A binary biomass-based solid electrolyte film (LSE) was successfully synthesized through the incorporation of lignin nanoparticles (LNP) with sodium alginate (SA). The impact of the mass ratio of SA to LNP on the microstructure, porosity, electrolyte absorption capacity, ionic conductivity, and electrochemical properties of the LSE was thoroughly investigated. The results indicated that as the proportion of SA increased from 5% to 15% LNP, the pore structure of the LSE became increasingly uniform and abundant. Consequently, enhancements were observed in porosity, liquid absorption capacity, ionic conductivity, and overall electrochemical performance. Notably, at a SA amount of 15% LNP, the ionic conductivity of resultant LSE-15 was recorded at 14.10 mS cm1, with porosity and liquid absorption rates reaching 308% and 58.4%, respectively. LSE-15 was employed as a solid electrolyte, while LNP-based carbon aerogel (LCA) served as the two electrodes in the construction of a symmetric all-solid-state supercapacitor (SSC). The SSC device demonstrated exceptional electrochemical storage capacity, achieving a specific capacitance of 197 F g1 at 0.5 A g1, along with maximum energy and power density of 27.33 W h kg1 and 4998 W kg1, respectively. Furthermore, the SSC device exhibited highly stable electrochemical performance under extreme conditions, including compression, bending, and both series and parallel connections. Therefore, the development and application of the binary biomass-based solid electrolyte film in supercapacitors represent a promising strategy for harnessing high-value biomass resources in the field of energy storage.

Advances in early detection of Breast cancer and treatment improvements have significant-ly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study a novel methodology for developing an interpretable computer-aided diagnosis (CAD) system for breast cancer detection, leveraging explaina-ble Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combine the continuous and binary representations of PSO and SMO to effectively manage trade-offs between Accuracy, feature selection and hyperparameter tuning. We evaluate several CAD models and investigate the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor filters, and edge detection. We further shedding light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. Also with HSMO our models achieved a high accuracy of 98.27% and F1- score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Obesity is one of the most prevalent and damaging metabolic conditions and is a risk factor for the development of other disorders that define the metabolic syndrome. Recent studies have identified a significant role of AGE accumulation and RAGE signaling pathways in the underlying mechanisms that cause the chronic inflammatory response present in obesity which, ultimately, leads to adipose tissue dysfunction. Given the current interest and increasing evidence on possible therapeutic opportunities in the AGE-RAGE axis, the purpose of this work is to review the key findings on new small molecules and bioactive compounds that act as AGE or RAGE inhibitors and their effects on metabolic parameters of people with obesity. The search queries were defined according to the following PICO question: “What are the effects of AGE-RAGE antagonism on adipose tissue metabolism and other metabolic parameters of people with obesity? “. Article search was performed in three online databases: PubMed, Scopus, and Web of Science. In the end, 20 studies were selected for this review. Overall, the reviewed molecules showed beneficial effects on body weight, glucose tolerance, insulin resistance, inflammatory profile, and adipose tissue metabolism in mice models with obesity. There are still very few studies on human application of these molecules, but the ones that were reviewed revealed controversial data when compared to animal studies. Therefore, further human clinical trials should be performed to retrieve more conclusive data.

Developing new antibiotics poses a significant challenge in the fight against antimicrobial resistance (AMR), a critical global health threat responsible for approximately 5 million deaths annually. Finding new classes of antibiotics that are safe, have acceptable pharmacokinetic properties, and are appropriately active against pathogens is a lengthy and expensive process. Therefore, high-throughput platforms are needed to screen large libraries of synthetic and natural compounds. In this review, we present bacterial cytological profiling (BCP) as a rapid, scalable, and cost-effective method for identifying the mechanisms of action of antibiotics, offering a promising tool for combating AMR and drug discovery. We present the application of BCP for different bacterial organisms and different classes of antibiotics and discuss BCP's advantages, limitations, and potential improvements. Furthermore, we highlight the studies that have utilized BCP to investigate pathogens listed in the Bacterial Priority Pathogens List 2024 and we identify the pathogens whose cytological profiles are missing. Lastly, we explore the most recent artificial intelligence and deep learning techniques that could enhance the analysis of data generated by BCP, potentially advancing our understanding of antibiotic resistance mechanisms and the discovery of novel druggable pathways.

Histological evidence of active prosthetic valve endocarditis (PVE) in cardiac valve tissue is widely regarded as a criterion established by Duke to confirm the presence of this condition, although the histological characteristics of mechanical prosthetic valve endocarditis have not yet been definitively established. The primary objective of this study was to determine the histologic differences in mechanical PVE in individuals with either early- or late-onset PVE, with the aim of distinguishing the unique aspects of this rare condition. Histological analysis of mechanical cardiac valve tissue was conducted in 27 patients who underwent surgical valve removal, comprising individuals with early onset and late-onset PVE. The findings were compared between the two groups. Histopathologically, PVE in individuals with mechanical heart valves is typically associated with the formation of vegetation and considerable neutrophil-rich infiltrates. In cases of early onset PVE, the affected valve tissue displays intense inflammation predominantly consisting of lymphocytes. In contrast, valve tissue samples from patients with late-onset PVE exhibit extensive fibrosis and inflammation, comprising macrophages, which are specialized immune cells and calcifications.

Theoretical physics addresses the fundamental principles of nature and the mysteries of the universe. In the last century, the theory of relativity challenged people's understanding of time and space, while quantum mechanics revealed various forms of energy. However, the concept of gravity remains elusive. All matter in the universe is composed of energy that can be converted into other forms, yet it is always conserved. However, achieving a state of equilibrium, represented by the equal sign, can be challenging to achieve on both macro and quantum scales. A phenomenon that can transition from one state to infinity is called collision and is essential for understanding natural phenomena and physical processes throughout the universe. In the study of general relativity cannot be three-dimensional problems to gain mathematical inspiration, understand the application of new dimensions. Here, we elucidate the nature of gravity, which inspires the solution of the Riemann hypothesis. Our results show that the conduction mode of dimensional energy is defined by Euler's formula. And a special mode of collision is derived which translates into vibration.

Modern advances in technology have increased the demand for traditional accounting systems to be upgraded for real-time data processing, security, and standardized reports. Thus, this paper proposes a new accounting information system that integrates IoT, blockchain, and XBRL. The proposed system aims to automate the accounting process by using IoT to collect data and send it automatically to a blockchain, which acts as a database that will generate journal entries automatically through smart contracts. XBRL will then be used as an output method for standardized financial reports based on the data transferred from the blockchain. This paper uses a qualitative research design based on semi-structured interviews with 13 industry experts from IT engineering, academia, and financial systems analysis. NVivo software was used to conduct a thematic analysis of interview transcripts. The findings revealed the technical feasibility, challenges, and benefits of the proposed integration and identified the most efficient mechanism for data transmission across the three technologies. The paper concluded that while integration complexity and user adoption are challenging, this integrated system can revolutionize accounting by enabling real-time data capture, automation of essential operations, data security, and integrity improvements. To our knowledge, this is the first paper to develop a comprehensive accounting information system framework using IoT, blockchain, and XBRL to automate data collection, improve data security, and streamline financial reporting. The study's potential to impact accounting systems and financial reporting research and practice emphasizes its importance.

Let an odd integer $\mathcal{X}$ be expressed as $\sum \limits_{M > m} 2^M + 2^m - 1$, where $2^m - 1$ is called the Governor. The general dynamics of the Collatz-type functions is such that a high index Governor is converted to $2^1 - 1$. For the $3\mathcal{Z} + 1$ sequence, the Governor occurring in the trivial cycle is $2^1 - 1$, while for the $5\mathcal{Z} + 1$ sequence, the Trivial Governors are $2^2 - 1$ and $2^1 - 1$. Therefore, for the specific cases of $3\mathcal{Z} + 1$ and $5\mathcal{Z} + 1$ sequences, it can be said that the Collatz function reduces $\mathcal{X}$ to $\sum \limits_{N > \mathcal{T}} 2^N + 2^{\mathcal{T}} - 1$, where $2^{\mathcal{T}} - 1$ is referred to as the Trivial Governor. Further, once the Trivial Governor is reached, it can again evolve to a higher index Governor by interacting with other terms. This feature allows the $\mathcal{X}$ to reappear in a Collatz-type sequence if its original Governor is expressible as a sum of series including the Trivial Governor, that is, $2^m - 1 = \cdots + 2^{\mathcal{T} + 1} + 2^{\mathcal{T}} + 2^{\mathcal{T}} - 1$. Therefore, if $\mathcal{X}$ reappears in the sequence, at least one odd ancestor of $\mathcal{X}$ in the sum of series format must have the Governor $2^m - 1$. Ancestor mapping shows that all the odd ancestors of $\mathcal{X}$ have the Trivial Governor of the respective Collatz sequence implying that $\mathcal{X}$ can repeat only if its original Governor is the Trivial Governor. Specifically for the $3\mathcal{Z} + 1$ sequence, the odd ancestors are separated by two even integers. Successor mapping for the $3\mathcal{Z} + 1$ further indicates that there are no auxiliary cycles, as the Trivial Governor is transformed to a different index Governor. Similarly, successor mapping for the $5\mathcal{Z} + 1$ sequence reveals that the smallest odd integers that form an auxiliary cycle are smaller than $2^5$. Finally, attempts to construct integers that diverge for the $3\mathcal{Z} + 1$ sequence suggest that no such integers exist.

Synopsis Corneal epithelial wavefront error and epithelial thickness variance qualify as highly sensitive and specific biomarkers for epithelial basement membrane dystrophy (EBMD). The biomarkers show a normalization after treatment of EBMD with phototherapeutic keratectomy. Purpose: To gauge the diagnostic value of a novel spectral-domain optical coherence tomography (SD-OCT) based imaging modality for simultaneous morphological (thickness profile) and refractive (optical wavefront) assessment of the corneal epithelial layer in one of the most common but often underdiagnosed corneal dystrophies: epithelial basement membrane dystrophy (EBMD). Methods: In this prospective observational study, a total of 32 eyes of 32 patients diagnosed with EBMD and 32 eyes of 32 healthy control subjects were examined with high-resolution anterior segment SD-OCT (MS-39; CSO, Florence, Italy). Various epithelial thickness and epithelial wavefront derived terms were compared between groups and receiver operating characteristic (ROC) curves were computed to analyze the diagnostic capacity of the respective parameters. A total of 17 of 32 EBMD patients underwent treatment with phototherapeutic keratectomy (PTK) and were followed up for 3 months. Results: Epithelial thickness variance (60.4 ± 56.7 µm versus 7.6 ± 6.1 µm) and interquartile range (11.0±6.9 versus 3.3±1.9 µm) were markedly elevated in EBMD patients as compared with healthy controls (both with p < 0.001). Epithelial wavefront analysis showed a highly statistically significant excess in all examined aberration terms in EBMD patients (all with p < 0.001). Significantly greater areas under the curve (AUC) were yielded by the epithelial wavefront derived parameters (e.g. total epithelial wavefront error: AUC = 0.966; 95% confidence interval (CI) 0.932 – 1) than by the epithelial thickness-derived parameters (e.g., variance: AUC = 0.919; 95% CI 0.848 – 0.990). Conclusion: Corneal epithelial wavefront aberrometry proved valuable as an objective biomarker for EBMD with high sensitivity and specificity. PTK resulted in a reduction of morphological and refractive epithelial irregularities in EBMD.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, e.g., low accuracy, slow speed, etc. The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Aerial Pedestrian Dataset, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Aerial Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperforms the most used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

This paper investigates rural mobility in the Global South using a participatory GIS approach to address the lack of mobility data. Limited rural mobility due to poor infrastructure and financial constraints hinders social and economic activities, impeding development. The study aims to explore the socio-economic impacts of limited mobility, identify software needs for data-scarce environments, and map daily mobility patterns. Fieldwork was conducted in Kenya (2022) and Nepal (2023), collecting geographic data related to mobility. The methodology included community engagement and digital mapping using the QField app for precise data collection. The study highlights the use of participatory GIS to fill data gaps, enabling more accurate mobility assessments. In Kenya, the research significantly increased the number of mapped roads and paths, providing a more realistic view of local mobility. Community engagement revealed gender-specific mobility patterns, with women facing particular challenges in daily activities. The study emphasizes the need for adaptable data models to reflect community-specific mobility needs and the integration of qualitative insights to propose effective mobility solutions. This research contributes to understanding rural mobility in the Global South and demonstrates the effectiveness of participatory GIS in overcoming data scarcity and enhancing mobility analysis.

