Recent advancements in computer vision, especially deep learning models, have shown considerable promise in tasks related to plant image object detection. However, the efficiency of these deep learning models heavily relies on input image quality, with low-resolution images significantly hindering model performance. Therefore, reconstructing high-quality images through specific techniques will help extract features from plant images, thus improve model performance. In this study, we explored the value of super-resolution technology for improving object detection model performance on plant images. Firstly, we built a comprehensive dataset comprising 1030 high-resolution plant images, named the PlantSR dataset. Subsequently, we developed a super-resolution model using the PlantSR dataset and benchmarked it against several state-or-the-art models designed for general image super-resolution tasks. Our proposed model demonstrated superior performance on the PlantSR dataset, indicating its efficacy in enhancing the super-resolution of plant images. Furthermore, we explored the effect of super-resolution on two specific object detection tasks: apple counting and soybean seed counting. By incorporating super-resolution as a pre-processing step, we observed a significant reduction in mean absolute error. Specifically, on the YOLOv7 model employed for apple counting, the mean absolute error decreased from 13.085 to 5.71. Similarly, on the P2PNet-Soy model utilized for soybean seed counting, the mean absolute error decreased from 19.159 to 15.085. These findings underscore the substantial potential of super-resolution technology in improving the performance of object detection models for accurately detecting and counting specific plants from images. The source codes and associated datasets are available at https://github.com/SkyCol/PlantSR.

Goal-conditioned reinforcement learning (RL) holds promise for addressing intricate control challenges by enabling agents to learn and execute desired skills through separate decision modules. However, the irregular occurrence of required skills poses a significant challenge to effective learning. In this paper, we demonstrate the detrimental effects of this imbalanced skill(sub-goal) distribution and propose a novel training approach, Classified Experience Replay (CER), designed to mitigate this challenge. We demonstrate that adapting our method to conventional RL methods significantly enhances the performance of the RL agent. Considering the challenges inherent in tasks such as driving, characterized by biased occurrences of required sub-goals, our study demonstrates the improvement in trained outcomes facilitated by the proposed method. In addition, we introduce a specialized framework tailored for self-driving tasks on highways, integrating Model Predictive Control (MPC) into our RL trajectory optimization training paradigm. Our approach, utilizing CER with the suggested framework, yields remarkable advancements in trajectory optimization for RL agents operating in highway environments.

Acanthamoeba genus amoebae can affect humans with diseases such as Granulomatous Amebic Encephalitis (GAE), a highly lethal neuroinfection. Several aspects about the disease still need to be elucidated. Animal models of GAE have allowed advances in these aspects. This work tested Wistar rats (Rattus norvegicus albinus) as an animal model of GAE. For this, 32 animals were infected with 1x106 A. castellanii trophozoites, T4 genotype. Ameba recovery tests were carried out on plates of agar, vascular extravasation assays, behavioral tests and histopathological technique with H/E staining. Data were submitted to linear regression analysis, one-way ANOVA and Tukey's test, performed in the GraphPad Prism® 8.0 program, with a significance level of p

The aim of this research is to investigate the influence of model height employed in the deep drawing of orthodontic aligner sheets on force transmission and aligner thickness. Forty sheets (Zendura FLX) were thermoformed over four models of varying heights (15, 20, 25, and 30 mm). Normal contact force generated on the facial surface of the upper right central incisor (Tooth 11) was measured using pressure-sensitive films. Aligner thickness around Tooth 11 was measured at five points. A digital caliper and a micro-computed tomography (µ-CT) were employed for thickness measurements. The normal contact force exhibited an uneven distribution across the facial surface of Tooth 11. Model 15 displayed the highest force (88.9±23.2 N), while Model 30 exhibited the lowest (45.7±15.8 N). The force distribution was more favorable for bodily movement with Model 15. Thickness measurements, conducted using a digital caliper and µ-CT, revealed substantial thinning of the aligner after thermoforming. This thinning was most pronounced at the incisal edge (50% of the original thickness) and least at the gingivo-facial part (85%). Additionally, there was a progressive reduction in aligner thickness with increasing model height, which was most significant on the facial tooth surfaces. We conclude that the thermoplastic aligner sheets undergo substantial thinning during the thermoforming process, which becomes more pronounced as the height of the model increases. As a result, there is a decrease in both overall and localized force transmission, which could lead to increased tipping by the aligner and a diminished ability to achieve bodily movement.

Microbicides, topical antiseptic agents, and a revolutionary advance in HIV prevention aim to prevent the entry of infectious agents into the human body, thus stopping the sexual transmission of HIV and other sexually transmitted diseases. Microbicides represent the promise of a new age in preventive measures against one of the world's most pressing health challenges. In addition to their direct antiviral effects during HIV transmission, microbicides also influence vaginal mucosal immunity. This article reviews microbicides by presenting different drug classifications and highlighting significant representatives from each group. It also explains their mechanisms of action and presents information about vaginal mucosal immune responses, emphasizing the critical role they play in responding to HIV during sexual transmission. For each chemical compound, the article provides a brief overview of relevant preclinical and clinical research, emphasizing their potential as microbicides.The article offers insights into the multifaceted impact of microbicides, which signify a pivotal step forward in the pursuit of effective and accessible pre-exposure prophylaxis (PrEP).

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

The challenge of missing data in scientific research prompts researchers to decide between imputing incomplete data or discarding observations, where discarding can lead to information loss. Various methods exist, from simple deletion to sophisticated approaches like Multiple Imputation (MI). However, these methods often fall short with high-dimensional datasets. Multiple Imputation by Chained Equations (MICE) and Random Forest (RF) proximity imputation offer promising alternatives. Therefore, in this paper, we propose integrating MICE with Bayesian random forest (BRF) to enhance imputation accuracy and predictive power, particularly in high-dimensional analyses. Our approach combines MICE’s efficiency with BRF’s robustness, offering a comprehensive solution to missing data challenges. By way of example, we provide empirical evaluations to validate its effectiveness using synthetic data of various missing data scenarios. The results from the simulations showed that the combination of BRF and MICE offered a promising strategy for high-dimensional analysis in the presence of missing data.

