The article discusses the problem of digital transformation of school mathematics education. Two traditions of mathematical education in Kazakhstan are considered in historical perspective: “factory” and creative, research. The key features of the current state of the school education system in Kazakhstan in the framework of the mass school system and alternative systems are considered. The system of state final and current student assessment, elements of the digital economy of education are characterized. The socio-economic and political prerequisites for the possibility of significant changes in the education system are formulated. The necessity is substantiated for the effective development of the country in the 21st century of personality qualities brought up in creative mathematical education in the context of tasks that are “not known how to solve”. It is shown that due to the use of digital technologies, in particular AI technologies, the process of transformation of the education system can be started in modern Kazakhstan. This transformation will be aimed at bringing the education system in line with the needs of the modern economy and modern society. The main components and dimensions of this process are considered. In particular, the necessary changes in the system of training and professional development of mathematics teachers and the necessary elements of continuity of this system with a comprehensive school are indicated.

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of recognition research in several studies. This paper continues our previous work on a recognition method of finger movement impairment levels using EMG. In contrast to our previous work, this paper provided a better recognition result with an improved experimental setting, such as higher sampling frequency and number of EMG channels. A large number of patients was recruited to provide a reliable recognition performance. This paper employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The employed machine-learning algorithms are SVM, random forest (RF), and multi-layer perceptron (MLP). The highest average recall score across impairment levels of 0.73 was achieved by MLP_ISCV in the spherical grasp task. Subsequently, the highest average recall score of non-majority classes of 0.72 was achieved by the SVM_DS-ISCV in the mass extension task. The cross-validation result showed that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the finger movement impairment level.

Background: The patients with head and neck cancer often experience impaired sleep. Moreover, the treatment may negatively affect sleep quality. The aim of this observational study was to evaluate the sleep quality after treatment for head and neck cancer, and its relationship with quality of life and psychological distress. Methods: One hundred fifty-one patients who underwent treatment for head and neck cancer at our department were included in the study. Quality of life, sleep quality, risk of sleep apnea, sleepiness, pain and psychological distress were assessed by means of specific questionnaires. Results: The median follow-up was 30 months. A poor sleep quality was observed in 55.6% of cases. An association between PSQI global sleep quality and EORTC global health status was found. The DT, the HADS anxiety and the HADS depression scores were associated to PSQI global score, sleep quality, sleep latency, sleep disturbances, daytime dysfunction. Conclusions: Sleep disturbances, particularly OSA and insomnia, are frequent in HNC patients, and significantly impact their quality of life and psychological well-being. Given the effect of sleep on overall well-being, addressing sleep disorders should be a priority in the care of HNC patients.

Modifying surface topography to prevent surface-induced thrombosis in cardiovascular implants allows endothelialization, which is the natural thrombo-resistance of the blood-contacting surfaces and is deemed to be the only long-term hemocompatible material. We have adapted a simulation framework to predict platelet deposition on the modified surface and developed an optimization strategy to promote endothelial retention and limit platelet deposition. Under supraphysiological bulk shear stress, a maximum of 79% linear coverage was achieved. This study concludes that the addition of microtrenches promotes endothelial retention and can be improved through optimal selection of geometric parameters.

This review investigates the impact of bioactive molecules produced by probiotics on child health, focusing on their roles in modulating gut microbiota, enhancing immune function, and supporting overall development. Key metabolites, including short-chain fatty acids (SCFAs), bacteriocins, exopolysaccharides (EPS), vitamins, and gamma-aminobutyric acid (GABA), are highlighted for their ability to maintain gut health, regulate inflammation, and support neurodevelopment. Clinical studies demonstrate that specific probiotics and their metabolites can reduce gastrointestinal disorders, enhance immune responses, and decrease the incidence of allergies and respiratory infections in pediatric populations. Additionally, postbiotics—bioactive compounds from probiotic fermentation—offer promising benefits, such as improved gut barrier function, reduced inflammation, and enhanced nutrient absorption, while presenting fewer safety concerns compared to live probiotics. By examining the mechanisms of action and clinical evidence, this review underscores the potential of integrating probiotics and postbiotics into pediatric healthcare strategies to promote optimal growth, development, and overall health in children.

Background/Objectives: Home and community care (HCC) nurses experienced increased occupational challenges during the COVID-19 pandemic including increased workloads, job stressors and occupational risks like virus exposure. The objective was to elucidate what factors influenced nurses’ decisions to stay in their role, take a temporary leave, or exit HCC during the COVID-19 pandemic. Methods: A secondary analysis of data collected using a cross-sectional online open survey distributed among HCC Registered Practical Nurses across Ontario between June and September 2022 was conducted. The factors contributing to nurses’ decision to remain in HCC, temporarily leave, or exit the sector were evaluated using multinomial logistic regression (p < 0.05). Results: Of the 664 participants, 54% (n=357) stayed in the HCC sector, 30% (n=199) temporarily left, and 16% (n=108) exited the sector. Nurses with greater years of experience working in HCC and those who avoided infection were more likely to stay in their role in HCC, which may reflect strong relationships with long-term clients, opportunity and accumulated experience to increase income, and maintained good health. Nurses with higher levels of emotional intelligence were more likely to take leaves and exit HCC, suggesting that stepping away may have been a strategy to safeguard themselves. Conclusions: HCC leadership should prioritize the development of solutions to support nurses in the HCC workforce, including those with fewer years’ experience. This may promote nurses’ participation in the sector, particularly during times of heightened occupational challenges and crises like COVID-19.

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multi-spectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multi-spectral range. Furthermore, a number of formations are annotated at a pixel level for the 10 m bands (Red, Green, Blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the 4-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.

Background and Objectives: Kidney transplantation is the preferred treatment for children with end-stage renal disease (ESRD), but its outcome can be affected by urological complications, with incidence rates of 1%-27%. The aim of this study was to evaluate the occurrence of urological complications and their management in a cohort of pediatric kidney transplant recipients. Materials and Methods: A retrospective analysis on 178 patients undergone to renal transplantation at our Pediatric Kidney Transplant Center between 2011 and 2023 was conducted. Demographic and clinical data were analyzed. Urological complications were categorized as early, intermediate, or late based on their onset time. Results: Out of 178 patients, 28 (15.7%) experienced urological complications. Most patients (61%) had a pre-existing uropathy. Early complications (7-30 days) were all obstructive, namely ureterovesical junction obstruction and perirenal collections. Intermediate complications (1-3 months) comprised ureteral stenosis, symptomatic vesicoureteral reflux (VUR), and obstructive lymphocele. Late complications (&gt;3 months) included symptomatic VUR and ureteral stenosis, with one case leading to ureteral rupture. Early complications were often detected due to acute graft dysfunction, while late ones were mainly identified during routine clinical, laboratory, or ultrasound follow-up. Urological complications requiring surgical or endoscopic therapy were 13.4%. Most ureteral stenoses were treated with initial endoscopic stents, followed by definitive surgery. VUR was treated with endoscopic correction with a high success rate (75%), while open surgery was reserved for cases where initial treatments failed, or complications recurred. No clear correlations were found between patient characteristics and risk of urological complication. Urological complications required multiple diagnostic procedures and therapeutic interventions (+2.5 admissions in mean and approximately +24,000 euros) compared to an uncomplicated post-transplant course. However, they did not significantly impact transplant outcomes, with a graft survival rate comparable to the control group. Conclusions: Regular post-transplant follow-up is crucial, especially for patients with known risk factors, to allow timely detection and treatment of urological complications, avoiding detrimental effects on graft function and improving transplantation outcomes.

Bands of interdendritic porosity and positive macrosegregation are commonly observed in pressure die castings, with previous studies demonstrating their close relation to dilatant shear bands in granular materials. Despite recent technological developments, the micromechanism governing dilatancy in the high pressure die casting (HPDC) process for alloys between liquidus and solidus temperature region is still not fully understood. To investigate the influence of fluid flow and the size of externally solidified crystals (ESCs) on the evolution of dilatant shear bands in HPDC, various filling velocities were trialled to produce HPDC samples of Al8SiMnMg alloy. This study demonstrates that crystal fragmentation is accompanied by a decrease in dilatational concentration, thus producing an indistinct shear band. Once crystal fragmentation stagnates, the enhanced deformation rate associated with a further increase in filling velocity (from 2.2 ms^-1 to 4.6 ms^-1) localises dilatancy into a highly concentrated shear band. The optimal piston velocity is 3.6 ms-1, under which the average ESC size reaches the minimum and the average yield stress and overall product of strength and elongation reaches the maximum value of 144.6 MPa and 3.664 GPa%, respectively. By adopting the concept of force chain buckling in granular media, the evolution of dilatant shear bands in equiaxed solidifying alloys can be adequately explained based on further verification with DEM-type modelling in OpenFOAM. Three mechanisms for ESC enhanced dilation are presented, elucidating previous reports relating the presence of ESCs to the subsequent shear band characteristics. By applying the physics of granular materials to equiaxed solidifying alloys, unique opportunities are presented for process optimization and microstructural modelling in HPDC.

Recent advances in technology have propelled Artificial Intelligence (AI) into a crucial role in everyday life, enhancing human performance through sophisticated models and algorithms. However, the focus on predictive accuracy has often resulted in opaque, black box models that lack transparency in decision-making. To address this issue, significant efforts have been made to develop explainable AI (XAI) systems that make outcomes comprehensible to users. Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction, and increase task performance. Evaluation research has emerged to define and measure the quality of these explanations, differentiating between formal evaluation methods and empirical approaches that utilize techniques from psychology and human-computer interaction. Despite the importance of empirical studies, evaluations remain underutilized, with literature reviews indicating a lack of rigorous evaluations from the user perspective. This review aims to guide researchers and practitioners in conducting effective empirical user-centered evaluations by analyzing several studies, categorizing their objectives, scope, and evaluation metrics, and offering an orientation map for research design and metric measurement.

The membrane dipole potential that arises from the interfacial water and constitutive dipolar groups of lipid molecules modulates the interaction of amphiphiles and proteins with membranes. Consequently, its determination for lipid mixtures resembling the existing diversity in biological membranes is very relevant. In this work the dipole potential of monolayers, formed at the air-water interface, from pure or mixed lipids (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE), 1-palmitoyl-2-ol0eoyl-sn-glycero-3-phosphatidyserine (POPS), Sphingomyelin (SpM) and cholesterol) were measured and correlated with the mean area per lipid. The results showed that, as previously observed, cholesterol increases the dipole potential in correspondence with the decrease in the average area per lipid and that the replacement of phosphocholine lipids by sphingomyelin leads to a decrease in the dipole potential. We have also observed that 20 mol% of POPE induces a monolayer condensation which is not affecting the measured dipole potential, except when cholesterol is added to the mixture. Additionally, at the small mole fractions encountered in biomembranes, the presence of the negatively charged lipid POPS increases the dipole potential of monolayers even though an enhancement in the monolayer packing is not evident. This increase in the dipole potential due to POPS and cholesterol and the smaller dipole potential of monolayers containing large amounts of SpM generates a non-zero transbilayer dipole potential in the asymmetric plasma membrane of eukaryotic cells.

Green leaf volatiles (GLV) are important signaling compounds that help to regulate plant defenses against pests and pathogens. Made through the hydroperoxide lyase (HPL) pathway, they are rapidly produced upon damage and can signal this to other parts of the same plant or even plants nearby, where they can induce rapid defense responses directly or prime them against the impending danger. In this primed state plants can respond faster and/or stronger should pests or pathogens attack. However, while all proteins and genes involved in the biosynthesis of GLV have been identified, little is still known about how the first two steps in the pathway, e.g., oxygenation by a lipoxygenase (LOX) and subsequent cleavage by HPL are facilitated within the damaged tissue, resulting in the production of Z-3-hexenal (Z3al) as the first committed product of the pathway. Here, we provide evidence that several factors might be involved in the production of Z3al including pH, Ca2+, and an environment that is highly hydrophobic. We present a model in which the extraordinary circumstances that are present at the site of Z3al production are considered and shine new light on potential regulatory mechanisms.

Theobroma grandiflorum (copoazu) is a plant native to South America that is cultivated in countries within the Amazon region. Its phytochemical composition imparts characteristic organoleptic properties, making it an exotic fruit. In this study, headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) was employed to identify the volatile organic compounds (VOCs) produced by copoazu. Initially, optimal sample pretreatment conditions were determined using a design of experiments (DoE) approach. Analysis of the volatile profiles enabled the identification of 103 copoazu VOCs during three ripening stages. 97 VOCs were classified based on their chemical compound families using spectral correlation analysis in different libraries and databases, as well as their molecular networks. Additionally, a volatilomic analysis was conducted to study the variation in VOCs in the three ripening stages of copoazu. Through molecular network analysis, it was determined that the VOCs emitted by the fruit are correlated with the interconversion of compounds, which is observed when studying the metabolic pathways. The results provide a detailed analysis of the copoazu volatilome and will provide insight into the organoleptic characteristics of this Amazonian fruit. Esters and terpenes such as α-terpineol, β-pinene, linalool, 2-methylbutyl butanoate, 3-methylbut-2-enoic acid, 2-methylpentyl ester, ethyl 4-methylpentanoate, vinyl acetate, isobutyl hexanoate among others were found as potential biomarkers in the copoazu ripening process.