Purpose: The impact of ethical leadership on employee emotional exhaustion has been widely analyzed. However, the effect of the leader's moral intensity on the employee's emotional state has gone unnoticed. This research seeks to show that ethical management by the leader affects employees' emotional exhaustion in accordance with the scope of the leader's moral standards. Their relationship is not necessarily linear but adopts a curvilinear pattern as a function of the leader's ethical intensity, and affective commitment acts as a mediating mechanism. Methods: A simple mediation model is used. The sample consists of 448 professionals in six organizations in the Colombian electrical sector who responded to an online questionnaire. The sampling is probabilistic by conglomerates. Findings: This research revealed that ethical behavior is voluntary in nature and develops in an environment of trust. When the ethical leader surpasses specific moral turning points, the follower's learning becomes an obligation, preventing affective fluidity. A search for acceptance implies constant consumption of resources that weakens the follower's capacity for self-regulation until it is exhausted. Furthermore, when the leader's expectations are difficult to achieve, affective commitment causes emotional ambiguity in followers, intensifying the stress associated with their role and increasing the likelihood of burnout. Originality: This research challenges conventional wisdom about the positive, linear effect of ethical leadership on employees' emotional state. It broadens the understanding of this management style and fills a crucial knowledge gap by introducing a new perspective. Practical Implications: A horizontal, balanced, and accessible leader can quickly convey a convincing ethical discourse. Emphasizing the vulnerability and importance of human beings can help prevent moral anguish and ethical distances. This, in turn, provides followers with resources to mitigate exhaustion and strengthen commitment.

The wastewater problem is becoming an increasingly significant environmental issue affecting various aspects of human activities. The generation of reactive oxygen species (ROS) in water through certain physicochemical processes, which can oxidize or degrade various organic and chemical compounds, offers an effective purification method. The main advantage of these methods is the elimination of chemical reagents. Aim of this work is to investigate the possibility of regulating the ROS content in water by altering the characteristics of the plasma discharge that occurs under hydrodynamic cavitation in the water flow. ROS concentration was determined using the permanganate oxidation titration technique. It was found that the content of various reactive oxygen species in water after treatment can be controlled by varying the power of the electrical pulses applied. Additionally, the study obtained the dependence of ROS concentration in treated water samples on storage time, indicating a prolonged effect of sonoplasma treatment on the antibacterial properties of water. Analysis of water samples using a biological test culture of Salmonella typhimurium revealed bacteriostatic properties of the water for at least 48 hours after sonoplasma discharge treatment.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc, an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 meters in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Mosquitoes are responsible for the transmission of numerous pathogens, including Plasmodium parasites, arboviruses and filarial worms. They pose a significant risk to public health with over 200 million cases of malaria per annum and approximately four billion people at risk of arthropod-borne viruses (arboviruses). Mosquito populations are geographically expanding into temperate regions and their distribution is predicted to continue increasing. Mosquito symbionts, including fungi, bacteria and viruses, have desirable traits for mosquito disease control including spreading horizontally and vertically through mosquito populations and potentially colonising multiple important vector species. Paratransgenesis, genetic modification of mosquito symbionts with effectors to target the pathogen rather than the vector, is a promising strategy to prevent the spread of mosquito-borne diseases. A variety of effectors can be expressed but venom toxins are excellent effector candidates because they are target specific, potent, and, stable. However, the only toxins to be explored in mosquito paratransgenesis to date are Scorpine and mutated phospholipase A2. To enhance the scope, effectiveness, and durability of paratransgenesis, an expanded arsenal of effectors is required. This review discusses other potential toxins effectors for future paratransgenesis studies based on prior in vitro and in vivo antiparasitic and antiviral studies and highlights the need for further research and investment in this area. In terms of mosquito-borne diseases, paratransgenesis strategies have been developed to target Plasmodium. We postulate the potential to apply this principle to target arboviruses using antiviral toxin effectors.

In this study, we aimed to investigate the differences in tissue surface adaptation and the variations in distances between reference points on the polished surfaces of 3D-printed denture bases with different build angles. The build angles were 0°, 30°, 60°, and 90°, with 15 denture bases printed for each angle. Using the Geomagic Control® software, a 3D best-fit alignment was conducted between the denture base tissue surface and the reference shape of the edentulous maxilla model to calculate the root mean square error. The distances between reference points on the polished surface were measured using digital calipers. One-way analysis of variance was conducted for statistical analysis. The adaptation, as measured by the root mean square error, varied significantly among denture bases with different build angles. The distances between the anterior and posterior reference points of the polished surface were also significantly different. However, within the limitations of this study, the variations in adaptations and dimensional accuracy across different build angles were within clinically acceptable ranges. In clinical practice, the print angle can be adjusted based on factors, such as printing time, resin consumption, and the number of denture bases being printed simultaneously.

Indoor residual spraying (IRS) and use of insecticide treated bed nets for malaria vector control have contributed substantially to reduction in malaria disease burden. However, these control tools have important shortcomings including being donor-dependent, expensive, and often fail because of insufficient uptake. We assessed the safety and efficacy of a user-friendly, locally tailored malaria vector control approach dubbed ‟House Decoration for Malaria Control” (HD4MC) based on incorporation of a WHO-approved insecticide, pirimiphos-methyl CS (Actellic 300CS), into a customary hut decoration practice in rural Uganda where millions of the most vulnerable and malaria-prone populations live in mud-walled huts. Three hundred and sixty households were randomly assigned to either HD4MC (120 households), IRS (120 households) or control group without any wall treatment (120 households). Entomological indices were assessed using pyrethrum spray catch, CDC Light traps and human landing catches. Organophosphate (OP) toxicity on acetyl cholinesterase activity among applicators of HD4MC was evaluated using Test-mate (Model 400) erythrocyte acetylcholinesterase (AChE) test V.2 whereas toxicity in household occupants was monitored clinically. OP level in house dust was analyzed using reversed-phase high-performance liquid chromatography (RP-HPLC). Entomological indices were compared between the 3 study arms at 1.5, 3- and 6-months post intervention. HD4MC- and IRS-treated huts had significantly reduced malaria vector density and feeding rate compared to control huts. There was no significant reduction in acetylcholinesterase activity at 1.5- and 24-hours post exposure. Organophosphate exposure did not result in any serious adverse event among the household occupants. In conclusion, HD4MC was safe and had comparable efficacy to canonical IRS.

Differential Evolution (DE) is an evolutionary algorithm that has become increasingly popular due to its effectiveness and efficiency. However, its adaptation for multi-objective optimization requires further investigation. To address this issue, a novel algorithm called ILSDEMO has been proposed, which incorporates indicator-based selection and local search into a self-adaptive DE. ILSDEMO employs an archive population to store non-dominated solutions, generating an initial population uniformly distributed over the feasible solution space using orthogonal design. Additionally, two variants of DE are used to expedite convergence, while q-Gaussian mutation is utilized to exploit better trial individuals. The k-nearest neighbor rule is used to eliminate crowded solutions, and indicator-based selection is employed to generate a new parent population without diversity preservation. The performance of ILSDEMO was investigated on the test instances from the ZDT series and DTLZ series in terms of the selected indicators. The results suggest that ILSDEMO accurately and evenly approximates the true Pareto front compared to NSGAII, IBEA, and DEMO.

This work focused on Diplectanum aequans, a gill parasite of European seabass Dicentrarchus labrax. The analyses allowed us to detect factors regulating parasite distributions on Corsican fish farms and to highlight the ecological structure of D. aequans communities on gills of fish. The study of parasite distribution showed that bigger fish appear more parasitized and that the infection dynamics of D. aequans can be explained by several factors such as biotic factors or farm environment conditions. The study of gill repartition of D. aequans showed that parasites tend to have a homogeneous distribution with no statistically significant difference in infection between the two sides of fish. However, the distribution of the number of parasites on gill arches varied according to the total number of parasites. The results differ depending on infection degree and host weight. When parasites are numerous, the individuals are distributed on the gill arches according to an anterior-posterior gradient, while with low rates of infection, the parasites are randomly distributed on the four arches. The spatial distribution of D. aequans appears to be determined by the differential action of water flow through the gill arches and the sizes of anterior arches. We also proposed a tool to reduce sampling effort and allow optimal exploitation for fish farmers.

The current era in which we live is centered on information technology because we live in the renaissance of information and its expansion on multiple scales and fields. People who have a high knowledge of technology and a passion for it seek to disseminate this information and process it correctly, and computers have now become one of the most essential devices for government operations. Large companies have already begun to use it in most businesses, and it has helped direct some government procedures for joint e-government, which allows people to access government services from home from their computers. It is also not hidden that with the increase in computer use, electronic crimes increased, and this contributed to the formation of laws. In various countries to deal with them. As technology evolves, the sophistication and impact of these cyber threats continue to grow, posing significant challenges to cybersecurity. This research discusses the role against cybercrime, the regulations, and the allowable behavior of using technology in Saudi Arabia. It analyzes cybercrime in the Kingdom and the system for combating cybercrime.

Introduction: Multiple sclerosis (MS) is a long-term inflammatory disease that affects the central nervous system due to an abnormal immune response. Hormonal and enzyme changes in these patients can influence the progression and prognosis of the disease. In this research, the levels of cortisol, adrenocorticotropic hormone (ACTH), melatonin, and lactate dehydrogenase (LDH) in the blood of MS patients were studied. Methods: This study involved 50 MS patients and 50 healthy individuals. The levels of ACTH, melatonin, and cortisol in the blood were measured using specific ELISA kits. The data obtained were then analyzed statistically using SPSS software. Results: The findings revealed a significant increase in the levels of cortisol and LDH in MS patients (P

More than 90% of patients affected by mastocytosis are characterized by a somatic point mutation of c-KIT, which induces ligand-independent activation of the receptor and downstream signal triggering, ultimately leading to mast cell proliferation and survival. The most frequent mutation is c-KIT p.D816V, but other rarer mutations can also be found. These mutations often have a very low variant allele frequency (VAF), well below the sensitivity of common next-generation sequencing (NGS) methods used in routine diagnostic panels. Highly sensitive methods are developing for detecting mutations. This review summarizes the current indications on the recommended methods and on how to manage and interpret molecular data for the diagnosis and follow-up of patients with mastocytosis.

Food tourism, an increasingly vital sector within tourism, significantly impacts both the economic vitality and cultural enrichment of tourist destinations. It attracts a diverse range of visitors, both local and international, while enhancing appreciation for regional culinary traditions. This study employs scenario planning to uncover pivotal factors influencing food tourism in rural Iran. Methodologically, it combines objective-driven, descriptive-analytical approaches with future studies methodologies. Data sourced from literature reviews, field studies, and expert surveys, facilitated by MICMAC and ScenarioWizard software, identified six key drivers: promotional activities, pricing, food quality, infrastructure, government support, and investment. The study forecasts future scenarios based on these drivers, revealing optimistic, stagnant, and crisis condi-tions impacting food tourism development. To achieve favorable outcomes, prioritizing these drivers is essential, ensuring sustainable growth and seizing tourism opportunities in rural Iran. This research contributes by systematically analyzing critical factors driving food tourism using scenario-based insights, essential for sector longevity and prosperity.

Anthocyanins are well known as colorants of flowers, but many other invisible functions might have been more important during the evolution of complex biosynthesis networks in plants. The number of anthocyanin decorating enzymes, the subtle control of structural genes by numerous transcription factors, and routes of intracellular anthocyanin transport are reviewed here. Various ecological functions of anthocyanins hold the key to understanding evolutionary trajectories that lead to the success of these pigments. Proposed functions include carbon sinks that prevent excessive sugar levels, sun blockers protecting the photosynthesis apparatus, antioxidants scavenging reactive oxygen species, providing camouflage, and attraction of pollinators and seed dispersers. Anthocyanins clearly fulfill different functions in different plant organs. It is currently believed that protective functions in leaves gave rise to the biosynthesis network and that flower and fruit coloration evolved later.

Background/Objectives: The aim of the narrative review was to identify information on the impact of technological interventions (such as telehealth and mobile health) on the health promotion of cardiac patients from diverse populations; Methods: Online databases of PubMed, and Cochrane Library were searched for articles in the English language regarding technological interventions for health promotion in cardiac patients. In addition, a methodological quality control process was conducted. Exclusion was based on first reading the abstract and then the full manuscript confirming content was not related to cardiac patients and technological interventions; Results: 11 studies were included in the review after quality control analysis. The sample size reported in these studies ranged from 12 to 1424 subjects. In 8 studies mobile phones, smartphones and apps were used as mHealth intervention with tracking and texting component; 2 studies used videoconferencing as a digital intervention program, while one study focused on using physical activity trackers; Conclusions: This review highlights the digital divide as individuals with limited eHealth literacy and lack of technological knowledge are not motivated or able participate in these interventions. Finding methods to overcome these barriers are important and can be solved to some extent by providing the technology and technical support.

An approach to estimate the dynamic characteristic of multilayered three-dimensional cubic quasicrystal cylindrical shells, annular plates, and truncated conical shells with different boundary conditions is presented. These investigated structures can be in a vacuum, totally filled with quiescent fluid, and subjected to internal flowing fluid, in which the fluid is incompressible and inviscid. The velocity potential, Bernoulli’s equation, and impermeability condition have been applied to the shell-fluid interface to obtain an explicit expression, from which the fluid pressure can be converted into the coupled differential equations in terms of displacement functions. The state-space method is formulated to quasicrystal linear elastic theory to derive the state equations for three structures along the radial direction. The mixed supported boundary conditions are represented by means of the differential quadrature technique and Fourier series expansions, respectively. A global propagator matrix, which connects the field variables at the internal interface to those at the external interface for the whole structure, is further completed by joint coupling matrices to overcome the numerical instabilities. Numerical examples show the correctness of the proposed method and the influence of the semi-vertical angle, different boundary conditions, and the fluid debit on the natural frequencies and mode shapes for various geometries and boundary conditions.

