobile genetic elements.

The growing demand for high-power DC Fast Charging (DCFC) stations for Electric Vehicles (EVs) is expected to lead to increased peak power demand and reduction of grid power quality. To maximize the economic benefits and station utilization under practical constraints set by regulatory authorities and DCFC station operators, this study explores and provides methods for connecting DCFC stations to the grid employing low power interconnection rules and Distributed Energy Resources (DERs). The system uses automotive Second Life Batteries (SLBs) and Photovoltaic (PV) systems as energy buffers and local energy resource to support EV charging and improve the station techno-economic feasibility through load shifting and charge sustaining. The optimal sizing of the DERs and the selection of the grid interconnection topology is achieved by means of a Design Space Exploration (DSE) by means of exhaustive search approach to maximize the economic benefits of the charging station and to mitigate high-power demand to the grid. Without loosing of generality, this study considers a range of DER sizes, grid interconnection specifications, and related electricity tariffs of American Electric Power (AEP) Ohio and the Public Utility Commission of Ohio (PUCO). Various scenarios and strategies have been defined to account for the interconnection requirements of the grid to the DCFC with DERs. The system’s techno-economic performance of different scenarios has been analyzed and compared using a multitude of metrics.

Dysbiosis, as reflected by the altered composition of the microbiome favoring pathogenic species, is a major contributor to colorectal cancer (CRC) development, which could lead to bacterial translocation into the bloodstream. This study aimed to evaluate the presence of circulated bacterial DNA (cbDNA) in CRC patients, with or without surgical removal of the primary tumor. In total, 100 participants enrolled in this prospective clinicolaboratory study, equally divided into four groups; healthy controls (Group 1), patients with non-metastatic CRC with surgical removal of the primary tumor (Group 2), patients with metastatic CRC with surgically excised (Group 3) or non-excised (Group 4) primary tumor. DNA extracted from peripheral blood was analyzed using PCR with specific primers targeting 16S rRNA, Escherichia coli (E. coli), and Fusobacterium nucleatum (F. nucleatum). Only the detection of F. nucleatum in the blood was significantly higher in Group 4 compared to Group 1 (p < 0.001), Group 2 (p = 0.023) and Group 3 (p = 0.023). The association of cbDNA with other clinical parameters or co-morbidities was also evaluated. These results highlighted the importance of bacterial translocation in CRC patients and the role of F. nucleatum as an intratumoral oncomicrobe associated with metastatic CRC.

Quantum chemical topology addresses the study of the chemical structure by applying the tools of differential topology to scalar and vector fields obtained by quantum mechanics. Here the magnetically induced isotropically averaged Lorentz force density has been computed and topologically analysed for 11 small molecules. Critical points (attractors, repellers and saddles) have been determined and trajectories connecting the attractors have been computed. It is shown that kinds and numbers of the critical points are rather transferable in similar molecules. CC bonds of different order are endowed with critical points of different kind close to their centre. The long range behaviour of the force allows understanding the value of the sum of topological indices of the isolated critical points.

Testicular cancer accounts for approximately 1% of adult cancers and 5% of all urologic cancers. The most common histopathological diagnosis of testicular neoplastic lesions are germ cell tumors (90-95% of cases), among which the majority of cases are seminomas, the most common malignant tumors among men aged 15-44. For better clinical diagnosis and treatment, it is important to understand the molecular mechanisms of tumor formation. In this study, the expression of the CacyBP/SIP protein and ERK1/2 and p38 kinases was analyzed for the first time in seminomas and normal testicular tissues. The research was carried out using archival tissue material from 30 patients undergoing surgery due to testicular seminoma, whereas the comparative material consisted of the adjacent normal tissues. Immunohistochemistry and qRT-PCR were used to identify the expression of CacyBP/SIP, ERK1/2, and p38. A very clear weakening of both immunoreactivity and gene expression of all tested parameters was demonstrated in seminomas compared to healthy tissues. Our findings suggest the involvement of the CacyBP/SIP protein in the ERK1/2 and p38 signalling pathways, which may be involved in the processes of testicular seminoma carcinogenesis. The results of our research provide the basis for further research in this area.

This paper focuses on the role of the most common mineralogical techniques applied to the identification of the different statuary qualities in white, grey and black Göktepe marble. For this purpose, a Roman case of sculpture marble collection from the rural villa of Valdetorres de Jarama (central Iberia), of the 4th century AD, is presented. The mythological statuary, combining white, grey and black marbles, are one of the most outstanding marble collections in Aphrodisian style found in Hispania. The analytical results (Petrography, Cathodoluminescence, C and O isotopes, and Sr and Mn concentration) support the identification of two varieties of “black” Göktepe, traditionally named as bigio morato and bigo antico, as well as the best statuary quality on white Göktepe. In addition, the analytical identification of other Asiatic marbles in Valdetorres collection, a white coarse-grained originally from the quarries of Aphrodisias city and one small piece identified as Carian red from Iasos, corroborates the already suggested strong connection existing between artists and the stone material they chose for their works. Finally, it is noteworthy the identification carried out on the marble of the bases that served as seats for the sculptures, as it is a white marble of lower quality whose analytical characteristics are consistent with the Microasiatic marble of Denizli. The use of these exotic and exceptional raw materials confirms the taste for luxury and decorative richness in Late Antique Hispanic rural villae and contributes to a better understanding of the distribution of Aphrodisian productions in the Western Roman provinces.