Urban public space comfort is vital for enhancing the quality of life and perceptions of livability, especially as climate change increasingly impacts outdoor thermal environments. This study investigates the use of ENVI-met, a 3D microclimate simulation tool, to assess thermal comfort concerning demographic factors like age and gender. While ENVI-met is often used to analyze urban microclimates, there is limited research on its application for simulating heat emission variations across different demographic groups. The findings highlight significant disparities in thermal stress vulnerability: elderly individuals are at higher risk due to elevated maximum PET values, while children experience broader ranges of thermal stress and higher baseline values. Despite cooling effects in the evening, children continue to face greater stress. The study emphasizes the need for tailored management strategies, as morning PET values are consistent but afternoon data reveals children’s ongoing vulnerability. Overall, the research calls for further investigation to enhance urban planning, thermal comfort strategies, and public health interventions amid rising temperatures. It advocates for expanding ENVI-met's capabilities to include diverse demographic factors, promoting a more comprehensive approach to urban resilience and well-being.

Given its substantial impact on project outcomes, increasing productivity in construction has been a major industry priority. Researchers studying construction have been focusing more on it and supporting various enhancement measures since determining the elements influencing labour productivity is a crucial step in this process. This research article uses questionnaires given to direct employees and midlevel employees to discover and comprehend the productivity elements influencing projects at a construction company located in the United Kingdom. After the questionnaire's answers were examined, it was simpler to pinpoint organisational and managerial flaws and to contrast the findings with those of previous productivity studies. The findings helped formulate suggestions for raising productivity. The primary conclusions show that truck availability, worker motivation dynamics, rework, tools, and machinery were the key factors influencing construction productivity. These findings are in line with earlier research conducted in the UK and the US. Salary expectations, the main reason for employee turnover in the organisation under investigation, was a component left out of previous studies. Finally, additional research seems to suggest that some productivity-affecting factors are common to international construction projects, supporting the gathering of data and the possibility of learning from experience in a variety of settings and even at different times of occurrence.

Usage of novel non-tobacco oral nicotine delivery products (ONDP) has been increasing among adolescents in the United States. It is presumed that they are less toxic than their tobacco containing counterparts, but that has not been examined in controlled studies. Most of the studies have focused on non-renal manifestations of tobacco consumption via different means. The renal manifestations of non-tobacco ONDPs are not very well known, especially in immunocompromised patients. Here, we describe a 19-year-old adolescent with kidney transplant who sustained acute tubular necrosis leading to advanced chronic disease apparently following the usage of non-tobacco ONDP. Further larger studies are needed to study the renal manifestations of these next generation products to raise awareness among public.

In Iran, fake news has been refined to weapon due to the popularity of new media and the growing social media-based assault on Afghan refugees. The recent geopolitical tensions and migrations, of course, have brought with them an increased number of false information reports and with them the rapid spread of negative stereotypes and the fueling of anti-Afghan sentiment. The article examines disinformation dispersion and its exacerbation in the digital space. It discusses in specifics ways in which misleading and hate speech against Afghan refugees pass through Iranian social media, in a thorough study of the psychological factors behind these narratives, that favors or fights such disinformation. The study screens the virtual screening areas that distribution of toxicity and disinformation by and engagement with social media. The disinformation technologies as well as the socio-political and historical contexts they derive from. Based on a detailed overview of the cyber-world, this study exposes the myriad challenges of suppressing false narratives and hate speech, focusing particularly on Afghan refugees in Iran. Besides, it points out to methods for both disallowing it to be distributed and curing the disease itself, basically, by redesigning digital agendas to present only the truth. The paper is aimed at unveiling the subversive forms of disinformation, armory with the data concerning digital tools and platforms, as well as input by external societies into the tapestry of the web that will construct a more inclusive digital discourse through critical engagement. Setting apart the genuine and not so wrong investigative procedures show great progress in society through these demanded efforts. Besides, the study impinges on more general issues such as media ethics and fact-checking, in what may be called the development of reliable information systems. This paper impinges on disinformation and humanizing toxic information, a vital aspect that currently has been managed only to a limited extend through the top-down disapproving or regulatory method and it creates strong ecosystem.

This study evaluates how automation and digital transformation affect Human Resource Development (HRD) strategies, focusing on AI-driven training programs, the effectiveness of remote work technologies, and the challenges related to digital transformation fatigue. Employing a mixed-methods approach that combines quantitative surveys (n=500) and qualitative interviews (n=30), the study offers comprehensive insights. Key findings reveal that 75% of respondents noted improved skill acquisition through AI, 60% expressed satisfaction with remote work technologies despite some challenges, and 55% reported experiencing digital transformation fatigue. The study suggests strategic investments in AI, robust remote work infrastructure, and wellness programs to mitigate fatigue, emphasizing the importance of balancing technological advancements with employee well-being.

Newcastle disease is a global disease, spreading quickly during outbreaks in poultry and leading to significant economic loss due to high morbidity, mortality, trade restrictions and embargoes. This disease has been present in India for the past 96 years, since 1928. Antigenic differences among Newcastle disease virus (NDV) strains are resulting in massive outbreaks in vaccinated and un-vaccinated poultry flocks around the globe. Synanthropic bird reservoirs contribute significantly to the extensive genetic diversity of the virus. This diversity, along with the vulnerability of many avian species, has led to diagnostic and vaccination failures. The genetic attributes of the circulating strains in India, meanwhile, remain mostly undisclosed. Ongoing evolution of spectrum of NDV genotypes in India, this review paper underscores the need for vigilant monitoring and adaptation of vaccination strategies to address emerging variants.

Granular material is one of the most common in nature. Recently, there has been a significant amount of interest in the granular materials, such as powders or sand. It is challenging to describe the motion of such kind of macroscopic size matter, because it behaves like solid, fluid or gas. Size division, pattern formation, avalanches, packing and convection - these are just a few examples from a wide range of observed phenomena that occur during the motion process of granular matter. Most of these processes are based by flow and no wonder that much effort has been devoted to obtain a hydrodynamic description in which granular materials are treated as a continuous medium. During the research of vertically shaken granular medium motion in an opened container it was found that experimental and numerical results are explained by hydrodynamic theory. This paper investigates the motion of a granular matter for a shallow, vertically shaken bed. We assume the Leidenfrost state as initial and the matter resembles a fluid heated up from below.A one-dimensional isothermal and a non-isothermal problem of granular material motion,simulatedby a hydrodynamic model are considered, and the material is assumed to be a continuous medium. The local temporal solvability of isothermal and non-isothermal initial boundary-value problem in Sobolev’s spaces is proved. This work presents a numerical study of the one-dimensional model of granular medium flow. The feature of this model is consideration of the Navier-Stokes equations accounting an interpolation form of VanderWaals constitutive equation for pressure. A finite-difference approximation for numerical solution is proposed.

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, including weak authentication, insufficient data protection, and firmware vulnerabilities. To address these issues, we propose a linguistic secret sharing scheme tailored for IoT applications. This scheme leverages neural networks to embed private data within texts transmitted by IoT devices, using an ambiguous token selection algorithm that maintains the textual integrity of the cover messages. Our approach eliminates the need to share additional information for accurate data extraction, while also enhancing security through a secret sharing mechanism. Experimental results indicate that the generated steganographic text effectively preserves the sentiment and semantic information of the cover text.

Metformin is a drug recommended as the first-line agent for the treatment of patients with type 2 diabetes mellitus due to its high efficiency and good safety profile. Additionally, literature has described its favourable effect in treating several other diagnoses. This review aims to summarize recent research on metformin modification and derivatization, which has led to agents with higher efficiency or expanded the application possibilities of metformin. Namely, buformin, phenformin, imeglimin, lixumistat, IM176 derivatives, moroxydine, proguanil, cycloguanil, metformin sulfenamides and sulphonamides, polypeptide derivative LysMET, artesinate-metformin conjugate AM2. Additionally, salts such as metformin hydrochloride, metformin hydrobromide, metformin hydroiodide, metformin acetate, metformin embonate, metformin threonate, metformin tartrate, metformin citrate, metformin mesylate and metformin maleate. The goal is to highlight recent research efforts and introduce perspectives for novel and effective biomedical substances within the still unexplored field.

Robot grasp modeling and implementation is one of essential abilities for a robot with embodied artificial intelligence. However, most existing deep learning-based grasp methods suffer a large number of parameters and heavy computation overheads. To address this issue, by fully exploiting the complementary capabilities of both CNNs and Transformers, we propose a smart grasp deep network with one-way fusion strategy via context path and spatial path (SGNet), which enjoys a lightweight structure, fast inference speed and easy deployment on devices with limited computation resources. Specifically, the context path employs lightweight depthwise separable convolution to achieve fast down-sampling while a novel DSFormer module mainly by integrating Transformer is to extract global and context-rich features. The spatial path efficiently fuses feature information from the context path in one-way manner and generate high-resolution feature maps via point-by-point convolution operations. Experimental results show the proposed model with only 1M parameters has a significantly overall performance, achieving 99.4% accuracy on Cornell dataset and 93.4% accuracy on Jacquard dataset, as well as within 12.5ms inference time.

As the cornerstone of severe accident management strategy, either in-vessel or ex-vessel retention of core melt (IVR or EVR) plays a pivotal role in the stabilization and termination of a severe accident, and ultimately secures the safety goal of “Practical elimination of large radioactive release” for light water reactors. In contrast to the IVR measures that are more or less identical in reactor designs, the EVR measures are quite different from design to design. This paper was intended to give a critical review on the EVR measures adopted in the reactor designs of VVER-1000, EPR, ESBWR, EU-APR1400 and APWR. The review study began with a general description of the existing EVR measures, including their principles, operational procedures and research efforts. We then focused our discussions on the pros and cons of each EVR measure, through the comparisons with the IVR and with the others in terms of simplicity, reliability and economy. We finally tried to identify the remaining issues and uncertainties in the qualification of the EVR measures, based on which potential design improvements and future research needs were recommended.

Atherosclerosis and cardiovascular disease are associated with high morbidity and mortality in industrialized nations. The Tyro3, Axl, and Mer (TAM) family of receptor tyrosine kinases is involved in amplification or resolution of atherosclerosis pathology and other cardiovascular pathology. The ligands of these receptors, Protein S (PS) and growth arrest specific protein 6 (Gas6), are essential for TAM receptor functions in amplification and resolution of atherosclerosis. The Axl-Gas6 interaction has various effects on cardiovascular disease. Mer and PS dampen inflammation, thereby protecting against atherosclerosis progression. Tyro3, the least studied TAM receptor in cardiovascular disease, appears to protect against fibrosis in post-myocardial infarction injury. Ultimately, PS, Gas6, and TAM receptors present an exciting avenue of potential therapeutic targets against inflammation associated with atherosclerosis and cardiovascular disease.

Lithraea molleoides fruit gum (LMFG) is a valuable product obtained from the total hydrolysis of the fruit. The hydrolysis process involves three methods: thermal (LMFGT), basic (LMFGB), and acid (LMFGA). Through these methods, the aim is to break bonds and de-esterify polysaccharides, resulting in increased solubility and decreased molecular weight. The resulting hydrolysates are then combined with pectins in a 1:2 ratios to form films. In this study, the focus is on utilizing the hydrolysates of Lithraea molleoides gums for film applications, with an evaluation of their structural and physicochemical characteristics. The films produced exhibit excellent mechanical properties and low water vapor permeability, as well as exceptional thermal stability. These properties make them highly suitable for industrial films in pharmaceutical and food applications. This research highlights the potential of LMFG-based films as a viable solution for various industrial needs due to their outstanding performance across multiple parameters.

Water scarcity presents one of the greatest challenges of our time. Especially in naturally water scarce regions, the need of additional water resources is rising, requiring innovative and site-adapted technologies. In this review, selected non-conventional water technologies for sustainable water management are strategically evaluated. Focused on a matrix-based assessment incorporating key indicators, the study discusses the availability, applicability, environmental impact, scalability, and economic viability of the selected technologies. Based on a comprehensive literature review, the methodology involves a systematic comparison of technologies for desalination, water reuse, groundwater utilization, agricultural reuse, and unconventional approaches like cloud seeding, dew water, and fog water harvesting. The indicators covering the respective categories are presented in the results. On this basis, the different technologies are analyzed by matrix-based evaluation, highlighting various strengths and weaknesses and providing insights into technology selection based on regional needs. The discussion interprets the findings, deriving implications for dry environments, acknowledging limitations and suggesting pathways for future research. Through a rigorous analytical framework, this study contributes to the discourse on sustainable water solutions, offering valuable insights for policymakers, researchers and industries during a decision-making progress.