Methylene blue (MB) is a dye hazardous pollutant widely used in several industrial processes that represents a relevant source of water pollution. Thus, the research of new systems to avoid its environmental dispersion, represents an important goal. In this work, an efficient and sustainable nanocomposite material based on green gold nanoparticles for MB water remediation, was developed. Starting from reducing and stabilizing characteristics of compounds naturally present in Lambrusco winery waste (grape marc) extracts, green gold nanoparticles (GM-AuNPs) were synthesized and deposited on a supporting membrane to create an easy and stable system for water MB decontamination. GM-AuNPs, with a specific plasmonic band at 535 nm, and the modified membrane were firstly characterized by UV-vis spectroscopy, X-Ray diffraction (XRD), and electron microscopy. Transmission electron microscopy analysis revealed the presence of two breeds of crystalline shapes, triangular platelets and round-shaped pentatwinned nanoparticles, respectively. The crystalline nature of GM-AuNPs was also confirmed from XRD. The photo-catalytic performance of the modified membrane was evaluated under natural sunlight radiation obtaining a complete MB degradation (100%) in 116 minutes. A pseudo-first-order kinetic described the photocatalytic process with a rate constant k equal to 0.044 ± 0.010 min-1. The modified membrane demonstrated high stability since it was reused, without any treatment, for 3 months and for at least 20 cycles maintaining the same performances. GM-AuNPs-based membrane was also tested with other water pollutants (methyl orange, 4-nitrophenol and rhodamine B revealing a high selectivity towards MB.

Background/Objectives: Patients with chronic kidney disease (CKD) have an increased risk of cardiovascular disease (CVD). Carotid intima-media thickness (C-IMT) is a non-invasive marker of atherosclerosis. Low omega-3 polyunsaturated fatty acids (PUFA) intake and vitamin D deficiency are potential cardiometabolic risk factors in patients with CKD. The aim of this study was to evaluate dietary intake and status of omega-3 PUFA and vitamin D in pre-dialysis and hemodialysis patients and to examine the link of dietary α-linolenic acid (ALA) and fish consumption with blood pressure and C-IMT in CKD patients. Methods: All 77 selected patients (36 pre-dialysis, 41 on hemodialysis) underwent standardized clinical, nutritional, and laboratory assessments. Repeated 24-hour recalls were performed to assess dietary intake. The fatty acid profile was determined by gas-liquid chromatography. Results: Inadequate vitamin D intake and vitamin D status were found in 95% of patients. PUFAs profiles did not differ between hemodialysis and pre-dialysis participants. Dietary intake of ALA was negatively correlated with systolic blood pressure (SBP) (p=0.007), C-IMT (p=0.002), serum CRP (p=0.044), iPTH (p=0.01) and 25(OH)D3 (p=0.006). ALA intake of more than 0.23 g/daily was associated with lower SBP (p=0.001), serum 25(OH)D3 (p=0.004), and C-IMT (p=0.002). Conclusions: This study contributes to a better understanding of the relationship between dietary ALA intake and C-IMT in CKD. The results of this study could emphasize the significant role of the high prevalence of vitamin D deficiency and inadequate omega-3 PUFA intake and status considering CVD health in CKD patients.

We consider the problem of finding the largest clique of a graph. This is an NP-hard problem and no exact algorithm to solve it exactly in polynomial time is known to exist. Several heuristic approaches have been proposed to find approximate solutions, Markov Chain Monte Carlo being one of those. In the context of Markov Chain Monte Carlo, we present a class of ``parallel dynamics'', known as Probabilistic Cellular Automata, which can be used in place of the more standard choice of sequential ``single spin flip'' to sample from a probability distribution concentrated on the largest cliques of the graph. We perform a numerical comparison between the two classes of chains both in terms of the quality of the solution and in terms of computational time. We show that the parallel dynamics are considerably faster than the sequential one while providing solutions of comparable quality.

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in Tunnel Boring Machine (TBM) construction tunnels, traditional methods have significant technical limitations. This paper analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. Utilizing numerical simulation, a model for advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space. The seismic wave reflection method can effectively receive information from unfavorable geological formations in front of the tunnel face. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, the method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Field verification of advanced geological forecasting based on ellipsoid positioning velocity analysis for TBM tunnels is conducted. The results indicate that this method can be used to obtain the three-dimensional velocity of the predicted unfavorable geological formations in front of the tunnel and is effective for the application of geological forecasting in TBM construction tunnels, thus providing crucial references and assurances for construction.

The finite volume method (FVM) is a kind of numerical analysis method that can take into account topography and apply local subdivision cells as the finite element method (FEM). The adjoint state method (ASM) can easily calculate the gradient vector of the objective function for the inversion. The author developed a program that applied these methods to 3-D Magnetotelluric (MT) inversion and verified it using test models. As a result, it was confirmed that the program can perform analysis properly and efficiently considering topography and subsurface resistivity heterogeneity.

This study explores the potential of yoga in developing crucial executive competencies within the context of transitional economies in Eastern Europe. As public sector enterprises face increasing competition, both domestically and internationally, there is a growing need for executives to acquire new skills. Through the lens of mindfulness, this research investigates the impact of yoga on the helping behavior and cultural intelligence of business leaders in a transitional economy. The findings reveal that yoga practices significantly enhanced participants' helping behavior and cultural intelligence. The study demonstrates that approximately two-thirds of the impact on helping behavior can be attributed to increased mindfulness, with the remaining one-third stemming directly from yoga practice. In terms of cultural intelligence, the results indicate that 88% of the improvement is due to heightened mindfulness, while 12% is directly attributable to yoga practices.

Digital image compression is applied to reduce camera bandwidth and storage requirements, but real-time lossless compression on a high-speed high-resolution camera is a challenging task. The article presents hardware implementation of a Bayer colour filter array lossless image compression algorithm on an FPGA-based camera. The compression algorithm reduces colour and spatial redundancy and employs Golomb-Rice entropy coding. A rule limiting the maximum code length is introduced for the edge cases. The proposed algorithm is based on integer operators for efficient hardware implementation. The algorithm is first verified as a C++ model and later implemented on Xillinx Zynq UltraScale+ device using VHDL. An effective tree-like pipeline structure is proposed to concatenate codes of compressed pixel data to generate a bitstream representing data of 16 parallel pixels. The proposed parallel compression achieves up to 56% reduction in image size for high resolution images. Pipelined implementation without any state machine ensures operating frequencies up to 320 MHz. Parallelised operation on 16 pixels effectively increases data throughput to 40 Gbit/s while keeping total memory requirements low due to real-time processing.

This thesis presents a comprehensive review of cancer, with a particular focus on its physical and biochemical basis. Cancer, a leading cause of mortality worldwide, is characterized by the uncontrolled growth and spread of abnormal cells. Understanding the fundamental differences between cancerous and normal cells is essential for developing effective diagnostic tools and therapeutic strategies. The review begins by exploring the physical anomalies of cancer cells, including their irregular shape, size, and structural organization. These cells often exhibit altered cell membranes, disrupted cytoskeletal architecture, and irregularities in cell adhesion and communication, which contribute to their invasive and metastatic potential. The biochemical basis of cancer is examined in detail, with an emphasis on the dysregulation of key metabolic pathways. Cancer cells undergo profound metabolic reprogramming, often referred to as the Warburg effect, where they preferentially utilize glycolysis over oxidative phosphorylation, even in the presence of oxygen. This metabolic shift supports the rapid proliferation of cancer cells and their survival in hypoxic conditions. Additionally, cancer cells produce abnormal proteins that contribute to tumor progression, such as mutant p53, and fail to produce critical tumor suppressor proteins like RB1, leading to unchecked cell division and evasion of apoptosis. Furthermore, the thesis reviews the role of oncogenes and tumor suppressor genes in cancer development, highlighting how mutations and epigenetic alterations drive the transformation of normal cells into malignant ones. The complex interplay between these genetic changes and the tumor microenvironment is also discussed, emphasizing the importance of signaling pathways, immune evasion, and angiogenesis in cancer progression. This detailed review synthesizes current research and insights from authoritative sources, including peer-reviewed journals and foundational medical texts, to provide a thorough understanding of the physical and biochemical underpinnings of cancer. The findings underscore the importance of continued research in these areas to advance the diagnosis, treatment, and prevention of cancer.

In Rwanda, following the 1994 Genocide against the Tutsi, many people were found guilty of genocide crimes and imprisoned. Their children ended up in a situation of ambiguous loss during and after a parent’s imprisonment. The article presents the multidimensional impact of this loss on the everyday lives of these children and their families according to key themes as they emerged from an ethnographic study in which 21 children and their family members participated. Themes include changed family dynamics and family stress, economic deprivation, incomprehension of a parent’s criminal past, the social stigma of being a child of a génocidaire, and strategies used to make the loss bearable. The uniqueness of the ambiguous loss as experienced by children of perpetrators of genocide in Rwanda compared to those of perpetrators of the Holocaust or other mass crimes relates to an amalgam of factors specific for the context of post-genocide Rwanda; major ones being the severity of genocidal crimes and gacaca courts Rwanda chose as its main form of transitional justice. The case study illustrates how using the prism of intergenerational relations helps to understand some of the transformative and enduring effects of a crisis that deeply affects a society.

This study investigated the relationship between environmental, social, and governance (ESG) disclosure and the performance of Saudi Arabian companies. We analysed panel data from the 100 non-financial companies listed on the Saudi stock exchange (Tadawul) from 2017 to 2022. Using fixed effects, random effects, and generalised method of moments (GMM) models to account for endogeneity concerns, we examined the impact of ESG disclosure on the return on assets (ROA), return on equity (ROE), and Tobin’s Q. An ESG index was constructed through principal component analysis of individual environmental, social, and governance scores. Our results indicate a significant positive relationship between ESG disclosure and companies’ key performance variables across all models. These findings are consistent with stakeholder theory and signalling theory, suggesting that comprehensive ESG practices can lead to better financial performance and serve as a positive signal to stakeholders. The study also reveals sector-specific differences, with non-manufacturing firms showing stronger positive relationships between ESG disclosure and performance measures compared to manufacturing firms. Additionally, we find that firm size, age, and liquidity are important factors influencing the ESG–performance relationship. This research contributes to the growing literature on ESG and corporate performance in emerging markets, offering valuable insights for policymakers, investors, and corporate practitioners in Saudi Arabia’s evolving sustainable business landscape. Our findings underscore the importance of ESG disclosure in driving sustainable and responsible business practices in the region.

This paper proposes a novel approach to transitioning the classical TLS 1.3 protocol to a post-quantum version with minimal changes to the existing infrastructure. By integrating a hybrid KEMTLS protocol with Identity-Based Encryption (IBE), the solution achieves post-quantum security without incorporating post-quantum keys into TLS certificates. This enables the protocol to function with Certificate Authorities that do not support post-quantum keys, ensuring its security while simplifying the transition. The main challenge addressed is the immediate need for support of post-quantum algorithms by Certificate Authorities and servers. The proposed solution leverages existing certificates and keys, facilitating a smoother transition.

Production planning managers need much information within the organization to create the best production plan. This information is scattered throughout the organization. The planning unit must gather data from sales orders and specified priorities, and use information from production capacity assessments, in-line inventory levels, and many other variables to develop a reliable production plan. This research aims to examine the production planning system at a car part manufacturing company, diagnose the current issues in this area, and endeavor to develop a hierarchical production planning model at the AP levels for the company through a mixed-integer linear programming model.

Work has been undertaken in healthcare sector to explore ways of co-produce design responses with different communities, and organisations. However, we lack empirical analysis of how design thinking can help tackle complexity. To assist the (anonymised for peer review) programme, we curated the Ripple Framework to develop trust and attempt to address the challenges of complexity in nursing. Through a generative design process data was used to collaboratively define bespoke co-design pathways with 31 participants from six Scottish care providers over ten months. Thematic, content and matrix analyses produced insights to inform vignettes illustrating how design responded to complex social care sector needs, with a particular focus on fulfilment and flourishing of the care workforce. Drawing on our empirical material and using the Design Research Value Model (Rodgers et al.), we illustrate how we have developed care's social, cultural, and economic value, through co-design, this way contributing to strategies to address the care crisis.

