Introduction: Detection of diaphragmatic injury in blunt trauma can be challenging in the absence of surgical exploration. Here we explore the mechanisms of injury and detection modes for patients with blunt trauma who had a diaphragmatic injury. Methods: This is a single-center, retrospective review conducted in a level 1 trauma center, 2016 to 2023, inclusive. We included all patients who presented with a blunt traumatic diaphragm injury (TDI). We identified nine patients with TDI using AIS injury description and type of trauma. Patient demographics, mechanism of injury, injury severity score (ISS), mode of diagnosis, and clinical outcomes were reviewed. Results: Of the nine patients identified with blunt TDI, two were associated with motor vehicle collisions, four were pedestrians struck, 1 from stab wounds, and 2 fell from the stairs. The mean ISS for the nine patients is 39.7. Of the nine patients with blunt TDI, four died in the trauma bay – two from traumatic arrest, and two died spontaneously. One patient died two days after admission. Of the four patients who survived, one had a diaphragm injury identified during video-assisted thoracic surgery (VATS) for retained hemothorax, and one patient had a diaphragm injury repaired during emergent exploratory laparotomy for other injuries. One patient had a diaphragm injury identified and repaired during diagnostic laparoscopy that was converted to open surgery, and the remaining patient had no diaphragm injury on diagnostic laparoscopy evaluation. Conclusions: Blunt trauma to the torso is usually a consequence of high-energy impact but can also occur following low-energy incidents, such as falls in elderly individuals. Imaging can be useful in detecting such injuries but is not 100% sensitive. A high index of suspicion should be maintained for diaphragmatic injury in high-energy blunt trauma.

This research presents a comprehensive study of 1D geomechanics modeling and wellbore stability analysis for directional wells in the S field, South Sumatra Basin, Indonesia. The primary goals include pre-drilling analysis and determining safe mud weight requirements about well inclinations. The study integrates data from nearby drilling experiences, incorporating wireline logs, FIT/LOT data, pressure readings, rock samples, drilling records, gamma ray profiles, and mud logs. Petrophysical analyses, shear wave velocity modeling, and the Mogi-Coulomb failure criterion were employed to define a safe mud weight range for distinct formations. The geomechanics model was calibrated using caliper data and drilling records to validate the breakout model. Additionally, the study transferred the earth mechanical model from wells S-3 and S-4 to S-8 and S-9, performing sensitivity analyses to refine mud weight recommendations and prevent shear failure. The study concludes that a maximum inclination of 30 degrees should be maintained for future wells to ensure wellbore stability. These findings provide critical insights for optimizing drilling operations in the South Sumatra Basin.

We propose a multi-sensor fusion localization framework for autonomous heavy-duty trucks suitable for mining scenarios, which enables high-precision, real-time trajectory generation and map construction. The motion estimated through pre-integration of the Inertial Measurement Unit (IMU) can eliminate distortions in the point cloud and provide an initial guess for LiDAR odometry optimization. The point cloud information obtained from LiDAR can assist in recovering depth information from image features extracted by the monocular camera. To ensure real-time performance, we introduce an iKD-Tree to organize the point cloud data. To address issues arising from bumpy road segments and long-distance driving in practical mining scenarios, we can incorporate a large number of relative and absolute measurements from different sources, such as GPS information and AprilTag-assisted localization data, including loop closure as factors in the system. The proposed method has been extensively evaluated on public datasets and self-collected datasets from mining sites.Our implementation is available at https://github.com/wangyicxy/iLM-slam

This study comprehensively investigates the stress distribution and aging effects in Ethylene Propylene Diene Monomer (EPDM) and Liquid Silicone Rubber (LSR) gasket materials through a novel integration of hyperelastic modeling and advanced machine learning techniques. By employing the Mooney-Rivlin, Ogden, and Yeoh hyperelastic models, we evaluate the mechanical behavior of EPDM and LSR under conditions of no aging, heat aging, and combined heat and sulfuric acid exposure. Each model reveals distinct sensitivities to stress distribution and material deformation, providing unique insights into material degradation processes. Additionally, we utilize machine learning for predictive analysis, enhancing our understanding of stress responses and identifying critical aging effects. The findings offer practical implications for selecting suitable gasket materials and developing predictive maintenance strategies in industrial applications, such as fuel cells, where material integrity under stress and aging is paramount. This dual approach highlights the mechanical resilience and vulnerabilities of EPDM and LSR and opens avenues for real-time predictive analytics and material performance optimization.

In an age of unprecedented global complexity, interconnectedness, and urgency, the inadequacy of traditional, reductionist models of knowledge production has become glaringly apparent. This pioneering, landmark article offers a sweeping, paradigm-shifting exploration of the revolutionary emergence of "Mode 4" knowledge production - a fundamental reconceptualization of the epistemological, organizational, and methodological foundations of the research enterprise. Drawing on cutting-edge theoretical frameworks and a vast corpus of rigorous empirical evidence, this work argues that Mode 4 represents a transformative leap towards a more collaborative, transdisciplinary, and adaptive approach to knowledge creation - one that holds the potential to catalyze a profound and lasting transformation in the way we conceive of, organize, and mobilize research to address the complex, interconnected challenges facing our world. At the heart of this paradigm shift lies the groundbreaking "decuple helix" framework, which expands the scope of stakeholder engagement and knowledge co-creation to incorporate a comprehensive range of actors, from academia and industry to marginalized communities, the natural environment, and international organizations. This article delves deeply into the multifaceted roles and invaluable contributions of this diverse array of stakeholders, demonstrating how their active integration can unlock the transformative power of collaborative, values-oriented research and innovation. Furthermore, the paper provides a comprehensive example of how Mode 4 knowledge production concepts could be implemented using cutting edge innovationology research. By drawing on a rich tapestry of theoretical foundations, including complexity theory, quantum physics, humanities, social sciences, spirituality, and the arts, innovationology exemplifies the transdisciplinary ethos at the core of this paradigm shift. The article delves deeply into the collaborative co-creation, iterative and adaptive methodologies, and holistic, values-driven vision that define this groundbreaking transdisciplinary science. However, this work also candidly explores the significant institutional, methodological, equity-related, and scalability challenges that continue to hinder the widespread adoption and implementation of the Mode 4 and decuple helix frameworks. In doing so, it charts a course forward, outlining a comprehensive set of practical implications and recommendations to address these barriers and unlock the transformative potential of these emerging paradigms. Ultimately, this article offers a sweeping, cohesive, and visionary analysis of the revolutionary emergence of Mode 4 knowledge production and the decuple helix framework - positioning itself as a landmark contribution that has the potential to catalyze a profound transformation in the way we conceive of, organize, and mobilize research for a sustainable and equitable future. With its groundbreaking insights, bold vision, and rigorous interdisciplinary foundation, this work stands as a clarion call for a new era of collaborative, transdisciplinary knowledge production that can truly address the complex, interconnected crises facing our world.

The therapeutic applicability of mesenchymal stem cells (MSC) for a range of conditions has garnered increasing interest. This study aimed to evaluate the mean major adverse cardiac and cerebrovascular event (MACCE)-free period in a population of 2,504 patients (age, 54.09 ± 11.65 years) receiving MSC therapy (mean dose, 1.49 ± 0.75 billion cells) for various indications (over two-thirds [n=1,700, 67.89%] received MSC therapy as an anti-ageing therapy) and evaluate for potential risk factors for MACCE development in this cohort. In multivariate Cox proportional hazards regression analysis, age, but not sex and MSC dose, was significantly associated with MACCE risk (hazard ratio=1.127; 95% confidence interval=1.0418, 1.219; p=0.0029). The adverse event rate was 0.8%, and none of the reported adverse events were severe. In conclusion, our study showed that stem cell therapy was safe in a large cohort of individuals and was associated with a low rate of MACCE.

Background: Cardiovascular diseases increasingly impact youth, with early development of risk factors such as obesity, hypertension, and inadequate nutrient intake. Proper nutrient intake and physical fitness are vital for reducing these risks, especially in pediatric populations. This study explores the connection between physical fitness, metabolic risk, and nutrient status among 1656 Chilean schoolchildren from diverse so-cio-economic backgrounds. Methods: Anthropometric measures included weight, height, skinfold thickness, waist circumference, and blood pressure. Physical fitness was assessed via handgrip strength, standing long jump, and a six-minute walk test. Nutrient intake was also evaluated, and a composite metabolic risk score was calculated based on waist circumference, skinfolds, and blood pressure. Results: Children from public schools exhibited higher obesity rates and waist circumferences than those from private schools. Physical fitness performance was strongly linked to cardiovascular health indicators: better fitness correlated with smaller waist circumferences, lower skinfold measurements, and healthier blood pressure levels. Children from private schools had better fitness test outcomes, indicating socio-economic disparities in access to physical activity and nutrient resources. Students with lower fitness and nutrient intake had higher metabolic risk scores, pointing to increased cardiovascular risk. Conclusions: Nutrient intake and physical fitness are essential to reducing cardiovascular risk in children. Significant differences in nutrient intake and fitness levels across socio-economic lines suggest that equal access to nutrition and physical activity programs in schools could benefit public health outcomes. Further research should investigate the lasting effects of nutri-ent-focused interventions on childhood metabolic health.

Background/Objectives: Clinical studies show that SARS-Cov-2 vaccination sometimes entails a severe and disabling chronic syndrome termed post-acute-Covid-vaccination syndrome (PACVS). PACVS shares similarities with long COVID. After more than three years, PACVS is still not officially recognised, while long Covid has been registered by health authorities within little over a year. Here we address possible reasons for that discrepancy. Methods: We analyse to which extent common symptoms of PACVS have been registered by European pharmacovigilance as adverse vaccination reactions and which consequences have been drawn thereof. Results: (i) PACVS is distinguished from normal vaccination reactions solely by prolonged duration. (ii) Symptom duration is poorly monitored by post-authorization pharmacovigilance. (iii) PACVS-specific signals were faithfully recorded by health authorities but haven’t prompted appropriate reactions. (iv) The most widely applied SARS-Cov-2 vaccine has been modified after roll-out without renewed phase III evaluation; the modification has increased DNA-contamination, which is thought to aggravate the spectrum of adverse events. (v) Crossing of Pharmacovigilance data with corresponding estimates of applied vaccine doses suggest a PACVS-prevalence of 0.003 % in the general population. In contrast, occupational surveillance studies suggest a PACVS-prevalence of 0.9 % in young and middle-aged persons. Conclusions: (a) Denial of official recognition of PACVS is unjustified. (b) PACVS seems to target preferentially young and middle-aged persons. (c) Without official disease recognition, access to public health care and welfare services is made difficult for PACVS-affected persons, which creates considerable socio-economic problems. (d) Without official disease recognition, development and evaluation of therapies for PACVS is impaired.

Artificial Intelligence (AI) has the disruptive potential to transform patients’ lives via innovations in pharmaceutical sciences, drug development, clinical trials, and manufacturing. However, it presents significant challenges, ethical concerns, and risks across sectors and societies. AI’s rapid advancement has revealed regulatory gaps as existing public policies struggle to keep pace with the challenges posed by these emerging technologies. The term AI itself has become commonplace to argue that greater “human oversight” for “machine intelligence” is needed to harness the power of this revolutionary technology for both potential and risk management, and hence to call for more practical regulatory guidelines, harmonized frameworks, and effective policies to ensure safety, scalability, data privacy, and governance, transparency, and equitable treatment. In this review paper, we employ a holistic multidisciplinary lens to overview the current regulatory landscape with a synopsis of the FDA workshop perspectives on the use of AI in drug and biological product development. We discuss the promises of responsible data-driven AI, challenges and related practices adopted to overcome limitations, and our practical reflections on regulatory oversight. Finally, the paper outlines a path forward and future opportunities for lawful ethical AI. This review highlights the importance of risk-based regulatory oversight, including diverging views in the field, in reaching a consensus.

This study combines two methodologies—key indicators from the Sustainable Livelihoods Framework (SLF) and the Sustainability Assessment of Food and Agriculture Systems (SAFA) approach—to evaluate sustainability among 330 producers from three associations working within the recently recognized Amazonian Chakra system, designated as a Globally Important Agricultural Heritage System (GIAHS). The SAFA framework's four dimensions—Good Governance, Environmental Integrity, Economic Resilience, and Social Well-being—were used to assess sustainability, while the SLF considered key capitals such as financial, natural, and human capitals as critical for understanding livelihood outcomes. The discriminant analysis revealed significant differences across the associations, with Kallari and Wiñak performing better in financial management, environmental practices, and governance compared to Tsatsayaku, which exhibited weaker outcomes in these domains. Key indicators such as chakra income, price determination, and civic responsibility were the most effective in distinguishing the groups. The income analysis further highlights that Tsatsayaku reported the highest total income but relies heavily on non-Chakra sources, while Kallari and Wiñak showed stronger reliance on Chakra income, which aligns with their more sustainable agricultural practices. Per-capita income was higher for Tsatsayaku, but extreme poverty levels remained similar across all associations, underscoring the need for targeted financial interventions. Policy recommendations include promoting access to financial resources and strengthening participatory governance models to enhance sustainability outcomes across associations. For future research, combining the SAFA and SLF provides a comprehensive and robust methodology for assessing sustainability in agricultural systems, enabling a deeper understanding of the interactions between social, economic, and environmental factors in diverse contexts.

We investigate a reliability of conservation of the vector current (CV) hypothesis in the neutron β−-decay. We calculate the contribution of the phenomenological term, responsible for the CVC in the hadronic current of the neutron β−-decay (or the CVC effect), to the neutron lifetime. We show that the CVC effect increases the neutron lifetime with a relative contribution 8.684×10−2. This leads to the increase of the neutron lifetime by 76.4 s with respect to the world averaged value τn=880.2(1.0)s (Particle Data Group, Chin. Phys. 40, 100001 (2016)). We show that since in the Standard Model (SM) there are no interactions, which are able to cancel such a huge increase of the neutron lifetime, we have to turn to the interactions beyond the SM the contribution of which to the neutron lifetime reduces to the Fierz interference term bF only. Cancelling the CVC effect at the level of the experimental accuracy we get bF=0.1219(12). If this value cannot be accepted for the Fierz interference term, the CVC effect induces irresistible problems for description and understanding of the neutron β−-decay.