This article proposes a superconducting shielding magnetic field model based on the Meissner effect of superconductors, which blocks magnetic fields. By utilizing the shielding effect of the superconductor on the magnetic field during the phase transition, the model achieves intermittent shielding of electromagnetic force, allowing the electromagnetic force to work intermittently on the moving magnet within the model during its operation. The model is analyzed on the basis of superconducting thermodynamic theory, with a focus on the electromagnetic energy of the system. It is concluded that the energy is not conserved during the operation of the model. To verify the correctness of the analysis, electromagnetic simulations and data analysis were conducted via the COMSOL electromagnetic module and Ampère's law superconducting model parameters, which yielded identical results.

A new family of inequality indices based on the deviation between the expected maximum and the expected minimum of random samples, called the nth Gini index is presented. These indices generalize the Gini index. At the same time, this family of indices and the S-Gini index are generalised by proposing the uv−Gini index, which turns out to be a convex combination of the S-Gini index and the Lorenz family of inequality measures. This family of Gini indices provides a methodology for achieving perfect equality in a given distribution of incomes. This is achieved through a series of successive and equal increases in the incomes of each individual.

Background/Objectives: Infusion pumps are critical in clinical settings, precisely delivering fluids such as medications and blood products. However, maintaining accuracy and preventing hemolysis is essential when administering blood, particularly to neonates. Conventional pumps may cause hemolysis due to pressure variations during transfusion. This study compares the blood transfusion accuracy and hemolysis rate between the conventional pump, Terufusion TE-LM700 (Terufusion) and the novel cylinder-type infusion pump, Anyfusion H-100 (Anyfusion); Methods: The study evaluated six different infusion rates (10-180cc/hr) and conducted 57 transfusion trials, 20 of which used a 1:1 blood-saline dilution. Blood transfusion accuracy was measured using research-grade packed red blood cells, and hemolysis rates were assessed before and after transfusion by chi-square tests and independent sample t-tests; Results: Anyfusion demonstrated an average transfusion error rate of 3.77%, compared to 4.00% for the Terufusion, with no statistically significant difference in hemolysis rates (p=0.697). Bland-Altman plots confirmed equivalent performance, with hemolysis rates of 0.566 ± 0.095% for Anyfusion and 0.518 ± 0.126% for Terufusion; Conclusions: Anyfusion provides accurate and reliable blood transfusion performance comparable to Terufusion, with no significant difference in hemolysis rates. Its integration of syringe and infusion methods shows potential for safer and more efficient transfusion practices, especially in pediatric and emergency settings.

Heat stress on workers wearing PPE(Personal protective equipment) in hot outdoor environments is of rising concern, especially in cases when rest breaks and clothing changes are impractical. Mist fan evaporative cooling could provide low-energy continuous cooling even during work activity. The cooling effect of a misting fan was compared to that of a fan alone, as well as natural convection. A thermal mannequin with heat flux sensors at 8 body locations was exposed to an outdoor misting fan while clothed in typical work clothes and PPE. Work clothes were dry or saturated with water to simulate sweat. The distance from the misting fan ranged from 4m (wetting common) to 7m (wetting unlikely). On average, the misting fan had a cooling effect of 0.31met (18.3W/m²) higher than natural convection when PPE with wet work clothes are worn, and 0.35met (10.63W/m²) higher when dry work clothes with PPE were worn. This equates to reducing thermal metabolic load from light industrial work to walking about in office work, or from standing to reclining. Under international standards for workers in hot environments based on metabolic rate and WBGT(wet bulb globe temperature), such as ISO 7243, this equate to an average of about a 0.35ºC (dry clothes with PPE) to 0.61ºC (wet clothes with PPE) increase in acceptable WBGT.

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system which enforces automatic deallocation of unused resources without using the garbage collector. In light of Rust's promise of safety, a natural question arises about the possible benefits of exploiting ownership to ensure memory safety of C programs. In our previous work, we developed a formal ownership checker to verify whether a C program follows ownership constraints. A C program that satisfies the ownership constraints is free of dangling pointers. In this paper, we further propose a static ownership-based safe memory deallocation approach, named SafeMD, to ensure memory-leak free in the C programs that satisfy ownership constraints defined in prior formal ownership checker. Benefitting from the C programs satisfying ownership constraints, SafeMD obviates alias and inter-procedural analysis during the finding of fixing patches. Also, the patches generated by SafeMD make the input C programs still satisfy ownership constraints. Usually, a C program that satisfies the ownership constraints is safer than its normal version. Our evaluation shows that SafeMD is effective in fixing memory leaks of C programs that satisfy ownership constraints.

Keywords: Security robot; kalman filtering; millimeter wave; inertial navigation system.

In recent years of technological advancements, the digitization of information has become a crucial factor for the growth and sustainability of small and medium-sized enterprises (SMEs), particularly in developing countries. These enterprises face a growing problem in accessing IT resources due to financial and lack of qualified IT personnel in their regions. This led to a growing interest in database and data warehouse technologies, which serve as foundational tools for organizing, storing, and analyzing vast amounts of data. This systematic review proposes to evaluate the impact of database and data warehouse technologies on organizational performance by employing the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review thoroughly synthesizes existing literature to identify the key benefits and challenges associated with the adoption of these technologies in SMEs. The proposed inclusion criteria were bounded by (1) publication date between 2014 and 2024, (2) research written in English, (3) research work focusing on evaluating the impact of database and data warehouse technologies on organizational performance, (4) research with a clear evaluation analysis of database and data warehouse technologies research framework. Following these guidelines, 150 eligible research studies were included. The analysis reveals a wide range of findings concerning the impact of database and data warehouse technologies. Key results include the identification of cost-efficiency trends, improvements in decision-making processes, enhancements in customer relationship management, and gains in operational efficiency. The findings provide actionable guidance for SMEs, policymakers, and IT professionals seeking to leverage these technologies to enhance organizational performance. To the best of our knowledge, this systematic review offers a comprehensive analysis that fills a gap in the existing literature on the impact of these technologies on organizational outcomes.

The diploid and autotetraploid of the eight tartary buckwheat cultivars were used to make a karyotype study by the conventional squash method to establish a reference for polyploid breeding. In this study, the eight cultivars exhibited similar karyotypic features between diploids and autotetraploids. The chromosome number in diploids was found to be 2n=2x=16, while 2n=4x=32 in autotetraploids. T6, T9, T18, T22, T27 and T36 possessed one pair of SAT chromosomes, whereas T25 and T39 had two pairs of SAT chromosomes. Compared with diploids, the total number of chromosomes and SAT chromosomes doubled in autotetraploids. However, the karyotype of autotetraploids remained unchanged in all cases which belonged to the 2A type. The karyotype of the same crop with different ploidy in diploid and autotetraploid buckwheat is the same. These eight cultivars' karyotype and chromosome number of autotetraploids are reported for the first time.

In the study of mountainous areas, mountain peaks play a vital role as geomorphic features that have significant influence on hydrological processes, vegetation distribution, and other important topographic characteristics. From the perspectives of geomorphology and hydrology, the river network water system is an interconnected entity that serves as a crucial component in describing regional topography, geomorphology, and hydrological features. The primary objective of this article is to extract water system information and mountain peak data in Chongli District, Hebei Province. To extract mountain peaks, coarse-resolution Digital Elevation Model (DEM) data is utilized to estimate their positions and initiate data sampling based on DEM elevations. Deep learning models are employed to avoid explicit feature extraction and instead implicitly learn from training data. By using weight sharing techniques, the training parameters of the network are reduced, achieving more accurate extraction and recognition of mountain peaks in Chongli District, along with their classification. For the extraction of the water system, the fast regional convolutional neural network algorithm from target detection is introduced to enhance the efficiency and accuracy of extracting water system information through image processing. This algorithm rapidly identifies the type, location, and width of the water systems. Additionally, the AlexNet transfer learning method is applied to locate the river skeleton within the extracted water system area. Morphological methods are then used to extract river morphology and calculate its length and width. Finally, a developed algorithm for water system information extraction detects the presence of water system coverage areas in Chongli District. Through experiments, a total of 2792 mountain peak data points were successfully extracted. In terms of water system information extraction, the Faster R-CNN and CNN-based methods demonstrate high efficiency and low detection rates. This study provides information services and data support for the operation and maintenance of the Chongli Winter Olympics. Furthermore, it serves as a reference for future research conducted by scholars in the Chongli area, offering insights into various fields such as environmental changes, vegetation classification, and geomorphic features.

Sulfated denitrification process has attracted more and more attention because it does not depend. on the addition of organic matter. Nowadays, the tailwater from urban wastewater treatment plant has high nitrogen concentration. In this case, we propose a sodium thiosulphate siderite autotrophic denitrifying (STSAD) biofilter for the removal of nitrogen from the tailwater of the urban wastewater treatment plant. Under the optimal conditions, the NO3--N removal rate was 100%, TN removal rate was above 95%. Many microorganisms were enriched on the surface of the ceramsite and siderite filler in the reactor, and the main denitrification microorganisms in the filter were Thiobacillus and Ferritrophicum. The process has certain technical and economic feasibility for denitrification in the tailwater of urban wastewater treatment plant, the cost is only 0.042 CNY/t(water), and the effluent TN and COD meet the requirements of water quality standards, so it is a suitable method.

Research in seaweed cultivation technologies aims to increase production and reduce costs, leading to more efficient and sustainable processes. In this study, we analyzed the outdoor production of Ulva compressa cultured in summertime at different stocking densities of 0.6, 0.8 and 1.0 kg Fresh weight (FW) m-2 in a raceway photobioreactor with 30 m2 surface (3000L) and its relation to pho-tosynthetic activity. Under the experimental conditions of high temperature (> 28-30 oC) and pH > 9 in culture water, higher seaweed density resulted in lower specific growth rate. The biomass pro-duction has been related to photosynthetic activity by using in vivo chlorophyll a fluorescence. Dynamic photoinhibition was observed at noon, which was less severe in cultures with higher algal densities. However, photosynthesis recovered in the afternoon. Seaweeds that were acclimatized for a week to the conditions of 1.0 kg FW m-2 stocking density showed an increase in biomass growth and absence of photoinhibition compared to non-acclimatized thalli. In conclusion, by using high densities (1.0 kg FW m-2), U. compressa presented the highest growth rate in a mid-scale raceway photobioreactor under stressed conditions related to the increase of photosynthetic capacity.

This review aims to describe the role of Limbic System-Associated Membrane Protein (LSAMP) in normal and pathophysiology and its potential implications in oncogenesis. We have summarized research articles reporting the role of LSAMP in the development of a variety of malignancies such as clear cell renal cell carcinoma, prostatic adenocarcinoma, lung adenocarcinoma, osteosarcoma, neuroblastoma, acute myeloid leukemia, and epithelial ovarian cancer. We also examine current understandings of how defects in LSAMP gene function may contribute to oncogenesis. Finally, this review discuss-es the implications of future LSAMP research and clinical applications. Recent Findings LSAMP has been originally described as a surface adhesion glycoprotein expressed on cortical and subcortical neuronal somas and dendrites during the development of the limbic system. It is categorized as part of the IgLON immunoglobulin superfamily of cell-adhesion molecules and is involved in regulating neurite outgrowth and neural synapse generation. LSAMP is both aberrantly expressed and implicated in the development of neuropsychiatric disorders due to its role in the formation of specific neuronal connections within the brain. Additionally, LSAMP has been shown to support brain plasticity via the formation of neuronal synapses and is involved in modulating the hypothalamus in anxiogenic environments. In murine studies, loss of LSAMP expression was associated with decreased sensitivity to amphetamine, increased sensitivity to benzodiazepines, increased hyperactivity in new environments, abnormal social behavior, decreased aggressive behavior, and decreased anxiety. Findings have suggest-ed that LSAMP plays a role in attuning serotonergic activity as well as GABA activity. Given its importance to limbic system development, LSAMP has also been studied in the context of suicide. In malignancies, LSAMP plays a significant role as a putative tumor suppressor, the loss of which leads to more aggressive phenotypes and mortality from metastatic disease. Loss of the LSAMP gene facilitates epithelial-mesenchymal transition, or EMT, where epithelial cells lose adhesion and gain the motile properties associated with mesenchymal cells. Additionally, LSAMP and the function of the RTK pathway have been implicated in tumorigenesis through the modulation of RTK expression in cell membranes and the activation of second messenger pathways and β-catenin.

Cisplatin (CDDP) remains a key drug for patients with advanced bladder cancer (BC), despite the emergence of new therapeutic agents. Identifying factors contributing to treatment resistance to CDDP is crucial. The acidity of the tumor microenvironment has been reported to be associated with treatment resistance and poor prognosis across various cancer types. Our objective was to investigate the effects of an acidic environment on BC cells and elucidate the mechanisms behind CDDP resistance. Our findings show that BC cells cultured in acidic conditions developed resistance to cisplatin as acidity increased. Notably, CDDP administered to BC cells in a pH 6.0 environment required twice the concentration compared to pH 7.5 to achieve equivalent toxicity. Using chloroquine and navitoclax, we identified the involvement of Bcl-2 and LC3B pathways in the acquisition of CDDP resistance under acidic conditions. Western blot analysis revealed that the activation of Bcl-2 and XIAP expression appears to inhibit both apoptotic and autophagic cell death. These results suggest that alleviating the acidity of the tumor microenvironment in clinical settings might enhance BC sensitivity to CDDP.