Link prediction is a significant field in network science, which focuses on predicting the probability of the existence or formation of a link between nodes in a social network based on currently observed connections. The effectiveness of traditional algorithms can vary depending on the type of network, making some methods more suitable than others for specific scenarios. Recently, several efficient link prediction algorithms have been developed, demonstrating robust results in both prediction accuracy and interpretability. However, existing research has not clearly established the relationship between network characteristics and link creation mechanisms for community influence analysis and anomaly detection. The ability to predict complex networks with diverse features still requires further investigation. In light of this, we introduce a novel framework designed to combine the best features of different link prediction algorithms when applied to the network, with the aim of achieving more reliable predictions about how networks will evolve. According to the proposed framework, we first focus on the feature extraction stage. During this phase, we systematically identify and extract a comprehensive set of features from the network before moving onto the classification phase. Here, we utilize state-of-the-art ensemble learning models to assess and classify potential links within the network. By training our machine learning models on the extracted features, we can effectively predict whether a particular link is likely to form (positive link) or unlikely to form (negative link). The ML models were trained and evaluated using two datasets: Twitch and Facebook. Results: Additionally, we assessed their performance on these datasets by conducting specific preprocessing and hyperparameter tuning steps. The final prediction model achieves AUC values between 94% and 99.3%. This research contributes significantly to efforts aimed at enhancing link prediction in dynamic socisal network contexts, providing valuable insights into the effectiveness of different ML algorithms in predicting future connections and enhancing our understanding of network dynamics.

Bio-char is a charcoal-like substance made through the pyrolysis of organic biomass. It has the potential to address issues related to agriculture and the environment. This thorough analysis examines the potentials of bio-char in Ethiopia to enhance crop productivity, reduce climate change, and improve soil fertility. Drawing from academic databases and peer-reviewed literatures, the review explores the production and characterization of bio-char, its impact on soil fertility and crop productivity, and its role in carbon sequestration and greenhouse gas mitigation. The review emphasizes on how bio-char may improve plant nutrition and soil fertility, which raises agricultural yields and quality while improving drought resistance. The review also emphasizes the potential of bio-char for greenhouse gas mitigation and carbon sequestration, enhancing Ethiopia's efforts to mitigate climate change. Yet the study also points out several challenges and opportunities that need to be addressed to allow Ethiopians to use bio-char technology on a large scale. These include the need for appropriate policy and regulatory frameworks, the availability and accessibility of bio-char feedstocks, and the importance of continued research and development to optimize production methods and application strategies. The review concludes by highlighting the groundbreaking potential of bio-char in promoting environmentally friendly agricultural practices and sustainable agriculture in Ethiopia. It necessitates a coordinated effort to solve the problems and grasp the opportunities by making sound decisions in policy integration, research, and development. Ethiopia can advance its sustainable development goals by using bio-char to create a road towards increased food security, healthy soil, and environmental resilience.

This report aims to document the extent of plastic waste pollution in lorry stations in Accra, assess its impact on public health and propose actionable solutions for effective waste management in stations. By highlighting the severity of the issue as well as providing recommendations for solutions, we seek to inform and inspire municipal assemblies, policymakers and other stakeholders to take decisive action against plastic waste pollution in lorry stations in Accra. Plastic waste pollution is a growing concern in Ghana, with lorry stations being particularly affected due to high foot traffic and inadequate waste management infrastructure within stations. This report is significant because of its implications for addressing public health hazards, continuing the ongoing efforts to reduce plastic pollution in Ghana and addressing community concerns. Through this comprehensive analysis, Mckingtorch Africa aims to drive meaningful change and create a cleaner, healthier Ghana for present and future generations.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.

Autoimmune polyglandular syndrome (APS) comprises a complex association of autoimmune pathological conditions. APS Type 1 originates from loss-of-function mutations in the autoimmune regulatory (AIRE) gene. APS2, APS3, and APS4 are linked to specific HLA alleles within the major histocompatibility complex, with single nucleotide polymorphisms (SNPs) in non-HLA genes also contributing to disease. In general, variability in the AIRE locus and the presence of heterozygous loss-of-function mutations can impact self-antigen presentation in the thymus. In this study, Whole Exome Sequencing (WES) was performed on a sixteen-year-old female APS3A/B patient to investigate the genetic basis of her complex phenotype. The analysis identified two variants (p.Arg111Trp and p.Thr101Ile) of the hepatitis A virus cell receptor 2 gene (HAVCR2) encoding for the TIM-3 (T cell immunoglobulin and mucin domain 3) protein. These variants were predicted, through in silico analysis, to impact protein structure and stability, potentially influencing the patient’s autoimmune phenotype. While Confocal Microscopy analysis revealed no alteration in TIM-3 distribution between PBMCs isolated from the patient and those of a healthy donor, RT-qPCR showed reduced TIM-3 expression in the patient’s cells. A screening conducted on a cohort of thirty APS patients indicated that the p.Thr101Ile and p.Arg111Trp mutations were unique for the proband. This study opens the pathway for the search of TIM-3 variants possibly linked to complex autoimmune phenotypes, highlighting the potential of novel variant discovery in contributing to APS classification and diagnosis.

. Studies were initiated to find new effective fungicides to use under field conditions to identify novel options for improved disease management in sugar beet. Cercospora-leaf spot (CLS) of sugar beet, caused by Cercospora beticola Sacc., is a major foliar disease of sugar beet in all sugar beet growing areas, worldwide. The disease is now dominant in almost all sugar beet growing areas of Europe, including Hungary. The epidemic spread of this disease can cause up to 50% yield loss. The use of fungicides has been being an integral part of the control of CLS of sugar beet, due to lack of efficacy from non-chemical alternatives. In recent decades, the emergence of resistant strains in C. beticola Sacc. populations have reduced the efficacy of some fungicides (Quinoine inside Inhibitors (QoI) – FRAC Group 11; and DeMethylation-Inhibitors (DMI) – FRAC Group 3) because of repeated use over many uears. Resistance of CLS to fungicides belonging to different groups of active substances has been described in many countries of the world, including Hungary. The picolinamides are a new distinct group of fungal respiration inhibitors (Inhibition of Complex III) belonging to Quinoine inside Inhibitors (QiI). Two novel fungicides from the group of picolinamides (Qil – FRAC Group 21) – fenpicoxamid and florylpicoxamid– were tested and evaluated over two seasons, in vegetation period of 2020 and 2021 for controlling CLS of sugar beet in Hungary. Both fungicides were applied as straight formulated products at a range of dose rates, and they showed very effective control of CLS compared to the untreated control check plots and the reference fungicide product difenoconazole. The results consistently showed all tested dose rates of fenpicoxamid and florylpicoxamid provided effective control against CLS of sugar beet in addition to a clear dose response curve. Disease severity, in terms of area under the disease progress curve values (AUDPC), was significantly correlated with yield decrease, while no significant context existed between disease severity and sugar content of the roots. Additionally, the results showed in two investigated years, the efficacy of both picolinamide fungicides applied at 75 g ai/ha provided significantly better % control on CLS of sugar beet than the DMI fungicide difenoconazole applied at 100 g ai/ha dose rate. Fenpicoxamid is already registered in Europe in cereals and label extension is planned in sugar beet and other crops. Florylpicoxamid is not yet registered in Europe but is beginning to be approved in countries across the globe in a range of crops and is continuously under evaluation for potential markets.

Abstract: The corrosion inhibition effects of Drimia maritima (L.) Stearn sin. Urginea matima (L.) Backer on three different materials, i.e. as-cast Al-7 wt.% Si alloy, SAE 1020 low carbon steel and commercially pure Al samples, into a stagnant and naturally aerated 0.5 M NaCl solution are evaluated. For this purpose, both the potentiodynamic polarization curves and electrochemical impedance spectroscopy with equivalent circuit are utilized. It is found that inhibition effect in-creases up to certain minor Drimia maritima content. Adsorption isotherms (e.g. Langmuir and Temkin) have indicated that all three examined material comprise physical adsorption mecha-nism. Al-Si alloys have attained inhibition efficiency of about 96% at 25 oC with 1250 ppm of Drimia maritima, and ~43% with 625 ppm at 45 oC. On the other hand, the cp. Al and SAE 1020 samples attain ~ 89% and 68% with 1250 ppm and 500 ppm at 25 oC, respectively. This clearly in-dicates that the dosage of Drimia maritima green inhibitor into NaCl solution posses certain sus-ceptibility for each distinctive material examined. Impedance parameters obtained by using CNLS (complex non-linear least squares simulations) are correlated and discussed.

Type II diabetes is a major coronavirus disease comorbidity that can be prevented through a diet. Capsicum annuum (chili) consumption is often suggested, and using functional food cultivars is the most effective strategy. However, the general combining ability (GCA), specific combining ability (SCA), and heterotic effect of functional biochemicals (polyphenol content, antioxidant activities, and α-glucosidase inhibitory compounds) remain poorly known in C. annuum. This study aimed to estimate those parameters in C. annuum by using five different genotypes and their hybrid com-binations based on growth characteristics, yield, yield components, and fruit functional biochem-icals. The F1 and F1R progenies were obtained from crosses in a greenhouse and full diallel design. Each parent used in this study had a GCA advantage for each character. The hybrid combination of 074 x 005 and 435 x 367 displayed the best yield results. However, the results indicated the opposite regarding α-glucosidase inhibitory. The heterotic effect of functional biochemicals was observed for traits related to genotypes, polyphenol content, antioxidant activity, α-glucosidase inhibitory compounds, and similar properties related to yield and yield components, indicating their hybrid use in chili production.

Videogames are recognized as significant tools and mediums to be used in civic participation in spatial planning and fostering local communities. As the phenomenon is widely recognized in papers presenting singular case studies and broader analyses in the field, selecting such serious games with certain characteristics remains unclear. The informed process of choosing games with particular properties regarding genesis, graphic style, genre, and complexity as the response for specified needs and process assumptions appears to be supportive in preventing unnecessary costs and data overproduction. Such avoidance is an important part of sustainable digital transformation. Therefore there is a need for the more conscious process of selecting videogames to be used in a participatory process. The following paper aims to propose a decisional instrument that could be useful for specifying the characteristics of games to be utilized in participation. They performed a multicriteria analysis of documented cases of implementing video games in civic engagement, allowing the creation of a set of numeric indicators that help determine the properties of games that will be most appropriate for given process assumptions. Such a tool can prevent overproducing data on the one hand and may cause dissemination presented way of handling the participation process on the other.

Recently, neglected and underutilized species (NUS) have deservedly come to the forefront of scientific interest because they can contribute to the human nutrition, due to the content of bio-active substances such as antioxidants. Despite the great diversity and rich tradition in use of Philippine NUS, the nutritional properties of many edible plants remain unexplored. The main objective of this study was to evaluate various parts of eleven NUS fruits and vegetables traditionally consumed in the Philippines, namely Allaeanthus luzonicus, Canarium ovatum, Dillenia philippinensis, Ficus pseudopalma, Flacourtia indica, Flacourtia inermis, Garcinia intermedia, Heliotropium arboreum, Posoqueria latifolia, Stelechocarpus burahol and Sterculia quadrifida for their total phenolic content (TPC) and in vitro antioxidant activity (DPPH and ORAC assays). The strongest antioxidant activity obtained in both assays were inflorescence of A. luzonicus (DPPH IC50 = 91.0 μg/mL, ORAC IC50 = 37.9 μg/mL) and fruit of S. burahol (DPPH IC50 = 253.7 μg/mL, ORAC IC50 = 32.2 μg/mL). These two species also had the highest TPC (202.1 and 133.0 µg GAE/mg extract, respectively). For all samples tested, the strong correlation was found between TPC and antioxidant activity. Based on our results, A. luzonicus and S. burahol have a promising potential as novel antioxidant rich food.

(1) Background: although current theories of Post-Traumatic Stress Disorder (PTSD) had focused on depressed mood and intrusion, the mechanism through which depression increases the risk of PTSD remains unclear. Research has concentrated on the hyper-active negative valence system (NVS) (e.g., increased fear and threat responses), but a significant role for hypo-active positive valence system (PVS) (e.g., less neural activation towards rewards) was conceivable. (2) Methods: To investigate the implicit processing in a refugee’s sample, ratings of arousal, dominance, and valence for 60 IAPS pictures (positive, neutral, and negative) were collected from 42 individuals with probable PTSD, and 26 trauma-exposed individuals (M age = 28.49 years, SD = ± 7.78). (3) Results: ANOVAs revealed a main group effect (η2p = 0.379) on arousal, dominance, and valence dimensions, and pictures’ categories (η2p = 0.620). Participants with PTSD responded to unpleasant images with enhanced arousal and lower valence ratings compared with individuals without PTSD. Moreover, arousal ratings were negatively correlated with valence (r = - 0.709, p &lt; 0.01). (4) Conclusions: Our findings supported evidence according to which PTSD origins a state of maladaptive hyperarousal and difficulties in regulating emotions. Positive stimuli are considered less pleasant providing evidence for a complementary involvement of NVS and PVS in PTSD development.