It remains inconclusive whether smoking is associated with an increased risk of fatty liver dis-ease (FLD). We investigated the association between smoking and the risk of FLD by using a meta-analysis of cohort studies. PubMed and EMBASE were searched using keywords from in-ception to September 2023 to identify relevant studies. Out of 806 articles searched from data-bases, a total of 20 cohort studies were included in the final analysis. In the meta-analysis, smoking was significantly associated with an increased risk of FLD (odds ratio/relative risk/hazard ratio, 1.14; 95% confidence interval, 1.05 – 1.24; n = 20). Subgroup analyses showed a significant positive association between them in prospective cohort studies (odds ratio/relative risk/hazard ratio, 1.15; 95% confidence interval, 1.05 – 1.18; n = 5), but not in retrospective cohort studies and cross-sectional studies based on cohort studies. In the subgroup meta-analysis by gender in Asians, smoking significantly increased the risk of FLD in men, while there was no significant association between them in women. This meta-analysis showed that smoking in-creases the risk of FLD. In addition to well-known risk factors of FLD such as obesity and alcohol consumption, clinicians should recommend smoking cessation for the management of FLD.

Availability of nasal spray treatments that inhibit the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) entry into nose and nasopharynx at early stages can be an appropriate approach to stop or delay the progression of the disease. We performed a prospective, randomized, double-blind, placebo-controlled, parallel-group, multicentric, phase II clinical trial comparing the rate of hospitalization due to COVID-19 infection between azelastine 0.1% nasal spray and placebo nasal spray treatment groups. The study furthermore assessed the reduction in virus load in SARS-CoV-2 infected subjects estimated via quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) using nasopharyngeal swabs in both the groups during the treatment period. A total of 294 subjects with mild COVID-19 infection were screened and randomized in a 1:1 ratio.

The rising incidence of prostate cancer necessitates innovative treatment approaches, particularly as diseases like the COVID-19 pandemic can disrupt traditional cancer care. This study aimed to evaluate the impact of hypofractionated versus conventionally fractionated radiotherapy on prostate cancer cell lines in vitro. Prostate cancer cell lines (PC-3 and DU-145) were exposed to varying doses of radiation alongside non-cancerous BPH-1 cells. We assessed radiation effects on cell proliferation, viability, colony formation, DNA repair, migration, invasion, and cytotoxicity. Results demonstrated that the prostate cell lines exhibited varying responses, with hypofractionation favourably impacting aggressive PC-3 cells while preserving non-cancerous cells. In contrast, conventional fractionation led to increased invasion and cytotoxicity in both prostate cancer cell lines. These findings advocate for personalised radiation therapy approaches that enhance treatment efficacy by considering the distinct behaviours of differing prostate cancer subtypes.

4E-BP2 deamidation significantly alters protein production in the brain and nervous system. It maintains the balance of protein production required for a healthy nervous system. Any imbalance in protein production in the brain and nervous system causes all neurodegenerative diseases. Discovering what causes 4E-BP2 deamidation only in neurons will make it possible to control this balance of protein production and cure neurodegenerative diseases such as memory loss, Alzheimer's, and Parkinson's. Alzheimer’s disease is caused by the absence of 4E-BP2 protein in the brain's neurons. This protein undergoes deamidation spontaneously only in the neurons and, in this way, controls the production of proteins in the neurons. This, in turn, determines whether or not Alzheimer’s and Parkinson’s disease start or progress. The purpose of this work is to discover the neurobiological mechanism that causes the deamidation reaction in the 4E-BP2 protein by performing immunoblotting in the retinal ganglia, the optic nerve, the dorsal root ganglia, the sciatic nerve, and the whole brain, extracted via dissection from 2-month-old, Wild-type male mice. The results showed that deamidation mainly occurs in the axons or axon-enriched regions of the optic nerve, the whole brain, and the sciatic nerve, confirming conclusively that axons are the critical factors behind the neurobiological mechanism of 4E-BP2 protein deamidation. The results confirm that they are also the reason why 4E-BP2 deamidation occurs only in neural tissue.

Hotel certifications have become increasingly vital in promoting sustainability within the hospitality industry, providing frameworks that guide hotels toward reducing their environmental footprint, enhancing operational efficiency and gaining a competitive edge in the marketplace. This review explores the development, types and impacts of hotel certifications, with a focus on prominent schemes such as ISO 14001, LEED, Green Key, and EarthCheck. Through an analysis of case studies from globally recognized hotels, we highlight how certifications contribute to significant reductions in energy and water use, improved waste management and enhanced biodiversity protection, while also delivering economic benefits through cost savings and increased guest loyalty. The paper also examines consumer perceptions of certifications and their willingness to pay (WTP) for certified hotels. Findings suggest that eco-conscious travelers, particularly younger generations, are willing to pay a premium for hotels that demonstrate a commitment to sustainability, though consumer awareness of certification schemes remains limited. The barriers to adopting certifications, such as high initial costs, resource limitations and management resistance, are also discussed, along with the challenges posed by greenwashing and the proliferation of certification schemes. Looking to the future, this review identifies key trends that will shape the evolution of hotel certifications, including increased transparency, the integration of smart technologies, the expanding role of social responsibility and alignment with global sustainability goals such as the United Nations Sustainable Development Goals (SDGs). Hotel certifications are not only crucial for environmental sustainability, but also serve as a powerful tool for enhancing the economic viability of hotels in an increasingly competitive and sustainability-driven market.

Generating executable code from natural language instructions using Large Language Models (LLMs) poses challenges such as semantic ambiguity and understanding task- specific contexts. To address these issues, we propose a system called DemoCraft, which enhances code generation by leveraging in-context learning and demonstration selection, combined with latent concept learning. Latent concept learning introduces additional concept tokens, which are trainable embeddings that capture task-specific knowledge. We then test our system on two major datasets: MBPP and HumanEval. Our experimental results demonstrate that the proposed system achieves an approximate 2x increase in the pass@k metric compared to baseline models. Furthermore, we introduce two novel evaluation metrics: correctness@k and similarity@k. Our empirical studies indicate that our system attains nearly a 3x improvement in these metrics as well.

The status of tropical fisheries is poorly known because the existing methodological choices have variable outcomes and recommendations. Therefore, multiple approaches to estimating sustainability were compared, namely fisheries independent stock biomass and recovery rates, fisheries dependent landed catches, balanced harvest and gear use metrics, and fish length measurements. Unfished biomass recovery time series was established by a 45-year stock recovery relationship from 7 fisheries reserves and compared to catch and length-based estimates of sustainability. The logistic production rates (r = 0.10 ± 0.06 95% CI) and maximum equilibrium total biomass (150 ± 30 tons/km²) indicated a broad range of potential maximum sustainable yields but likely ranging from 1.1 to 3.9 (95% CI; mean = 3.8) tons/km²/year. In contrast, mean annual biomass growth rates in reserves was lower but less variable than surplus production estimates, ranging from 2.1 to 3.5 (mean=2.8 tons/km²/year). Realized catches at landing sites was lower still, ranging from 1.43 to 1.52 (mean=1.48± 0.2 tons/km²/y). Differences were largely attributable to changes in taxonomic composition and an imbalance in the proportionality of production potential versus actual capture rates. Differences in the vulnerability of taxa to fishing and a lack of compensatory increased production among fishing resistant taxa could largely account for the lost potential production. Large proportional losses of catch were measured among snappers, unicorn fish, sweetlips, goatfish, and soldierfish, while smaller proportional gains in the catch were found among resident herbivorous rabbitfish, parrotfish, and groupers. Many of these declining taxa had vulnerable schooling life histories likely to require special habitat and reserve characteristics. Evaluations of sustainability from length measurements found 17 or 7% of total and 12% of caught species had a minimum sufficient sample sizes (>30 individuals, 413 catches, 2299 individuals, and 144 species) to evaluate length and spawning metrics of sustainability. Seven of these species met length-based and 3 meet spawning potential ratio thresholds for sustainability. Consequently, length-based evaluations were unable to evaluate the sustainability of the larger community. Recommendations include a management focus on the recovery of “ghost taxa”, species abundant in the oldest reserve but not contributing to fisheries yields.

This paper reviews the measurement challenges associated with 3D scanning techniques in civil engineering. It explores the practical aspects of scanning buildings and complex surfaces through various case studies. The paper details the conventional use of Terrestrial Laser Scanning (TLS) for reconstructing the technical documentation of a hall. It then describes an unconventional application of this technique for measuring an ETICS (External Thermal Insulation Composite System) wall, aiming to detect microdeformations caused by environmental factors controlled within a climatic chamber. The measurements of the insulated wall were subsequently repeated using a metrological grade laser scanner. Numerical data was analyzed with inspection engineering methods. This approach yielded qualitative and quantitative results. Although the qualitative results were consistent, the quantitative data revealed some inconsistencies. To address discrepancies in the quantitative data, a comparative analysis was performed, which highlighted critical findings and areas of divergence.

This paper presents four novel schemes for secure data encryption utilizing mathematical transformations. The first scheme employs matrix manipulations and partitioning of numerical values derived from plain text characters, constructing a cipher through matrix additions. The second scheme introduces division operations and additional transformation layers for enhanced data obscurity. The third scheme incorporates tridiagonal matrices, creating a structured encryption process. The fourth scheme refines this approach with quotient-remainder operations and symmetric keys to construct a tridiagonal matrix cipher. Detailed algorithms for encryption and decryption are provided, with examples illustrating practical applications. This work contributes to mathematical cryptography by offering enhanced security methods for digital communication.

Today, reducing vehicle energy consumption is a crucial topic. For electric vehicles, reducing energy consumption is essential to address some of the most critical issues associated with this type of vehicle, such as the limited range of electric powertrains and the long battery recharging times. To lower the environmental impact during the vehicle’s use phase and reduce energy consumption, vehicle mass reduction (lightweighting) is an effective strategy. The objective of this work is to analyze the vehicle parameters that influence lightweighting outcomes on a real driving cycle, representative of the home-to-work travel in the northern Italy. In particular, a previous work carried out on standard driving cycles is repeated, in order to observe whether it is possible to draw the same conclusions regarding the variability of the lightweighting outcome. This study was conducted using two opposite vehicle models, a compact car and an N1 vehicle, simulated through a well-established vehicle simulation tool for energy consumption estimation. The study reveals that even for the real driving cycle, as for the WLTC and US06 standards, the parameters that most influence the outcome of the lightening are rolling resistance, the characteristics of the battery pack, the aerodynamic coefficients and the efficiency of the transmission. Finally, the standard cycle that best fits with the real one considered in this study is the Artemis Urban Cycle.

Acute pancreatitis (AP) is a potentially fatal organ disease that is widespread globally. Although significant progress has been made in the previous decade, the study of mechanisms and therapeutic strategies is still far from being completed. Xanthine oxidase (XO) is an enzyme that generating urate and also responsible to produce reactive oxygen species (ROS). Considerable clinical and mechanical research has been conducted over many decades to explore the function of XO in AP and its promising therapeutic applications. Unquestionably, XO is a key part of starting and progressing AP by ROS production. Not fully known, though, are XO's many other functions besides ROS resource in both local and global processes, such as its role in cellular signaling or metabolism regulation. This review seeks to summarize the function of XO in AP pathophysiology by analyzing the inherent biological and chemical properties of XO, and lastly provide fresh concepts for future research.

The increasing prevalence and heightened severity of extreme weather events, which are consequences of climate change, pose significant challenges to the global property insurance sector. This study examines the concurrent crises faced by insurance companies in maintaining profitability and by property owners in affording coverage, thereby necessitating a reevaluation of current insurance paradigms. We present an innovative approach that utilizes a back-propagation (BP) neural network to forecast insurance losses and guide strategic underwriting decisions in regions prone to perils. Our advanced model combines crucial factors such as the likelihood of extreme meteorological phenomena, insurance risk premiums, and capital adequacy ratios to predict impending losses and inform insurance policy formulation. Through an extensive analysis of nearly two decades of catastrophe data from the United States and New Zealand, we determine the model's effectiveness in anticipating insurance-related losses and providing strategic guidance to insurance entities. The study concludes with recommendations for insurers to enhance risk assessment, innovate products, collaborate with governments, invest in green projects, and establish dedicated climate risk management teams. The study contributes to the discourse on climate change adaptation in the insurance industry and offers practical solutions for navigating the complexities of extreme weather risk.

Over the years, the construction sector has used traditional methodologies that often fail to meet established budgets and deadlines, making it imperative to adopt collaborative methodologies such as IPD and BIM. This research evaluates the influence of these methodologies in the design stage of residential buildings, through a case study in Peru managed by an SME. The methodology is divided into three parts: first, a diagnosis of traditional management is carried out through documentary review and semi-structured interviews; second, a proposal for improvement is proposed using tools based on BIM and IPD; third, the proposal is validated through statistical analysis and a validation matrix. Nine typical management problems were identified, such as deficiencies in plans, measurements and budgets, and delays due to inadequate planning. Eight optimization tools are suggested, such as the NEC4 ECC Collaborative Contract, ECD, Revit, Navisworks, early integration of the contractor, ICE Sessions, 3D, 4D and 5D BIM models. The 3D model showed 0.48 interferences per m², the 4D model allowed monitoring the progress of the project, and the 5D model allowed a cost optimization of 4.73%. The profitability analysis showed a return on investment of S/ 3.42 per “sol” invested. The validation of the optimization was demonstrated by the Z Test. The validation matrix highlighted the NEC4 ECC Collaborative Contract and the 3D BIM Model as the most effective tools. In this way, the study contributes to closing the knowledge gap regarding the implementation of BIM and IPD to optimize the management of residential buildings.