Effective teamwork is essential to the success of sport and athletic organizations. Interprofessional Collaboration (IPC) and team-based care has also been identified as a priority for health and social care organizations.(World Health Organization, 2010) The Interprofessional Education Collaborative (IPEC) Core Competencies of (1) Values and Ethics; (2) Roles and Responsibilities; (3) Communication; and (4) Teams and Teamwork provide a guiding framework for IPC. (Interprofessional Education Collaborative, 2023). Student-athlete well-being is a key objective for individuals working with or for university athletic departments, with high levels of well-being being correlated with sport, academic, and personal improvements (Navarro et al., 2020). Due to the multifaceted factors that influence student-athlete well-being (e.g., cultural factors, coaches, academic pressure, family stress, etc.), there is an increasing need for an interdisciplinary/interprofessional approach to well-being (Lemelin et al., 2022, Navarro et al., 2020; Ulrich et al., 2022) However, significant barriers exist in implementing IPC into university athletic departments and little research exists on how to overcome roadblocks to integrating IPC in university athletic departments for enhancing student-athlete wellness. This paper will describe how a university athletic department used a team approach to enhance student-athlete health and well-being. The Interprofessional Wellness Team (IWT) included a clinical sport psychology doctoral student, a licensed mental health professional, athletic trainer, and sport dietitian. A case vignette is used to demonstrate how IPEC core competencies are operationalized by the team to address athlete health and well-being through IPC. Recommendations on the further implementation of IPC centered around student-athlete well-being will be provided.

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, the generalizability of these models across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the model's applicability across diverse development environments by integrating various datasets from distinct projects into a unified, using a more interpretable ML approach. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to seven times more defective modules with the same testing effort as a benchmark.

The notion of Critical Point Theory has extensive application in the filed of Partial Differential Equations, one of which is studying the existence and commenting on the uniqueness and multiplicity (in case when the solution is not unique) of weak solutions to Elliptic PDEs under certain boundary conditions.\par This article provides a brief survey about specific concepts like : differentiation on Banach Spaces, maxima and minima and its applications to PDEs. Furthermore, we've discussed the notion of defining a weak topology on Banach Spaces, before introducing the Variational Principle and its applications.\par The epilogue of the article primarily deals with results which studies the existence of weak solutions to Dirichlet Boundary Value Problems under specific conditions. The most notable aspect of this article is the proof of \textit{Rabinowitz's Saddle Point Theorem} using the method of \textit{Brouwer Degree}. Readers who are highly motivated in pursuing research in any of the topics relevent to the contents of this paper will surely find the \texttt{References} section to be extremely resourceful.

The Lobo reservoir designed to supply water to the Daloa city populations (Central west of Côte d'Ivoire) is facing the phenomenon of eutrophication due to the agricultural plots located upstream the reservoir inputs. Studies have highlighted the reservoir pollution and sedimentation problem. This study was initiated to provide a simple and effective solution to the eutrophication problem. The main objective is to determine the potential optimal dimensions of a grassed strip in order to limit the eutrophication phenomenon of the Lobo reservoir. The sizing of this grassed strip was done using the VFSMOD model. The flow from the pollution source plots was simulated and integrated into the model. The different scenarios tested from the band width variation have concerned the return periods of 1 and 2 years. The results obtained revealed that for the one-year return period, the grassed strip to be installed should have a width of 12.70 m in order to reduce by 70% the flow entering the strip considering the diffuse runoff over a length of 300 m. For the two-year return period, a width of 22.95 m should be required with the same length. The scenarios showed that a grassy strip width equal to 3 m should allow a reduction of 75% of upstream sediments.

Reed (Phragmites autralis) is a plant species with a seasonal reproductive cycle, it has a very high biomass in U Minh Thuong National Park. This characteristic that management of forest fire is dificult in Vietnam. To evaluate height, diameter, density, growth, biomass of fresh and dry, biochar on reed plants; it include plant, stem, leaves, flowers; analysis indicators of growth, biomass, biochar on reed. compare chemical indicators of peat and biochar in U Minh Thuong and Kien Luong of Mekong Delta. Relationship of peat chemical with growth and biomass. Study adsorption from reed biochar with pig urine and inorganic chemical of nitrogen and phosphorus. Study on types of peatland thickness, each one had 5 plots, investigated height (Ht), diameter of stem (D0.0); weigh part of plants, stems, leaves and flowers. the soil samples collected on investigated 20 plots, biochar is analyzed for its chemical composition, then the biochar is adsorbed with pig urine and phosphorus and nitrogen inorganic. The results evaluated fresh biomass on parts of reed plant, dry biomass and biochar on parts of them. Results height 3.39 – 4.74 m, diemeter 1.8 – 3.17 cm, biomass 0.04 – 0.1 m3 / m2, weight 15.46 – 20.54 kg / m2. The dry weight / plant 127.34 – 358.58 g, a dry plant trunk 79.55 – 217.78 g, a dry plant leaves 33.78 – 112.16 g, a dry plant flowers 4.4 – 19.64 g. A plant Biochar 26.16 – 73.57 g, a trunk biochar 14.41 – 39.44 g, a leaf biochar 8.16 – 22.59 g, a flower biochar 2.59 – 11.54 g, Peat analysis, indicators decrease P2O5, SO42-, NH4+ and increase are pH, humic acid, N%, K2O, Fe2+. Compare biochar indicators of U Minh Thuong and Kien Luong indicators higher are 9/11 as humic acid, OM%, Ash%, C%, N%, P%, K%, Ca%, Mg%. Indicators lower are as pH and SiO. Reed biochar can adsorption the pig urine as ammonium, nitrate, nitrogen, phosphorus and it also adsorption inorganic as nitrogen and phosphorus. This discovery it is possible to propose the use of data for environment treatment and application of biochar fertilizer for agriculture in region.

The leading cause of gastroenteritis in children under the age of five is rotavirus infection, accounting for 37% of diarrhoeal deaths in infants and young children globally. Oral rotavirus vaccines have been widely incorporated into national immunisation programs, but whilst these vaccines have excellent efficacy in high-income countries, they protect less than 50% of vaccinated individuals in low- and middle-income countries. In order to facilitate the development of improved vaccine strategies, a greater understanding of the immune response to existing vaccines is urgently needed. However, the use of mouse models to study immune responses to human rotavirus strains is currently limited as rotaviruses are highly species-specific and replication of human rotaviruses is minimal in mice. To enable characterisation of immune responses to human rotavirus in mice, we have generated chimeric viruses that combat the issue of rotavirus host range restriction. Using reverse genetics, the rotavirus outer capsid proteins (VP4 and VP7) from either human or murine rotavirus strains were encoded in a murine rotavirus backbone. Neonatal mice were infected with chimeric viruses and monitored daily for development of diarrhoea. Stool samples were collected to quantify viral shedding, and antibody responses were comprehensively evaluated. We demonstrated that chimeric rotaviruses were able to efficiently replicate in mice. Moreover, the chimeric rotavirus containing human rotavirus outer capsid proteins elicited a robust antibody response to human rotavirus antigens, whilst the control chimeric murine rotavirus did not. This chimeric human rotavirus therefore provides a new strategy for studying human rotavirus-specific immunity, and could be used to investigate factors causing variability in rotavirus vaccine efficacy. This small animal platform therefore has the potential to test the efficacy of new vaccines and antibody-based therapeutics.