Antigen-presenting cells (APCs) are essential for modulating immune responses, and optimizing their differentiation protocols is critical for cell-based immunotherapies. This study compares eight different protocols for differentiating APCs from C57BL/6J mice using GM-CSF and L-929 supernatant. Four groups treated with GM-CSF and four with L-929 supernatant at various concentrations, alongside a control group were evaluated. Flow cytometry was used to analyze the expression of key markers, including MHC II, CD11c, F4/80, and CD11b. Our findings show that GM-CSF resulted in superior differentiation outcomes compared to L-929 supernatant, with higher expression levels of the analyzed markers. This study underscores the importance of considering both cell yield and protein expression quality in selecting differentiation protocols. For researchers developing therapies targeting specific cell populations, these findings demonstrate how different culture conditions influences both the quantity and phenotypic characteristics of differentiated cells. Balancing efficiency and phenotypic fidelity is crucial for optimizing cell-based immunotherapy strategies. Our data provide a valuable reference for future studies and applications involving APC differentiation from bone marrow precursors, ensuring robust foundations for developing advanced immunotherapies. This comprehensive comparison of differentiation protocols offers insights essential for enhancing the effectiveness and cost-efficiency of cell-based therapies.

Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Alpha emitters like plutonium pose severe health risks when ingested, damaging DNA and potentially causing cancer. Traditional detection methods require proximity within millimeters of the contamination source, presenting safety risks and operational inefficiencies. Long-range detection through alpha radioluminescence (RL) offers a promising alternative. However, most of the previous experiments have been carried out under controlled conditions that preclude the overwhelming effect of ambient light. This study demonstrates successful detection of a 3 MBq alpha emitter in an open environment using a newly developed, lightweight compact alpha camera. This camera incorporates a deep-cooled CCD and a specially designed lens system with a low f-number to maximum the signal intensity. The lens was also designed to minimize blue shift effects of filters. Nighttime imaging was achieved with a dual-filter system using a sandwich filters assembly centered at 337 nm and 343 nm for capturing alpha RL and subtracting background light, respectively. At night, the alpha source was detected from 1 meter away within one minute. The system was also evaluated under simulated urban lighting conditions. For daytime imaging, a stack of tilted 276 nm short pass filters minimized sunlight interference, enabling clear detection of the alpha source at 70 cm within 10 minutes under indirect sunlight. This research not only underscores the viability of long-range optical detection of alpha emitters for environmental monitoring and scientific research but also highlights its ability to enhance nuclear safety and public health in real-world settings.

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing using IA information from different sensor data such as images or X-ray radiographs can help to improve medical decisions and to achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. In this work, we compare three different DL object detection models, which are Faster R-CNN, YOLO V2, and SSD. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using X-ray panoramic radiographs by using Winter's classification criterion. This criterion characterizes the third molar's position regarding the second molar's longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-ray images. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic X-rays represents a promising solution in dental applications.

Immunohistochemistry (IHC) helps identify different types of tissues and provides important information about the pattern, shape, and structure of cells in a tissue sample. It is a very useful tool and widely used to uncover histology and pathology diagnosis uses. The IHC is a very useful tool to conduct research in the developmental biology area. Some insects, like butterflies, bees, beetles, ants, wasps, and moths, undergo a unique life cycle called complete metamorphosis, which has four distinct stages: egg, larva, pupa, and adult. Larvae are often wingless and have different habits and forms than adults, which are better suited for growth and development. During the metamorphosis, how does the insect legs change is a really an interesting issue.To better understand insect development, agriculture pest control, and insect pathogen interaction, here we use an insect Bombyx mori (domestic silk moth) as a model to detail how to conduct the IHC assays, such as HE staining, TUNEL to see the cell apoptosis, pH3 and BrdU staining to check cell division. The reagents can be easily found, and this step-by-step protocol is easily reproduced.

Optimization techniques are pivotal across various scientific domains, typically involving solving problems among feasible alternatives based on specific goals, alternatives, or constraints. In mathematical optimization, traditional methods like Simulated Annealing (SA) do not guarantee finding global minima/optima, especially in cases that contain high dimensions in objective functions. This paper introduces a novel approach where a free-energy driven self-adaptive SA algorithm is designed to handle such cases by incorporating free-energy costs within the Metropolis-Hastings framework. This algorithm not only dynamically adjusts to the changing dimensions of the objective function, but it also enhances a faster optimization process. Furthermore, as examples we demonstrate its capability on a convex unimodal function and the non-convex Rastrigin function, revealing faster convergence to search global minima compared to standard SA algorithm. At last, parameter estimation of noisy exponential data has been executed by our Simulated Annealing driven by the Free-Energy (SAFE) algorithm. Our results suggest that this novel approach may significantly improve optimization speed and accuracy, providing a robust tool for complex multidimensional optimization problems.

Background: The use of complementary therapies in the population is increasing, so it is necessary to understand the training that health professionals receive in this type of therapy in their training plans, as they are often the primary source of information for patients. Our objective was to investigate Spanish universities that offer subjects on complementary therapies in their Nursing degree programs. Methods: Observational descriptive cross-sectional study. For this purpose, we used the document published on the website of the Ministry of Universities as a working document. Additionally, a literature search was conducted up to September 2023 in the PubMed database, along with reverse searches. Results: Out of a total of 62 universities, only 16 (29%) offer a subject related to this type of therapy, with most of them being elective courses. Conclusions: The training content on complementary care in Nursing degree programs in Spanish universities is scarce, highlighting the potential benefit of expanding and promoting it, in line with the recommendations of the World Health Organization.