Background/Objectives : This study investigates the utility of vision transformers (ViTs) for predicting early responses to SRS using a minimal pre-processing approach on MRI images. Methods: We analyzed MRI scans from 19 patients with BM, focusing on axial fluid-attenuated inversion recovery (FLAIR) and high-resolution contrast-enhanced T1-weighted (CE T1w) sequences. Patients were classified as responders (complete or partial response) or non-responders (stable or progressive disease). Results: Our findings demonstrate that ViTs can effectively predict treatment responses, achieving an overall accuracy of 97%. The model exhibited high precision (96% for progression and 97% for regression) and strong recall rates (92% for progression and 99% for regression), distinguishing between treatment outcomes reliably. The confusion matrix analysis further supports its reliability, with minimal misclassifications. The model also achieved an almost perfect area under the ROC curve (AUC = 0.99), indicating accurate differentiation between responders and non-responders across various thresholds. Conclusions : These findings highlight the potential of the Vision Transformer model as a non-invasive predictive tool in clinical settings, significantly influencing clinical decision-making processes and enhancing patient management for brain metastases. By improving early response predictions, this model contributes to the development of personalized treatment strategies in oncology. Future research should focus on validating these results in larger, diverse cohorts, integrating additional data types, and refining the model to further enhance its utility in clinical practice.

This study examines the chemical recycling of polymethylmethacrylate (PMMA) dental waste via pyrolysis in a semi-batch fixed-bed reactor, aiming to convert it into the valuable monomer methyl methacrylate (MMA). Experiments were conducted in a 2L reactor at temperatures of 425 °C, 450 °C, and 475 °C to understand the effects of heating rate and temperature on product yield and composition. Results show that at 425 °C, MMA was the primary liquid component, with minimal by-products, suggesting that lower temperatures enhance monomer recovery. Higher temperatures, however, increased gas yields and reduced MMA yield due to intensified thermal cracking. The study also highlights that char formation and non-condensable gases increase with reactor scale, indicating that heat transfer limitations can influence MMA purity and yield. These findings emphasize that for effective MMA recovery, lower temperatures and controlled heating rates are optimal, especially in larger reactors where heat transfer issues are more prominent. This research contributes to scaling up PMMA recycling processes, supporting industrial applications to achieve efficient monomer recovery from waste.

Background: Since the introduction of the SLICC criteria in 2012, biopsy-proven lupus nephritis (LN) has been the only independent diagnostic criterion for systemic lupus erythematosus (SLE). This was reaffirmed by the EULAR/ACR in 2019, emphasizing the importance of renal biopsy in LN. However, the current classification lacks specific histopathological criteria for defining LN. This study describes the histological findings of patients with LN, compares them with those of other glomerular diseases, and evaluates their diagnostic accuracy. Methods: This retrospective cohort included 731 kidney biopsies. The patients were divided into two groups: a LN group and a control group comprising patients with membranous nephropathy, IgA nephropathy, membranoproliferative glomerulonephritis, pauci-immune glomerulonephritis, and proliferative glomerulonephritis. Sensitivity and specificity analyses were conducted for various histopathological features. Results: We identified five features strongly correlated with LN: mesangial proliferation, subendothelial deposits, C1q staining ≥ 1+, dominant IgG, and ≥ 4 positive immunofluorescence elements. Combined, these features yielded an area under the ROC curve of 0.94 (95% CI: 0.91–0.95). In membranous nephropathy, histological features such as mesangial deposits, C1q positivity, and ≥ 4 positive immunofluorescence elements effectively distinguished class V LN from non-lupus membranous nephropathy, with an area under the ROC curve of 0.85 (95% CI: 0.76–0.93). Conclusion: The combination of mesangial proliferation, subendothelial deposits, C1q staining ≥1+, dominant IgG, and ≥ 4 positive immunofluorescence elements offer good accuracy for diagnosing renal involvement in SLE. In the absence of pathognomonic features, combined criteria are valuable diagnostic tools, particularly when other SLE criteria are lacking.

The immunization against Gonadotropin-Releasing Hormone (GnRH) has been successfully explored and developed for the parenteral inoculation of animals, aimed at controlling fertility, reducing male aggressiveness, and preventing boar taint. Although effective, these vaccines may cause adverse reactions at the injection site, including immunosuppression and inflammation, as well as involvement of laborious and time-consuming procedures. Oral vaccines represent an advancement in antigen delivery technology in the vaccine industry. In this study, a Salmonella enterica serovar Typhimurium (S. Typhimurium) mutant lacking the pathogenicity island 2 (S. Typhimurium ΔSPI2) was used as a vehicle and mucosal adjuvant to deliver two genetic con-structs, in an attempt to develop an oral immunological preparation against Gonadotropin hormone-releasing hormone (GnRH). S. Typhimurium ΔSPI2 was transformed to carry two plasmids containing a modified GnRH gene repeated in tandem (GnRXG/Q), one under eu-karyotic expression control (GnRX GQ_OptMamm_V3::GnRXG/Q) and another under prokar-yotic expression control (pJexpress::GnRXG/Q). Group of three male BALB/c mice were orally immunized and tvaccination boosted 30-days later. Oral administration of S. Typhimurium ΔSPI2transformed with both plasmids was effective in producing antibodies against GnRXG/Q, leading to a decrease in serum testosterone levels and testicular tissue atrophy, evidenced by a reduction in the transverse tubular diameter of the seminiferous tubules and a decrease in the number of layers of the seminiferous epithelium in the testes of the inoculated mice. These re-sults suggest that S. Typhimurium ΔSPI2can be used as a safe and simple system to produce an oral formulation against GnRH, and that Salmonella-mediated oral antigen delivery is a novel, yet effective, alternative to induce an immune response against GnRH in a murine model, war-ranting further research in other animal species.

To reduce weight and simplify maintenance, ship structures frequently include openings and cut-outs. While these features offer practical advantages, they can weaken the structural integrity of key components. This study explores the effects of these geometric discontinuities on the double-bottom floor plates of a Panamax-class container ship under axial and transverse loads. Through numerical simulations and experimental testing, we analyzed different cut-out configurations and stiffening strategies to assess their impact on stress distribution, plate thickness and fatigue behavior. The results reveal that side cut-outs significantly increase stress, particularly under transverse loads, while central openings have less impact. Additionally, increasing plate thickness consistently reduces stress levels across all models, improving structural durability. Fatigue analysis shows that certain stiffening configurations, particularly those with longitudinal stiffeners in the bottom plates, enhance fatigue life. These findings offer critical design insights for optimizing hull structures, balancing weight reduction with strength improvement. The study provides valuable recommendations for reducing stress concentrations and extending the fatigue life of ship components, contributing to more efficient and safer ship designs.

Formaldehyde (HCHO) is harmful to human health and an adequate assessment of its concentrations, both in outdoor and indoor environments, is necessary in the effort of risk mitigation. In this research, ground indoor and outdoor HCHO measurements have been integrated with the analysis of tropospheric total columns obtained by satellite surveys to assess the concentrations of HCHO in a number of environments, exploiting the proximity of a World Meteorological Organization – Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy to a National Institute for Insurance against Accidents at Work (INAIL) department in the municipality of Lamezia Terme. Meteorological parameters gathered at the WMO station have also been used to provide additional data and test new correlations. Using statistical significance tests, the study demonstrates the presence of a correlation between indoor and outdoor HCHO concentrations, thus showing that an exchange between indoor and outdoor formaldehyde does occur. Rooms located in the building where indoor measurements took place have also demonstrated degrees of susceptibility to HCHO exposure which are correlated with the orientation of prevailing wind corridors in the area. The new findings constitute an unprecedented characterization of HCHO hazards in Calabria and provide regulators with new tools on how to mitigate formaldehyde-related risks.

Coxsackievirus A6 (CVA6) has become increasingly clinically relevant as a cause of Hand, Foot and Mouth Disease (HFMD) globally since 2008. However, most laboratories do not routinely determine the enteroviral type of positive samples. The non-pharmaceutical measures introduced to curb transmission during the SARS-CoV-2 pandemic may also have been perturbed CVA6 epidemiology. We thus aimed to determine the prevalence, clinical presentation and genetic relationship of CVA6 across three complete epidemic seasons: one pre- and two post-SARS-CoV-2-emergence in our regional healthcare setting. Surplus diagnostic nucleic acid from diagnosed enteroviral positives diagnosed between September to December 2018 and May 2021 to April 2023 was subject to VP1 gene sequencing to determine CVA6 cases and interrogate their phylogenetic relationship. Confirmed CVA6 cases were also retrospectively clinically audited. CVA6 infections were identified in 33 and 69 individuals pre- and post-pandemic respectively, with cases peaking in November of 2018 and 2022, but in October of 2021. HFMD was the primary diagnosis in 85.5% of post-pandemic cases, but only 69.7% pre-pandemic, where respiratory and neurological symptoms (45.5% and 12.1% respectively) were significantly elevated. Complete VP1 sequence was retrieved for 94% of CVA6 cases, revealing study infections were genetically diverse and suggestive of multiple local and international transmission chains. CVA6 presented a significant clinical burden in our regional UK hospital setting both pre- and post-pandemic and was subject to dynamic clinical and genetic epidemiology.

Early detection of cervical cancer is the need of the hour to stop the fatality from this disease. There have been various CAD approaches in the past that promise to detect cervical cancer in an early stage, each of them having some constraints.This study proposes a novel ensemble deep learning model, VLR (variable learning rate), aimed at enhancing the accuracy of cervical cancer classification. The model architecture integrates VGG16, Logistic Regression, and ResNet50, combining their strengths in an offbeat ensemble design. VLR learns in two ways: firstly, by dynamic weights from three base models, each of them trained separately; secondly, by attention mechanisms used in the dense layer of the base models. Hyperparameter tuning is applied to further reduce loss, fine tune the model’s performance, and maximize classification accuracy. We performed K-fold cross-validation on VLR to evaluate any improvements in metric values resulting from hyperparameter fine tuning. We have also validated our model on images captured in three different solutions and on a secondary dataset. Our proposed VLR model outperformed existing methods in cervical cancer classification, achieving a remarkable training accuracy of 99.95% and a testing accuracy of 99.89%.

This paper explores the pivotal role of personalized product recommendations in enhancing the e-commerce experience. As online shopping becomes increasingly prevalent, the demand for tailored user experiences has surged, prompting the development of sophisticated recommendation systems. This study presents a comprehensive analysis of various methodologies employed to deliver personalized suggestions, including collaborative filtering, content-based filtering, and hybrid approaches. The implementation of a user-centric recommendation engine demonstrates significant improvements in user engagement, satisfaction, and conversion rates. Furthermore, the paper discusses the importance of real-time adaptation mechanisms and user feedback loops in optimizing recommendations. By providing insights into the challenges and solutions associated with recommendation systems, this research aims to equip e-commerce businesses with the tools necessary to leverage personalization effectively, ultimately leading to enhanced customer experiences and increased sales

In this study, we explored the integration of evaporative cooling pads (ECPs), typically used for cooling open areas in arid climates, with split air conditioning systems. The objective was to assess the improvement in cooling efficiency and the reduction in electricity consumption. Two experimental setups were evaluated: a standard setup without an evaporative cooling system (ECS) and an enhanced setup incorporating ECS. The ECS was installed behind the outdoor air conditioning unit, positioned near the condenser, and supplied with water through a pump system. The key performance metric was electricity consumption, and the findings revealed a significant 36% reduction in energy usage when the ECS was employed. Additionally, the cooling performance within the conditioned space improved. This study demonstrates the potential of integrating evaporative cooling systems into air conditioning units to enhance energy efficiency in desert environments, with a detailed comparison of system performance based on air conditioner capacity and electrical current readings.

The introduction of HPAI H5N1 clade 2.3.4.4b viruses to North America in late 2021 resulted in avian influenza outbreaks in poultry, mortality events in many wild bird species, and spillover into many mammalian species. Reassortment events with North American low pathogenic virus were identified as early as February 2022 and over 100 genotypes have been characterized. Such diversity increases the complexity and time required for monitoring virus evolution. Here, we performed ordination and clustering analyses on sequence data from H5N1 viruses identified in North America between January 2020 to December 2023 to visualize virus genotypic diversity in poultry and wildlife populations. Our results reveal that ordination and cluster-based approaches can complement traditional phylogenetic analyses specifically for the preliminary assignment of H5N1 viruses to genotypic groups or to identify novel genotypes. Our study expands current knowledge on genotype diversity of H5N1 viruses in North America and describes a rapid approach for early virus genotype assignment.

Multisensory spaces have been used across a number of health and social care contexts to promote well-being, engage the senses and promote inclusion by providing places where people can come together. Much of the existing research on such spaces focus on children or older adults with little investigation dedicated to spaces inclusive of both. Utilising multisensory spaces which bring together children and older adults can reduce costs and lead to opportunities for shared learning. We sought to identify empirical research which investigated the use of multisensory spaces for intergenerational participants. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to guide this review. Five electronic databases (PsycINFO, CINAHL, Web of Science, Embase, Pubmed) were interrogated using search terms developed in collaboration with a specialist subject librarian around the concepts of ‘intergenerational’ and ‘multisensory’. Two authors independently applied the agreed upon eligibility criteria to the titles and abstracts of potential articles for inclusion. Once the final articles had been identified, the authors independently extracted data and assessed quality of the included studies. Data were subjected to narrative synthesis. Following review of 979 titles and abstracts, 3 articles were identified for inclusion. Quality appraisal rated these as between 3 to 6 out of a possible total of 7. Narrative synthesis identified three themes named as Independence, Connecting with others and Equitable multisensory space design. Multisensory spaces offer the potential to deliver care within health and social care contexts which caters to the needs of children and older adults. There is a need to co-create such spaces with service users and other stakeholders to ensure they adequately meet the needs of all.