With the rapid advance in quantum computing, quantum security is now an indispensable property for any cryptographic system. In this paper, we study how to prove the security of a complex cryptographic system in the quantum random oracle model. We first give a variant of Zhandry’s compressed quantum random oracle (CStO), called compressed quantum random oracle with adaptive special points (CStOs). Then, we extend the on-line extraction technique of Don et al (EUROCRYPT’22) from CStO to CStOs. We also extend the random experiment technique of Liu and Zhandry (CRYPTO’19) for extracting the CStO query that witnesses the future adversarial output. With these preparations, a systematic security proof in the quantum random oracle model can start with a random CStO experiment (that extracts the witness for the future adversarial output) and then convert this game to one involving CStOs. Next, the on-line extraction technique for CStOs can be applied to extract the witness for any on-line commitment. With this strategy, we give a security proof of our recent compact multi-signature framework that is converted from any weakly secure linear ID scheme. We also prove the quantum security of our recent lattice realization of this linear ID scheme, by iteratively applying the weakly collapsing protocol technique of Liu and Zhandry (CRYPTO 2019). Combining these two results, we obtain the first quantum security proof for a compact multi-signature.

Introduction: People with severe and persistent mental illness (SPMI) present unique challenges in mental healthcare due to the enduring nature and complexity of their conditions. This study evaluated the clinical effectiveness and potential cost benefits of a multidisciplinary community psychosocial rehabilitation team catering to individuals with SPMI in Nova Scotia, Canada. Method: The Canadian billing system of Medical Service Insurance (MSI) was used to compare inpatient service use and emergency department visits during the year before and the year after receipt of one year of community rehabilitation for patients referred to the team from September 2016 to September 2017. Result: Results demonstrated a statistically significant reduction in mean admission rates and length of inpatient admissions in the year following rehabilitation compared to the pre-rehabilitation year. A substantial percentage of patients experienced no inpatient admissions (88% v. 60%) or ED visits (82% v. 67%) in the post-rehabilitation year compared to the pre-rehabilitation year. There was a significant reduction in inpatient days by 90%, translating into substantial cost savings. The findings highlight the potential economic benefits of community rehabilitation for people with SPMI. Conclusion: This uncontrolled study suggests that community rehabilitation is associated with favourable clinical outcomes for individuals with SPMI in terms of reduced inpatient service use and related costs. Further research into community psychosocial rehabilitation services in the Canadian setting is needed, including controlled and cost-effectiveness studies.

Background/Objectives: Multisystem Inflammatory Syndrome in children (MIS-C) is a rare but severe post-infectious complication of COVID-19, that often requires admission to the Pediatric Intensive Care Unit (PICU). The present study aimed to compare the demographic, clinical and laboratory characteristics between children diagnosed with MIS-C who were admitted to the PICU or did not require PICU admission. Methods: Children diagnosed with MIS-C from September 2020 to April 2023 were included in this case-control study. Patient’s demographic, clinical and laboratory data, were collected from medical records. Results: Fifty children with MIS-C were included in the study [median (IQR) age: 7.5 (4.3, 11.4) years, 28/50 (56%) males]. Twenty-two (22/50, 44%) children required admission to the PICU. In the multivariate regression analysis, hepatic (OR:12.89, 95%CI: 1.35-123.41, p-value=0.03) and cardiological involvement (OR:34.55, 95%CI: 2.2-541.91, p-value=0.01) were significantly associated with hospitalization at the PICU. Regarding the laboratory and imaging parameters during the first 48 hours from admission, D-dimer levels higher than 4μg/mL and decreased Left Ventricular Ejection Fraction were associated with an increased risk of PICU admission (OR:7.95, 95%CI:1.48-42.78, p-value=0.02 and OR=1.28, 95%CI:1.07-1.53, p-value=0.01). Children who were admitted to the PICU were more likely to develop complications during their hospitalization (10/22, 45.5% vs. 3/28, 10.7%, p-value=0.005) and were hospitalized for more days, than children in the pediatric wards (median length of stay (IQR): 20 (15,28) days vs. 8.5 (6, 14) days, p-value&lt;0.001). Conclusions: In the present study, cardiovascular, hepatic involvement and increased D-dimer levels in children with MIS-C were associated with admission to the PICU.

Rationale: Post-hepatectomy liver failure (PHLF) remains a significant risk for patients undergoing partial hepatectomy (PHx). Reliable prognostic markers and treatments to enhance liver regeneration are lacking. Plasma nanoparticles, including lipoproteins, exosomes, and extracellular vesicles (EVs), can reflect systemic and tissue-wide proteostasis and stress, potentially aiding liver regeneration. However, their role in PHLF is still unknow. Methods: Our study included 9 patients with hepatocellular carcinoma (HCC) undergoing PHx: 3 patients with PHLF, 3 patients undergoing the Associating Liver Partition and Portal vein Ligation for Staged hepatectomy (ALPPS) procedure and three matched controls without complications after PHx. Patient plasma was collected before (PRE), 1 and 5 days (POD1, POD5) after PHx. EVs were isolated by ultracentrifugation and extracted proteins subjected to quantitative mass spectrometry using a super-SILAC mix prepared from primary and cancer cell-lines. Results: We identified 2625 and quantified 2570 proteins in EVs in PHx patients. Among these, 53 proteins were significantly upregulated and 32 were downregulated in patients with PHLF compared to those without PHLF. Furthermore, 110 proteins were upregulated and 78 were downregulated in PHLF compared to patients undergoing ALPPS. The EV-proteomic signature in PHLF indicates significant disruptions in protein translation, proteostasis, intracellular vesicle biogenesis, as well as alterations in proteins involved in extracellular matrix (ECM) remodeling, metabolic and cell cycle pathways, present already before PHx. Conclusion: Longitudinal proteomic analysis of EV´s circulating in human patient plasma undergoing PHx uncovers proteomic signatures associated with PHLF which reflect dying hepatocytes and endothelial cells and are present before PHx already. Our results support EVs as potential biomarkers and therapeutic targets for liver regeneration and failure.

Electric vehicles are in focus and growing, with new adoptions every day. Its wide use represents a new scenario and challenge for the electrical power system due to the high charge storage requirements. Predicting these loads using artificial neural networks proves to be a very efficient tool for solving time series. This work uses a multilayer perceptron network through supervised backpropagation training with Bayesian regularization to improve generalization, reducing overfit- ting errors. In this research, the aggregation of the actual consumption of 200 homes and 348 plug-in electric vehicles is used in the training and forecasting process, and to validate the method developed, MAPE was used. Short-term forecasts were made considering the year’s four seasons, predicting the next 24 hours of total aggregate demand from homes and vehicles. The methodology presented significant and relevant results using hybrid training for the problem, with the potential to be applied in the real world.

Introduction: Low back pain (LBP) is the leading cause of lost productivity, absenteeism, disability, and early retirement worldwide. LBP treatment should be comprehensive, including prevention, education, diagnosis, and various treatment methods. Treatment can be divided into pharmacological and non-pharmacological. The role of physiotherapy in the comprehensive treatment of LBP is very important. Elastic therapeutic tape, also called kinesiology tape or therapeutic kinesiology tape, has been used for about 50 years. Purpose: The aim of this study was to evaluate the effectiveness of Kinesio taping in patients suffering from (LBP) and its possible effect on the swing of the center of pressure (COP), balance, gait, improvement of coordination, and pain reduction. Material and methods: A total of 60 patients aged 20 to 83 years (54% women) were examined, all of whom met the inclusion and exclusion criteria. In the experimental group, in addition to comprehensive rehabilitation, kinesiotaping of the L1-L5 spine was used. The control group underwent balance control training. The research tools used included the Bohannon single-leg standing test, the FAC scale - Functional Ambulatory Category, the Podsiadlo and Richardson Standing and Walking Test - Timed Up and Go (TUG), lumbar spine mobility assessment, Lasegue's sign and examination of neurological deficit symptoms. and self-assessment of pain using the Visual Analogue Scale (VAS). Results: Kinesio taping has a statistically significant effect on postural control, balance, gait, coordination, and pain. The main parameter with a statistically significant effect on the effects of Kinesio taping was age (below 55 years), which was also observed in the control group. Conclusions: Kinesio taping is an effective method in the treatment of LBP. It improves postural control, balance, gait, coordination, and pain.

Doravirine (DOR) is a non-nucleoside reverse transcriptase inhibitor (NNRTI) with efficacy against some NNRTI resistant mutants. Although DOR resistance mutations are established for HIV-1 subtype B, it is less clear for non-B subtypes. This study investigated prevalent NNRTI resistance mutations on DOR susceptibility in HIV-1 subtype C. Prevalent drug resistance mutations were identified from a South African genotypic drug resistance testing database. Mutations, single or in combination, were introduced into replication defective pseudoviruses and assessed for DOR susceptibility in vitro. The single V106M and Y188L mutations caused high-level resistance while others didn’t significantly impact DOR susceptibility. We observed agreement between our in vitro and the Stanford HIVdb predicted susceptibilities. However, the F227L mutation was predicted to cause high-level DOR resistance but was susceptible in vitro. Combinations of mutations containing K103N, V106M or Y188L caused high-level resistance, in agreement with the predictions. These mutations are frequently observed in patients failing efavirenz or nevirapine-based first-line regimens. However, they are also observed in those failing a protease inhibitor-based second-line regimen, as we have observed in our database. Genotypic drug resistance testing is therefore vital prior to initiation on DOR-based treatment for those previously exposed to efavirenz or nevirapine.

Mitochondria, crucial intracellular organelles, are central to energy metabolism, signal transduc-tion, apoptosis, calcium homeostasis, and a myriad of other biological processes, making them a focal point in diverse research fields. The capacity to fluorescently labeling and visually track mitochondria is crucial for understanding their biological roles. We present Mulberrin-Cy3, a novel small molecule fluorescent probe that selectively labels mitochondria in animal cells, in-cluding cancer cells, with relative ease. This protocol details the synthesis of Mulberrin-Cy3 and its use for visualizing mitochondria in living cells. The synthesis is straightforward and time-efficient, and the labeling method is more accessible than traditional approaches, providing a cost-effective option for mitochondrial visualization at room temperature. The labeling is rapid, with effective labeling achieved within 5 minutes of incubation. The fluorescent signal is stable and brighter, offering a significant advantage over existing methods. Mulberrin-Cy3 represents a promising mitochondrial labeling compound, providing researchers with a novel experimental tool to explore the complex biological functions of mitochondria. This innovation has the potential to significantly advance our comprehension of mitochondrial dynamics and their role in cellular health and disease.

This study innovates upon the field of dialog-driven query resolution (DDQR) utilizing knowledge graphs (KGs). Traditional methods in DDQR predominantly depend on fully supervised signals that presuppose the existence of perfect logical forms for queries. This reliance on gold logical forms for answer extraction is impractical in diverse real-world applications. When such forms are absent, contemporary methods, which lean on weak supervisory signals or employ heuristic and reinforcement strategies, recast DDQR as a knowledge graph path optimization problem. Despite the non-availability of gold logical forms, the rich conversational context provided by comprehensive dialog histories and domain-specific knowledge can be leveraged to optimize path selection in KGs effectively. We introduce an advanced method, termed CONVEX (CONVersational EXploration), which utilizes contrastive learning for path ranking. CONVEX addresses critical challenges by enabling learning under weak supervision and integrating the conversational context to enhance the representation quality for more effective path discrimination. Extensive evaluations of CONVEX on established benchmarks demonstrate its superiority across various metrics over current methods, notably improving Mean Reciprocal Rank (MRR) and Hit@5 by up to 20 and 36 percentage points respectively.

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

This paper addresses the historically biased selection of problems in technoscience, which has been predominantly shaped by Western male perspectives. We propose a novel framework with objective criteria for identifying and prioritizing human problems worthy of scientific and technological attention. Our approach aims to transcend traditional biases, incorporating a more inclusive, global perspective. We outline metrics considering factors such as scale, urgency, potential impact, and relevance across diverse populations. Using these criteria, we identify key global challenges including climate change, pandemic preparedness, food and water security, education access, sustainable energy transition, AI governance, biodiversity conservation, and mental health. We present a network analysis of their interconnections, revealing complex interdependencies and potential cascading effects of interventions. Our mathematical framework, based on graph theory and network analysis, provides a rigorous foundation for understanding and quantifying these relationships. This research contributes to decolonizing and degendering technoscience, offering a more equitable framework for addressing pressing human problems \cite{roy2018}. Our findings have significant implications for research prioritization, policy-making, and resource allocation in scientific and technological endeavors, potentially reshaping how we approach and address the most critical issues facing humanity.

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. Krustal-Wallies test, and Dunn test with Bonferroni adjustment, revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

The development of high-performance hydrogen peroxide (H2O2) sensors is critical for various applications, including environmental monitoring, industrial processes, and biomedical diagnostics. This study explores the development of efficient and selective H2O2 sensors based on bismuth oxide/bismuth oxyselenide (Bi2O3/Bi2O2Se) nanocomposites. The Bi2O3/Bi2O2Se nanocomposites were synthesized using a simple solution-processing method at room temperature, resulting in a unique heterostructure with remarkable electrocatalytic properties for H2O2 detection. Characterization techniques, including powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM), confirmed the successful formation of the nanocomposites and their structural integrity. The synthesis time was varied to obtain the composites with different Se content. Electrochemical measurements revealed that the Bi2O3/Bi2O2Se composite formed under optimal synthesis conditions displayed high sensitivity (152.7 µA µM-1 cm-2) and excellent selectivity towards H2O2 detection, along with a wide linear detection range (0.02 - 15 µM) and low detection limit (1.62 µM). The superior performance is attributed to the synergistic effect between Bi2O3 and Bi2O2Se, enhancing electron transfer and creating more active sites for H2O2 oxidation. These findings suggest that Bi2O3/Bi2O2Se nanocomposites hold great potential as advanced H2O2 sensors for practical applications.