A novel approach to enhance Additive Friction Stir Deposition (AFSD) is presented through the integration of Multimodal Retrieval Augmented Generation (RAG). AFSD which is a solid-state metal additive manufacturing process characterized by its complexity, necessitating advanced analytical tools. A multimodal RAG system is implemented, integrating textual, and visual data. The methodology involves text extraction using LangChain's PyPDFLoader, followed by chunking and embedding generation via Google's Generative AI model. ChromaDB is utilized for vector storage and efficient information retrieval. The system, powered by Google's Gemini large language model, demonstrates proficiency in generating detailed explanations of friction-based deposition processes, identifying manufacturing techniques from visual data, and evaluating material microstructures. Information from multiple sources is synthesized to produce context-aware responses. Process control, quality assurance, and decision-making in AFSD are significantly enhanced by this approach. The system's capabilities are illustrated through the analysis of various friction-based deposition techniques and microstructure evaluation.

Batched network coding (BNC) is a practical realization of random linear network coding (RLNC) designed for reliable network transmission in multi-hop networks with packet loss. By grouping coded packets into batches and restricting the use of RLNC within the same batch, BNC resolves the issue of RLNC that has high computational and storage costs at the intermediate nodes. A simple and common way to apply BNC is to fire and forget the recoded packets at the intermediate nodes, as BNC can act as an erasure code for data recovery. Due to the finiteness of batch size, recoding strategy is a critical design that affects the throughput, the storage requirements and the computational cost of BNC. The gain of the recoding strategy can be enhanced with the aid of a feedback mechanism, however the utilization and development of this mechanism is not yet standardized. In this paper, we investigate a multi-phase recoding mechanism for BNC. In each phase, recoding depends on the amount of innovative information remained at the current node after the transmission of the previous phases was completed. Relevant information can be obtained via hop-by-hop feedback, then a more precise recoding scheme that allocates networking resources can be established. Unlike hop-by-hop retransmission schemes, the reception status of individual packets does not need to be known, and packets to be sent in the next phase may not be the lost packets in the previous phase. Further, due to the loss-tolerance feature of BNC, it is unnecessary to pass all innovative information to the next node. This study illustrates that multi-phase recoding can significantly boost the throughput and reduce the decoding time as compared with the traditional single-phase recoding approach. This opens new window in developing better strategies for designing BNC, rather than sending more batches in a blind manner.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. Addressing critical challenges in EMS solutions such as data overhead, single-point failures, nonstationary environments, and scalability, the proposed LSD-MADDPG effectively harmonizes individual building needs with entire community energy management goals. By leveraging and sharing only strategic information among agents, the proposed approach demonstrates to optimize the EMS decision-making processes, while enhancing training efficiency and safeguarding data privacy - a critical concern in the community setting. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings, aligning individual buildings’ energy management actions with overall community goals in a fair manner, highlighting its potential for future smart community energy management advancements.

Simulations of the pump response time refer to the determination of the time constant of the transient process when the flow and pressure change. The changes mentioned in the standards are precisely defined and prescribed by the "MIL-P-19692E" norms. The simulation showed that the time constants are within the permissible limits prescribed by these norms. The diagram shows the responses for the considered pump and for the case when the flow changes from Qn to Qmin and changes from Qmin to Qn. The time constants t1 and t2 are defined on the diagrams, with the parameters that the pump has. In addition, the standards define the pressure jump that occurs during the transient process as well as time constants. From the flow change diagram, it can be seen that the flow change is also very fast and takes place in time intervals shorter than 0.1 s. By evaluating the size of the time constants, t1 and t2, it can be concluded that they have a value below 0.05 s, which meets the regulations in that area. Also, the size of the jump pressure meets the regulations because it is only 1.2 M Pa above the nominal pressure.

The impact of climate change is undeniable: considering the significant carbon footprint of surgical activity, there is an urgent need to pursue sustainability in the operating room as well. At the Breast Surgery Clinic of the IRCCS Ospedale Policlinico San Martino in Genoa, a prospective study was conducted from September 2023 to May 2024 to evaluate the effectiveness of a series of measures aimed at reducing the environmental impact in the operating room. 34 women affected by invasive breast cancer T1-2, cN0 underwent quadrantectomy and sentinel lymph node biopsy (SNLB): 17 according to a standard, high environmental impact approach; 17 according to a green approach based on the "5 R rule" (reduce, reuse, recycle, rethink, research). The implementation of measures such as the use of indocyanine green (ICG) for SNLB, the use of local anesthesia and intravenous sedation, the minimization of waste production, the use of reusable gowns, drapes and surgical instruments and the separate collection of non-infectious plastic waste resulted not only in a reduced environmental impact, but also in benefits for patients, who were spared a hospital admission the day before surgery, the administration of a radiotracer, and, in 58.8% of cases, an overnight stay. Considering the alarming data regarding the threat to our health, it is both possible and necessary to adopt virtuous behaviors in the operating room that reverse the current trend of emitting greenhouse gases.

Demand for livestock products in East Africa is anticipated to triple by 2050. Therefore, sustainable intensification of livestock production systems for increased productivity is necessary in line with minimal negative environmental consequences. An agronomic field experiment was set up at the International Livestock Research Institute in Nairobi, Kenya, and the effects of organic and inorganic soil amendments on greenhouse gas emissions (particularly N2O) from a Humic Nitisol planted with Brachiaria brizantha cv. xaraes were evaluated between October 2018 and August 2019. The treatments comprised mineral NPK fertilizer, Lablab intercrop, FYM, FYM-BC, Bioslurry, and control. Fertilizer treatments were applied at a rate of 45 kg N ha-1 following each harvest. GHG emissions were measured using the static vented chamber technique. Treatment and season significantly influenced daily N2O emissions. The lowest (4.51±3.30 µg N m-2 h-1) and highest (27.16±3.61 µg N m-2 h-1) mean N2O emissions were recorded under NPK and Control treatments during the short rains and dry seasons, respectively. Cumulative N2O emissions and the corresponding yield-scaled emissions were similar across all the treatments but varied significantly (p < 0.001) between the wet and dry seasons. Cumulative N2O emissions were 0.31±1.49, 0.33±1.47, 0.33±1.74, and 0.37±1.74, 0.38±2.3 and 0.42±1.81 Kg N ha-1 under FYM-BC, Lablab, NPK fertilizer, and FYM, Bioslurry and control treatments respectively. The corresponding yield-scaled emissions were also higher during the wet (0.23±1.16 g N kg-1 DM) than in the dry seasons (0.16±0.50 g N kg-1 DM). Higher (-21.86±4.47 mg CH4-Ch-1) CH4 uptake was recorded under the control treatment whereas the lowest (-2.69±17.97 mg CH4-Ch-1) uptake was recorded under Bioslurry (P < 0.01). Treatment and season exhibited individual effects on daily CO2 emissions (P < 0.001), with a significant interaction effect (P < 0.001). The highest (157.5±28.76 mg CO2-C m-2h-1) and lowest (44.33±8.37 mg CO2-C m-2h-1) CO2 emissions were recorded under Control and FYM treatments during the October 2018-January 2019 and January-March 2019 HD. Since the experiment was newly established via ploughing a field which had been used as a permanent pasture during previous years, did not expect considerable yield differences between treatments. Yet, it is interesting to see first effects of fertilizer amendments, pointing to their potential as climate-smart forage intensification strategies. The study established that Manure + biochar is a better strategy for forage soil amendments in mitigating soil N2O emissions.

Interferons (IFNs) are cytokines involved in the immune response with synergistic regulatory action. They are therapeutics for various viral and proliferative conditions, with proven safety and efficacy. Their clinical application presents difficulties due to the molecules' size, degradation, and pharmacokinetics. We developed a controlled release system for viral respiratory tract infections. A core-shell nanoparticle was prototyped, which hydrolyzed the shell (polyvinylpyrrolidone), releasing the active ingredients IFN-α and IFN-γ. The core (chitosan) degraded slowly, with a controlled release of IFN-α. The primary and rapid effect of the combination of interferons ensured an antiviral and immunoregulatory response from day one, induced by IFN-α and enhanced by IFN-γ. The multilayer design demonstrated an optimal toxicity profile. This formulation is an inhaled dry powder targeted for the non-invasive intranasal route. This prototype would ensure greater bioavailability, controlled release, fewer adverse effects, and robust biological action thanks to the simultaneous impact of both molecules.

Lidar and camera sensors are widely utilized in autonomous vehicles (AVs) and robotics due to their complementary sensing capabilities—lidar provides accurate depth information, while cameras capture rich visual data. However, effective fusion of these sensors requires precise calibration, which is often challenging due to the differences in sensing modalities. This paper presents a self-calibration algorithm based on Singular Value Decomposition (SVD) to address this problem. Singular Value Decomposition is a powerful matrix factorization technique that helps identify key calibration parameters by decomposing matrices into singular vectors and values, representing essential calibration data. Our method not only automates the calibration process but also provides a reliable mechanism for detecting calibration errors, ensuring long-term accuracy and robustness in sensor fusion. The algorithm simplifies deployment in real-world applications by eliminating the need for manual calibration or external targets. Experimental results show that our approach improves sensor alignment and fusion accuracy, making it particularly useful for AV and robotic systems where maintaining precise calibration is critical.

(1)Background: Evidence on the association between air pollution and the risk of exacerbations of chronic obstructive pulmonary disease (COPD) is scarce and inconsistent. We aimed to analyze the association between long-term exposure to particulate matter with a diameter of less than 2.5 µm (PM2.5) and nitrogen dioxide (NO2) and exacerbation of COPD (COPD-E) in Bogotá, Colombia; (2) Methods: Retrospective cohort study of 722 COPD patients in the AIREPOC program of the Colombian Pneumological Foundation between 2018 and 2021. Exposure to PM2.5 and NO2 was estimated for residential locations using the inverse squared distance weighted regression (IDWR) method. Hazard Ratios (HR) were calculated for the first COPD-E during follow-up using Cox models, adjusted for meteorological, demographic, and clinical variables; (3) Results: COPD-E was associated with long-term exposure to PM2.5 (HR:1.03, 95%CI: 1.02-1.05) and NO2 (HR:1.05, 95%CI: 1.03-1.07) controlling for the effect of quality of life, history of COPD-E, Forced Expiratory Volume in One Second (FEV1) pre-bronchodilator and exposure to solar radiation. An association was found between COPD-E and concentrations ≥ 25µg/m3 of NO2 (HR:1.49, 95%CI: 1.12-1.98) but not with concentrations ≥15 µg/m3 of PM2.5; (4) Conclusions: This study suggests that people with COPD exposed to higher concentrations of PM2.5 and NO2 had an increased risk of COPD-E. Solar radiation was also associated with an increased risk of COPD-E. The results highlight the importance of continuing efforts to reduce air pollution.

Neuropsychiatric disorders are worldwide public-health concern. Schizophrenia is one of the well-known neuropsychiatric disorders, which may affect millions globally. Maternal immune activation might be one of the key factor for the development of neuropsychiatric disorders. Previously, a mouse model of neuropsychiatric disorders has originally been made, in which poly-I:C, sodium dextran sulfate (DSS) and κ-carrageenan (CGN) were used for the maternal immune activation of mice. In the neuropsychiatric disorder mice, a significant link between biochemical changes of p62 and/or GLAST in the mouse brain and the alteration of experimental behaviors. Therefore, potential therapeutic study has been achieved for the development of effective treatment against neuropsychiatric disorders with using butyric acid, trehalose, piceid, and biochanin A. As a result, some of the combination with these orally available molecules could be effective for the improvement of behavioral alterations as well as biochemical changes of p62 and/or GLAST in the mouse brain. Importantly, the correlation between behavioral score and p62/GLAST protein expression has been again recognized. The significant correlation between pathological behavior and the biochemical alterations would contribute to develop further innovative therapeutics for vaious psychiatric disorders.

Chemosensory proteins (CSP) are found in the olfactory sensory organs (antennae and maxillary palps) and/or gustatory sensory organs (labellum and legs) and long been accepted to function through binding ‘odorants’. Yet, the same CSPs are also expressed in many tissues other than olfactory and gustatory organs, such as gut, brain, fat body, wing, epidermis, corpora allata, pheromone gland, prothoracic gland, etc. In this review, we suggested the rename “chemosensory protein (CSP)” to “4 Cysteines Soluble Protein (4CSP). To support our idea, we reviewed CSPs from several aspects: their primary sequences, existence of CSP in insects, hexapods, and bacteria, expression during development and in other tissues, evolution of CSP genes, genomic mining, their ancestral functions and lipoid binding, function outside the chemosensory/binding aspect, their intracellular function, and from structure to function. The scope of the review per se is broad, and this is especially true given the volume of data that has been gathered on CSP expressed in ways that are not consistent with the olfactory paradigm. We discuss the Mp10 story in aphids, our research on insecticide resistance, and other lipid transport and immunity-related processes. There is substantial evidence to support the non-chemosensory aspects of CSPs, which is where the majority of discussions lean. There are still very few, if any, statements outlining the aspects of CSPs’ chemosensory properties, specifically how they activate olfactory receptor neurons (ORNs). We present an analysis showing a stronger correlation between them and the actin complex family proteins, mucins, and translation initiation factors. An additional argument is the existence of CSPs outside of insects. Therefore, strong arguments in favor of renaming chemosensory proteins are becoming evident here, outweighing the drawbacks.