Background: While exercise is known to promote sleep quality, the specific effects of different types of exercise interventions on the sleep quality of college students remain inadequately researched. Methods: Initially, the sleep quality of 182 college students was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Subsequently, 23 volunteers underwent an 8-week exercise intervention. They were randomly assigned to either the aerobic or resistance exercise group to analyze and compare the effects of aerobic and resistance exercises on college students' sleep quality. Sleep quality and exercise safety were monitored using Huawei Band 6 smart wearable devices. Results: The prospective survey highlighted sleep quality challenges among college students, evidenced by elevated PSQI scores. Aerobic exercise and resistance training improved sleep quality and parameters. In the aerobic exercise group, sleep duration increased, with improved blood oxygen saturation and reduced wakefulness. Resistance training increased deep sleep duration and REM sleep. Conclusion: Through smart wearable devices like the Huawei Band, monitoring of both exercise and sleep revealed the beneficial effects of aerobic exercise and resistance training on improving sleep quality among college students. Aerobic exercise increased sleep duration, blood oxygen saturation, and reduced waking frequency, while resistance training significantly increased deep sleep and REM sleep duration.

The adhesion of two-component lipid vesicles to a flat substrate is investigated. The components are characterized by different spontaneous curvatures. The shape of the vesicles is coupled to the lateral distribution of membrane components. The influence of the adhesion on the shape transformations and the lateral distribution of membrane components is investigated. The evolution of the vesicle’s shape and the lateral distribution of membrane components in the process of vesicle adhesion is studied. It has been shown that vesicle adhesion may induce mixing or demixing of membrane components. The change of the vesicle shape due to adhesion facilitates the curvature driven segregation of the membrane components and results in the formation of large membrane domains with various mean curvatures.

The continued rise in SMEs' corruption-related activities results in uncertainty around their ability to sustainably contribute to economic growth, leaving SMEs financially fragile and exposed to problems associated with trade credit management resulting in business exits. Given that little research has been conducted on how corruption affects smaller businesses while corruption's impact on SMEs' trade credit management effectiveness remains largely unexamined, the study aims to determine the impact of corruption on SMEs' trade credit management effectiveness. By addressing this unanswered research gap, SMEs could be better equipped to understand how corruption affects their trade credit management in support of their overall finances. The study employed a quantitative research design with purposive sampling using a survey by administrating 10 450 online questionnaires tested by a sample of 450 SMEs across South Africa. The result aligns with expectations around corruption being detrimental to SMEs' trade credit management effectiveness while also indicating, unexpectedly, SMEs’ willingness to partake in corruption, given that SMEs benefit from increased effectiveness in managing trade credit. The study adds to exisiting literature on corruption and SMEs' trade credit management while also providing anti-corruption recommendations to SMEs that are dependent on trade credit. In so doing, SMEs could be better equipped to understand how corruption affects their trade credit management to support their overall finances contributing to improved SME creation rates and fostering entrepreneurship as a pivotal mechanism for improving South Africa’s sustainable development goals.

In recent decades, the aviation industry has increasingly adopted composite materials for various aircraft components, due to their high strength-to-weight ratio and durability. To ensure the safety and reliability of these structures, Health and Usage Monitoring Systems (HUMS) based on optical fibers (FO), particularly Fiber Bragg Grating (FBG) sensors, have been developed. However, both composite materials and optical fibers are susceptible to environmental factors such as moisture, in addition to the well-known effects of mechanical stress and thermal loads. Moisture absorption can lead to the degradation of mechanical properties, posing a risk to the structural integrity of aircraft components. This research quantifies the impact of humidity on FBG sensors and introduces a new formulation of the Bragg equation, which accounts for the effects of mechanical strain, thermal expansion, and hygroscopic swelling. The developed mathematical model, derived by differentiating the Bragg equation and applying the rules of mixtures, provides an accurate measurement of the Bragg wavelength variation, addressing potential measurement errors caused by moisture absorption. This approach is crucial for improving the performance and reliability of HUMS in monitoring composite structures, ensuring long-term safety in extreme environmental conditions.

This paper explores the implementation of a VCO-based ADC, achieving an ENOB of 12 bits with 1MHz of sampling rate in the audio bandwidth. The solution exploits the scalability and PVT invariance of a novel digital-to-frequency converter, to reduce the size and consumed power. The architecture has been validated in a 130-nm CMOS technology node, displaying a power consumption of 105.57 μW, and a silicon footprint of 0.034 mm2, in a pseudo differential configuration. Performance can be dynamically adjusted to trade off power consumption by resolution without changing the sampling rate. In addition, the proposed architecture benefits from multiple instantiations in the same SoC, making it particularly suitable for sensor array applications, such as biomedical sensors and spatial audio arrays.

Background: The present study investigated the effects of a micronutrient combination with proven impact on prostate cell metabolism and growth with regard to PSA dynamics. Methods: In an open single-arm multicenter study, 46 men with different baseline situations were recruited: 16 men with constantly elevated PSA values and negative prostate biopsy (group A), and 30 men with low risk prostate cancer on Active Surveillance (Group B) Results: In group A, 12 out of 16 men (75%) showed a decrease of PSA after 6 months from an average 9.26 to an average 8.84 ng/ml (4.5%), whereas in group B, PSA level decreased in 16, increased in 12 men, and remained unchanged in 1 man. The average PSA in group B dropped from 7.47 to 6.82 ng/ml (8.6%).Both PSA decreases were not statistically significant. Conclusion: The results of the present study are consistent with previous findings on the positive effects of individual micronutrients on prostate health. Longer administration of micronutrients or different dosage or combinations may further improve PSA outcomes.

In a series of publications, we attempt to demonstrate the prophylactic role of bromhexine hydrochloride (BHH) and the curative role of high colchicine doses against COVID-19. Effects of long-term prophylaxis with BHH are presented in two cases of elderly women with severe comorbidity. After long-term prophylactic use of BHH from 3 to 6 months, one does not get sick from COVID-19, and the other passes it almost asymptomatically, even though all their family members fall ill. The dramatic healing effect of colchicine was demonstrated in a family in which both spouses became ill with COVID-19 almost simultaneously. They are treated in different hospitals, and where colchicine is administered in high doses, the patient recovered. The other patient developed complications and died on standard treatment without high doses of colchicine. An outpatient accidentally overdosed on colchicine (12.5mg). The therapy was stopped immediately. Clinically, on the third day, there are absolutely no symptoms. These cases reaffirm the prophylactic effect of BRH and the curative effect of high-dose colchicine

This study investigated vegetable farmers’ willingness-to-pay (WTP) for private advisory services in northwestern Ethiopia. Understanding farmers' preferences for modernizing agricultural advisory services is essential for enhancing the productivity and profitability of smallholder farming. The study assessed WTP for improved advisory services using data from 393 farm households in northwestern Ethiopia. Data from discrete choice experiments were analysed using a random parameter logit model. The results revealed the existence of heterogeneity in farmers’ WTP for vegetable advisory services. Household educational status and age influenced preferences for advisory service features. Farmers preferred enhanced services with frequent visits from agricultural experts and hands-on, field assistance over digital short message service (SMS) and voice-based advice. Additionally, they were more willing to invest in services that focused on leafy vegetables than on fruity vegetables or roots and tubers. The results suggest that the push for commercializing agricultural advisory services should consider contextual differences in smallholders’ preferences.

Poly(butyl cyanoacrylate) (PBCA) nanoparticles have numerous applications, including drug and gene delivery, molecular imaging, and cancer therapy. To uncover the molecular mechanisms underlying their interactions with cell membranes, we utilized a Langmuir monolayer as a model membrane system. This approach enabled us to investigate the processes of penetration and reorganization of PBCA nanoparticles when deposited in a phospholipid monolayer subphase. Atomic force microscopy (AFM) was employed to visualize Langmuir–Blodgett (LB) films of these nanoparticles. Additionally, we examined the state of a monolayer of Pluronic F68, a stabilizer of PBCA nanoparticles in suspension, by measuring changes in relative surface area and surface potential over time in the barostatic regime following PBCA suspension spreading. Based on these findings, we propose a molecular mechanism for nanoparticle reorganization at the air–water interface.

Diverse exogenous nitrogen (N) sources have a considerable impact on microbial community structure in terrestrial ecosystems. Legume plants and N deposition can relieve N limitations and increase net primary productivity. However, the differences in their effects on soil microbial communities remain unclear. Here, the responses of the soil microbial community to a legume-planting system and simulated N deposition were examined in karst grasslands in Southwest China over five years by analyzing soil microbial phospholipid fatty acids (PLFAs). The experiment included three treatments—legume plant introduction (NL, Indigofera atropurpurea), N deposition (ND, NH4NO3:10 g N m−2 yr−1), and a control with no treatment. The effects of NL and ND on soil microbial community composition were significantly different. ND significantly reduced the biomass of bacteria, Actinobacteria, and arbuscular mycorrhizal fungi. NL insignificantly increased the biomass of all microbial groups. However, the total amounts of PLFAs and fungal biomass were significantly higher in NL than in ND. The effect of legume plant introduction on soil microbial community composition was more powerful than that of ND. Overall, the introduction of legume plants is beneficial for increasing the biomass of the soil microbial community and stabilizing the soil microbial community structure in karst grassland ecosystems.

Citizens Advice on Prescription (CAP), a Liverpool (UK) based service, provides welfare advice and link worker social prescribing support to people experiencing and at risk of financial or social hardship. CAP, which receives referrals from healthcare and third sector services, aims to improve service users’ financial security, health, and wellbeing. A mixed-method Social Return on Investment (SROI) analysis was used to evaluate the service. Between May 2022 and November 2023, a subset of service users (n=538) completed the Short Warwick-Edinburgh Mental Wellbeing Survey (SWEMWBS) at baseline and 2-month follow-up. Supporting quantitative and qualitative economic data was also collected (Feb 2023 - Feb 2024) through semi-structured interviews (n=16). Social value changes were generated using the change in SWEMWBS scores and social valuations from the Mental Health Social Value Bank (MHSVB). SROI ratios were calculated from change of social value against service provision costs. The mean service provision cost was £148.66 per person. The overall study reported a positive SROI return of £1: £3.40-£4.69. Results indicate that non-clinical support services, like CAP, can be an effective intervention to address the wider determinants of health and wellbeing.

The advent of the era of big data has led to the emergence of heterogeneous network data fusion as a prominent area of research. Heterogeneous network data is characterised by multi-modality, multi-source, and high dimensionality, which presents significant challenges for traditional data fusion methods. These methods often encounter difficulties in processing such data, including issues such as information redundancy, data inconsistency, and high computational complexity. This paper proposes a heterogeneous network data fusion model based on a deep neural network. The model employs the Multi-Layer Perceptron (MLP) as its fundamental framework, utilising the deep neural network to facilitate joint feature representation learning on data from disparate modalities. The Adaptive Feature Reconstruction Module enables the model to learn the interrelationships between different modalities and to balance the importance of different modal features in the fusion process in a dynamic manner. Furthermore, we introduce an innovative cross-modal attention mechanism, which is capable of effectively capturing the coupling relationship between deep features in heterogeneous data, thereby enhancing the expressiveness and data fusion efficacy of the model. The experimental results demonstrate that the proposed model markedly enhances the accuracy of classification and regression tasks in comparison to traditional methodologies.

Background: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial neural networks (ANNs) may identify complex prognostic factors in patients with SCI. However, psychological variables’ influence on rehabilitation outcomes remains underexplored, despite their potential impact on re-covery success. Materials and Methods: A cohort of 303 patients with SCI was analyzed with an ANN model that employed 17 input variables, structured into two hidden layers and a single output node. Clinical and psychological data were integrated to predict functional outcomes, measured by the Spinal Cord Independence Measure (SCIM) at discharge. Paired Wilcoxon tests evaluated pre-post differences, and linear regression assessed correlation, with Pearson’s coefficient and Root Mean Square Error calculated. Results: Significant improvements in SCIM scores were observed (21.8 ± 15.8 at admission vs. 57.4 ± 22.5 at discharge, p < 0.001). The model assigned the highest predictive weight to SCIM at admission (10.3%), while psychological factors accounted for 36.3%, increasing to 40.9% in traumatic SCI cases. Anxiety and de-pression were the most influential psychological predictors. Correlation between predicted and actual SCIM scores was R=0.794 for the entire sample and R=0.940 for traumatic cases. Conclusion: ANN model demonstrated strong impact, especially for traumatic SCI, of psychological factors on functional outcomes. Anxiety and depression emerged as dominant negative predictors, conversely self-esteem and emotional regulation functioned as protective factors increasing functional outcomes. These findings support the integration of psychological assessments into predictive models to enhance accuracy in SCI rehabilitation outcomes.

This article provides a comprehensive overview of lung cancer from a genetic perspective, emphasizing the molecular pathways involved in its pathogenesis. This review presents the genetic syndromes and key genomic variations, including alterations and instabilities, that contribute to tumor development and progression. Furthermore, advancements in genomic testing, such as liquid biopsy, are explored, highlighting their utility in early diagnosis, prognostic evaluation, and personalized treatment strategies. The role of biomarkers in informing therapeutic decisions and optimizing patient outcomes is also examined, offering insights into their potential to refine clinical interventions. This review aims to enrich the understanding of the genetic landscape of lung cancer, providing insights that may lead to improved diagnostic and therapeutic approaches.

Background/Objectives: Stroke patients generally have balance and gait dysfunction due to decreased range of motion (ROM) and muscle strength of the ankle joint. A therapist can apply a floss band to enhance ROM, pain control, jumping performance, strength, myofascial release, and recovery from fatigue. This study compared the immediate effects of floss band application on ankle ROM, balance, and gait ability in stroke patients.; Methods: This study randomized 40 chronic stroke patients to either the floss (n = 20) or sham (n = 20) band group. Outcome measures were ankle ROM, weight bearing lunge test (WBLT) performance, standing balance, and gait ability. Outcome measures were assessed at baseline and immediately after applying the floss or placebo band.; Results: There were significant improvements in dorsiflexion (DF), WBLT, static balance, and foot strike in those who used the floss band compared to the sham group (all p &lt; 0.05).; Conclusions: The floss band improved ankle DF, balance, and gait, indicating that it is a feasible therapeutic method for improving ankle DF, balance, and gait in chronic stroke patients.