Background: Chronic exposure to severe hypoxia causes an increase in hematocrit (Hct) and hemoglobin concentration ([Hb]), which can lead to Excessive Erythrocytosis (EE), which could impact physical performance. This work aims to determine the differences in the six-minute walking test (6MWT) outputs between EE and healthy subjects residing at more than 5000 meters. Methods: A prospective, cross-sectional study was performed on 71 men (36 healthy and 25 suffering EE) living in La Rinconada, Peru (5100 m). Basal levels of [Hb] and Hct were obtained. All the subjects per-formed the 6MWT, and distance reached, vital signs, dyspnea, and fatigue (Borg scale) at the end of the test were recorded. Results: The average [Hb] and Hct levels in the control group were 18.7 ± 1.2 g/dL and 60.4 ± 7.1%, respectively, contrasting with EE subjects who showed 23.4 ± 1.6 g/dL and 73.6 ± 5.9% (p < 0.001). However, no statistically significant differences were observed in BMI or other anthropometric parameters. At the end of the 6MWT, the distance traveled, and vital constants were similar between both groups, except for arterial oxygen saturation which was consistently lower in subjects with EE along the test. Conclusion: EE does not significantly affect 6MWT performance at high altitudes nor the hemodynamic control during moderate aerobic exercise of subjects who live permanently in a severe hypoxic environment.

This study investigates the critical role of Supplier Relationship Management (SRM) in enhancing the e-commerce user experience. In an increasingly competitive digital marketplace, effective SRM practices are essential for optimizing supply chain operations and meeting customer expectations. The research employs a qualitative approach, combining semi-structured interviews with industry stakeholders, extensive literature review, and case studies to explore various dimensions of SRM. Key findings reveal that strategic supplier selection criteria, including quality, cost efficiency, reliability, innovation, and sustainability, are pivotal in ensuring a robust and adaptable supply chain. Effective communication practices, such as transparency, frequent updates, and digital tools, foster strong supplier relationships and enable timely issue resolution. Performance management strategies, centered on KPI monitoring and continuous improvement, support operational excellence and customer satisfaction. Risk management practices, including diversification, contingency planning, and advanced analytics, are crucial for mitigating disruptions and ensuring supply chain resilience, as highlighted by the challenges posed during the COVID-19 pandemic. Technological integration with blockchain, AI, IoT, data analytics, and automation enhances efficiency, transparency, and decision-making capabilities in SRM. Furthermore, the study underscores the evolving integration of sustainability and ethical sourcing into SRM strategies, reflecting broader corporate responsibility goals. Ultimately, this research contributes to understanding how effective SRM strategies drive competitive advantage in e-commerce by enhancing supply chain reliability, responsiveness, and customer-centricity. As e-commerce continues to evolve, adapting and innovating SRM practices will be vital for companies aiming to sustain growth and meet the dynamic demands of global markets.

Green peppers (Capsicum annuum L.) is a fruit vegetable with great culinary versatility, and present important nutritional properties for human health. Water deficit negatively affects nutritional quality of green peppers fruits. This study aimed to investigate the influence of zinc oxide nanoparticles (ZnONPs), associated with plant growth-promoting bacteria (PGPB), on the post-harvest quality nutritional of green peppers subjected to water deficit. In an open-field ex-periment, two irrigation levels (50 and 100% of crop evapotranspiration - Etc), four treatments composed by combination of ZnONPs, zinc sulfate (ZnSO4) and PGPB (T1 = ZnSO4, T2 = foliar ZnONPs, T3 = foliar ZnONPs + PGPB, T4 = ZnSO4 via soil + PGPB via soil) and a control treatment (Check) were tested. Water deficit or water deficit mitigation treatments did not interfere with the physical-chemical parameters (except vitamin C content) and physical color parameters (except L*) of green pepper. The water deficit reduced the levels of Ca, Mg, P, K, Mn, Fe, B, and Zn in the fruits. The foliar application of ZnONPs in association with PGPB provided better nutritional quality of green pepper in terms of vitamin C and mineral nutrients. Under water deficit, ZnONPs or ZnSO4 via foliar, associated or not with PGPB, increased the levels of Ca, P, Mg, and Fe in the fruits. Applying Zn via foliar as ZnONPs or ZnSO4 mitigated the negative effects of water deficit on the quality of pepper fruits that was enhanced by the Bacillus subtilis and B. amyloliquefaciens inoculation.

The effect of doublet state metalloradical complex in a solar cell inside the common active layer poly(3-hexylthiophene)/ [6,6]-phenyl-C61-butyric acid methyl ester (P3HT/PC60BM) is explored. In this work is demonstrated that the role of the Bis-[1,3-Bis-(2,6-diisopropylphenyl)-4,5-dichloroimidazol-2-ylidene]palladium(I) hexafluoridophosphate dopant, [Pd(IPrCl)2][PF6], is crucial because, by having a stable unpaired electron in the molecule, it significantly improves the optoelectronic performance of the devices. We found the optimal concentration of this molecule in the active layer, and we demonstrated that the presence of this additive in the active layer helps to significantly improve the morphology of the device. The improvements in optoelectronic and morphological parameters are associated with a remarkable increase of photocurrent generation due to more favourable mechanisms of charge separation at the donor/acceptor (D/A) interfaces of the active layer and reduced recombinations. Moreover, the presence of this additive improves the stability of the unencapsulated solar cell against photochemical degradation produced by sunlight.

Most consumer purchases are influenced by their evaluation of a product's advantages, disadvantages, and emotional components. Research in psychology and marketing indicates that customer emotions play a role at different stages of the purchasing process. This study aims to identify the elements that may influence consumer feelings when purchasing high-end cosmetics. To explore the various aspects of customer emotions, a qualitative study was conducted using in-depth semi-structured interviews with 23 users of luxury cosmetics and health products in Telegram groups. This research uncovered multiple emotional dimensions and pinpointed factors that could elicit emotions in the target market. The next step, based on collective consensus, was to determine the variables impacting customer emotions. At this stage, the panel included 15 experts in marketing, psychology, luxury cosmetics import businesses active on digital platforms, and managers of luxury cosmetics and hygiene groups in online environments. Through a three-step consensus method, experts identified and assessed 36 factors affecting customer emotions based on their significance and perceived impact. These factors were then categorized into three groups: personal variables, group and product variables, and contextual variables.