The vast network of interconnected devices of the Internet of Things poses significant security challenges, necessitating the upgrade of current wireless networks to the 6G standard and an improved intrusion detection system for host networks. Thus this article therefore underscores a game theory concept to analyze the security impediments in wireless communication systems, where a host network bandwidth is jeopardized by the intruder while acting as a legitimate user. While game theory can help with the impending security concerns, network slicing is saliently proposed for large and complicated networks, precipitating a layer wise fragmentation of the concerned network and effectively addressing the security issues in different layers of the IoT framework. However, with increasing network complexity, network security issues incorporating traditional measures become increasingly cumbersome. Therefore, this paper also briefly mentions some of the ML techniques that can be used to classify and segregate valid users from the compromised nodes in the host network. The paper discusses the potential of incorporating intelligence into wireless communication systems to enhance the host network security. The paper further aids in analyzing existing security issues in different layers of wireless communication systems, underscoring network slicing for further classification of intruders from user nodes and training the host network using ML/AI per the user application specification.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with on-board memory for direct high-resolution recording of brain activity for >48 hrs. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). Mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between drug-treatments. Hypoactivity was globally observed as a consistent outcome in MK801-treated subjects and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in e.g. drug discovery, models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

The lack of defect image data is one of the main factors affecting the accuracy of supervised deep learning-based defect detection models. In response to the insufficient training data of defect images with complex backgrounds such as rust and surface oil leakage in substation equipment, leading to poor performance of the detection model, this paper proposed a novel adversarial deep learning model for substation defect generation: ADD-GAN. In comparison to existing generative adversarial networks, this model generates defect images based on effectively segmented local areas of substation equipment images, avoiding image distortion caused by global style changes. Additionally, the model utilizes a joint discriminator for overall image and defect image to address the issue of low attention to local defect areas, thereby improving the loss of image features. This enhances the overall quality of generated images as well as locally generated defect images, ultimately improving image realism. Experimental results demonstrate that the YOLOV7 object detection model trained on the dataset generated using the ADD-GAN method achieves an mAP of 81.5% on the test dataset, representing an improvement of 9.6% over the original dataset, 5.7% over traditional augmentation methods, and 7% over typical adversarial deep learning methods. This confirms that the ADD-GAN method can generate a high-fidelity dataset of substation equipment defects.

In response to the escalating global waste production rate, sustainable waste management be-comes indispensable. In parallel, the transformation of energy system is vital in order to ensure affordable, decentralized, and sustainable energy for the global community, based on economic, environmental, and social criteria. Waste-to-Energy (WtE) is an effective option in order to put a stepping stone to address both issues provided that multiple views and criteria are considered. This paper puts forward a generic methodological framework to holistically assess WtE technologies based on the PROMETHEE approach. In addition to environmental, economic, and social aspects, demonstration of applicability of the proposed methodology is realized at national level the case of Greece. Anaerobic Digestion seems to be the most preferable choice, recognized for its cost-effectiveness and lower environmental burden to other WtE technologies (i.e. gasification, pyrolysis, incineration). Insightful expert opinions and public preference are of paramount importance to the adopted approach, revealing technology-specific concerns and preferences. In addition, the material presented underscores a crucial gap in Life Cycle Assessment studies for large-scale WtE, emphasizing the need for future scientific attention and exploration. Public consensus indicates a positive overall attitude towards waste valorization in the context of energy production.

The landscape of business intelligence (BI) is marked by rapid growth, extensive research, and practical implementations, reflecting its sustained relevance in modern business strategies. This paper delves into the dynamics of BI, particularly focusing on the interplay between maturity and agility. While BI maturity emphasizes optimization and predefined goals, BI agility prioritizes adaptability and response to change. The paper highlights the contradictions between these concepts, emphasizing the need for a balanced approach in BI strategy formulation.The role of organizational culture in fostering BI agility is explored through literature review and interviews with BI professionals. Key insights include the necessity of an open culture for effective BI utilization, clear strategic vision, decision delegation, information sharing, business-IT cooperation, and a learning-oriented mindset. These cultural factors contribute significantly to BI effectiveness and the ability to navigate dynamic business landscapes. The paper proposes pillars of agile maturity supported by an open organizational culture, including efficient communication, information integration, creative problem-solving, and close collaboration between business and IT. These pillars underscore the importance of a flexible and open mindset in navigating BI challenges and ensuring sustained value. Empirical validation and further research are suggested to deepen our understanding of the impact of organizational culture on BI agility and effectiveness.

Evaluation of biliary strictures remains a challenge due to the low sensitivity of standard diagnostic work up, but the introduction of direct single-operator cholangioscopy (DSOC) changed this paradigm. Aim of our study was to evaluate diagnostic performance of DSOC and DSOC-targeted biopsies, intraductal ultrasound (IDUS) and brush cytology in patients with indeterminate biliary stricture (IBS). We evaluated patients who underwent advanced diagnostic work up for IBS in our endoscopy unit from ‘01/2018 to ‘12/2022; all patients had at least one previous attempt to characterize the biliary stricture. Final diagnosis was based on surgical pathology and/or clinical and radiological follow-up of at least 12 months. A total of 57 patients with a mean age of 67.2 ± 10.0 years were included, with a mean follow up of 18.2 ± 18.1 months. IBS were mostly located in the distal common bile duct (45.6%) and final diagnosis was consistent with malignancy in 35 patients (61.4%). DSOC and IDUS showed a significantly higher accuracy (89.5% and 82.7% respectively) compared to standard cytology (61.5%, p <0.05). In our experience, DSOC visualization and IDUS both demonstrated an optimal diagnostic yield in the differentiation of IBS, with an acceptable safety profile both quantitatively and qualitatively.