Objectives: Cerebral edema, a prevalent consequence of brain injury, is associated with significant mortality and disability. Timely diagnosis and monitoring are crucial for patient prognosis. There is a pressing clinical demand for a real-time, non-invasive cerebral edema monitoring method. Ultrasound methods are prime candidates for such investigations due to their non-invasiveness nature; Methods: Acute cerebral edema by permanently occluding the left middle cerebral artery (MCA) was introduced in rats. Ultrasonic echo signals were collected at nine time points over a 24-hour period to extract features from both the time and frequency domains. Concurrently, histomorphological changes were examined. We utilized support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, and feedforward neural network (FNN) for predicting the cerebral infarction volume ratio; Results: The integration of 16 ultrasonic features associated with cerebral edema development with the RF model enabled effective classification of cerebral edema types with a high accuracy rate of 97.9%. Additionally, it provided an accurate prediction of the cerebral infarction volume ratio, with an R2 value of 0.8814; Conclusions: Our proposed strategy classifies cerebral edema and predicts the cerebral infarction volume ratio with satisfactory precision. The fusion of ultrasound echo features with machine learning presents a promising non-invasive approach for the monitoring of cerebral edema.

The main focus of this research is to explore the development of machine learning (ML) within a microservices setting for travel applications that offer real-time personalization to create user engagement and satisfaction. The system utilizes context awareness that is dynamic and relevant to the user, such as user profiles, location, and current activities to generate appropriate recommendations that develop with the user. It emerged from testing that there was a marked increase in engagement as evidenced by the click-through rates (CTR) which improved by 15% from what was observed in the conventional systems signifying that the model had worked. Furthermore, the accuracy of the recommendations in relation to the user preferences registered 85% accuracy for the implemented ML models and in addition to that the system employed microservices therefore tools for scaling up while integrating all the components kept the latency at less than 500 ms even during the busiest loads. In addition, the level of adaptation that was real-time was also improved as the system was able to react at a faster pace to changes in data, such as even the delay of flights, and made recommendations to passengers that were situationally appropriate.Finally, it is established that personalization enabled by machine learning contributes towards enhancing customer satisfaction, retention rate, and response time in the travel industry. The convergence of micro services and machine learning can be viewed as a great solution to the performance evolution and scaling requirements of data intelligence responsive markets like the travel industry which are highly active and interactive in nature.

One of the most important requirements in the installation of landfills is the identification and evaluation of suitable areas, a complex decision due to the various factors and criteria involved. The aim of this work was to present the main criteria and methods of Multicriteria Decision Analysis (MCDA), with emphasis on the Hierarchical Analysis Process (AHP) method, used in the selection of landfill sites in Brazil and other countries. The methodology consisted of creating a ranking of the most used criteria for selecting areas suitable for landfill, based on a Systematic Literature Review, with data from a search strategy in the journals Scopus, Web of Science and Google Scholar. The criteria selected were based on those established in standards, resolutions and laws in force in Brazil and criteria from other countries. Of the 89 studies selected, there was a recurrence among the main criteria used, namely: distance from water resources (96%), topographical characterization (90%), access (89%), distance from population centers and geology and existing soil types, both with 80%. As for the AHP method, studies have revealed other methods associated with it, such as the Geographic Information System (GIS), Weighted Linear Combination (WLC), Fuzzy, Fuzzy-AHP and Remote Sensing.

The aim of this study was to evaluate the growth and flowering characteristics of three Oncidesa cultivars under four distinct canopy treatments in a greenhouse environment. The control canopy treatment (canopy D) involved covering the greenhouse with black shade nets to reduce natural light penetration. Canopy B included black shade nets in combination with a transparent plastic screen on the sides to further modify light conditions. Additionally, two other treatments were applied: black shade nets with (canopy A) or without (canopy C) plastic sheeting, supplemented with daily lighting from high-pressure sodium lamps. Ten horticultural traits were measured to assess the impact of these canopy treatments on different genotypes at various growth stages. The results showed that canopy treatments significantly affected all measured traits of the Oncidesa cultivars, except for leaf width. Apollo plants treated under canopy A exhibited longer and thicker pseudobulbs, wider leaves, higher chlorophyll content, and a greater number of branches and florets compared to Honey Angel and Golden Star. Plants under canopy C showed superior growth traits across all cultivars, while those under canopies A and D exhibited better flowering quality. These findings suggest that different canopy treatments had varying effects on the growth and flowering traits of Oncidesa cultivars, with each cultivar showing distinct adaptability to specific canopy conditions.

(1) Background: General nutrition knowledge is a fundamental pillar of well-being and healthy lifestyles. The aim of this study was to investigate the effect of a weight loss intervention sup-ported with telemonitoring and telehealth coaching, on improving the general nutrition in over-weight and obese individuals. (2) Methods: A total of 30 participants have completed the trial at the 3-month visit, and a total of 25 have completed the trial at the 6-month visit. All participants have enrolled in a randomized controlled trial (RCT) and received a hypocaloric tailored diet and 3 online nutrition education sessions over a 6-month period. Participants were randomly di-vided into two groups, where the intervention group were supported with weekly telemonitoring and monthly telehealth coaching vs. a control group. The Arabic validated General Nutrition Knowledge Questionnaire (GNKQ) was used, which covered 4 sections; dietary recommenda-tions; food groups and nutrient sources; healthy food choices; associations between diet–disease and weight (3) Results: Findings showed that both intervention and control groups have im-provements in GNKQ scores over time, with the intervention group demonstrating significant in-creases in overall nutrition knowledge and specific areas such as the diet-disease relationship and weight management at 3 months. In addition, changes in GNKQ scores had a significant negative association with BMI and visceral fat percentage. The findings underline the benefits of supporting nutrition interventions with telemonitoring and telehealth coaching suggesting that increased nutrition knowledge may relate to lower body fat metrics. Still, small sample size and high attrition rate of participants were considered a significant limitation in the current study, where large populations is required to confirm reliability of the obtained findings.

The so-called controversy in elastohydrodynamic lubrication (EHL) regarding the nature of the shear dependence of viscosity, Eyring versus Carreau, is truly a controversy regarding the pressure and temperature dependence of the low-shear viscosity. The Roelands hoax became acceptable in EHL because ignoring the universal previtreous piezoviscous response made the traction calculated with the Eyring assumption appear to be reasonable. Traction and minimum film thickness calculations sometimes require the description of viscosity at pressures up to the glass transition pressure. There have been few measurements of viscosity at pressures up to the glass pressure. Therefore, a need exists for a piezoviscous model that extrapolates accurately and the Hybrid model fills that need. Here, an improved relation for the temperature dependence of the divergence pressure is offered and extrapolation is demonstrated for a polyalphaolefin.

Psychological momentum dynamics in tennis have triggered interest for a long time, but measuring their impact presents substantial obstacles. In this paper, we present an approach to quantify momentum that combines real-time winning probabilities, leverage, and an exponentially weighted moving average (EWMA). We test the method on a high-profile match between Carlos Alcaraz and Novak Djokovic, demonstrating how changes in leverage affect momentum. Furthermore, we use feature extraction methods from time series analysis to derive momentum-related characteristics, which are critical inputs for creating an eXtreme Gradient Boosting (XGBoost) binary classification model to predict game winners. The algorithm has an average accuracy of 84% and provides real-time predictions of each player’s chances of winning the match. Our findings indicate that momentum is a somewhat relevant element in forecasting match outcomes, highlighting its potential value in improving match prediction systems.

Background: Male factors contribute to approximately 50% of infertile couples. However, obvious causes remain unknown in many cases. This observational study aimed to investigate the associations of clinical and lifestyle parameters with sperm parameters. Materials and Methods: This study enrolled 41 men of infertile couples without obvious causes for male infertility from July 2023 to April 2024. Semen samples were evaluated for sperm number, motility, DNA fragmentation, and oxidative stress (OS) marker oxidation-reduction potential (ORP). Blood samples were analyzed for biochemical parameters, including advanced glycation end products (AGEs) and systemic OS marker diacron-reactive oxygen metabolites (d-ROMs). Skin-accumulated AGEs levels were identified with an autofluorescence method. Lifestyle factors were assessed with a lifestyle questionnaire. Results: Most of the participants were under 40 years old and non-obese with normal clinical and sperm parameters. Multiple regression analyses revealed that body mass index, serum d-ROMs, and semen ORP levels were independently associated with decreased sperm number. Additionally, serum zinc and semen ORP levels were associated with sperm motility. Furthermore, serum zinc and high-density lipoprotein-cholesterol levels were associated with sperm progressive motility and DNA fragmentation, respectively. The rest of the clinical and lifestyle factors, including skin-accumulated and serum AGE levels were not correlated with any sperm parameters. Furthermore, serum d-ROMs and semen ORP levels were not correlated with each other or any of the clinical and lifestyle factors. Conclusions: Our present study indicates that both systemic and local OS may be independently involved in sperm abnormality of healthy men without obvious causes for male infertility.

Ponte de Vilanova, a masonry bridge, was built in Allariz, Galicia, in the 13th-14th centuries. It is still standing. The structure, generally well-preserved, shows minor deformations and wear signs caused by environmental factors. To conduct a comprehensive assessment without impacting the bridge’s integrity, drones with thermal and underwater imaging technology were employed. Aerial inspections revealed vegetation growth and minor efflorescence (salt deposits) in some areas, while aerial thermography detected temperature variations along the stone joints, indicating moisture presence. The granite blocks comprising the bridge showed consistent quality and preservation. The underwater inspection confirmed that the bridge’s piers are well-set on the riverbed, with no major damage observed, ruling out immediate repair needs. This approach allowed a thorough evaluation of submerged parts without requiring divers, enhancing safety and reducing costs.

This study investigated the genetic parameters for serum IGF-I concentrations and growth traits in beef cattle. A divergent selection experiment for serum IGF-I concentration was initiated in 1989. One hundred spring-calving (50 high line and 50 low line) and 100 fall-calving (50 high line and 50 low line) Angus cows with unknown IGF-I concentrations were randomly assigned to the two divergent selection lines. For the 2009 through 2017 breeding seasons, the selection criterion in the two lines was changed from serum IGF-I concentration to high vs. low maintenance energy expected progeny differences (ME EPD). The number of records available for analysis varied from 2,056 for IGF-I concentration on day 42 of the postweaning performance test to 2,988 for birth weight, with the exception that only 617 records were available for IGF-I concentration at weaning. (Co)variance components were estimated for direct and maternal additive genetic effects using an animal model and multiple-trait, derivative-free, restricted maximum likelihood (MTDFREML) computer programs. Direct heritability estimates for serum IGF-I concentrations ranged from 0.34 ± 0.07 to 0.46 ± 0.07, whereas direct heritability estimates for weight traits ranged from 0.32 ± 0.05 to 0.39 ± 0.07. Maternal genetic effects on IGF-I were minimal, with estimates ranging from 0.02 ± 0.11 to 0.17 ± 0.05, and were similarly low for weight traits. Maternal permanent environmental effects were negligible for IGF-I but were larger for weight traits, especially for weaning weight (0.22 ± 0.04). Direct-maternal correlations for postweaning IGF-I concentrations were approximately -0.90. Genetic correlations of IGF-I concentration at weaning with postweaning IGF-I measures ranged from 0.63 to 0.90. Genetic correlations among IGF-I measures at the postweaning time points were also large (≥ 0.89). The genetic correlations of IGF-I with weight traits were generally low, with negative correlations observed for birth weight and small positive correlations for the other weight traits. Environmental and phenotypic correlations between IGF-I concentrations and growth traits were generally low, indicating that shared environmental influences are minimal and that circulating IGF-I has a modest impact on growth traits in beef cattle.

Iron-sulfur (Fe-S) clusters are essential cofactors found in many proteins in the mitochondria, cytosol, and nucleus of the cell. These versatile cofactors may undergo reversible oxidation-reduction reactions to enable electron transfers; they may be structural and confer stability to a folded protein; they may be regulatory and transduce an iron signal that alters the function or stability of a recipient protein. Of the nearly 70 proteins described in mammalian cells that bind Fe-S clusters, about half localize exclusively or partially to the nucleus, where they are required for DNA replication and repair, telomere maintenance, transcription, mitosis, and cell cycle control. Most nuclear Fe-S cluster proteins interact with DNA, including DNA polymerases, primase, helicases, and glycosylases. However, the specific roles of the clusters in the enzymatic activities of these proteins and their interplay with DNA remain a matter of debate. Defects in the metallation of nuclear Fe-S proteins cause genome instability and alter the regulation of cell division and proliferation, which are hallmarks of various genetic diseases and cancers. Here, we provide an inventory of the nuclear Fe-S cluster binding proteins and discuss cluster types, binding sites, the process of cluster acquisition, and potential roles of the cluster in the function of the proteins. However, many questions remain unresolved. We highlight critical gaps in our understanding of cluster delivery to nuclear client proteins, the potential for cluster repair, and the mechanistic roles that clusters play in these enzymes. Taken together, this review brings the focus to the nucleus of the human cell as a hotspot for Fe-S cluster proteins and aims to inspire new research in the roles of iron in DNA metabolism and the maintenance of genome integrity.