This study explores the influence of cultural differences on Supplier Relationship Management (SRM) within global e-commerce, examining communication styles, trust-building, negotiation strategies, and strategic alignment across diverse cultural contexts. Through qualitative research including interviews with e-commerce professionals, the study identifies how cultural factors shape SRM practices and outcomes, highlighting the importance of cultural sensitivity and adaptive strategies in fostering effective supplier relationships. Findings underscore the significance of communication preferences, trust norms, and strategic orientations in navigating cultural complexities to enhance operational efficiency and mitigate risks in global markets. Practical implications suggest integrating cultural dimensions into SRM frameworks, prioritizing cultural competence development, and leveraging cultural diversity for innovation and collaboration. Future research directions include exploring digitalization's impact on cross-cultural SRM and longitudinal studies on evolving cultural dynamics in global business. By advancing understanding in this area, e-commerce firms can enhance resilience, maintain competitive advantage, and achieve sustainable growth in the global marketplace.

The formation of atroposelective biaryl compounds in plants and fungi is well understood; however, the polyketide aglycone synthesis and dimerization in bacteria remain unclear. Thus, the biosynthetic gene cluster (BGC) responsible for antibacterial setomimycin production from Streptomyces nojiriensis JCM3382 was examined in comparison with the BGCs of spectomycin, julichromes, lincolnenins, and huanglongmycin. Setomimycin BGC includes post-PKS assembly/cycling enzymes StmD (C-9 ketoreductase), StmE (aromatase), and StmF (thioesterase) as key components. The heterodimeric TcmI-like cyclases StmH and StmK are proposed to aid in forming the setomimycin monomer. In addition, StmI (P-450) is predicted to catalyze biaryl coupling of two monomeric setomimycin units, with StmM (ferredoxin) specific to setomimycin BGC. The roles of StmL and StmN, part of the nuclear transport factor 2 (NTF-2)-like protein family and unique to setomimycin BGCs, could particularly interest biochemists and combinatorial biologists. α-Glucosidase, a key enzyme in type 2 diabetes, hydrolyzes carbohydrates into glucose, thereby elevating blood glucose levels. This study aimed to assess the α-glucosidase inhibitory activity of EtOAc extracts of JCM 3382 and setomimycin. The JCM 3382 EtOAc extract and setomimycin exhibited greater potency than the standard inhibitor, acarbose, with IC50 values of 285.14 ± 2.04 μg/mL and 231.26 ± 0.41 μM, respectively. Kinetic analysis revealed that setomimycin acts as a competitive inhibitor of α-glucosidase. Molecular docking demonstrated two hydrogen bonds with maltase-glucoamylase chain A residues Thr205 and Lys480 (binding energy = −6.8 kcal·mol⁻¹), two π-π interactions with Trp406 and Phe450, and one π-cation interaction with Asp542. Residue-energy analysis highlighted Trp406 and Phe450 as key in setomimycin's binding to maltase-glucoamylase. These findings suggest that setomimycin is a promising candidate for further enzymological research and potential antidiabetic therapy.

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in oil and gas industry, impacting the efficiency, integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and the concentration of chemical inhibitors. And then the effect of selected chemical reagent on inhibiting the ARPD amount and thus en-hancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil is decreased from 12 °C to (-4) °C, ARPD amount is declined from 0.185 to 0.016 g and oil effective viscosity is reduced from 16.2 mPa·s to 3.1 mPa·s. It is determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

Background: Routine childhood vaccination, e.g., for diphtheria-tetanus-pertussis (DTP), might provide an additional protection towards SARS-CoV-2 infection. This concept of heterologous immunity was explored in healthy children receiving both DTP and inactivated SARS-CoV-2 vaccines. Methods: A cross-sectional study was performed on 154 healthy children aged 6–8 years old in Jakarta, Indonesia. Their vaccination status of DTP (including diphtheria-tetanus booster vac-cine at 5 years old) and CoronaVac (from 6 years old) were recorded. Peripheral blood samples were collected from all participants, in which anti-diphtheria toxoid IgG, anti-SARS-CoV-2 S-RBD antibodies and T cell-derived IFN- were measured. Results: Study participants with complete DTP vaccination had significantly higher titer of an-ti-diphtheria toxoid IgG than the ones without (median=0.9349 versus 0.2113 IU/mL; p

This study proposes a risk analysis model based on the principles of ISO 31000 and decision theory for biological agents with potential for offensive use in Brazil. Bibliographic research was conducted on the main models already published on the subject. The German risk classification system was adopted as the main reference because it is adjustable and adopts a semiquantitative approach. After translating and adapting this model to the Brazilian context, the analytic hierarchy process (AHP) was applied to generate a hierarchical tree with criteria and subcriteria to be considered in the risk assessment. A questionnaire was administered to a group of experts to evaluate the relevant criteria and subcriteria and the risks related to three biological agents (Bacillus anthracis, Yersinia pestis and Francisella tularensis), which were selected in an exploratory manner to exemplify the application of the model. According to the results, the criterion of impact of a biological attack was given greater importance when compared with the probability of occurrence of this event. According to the subcriteria, the greatest weight was attributed to human health when compared with agriculture, the environment, or the economy. Finally, in the evaluation of the three pathogens, B. anthracis was identified as having the highest risk for offensive use in Brazil. This research is focused on a practical approach and can be implemented by competent agencies to foster their capacity for biological defense by determining which pathogens represent the greatest risk to society.