Objectives: To assess the role of soluble PD-L1 and PD-1 in combination with mRNA transcripts of PCA3, PSMA, AR genes in diagnostics of clinically significant prostate cancer (PCa). Methods: For 68 PCa patients, plasma sPD-L1 and sPD-1 were measured by ELISA method and the urinary PSMA, PCA3 and AR were tested using RT-qPCR. Results: PSMA and AR were iden-tified as the most reliable biomarkers for predicting clinically significant prostate cancer, with AUCs of 0.81 and 0.78, respectively. Additionally, the expression levels of PSMA and PCA3 were associated with the pT3 stage. Despite the diagnostic potential of mRNA transcripts alone, the ad-dition of sPD-L1 and sPD-1 significantly enhanced diagnostic accuracy. Combination doubled the specificity of PCA3 from 44.1% to 88.0%, compared to PCA3 alone. sPD-L1, sPD-1 and AR resulted in the best distinction between clinically significant and insignificant prostate cancer, achieving an AUC of 0.97, an accuracy of 0.95, a sensitivity of 100%, a specificity of 95%, revealing the potential of AR in distinguishing clinically significant PCa, more effectively than PSMA and PCA3. Our find-ings highlight the significant potential of sPD-1 in enhancing diagnostic accuracy when added to a triple gene panel, achieving an AUC of 0.90 compared to an AUC of 0.78 without sPD-1. This un-derscores the value of sPD-1, which has been less explored than sPD-L1 in cancer diagnostics. Con-clusions: sPD-L1 and sPD-1 has a potential to significantly enhance the accuracy of PCa diagnostics when added to the diagnostic panel, alongside PSMA, PCA3, and AR mRNA transcripts.

Background: Falls in older adults are prevalent and can result in an injury affecting their quality of life. Whether a fall is fatal or not, it has a high financial impact. Older adults with dementia are more likely to fall than their cognitively healthy counterparts, increasing their risk of serious injury or even death. This quality improvement project developed, implemented, and evaluated an evidence-based fall prevention bundle to reduce preventable falls in a long-term memory care unit.; Methods: The interventions that were implemented includes fall risk screening, physical exercise, vitamin D supplementation, purposeful hourly rounds, and post-fall huddles. Kurt Lewin’s force field analysis guided the project’s implementation. Nurses, healthcare providers, certified nursing assistants, restorative aides, and life enrichment staff from all shifts at a long-term memory care unit participated in the project. The participants implemented the evidence-based fall prevention bundle based on their scope of practice. They participated for 12 weeks. The frequency of falls was compared three months before, during, and after implementation. Paired t-test and independent samples t-test were used to analyze statistically significant differences between variables.; Results: Thirty staff members attended educational sessions, and 22 residents received interventions. Fall rates dropped 50% during the project's implementation but increased subsequently. The difference in the number of falls before, during, and after implementation was not statistically significant.; Conclusions: Staff working in a long-term care facility may help reduce fall rates using evidence-based interventions, but measures should be taken to sustain the initiative's implementation.

The use of probiotics has been gaining popularity in terms of inclusion into human diets over recent years. Based on properties exerted by these living organisms, several benefits have been elucidated and conferred to the host. Bacteria has been more commonly used in probiotic preparations, in comparison to yeast candidates, however, yeast exhibit several beneficial properties such as the prevention and treatment of diarrhoea, production of antimicrobial agents, prevention of pathogen adherence to intestinal sites, maintain microbial balance, modulation of the immune system, resistant to some antibiotics, amongst others. This review details the use of yeast organisms as biotherapeutics and has a special focus on production considerations and their formulation into different delivery formats.

Recent clinical trials using synthetic incretin hormones, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) receptor agonists have demonstrated that these treatments ameliorated many complications related to obesity, emphasizing the significant impact of body weight on overall health. Incretins are enteroendocrine hormones secreted by gut endothelial cells triggered by nutrient ingestion. The phenomenon that oral ingestion of glucose elicited much higher insulin secretion than intra-venous injection of equimolar glucose is known as incretin effect. This also alludes to the thesis that food intake is the root cause of insulin resistance. Amylin is co-expressed with insulin from the pancreas β-cells but does not have insulinotropic function. Amylin suppresses glucagon secretion, slowing gastric emptying, and suppressing the central nervous system (CNS) reward center leading to weight loss. However, amylin can self-aggregate and cause serious cytotoxicity and may cause β-cell apoptosis. Glucagon is secreted by the pancreatic α-cells and participates in glucose homeostasis in glucose-dependent manner. In hypoglycemia, glucagon increases blood glucose level by glycogenolysis and gluconeogenesis and inhibits glycogenesis in the liver. Some triple agonists in combination with glucagon and dual incretins are already being developed. These advances bring to the question “Are the benefits of these anti-obesity treatments sustainable?” Chronic agonism may decrease the number of receptors. Also, long-term stimulation may cause β-cell exhaustion and failure. Additionally, instead of endogenous control of the appetite, exogenous control of satiety and food intake may hinder the long-term sustainability of these treatments. We will discuss the incretins’ mechanism of action, challenges, and future directions.

Water desalination technology has emerged as a critical area of research, particularly with the advent of more cost-effective alternatives to conventional methods such as reverse osmosis and thermal evaporation. Given the vital importance of water for life and the scarcity of potable water for agriculture and livestock—especially in the Kingdom of Saudi Arabia—the capacitative deionization (CDI) method for removing salt from water is highlighted as the most economical choice compared to other techniques. CDI applies a voltage difference across two porous electrodes to extract salt ions from saline water. This study will investigate water desalination using CDI, utilizing a compact D.C. power source under 5 volts and a standard current of 2 amperes. We will convert waste materials like sunflower seeds, peanut shells, and rice husks into activated carbon through carbonization and chemical activation to improve its pore structure. Critical parameters for desalination, including voltage, flow rate, and total dissolved solids (TDS) concentration, have been established. The initial TDS levels are set at 2000, 1500, 1000, and 500 ppm, with flow rates of 38.2, 16.8, and 9.5 mL/min across various voltage settings of 1.5, 2, and 2.5 volts, applicable to both direct and inverse desalination methods. Results indicate that desalination processes function effectively at a flow rate of 9.5 mL/min, achieving a performance efficiency of 65%, while a voltage of 2.5 volts yields a performance level of 44%.

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have better theoretical geolocation accuracy than 1 meter but maybe unrealizable with less accurate external data, including the atmospheric parameters and the ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a set of geolocation procedures, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 meter for TerraSAR-X and 2 meters / 4 meters for the Tianhui-2 reference / secondary satellite. Based on the WorldDEM and the SRTM, an extra geolocation error of 2 and 4 meters will be introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that the tropospheric error and the ground elevation error are the primary factors influencing the geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.

The adversarial vulnerability of deep learning models is a critical issue that must be addressed to ensure the safe commercialization of AI technologies. Although numerous studies on adversarial defense methods are actively being conducted from various perspectives, most of them still provide limited robustness, and even the relatively trusted adversarial training is no exception. To develop more reliable defense methods, ongoing research exploring the properties and causes of adversarial vulnerabilities is essential. In this study, we focus on a hypothesis regarding the existence of adversarial examples: The adversarial examples represent low-probability “pockets” in the manifold. Assuming that the hypothesis holds true, we propose a method for generating perturbation: “mixed perturbation (MP)”, which aims at discovering diverse pocket samples in a defensive perspective. The proposed method generates perturbations by leveraging information from both the main task and auxiliary tasks in multi-task learning scenarios, combining them through random weighted summation. The generated mixed perturbation intends to maintain the primary directionality of the main task perturbation to improve the model’s main task recognition performance while introducing variability in the perturbation directions. We then utilize them for adversarial training to form more robust decision boundary. Through experiments and analyses conducted on five benchmark datasets, we validated the effectiveness of our proposed method.

Background/Objectives: Pulmonary diseases are common in patients with thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA). Although high prevalences of chronic obstructive pulmonary disease and lung cancer (LC) are known, the prevalence of these and other pulmonary diseases regarding their relation to outcome of TAA and/or AAA are not determined. Methods: Pulmonary diseases present at aortic aneurysm (AA) diagnosis and follow-up periods and cause of death of 952 patients with TAA, AAA, or TAA+AAA (including thoracoabdominal AA) treated at our institution in Japan, were retrospectively analyzed. Cox regression analysis was used to investigate potential risk factors of mortality. Results: Mean patient age was 72.4 years, and median follow-up was 4.92 years. At diagnosis, 528 (55.5%) patients had pulmonary diseases, including emphysema without interstitial lung disease (ILD) or LC, LC, idiopathic pulmonary fibrosis (IPF) without LC, non-IPF ILD without LC, and interstitial lung abnormalities (ILAs) without LC in 250, 85, 65, 15, and 58 patients, respectively. During follow-up, LC and acute exacerbation (AE) of IPF developed in 50 and 12 patients, respectively. In 213 patients who died, there were 45 (21.1%) aortic disease-related deaths. Other causes of death included LC (27.7%), cardiovascular event (9.4%), pneumonia (5.6%), and interstitial lung disease (4.7%). In a multivariate Cox regression hazard model, age, larger maximum aneurysm diameter, and coexisting LC, IPF, or concomitant cancer were associated with poor prognosis. Conclusions: In patients with AA, not only age and aneurysm diameter but also coexisting LC and IPF were prognostic factors for mortality.

Cardiovascular disease (CVD) is the primary cause of mortality globally with a multifactorial etiology that involves epigenetics. Chromobox 3 (CBX3), the major isoform of heterochromatin protein 1, is involved in intricate epigenetic mechanisms affecting congestive heart failure. In patients with CVD affected by lung cancer risk, CBX3 exerts a sophisticated mechanism of action, suppressing the proliferation, migration, and formation of neointima in vascular smooth muscle cells (VSMCs) by affecting the Notch3 pathway, indicating a potential protective function against vascular remodeling and atherosclerosis. However, the broader impact of CBX3 on endothelial function, as well as its effects on monocyte/macrophage and lymphocyte infiltration and function within the arterial wall, remain poorly understood. Since very little is known so far, more definite research would be needed to reveal the fine mechanisms of CBX3 action, along with its relationship in molecular processes and prospects as a biomarker. Specifically, CBX3 biological features could be examined to gain a greater insight into CVD risks. This review outlines the role of CBX3 in mechanisms associated with CVD and feasibility for optimizing pre-existing therapy and developing new therapeutic strategies based on personalized medi-cine.

Accurate determination of an individual’s unique human leukocyte antigen (HLA) allele holds important significance in evaluating the risk associated with autoimmune and infectious diseases, such as human immunodeficiency virus (HIV) infection. Several allelic variants within the HLA system have been linked to either increased protection or susceptibility in the context of infectious and autoimmune diseases. This study aimed to determine the frequency and association of HLA alleles between people living with HIV (PLHIV) as the case group and HIV-uninfected Peruvian individuals with high-risk behavior of sexual transmission diseases as the control group. Whole exome sequencing (WES) was used to determine high-resolution HLA allelotypes using OptiType and arcasHLA tools. The HLA alleles present in HLA classes I (A, B, and C loci) and II (DPA1, DPB1, DQA1, DQB1 and DRB1 loci) were determined in a cohort of 59 PLHIV (cases) and 44 HIV-uninfected individuals (controls). The most frequent HLA alleles were A*02:01, DPB1*04:02 and DQB1*03:419 at 36%, 30% and 28% prevalence in general population. We found that C*07:01 (p = 0.0101; OR = 10.222, 95% IC: 1.40–74.55), DQA1*03:02 (p = 0.0051; OR = 5.297, 95% IC: 1.48–19.02) and DRB1*09:01 (p = 0.0119; OR = 4.788, 95% IC: 1.39–16.44) showed an association with susceptibility to HIV infection, while DQB1*03:419 (p = 0.0478; OR = 0.327, 95% IC: 0.11–0.96) was associated with protection from HIV infection. Our findings contribute to the knowledge of HLA allele diversity in the Peruvian population (around 70% South American indigenous ancestry), lay the groundwork for further valuable large-scale use of HLA typing and offer a novel association of HIV infection that is relevant to vaccine studies.