Larus michahellis increased its population throughout the 20th century in its worldwide distribution area. The increase in the Salinas de San Pedro del Pinatar has produced changes in the habitats due to the contribution of guano, predation on birds, incidents with workers, and the production of salt. The objective is to analyze the impacts of L. michahellis on the landscape, habitats, waterfowl, salt production, and workers, as well as to evaluate the effectiveness of control activities. Censuses of wintering L. michahellis have been carried out between 1990-2021, of nesting aquatic birds between 1994-2021, and nests and eggs of L. michahellis have been eliminated between 2000-2021. The result has been a decrease in pairs of L. michahellis, recovery of waterfowl populations, colonization of others, absence of incidents with workers, and reduction of damage to salt production. However, to reach a definitive solution, measures should be adopted to prevent L. michahellis from accessing the main sources of human food: urban solid waste dumps, aquaculture farms, and fish discards.

Spatiotemporal variations of sediments transported along rivers play a crucial role in a wide spectrum of uses, such as navigation, recreation, habitats or hydropower production. The advancement in technology has made it possible to use various indirect techniques to study and evaluate the transport of suspended sediment in fluvial environments. To investigate large-scale phenomena, remote sensing is becoming a largely utilized approach, as it allows to combine spatially-distributed and local information. Dam-induced sediment reduction occurs in large and small rivers worldwide, having profound implications on the fluvial systems. However, the systematic change of suspended sediment concentration (SSC) and its dynamic processes are not well known, and generally investigated only at the very local scale using field information. In this work, SSC maps were created by combining satellite images with local monitoring SSC data, using the Zhijiang -Chenglingji reach of the Changjiang River (China) as a case study, and analyzing how SSC dynamics changed over the period 2017-2022 in correspondence of extreme events. Multiple relationships between measured SSC and bands reflectance were tested, showing the potential of the best-performing one (R2=0.43) in mapping SSC spatiotemporal variations over an extent of dozens of kilometres, eventually providing new insights into the SSC dynamics at hotspots of the river systems, such as confluence zones, barriers, and reservoirs.

The objective of the research was to examine the determinants that determine farmer participation and shallot farming development in search of effective farm management practices. The study used Structural Equation Modeling data analysis. The primary data was collected from direct-structural interviews with 150 randomly chosen shallot farmers in Bantaeng Regency, Indonesia. It was found that the physical attributes of the land, the economic framework of the community, and the political system of the agricultural community were fundamental factors that exerted a positive and significant influence on farmer participation. Therefore, improvements in the physical aspects of land, the economic framework, and the political structure of agricultural communities could promote farmer participation. Furthermore, the farmers’ participation and the political structure of agricultural communities have a positive and significant impact on the development of shallot farming. Thus, by increasing the influence of government officials and community leaders, shallot farming can be promoted. The farmers can then enhance their participation in the shallot farming plan's formulation and implementation, providing the continued development of shallot farming. The findings of this study contribute significantly to the body of knowledge by validating previous research and proposing different ways to improve effective farm management practices in shallot farming.

Food safety in dairy shops has gained increasing attention. Consequently, this study aims to evaluate dairy handlers' knowledge of food safety practices, assess compliance with food safety codes regarding facility maintenance and personal hygiene, and understand retail dairy shops' capacity to implement food safety standards. This research involved surveys and interviews with 110 dairy retail workers to gauge their grasp of food safety principles, their recognition of its importance, and the obstacles they encounter in implementing proper food safety measures. Among the participants, 93% were males under 35 years old, with experience ranging from 1 to 8 years. The study revealed that while over 83% of participants were familiar with standard procedures ensuring dairy product safety, only 37% understood “HACCP” in relation to food safety. Additionally, 42% believed that freezing food at -18°C eliminates food bacteria, and 56% thought food contamination could be identified by taste, indicating misconceptions among respondents. Results showed that 90% of survey participants had a strong awareness of food safety, with 98% emphasizing the importance of regular inspection of production areas for cleanliness to ensure a safe dairy production environment. However, less than 45% had received formal training on dairy-specific food safety practices, and only 53.6% had a documented system for correctly labeling and providing production dates for dairy products. These findings have significant implications for public health and food safety policies in Jordan. Furthermore, the study provides valuable insights to enhance food safety practices among dairy retail vendors.

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is a 320 μm high resistivity Si and the bridge dielectric is a 15μm polyimide PI2525. The microfabrication process involves steps of metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results are presented and discussed, exhibiting reasonable agreement.

Thioethers are critical in the fields of pharmaceuticals and organic synthesis, but most of the methods for synthesizing alkyl thioethers employ foul-smelling thiols as starting materials or generate them as by-products. Additionally, most thiols are air-sensitive and are easily oxidized to produce disulfides under atmospheric conditions; thus, a novel method for synthesizing thioethers is necessary. This paper reports a simple, effective, green method for synthesizing dialkyl or alkyl aryl thioether derivatives using odorless, stable, low-cost ROCS2K as a thiol surrogate. This transformation offers a broad substrate scope and good functional group tolerance with excellent selectivity. The reaction likely proceeds via xanthate intermediates, which can be readily generated via the nucleophilic substitution of alkyl halides or aryl halides with ROCS2K under transition-metal-free and base-free conditions.

The use of tuning forks to measure fluid density and viscosity is widely employed in fields such as food, medicine, textiles, automobiles, petrochemicals, and deep drilling. The explicit analytical model based on Euler-Bernoulli cantilever beam theory for the relationship between tuning fork resonance characteristics and the density and viscosity of fluid is only applicable to the situation where the fluid viscous effect is very small. In this paper, the finite element method is used to simulate the influence of large variations in fluid density and viscosity on the resonance characteristic parameters (resonant frequency and quality factor) of the tuning fork. The numerical simulation results are compared with the analytical analysis results and experimental measurement results. Then, the sensitivity of tuning fork resonance characteristic parameters to fluid density and viscosity is studied. The results show that compared with the analytical results, the numerical simulation results have a higher degree of agreement with the experimental measurement results. The relative difference in resonant frequency is less than 2%, and the relative difference in quality factor is less than 4%. This indicates that the finite element method includes the influence of fluid viscosity on tuning fork resonance parameters, which is more in line with the actual conditions than the analytical model. Simulating and analyzing the sensitivity of the tuning fork to fluid density and viscosity by the finite element method, it is possible to consider the situation where fluid density and viscosity vary over a large range. Compared with experimental measurements, this method has higher efficiency and can significantly save time and economic costs. This study can overcome the limitation of existing explicit analytical models, which are only applicable when the viscous effects of the fluid are very small. It enables a more accurate simulation of the coupling vibration between tuning forks and fluids, thereby providing theoretical references for further optimizing tuning fork structural parameters to enhance the accuracy of measuring fluid characteristic parameters.