This qualitative research explores Supplier Relationship Management (SRM) practices within the e-commerce fashion industry, aiming to uncover key strategies and their impacts. Through semi-structured interviews with supply chain managers, procurement officers, and supplier representatives, critical themes emerged: communication, trust, risk management, sustainability, and digital technologies. Effective communication, characterized by transparency and regular updates, emerged as pivotal for aligning expectations and resolving issues promptly. Trust-building practices, including fair dealings and formal agreements, were essential in fostering long-term partnerships, reducing transaction costs, and enhancing collaboration. Robust risk management strategies, such as supplier diversification and contingency planning, were identified as crucial for mitigating disruptions and ensuring supply chain resilience. Sustainability was highlighted as a strategic imperative, driven by ethical sourcing and environmental standards, supported by technologies like blockchain for transparency. The transformative role of digital technologies—supply chain management software, blockchain, IoT, and data analytics—was evident in enhancing visibility and decision-making across global supply chains. Viewing suppliers as strategic partners, rather than mere vendors, promoted collaborative innovation and mutual growth. Despite challenges like cultural differences and regulatory complexities, opportunities exist to enhance SRM through integrated best practices. By prioritizing effective communication, trust-building, risk management, sustainability, and digital innovation, companies can strengthen supplier relationships, optimize supply chain performance, and achieve competitive advantage in the e-commerce fashion sector.

Despite advances in conservation-restoration treatments, most surface cleaning tests are subjectively evaluated. Scores according to qualitative criteria are employed to assess results, but these can vary by user and context. This paper presents a range of cleaning efficacy and homogeneity evaluation metrics for appraising cleaning trials, which minimise user bias by measuring quantifiable changes in the appearance and characteristic spectral properties of surfaces. The metrics are based on various imaging techniques (optical imaging by photography using visible light (VIS); spectral imaging in the visible-to-near-infrared (VNIR) and shortwave infrared (SWIR) ranges; chemical imaging by Fourier transform infrared (FTIR) spectral mapping in the mid-infrared (MIR) range; and scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX) element mapping). They are complemented by appearance measurements (glossimetry and colorimetry). As a case study showcasing the low-cost to high-end metrics, agar gel spray cleaning tests on exposed ground and unvarnished oil paint mock-ups are reported. The evaluation metrics indicated that spraying agar (prepared with citric acid in ammonium hydroxide) at a surface-tailored pH was a safe candidate for efficacious and homogenous soiling removal on water-sensitive oil paint and protein-bound ground. Further research is required to identify a gel-based cleaning system for oil-bound grounds.

Dimethylformamide (DMF), a polar solvent, is commonly used for preparing graphene/epoxy nanocomposites. While commonly previous research has predominantly highlighted the improvement in physio-mechanical properties of these nanocomposites, the effect of DMF on processing and its direct influence on the final characteristics of MXene/epoxy nanocomposites have not been investigated.This unexplored link between DMF dosage, MXene concentrations, and the final composite properties presents an exciting direction for future research. In this study, a fixed dosage of DMF was used with varying MXene concentrations to fabricate the nanocomposites. To assess the reliability of DMF dosage on the characteristics of the fabricated nanocomposites, various evaluation techniques were employed, including dispersion evaluation, mechanical tests, thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), scanning electron microscopy (SEM), electromagnetic interference (EMI) shielding, and surface roughness measurements. The research outcomes revealed that as MXene concentration increased, the characteristics of the MXene/epoxy nanocomposites, improved across the board, indicating their potential for use in energy storage applications.

This review focuses on social play, a complex behavior that is often difficult to categorize. Although play has been typically associated with positive emotions, emerging research indicates that it relates to various emotional systems, ranging from attachment to conflict. Play oscillates between compe-tition and cooperation, and includes a spectrum in-between, thus quantitatively identifying and demonstrating the emotional nature of play remains challenging. We examined some examples from human and non-human animal studies and explored the emotional and neuro-hormonal systems involved in play. We considered ethological data possibly indicating the emotional states underlying play and we focused on the cooperative and competitive elements of play. We investigated the relationship between play and affiliative/aggressive behaviours, the communicative meaning of play signals (especially primate play faces), and the motor and possibly emotional contagion func-tion of rapid motor mimicry during play. Of all the literature on play, this review selects and com-bines the studies in an innovative way, to present the methods, tools, and evidence indicative of the emotional nature underlying play, which is much more complex than previously thought.

(1) Background: A wide literature based on loading and unloading bending tests has been carried out over the half past century for assessing the force released by superelastic wires. The aim of this investigation is not only to characterize a broadly described superelastic NiTi and a novel CuNiTi archwire, but also to provide novel insights into the load vs deflection profiles. A harmonic scenario for superelastic wires will be shown as temperature is kept constant at 37°C. However, a completely different picture will be highlighted as a temporary heating step up to 50°C is introduced within the force vs deflection profiles. (2) Methods: A variety of NiTi and a novel CuNiTi round and rectangular cross-section archwires have been investigated. Static 3-point bending tests up to a deflection level of 3.0 mm have been carried out for detecting the unloading force plateau levels occurring at 37°C. A hold deflection step of 5 min at deflection levels occurring along the unloading plateau extension is introduced. The effect of a temporary heating up to 50°C on the force plateau level released by the investigated wires is analyzed. (3) Results: Mechanical profiles suggest that the plateau force levels detected at 37°C are consistent with those broadly reported in the literature, but they only represent a single block puzzle of the force scenario that a superelastic wire releases during the orthodontic treatment. (4) Conclusions: Once a superelastic wire is bent and clinically applied, the effect of a temporary heating up to 50°C (e.g. hot food or drink ingestion) produces a significant plateau force increase whose magnitude depends on the deflection level. The highest force increase of about 50% and 40% have been detected at deflection levels located at the beginning of the unloading plateau extension for NiTi and CuNiTi wires, respectively.