Wearing masks to protect against communicable diseases is an effective tool used in many countries affected by the COVID-19 pandemic. Antibacterial activity, antibacterial efficiency, microbial purity and breathability properties of medical disposable masks are very important. Ag is most commonly applied to antimicrobial textiles, but several studies are known about Ag nano-particles. In this work three antimicrobial additives were used. One of them Rudolf GmBH's RUCO-BAC AGP with silver chloride (AgCl) active ingredient also has antimicrobial activity. Four compositions of the binders with antimicrobial additives were prepared and applied to one-layer nonwoven PP material using screen printing, knife over roll coating and laminating machine methods. The influence of the binder antimicrobial polymer coating on the breathability and antibacterial activity of the nonwoven PP material was evaluated. Results had showed that the composition of the polyacrylic acid binder Tubicoat Thickener LP had the least effect on their breathability and samples with the RUCO-BAC AGP formulation had showed the best antimicrobial response. Therefore, further studies of the antimicrobial coating composition were performed using the binder Tubicoat Thickener LP and antimicrobial additives of different chemical nature and concentration - RUCO-BAC AGP, BI-OME AM10 and Nano Ag aqueous dispersion. Based on the microbiological and air permeability results of the samples of the one-layer nonwoven material with coating, the samples of two-layers and three-layers of medical mask model were prepared. Microbiological studies have shown that three-layered medical mask model with RUCO-BAC AGP composition in the middle layer, in both sides of the model has antibacterial efficiency against three pathogens (E. Coli, K. Pneumoniae, an S. Aureus). The performance of this medical mask model has been found to meet requirements for type I medical masks according to the EN 14863 standard. Study have shown that the microbial purity of the mask model is CFU/g <3.

Forensic speaker recognition plays a key role in criminal investigations, providing important conclusions for the justice system. The mandatory use of protection masks during the COVID-19 pandemic has posed a challenge for forensic speaker recognition, as they act as voice barriers or filters. Although the pandemic has been declared over, analyzing their impact in forensic speaker recognition contributes to a better understanding of the use of coverings as a voice disguise technique. This study aims to evaluate the impact of two types of face masks on an automatic forensic speech recognition system. For this purpose, the Multilingual Forensic Voice Database (FMVD), developed under the CERTAIN-FORS project, funded by the European Union, was used. Comparisons were made between dialog speech samples and reading samples collected without a mask, with a surgical mask, and with an FFP2 mask for both sexes in eight different languages. The performance metrics equal error rate (EER) and cost of likelihood ratio (Cllr) were calculated and analyzed. The results show that the presence of face masks has an impact on the performance metrics. The effect observed varies according to the language spoken, the gender of the speaker, and the type of mask.

This review explores the intricate relationship between the microbiome and cancer metabolism, focusing on the Warburg effect—a shift where cancer cells rely on glycolysis for energy even in oxygen-rich environments. This metabolic reprogramming fuels tumor growth and alters the tumor microenvironment. Recent studies highlight the microbiome's influence on cancer metabolism, suggesting that microbial imbalances can either promote or hinder the Warburg effect. Microbiome alterations impact metabolic pathways, immune responses, and gene expression, which can accelerate or mitigate cancer progression. We examine how dysbiosis affects the Warburg effect and its implications for tumor growth, metastasis, and treatment resistance. Additionally, we discuss the potential of microbiome-targeted therapies, such as probiotics and fecal microbiota transplants, to modulate cancer metabolism. These interventions offer the possibility of reversing or controlling the metabolic shifts in cancer cells, enhancing the efficacy of traditional treatments like chemotherapy and immunotherapy. Despite promising developments, challenges remain in identifying key microbial species and pathways, as well as in validating microbiome-targeted therapies through large-scale clinical trials. Nonetheless, the intersection of microbiome research and cancer metabolism presents an exciting frontier for innovative therapies. This review offers a fresh perspective on cancer metabolism by integrating microbiome insights, highlighting the potential for interdisciplinary research to enhance our understanding of cancer progression and treatment strategies.

This scoping review explores the development and application of sustainability competence frameworks in secondary education globally, identifying key trends and challenges within the field. An initial sample of 2,659 peer-reviewed publications from 2003 to 2023 was subjected to a rigorous multi-stage screening process, refining the selection for in-depth analysis. Through qualitative clustering, the review identifies two primary perspectives on sustainability competencies. The first emphasizes transversal competencies, relevant across diverse educational settings, including national policies, global educational guidelines, various subject domains, and innovative teaching-learning approaches. The second perspective focuses on specific frameworks that address the cognitive, affective, and behavioral dimensions of sustainability. The review highlights a global consensus on the importance of key competencies such as critical thinking, systems thinking, and action competence, which are essential for preparing secondary students to tackle sustainability challenges. Additionally, it stresses the need for a holistic approach to competence development that integrates cognitive, affective, and behavioral aspects. Despite this consensus, the review reveals a research bias, with a predominance of studies from Europe, particularly Germany and Sweden, and calls for increased regional diversity and collaboration in future research.

Diabetic foot ulcers (DFUs) are a multifactorial pathophysiologic condition that occurs in patients with diabetes mellitus (DM). Approximately 9.1 to 26.1 million people are affected by DFUs annually. This condition can lead to progressive foot infections and, ultimately, foot amputation. Various microbes contribute to DFUs, including methicillin-resistant Staphylococcus. aureus, Pseudomonas aeruginosa, and Escherichia coli can form biofilms composed of complex matrices that create a protective shield around the microorganisms, enhancing their survival and resistance to treatment. Biofilm formation is a critical virulence factor directly associated with the onset and persistence of DFUs. It not only complicates the clinical management of these ulcers but also facilitates antibiotic resistance, necessitating a comprehensive approach to treatment. Effective management strategies must therefore target biofilm formation and the virulence factors associated with these pathogens. By addressing these elements, conventional antibiotics can be rendered more effective in treating diabetic foot ulcers. This approach aims not only to promote wound healing but also to address underlying causes and prevent further complications. This article seeks to highlight the significance of biofilm formation in DFUs, explore methodologies for studying this condition, as well as discuss demographic considerations and treatment options.

Avoiding global dependence on fossil oils, as well as improving the environmental impact during energy production, are factors that drive research into renewable energies. Considering lignocellulosic biomass residues as a raw material for gasification, a thermochemical process that converts lignocellulosic resources into synthesis gas (H2, CO, CH4 and CO2) is an alternative under study, due to its relatively low costs, high efficiency and the wide variety of applications of synthesis gas. Fortunately, there are still fields for its improvement and technological development. Gasification, for example: Different types of lignocellulosic biomass, such as sugarcane bagasse, wheat straw, pine sawdust or corn cob, differ in their physical, chemical and morphological properties, which can affect the characteristics of the gasification process. This work uses the methodology of generalized stoichiometry and mass and atomic balances in the gasification reactor, to predict the composition of syngas produced by gasification of both individual substrates and mixtures. The results provide useful information for the design and operation of gasification reactors, with their operating region between 2.0 bar and 4.5 bar, and 1024 K and 1224 K, understanding the effects of different types of biomasses, its humidity and molecular weight on the operation and performance of the process.

Posterior retroperitoneal laparoscopic adrenalectomy (PRLA) has emerged as a revolutionary minimally invasive technique for adrenal gland surgery, offering significant advantages over traditional open approaches. This narrative review aims to provide a comprehensive update on PRLA, focusing on its anatomical foundations, surgical technique, and clinical implications. We conducted an extensive review of the current literature and surgical practices to elucidate the key aspects of PRLA. The procedure leverages a unique "backdoor" approach, accessing the adrenal glands through the retroperitoneum, which necessitates a thorough understanding of posterior abdominal wall and retroperitoneal anatomy. Proper patient selection, meticulous surgical planning, and adherence to key technical principles are paramount for successful outcomes. The surgical technique is described step-by-step, emphasizing critical aspects such as patient positioning, trocar placement, and adrenal dissection. PRLA demonstrates reduced postoperative pain, shorter hospital stays, and faster recovery times compared to open surgery, while maintaining comparable oncological outcomes for appropriately selected cases. However, the technique presents unique challenges, including a confined working space and the need for surgeons to adapt to a posterior anatomical perspective. We conclude that PRLA, in the right clinical setting, offers a safe and effective alternative to traditional adrenalectomy approaches. Future research should focus on expanding indications and refining techniques to further improve patient outcomes.

Background: Somatic tinnitus (ST) is a type of tinnitus that is influenced by changes in somatosensory input from the cervical spine or temporomandibular area. Although traditional physiotherapy has been shown to reduce ST symptoms, in-clinic treatment is not always available, and adherence to home exercise programs is often low. This study aims to investigate the effectiveness of an app-based physiotherapy program to enhance compliance and availability of treatment for ST patients. Methods: This pilot randomized controlled trial included 38 adult patients with chronic somatic tinnitus. Participants were randomly assigned to a treatment group receiving a 9-week app-based cervical spine exercise program or a control group. The primary outcome was tinnitus distress measured using the Tinnitus Handicap Inventory (THI). As a secondary outcome we the Mini-Tinnitus Questionnaire (Mini-TQ). Results: Participants in the treatment group showed significant reductions in tinnitus distress, with a large effect size for both the THI and Mini-TQ (Cohen’s d = 1.71 and 1.02, respectively). The control group showed no significant changes. Conclusion: This study provides evidence that an app-based physiotherapy intervention is a feasible and effective treatment for ST. Further research with larger sample sizes, comparisons to traditional in-clinic treatments is needed to confirm these findings. Additional studies on personalized treatment might further improve the treatment.

Geography has been a fundamental determinant of economic development throughout history, influencing resource distribution, trade routes, climate conditions, and the spatial organization of economic activity. This essay delves into the multifaceted role of geography in shaping economic outcomes, drawing upon historical analyses and contemporary economic theories. By examining the historical origins of economic growth, the impact of geographical barriers and advantages, the influence of climate and natural resources, and the spatial distribution of industries, we explore how geographical factors contribute to international inequality and the divergent development trajectories of nations and regions. The discussion incorporates insights from seminal works, including Koyama and Rubin's "How the World Became Rich," Davis and Weinstein's analysis of the geography of economic activity, and Redding and Venables' exploration of economic geography and international inequality, among others. We also consider the roles of institutions, governance, and technology as mediating factors that can amplify or mitigate the influence of geography on economic development. By highlighting the interplay between geography and these mediating factors, the essay underscores the importance of tailored policy interventions that consider geographical realities. Understanding this complex relationship is essential for addressing global and regional disparities and formulating effective development strategies that leverage geographical advantages while mitigating disadvantages.

Arbutus unedo (strawberry tree) is a small Mediterranean tree capable of vigorous regrowth after disturbances like fire. Traditionally used for biomass fuel, its bark and branches hold potential for higher-value products through ecovalorisation into liquid mixtures that could replace petro-leum-based materials. This study aimed to explore the chemical composition of various compo-nents of Arbutus unedo and to produce a liquefied material from its internal (IB) and external bark (EB). Chemical compositions of internal and external bark were determined using TAPPI standards including ash, extractive content, lignin, and cellulose. Metal cations were analyzed by ICP. Liquefaction of bark was optimized in a PARR reactor, evaluating factors such as particle size, temperature, and time and the best polyols were monitored by FTIR-ATR. Polyurethane foams were made with internal and external bark liquefied materials by polymerization with isocyanate, a catalyst and water as blowing agent. Results showed that EB has a higher extractive and lignin content, while IB contains more cellu-lose. Liquefaction yields were higher for IB (74%) than EB (68%), with IB yielding polyols that produced stronger and more resilient foams. Mechanical properties of the foams were influenced by the NCO/OH ratio and catalyst levels. Overall, the internal bark demonstrated superior performance for foam production, highlighting its potential as an eco-friendly alternative to petroleum-derived materials.

In this study, we address key challenges in enhancing ruminant productivity across Ethiopia, Tanzania, Kenya, Uganda, Rwanda, and Burundi in eastern Africa. A critical focus is on improving access to forage seeds essential for cattle systems. Key factors such as ruminant population, feed deficits, and governmental priorities are examined. The primary challenge lies in inadequate access to affordable forage seeds, compounded by reluctance to invest in perceived expensive elite varieties. Many farmers rely on outdated planting materials, lacking purity and vigor. To overcome these barriers, our analysis stresses the urgency of transitioning to recently improved seed varieties through rigorous selection and breeding. Promoting domestic seed production within Africa is crucial to mitigate high import costs and logistical complexities like variety registration. A coordinated regional approach to seed production could enhance economic viability and foster widespread adoption of improved forages. Such initiatives align with governmental agendas for sector development, offering triple benefits: increased productivity, environmental gains through reduced methane emissions, and regional economic growth.

The present paper focuses on the practical application of software tools for calculating and verifying trusses systems. It covers externally and internally statically indeterminate trusses systems and compares results from two software tools. The MATLAB Simscape – Multibody simulation tool, specially designed for mechanical systems, is utilized to calculate the trusses system. The model is optimized to minimize errors compared to theoretical calculations. By using blocks, it is possible to build a system, including repeating parts quickly.