One of the major sources of catastrophic failures and deterioration of the mechanical properties of metals such as ductility, toughness, and strength, of various engineering components during application is hydrogen embrittlement (HE). It occurs as a result of adsorption, diffusion, and interaction of hydrogen with various metal defects like dislocations, voids, grain boundaries and oxide/matrix interfaces due to its small atomic size. Over the years, extensive effort has been dedicated to understanding hydrogen embrittlement sources, effects, and mechanisms. This research aimed at assessing the tensile properties; toughness, ductility, and susceptibility to hydrogen embrittlement, of cold-finished mild steel. Steel coupons were subjected to electrochemical hydrogen charging in a carefully chosen alkaline solution over a particular time and at various charging current densities. Tensile property tests were conducted immediately post the charging process, and the results were compared with those of uncharged steel. The findings revealed a clear drop in toughness and ductility with increasing hydrogen content. Fracture surfaces were examined to determine failure mechanisms. This evaluation has enabled the prediction of the steel's ability to withstand environments with elevated hydrogen concentrations during practical applications.

We present a laser-plasma electron accelerator module designed to be driven by high repetition rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet. The core methods involve comprehensive metrological assessments, including rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatio-temporal distribution analyses of the laser system. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We demonstrate the full functionality of the laser-plasma accelerator module and perform target density and laser-plasma characterizations. Our work is a first step towards the wide dissemination of fully integrated laser-plasma accelerator technology.

Medicinal activities carried out within hospitals lead to a high consumption of antibiotics and subsequently result in a high excretion rate of antibiotic residues. These antibiotics consumed in human medicine are not fully absorbed by the body and are often discharged into the environment as biological waste from infected human patients. The significant water consumption in hospitals and the pharmaceutical impact on wastewater from healthcare facilities facilitate the emergence and spread of antibiotic-resistant bacteria and antibiotic-resistant genes in the environment. Hospital effluents could be doubly involved in the dissemination of antibiotic molecules and multidrug-resistant bacteria in various ecosystems. The objective of this study is to characterize hospital effluents by assessing antibiotic concentrations and the diversity of antibiotic-resistant bacteria in these hospital wastewaters and within the environment (aquatic environments; rivers), as well as to inventory the bacteria present and those carrying antibiotic resistance in both hospital wastewater and the environment.

The Roma population constitutes the largest ethnic minority in Europe, however, data regarding the prevalence of non- communicable diseases are still scarce in the medical literature. The aim of this study was to assess the clinical and metabolic particularities of a Roma population with diabetes, in comparison with a group of Romanian Caucasians. We conducted an observational, transversal study and evaluated 808 adult patients with diabetes mellitus, from a tertiary hospital providing diabetes care, aged between 18 and 89 years old, of whom the majority were men (52.84%). Most of them had type 2 diabetes mellitus (T2DM), reaching 95.1% the Caucasian group and 87.8% among the Roma patients. 81.1% of the Caucasian patients had hypertension, approximately 15% higher than the Roma group, while more than half of the patients in both groups had obesity. There were no differences concerning myocardial infarction, however, the number of patients with history of stroke was 2.1 times higher in the Roma group compared to Caucasians. Chronic kidney disease was present in 22.1% of the Roma patients, 33.25% had retinopathy, and 72.7% presented polyneuropathy. The prevalence of cardiovascular risk factors, cardiovascular disease and microvascular complications among the study Roma population are quite significant, underscoring the importance of ethnic disparities in approaching healthcare management strategies.

Molecular dynamics simulations were performed to examine the impact of temperature (ranging from 250K to 350K) on the structural behavior of cellulose chains when dissolved in an aqueous cuprammonium hydroxide Cuam solvent system. By monitoring the root mean square deviation (RMSD) and radius of gyration (Rg) values at each temperature, we found that the geometric deformation of the cellulose chains changed significantly at T=300K. In contrast, Rg and RMSD values remained stable at temperatures in the range of 250K, 270K and the range of 330K, 350K. Calculating the interaction energy between the solvent and cellulose chains revealed that temperature significantly influences the cellulose dissolution process in the Cuam solvent system and that T=300K is an efficient temperature for its dissolution. The number of intra- and interchain hydrogen bonds was also calculated as a function of temperature, and the analysis confirmed that there is no breakage of these bonds at temperatures below 300 K (i,e: 250K, 270K) and above 310 K (I,e, : 330K and 350K) either between two native chains or between a native chain and another derivative.

Cold chains are essential in providing us humans with food and medicine across the globe. However, cold transportation, like all transportation, has massive negative impacts on the environment and human well-being, which especially culminate in cities. Thus, it is important to research the ways to reduce these impacts and reinvent urban logistics infrastructure. In this article, we articulate the massive problems the road-based transportation sector imposes on the environment and on the health of humans, especially in cities. We present algorithms that show promise in optimizing the whole road transportation sector as well as some innovations and alternative ways to model cities and the whole urban transportation infrastructure. We approach the issues discussed specifically from the point of view of refrigerated transportation, but many of the solutions can be applied to all the transportation sector in general.

Background: Removable prostheses have seen a fundamental change recently because of advances in polymer materials, allowing improved durability and performance. Despite these advancements, notable differences still occur amongst various polymer materials and processing technologies, requiring a thorough grasp of their mechanical, physical, and therapeutic implications. Methods: Fours different processing techniques, all utilizing Polymethyl methacrylate (PMMA) as the main material, were used to construct complete dentures. The specimens underwent a mechanical test. Results: All the specimens broke under the influence of a certain force, rather than yielding through flow, as is characteristic for plastic materials. For each specimen, the maximum force (N) was recorded (figure 4), as well as the breaking energy, Conclusions: The best results were obtained in the case of the thermoplastic injection system and, surprisingly and contrary to those in the specialized literature, the worst results were recorded in the case of 3D printed samples.

Within environmental perception, automatic navigation and object detection, computer vision is a crucial and demanding field with many applications in modern industries, such as multi-target long-term visual tracking in automated production, defect detection, and driverless robotic vehicles with sensing ability. The performance of computer vision has greatly improved recently thanks to developments in deep learning algorithms and hardware computing capabilities, which has spawned the creation of a large number of related applications. This paper presents the results of a detailed review of over 50 papers published over the course of two decades (1999–2024), with a primary focus on the technical advancement of computer vision. To elucidate the foundational principles, an examination of typical visual algorithms based on traditional correlation filtering was initially conducted. Subsequently, a comprehensive overview of the most recent advancements in deep learning-based computer vision techniques was compiled. Furthermore, a comparative analysis between conventional and novel algorithms was undertaken to discuss the future trends and directions of computer vision. Lastly, the feasibility of employing visual SLAM (Simultaneous Localization and Mapping) algorithms in the context of autonomous vehicles with sensing capability was explored. Furthermore, we explored a thorough and efficient architecture for utilizing these techniques in autonomous robotic vehicles into unmanned green and low-carbon smart factories, underlining novel advances as well as potential prospects for future research.