Objective: This study explores the impact of public policies that promote social equity and rights to nature on subjective well-being (SWB). The focus is on how sustainability policies, particularly on environmental protection and poverty reduction, have influenced life satisfaction in Ecuador. Design/methodology/approach: The research uses ordinal logistic regression models to analyze data from the National Survey of Employment, Unemployment and Underemployment of Ecuador. Life satisfaction is the dependent variable, while independent variables include income, education, job satisfaction, concern for the environment, and regional differences. Findings: The results indicate that the implementation of public social investment policies increases life satisfaction, particularly in regions such as the Coast. After the implementation of the policy, income, higher education, job satisfaction, and concern for the environment were associated with higher levels of subjective well-being. However, disparities persist, with lower life satisfaction among indigenous groups, women, the elderly, and those living in poverty. Originality/value: This study provides valuable insights into the relationship between sustainable policies and subjective well-being, highlighting Ecuador's unique constitutional framework. It offers lessons for other developing nations seeking to balance social equity, economic growth, and environmental sustainability in pursuit of improving overall quality of life.

The application of deep learning convolutional neural networks (CNNs) for rock image characterization has become a prominent method in petroleum geology both nationally and internationally. Precise and automated segmentation of rock sheet images is essential for effective analytical studies. This research addresses the limitations of traditional image segmentation methods, which suffer from low accuracy and high costs in analyzing reservoir rock flakes. We propose an advanced model based on the U-Net architecture, incorporating both attention mechanisms and residual networks. The inclusion of residual modules enhances the network's depth, while the Convolutional Block Attention Module (CBAM) improves feature representation by adjusting the weighting of learned information. Extensive experimental results demonstrate that this approach delivers superior segmentation performance, marking a significant advancement in the field of reservoir rock image analysis.

Lomami National Park, located in the Democratic Republic of Congo, is renowned for the integrity of its forest ecosystems, safeguarded by the absence of agricultural activities and limited road access. However, these ecosystems remain under-researched, particularly regarding forest cover dynamics. This research gap poses a significant challenge to establishing rigorous monitoring, which is essential for ensuring the long-term preservation of these valuable ecosystems. This study evaluates land cover dynamics within Lomami National Park through supervised classification of Landsat images from 2008, 2016, and 2024. Seven spatial structure indices were calculated to highlight the dynamics of landscape composition and configuration, distinguishing between the park's core and buffer zones. The underlying spatial transformation processes were identified using a decision tree approach. The results highlight a striking contrast in forest cover stability between Lomami National Park and its surrounding periphery. Within the park, forest cover has not only been preserved but has also shown a modest increase, rising from 92.60% to 92.75%. In contrast, the peripheral zone experienced a significant decline in forest cover, decreasing from 79.32% to 70.48% over the same period. This stability within the park goes beyond simply maintaining forested areas; it includes the preservation and strengthening of the spatial structure of forest ecosystems. For instance, edge density, which is a key indicator of forest edge compactness, remained stable in the park, fluctuating between 8 m/ha and 9 m/ha. However, in the peripheral zone, edge density exceeded 35 m/ha, suggesting that forest edges in the park are considerably more cohesive and intact than in the surrounding areas. Furthermore, the spatial transformation processes within each area underscore these contrasting dynamics. In the park, the main process was aggregation of primary forest patches, which reflects a trend toward continuous and connected forest landscapes. By contrast, the peripheral zone experienced dissection, indicating fragmentation and breakdown of forest patches. Together, these findings underscore the park's role in maintaining both the extent and the structural integrity of forest ecosystems, setting it apart from the more degraded periphery. These findings highlight not only the resilience of forest ecosystems in the face of limited anthropogenic pressures but also the crucial importance of effective land management and rigorous conservation strategies in addressing the challenges of urbanization and surrounding rural expansion. They further emphasize that well-adapted conservation measures, combined with specific demographic and socio-economic conditions, can play a pivotal role in long-term forest preservation and ecological stability.

This paper explores developing and deploying a fog computing platform tailored for industrial environments, focusing on predictive maintenance and advanced data management. The research investigates the integration of custom sensors and algorithms to meet specific industrial requirements, emphasizing the application of Long Short-Term Memory (LSTM) models for predicting equipment failures. The study enhances proactive maintenance strategies and real-time decision-making by applying machine learning techniques within a fog computing architecture. The findings underscore the platform’s potential to advance industrial data processing, edge intelligence, and maintenance practices, contributing to improved efficiency and reduced downtime in industrial operations.

Multiple recent studies, namely laboratory-based systematic review articles and applied or experimental research, have indicated the association, particularly in young or adult users, between results in measurements of short-term memory (STM) with the frequency and intensity of playing games using various forms of electronic support, globally known as digital games (DG). In this sense, we used an experimental study applied to the assessment of STM among university students, based on a comparative analysis between two groups, one of intensive users of digital games (IDG), and the other not (NIDG), statistically comparing the two results. The starting hypothesis holds that it is possible to statistically demonstrate that subjects who intensely use DG present, when tested in a laboratory context, higher results in STM tests. A self-assessment scale for the use of DG was used to define the experimental groups and, for data production, a subtest from the Wechsler Intelligence Scale for Adults measured for the Portuguese population, the digit span (or digit memory), experimentally adapted from a program produced using stimulus presentation software, SuperLab6. It was developed with 164 university students, with identical gender distribution, from a higher education institution. The results of the student t test indicated the presence of statistically significant differences between the groups. There are significant differences between the groups regarding STM according to the DOD. The potential applicability of the STM assessment program to different groups and objectives is concluded, particularly in an experimental context, and suggests its possible adoption as a Psychology tool in the areas of cognition.

Infrastructure financing in least-developed countries (LDCs) faces various obstacles, including fragile economic conditions, inadequate regulatory frameworks, and concerns over transparency and investor security. This deters private sector involvement, impeding vital infrastructure development and long-term economic progress. Blockchain technology, on the other hand, has demonstrated its ability to enhance transparency and improve operational efficiency. To bridge this gap, this study presents a novel blockchain-based framework for private financing governance in such projects. The proposed framework was built on three components: a foundational blockchain layer for secure transactions, a DAO layer for governance, and a node layer for user interaction. Moreover, a decentralized decision-making structure was outlined, which allocates voting power according to roles and reputations. The framework also introduces well-defined entry and exit criteria to ensure commitment and transparency. Further, it incorporates a member-driven proposal process and a reputation-based compensation system to encourage active involvement. Finally, a real-world case study was used to assess the framework's practicality and efficiency. According to the findings, the proposed framework has the potential to address and improve the identified gaps. The results of this study provide conceptual insights for LDC infrastructure project stakeholders to enhance the efficiency of infrastructure development in these nations.

This paper takes a novel approach to simulating fractional stride decreases in binary artificial neural networks (ANNs), addressing the limitations of fixed integer strides typically used in traditional convolutional neural networks (CNNs). By introducing a dynamic stride adjustment mechanism that reduces stride over time using either exponential or logarithmic decay functions, the network can simulate fractional strides, enabling the capture of increasingly fine-grained spatial details at deeper layers. This method improves output resolution, feature extraction without increasing network complexity, and possible solutions for binary ANN architectures where computational efficiency is critical. Using synthetic data, we analyze how both decay functions impact output resolution in a comparative experiment. Exponential decay produces a rapid initial stride decrease, leading to early significant resolution gains, while logarithmic decay offers a more gradual and controlled resolution increase. Both methods are enhanced by an attention mechanism that selectively emphasizes important regions in feature maps, improving spatial feature extraction efficiency. Our findings demonstrate that dynamic, asymptotic stride adjustment provides an effective method for simulating fractional strides in binary ANNs, with potential applications in areas such as object detection, segmentation, super-resolution, and autonomous systems, including spacecraft navigation and hazard detection. We also examine the trade-offs between the two decay methods in terms of resolution growth and computational overhead, contributing to ongoing research in adaptive ANN architectures and binary neural network design.

Background/Objectives: The World Federation of the Deafblind Global Report 2023 reports that many countries do not have a comprehensive identification, assessment, and referral system for persons with deafblindness, a combination of hearing and vision loss, across all age groups and geographic regions. The scan seeks to inform researchers, policymakers, and community-based organizations about the status of and gaps in sensory healthcare initiatives in Canada, with intent to raise awareness to enhance integration and coordination of eye and ear care services. Methods: We conducted an environmental scan of Canada’s healthcare system and current public health policies addressing vision and hearing care in Canada at federal and provincial levels. The scan was conducted using published literature searches from five scientific databases – Embase, Medline, PsycINFO, PubMed and CINAHL, in combination with the grey literature review of federal, provincial, and territorial government and non-profit organizations websites during April-October 2022. Out of 1257 articles screened, 82 studies included that met the inclusion/exclusion criteria. 13 reports were included in the grey literature search. Results: Thematic findings indicate stigma and discrimination towards individuals with disabilities and marginalized groups, including hearing, vision, or dual sensory loss, persist. Barriers to vision and hearing healthcare access include inadequate policies, underinvestment in vision and/or hearing services, limited collaboration and coordinated services between hearing and vision services, discrepancies in insurance coverages, and lack of health system support. Conclusions: This scan demonstrates the persistent barriers to vision and/or hearing services present in Canada, stemming from inadequate policy and limited-service coordination. Future work to address gaps, evaluate public education, and develop integrated sensory health care initiatives to enhance coordinate eye and ear care services as recommended in WHO Report on Hearing and Vision is imperative.

We study multivariate $L_2$-approximation problem $\textrm{APP}_d$ defined over a Banach space in the average case setting. The space is equipped with a zero-mean Gaussian measure with weighted covariance kernel which depends on parameter sequences \texorpdfstring{$\a=\{\az_j\}_{j\in\N}$ and $\ba=\{\bz_j\}_{j\in\N}$}{} with $1 0$. In this paper two interesting weighted covariance kernels are considered, which model the importance of the covariance kernels. Under the absolute error criterion or the normalized error criterion, we discuss \texorpdfstring{$(s,t)$}{}-weak tractability of the \texorpdfstring{$L_2$}{}-approximation problem $\textrm{APP}=\{\textrm{APP}_d\}_{d\in\N}$ with the above two weighted covariance kernels for some positive numbers $s$ and $t$ in the average case setting, where \texorpdfstring{$(s,t)$}{}-weak tractability means that how the information complexity depends on $d$ and $\va^{-1}$ for large dimension $d$ and small threshold $\va$. In particular, for all $s>0$ and $t\in(0,1)$ we find the matching sufficient and necessary condition on the parameter sequences \texorpdfstring{$\a=\{\az_j\}_{j\in\N}$ and $\ba=\{\bz_j\}_{j\in\N}$}{} to obtain average case \texorpdfstring{$(s,t)$}{}-weak tractability under the absolute error criterion or the normalized error criterion.

This paper discusses the design and evaluation of a set of heuristics (U+A) to assess the usability and accessibility of interactive systems in terms of cognitive diversity in an integrated way. Method: An analysis of the usability and accessibility aspects that have the greatest impact on people with cognitive disabilities is presented. A set of cognitive usability and accessibility heu-ristics targeted at people with cognitive disabilities is generated with the support of Generative Artificial Intelligence (GenAI). Result: Several interactive systems are evaluated to validate the U+A heuristics. A quantitative classification of the evaluated systems is obtained, i.e., the “U+Aindex”, which reveals the most and least usable and accessible systems for people with cognitive and learning disabilities. Conclusion: This study demonstrates that it is possible to car-ry out a classification of heuristic criteria considering accessibility, usability, and cognitive disa-bility. The results also show that the selection of criteria proposed by GenAI is optimal and that this technology can be used to improve usability and accessibility of interactive systems in a practical way. Yet, further evaluations are needed to support the results of the analyzed proposal.

In the aerospace industry, composites based on polymer or metal matrices and fillers made from granite, marble, lime and other rock powders are used. These fillers are similar in composition to cement and glass used for immobilization of radioactive waste (RW). In this paper, it is proposed to grind cemented or vitrified radioactive waste into micro or nanopowder and introduce this powder into a polymer or metal matrix to obtain a composite with improved physical characteristics, which can be used as a structural element or equipment element of automated space stations, satellites or rovers. In this way, it is possible to dispose of a huge amount of accumulated radioactive waste into space as a payload.

Background: Conventional echocardiography used to assess volumes of the left ventricle (LV) and left atrium (LA) along with mitral regurgitation grade is routine studies before and after transcatheter edge-to-edge mitral valve repair (Mitral TEER). Previous studies focus on LV parameter changes and comparison of the functions before and a few months following Mitral TEER implantation, as well as LA reverse remodeling, by assessing LV volumes. However, less is known regarding LA strain changes in the early phase after the procedure. The objective of the study was to assess the effect of Mitral TEER on LA strain early after TEER procedure. Methods: The retrospective study included 44 patients who underwent Mitral TEER. LA strain and volumes were evaluated by speckle tracking echocardiography at baseline and 24-48 hours following the procedure. Demographic, echocardiographic, and clinical characteristics were obtained and statistically analyzed. Results: LA global longitudinal strain (GLS) reservoir improved significantly (from 12.2±7 to 14.7±6.4, p=0.0079) after Mitral TEER. Significant improvements were also seen in LA volumes (LA maximal and minimal volume), which reduced by 17% and 22.5% respectively. LV GLS was significantly changed (from -9.8% to -12.8%, p

Traumatic brain injury (TBI) causes multiple cerebrovascular disruptions and oxidative stress, resulting in pathophysiological mechanisms. These are often accompanied by serious impairment in cerebral blood flow autoregulation, as well as neuronal and glial degeneration. Most currently available early antioxidant therapies lack sufficient efficacy. However, the application of a newly developed class of biologically compatible carbon-based nanoclusters, namely poly-ethylene-glycol-functionalized hydrophilic carbon clusters (PEG-HCCs), mitigate secondary injury after a TBI in early studies. In the present pilot study, we explore the efficacy of PEG-HCCs in an open blast rat model of TBI. Here we report on the neuroprotective efficacy of PEG-HCCs.