Optical communication systems pose significant challenges, including the effects of nonlinear noises. The nonlinearities, including Kerr-induced phase noise, become more problematic in m-QAM as the orden of the format increases becoming a highly densed set of data-symbols and therefore, requiring advanced signal processing for successful separation of symbols at the demodulation stage. Machine learning techniques have recently been applied to improve signal integrity in such scenarios. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in -90, -100, and -110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.

In the context of high frequency and intensity forest fires driven by future warming and drying climate, early detection and effective control of fires are extremely important to reduce losses. Satellite remote sensing, characterized by its broad observational scope and strong repeat observation capability, has become an important method for rapid monitoring of forest fires, replacing traditional means such as manual measurement and aerial photography. This paper addresses the misjudgments caused by solely relying on changes in infrared band brightness values and single-band separation in forest fire discrimination. It constructs the angle slope index ANIR based on the red band, near-infrared band and short-wave infrared band, Angle slope index AMIR based on the short-wave infrared band, mid-infrared band and far-infrared band, and angle slope index AMNIR based on the difference between the AMIR and ANIR, integrating the strong inter-band correlations and the reflectance characteristics of visible and short-wave infrared bands to simultaneously monitor smoke and fuel biomass changes in forest fires. To address the omissions caused by single-image fire discrimination algorithms, the paper constructs time-series Angle slope difference indices ∆ANIR and ∆AMIR. To address the different fire point discrimination thresholds caused by background difference in different time and space, the paper uses the decomposed three-dimensional OTSU(Maximum inter-class variance method) algorithm to calculate the segmentation threshold of the sub-regions constructed from the AMNIR data. The methods were validated using high temporal resolution meteorological satellite Himawari-8 and ground truth fire data from Hunan Province, China, from 2018-2019. The results show that the discrimination method based on the ASITR(Angle slope indices thresholds) method exhibits an accuracy of 0.88, a missed detection rate of 0.17, and an overall evaluation value of 0.85, which is 0.09 higher in accuracy and 0.03 higher in overall evaluation than the discrimination method based on infrared bands brightness values proposed by Feng et al. [1] (accuracy 0.79, missed detection rate 0.14, overall evaluation value 0.82); the method based on the FAMN_OTSU_ASITR (Fusion of Angle slope difference index (false) data, Decomposed three-dimensional OTSU adaptive threshold segmentation Algorithm and ASITR) exhibits an accuracy of 0.85, a missed detection rate of 0.08, and an overall evaluation value of 0.88, which is 0.09 lower in missed detection rate and 0.03 higher in overall evaluation as compared to the ASITR. The results demonstrate that the forest fire discrimination method based on Angle slope indices, Himawari-8 satellite data, and the decomposed OTSU adaptive threshold segmentation algorithm can improve the accuracy of forest fire discrimination and reduce errors in missed detections.

This study aimed to investigate the prevalence of cardiovascular disease (CVD) risk factors among Algerian aircrew, a population exposed to unique environmental and occupational stressors. By comparing these rates to the general population, we sought to identify specific health challenges faced by aircrew. Methods: A cross-sectional study was conducted on 154 Algerian aircrew participants. Prevalence rates for current smoking, hypertension, diabetes, and dyslipidemia were compared between the aircrew and a general population control group. Results: Our findings revealed significantly higher rates of current smoking among aircrew compared to the general population. Additionally, smoking, dyslipidemia, hypertension, and diabetes emerged as the most prevalent risk factors within the aircrew population. Conclusions: Despite the rigorous selection process for aircrew recruitment, our study confirms findings from previous literature, highlighting the high rate of normal qualifying examinations. The substantial sample size and homogeneous nature of the aircrew population enhance the reliability and generalizability of our conclusions. These findings underscore the importance of targeted interventions to mitigate CVD risk factors within this specific population, given the unique environmental and occupational factors associated with aircrew.

Secure Secret Sharing Single-Source Shortest Distance (SSSD) protocols, built on the Secure Multi-Party Computation (SMC) foundation, have emerged as a promising solution to address the secure distribution and management of sensitive secrets among multiple parties. This paper focuses on the crucial aspect of security proof within SSSD protocols based on SMC, aiming to provide a scientifically rigorous understanding of the formal verification process and the robust guarantees they offer. Through a comprehensive examination of the fundamental principles underpinning SMC and the specific design considerations unique to SSSD protocols, this paper meticulously explores the key components and intricate steps involved in constructing a rigorous security proof. Furthermore, it discusses the adversarial model, precise security definitions, cryptographic assumptions utilized within the proof, and sophisticated techniques and reductions to establish its validity. By means of a meticulous analysis of the security proof for SSSD protocols, this paper critically evaluates both the strengths and limitations of the proposed approach, offering valuable insights to inform future research and development within this significant domain of study.

The research identified challenges faced by teachers, suggested effective pedagogical strategies, and proposed practical recommendations to mitigate the negative impacts of ChatGPT in higher education. These strategies promote the ethical and responsible use of artificial intelligence, ensuring academic integrity. The article deeply analyzes pedagogical strategies to prevent plagiarism and promote academic integrity, offering a practical guide for educators on how to ethically integrate AI into their pedagogical practices. By filling a gap in the literature, the study contributes to the discussion on the ethical use of AI in education, offering evidence-based recommendations and fostering critical reflection on the impact of emerging technologies in the academic environment. The research highlights effective pedagogical strategies to mitigate the negative effects of ChatGPT, promoting an educational approach that prevents plagiarism and encourages the ethical use of AI. Through documentary analysis of scientific articles and data coding according to Bardin's methodology, patterns and trends were identified for the development of innovative pedagogical practices. The qualitative approach included a literature review and content analysis of articles published in the last five years. The results indicated that, although ChatGPT can personalize teaching and support content development, it also increases the risk of plagiarism and reduces students’ critical engagement. To combat these challenges, it is suggested to create personalized assessments, promote critical thinking, and use plagiarism detection tools. Continuous teacher training and the development of clear policies on the ethical use of AI are essential to ensure academic integrity and quality of learning.

The study examines the effects of transitioning to hydrogen production in the National Capital Region (NCR) and Palawan Province, Philippines, focusing on technology, environment, and stakeholder impact. The research, conducted through a July 2022 survey, aimed to assess public awareness, knowledge, risk perception, and acceptance of hydrogen and its environmentally friendly variant, green hydrogen, infrastructure. Disparities were found between urban NCR and rural Palawan, with lower awareness in Palawan. Safety concerns were highlighted, with NCR respondents generally considering hydrogen production safe, while Palawan respondents had mixed feelings, particularly regarding nuclear-based hydrogen generation. The report emphasizes the potential ecological advantages of hydrogen technology but highlights potential issues concerning water usage and land impacts. It suggests targeted public awareness campaigns, robust safety assurance programs, regional pilot projects, and integrated environmental plans to facilitate the seamless integration of hydrogen technology into the Philippines' energy portfolio. This collective effort aims to help the country meet climate action obligations, foster sustainable development, and enhance energy resilience.

Chronic mountain sickness (CMS) poses a significant health risk to individuals who rapidly ascend to high altitudes, potentially endangering their lives. Inorganic nanoparticles offer an effective means of transporting and delivering drugs, protecting nucleic acids from nuclease degradation, and mediating the expression of target genes in specific cells. These nanoparticles are almost non-toxic, easy to prepare and store, possess a large surface area, exhibit good biocompatibility and degradability, and maintain good stability. They can be utilized in the treatment of CMS to enhance the therapeutic efficacy of drugs. This paper provides an overview of the impact of inorganic nanomaterials on CMS, discussing their roles as nanocarriers and their potential in CMS treatment. It aims to present novel therapeutic strategies for the clinical management of CMS and summarizes the relevant pathways through which nanomaterials contribute to plateau disease treatment, providing a theoretical foundation for future clinical research.

The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.

The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn’s disease (CD), where microbiome dysregulation can alter immune responses leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of our recently developed probiotic+amylase blend in the SAMP1/YitFc (SAMP) mouse model of CD-like ileitis. Four groups of SAMP mice were gavaged for 56 days with: 1) probiotic strains + amylase (concentration: 0.25 mg/100 uL PBS); 2) only probiotics; 3) only amylase; PBS (vehicle-treated controls). Ilea were collected for GeoMx Digital Spatial Profiler (DSP) analysis and histological evaluation. Histology assessment for inflammation indicated a significantly reduced level of ileitis in mice administered probiotics+amylase blend. DSP analysis showed decreased abundance of neutrophils and increased abundance of dendritic cells, innate lymphoid cells and macrophages, with a significant enrichment of five intracellular pathways related to apoptosis, in probiotics+amylase treated mice. Increased apoptosis occurrence was confirmed by TUNEL assay. Our data demonstrate a beneficial role of the probiotic and amylase blend, highlighting an increased apoptosis of innate immunity-associated cell subsets, thus promoting the resolution of inflammation. Hence, we suggest that our recently developed probiotic blend may be a therapeutic tool to manage CD and therefore could be a candidate compound to be tested in clinical trials.

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.

Graph neural networks and graph convolutional networks are effective at representing complex relationships. We attempt to apply them to sports analytics by reviewing existing applications in sports. These methodologies leverage the inherent graph structure of sports data, capturing interactions among players, teams, and game events. By systematically examining various studies, we highlight how GNNs and GCNs can enhance performance prediction, player evaluation, and tactical analysis in sports. Our review emphasizes the unique advantages these models offer over traditional analytical approaches, particularly in their ability to account for the dynamic and interconnected nature of sports activities. We conclude by identifying potential areas for future research and application, suggesting that the integration of advanced graph-based techniques could significantly advance the field of sports analytics.

Magnetorheological elastomers (MREs) are in demand in the field of high-tech micro- and nano-industries such as biomedical applications and soft robotics due to their exquisite magneto-sensitive response. Among various MRE applications, programmable actuators are emerging as promising soft robots because of their combined advantages of excellent flexibility and precise controllability in a magnetic system. Here, we present the development of a magnetically programmable soft magnetic microarray actuators through field-induced injection molding using MREs, which consist of styrene-ethylene/butylene styrene (SEBS) elastomer and carbonyl iron powder (CIP). The ratio of the CIP/SEBS matrix is designed to maximize the CIP fraction based on a critical solids loading. Further, as part of the design of the magnetization distribution in micropillar arrays, the magnetorheological response of the molten composites is analyzed using the static and dynamic viscosity results for both the on- and off- magnetic states, which reflect the particle dipole interaction and subsequent particle alignment during field-induced injection molding process. To develop high aspect ratio soft magnetic microarray, X-ray lithography is applied to prepare the sacrificial molds with a height-to-width ratio of 10. The alignment of CIP is designed to achieve a parallel magnetic direction along the micropillar columns, and consequently, the micropillar arrays successfully achieve the uniform and large bending actuation of up to approximately 81˚ with an applied magnetic field. This study suggests that the injection molding process offers a promising manufacturing approach to build a programmable soft magnetic microarray actuator.

Clear cell renal cell carcinoma (ccRCC) is a highly aggressive subtype of renal cancer, often associated with poor prognosis and high mortality rates. In this study, we identified Nicotinamide N-methyltransferase (NNMT) as a key gene involved in the pathogenesis of ccRCC through RNA sequencing analysis of tumor and normal tissues from 14 patients. NNMT was among the top five upregulated genes, and its high expression was significantly correlated with reduced overall, progression-free, and disease-specific survival in patients, as well as advanced pathological stages and higher histologic grades. Gene ontology (GO) enrichment analysis revealed NNMT's role in biological processes such as signal transduction and immune response. Immune cell infiltration analysis indicated that high NNMT expression was associated with increased infiltration of activated NK cells and macrophages, while low expression correlated with a higher presence of regulatory T cells. Functional studies using siRNA-mediated NNMT knockdown in ccRCC cell lines demonstrated a significant reduction in cell proliferation, with further analysis revealing altered gene expression profiles related to cell adhesion and calcium ion binding. These findings suggest that NNMT not only plays a pivotal role in ccRCC aggressiveness but also may serve as a valuable prognostic biomarker and a potential therapeutic target for improving clinical outcomes in patients with this malignancy.

Objectives: The male urethra is a complex structure that plays a critical role in genitourinary health and function. Despite its importance, histological descriptions of the penile urethra, par-ticularly its epithelial components, remain incomplete. This study offers a comprehensive his-tological analysis of the penile urethra, focusing on the epithelium across distinct anatomical regions, including the glans, distal and proximal fossa navicularis, and spongy urethra. Meth-ods: Utilizing four human penile specimens, we employed various staining techniques to eluci-date the structural characteristics of these epithelial tissues. Results: Our findings reveal notable variations in epithelial composition, such as the presence of glycogen-rich cells in the distal fos-sa navicularis and the presence of mucous glands in the spongy urethra and proximal fossa na-vicularis. Additionally, we identified a previously underreported valvule-like structure in the distal fossa navicularis in two of the specimens. Conclusions: This study provides new insights into the histological organization of the penile urethra, offering critical reference data that can enhance our understanding of urethral pathologies and improve the outcomes of surgical inter-ventions, particularly in the context of tissue engineering and reconstructive surgery.