Maize is the staple cereal in Malawi, with a daily per capita consumption of 383g (dry matter basis), primarily consumed as dehulled maize flour used to cook nsima, a thick porridge. However, the extent of mass and nutrient loss during dehulling remains unclear in this food-insecure country, where the household maize harvest typically lasts only 6–7 months. In the current study, a milling experiment to determine yield and micronutrient losses was conducted. Milling batches (30 kg) of four maize grain varieties were dehulled at three abrasive disk dehullers under controlled conditions. Mass losses ranged from 18.8% to 42.7%, with a mean of 28.2±5.7%. Maize variety and dehuller significantly affected mass loss (p&lt;0.05). Initial protein, iron, and zinc concentrations were 8.8 ±1.0 g/100g, 20.5±2.0 µg/g, and 18.2 ±2.6 µg/g, respectively. Post-dehulling nutrient true retention varied for protein (49.7% to 75.6%, mean=64.7±7.0%), iron (18.7% to 71.6%, mean=37.6±12.0 %), and zinc (11.0% to 46.2%, mean=27.9±8.7 %). Maize variety significantly influenced mass and protein loss (p &lt; 0.05), while dehuller influenced mass, iron, and zinc losses. However, no significant difference (p &gt; 0.05) was found in the interaction between dehullers and variety on mass and nutrient losses, respectively. Given Malawi's precarious food insecurity situation, transitioning from dehulled maize flour nsima to whole maize flour or less refined nsima, is imperative. Our study findings can have food and nutritional savings for other southern Africa countries where the dehulling practice is rampant.

As a precious natural resource, forests are being destroyed. In previous studies, there is a lack of interactive assessment of its distribution that comprehensively considers multiple external disturbances. This paper takes the Barcelona Metropolitan Region as an example. Based on remote sensing, it analyzes the development process of the forest from 2006 to 2018 through multiple landscape indicators, and OLS models were established to analyze variables that have direct and indirect effects on forest distribution. In addition, the ecological structure of the forest was analyzed based on NDVI. It was found that the forest area is the largest but has been decreasing, becoming more complex in distribution structure. Much of the forest was converted to agricultural land and grassland. The green quality of forests was increasing, and the broad-leaved forest, the second largest area, contributes the most. NDVI is the most important positively correlated variable, and daytime surface temperature is an important inverse factor related to NDVI. In addition, NDBI is also a negative condition that inhibits forest development. In conclusion: The BMR forest area is decreasing and becoming more fragmented. NDVI and daytime LST are the two most significant factors. Climate warming may lead to worse forest development.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

The modern textbook analysis of the thermal state of photons inside a three-dimensional reflective cavity is based on the three quantum numbers that characterize photon’s energy eigenvalues coming out when the boundary conditions are imposed. The crucial passage from the quantum numbers to the continuous frequency is operated by introducing a three dimensional continuous version of the three discrete quantum numbers, that leads to the energy spectral density and to the entropy spectral density. This standard analysis obscures the role of the multiplicity of energy eigenvalues associated to the same eigenfrequency. In this paper we review the past derivations of Bose’s entropy spectral density and present a new analysis of energy spectral density and entropy spectral density based on the multiplicity of energy eigenvalues. Our analysis explicitly defines the eigenfrequency distribution of energy and entropy and uses it as a starting point for the passage from the discrete eigenfrequencies to the continuous frequency.

A two-qubit Hamiltonian model exhibiting an SU(2) symmetry is considered. The related dynamical problem results to be then exactly solvable both in the time-independent and in the time-dependent case. Basing on the formal, general form of the related time evolution operator, the time-dependence of the level of entanglement of some initial conditions is studied within both the Rabi and the Landau-Majorana-Stückelber-Zener scenarios.

Nonlinear loads, a crucial component of power systems grids, pose a challenge due to harmonics injection. Our research tackles this issue with a novel approach - a modified green plug / switched filter compensation scheme using fuzzy logic controllers. This innovative scheme, presented in the paper, utilizes dual action pulse width modulation to ensure switching functions from harmonic reduction and capacitive compensation for inrush nonlinear type AC loads.  The scheme's multi-loop regulations and online switching effectively handle dynamic type slow acting inrush, motorized and other rectifier type-nonlinear loads, enhancing power factor, power quality at source and load buses, and reducing total harmonic distortion at key source and sensitive nonlinear load buses. A simulation model in the MATLAB/SIMULINK software environment demonstrates the efficiency of the proposed FACTS technique. The results show that voltage, current, active power, reactive power, power factor regulation, and effective energy utilization are achievable with the designed FACTS-MFCC-SFC. The switched modulated AC green plug filter significantly improves power quality and enhances power factor in the case of inrush and nonlinear loads.

The pathogenicity of grapevine geminivirus A (GGVA), a recently identified DNA virus, to grapevine plants remains largely unclear. Here, we report a new GGVA isolate (named GGVAQN) obtained from grapevine ‘Queen Nina’ plants with severe disease symptoms; the infectious clone of GGVAQN (pXT-GGVAQN) was also constructed. Infection assays indicated that Nicotiana benthamiana plants inoculated with GGVAQN display upward leaf curling and chlorotic mottling symptoms. Direct injection of Agrobacterium tumefaciens cells carrying pXT-GGVAQN into roots of in vitro-grown ‘Red Globe’ grape plantlets by syringe led to systemic infection with GGVAQN, and the plants exhibited chlorotic mottling symptoms on their upper leaves, and downward curling, interveinal yellowing, and leaf-margin necrosis symptoms on their lower leaves. Our results provide insights into the pathogenicity of GGVA, and a simple and efficient inoculation method to deliver infectious viral clones to woody perennial plants.

The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet successfully simplified post-processing and integrated the post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent(HTBNet). First of all, we propose Binarization of Hyperbolic Tangent (HTB), optimized along with which, the segmentation network can expedite the initial convergent speed by reducing the amount of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention(MSCA). Meanwhile considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial(FMCS), which can fuse the multi-scale feature maps from channel and spatial dimension. Finally we adopt cross entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet compared with lightweight models has achieved competitive performance and speed on Total-Text(F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).