Atherosclerosis, a major risk factor for cardiovascular diseases is influenced by modifiable factors such as adiposity and blood cholesterol. Diet is crucial in these areas, particularly regarding antioxidant, inflammatory, and obesity effects. Coffee, a globally popular stimulant beverage has garnered significant attention for its potential impact on cardiovascular diseases. Recent insights reinforce the need to re-examine the relationship between coffee consumption and atherosclerosis progression. Coffee’s complex composition includes polyphenols, renowned for their antioxidant and anti-inflammatory properties, as well as potential weight-reducing effects. In addition, studies have demonstrated that certain coffee compounds such as chlorogenic acid, caffeic, p-coumaric, and ferulic acid can prevent atherogenesis by preventing oxidation of low-density lipoproteins. Conversely, diterpenes, found in some coffee brews, can elevate cholesterol levels posing risk to coronary health. Notably, coffee intake has been shown to influence gut microbiota diversity, potentially contributing to anti-obesity effects. This review explores the insights from preclinical and clinical studies investigating the potential mechanisms through which coffee consumption may reduce the risk of atherosclerosis. Highlighting the potential benefits of moderate filtered-coffee consumption, as well as the potential risks associated with excessive coffee consumption. Understanding this relationship is crucial for informing public health recommendations and guiding future research.

Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In those unseen or complex environmental information, agents often perform sub-optimally and require more training time. In response to these challenges, this manuscript pioneers a framework integrating zero-shot learning combined with hierarchical reinforcement learning to enhance agent decision-making in complex environments. Zero-shot learning enables agents to infer correct actions for previously unseen objects or situations based on learned semantic associations. Subsequently, the path planning component utilizes hierarchical reinforcement learning with adaptive replay buffer, directed by the insights gained from zero-shot learning, to make decisions effectively. Two parts are trained separately, so zero-shot learning is available in different and unseen environments. Through simulation experiments, the proposed method proves that this structure can make full use of environmental information to generalize across unseen environments and plan collision-free paths.

This study explored market actors’ perspectives on data sharing within Poland’s food passports pilot, which tested the feasibility of a digital system that provides reliable and comprehensive information on the origin, journey, and quality of agri-food products. Using thematic analysis, we identified factors influencing stakeholders’ willingness and motivations to share data. Our findings highlight the necessity of a clear value proposition for all actors within the production and distribution chain to invest resources and time in additional data exchange systems. For farmers, reducing burdensome reporting procedures and providing direct financial incentives were key motivators. Agri-food processing, transport and packaging companies viewed data collaboration as a practical tool to ensure high-quality raw materials and promote premium-priced food. Appropriate data management policies were critical for all stakeholders. While sustainability was recognized as important, opportunities for collaboration going beyond economic considerations were not widely explored. Additionally, concerns about data interpretation emerged as a significant issue. These findings suggest the need for data-sharing strategies that better align economic benefits with broader sustainability goals. Further research is also needed to examine how concerns about potential data misinterpretation limit stakeholders’ willingness to engage in data sharing initiatives.

Background: Conventional audiometry assesses hearing in the frequency range of 250–8,000 Hz, while humans can detect sounds from 20–20,000 Hz. Hearing sensitivity above 8,000 Hz can be assessed through “Extended high-frequency audiometry (EHFA)”, which is useful for early detection and monitoring of hearing loss and counseling for tinnitus patients. Despite its usefulness, age significantly affects hearing sensitivity. Currently, there are no standard hearing thresholds in the EHF range categorized by age groups in Thailand. This study aimed to determine normative EHFA data across different age groups in a healthy Thai population. Methods: The study included 134 participants aged 18–70 years, categorized into five age groups. All participants had normal hearing thresholds and type A tympanograms bilaterally. Following this, EHFA measurement was performed. Results: Across all age groups, there was an observed increase in median hearing thresholds as age increased. In comparison, no significant difference in median hearing thresholds was found between individuals aged 18–30 and >30–40 years. However, those over 40 years had significantly higher median thresholds across all frequencies compared to those under 40 years (p40–70 years. Median hearing thresholds for those aged 18–40 was ≤20 dB HL across most frequencies, except at 16,000 Hz (30 dB HL). In the >40–70 age group, thresholds ranged from 20 dB HL at 9,000 Hz to 70 dB HL at 14,000 and 16,000 Hz. However, no significant differences were found in hearing thresholds between sexes and ear sides. Conclusions: This study establishes normative EHFA data for healthy Thai adults, appropriately divided into two groups (18-40 and >40-70 years) for clinical use. Further studies are needed to assess EHFA thresholds in populations with hearing loss-related risk factors.

Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 stroke patients enrolled in the DEFINE cohort. We investigated the associations between baseline and post-intervention changes in EEG Theta/Alpha Ratio (TAR) and TMS metrics with upper limb motor functionality, assessed by five outcomes: Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Pinch Strength Test (PST), Finger Tapping Test (FTT), and Nine-Hole Peg Test (9HPT). Results: Our multivariate models identified that a higher baseline TAR in the lesioned hemisphere was consistently associated with poorer motor outcomes across all five assessments. Conversely, a higher improvement in TAR was positively associated with improvements in FMA and 9HPT. Additionally, increased TMS Motor Evoked Potential (MEP) amplitude in the non-lesioned hemisphere correlated with greater FMA-diff, while lower TMS Intracortical Inhibition (ICI) in the non-lesioned hemisphere was linked to better PST improvements. These findings suggest the potential of TAR and TMS metrics as biomarkers for predicting motor recovery in stroke patients. Conclusion: Our findings highlight the significance of TAR in the lesioned hemisphere as a predictor of motor function recovery post-stroke and also a potential signature for compensatory oscillations. The observed relationships between TAR and motor improvements, as well as the associations with TMS metrics, underscore the potential of these neurophysiological measures in guiding personalized rehabilitation strategies for stroke patients.

The incidence of melanoma, the most lethal form of skin cancer, has increased due to ultraviolet exposure. The molecular characterization of melanomas has shown a high mutational burden led to the identification of some recurrent genetic alterations. BRAF gene is mutated in 40-50% of melanomas and its role in melanoma development is paramount. BRAF mutations confer constitutive activation of MAPK signalling. The large majority (abou 90%) of BRAF mutations occur at amino acid 600; the majority are BRAFV600E mutations and less frequently BRAFv600K, V600D and V600M. The introduction of drugs that directly target BRAF-mutant protein (BRAF inhibitors) and of agents that stimulate immune response through targeting of immune check inhibitor consistently improved the survival of melanoma BRAFV600-mutant patients with unresectable/metastatic disease. In parallel, studies in melanoma stage II-III patients with resectable disease have shown that adjuvant therapy with ICIs and/or targeted therapy improves PFS and RFS, but not OS compared to placebo; however, neoadjuvant therapy plus adjuvant therapy improved therapeutic response compared to adjuvant therapy alone.

This paper presents a novel machine learning framework that applies evolutionary ensembles of artificial agents to mineralogical analysis and classification. The approach is based on hybridiza-tion techniques that combine diverse machine learning algorithms, creating large and effective communities of agents. These progressively mute and improve through crossover, hybridization, and selection, addressing the challenges of mineral recognition and classification from thin section images. By combining multiple machine learning techniques, the ensemble of agents autono-mously improves by evolving to adapt and enhance its ability to identify mineral species and classify different types of alterations. We detail the method, provide examples using synthetic and real data, and explore the potential to improve mineralogical analysis workflows through this dynamic, self-improving system.

The production of recombinant proteins in Escherichia coli is often challenged by cytoplasmic expression due to proteolytic degradation and inclusion body formation. Extracellular expression can overcome these problems by simplifying downstream processing and improving protein yields. This study aims to compare the efficiency of two Bacillus subtilis chitosanase signal peptides in mediating extracellular secretion in E. coli. We identified a naturally occurring mutant signal peptide (mCsn2-SP) from B. subtilis CH2 chitosanase (CH2CSN), which is characterized by a deletion of six amino acids in the N-region relative to the signal peptide (Csn1-SP) from B. subtilis CH1 chitosanase (CH1CSN). The CH1CSN and CH2CSN genes were cloned into the pET-11a vector and protein secretion was evaluated in E. coli BL21(DE3) host cells. Expression was induced with 0.1 mM and 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 30°C for one and three days. CH2CSN showed higher secretion levels compared to CH1CSN under all experimental conditions, especially with 0.1 mM IPTG induction for 3 days, which resulted in a 2.37-fold increase in secretion. Furthermore, it was demonstrated that mCsn2-SP is capable of secreting human Cu,Zn-superoxide dismutase (hSOD) in E. coli BL21(DE3) and successfully translocating it to the periplasmic region. This study represents the inaugural investigation into the utilisation of a naturally modified signal peptide, thereby corroborating the assertion that signal peptide deletion variants can influence protein secretion efficiency. Furthermore, the findings substantiate the proposition that such variants can serve as a viable alternative for the secretion of heterologous proteins in E. coli.

Digital Twins’ design frequently incorporates machine learning and, more recently, deep reinforcement learning techniques in order to interpret data and forecast future outcomes based on incoming data. However, because neural networks are typically considered a ”black box” model, doubts over the reliability of the output from deep learning models continue. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We created five-step rotations, or a succession of five successive crops, to compare these policies to those produced by a deep Q-learning technique. The purpose of the rotations is to maximize crop yields while upholding prescribed planting guidelines and preserving a healthy amount of nitrogen in the soil. We incorporated phenomena related to weather and price fluctuations into the reward signal to account for the potential impact of external factors on crop production. The deployed explainable tabular reinforcement learning methods outperform the deep Q-learning approach regarding collected rewards when the rewards are not perturbed. For the perturbed case, robust tabular reinforcement learning methods outperform the deep learning approach even more while maintaining human interpretable policies. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies. Digital Twins are often intertwined with machine learning and, more recently, deep reinforcement learning methods in their architecture to process data and predict future outcomes based on input data. However, concerns about the trustworthiness of the output from deep learning models persist due to neural networks generally being regarded as a black box model. In our work, we developed crop rotation policies using explainable tabular reinforcement learning techniques. We compared these policies to those generated by a deep Q-learning approach by generating five-step rotations, i.e. producing a series of five consecutive crops. The aim of the rotations is to maximise crop yields while maintaining a healthy nitrogen level in the soil and adhering to established planting rules. Crop yields may vary due to external factors such as weather patterns or changes in market prices, so perturbations have been added to the reward signal to account for those influences. The deployed explainable tabular reinforcement learning methods collect, on average, at least as much reward over 100 crop rotation plans when randomly starting with any crop compared to the deep learning model. For the perturbed case, robust tabular reinforcement learning methods collect similar amounts of reward across 100 crop rotation plans compared to the non-random reward setting, whereas the deep reinforcement learning agent collects even fewer rewards compared to learning on non-perturbed rewards. By consulting with farmers and crop rotation experts, we demonstrate that the derived policies are reasonable to use and more resilient towards external perturbations. Furthermore, the use of interpretable and explainable reinforcement learning techniques increases confidence in resulting policies, thereby increasing the likelihood that farmers will adopt the suggested policies.

Volunteering among older adults in the United States is rising, with rates up by 65% since 1974. With this tremendous growth in volunteering among seniors, examining how it affects them is essential. One such volunteering program we look at in this research is the Foster Grandparents Program (FGP) of the High Country, which allows older adults to volunteer at local schools to help in-need children. Previous research has shown how volunteering as an older adult positively impacts people's quality of life. This paper will explore the quality-of-life changes for the FGP older adult volunteers, explicitly comparing the quality-of-life changes with demographics and program satisfaction. This study looked at 93 participants of the FGP and their survey responses. We used cross-tabulation and correlation to look for relationships between demographics, satisfaction with volunteer experience, and overall changes in the quality-of-life of volunteers. Age, gender, and years of service were not good predictors of quality-of-life changes. On the other hand, overall satisfaction with the program proved to be the best predictor for change in quality of life. When satisfaction with positive program experiences is high, volunteers are more likely to have less symptoms of depression, state they have a greater sense of belonging to a community and were able to build a positive identity for themselves, all of which impacted their overall quality-of-life.

Federated learning (FL) is a decentralized machine learning paradigm that allows multiple local clients to collaboratively train a global model by sharing their model parameters instead of private data, thereby mitigating privacy leakage. However, recent studies have shown that gradient-based data reconstruction attacks (DRA) can still expose private information by exploiting model parameters from local clients. Existing privacy-preserving FL strategies provide some defence against these attacks but at the cost of significantly reduced model accuracy. Moreover, the issue of client heterogeneity further exacerbates these FL methods, resulting in drifted global models, slower convergence, and decreased performance. This study aims to address the two main challenges of FL: data heterogeneity, particularly in Non-Identical and Independent Distributions (Non-IID) clients, and client privacy through DRA. By leveraging the Lagrange duality approach and employing a generator model to facilitate knowledge distillation (KD) between clients, thereby enhancing local model performance, this method aims to concurrently address the primary challenges encountered by FL in real-world applications.

Endohedral metallo-borospherenes M@B40 have received considerable attention since the discovery of B40 in 2014. However, the coordination bonding nature of most of the actinide-doped endohedral An@B40 still remains in disputes or unexplored. Extensive first-principles theory calculations performed herein unveil the ground states of triplet U@B40 (1, C2v, 3A2), quartet U@B40- (2, C2v, 4B1), quintet Np@B40+ (3, C2v, 5A1), sextet Np@B40 (4, C2, 6A), septet Pu@B40 (5, C2v, 7A2), octet Am@B40 (6, C2v, 8A2), and octet Cm@B40+ (7, C2v, 8A2) at the coupled-cluster with triple excitations CCSD(T) level. Detailed principal interacting spin orbital (PISO) and adaptive natural density partitioning (AdNDP) analyses reveal their coordination bonding patterns and show that, with the numbers of unpaired α-electrons in parallel spins varying from nα = 2, 3, 4, 5, 6, 7, to 7 in these complexes, the percentage contribution of the An 5f-involved PISO pairs to overall coordination bonding interactions decreases monotonously from 41% to 1%, the contribution of An 6d-involved PISO pairs increases monotonously from 47% to 72%, while the marginal contribution of An 7s-involved PISO pairs remains basically unchanged (4~7%). The IR, Raman, and photoelectron spectra of the most concerned species are computationally simulated to facilitate their characterizations in future experiments.