Breakthrough Sparsity Theorem, derived independently by Donoho and Elad \textit{[Proc. Natl. Acad. Sci. USA, 2003]}, Gribonval and Nielsen \textit{[IEEE Trans. Inform. Theory, 2003]} and Fuchs \textit{[IEEE Trans. Inform. Theory, 2004]} says that unique sparse solution to NP-Hard $\ell_0$-minimization problem can be obtained using unique solution of P-Type $\ell_1$-minimization problem. In this paper, we derive noncommutative version of their result using frames for Hilbert C*-modules.

In the realm of Lithium-ion batteries, issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting Remaining Useful Life (RUL) serves as a crucial method to assess the health of batteries, thereby enhancing reliability and safety. Currently hybrid approaches for batteries RUL estimation are prevalence and gained fruitful development. In this paper, a hybrid voting ensembles combining Gradient Boosting, Random Forest and K-Nearest Neighbors is proposed to predict the capacity fade trend and knee point. Finally, extensive experiments are conducted using CALCE CS2 datasets. According to the experimental results, the proposed approach supersedes the single deep learning approach for RUL prediction and the knee point is predicted accurately. Besides prediction purpose, this innovated approach can be also proposed to be integrated into the real-world application for wider usage.

The manuscript deals with a new two-parameter unit stochastic distribution, obtained by transforming the Gumbel distribution, using generalized logistic mapping, into a unit interval. The distribution thus obtained is called the Gumbel–Logistic Unit (abbr. GLU) distribution, and its key stochastic properties have been investigated in detail. Among others, it is shown that the GLU distribution, unlike the Gumbel one which is always positively asymmetric, can take both asymmetric forms. Also, the procedure for estimating parameters based on quantiles, along with the asymptotic properties of the obtained estimators and the study of their numerical simulation, is presented. Finally, the application of the GLU distribution in modeling some real–world data related to telecommunications is considered.

Currently, there are several drugs used for the treatment of type 2 diabetes (T2D), however, all of them have adverse effects. Benzothiazoles have a broad spectrum of biological activities such as antidiabetic. This study aimed to evaluate in silico and in vivo two series of 2-aminobenzothiazole derivatives linked to isothioureas (3a-w) or guanidines (4a-z) for the treatment of T2D. The ADMET properties were determined in silico from which it was possible to select compounds 3a, 3b, 4a, 4b, 4c, 4r, 4s, 4x, and 4y and, with molecular docking, it was shown that 3b and 4y showed high affinity for PPAR gamma (ΔG = -7.8 and -8.4 kcal/mol, respectively). In vivo, the LD50 value was estimated in rat based on OECD Guideline 425, being > 1750 mg/kg for both compounds. The pharmaco-logical effect of 3b and 4y was evaluated in the T2D rat model, showing that after oral admin-istration in equimolar ratio to pioglitazone (15 mg/kg) for 4 weeks, both compounds were able to reduce blood glucose levels (< 200 mg/dL) and improve the lipid profile. Therefore, 3b and 4y could be used in the future as antidiabetic agents.

Lichenochemical composition and antimicrobial, antibiofilm and antioxidant effects of a newly described arctic lichen Placidium doesaiense growing in Pakistan were investigated. HPLC–DAD methods were used for identification of secondary metabolites in acetone and methanol extracts. The total polyphenol and the total flavonoid content was determined spectrophotometrically. The study examined the antioxidant (DPPH scavenging activity and reducing power), antibacterial (minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC)) and antibiofilm (inhibition of biofilm formation and reduction of mature biofilm) activities of extracts of the lichen P. doesaiense and isolated parietin. The chemical constituents olivetol, olivetolic acid, haematommic acid, fallacinol and parietin, were identified as major compounds in the tested extracts of the lichen. Parietin was isolated from the acetone extract on a separation column. The methanol extract had higher values of TPC (21.67 mg GA/g) and TFC (377.40 mg RE/g) than the acetone extract. Although the extracts showed the best antibacterial activity (especially against Proteus mirabilis ATCC 12453), parietin demonstrated superior antibiofilm activity (especially against Staphylococcus aureus ATCC 25923). Isolated parietin showed the best antioxidant activity measures according to DPPH scavenging activity (IC50= 51.616 μg/mL) and reducing power. This is the first report on the phytochemical composition of the lichen Placidium doesaiense and the first description of the chemical composition of some of the 28 species of the genus Placidia. This research will pave the way for further exploration of new activities of this lichen and its metabolites, which are important for medicine and pharmacy.

The search for materials with exceptional properties has led to significant advances in the field of non-carbon diamond-like conductive thin films (DLC-CTFs). These films, synthesized by chemical vapor deposition (CVD) and other techniques, are being actively researched for their potential in high-tech applications. This literature review discusses the synthesis methods, material properties, and applications of DLC-CTFs, highlighting the role of non-carbon dopants in enhancing electrical conductivity. This literature review aims to provide a comprehensive overview of the historical development, synthesis techniques, material properties, characterization methods, and applications of DLC-CTFs, with a focus on the impact of non-carbon doping on their conductive properties. By reviewing the literature. The review also addresses the challenges faced and suggests future research directions.

Abstract Background: Oxidative stress has emerged as a key contributor to numerous NCDs (Non Communicable Diseases), including cardiovascular diseases, cancer, and diabetes. This study aims to explore the potential of targeted interventions to mitigate oxidative stress as part of a primary prevention strategy. Methods: The study included 32 healthy participants (11 men, 21 women) aged 45-65 who completed both the initial and follow-up assessments of the Healthy Days Initiative, a community-based wellness program organized by the non-profit Associazione O.R.A. ETS. Through blood analysis, vital sign assessment, lifestyle questionnaires, and individualized recommendations, participants received guidance on improving their health and reducing disease risk. The initiative also offered the opportunity for participants to consume a flavonoid supplement containing quercitrin, rutin and hesperidin, with the goal of reducing oxidative stress. Participants who opted for supplementation were instructed to take 1-2 tablets daily for two weeks. Data collected included demographic information, anthropometric measurements, vital signs, dietary and lifestyle habits, medical history, WHO-5 Well-Being Index scores, and blood parameters. Results: Significant reductions were observed in glucose levels (from 82 to 74.5 mg/dL), reactive oxygen metabolites (d-ROMs) (from 394.5 to 365.5 U.CARR), and systolic blood pressure (from 133 to 122 mmHg) after the two-week flavonoid intervention. Most participants (26/31) reported no side effects, and the majority (30/31) expressed a willingness to continue using a product combination of quercitrin, rutin and hesperidin or a similar product long-term. Conclusions: While limited in scope and duration, the PREVES-FLAVON study contributes valuable insights to the growing body of evidence suggesting that flavonoid supplementation may play a significant role in reducing risk factors associated with NCDs in primary prevention settings. By targeting novel risk factors such as oxidative stress, this intervention holds promise for mitigating the global burden of NCDs and promoting healthy aging.

Background: The high global prevalence of prediabetes requires its early identification. Amino acids have emerged as potential predictors of prediabetes. This study aims to investigate the association between BCAAs and prediabetes in the Kazakh population. Materials and Methods: In this case-control study, plasma BCAAs levels were measured using the Trace GC 1310 gas chromatography system coupled with the TSQ 8000 triple quadrupole mass spectrometer (Thermo Scientific, Austin, TX, USA) followed by silylation with the BSTFA + 1% TMCS derivatization method. Biochemical parameters, including total cholesterol, HDL-C, LDL-C, triglycerides, fasting glucose, HbA1c, and creatinine, were assessed for each participant. Anthropometric and physical examinations, including blood pressure, heart rate measurements, and family history collection, were conducted by trained professionals. Results: The final analysis included data from 112 Kazakhs with prediabetes and 55 without prediabetes, aged 36-65 years. Of the thirteen AAs analyzed, only alanine showed a significant association with prediabetes risk. Our findings revealed an inverse relationship between alanine and aspartic acid levels, and direct association of lysine and prediabetes in Kazakh ethnicity. Conclusion: Lower plasma level of alanine and aspartic acid, and higher level of lysine may serve as predictive biomarkers for prediabetes in the Kazakh population. This research is funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (grant AP14871855).

We investigate solutions of the classical Mathieu-Hill (MH) equation which characterizes the dynamics of trapped ions, based on the Floquet theory and starting from a well known analytical model. The analytical model we introduce demonstrates that the equations of motion are equivalent to those of the harmonic oscillator (HO). Two independent approaches are used, based on two classes of complex solutions of the MH equation. The paper addresses both the damped HO and parametric oscillator (PO) for an ion confined in an electrodynamic (Paul) trap. Stability and instability regions for the periodic orbits associated to the MH equation which describes a HO (PO) confined in an electrodynamic trap are also discussed, depending on the values of the Floquet exponent, for very small values of the $q$ parameter. The main objective of the paper is to identify stable and bounded solutions for the dynamics of a HO levitated in an electrodynamic trap. This paper represents a follow-up of a recently published article in Photonics, 11 (6) 551.

Magnaporthe oryzae is a filamentous heterothallic ascomycete fungus globally distributed in rice-growing regions and serves as the causative agent of rice blast disease. Populations shaped by environmental factors and human intervention play important roles in the formation of genetic structure. In this study, population structures and spatiotemporal dynamics were investigated based on the large-scale whole genomic sequences of rice-infecting M. oryzae around the world. By analyzing the genetic structures, we identified divergent clades that crossed geographic boundaries. While we observed associations between the isolates and their geographic origins, we also found that there were frequent migration events occurring worldwide. The populations in Asia demonstrated the highest genetic diversity due to the continent’s history of rice domestication, followed by separate gene flows to Africa, North America, South America and Europe. Within Asia, China was the migration origin, facilitating gene flows to Japan and South Korea. Additionally, our analysis of the evolutionary history of global M. oryzae populations provided insights into the population expansion that has taken place in recent decades. Overall, our findings indicate that human-mediated gene flows played a pivotal role in shaping the genetic structure of M. oryzae.

The material properties and structural characteristics of ballistic composites are crucial to their ballistic performance. Investigation for the influence of the material and structural parameters of UHMWPE body armor on anti-fragmentation penetration performance has been made by numerical simulation in this paper, with a numerical modeling simulating the penetration of a 1.1 g FSP into the target plate of UHMWPE body armor, by which the simulation results demonstrate that the failure process of the body armor target plate primarily involves shear damage, interlayer delamination, and tensile damage. Furthermore, the study evaluates the effects of elastic modulus, tensile strength, shear strength, number of layers, and interlayer strength on the ballistic limit velocity of UHMWPE body armor. The findings reveal that the ballistic limit velocity is most sensitive to changes in shear strength, with variation rates ranging from -18.02% to 10.81%, showing an approximate positive correlation, while the elastic modulus has the smallest impact on ballistic limit velocity, with variation rates ranging from -1.8% to 3.6%. Additionally, appropriate interlayer strength can improve the ballistic limit velocity of the body armor to a certain extent. This study provides theoretical methods and recommendations for optimizing anti-penetration performance of UHMWPE body armor.

With the fast growth of smartphones and digital cameras, massive images are generated every day in the world. They are easily transmitted on the insecure channel-Internet. It has been a hot spot for protecting sensitive images during communication. A new cryptosystem is proposed using a six dimensional (6D) hyperchaotic system and DNA computing. The hash value is obtained with the function of SHA-512. It keeps the encrypted result closely connected with the original image. The initial values of the cryptosystem are produced with the hash value and the secret key. The pixel is divided into four parts, and a large matrix is formed. Scrambling is performed on the new image. DNA coding, modern DNA complementary rules, DNA computing, and DNA decoding are performed on the new image. Diffusion is also executed, and ciphered image is achieved finally. The experimental result reveals the outcome of the proposed algorithm. Security analysis shows that the designed algorithm has a huge secret key space, low correlation, and high sensitivity. It also signifies that the designed algorithm could protect against common attacks and is more secure than some existing methods.