Vaginitis is a widespread issue for women worldwide, yet current diagnostic tools are lacking. Bacterial vaginosis (BV) is the most prevalent type of vaginitis, found in 10-50% of reproductive aged women. Current diagnostic methods for BV rely on clinical criteria, microscopy, or detection of a few microbes by qPCR. However, many vaginal infections lack a single etiological agent and are characterized by changes in the vaginal microbiome community structure (e.g. BV is defined as a loss of protective lactobacilli resulting in an overgrowth of anaerobic bacteria). Shotgun metagenomic sequencing provides a comprehensive view of all the organisms present in the vaginal microbiome (VMB) allowing for a better understanding of all potential etiologies. Here, we describe a robust VMB metagenomics sequencing test with a sensitivity of 88.5%, specificity of 96.2%, and a positive predictive value of 95.8% certified by Clinical Laboratory Improvement Amendments (CLIA), the College of American Pathologist (CAP) and the Clinical Laboratory Evaluation Program (CLEP). We sequenced over 7,000 human vaginal samples with this pipeline and described general findings and comparison to US census data.

Salmonella sp. is a globally prevalent organism responsible for causing salmonellosis, a foodborne illness. Salmonella Typhimurium represents a type of Salmonella sp. not associated with typhoid fever but capable of causing stomach and intestinal inflammation or severe infection. In the present study, 150 uncharacterized proteins of S. Typhimurium were randomly selected from UniProtKB and analyzed using TMHMM, PROSITE, STRING, DEG, and BLASTp. Results indicated that 32 uncharacterized proteins (21%) were predicted to be transmembrane proteins involved in various biological pathways. Among them, 30 transmembrane proteins were predicted to be essential and non-host homologous. This study's findings suggest their potential as drug targets against salmonellosis.

Background: Individuals living in rural areas often face challenges in accessing healthcare, increasing their risk of poor health outcomes. Farmers, a sub-population in rural areas, are particularly vulnerable to mental health issues and suicide, yet exhibit low rates of help-seeking behavior. The aim of our study was to develop an in-depth understanding of the issues influencing mental help-seeking among farmers living in rural areas from the perspectives of healthcare providers, as well as to explore the strategies providers use to navigate through these issues to effectively engage with this vulnerable population. Methods: We used a descriptive phenomenological approach to understand healthcare providers’ perspectives, experiences, and approaches to providing mental healthcare to farmer clients in rural areas. Semi-structured interviews were conducted with 21 participants between March and May 2023. Results: Our analysis yielded five thematic areas: (1) ensuring accessibility, (2) establishing relatability, (3) addressing stoicism and stigma, (4) navigating dual roles, and (5) understanding community trauma. Conclusions: Healthcare service delivery for farmers is multifaceted. This study fills a gap in knowledge by translating these the data to inform an evidence-based model and a list of recommendations for implementing agriculturally-informed practices in rural areas.

Word Sense Disambiguation (WSD) stands as a persistent challenge within the Natural Language Processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library, a widely recognized tool, demonstrates suboptimal accuracy. Conversely, the application of deep neural networks offers heightened classification precision, yet their practical utility is constrained by demanding memory requirements. This research paper introduces an innovative method addressing WSD challenges by optimizing memory usage without compromising state-of-the-art accuracy. The presented methodology facilitates the development of WSD system that seamlessly integrates into NLP tasks, resembling the functionality offered by the NLTK library. Furthermore, this paper advocates treating the BERT language model as a gold standard, proposing modifications to manually annotated datasets and semantic dictionaries such as WordNet to enhance WSD accuracy. The empirical validation through a series of experiments establishes the effectiveness of the proposed method, achieving state-of-the-art performance across multiple WSD datasets. This contribution represents advancement in mitigating the challenges associated with WSD, offering a practical solution for integration into NLP applications.

The Western Balkans region is persistently vulnerable to natural hazards, inadequate infrastructure, and limited institutional capacities, necessitating a collaborative approach to disaster risk management. This paper presents a comprehensive project led by the Scientific-Professional Society for Disaster Risk Management (NSD-URVs) in Belgrade, aiming to establish and strengthen a regional network of experts in disaster risk management. The project focuses on building expertise, facilitating knowledge exchange, promoting expert coordination, and enhancing cross-border collaboration. Key activities include developing a web platform, organising an international conference, and advocating for regional policies that integrate best practices and innovations in disaster risk management. The expected outcomes are the establishment of a cohesive network, enhanced expertise and coordination, and improved preparedness and responsiveness to disasters. By leveraging collective expertise and resources, this initiative aims to fortify the region's resilience against potential disasters. Addressing the identified gaps and implementing the proposed policy recommendations can significantly enhance the region's resilience to natural hazards. Future research should focus on the implementation and impact of these recommendations to ensure continuous improvement in disaster preparedness and response.

Breast cancer remains a major public health concern, and early detection can result in more treatment options, which are crucial for improving survival rates. Metabolomics offers the potential to develop blood-based screening and diagnostics tools that are less invasive and more cost-effective. However, the inherent complexity of metabolomic datasets makes identifying the most diagnostically relevant biomarkers a difficult task, with multiple studies demonstrating lim-ited agreement on the specific metabolites and pathways involved. This study aims to identify a set of biomarkers for early diagnosis of breast cancer using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (CHTN) were analyzed. We utilized univariate Naïve Bayes, L2-regularized Support Vector Machines, and Principal Component Analysis (PCA), along with feature engineering techniques, to select the most informative features. Multiple ma-chine learning models, including Support Vector Machines, Multidimensional Scaling, Logistic Regression, and Ensemble Learning were utilized for classification. The best-performing feature set comprised 4 biomarkers and 2 demographic variables, achieving an accuracy of 98%, demon-strating the potential for a robust, cost-effective, non-invasive breast cancer screening and diagnostic tool.

We overview the main equations of the Rayleigh-Ritz variational method and discuss their connection with the problem of simultaneous diagonalization of two Hermitian matrices.