The world health organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm which are unable to deal with data complexities, prone to overfitting and limited in generalisation. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicator to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into stacking ensemble model achieved comparable F1 scores of 69% in dataset (D1) and 76% in dataset (D2). Our findings suggest that utilising sentiment indicators as additional feature for depression detection yields an improved model performance and thus, we recommend the development of depressive term corpus for future work.

The keratinocyte carcinomas, basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), are the most common cancers in humans. Recently, an increasing body of literature has investigated the role of miRNAs in keratinocyte carcinoma pathogenesis, progression and their use as therapeutic agents and targets, or biomarkers. However, there is very little consistency in the literature regarding the identity of and/or role of individual miRNAs in cSCC (and to a lesser extent BCC) biology. miRNA analyses that combine clinical evidence with experimental elucidation of targets and functional impact provide far more compelling evidence than studies purely based on clinical findings or bioinformatic analyses. In this study, we review the clinical evidence associated with miRNA dysregulation in KCs assessing the quality of validation evidence provided, identify gaps, and provide recommendations for future studies based on relevant studies that investigated miRNA levels in human cSCC and BCC. Furthermore, we demonstrate how miRNAs contribute to the regulation of a diverse network of cellular functions, and that large-scale changes in tumor cell biology can be attributed to miRNA dysregulation. We highlight the need for further studies investigating the role of miRNAs as communicators between different cell types in the tumor microenvironment. Finally, we explore the clinical benefits of miRNAs as biomarkers of keratinocyte carcinoma prognosis and treatment.

Globalization is a basic feature of contemporary digital society related to increasing level of informatization, electroniztion and communications through the global network and share different information. Regardless of the many advantages of the digital age, related to the intellectualization of various activities and objects (e-governance, e-health, social computing, smart home, smart city, etc.), the need for secure protection of users' privacy should not be overlooked. In this reason, the goal of the article is to determine the specific features of the main components of the globalization and the impact on the privacy and data protection. Based on the analysis of possible impacts the main recommendation for adequate data security and privacy protection are determined.

Session-based recommendations which aim to predict subsequent user-item interaction based on historical user behavior during anonymous sessions can be challenging tasks. Two main challenges need to be addressed and improved: (1) How to analyze these sessions to accurately and completely capture users’ preferences, and (2) how to identify and eliminate any interference caused by noisy behavior. Existing methods have not adequately addressed these issues since they either neglected the valuable insight that can be gained from analyzing consecutive groups of items or failed to take these noisy data in sessions seriously and handled them properly, which can jointly impede recommendation systems from capturing users’ real intentions. To address these two problems, we designed a multi-order semantic denoising (MSD) model for session-based recommendations. Specifically, we grouped items of different lengths as varying multi-order semantic units to mine the user’s primary intention from multiple dimensions. Meanwhile, a novel denoising network was designed to alleviate the interference of noisy behavior and provide a more precise session representation. The result of extensive experiments on three real-world datasets demonstrated that the proposed MSD model exhibited improved performance compared with existing state-of-the-art methods in session-based recommendation.

In this 2 x 2 between-subjects experimental study, a virtual reality (VR) laboratory simulation is paired with a VR molecular world intervention to teach chemistry concepts. The independent variables are the implementation timing of the molecular world intervention (Pre-lab vs. Integrated) and the level of embodiment (Traditional vs. VR). Eighty students (N = 80), ages 11-18 years old, from a community center in New York City participated, completing a pretest, the laboratory simulation, the molecular intervention, and a post-test. The pre- and post-test measures included multiple-choice, free-response, and drawing questions. A key finding was that integrating the intervention within the lab simulation, no matter which level of embodiment, led to significantly higher gains in learning. The combination of using physical manipulatives and integrating them within the lab exercise (Integrated Traditional condition) demonstrated the greatest gains overall. On drawing measures, the Integrated VR condition showed significant improvement in three out of the four drawing categories (i.e., molecule shape, atom quantity, and relative sizes). The implications are that even though using a VR molecular world intervention can lead to significant learning of abstract chemistry content, the use of physical manipulatives is still a more effective tool.

The construction of tunnels excavated by the conventional method in densely populated urban environments requires an adequate characterization of the loads acting on the primary lining during the excavation process to ensure that the ground is deformed and stresses around the tunnel are relieved, simultaneously complying with the failure and serviceability limits of international standards while minimizing damage to nearby structures. In this paper, common lining design criteria are revisited, through the numerical simulation of an instrumented tunnel section which is part of a 4.5 km long metro line currently under construction in Mexico City. Key needs for improvement in current design approaches are identified. The tunnel was instrumented with load cells, extensometers, and topographical references for convergences and divergences. A three-dimensional finite difference model of the instrumented section was developed, and the load transfer mechanisms between the excavated soil and the primary lining was analyzed. Then, the numerical simulation of the contribution of the secondary lining in the overall stability for sustained load was established, along with the expected ground settlements, which can significantly affect nearby structures. Results gathered from this research are key for updating lining design criteria for urban tunnels built in stiff brittle soils.