Clopidogrel; a prescription drug to reduce ischemic events in cardiovascular patients; has been extensively studied in mostly European individuals but not among Caribbean Hispanics. This study evaluated the low abundance and reduced activity of paraoxonase-1 (PON1) in clopidogrel-resistant patients as a predictive risk biomarker of poor responders and disease severity in this population. Thirty-six patients on clopidogrel (cases; divided into poor and normal responders) were enrolled along with 11 cardiovascular patients with no clopidogrel indications (positive control) and 13 healthy volunteers (negative control). Residual on-treatment platelet function (PRU); PON1 abundance by western blotting and PON1 activity by enzymatic assays were measured. PON1 genotyping and computational haplotype phasing were performed on 512 DNA specimens for two genetic loci (rs662 and rs854560). No statistical differences in mean relative PON1 abundance were found among the groups (p> 0.05). However; a significantly lower enzymatic activity was found in poor responders (10.57 ± 6.79 µU/mL) when compared to controls (22.66 ± 8.30 µU/mL and 22.21 ± 9.66 µU/mL; p= 0.004). PON1 activity among carriers of the most prevalent PON1 haplotype (AA|AA) was significantly lower than in wild-types (7.90 µU/mL vs 22.03 µU/mL; p= 0.005). Our findings suggested that PON1 is a potential biomarker of cardiovascular disease severity in Caribbean Hispanics.

Maternal obesity has been found to be impaired oxidative status in placenta and newborn which can lead to adverse pregnancy outcomes and long-term adverse influence on metabolic programming in the offspring. This study aimed to investigate the role of maternal obesity on maternal and umbilical cord plasma oxidative status and placental oxidative adaptation. Maternal obesity (n=20) defined as pre-pregnancy BMI ≥ 25 kg/m² and maternal lean (n=20) defined as pre-pregnancy BMI < 25 kg/m² were recruited into this study. Both groups were matched by gestational age at delivery. Maternal blood, umbilical cord blood and placental tissue were collected to assess nutritional content (cholesterol, triglyceride, and protein), oxidative stress marker (MDA and protein carbonyl) and antioxidant activity (SOD and catalase). Placenta protein expression (SOD2, catalase, UCP2 and Nrf2) was evaluated by western blot analysis. Catalase activity in maternal plasma significantly increased in maternal obesity group (p = 0.0200) with an increased trend in MDA and protein carbonyl levels. Umbilical cord plasma triglyceride, protein carbonyl and catalase activity significantly increased in maternal obesity compared with that in maternal lean (p = 0.0462, 0.0485, and 0.0348, respectively). Although placenta protein expression analysis exhibited significant decreased SOD2 (p = 0.0169) and catalase (p = 0.0067), accompanied by Nrf2 downregulation (p = 0.0453). An increased mitochondrial antioxidant UCP2 expressions was observed (p = 0.0143). Hence, our study provided evidence that maternal obesity is associated with placental and fetal oxidative stress with the parallel increased placental antioxidant UCP2 expression.

This review investigates the pivotal challenge of recycling material waste in the context of Additive Manufacturing with an emphasis on decentralized 3D printing, shedding light on its environmental and economic implications. With additive manufacturing experiencing exponential growth, the environmental impacts of waste generation during 3D printing processes have become increasingly significant. The paper explores various recycled materials commonly used in 3D printing, including polymers like polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PETG), evaluating their characteristics and usability. General recycling methodologies, encompassing mechanical and chemical processes, are examined with attention to challenges such as polymer sorting, additives, coatings, contamination, and thermoset reprocessing. The economic and societal impact of integrating recycled materials into 3D printing and the environmental impacts are examined. By identifying research gaps and proposing future trends, this review contributes to a deeper understanding of how recycling can play a pivotal role in achieving environmental sustainability and economic viability within the decentralized 3D printing landscape.

Carbon fiber-reinforced polymer (CFRP), noted for its outstanding properties including high specific strength and superior fatigue resistance, is increasingly employed in aerospace and other demanding applications. This study investigates the interactions between CFRP composites and high-energy lasers (HEL), with continuous wave laser powers reaching up to 120 kW. A novel automated sample exchange system, operated by a robotic arm, minimizes human exposure while enabling a sequence of targeted laser tests. High-speed imaging captures the rapid expansion of a plume consisting of hot gases and dust particles during the experiment. The research significantly advances empirical models by systematically examining the relationship between laser power, perforation times, and ablation rates. It demonstrates scalable predictions for the effects of high-energy laser radiation. Detailed examination of the damaged samples, both visually and via micro-focused computed X-ray tomography, offers insights into heat distribution and ablation dynamics, highlighting the anisotropic thermal properties of CFRP. Compression-after-impact (CAI) tests further assess the residual strength of the irradiated samples, enhancing the understanding of CFRP’s structural integrity post-irradiation. Collectively, these tests improve the knowledge of the thermal and mechanical behavior of CFRP under extreme irradiation conditions. The findings not only contribute to predictive modeling of CFRP's response to laser irradiation but also enhance the scalability of these models to higher laser powers, providing robust tools for predicting material behavior in high-performance settings.

Continuing the investigation already started (See Farcaş, A.-A.; Bende, A. PCCP 2024, 26, 14937), the light absorption and charge-transfer properties of the dopamine (DA), dopamine zwitterion (called as dopamine-semi-quinone or DsQ) and dopamine-o-quinone (DoQ) adsorbed on the graphene nanoparticle surface have been investigated using the ground state and linear-response time-dependent density functional theories, considering the B97X-D3BJ/def2-TZVPP level of theory. In terms of the strength of molecular adsorption on the surface, the DsQ form has 50 % higher binding energy than that for the DA or DoQ cases (-20.24 kcal/mol vs. -30.41 kcal/mol). The results obtained for electronically excited states and UV-Vis absorption spectra show that the photochemical behavior of DsQ is more similar to DA than that observed for DoQ. Of the three systems analyzed, the DsQ-based complex showed the most active CT phenomenon, both in terms of the number of CT-like states and the amount of charge transferred. Of the first thirty electronically excited states computed for the DsQ case, eleven are purely CT-type and nine are mixed CT and localized (or Frenkel) excitations. By varying the adsorption distance between the molecule and the surface vertically, the amount of charge transfer obtained for DA decreases significantly as the distance increases, for DoQ it remains stable, while for DsQ there are states for which little change is observed, while for others there is a significant change. Furthermore, the mechanistic compilation of the electron orbital diagrams of the individual components cannot describe in detail the nature of the excitations inside the complex.

The behavior of low concentrations of methanol (0.5 and 1.0 wt%) as a promoter for hydrate formation in seawater or pure water metastable solutions of methane was investigated using the classical molecular dynamics method at moderate temperature and pressure. The influence of methanol on the dynamics of the re-arrangement of the hydrogen bond network in seawater and pure water solutions of methane was studied by calculating the order parameters of the tetrahedral environment and intermolecular torsion angles for water molecules, as well as by calculating the number of hydrogen bonds, hydrate and hydrate-like cavities. It was found that hydrate nucleation can be considered a collective process in which the rate of hydrate growth is faster in systems with low concentrations of methanol and confident hydrate growth begins earlier in a metastable solution without sea salt with a small amount of methanol than in systems without methanol.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

While Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), it also presents challenges that can affect model accuracy and performance. Practical applications show that RAG can mask the intrinsic capabilities of LLMs. Firstly, LLMs may become overly dependent on external retrieval, underutilizing their own knowledge and inference abilities, which can reduce responsiveness. Secondly, RAG techniques might introduce irrelevant or low-quality information, adding noise to the LLM. This can disrupt the normal generation process, leading to inefficient and low-quality content, especially when dealing with complex problems. This paper proposes a RAG framework that uses reflective tags to control retrieval. This framework evaluates retrieved documents in parallel and incorporates the Chain of Thought (CoT) technique for step-by-step content generation. The model selects the highest quality and most accurate content for final generation. The main contributions include: 1) Reducing the hallucination problem by selectively utilizing high-scoring document, 2) Enhancing real-time performance through timely external database retrieval, and 3) Minimizing negative impacts by filtering out irrelevant or unreliable information through parallel content generation and reflective tagging. These advancements aim to optimize the integration of retrieval mechanisms with LLMs, ensuring high-quality and reliable outputs.

Graphical User Interface (GUI) agents are expected to precisely operate on the screens of digital devices. Existing GUI agents merely rely on current visual observations and plain-text action history, ignoring the significance of history screens. To mitigate this issue, we propose UI-Hawk, a visual GUI agent specially designed to processing screen streams encountered during GUI navigation. UI-Hawk incorporates a history-aware visual encoder and an efficient resampler to handle the screen sequences. To acquire a better understanding of screen streams, we define four fundamental tasks—UI grounding, UI referring, screen question answering, and screen summarization. We develop an automated data curation method to generate the corresponding training data for UI-Hawk. Along with the efforts above, we have also created a benchmark FunUI to quantitatively evaluate the fundamental screen understanding ability of MLLMs. Extensive experiments on FunUI and GUI navigation benchmarks consistently validate that screen stream understanding is not only beneficial but also essential for GUI navigation.

The interaction forces and mechanical properties of the interaction between melittin (Mel) and lipopolysaccharide (LPS) are considered a crucial driving force for Mel in killing Gram−negative bacteria (GNB). However, how their interaction forces are performed at the single−molecule level and the dissociation kinetic characteristics of the Mel/LPS complex remain poorly understood. In this study, the single−molecule level interaction forces between Mel and LPSs from E. coil K−12, O55:B5, O111:B4, and O128:B12 were explored using atomic force microscopy (AFM) based single−molecule force spectroscopy (SMFS). Further, the AFM−based dynamic force spectroscopy (DFS) and advanced analytical model were employed to investigate the kinetic characteristics of the Mel/LPSs complex dissociation. The results indicated that Mel could interact with both rough (R)−form LPS (E. coli K−12) and smooth (S)−form LPSs (E. coli O55:B5, O111:B4, O128:B12), and the S−form LPSs showed a more robust interaction with Mel than R−form LPS, and a slight difference existed in the interaction forces between Mel and diverse S−form LPSs. The Mel interacts with S−form LPSs showed greater specific and non−specific interaction forces than R−form LPS (p &lt; 0.05), as determined by AFM−based SMFS. However, there was no significant difference in the specific and non−specific interaction forces among the three S−form LPSs (p &gt; 0.05), indicating the variability of O−antigen could not affect the interaction between Mel and LPSs. The DFS result showed that the Mel/S−form LPSs complexes had a lower dissociation rate constant (koff), a shorter energy barrier width (xβ), a longer bond lifetime (τoff), and a higher height of the energy barrier (∆G), demonstrating that Mel could interact with S−form LPSs to form more stable complexes. In conclusion, this study promotes our knowledge of the interaction micromechanics and kinetics characteristics between Mel and LPS at the single−molecule level. Moreover, it can help us design and evaluate new anti−GNB drugs.

Pipes have been utilized since ancient times and have seen significant renewal, modification, and replacement to improve their effectiveness and efficiency in the modern era. As early as 4000 BC, Egyptians used clay pipes for drainage systems. The U.S. has the largest network of energy pipelines in the world. The ASCE 2017 Infrastructure Report Card assigned grades of D and D+ to water and wastewater pipelines, highlighting a substantial need for improvement. The paper investigates the infrastructure required for the sustainability of pipeline systems, as well as the construction of underground systems using both open-trench and trenchless technologies, along with the associated risks in the US. It concludes that trenchless methods are increasingly preferred for pipeline construction due to technological advancements, reduced surface disruptions, shorter construction times, and lower social costs. Tackling this challenge will necessitate strategic investment, decisive leadership, thorough planning, and meticulous preparation for future demands. Enhancing pipeline infrastructure can stimulate the U.S. economy, whereas postponing action will lead to higher costs.

This study examines the impact of natural resource exploitation, renewable energy consumption, economic growth, biocapacity, and information and communication technologies (ICT) on the ecological footprint of BRICS+ nations using panel data from 1991 to 2019. Advanced econometric techniques, including FMOLS, DOLS, CCR, and GMM, reveal significant findings relevant to sustainability-focused policymakers. The results show that natural resource exploitation significantly contributes to environmental degradation, while renewable energy consumption mitigates these effects to varying degrees across the studied countries. Although economic growth is crucial, it worsens ecological footprints unless accompanied by sustainable practices. ICT and biocapacity emerge as key factors in promoting environmental sustainability, with ICT enhancing innovation and efficiency to reduce ecological impacts. The study emphasizes the need for tailored policy frameworks that align economic growth with environmental sustainability, advocating for the adoption of renewable energy, eco-innovations, and green finance initiatives suited to the unique conditions of BRICS+ nations. The findings aim to guide policymakers in advancing sustainable development and mitigating the adverse environmental impacts of rapid industrialization and urbanization, while contributing to the global discourse on achieving a balance between economic growth and ecological well-being in developing countries.

Microwave-induced thermoacoustic tomography (MITAT) holds significant promise in biomedical applications. It creates images using ultrasonic sensors to detect thermoacoustic signals induced by microwaves. The key to generating thermoacoustic signals that accurately reflect the fact is to achieve sufficient and uniform microwave power absorption of the testing target, which is closely tied to the microwave illumination provided by the antenna. In this article, we introduce a novel design and implementation of an ultra-wideband water immersion antenna for an MITAT system. We analyze and compare the advantages of selecting water as the background medium. Simulations are conducted to analyze the ultra-wideband characteristics in impedance matching, axial ratio, and radiation pattern of the proposed antenna. The measured |S11| shows good agreement with the simulated results. We also simulate the microwave power absorption of tumor and brain tissue, and the uniform microwave power absorption and high contrast between the tumor and brain indicate the excellent performance of the proposed antenna in the MITAT system.