Introduction: The COVID-19 pandemic has triggered several challenges to mental health worldwide. Undergraduate medical students face considerable stress in their academic routines. Thus, there is a need to explore the implications of the mental health of undergraduate medical students during the COVID-19 pandemic. Objective: To review the global literature about anxiety and depression disorders in undergraduate medical students during the COVID-19 pandemic. Method: we developed an integrative literature review on the occurrence of anxiety and depression symptoms in undergraduate medical students during the COVID-19 pandemic. We assessed the results on the occurrence of anxiety and depression and the severity of symptoms in medical students during the COVID-19 pandemic using quantitative studies applying the GAD-7 questionnaire for anxiety or PHQ-9 for depression. Results: We reviewed 85 selected studies, and the results showed a significant prevalence of moderate and severe symptoms of anxiety and depression, with 28.2% of participants presenting scores ≥ 10 on the GAD-7 and 38.9% on the PHQ-9. Statistical analyses revealed associations between higher rates of anxiety symptoms in developing countries and data collected after the lockdown period in 2020, at the Pandemic lockdown. Conclusion: Our findings highlight the need for specific interventions to support the mental health of undergraduate medical students, critically in female students from developing countries during a Pandemic crisis.

The incremental sheet forming is born as an excellent alternative to other material forming pro-cedures to deform incrementally flat metal sheets into the preferred complex three-dimensional profile. The main characteristics of this process are its versatility and cost-effectiveness; in addition, it allows for reaching greater formability, compared to conventional sheet forming processes. Re-cently, its use has been extended to polymers and composites. The following review aims to present the current state of the art of the incremental sheet forming of polycarbonate, an outstanding en-gineering plastic, starting from the first studies on the feasibility of this process applied to polymers. Attention is given to the advantages, drawbacks and main applications of the incrementally formed polycarbonate sheets, as well as the influence of the process parameters and the toolpath strategies on features like formability, forming forces, deformation and failure mechanisms, geometric accu-racy, surface quality, etc. Moreover, new hybrid forming methods for process optimization are presented. Finally, a discussion is provided on the technical challenges and research directions for incremental sheet forming of polycarbonate, and more generally thermoplastics, in the future. Therefore, this review aims to offer an extensive overview of the incremental forming of polycar-bonate sheets, useful to academic and industrial researchers who work on this topic.

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant buses is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

The physiological impact of walking in nature was quantified via continuous heart rate variabil-ity (HRV), pre- and post-walk saliva cortisol measures, and self-reported mood and mindfulness scores for N=17 participants who walked “The Green Road” at Walter Reed National Military Medical Center in Bethesda, Maryland. For N=15 of the participants, HRV analysis revealed two main groups: group 1 individuals who had a 104% increase (mean) in root mean square standard deviation (RMSSD) and 47% increase (mean) in standard deviation of NN values (SDNN) indi-cating an overall reduction in physiological stress from walking the Green Road, and group 2 in-dividuals who had a decrease (mean) of 42% and 31% in these respective HRV metrics signaling an increase in physiological stresses. Post-walk self-reported scores for vigor and mood disturb-ance were more robust for the Green Road than for a comparable urban road corridor and showed that higher HRV during the walk was associated with improved overall mood. Saliva cortisol was lower after taking a walk for all participants and showed that walking the Green Road elicited a significantly larger reduction in cortisol of 53% on average when compared with 37% of walking along an urban road. It is also observed that the order in which individuals walked the green road, and urban road also impacted their cortisol response with those walking the urban road before the green showing a substantial reduction in cortisol, suggesting a possible attenuation effect of walking the green road first. These findings provide quantitative data demonstrating the stress-reducing effects of being in nature, thus supporting the health benefit value of providing access to nature more broadly in many settings.

This article examines empirically the research on digital strategy, addressing its temporal evolution, sources of scientific production, countries and organizations generating knowledge, and the topics investigated. It highlights key authors and journals from 1991 to 2023, contributing to Sustainable Development Goal (SDG) 9. Methodologically, traditional bibliometric laws and computational tools (VOSviewer software) were applied for data processing. The results show an exponential increase in publications from 2005 to 2022 with a critical mass in digital strategy studies, concentrated in 11 journals and 83 authors with 2 or more publications. The main organizations leading scientific production in this field are in the USA. Finally, the primary Web of Science categories for the leading journals are Business, Management, and Psychology. The research examines the use and adoption of digital strategies in marketing, the impact of digital transformation on business models, and the challenges it poses for human resources. In addition, the challenges arising from the impact of COVID-19, the rise of artificial intelligence, and the integration of digital strategies in public administration should continue to be addressed. Finally, industrial digital transformation and the challenges of Big Data in higher education have less connection with other key concepts analyzed.

The detection of microplastics (MPs) in drinking water has emerged as a critical environmental concern, with significant implications for public health. However, the variability in identification methods across studies has made it difficult to compare results, underscoring the urgent need for standardized protocols. This study presents a comprehensive meta-analysis and practical ap-proach to standardizing Fourier Transform Infrared Spectroscopy (FTIR)-based methodologies for detecting MPs in drinking water. By systematically reviewing studies from 2019 to 2023, this research identifies the most effective techniques for MP quantification, with an emphasis on the use of 0.45 μm cellulose nitrate filters and Nile red staining for fluorescence detection. The me-ta-analysis demonstrates consistent results across studies, supported by statistical analysis con-firming the reproducibility and reliability of the methodologies employed. Experimental findings highlight that the cellulose nitrate filter offers superior microplastic retention with low back-ground fluorescence, making it ideal for FTIR analysis. Moreover, the Nile red spray method is recommended for its efficiency and uniformity in staining, and random counting proves to be a rapid and reliable quantification technique. This study advocates for the adoption of standard-ized protocols, particularly in filter selection and staining methods, to ensure accurate and com-parable MP detection. The findings contribute valuable insights for optimizing water treatment processes and provide a solid foundation for future research and regulatory frameworks aimed at mitigating microplastic contamination in water resources.

The potential of technical lignins slow pyrolysis is briefly summarized. The pyrolysis process, which can be self-sustained by burning the co-produced gas, can primarily produce high-quality biochar in significant amounts, to be used as a fuel and as a reductant in metallurgy. Together, significant amounts of bio-oil can be produced, rich in guaiacols, which are commercial and expensive chemicals produced today via petrochemical routes and used in pharmacology, food chemistry, polymer chemistry, etc. Such compounds or bio-oil itself can also be converted by hydrodeoxygenation into biofuels.

Background: Nanotechnology has emerged as a promising field of study, aiming to revolutionize healthcare through the innovative development of drugs, diagnostic tools, and medical devices. As a result, healthcare professionals in training are constantly faced with the challenge of understanding and applying these emerging technologies. The objective of this study is to determine the factors to consider for including the teaching of nanotechnological tools in medical specialty postgraduate programs. Methods: A study based on the Nominal Group Technique (NGT) was conducted to identify the factors that promote and hinder the implementation of nanotechnology in certain postgraduate programs where nanotechnology is employed, such as Internal Medicine, Critical Care Medicine, Family Medicine, and Clinical Pharmacology at the School of Medicine of Universidad de La Sabana. A framework analysis was used to analyze the collected data. Results: Based on the two research questions, several aspects were identified for the inclusion of nanotechnology in the medical practice of specialties. Key challenges include lack of knowledge and resource availability, misinformation, and lack of knowledge, as well as paradigmatic analysis as the main obstacles to the use of nanotechnology. Conclusions: The findings provide a first-time study on nanotechnology curricula within medical specialty programs in Colombia, with potential extrapolation to similar situations in other medical schools.

Through the integration of genomic information, transcriptome sequencing data, and bioinformatics methods, we conducted a comprehensive identification of the ALDH gene family in melon. We explored the impact of this gene family on melon growth, development, and their expression patterns in various tissues and under different stress conditions. Our study discovered a total of 17 ALDH genes spread across chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12 in the melon genome. Through a phylogenetic analysis, these genes were classified into 10 distinct subfamilies. Notably, genes within the same subfamily exhibited consistent gene structures and conserved motifs. Our study discovered a pair of fragment repeats within the melon ALDH gene. Furthermore, there was a noticeable collinearity relationship between the melon's ALDH gene and that of Arabidopsis (12 times), and rice (3 times). Transcriptome data reanalysis revealed that some ALDH genes consistently expressed highly across all tissues and developmental stages, while others were tissue or stage-specific. We analysed the ALDH gene's expression patterns under six stress types, namely salt, chilling, waterlogged, powdery mildew, fusarium wilt, and gummy stem blight. The results showed differential expression of MELO3C024328 and MELO3C004430 under all stress conditions, signifying their crucial roles in melon growth and stress response. RT-qPCR analysis further corroborated these findings. This study paves the way for future genetic improvements in melon molecular breeding.

The modulation of vocal elements such as pitch, loudness, and duration plays a crucial role in conveying both linguistic information and the speaker’s emotional state. While acoustic features like fundamental frequency (F0) variability have been widely studied in emotional speech analysis, challenges remain in accurately classifying emotions due to the complex and dynamic nature of vocal expressions. Traditional analytical methods often oversimplify these dynamics, potentially overlooking intricate patterns indicative of specific emotions. This study aims to enhance emotion classification in speech by directly incorporating dynamic F0 contours into the analytical framework using Generalized Additive Mixed Models (GAMMs). We utilized the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), focusing on eight distinct emotional states expressed by 24 professional actors. Sonorant segments were extracted, and F0 measurements were converted into semitones relative to a 100 Hz baseline to standardize pitch variations. By employing GAMMs, we modeled non-linear trajectories of F0 contours over time, accounting for both fixed effects (emotions) and random effects (individual speaker variability). Our analysis revealed that incorporating emotion-specific non-linear time effects and individual speaker differences significantly improved the model’s explanatory power, ultimately explaining up to 66.5% of the variance in F0. The inclusion of random smooths for time within speakers captured individual temporal modulation patterns, providing a more accurate representation of emotional speech dynamics. The results demonstrate that dynamic modeling of F0 contours using GAMMs enhances the accuracy of emotion classification in speech. This approach captures the nuanced pitch patterns associated with different emotions and accounts for individual variability among speakers. The findings contribute to a deeper understanding of the vocal expression of emotions and offer valuable insights for advancing speech emotion recognition systems.

Cassava (Manihot esculenta Crantz) is used as biomass energy materials and an effective supplement for food and feed. Cinnamyl alcohol dehydrogenase (CAD) catalyzes the final step of lignin biosynthesis and is responsible for various stresses. However, systematic investigation of CAD gene family in cassava has been poorly understood. In this study, we performed a genome-wide survey and identified 36 MeCADs genes that were unevenly harbored in 12 chromosomes. Through phylogenetic analyses along with Arabidopsis counterparts, these MeCADs were divided into four groups, each gene contains a similar structure and conserved motifs. Interestingly, transcriptome data analyses unveiled 32 MeCAD genes during the postharvest physiological deterioration (PPD) process of cassava tuberous roots, whereas 27 MeCAD genes significantly changed. Meanwhile, the relative quantitative analysis of 6 MeCAD genes demonstrated that they were sensitive to PPD, suggesting they may involve in PPD regulation. Silencing MeCAD13 and MeCAD28 further showed that lignin content significantly decreased in the leaves. The wound-stress tolerance of transgenic yeast cells was enhanced after transformation with MeCAD13 and MeCAD28. In conclusion, MeCAD13 and MeCAD28 may play positive roles in lignin biosynthesis and PPD response, respectively. These results provided a systematic functional analysis of MeCADs in cassava and paved a new way to genetically modify of lignin biosynthesis and PPD tolerance.

The aim of the present review is to investigate the role of a toothpastes containing enzymes and proteins in supplementing natural salivary defenses. A systematic review was conducted using PRISMA with PROSPERO ID code CRD42024558854. An exhaustive search of PubMed, Web Of Science, and Scopus, using the PICO format was carried out. The used MeSH were enzymes; AND proteins; AND toothpaste. In vivo studies on humans analyzing the gum effects of a toothpaste containing enzymes and proteins, in the English language, published in the last 10 years were included. The search period started on 04 June 2024 and ended on 20 July 2024. The risk of bias was evaluated according to the Revised Cochrane risk-of-bias tool for randomized trials (RoB 2.0) while non-randomized studies were evaluated with the Cochrane risk-of-bias tool in Non-Randomized Studies (ROBINS-I). From a total of 62 studies, 3 were included. Two RCTs and had low and medium risk of bias, the N-RCT had low risk of bias. With the limitations of the present study, a toothpaste containing enzymes and proteins can improve gum health after 12 week and after 12 months, the role against black stains seemed not to be confirmed.

(1) Background: Arrhythmia, or irregular heart rhythms, are a prevalent cardiovascular condition and can be diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends heavily on the quality of signal preprocessing. This study evaluates the impact of different windowing techniques applied to Fourier Transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A 1D Convolutional Neural Network (CNN) was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal.(3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.