Modular nanotransporters (MNTs) are drug delivery systems for targeted cancer treatment. Being composed of several modules, they offer the advantage of high specificity and biocompatibility in delivering drugs to the target compartment of cancer cells. The large carrier module brings together functioning MNT modules and serves as a platform for drug attachment. The development of smaller-sized MNTs via truncation of the carrier module looks advantageous in facilitating tissue penetration. In this study, two new MNTs with a truncated carrier module containing either an N-terminal (MNTN) or a C-terminal (MNTC) part were developed by genetic engineering. Both new MNTs demonstrated high affinity for target receptors, as revealed by fluorescently labeled ligand-competitive binding. The liposome leakage assay proved the endosomolytic activity of MNTs. Binding to the importin heterodimer of each truncated MNT was revealed by a thermophoresis assay, while only MNTN possessed binding to Keap1. Finally, the photodynamic efficacy of the photosensitizer attached to MNTN was significantly higher than when attached to either MNTC or the original MNT. Thus, this work reveals that MNT’s carrier module can be truncated without losing MNT functionality, favoring the N-terminal part of the carrier module due to its ability to bind Keap1.

Introduction: Urological conditions significantly impact global health, with increasing demand for urologists in both developed and developing countries. Disparities in access to surgical care between high-income countries (HICs) and low- and middle-income countries (LMICs) are evident. Despite advancements in urology, LMIC training programs often follow outdated curricula and traditional methods. Methodology: A comprehensive search strategy identified urology training programs in LMICs using the EduRank website, Google searches, and PubMed. Data were collected from literature, official documents, and online resources, focusing on variables such as program duration, research requirements, and resident salaries. Results: The analysis revealed significant variability in program structures and requirements across LMICs. Residency training durations ranged from 4 to 6 years, with inconsistent research obligations and resident salaries averaging $12,857 annually, with a range from 5,412 to 18,174$. Fellowship opportunities were limited, with only a small number of programs achieving international accreditation. Conclusion: The study highlights substantial disparities in urology training between HICs and LMICs. There is an urgent need for standardized and locally tailored training frameworks to enhance the quality of urology education in LMICs. Future research should focus on developing strategies to improve training opportunities, ensuring equitable access to advanced urological care and education globally.

The overall comfort experienced by an individual in an n-dimensional space can be theoretically constructed using a mathematical equation, that relates the comfort experienced by an individual in a particular point in space with spatial points associated with higher comfort attributes and the desirable factors affecting the current environmental space.

The article "Ensuring Quality Medicine is Not a Single Event but a Combined Effort of a Pharmaceutical Company" explores the multifaceted approach required to maintain high standards in pharmaceutical manufacturing. It emphasizes that quality assurance in medicine production is not an isolated event but a continuous, integrated process involving multiple departments and stages. The article highlights the roles of research and development, rigorous clinical trials, regulatory compliance, and robust manufacturing practices in ensuring the efficacy and safety of medicines. It also discusses the importance of quality control measures, such as stringent testing protocols and regular audits, to detect and mitigate potential issues early. The collaboration between different teams within a pharmaceutical company, including scientists, engineers, and quality assurance professionals, is crucial for sustaining quality throughout the product lifecycle. Moreover, the article addresses the necessity of adhering to global standards and guidelines, such as Good Manufacturing Practices (GMP), to ensure consistency and reliability in drug production. By fostering a culture of continuous improvement and vigilance, pharmaceutical companies can effectively safeguard public health and maintain trust in their products. This holistic approach underscores that delivering quality medicine is an ongoing commitment requiring coordinated efforts across the entire organization.

Keywords: Forest ecosystem; Biodiversity; Ecosystem multifunctionality

While the number of publications on wine and health is steadily increasing, ranging from molecular level to epidemiological studies, often with contradictory results, little attention has been given to an holistic approach. In this review some unusual concepts are considered, such as phytocomplex, vehicle and the Matrix effect. As an example, three mono/polyphenols present in wine, Tyrosol, Hydroxytyrosol and Resveratrol, have been selected and the consequences of respecting, disregarding or ignoring the Matrix effect are shown. Underestimation of the wine Matrix effect could lead to deceiving results, as in the case of de-alcoholized wine or wine-compound-based nutritional supplements,or in emphasizing a single component toxic activity, in this case alcohol, ignoring the specific protective action at molecular level of other compounds (polyphenols) present in the same Matrix. The dark side of Matrix effect is also discussed. It is suggested that specificity of wine should be always taken in consideration by public health Authorities.

The artificial general intelligence (AGI) industry, represented by ChatGPT, has impacted social order during its development, and also brought various risks and challenges such as ethical concerns in science and technology, attribution of liability, intellectual property monopolies, data security, and algorithm manipulation. The development of AI is currently facing a crisis of trust. Therefore, the governance of AGI industry must be prioritized and the opportunity of the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken. It is necessary to enhance the norms for the supervision and management of scientific and technological ethics within the framework of the rule of law. Additionally, it is also essential to continuously improve the regulatory system for liability, balance the dual values of fair competition and innovation encouragement, and strengthen data security protection systems in the field of AI. All of these will enable coordinated governance across multiple domains, stakeholders, systems, and tools.

In genomics analysis, with the development of sequencing technology and the rapid growth of data volume, how to effectively identify and analyze important information in genomic data from massive data has become an important challenge. Identify key patterns and features in genomic data through advanced community testing methods. First, genomic data often contains a lot of noise and redundant information that needs to be processed through steps such as normalization, filtering, and dimensionality reduction. Normalization makes data at different scales comparable, filtering out data points with low quality or missing values, and dimensionality reduction reduces data dimensions through principal component analysis (PCA) and retains the main information. The gene co-expression network establishes the connection relationship between genes by calculating the expression similarity matrix between genes, and applies the Louvain algorithm to maximize the modularity of the network, aggregate nodes layer by layer, form a community structure, and identify the complex relationship and potential structure between genes. Finally, we utilize the cancer dataset to evaluate the proposed model. From our extensive experimental results, we can observe that Louvain's algorithm indicates outperformances and accuracy in the recognition and analysis of genomic data.