(1) Background: Sarcopenia, a condition characterized by a decrease in muscle mass and strength, is very common in patients with obesity and can be aggravated by bariatric surgery, potentially compromising the clinical results of surgery. There is increasing interest in pa-tient-reported outcome measures (PROMs) to assess results in other health variables that may be especially important for individual patients. The role of exercise in enhancing health-related quality of life (HRQoL) after surgery remains underexplored. This study investigated the impact of exercise on HRQoL in bariatric sarcopenic patients via the Sarcopenia Quality of Life (SarQoL) questionnaire. (2) Method: Candidates for surgery took part in the EXPOBAR program and were randomized into experimental and control groups. The intervention lasted 16 weeks, starting one month after surgery, and included combined aerobic and resistance exercise. Outcomes, including body composition and physical fitness parameters, were measured before and after the intervention. All participants underwent gastric bypass surgery (RYGB). (3) Results: The baseline characteristics of the participants were not significantly different between the two groups. All patients met the criteria for moderate/severe obesity and the EASO/ESPEN criteria for sarcopenia, namely, reduced muscle strength assessed by grip strength and reduced muscle mass assessed by the ASMM score/weight. After completing the study protocol, patients in both groups had increased HRQoL, but the increase was significantly greater in the intervention group than in the control group. (4) Conclusion: Our findings suggest that exercise plays a crucial role in improving both physical and psychological well-being in postbariatric patients, highlighting the need to integrate structured exercise programs in post-surgery care. This study was prospectively registered at Clinicaltrials.gov (NCT03497546).

A constant effort is made in the physical metallurgy of transformation induced plasticity (TRIP) steels. These are multiphase steels, whose mechanical behavior depends on the different phases present, the intrinsic strength of each phase, the work hardening of each constituent and the austenite to martensite transformation, among others. Hard and soft phases can be distinguished in these steels and both stress and strain are distributed between them. The objective of this work was to simulate the stress-strain behavior for two TRIP steels. Bouquerel’s model was modified to account for the initial martensite, a moderate hard austenite and adjustable strain hardening parameters. The austenite fraction that transforms into martensite was computed through an Olson and Cohen model. The Mecking-Kocks model was used to evaluate the increment in the dislocation density and the Rodriguez-Gutierrez model, to compute the hardening of martensite. Ferrite is considered a soft phase, martensite and bainite, hard phases and austenite, a phase with a moderate hardness. The routine was implemented in MATLAB, and results were compared with those obtained experimentally in a tension test of two TRIP steels of the same composition but different heat treatments, obtaining in both cases a good agreement for the stress-strain-curves.

Background: The CoBaTrICE initiative standardized intensive care training throughout Europe. Pericardiocentesis, an important yet infrequent operation, poses unique training challenges often unmet by conventional methods. This study evaluates the efficacy of virtual reality (VR) simulations and 3D-printed mannequins in pericardiocentesis training, both crafted from scratch using highly affordable materials and free software. Methods: Thirty-five final-year medical students participated in this prospective, quasi-experimental study. Initially, students underwent VR simulation training, followed by training using 3D-printed self-made mannequins. Learning outcomes were assessed using the Objective and Structured Clinical Examination (OSCE) questionnaire. Heart rate variability (HRV) analysis was employed to monitor stress responses, while the NASA Task Load Index (NASA-TLX) was used to gauge self-perceived difficulty. Results: Although the mannequin model induced more stress reactions, it excelled in tasks requiring fine motor skills, such as correct placement and asepsis maintenance. In contrast, lower NASA-TLX scores indicated that the VR model imposed less mental demand and effort, reflecting a reduced cognitive load. Conclusion: Both VR simulations and 3D-printed mannequins effectively taught pericardiocentesis skills, each offering distinct advantages. Mannequins enhanced fine motor skills, whereas VR reduced cognitive load and increased engagement. The combination of both approaches could maximize training outcomes, particularly in resource-constrained environments, thus broadening access to advanced simulation education.

Umbria is an Italian region with a highly fragmented water supply, with many small water distribution systems fed by local sources. The chlorination in these small water systems faces many difficulties related to the high demand variation, low economic relevance, and access to infrastructures. As a result, there are many noncompliances with the quality of the supplied water, which causes a decrease in the performance indicators and tariff. This research investigates the possibility of using a low-cost chlorination system to consider different environmental conditions (e.g., temperature variations of water and environment, pH, ...) and discontinuous consumption. A device for this specific use is developed and tested by laboratory tests and is ready for use in the field.

Applying cold to a bone injury can aid healing, though its mechanisms are complex. This study investigates how cold therapy impacts bone repair to optimize healing. Cold was applied to a rodent bone model, with physiological responses analyzed. Vasoconstriction was mediated by an increase in TRP channels, TRPA1 (p=0.012) and TRPM8 (p&lt;0.001), within cortical defects enhancing sensory response and blood flow regulation. Cold exposure also elevated hypoxia (p &lt; 0.01) and VEGF expression (p &lt; 0.001), promoting angiogenesis vital for bone regeneration. Increased expression of osteogenic proteins PGC-1α (p = 0.039) and RBM3 (p&lt;0.008) suggests stimulated reparative processes. Enhanced osteoblast differentiation and ALP presence at days 5 (three-fold, p=0.014) and 10 (two-fold, p=0.010) were observed, along with increased osteocalcin (OCN) at day 10 (two-fold, p = 0.010) indicating presence of mature osteoblasts capable of mineralization. These findings highlight cold therapy's multifaceted effects on bone repair, offering insights for therapeutic strategies.

The cultivation of sunflower (Helianthus annuus L.) as a cut flower stands out in floriculture due to its aesthetic beauty and commercial value. Understanding how cut sunflower genotypes adapt to different edaphoclimatic regions and management practices is essential to optimize flower quality and productivity. This study aimed to evaluate the effect of plant density and location on the development, growth, and quality of cut sunflower in tropical and subtropical environments. Plant densities of 10, 20, 30, 40, and 50 plants/m² were evaluated in tropical climate and subtropical climate using a randomized block design in a factorial scheme. Results showed significant differences between locations for plant height, capitulum and stem diameter, final number of leaves, leaf area, leaf area index, phyllochron, and the developmental cycle. Plant density significantly influenced these variables except for plant height and developmental cycle. The interaction between location and plant density was significant only for capitulum diameter and final leaf number. The findings indicate that both planting density and location significantly influence the developmental cycle of cut sunflowers, with lower densities favoring more robust plants at harvest. A density of 30 plants/m² is recommended for efficient space use without significantly compromising floral stem quality. All produced stems are marketable, suggesting that adjusting planting density can optimize production without compromising quality, adapting to specific regional conditions.

Diversification of cropping sequences has a positive impact on soil organic carbon, while improving nutrient cycling and crop yields. The objective of this research was to assess amylase and cellulase activity, C and N dynamics, and maize yield on a low fertility Oxisol in the Brazilian Cerrado. The experiment was conducted under field conditions during three maize crop succession cycles. Treatments consisted of cultivating maize during the summer, after sorghum and lablab cropped as green manure and fallow during the winter. Higher maize yields were achieved by sorghum-maize succession compared to monocropping, due to higher N fertilizer and biomass in-puts to topsoil. Sorghum-maize succession also provided a higher proportion of stable C and N compared to other successions. Maize yields declined as tropical soil fertility intrinsically de-creased along three crops succession cycles. Cellulase activity decreased over time, whereas amylase activity increased as the plant residues were already in advanced stages of decomposition. The sorghum-maize crop succession stood out compared to lablab and fallow as it provided the high-est maize yields, while, maintaining higher C and N levels, and amylase activity. This better performance was likely due to larger amounts of incorporated biomass and better mineral N fertilizer management.

The aim of the study was to assess the frequency of fish consumption in Poland including sea and freshwater fish species referring to some socio-economic determinants. The frequency of consumption was assessed using Food Propensity Questionnaire data from 1489 adults (722 males, 767 females) in 2019–2020. The fish consumption frequency in Polish adults was too rare compared to the recommendations (at least twice a week). About 80% of respondents consumed fish less frequently than recommended. Sea fish were mostly consumed less than once a month (31.8%) or 1-3 times a month (30.9%). The most of subjects (44.8%) consumed freshwater fish less than once a month. It was also depended on: educational level, economic status, place of residence, declared health status, and on BMI value or BMI status. However, taking into account sex, the occurrence of these relationships was different in men and women. It seems that educational activities and dissemination of proper nutrition principles is necessary in order to increase the consumption of fish in Poland. It should be important to emphasize health benefits of fish consumption in relation to possible adverse effects of their contamination.

Aim: To elucidate the international network structures and evolution of collaborations in capsule endoscopy research from 2000 to 2023 by analyzing co-authorship networks. Method: This study analyzed co-authorship patterns in capsule endoscopy research articles published from 2000 to 2023 using data from the Web of Science (WoS). I employed Python (version 3.10.5) in the PyCharm development environment (version 2022.1.3) to conduct a network analysis using social network analysis principles. The analysis included macro-level metrics (network density, clustering coefficient, components, and average path length) and micro-level metrics (degree centrality, closeness centrality, and betweenness centrality) to assess collaboration intensity, key contributors, and network cohesion. Result: The co-authorship network analysis revealed evolving collaboration patterns over three distinct periods (2000-2009, 2010-2019, and 2020-2023). Initially, the network exhibited low density and high fragmentation, with key contributors such as Guido Costamagna and Jonathan A. Leighton prominently positioned. Over time, the network expanded with increased density and clustering, though it remained fragmented. In recent years, the network showed increased cohesion with reduced components and notable contributors like Anastasios Koulaouzidis and Xavier Dray, reflecting the growing importance of international collaborations, particularly involving researchers from Japan, Italy, and Israel. Conclusion: The study highlights the dynamic nature of international collaborations in capsule endoscopy research, with increasing connectivity and influential contributors playing pivotal roles in advancing the field. These findings underscore the value of international cooperation in enhancing diagnostic and therapeutic approaches in capsule endoscopy, pointing to future opportunities for deeper collaboration and integration.

Solid-phase joining is a technique used to fuse two materials by applying heat and pressure, causing plastic deformation without reaching the melting point. This method is applicable for similar and dissimilar materials, including metals, polymers, and ceramics, without the need for filler material. Among the various methods, Friction Stir Welding (FSW) is preferred for joining dissimilar materials due to its solid-state process, which enhances mechanical properties, metallurgical compatibility, and reduces distortion. FSW is particularly effective for materials with significant differences in melting points. The current project focuses on assessing the strength and mechanical properties of FSW joints between aluminum and polymer-based composites. FSW was selected for its ability to produce fine-grained, strong welds free from fusion-related defects such as porosity and cracks. Applications in sectors like automotive, aerospace, and shipbuilding are explored, with impact, hardness, and tensile tests conducted to evaluate joint performance. This comprehensive study includes literature review, fabrication, testing, and tool design to ensure a thorough understanding of dissimilar joint properties.

Sinonasal carcinomas are aggressive neoplasms that present a high morbidity and mortality rate with unfavorable prognosis. This group of tumors exhibits morphological and genetic diversity. Genetic and epigenetic alterations in these neoplasms are currently targets for diagnosis and treatment. The most common type of cancer originated in the sinonasal tract are sinonasal squa-mous cell carcinomas (SNSCC), which presents different histological patterns and variable his-tological aggressiveness. A significant number of alterations have been reported in sinonasal tu-mors, including deficiencies in the SwItch/Sucrose non-fermentable (SWI/SNF) chromatin remod-eling complex. In the sinonasal tract, deficiencies of the subunits SMARCB1/INI1, SMAR-CA4/BRG1 and SMARCA2 have been noted in carcinomas, adenocarcinomas, and soft tissue tu-mors with distinctive high-grade morphology and fatal prognosis. Objective: To identify the status of the SWI/SNF complex using immunohistochemistry in sinonasal squamous cell carci-nomas and its association with morphology and survival. Methods: A total of 1582 SNSCC cases were analyzed, selecting those with high-grade malignancy morphology, which were then evalu-ated immunohistochemically for SMARCB1 and SMARCA4 proteins. Their expression was compared with the biological behavior and survival of the patients. Results: Among the SNSCC, 47% corresponded to the non-keratinizing squamous cell carcinoma (NKSCC) type with high-grade characteristics, 40% were keratinizing squamous cell carcinomas (KSCC), 9% were SMARCB1-deficient carcinomas, and 4% were SMARCA4-deficient carcinomas. Mosaic expres-sion for SMARCB1 (NKSCC 33%, KSCC 21.9%) and SMARCA4 (NKSCC 14.6%, KSCC 12.2%) was identified, showing an impact on tumor size and progression. Conclusions: We identified that the mosaic expression of INI1 in NKSCC exhibited a worse prognosis compared to intact carcinomas and was similar to SMARCB1-deficent carcinoma.