Lactococcus spp. has emerged as a pathogen that is affecting global aquaculture, with L. garvieae, L. petauri, and L. formosensis causing piscine lactococcosis. While antimicrobials are commonly used to treat diseases in aquaculture, reports of antimicrobial resistance in fish isolates are increasing. However, little is known about the susceptibility patterns of Lactococcus spp. strains isolated from native fish species in Brazil. This study aimed to assess the antimicrobial susceptibility of these strains and establish a provisional epidemiological cut-off value for L. garvieae using the normalized resistance interpretation approach. A total of 47 isolates were tested: 17 L. garvieae, 24 L. petauri, and 6 L. formosensis. The isolates were classified as wild-type or non-wild-type (NWT) based on inhibition zone diameters. Isolates classified as NWT for three or more antimicrobial classes were considered multidrug-resistant, and the multiple antibiotic resistance (MAR) index was calculated. The results revealed heterogeneity in antimicrobial resistance profiles, with higher resistance to trimethoprim/sulfamethoxazole and norfloxacin. Resistance to other antimicrobials, including florfenicol and oxytetracycline (approved for use in Brazil), varied according to the bacterial species. Lactococcus petauri (87.5%) and L. formosensis (66.7%) showed the highest multidrug resistance, compared to L. garvieae (11.7%), along with higher MAR index values. These findings suggest that multidrug-resistant strains could pose future challenges in the production of native species, underscoring the need for ongoing monitoring of antimicrobial resistance and responsible use of antimicrobials in aquaculture.

The purpose of this paper is to design a mechanism to drive a propeller deform between an aerial shape and an aquatic shape. This mechanism can realize the deformation of blade angle, radius, blade twist angle distribution and blade section thickness. Inspired by the Kresling origami structure and utilizing its rotation-folding motion characteristics, a propeller hub structure with variable blade angle is designed. Through the motion analysis of spherical four-bar mechanism, a blade deformation unit (S-unit) with extensional-torsional kinematic characteristics is designed. A rib support structure fixed to the linkages of S-unit is designed to achieve the change of blade section thickness. Based on motion analysis, the coordinate transformation method is used to establish the relationship between propeller shape and deformation mechanism. The deformation of blade extension, blade twist distribution, blade section thickness are analyzed. The deformation ability of the proposed structure is verified by kinematic simulation and rapid prototyping based on 3-D printing.

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common neuropsychiatric disorders in childhood and can persist into adulthood. Despite its prevalence, diagnosis remains primarily clinical, and it is based on behavioural observation and standardised questionnaires but with few objective tools to validate diagnoses. The use of electroencephalogram (EEG) has emerged as a potential tool to identify neurophysiological biomarkers of ADHD, and research on EEG-based neurofeedback has shown promising results as a therapeutic intervention. This article reviews recent studies looking at the role of EEG in the assessment and treatment of ADHD, with a focus on key findings, current limitations, and future directions.

(1) Background: Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder with poorly detailed subtypes/dimensions, such as religious OCD (ROCD). To date, little is known about ROCD characteristics. This work aimed to describe the sociodemographic and clinical characteristics, as well as the religiosity and spirituality among Lebanese Muslim citizens diagnosed with OCD and exhibiting religious symptoms. (2) Methods: Outpatients were enrolled according to the following inclusion criteria: 18 years of age or above, diagnosis of OCD, presence of religious symptoms, and ability to read and understand the questionnaire. Exclusion criteria were as follows: other psychiatric disorders, and cognitive or physical impairments preventing participation. They filled in a questionnaire including the 25-item Arabic Scale of Obsessions and Compulsions (10 questions addressing obsessions, 10 questions addressing compulsions and five filler items, all of which rated on a 4–point Likert scale; higher total scores indicating increasing severity), the 26-item Spiritual Involvement and Beliefs Scale (rated on a 5-point Likert scale; higher scores indicating higher spirituality), and questions assessing the sociodemographic, clinical, and religiosity variables. (3) Results: Fifty adults (62% females, 52% aged between 18-29 years) completed the study. They had mild (26%), moderate (48%) and severe (26%) OCD symptoms. The majority had a family history of psychiatric disorders (48% OCD) and reported traumatic life events (58%). The majority attended religious school at least at one point in their life and described moderate to very high degree of self-religiosity and parental religiosity. Group comparisons (patients with mild vs. moderate vs. severe OCD symptoms) showed significant differences with regards to family history of psychiatric disorders (p=0.043), frequency of self-questioning if praying correctly (p=0.005), higher rating of partial ablution repetition (p=0.006), and frequency of partial ablution repetitions (p=0.041). No significant groups differences were noted with regards to sociodemographic or spirituality outcomes (4) Conclusion: Some religiosity variables (thoughts and practices) seem to be associated with OCD severity. The current results support the need for further research pertaining to ROCD.

This report summarizes the findings of a deep retrofitting modelling study of four government buildings across the UAE. Buildings envelope assessment, HVAC, and electrical systems design, installed conditions, maintenance practices and operation methods, and rigorous engineering analysis has been performed for the four buildings to produce calibrated energy models that confirm a good correlation between actual annual energy consumption and advanced simulated energy modeling results. IES-VE software, ASHRAE Guideline 14, and IPMVP were the basis of the energy model calibration based on actual data. Energy retrofitting options were applied to the calibrated energy models of the four government buildings where advanced computational modeling studies have been utilized to analyze different thermal envelope components and external fabric energy efficiency measures in conjunction with the actual HVAC systems. The results have shown that it may be financially challenging to implement a single envelope retrofitting measure on any of the four buildings due to high payback periods. Nevertheless, when conducting an optimization through a holistic building approach targeting the overall envelope enhancements, the financial modeling results provide significantly improved and provide reduced payback periods of the solutions, making them more attractive to investors. Based on the energy modeling results and the cost of the various proposed building envelope solutions provided by Saint-Gobain, the payback period in all cases varies between 15 to 20 years. However, huge savings in terms of carbon emissions are achieved by implementing the deep retrofitting measures, crucial to achieving carbon neutrality within the UAE built environment. Deep retrofitting is a promising segment of the execution roadmap as part of the UAE's decarbonization journey; even though it is capital-intensive upfront, it has a major impact on long-term operational savings. Moreover, it is often more cost-effective than building new and energy-efficient structures from scratch. The findings indicate that deep retrofitting can be a techno-financially viable solution if it is well-planned during major refurbishment or as part of building maintenance. It has significant potential to reduce carbon emissions while also providing a range of benefits such as operational cost savings, improved thermal comfort – a feature which has a direct impact on perceived efficiency and productivity at work, indoor air quality, improved asset value, reputational enhancement, and better choice for environmentally-cautious tenants or buyers.

Contrary to a prevailing dogma in twentieth-century neuroscience, emerging evidence suggests that the generation of new neurons continues throughout life, contributing significantly to crucial life functions owing to their robust ability to enhance neuroplasticity. Hippocampal neurogenesis occurs in the subgranular layer of the dentate gyrus, a region characterized by a specific microenvironment conducive to the proliferation of neuronal cells. A substantial body of evidence supports the belief that this phenomenon impacts various functions, including learning, emotional responses, and the formation of new memories. Diseases affecting these areas can significantly disrupt cognition and general behavior. For instance, in mesial temporal lobe epilepsy, aberrant neurogenesis has been observed, leading to increased cell proliferation, altered patterns of cellular integration, errors in information processing, and the formation of abnormal synaptic connections and transmissions. These abnormalities have the potential to generate epileptic seizures, underscoring the profound impact of these diseases on cognitive functions. Conversely, the chronic recurrence of seizures, can result in chronic recurrence of seizures, which can lead to a persistent reduction of neurogenesis in the epileptic brain. This reduction directly impacts various learning processes and the formation of hippocampus-dependent declarative memory. The mechanisms behind these processes remain fully elucidated and involve the delicate balance between neuronal excitation and inhibition, directly influencing brain performance and potentially leading to a decline in cognitive functions. In mesial temporal lobe epilepsy, this condition stands out as one of the most severe disorders affecting the well-being of epileptic patients. While memory is highlighted as one of the most affected functions, it is not the sole cognitive function compromised by this comorbidity. The alterations can extend from language to executive and spatial functions.

The study describes the features of manifestations and interrelationships of job stress, working capacity, professional performance and safety in loggers with shift work organization in the North. The study involved 402 loggers. The research methods include questionnaires and psychophysio-logical diagnostics of stress and working capacity, as well as questionnaires on the professional performance and safety of workers. The following statistical methods were used: correlation, mul-tivariate dispersion and multi-regression stepwise analysis. It was found that the severity of stress, speed, accuracy and operator working capacity, as well as well-being, activity and mood in logging equipment operators, timber truck drivers and maintenance specialists statistically differ signifi-cantly. Higher professional stress and more frequent decrease in professional performance are characteristic of truck drivers. Psychological and psychophysiological job stress and working ca-pacity parameters are associated with professional performance parameters of loggers. According to the results of a number of multi-regression analyses, reduced professional performance markers of loggers include the index of stress, satiety, monotony and fatigue, vegetative balance and stress (method of M. Luscher and coefficients of G.A. Aminev), operator working capacity and reaction speed (CVMR).

The Bloch sphere is a powerful geometrical representation of qubit states, fundamental to understanding quantum information theory. This paper explores the stereographic projection of the Bloch sphere, focusing on its utility in addressing decoherence and quantum error correction, two major challenges in quantum computing. By mapping the complex quantum states onto a two-dimensional plane, the stereographic projection simplifies the visualization and manipulation of qubit transformations. We investigate how this projection aids in identifying and correcting errors that arise from decoherence---such as phase flips and bit flips---while maintaining the integrity of quantum states. Additionally, we investigate the influence of decoherence on qubits through this projection, demonstrating how error correction protocols can be optimized by mapping errors onto the complex plane. The analysis highlights the potential of stereographic projection in developing more robust strategies for mitigating decoherence, thereby advancing the overall performance of quantum systems.

A review of recent advances in spacetime optics is given, with special emphasis on time-refraction. This is a basic optical process, occurring at a temporal discontinuity ou temporal boundary, that is able to produce various different effects, such as frequency shifts, energy amplification, time-reflection and photon emission. If, instead of a single discontinuity, we have two reverse temporal boundaries, we can form a temporal beam-splitter, where temporal interferences can occur. It will also be shown that, in the presence of an axis of symmetry, such as a magnetic field, the temporal beam-splitter can induce a rotation of the initial polarization state, similar to a Faraday rotation. Recent work on time-crystals, superluminal fronts and superfluid light will be reviewed. Time-gates, based on spacetime optical effects will be discussed. We also mention recent work on optical metamaterials. Finally, the quantum properties of time-refraction, which imply the emission of photon from vacuum, are considered, while similar problems in high energy QED associated with electron-positron pairs are briefly mentioned.

The environmental paradigm emphasizes the importance of political-economic and socio-economic factors that can significantly affect environmental health. One of the above-mentioned factors in the EU's so-called Eastern bloc is also the issue of unresolved environmental burdens. Our case study sought answers to the questions: How and why was Niklová Huta (Nickel smelter) Sereď created? How is it possible that, despite the knowledge of nickel's harmfulness to the environment, production continued for so long? Why, even 30 years after the end of production, the solid leaching residue (lúženec) landfill was not disposed of? The main methods of data collection were a content analysis of secondary data, a self-constructed questionnaire and semi-structured interviews. The research group for the quantitative part of the case study consisted of 354 respondents, of which 274 (77.40%) were women and 80 were men (22.60%). The data obtained by the questionnaire are presented through descriptive statistics. 12 informants participated in the quantitative research through semi-structured interviews, of which 9 were men and 3 were women. The interviews lasted between 30-45 minutes, were transcribed and we used qualitative thematic analysis to process them. The result of our case study is the creation of a conceptual model that captures the key points in the process of the formation of a toxic landfill and its persistence in the environment during the last 30 years.

Designing a ship hull is a complex procedure that involves constructing the basic hull using predetermined criteria or specified lines. Grasshopper, a visual programming environment for Rhinoceros 3D (CAD software), has a parametric modeling and analysis tool that automates the generation of ship hull and line plans using offset tables. This strategy reduces the necessity of manual design while enhancing efficiency and precision. Grasshopper is widely used for generating designs, enabling designers to visually create intricate algorithms by connecting components instead of writing code. This paper proposes a novel technique to automate the construction of ship hull and line plans with Grasshopper, leveraging its parametric capabilities to produce hull shapes and plan lines readily from offset table data. Future advancements may involve combining hydrostatic and hydrodynamic evaluations, improving hull performance, and automating the fairing process. A user-friendly interface and real-time collaboration tools can boost accessibility and design flexibility.

This paper presents the development of a Generative Adversarial Network (GAN) for the generation of synthetic dental panoramic radiographs to address the scarcity of data in dental research and education. A Deep Convolutional GAN (DCGAN) with Wasserstein Loss and Gradient Penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus for this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing was conducted to standardize input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated generated synthetic radiographs using a ranking system based on visibility and realism from 1 (Very Poor) to 5 (Excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. After averaging evaluation scores, a trade-off was observed between the model trained on non-denoised data, which showed the highest subjective quality for finer structures such as the Mandibular Canal and Trabecular Bone, and one of the models trained on denoised data, which offered better overall image quality, especially for Clarity &amp; Sharpness, and Overall Realism. The outcome serves as a foundation for further research into GAN architectures for dental applications. A GitHub repository containing the WGAN-GP model, source code, and synthetic data has been made publicly available at https://github.com/ViktorLaden/DataProjectGAN.