In central area of Liaoning province, the thermal performance of rural building envelopes are mostly non-standardized in Northern China, resulting in a significant heat loss. In this study, The objective was to propose some low-cost and energy-efficient solutions. Through a field of inves-tigations, heating energy consumption was saved by 20% and increased construction costs was less than 8000 RMB, which was acceptable for rural residents. In order to achieve this target, NSGA-II algorithm was integrated with Rhino+Grasshopper and EnergyPlus simulation kernel to establish a thermal performance optimization model for heat transfer of the rural building envelopes. Among the above Pareto optimal solutions, the recommended thickness of insulation layer for the room floors, ceilings, and external walls were 70mm, 50mm, and 40mm respectively. Furthermore, try to reduce the window to wall ratio as much as possible. Finally, based on the lower total cost, three technical solutions that rural residents could select according to their specific needs have been put forward.

The integration of Unmanned Aircraft Systems (UAS) into the current airspace poses significant challenges in terms of safety, security and operability. As an example, the European Union has defined in 2019 a set of rules to support the digitalization of UAS Traffic Management (UTM) systems and services, namely the U-Space regulations. Current propositions opted for a centralized and private model, concentrated around governmental authorities (e.g., AlphaTango provides the Registration service and depends on the French government). In this paper, we advocate in favor of a more decentralized and transparent model in order to improve safety, security and operability among UTM stakeholders. As such, we propose DFly, a Publicly Auditable and Privacy-preserving UAS Traffic Management system on Blockchain, and developed two initial services: Registration and Flight Authorization. We demonstrate that the use of a blockchain guarantees the public auditability of the two services and corresponding service providers’ actions. In addition, it facilitates the comprehensive and distributed monitoring of airspace occupation and the integration of additional functionalities (e.g., the creation of a live UAS tracker). The combination with zero knowledge proofs enable the deployment of an automated, distributed, transparent and privacy-preserving flight authorization service, performed on-chain thanks to the blockchain logic. In addition to its construction, this paper details the instantiation of the proposed UTM system with the Ethereum Sepolia’s testnet and the Groth16 ZK-SNARK protocol. On-chain (gas cost) and off-chain (execution time) performance analyses confirm that the proprosed solution is a viable and efficient alternative in the spirit of digitalization and offers additional security guarantees.

With the development of social economy, the quality of national life has been rapidly improved, along with the accelerated aging of the population and the continued prevalence of metabolic risk factors. Cardiometabolic diseases (CMDs) have become a major disease burden for both urban and rural residents in China, and most of them are related to lifestyle changes in modern society, especially circadian rhythm disorders. Circadian rhythm regulating the activity of metabolism, such as carbohydrates, lipids, and other nutrients, is the most important pathway modulating the health. Nowadays, studies have shown that the interaction between circadian rhythmicity and gut microbiota counts for the homeostasis of metabolic health, while circadian rhythm disruption is associated with gut microbiota dysbiosis and metabolic diseases. However, there is a lack of research on the topic of gut microbiota mechanisms regulating circadian rhythm disruption leading to CMDs. With increased burden of CMDs prevention and treatment, as well as the demand of healthcare resource allocation, it prompts us to put forward a new strategy to solve this problem. This review focuses on the interactions between circadian rhythm and gut microbiota to elucidate the potential mechanisms of circadian rhythm disruption induced CMDs, providing a solid support for further prevention and treatment.

This study investigated subclinical atherosclerosis progression in low-risk, middle-aged adults (N=141; mean age 49.6±4.7 years) using a 5-year ultrasound follow-up. We compared in-volvement of the carotid and femoral arteries. Methods: Clinical data, risk factors, and carotid/femoral intima-media thickness (IMT) and plaque presence were analyzed. Results: Cardiovascular risk factors and scores increased significantly at follow-up. Both carotid and femoral IMT increased (p&lt;0.001) but remained within the normal range. While plaque prev-alence rose and was similar in both arteries (carotid: 4.8% to 17.9%, femoral: 3.6% to 17.7%, p&lt;0.001 for both), the progression of plaque burden was greater in femorals. Notably, carotid IMT demonstrated a faster yearly progression rate compared to femoral IMT. The age- and sex-adjusted increase in IMT was also more frequent in the carotids (52.9% to 78.8%, p&lt;0.001) compared to femorals (23.2% to 44.7%, p&lt;0.001). Conclusions: This study demonstrates significant subclinical atherosclerosis progression in low-risk middle-aged adults during 5 years. Carotid arteries showed a faster progression rate and higher prevalence of increased age- and sex-adjusted IMT compared to the femoral arteries. However, plaque burden was similar in both territories, with greater progression in femorals. Identifying carotid and femoral atherosclerosis burden may be a valuable tool for risk stratification in this population.

Benzazoles, such as benzoxazoles and benzothiazoles, are compounds with important biological and pharmacological activities and important intermediaries in synthesis. This report presents the synthesis of a butanamide derived from linking 5-chloro-2-aminobenzoxazole and 2-aminobenzothiazole via 4-chlorobutanoyl chloride. The corresponding compound N-(benzothiazol-2-yl)-4-((5-chlorobenzoxazol-2-yl)aminobutanamide was obtained 76 % yield global using accessible starting materials and methodology in two reaction steps. Furthermore, we made docking studies of this compound on 3-TOP protein to explore its potential as an antidiabetic agent.