Background: Study to assess glucose levels of people with type 1 diabetes (T1D) overnight based on insulin type and timing. Methods: Real-world, retrospective study in T1D using multiple daily insulin injections. Contin-uous glucose monitoring and insulin injections data were collected for ten hours after dinner us-ing the Insulclock® connected cap. Meal events were identified using ROC detection methodology. The timing of rapid insulin, second injections, and the type of insulin analogs used were evaluat-ed. Results: Nocturnal profiles (n=775, 49 subjects) were analyzed. Higher glucose AUC over 180mg/dL was observed in delayed injections (number; %; mg/dL x h): -45-15min (n=136; 17.5%, 175.9 ±271.0); -15-0min (n= 231; 29.8%, 164.0 ±237.1); 0+45min (n= 408; 52.6%, 203.6 ±260.9), (p 0.049). The use of ultrarapid (FiAsp®) (URI) vs rapid insulin analogs (RI) was associated with less hypoglycemia events (7.1 vs 13.6%; p 0.005) and TBR70 (1.7 +6.9 vs 4.6 +13.9%; p 0.003). Us-ers of glargine U300 vs degludec had higher TIR (70.7 vs 58.5 %) (Adjusted R-squared: 0.22, p < 0.001). The use of a correction injection (n= 144, 18.6%) was associated with higher hypoglycemia events (18.1 vs 9.5%; p 0.003), TBR70 (5.5 +14.2 vs 3.0 +11.1%; p 0.003), glucose AUC over 180 mg/dL (226.1 +257.8 vs 178.0 +255.3 mg/dL x h; p 0.001), and lower TIR (56.0 +27.4 vs 62.7 +29.6 mg/dL x h; p 0.004). Conclusions: Dinner rapid insulin timing, insulin types and correction injections affect nocturnal glucose profile in T1D.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

The article examines the relationship between household food waste and GDP in various European regions, aiming to determine how economic prosperity influences household food waste levels. Using comparative analysis of secondary and tertiary data, we developed a synthetic indicator (IpFW), to assess the interaction between GDP per capita and household food waste per capita. Despite expectations, higher GDP did not consistently correlate with lower food waste, challenging assumptions about economic prosperity and environmental stewardship. The research underscores the complex interplay between economic factors and food waste management, revealing no significant correlation even at regional levels. It also highlights the potential of e-commerce in enhancing food waste reduction strategies. E-commerce platforms can optimize food supply chains and consumer behavior, contributing to more sustainable consumption patterns. The conclusions call for a reevaluation of policies and suggest that integrating e-commerce could bolster efforts to manage food waste more effectively, aligning with the Sustainable Development Goals.

We introduce two symmetric concepts to physics: “distance” (space and time in one) and “wavematter” (electromagnetic wave packet and matter in one). We claim that physics has chosen the wrong concept of time: It is not aware that the time of a moving object flows in a direction other than my time. We provide 15 proofs for our claim by solving 15 mysteries of physics graphically! For example, we prove that length contraction and time dilation are actually geometrical effects in a 4D manifold that we call “Euclidean spacetime” (ES). We prove that the discrepancy in calculating the Hubble constant stems from a systematic error in the redshift measurement. We prove that what I deem wave, deems itself matter, which solves wave–particle duality. We even untangle quantum entanglement without the issue of non-locality. We finally understand why alternative theories of Euclidean relativity have failed: They are all based on four dimensions of space rather than distance. A huge amount of energy was injected into ES at a point that we take as origin. Ever since has this energy been moving radially away at the speed of light. We live in the 3D hypersurface of an expanding 4D hypersphere. Hyperspherical coordinates have the great advantage that they reduce all that is ever happening to just one formula. So, it is the Theory of Everything in these coordinates: “Energy is covering radial distance which, divided by Euclidean time, is equal to the speed of light.” Acceleration and force like gravitation emerge from a rotation of objects in ES and a subsequent projection to 3D space. Matching the symmetry simplifies physics.

(1) Background: To determine the diagnostic accuracy that optical coherence tomography (OCT) can add to fundus photography (FP) in assessing the condition of the retinal posterior pole; (2) Methods: Two blocks of analysis: at first, the posterior pole of each eye was examined using FP non-mydriatic imaging device. Secondly: OCT was used in addition to FP. After consolidating specific diagnostic criteria, the assessments were evaluated by two blinded independent investigation groups (by optometrists, and by ophthalmologists who was considered the gold standard); (3) Results: We calculated the diagnostic accuracy of FP compared to OCT and found that both had similar sensitivity, FP had slightly higher specificity, (p-value: 0.01) and OCT had a higher kappa coefficient 0.50 (95%CI: 0.46-0.55) vs 0.39 (95%CI: 0.34-0.45) of FP; (4) Conclusions: Based on the premise that the role of the optometrist in Spain is not to diagnose but to detect lesions and refer patients to an ophthalmologist, the results of this study support the use of OCT, that it provided gradable images in almost all examined eyes (97.5%), compared to FP (73.5%). However, the optometrist need a detailed and standardized guide in order to evaluate according to the ophthalmologist's criteria.

Not applicable

Within the polyurethanes market, only 0.1% are from renewable sources. With the continuing concern about climate change and greenhouse gases in the atmosphere, the substitution of monomers and processes derived from petrochemical sources is crucial for the development of biomaterials. Therefore, polyhydroxyurethanes have propose due to their synthesis route with the incorporation of monomers from renewable sources such as vegetable oils and the substitution of isocyanates known for their high toxicity, carcinogenicity and petrochemical origin, and the use of carbon dioxide. There are few reports on the use of soybean oil and the possible application of these materials in wound healing applications. Based on this, polyhydroxyurethanes were obtained from carbonated soybean oil and two diamines, one aliphatic: 1,4-butadiamine (putrescine), and one cycloaliphatic: 1,3-cyclohexanobis(methylamine), and they were physiochemically, mechanically, and thermally characterized. Four polyhydroxyurethanes were obtained, with stability to hydrolytic and oxidative media, thermal stability above 200 °C, tensile strength between 0.9-1.1 MPa, elongation at break between 81 and 222%. And water absorption up 102% and contact angle between 63,70 and 101,39. Achieving new formulations of biobased NIPHUs that represent a more sustainable process which allow to achieve the physicochemical, mechanical, and thermal properties required for the preparation of wound dressings.

Although the empirical Kepler’s laws can be proven by applying Newton’s laws to the dynamics of two particles attracted due to the gravitational interaction, there is no explicit formula for the motion as a function of time. In this paper, a quasi-analytical solution for this problem is proposed. It approximates the real behavior of celestial bodies with an acceptable degree of accuracy. All calculations involved require a low computational cost. This problem is closely related to Kepler’s equation, since, the solution for the equations of motion as a function of time gives us the solution to Kepler’s equation as well. The results are presented for each planet of the solar system (including Pluto), and the solution is compared against the real orbits.

Quasi-bound state in the continuum (QBIC) can effectively enhance the interaction of terahertz (THz) wave with matters due to the tunable high-Q property, which has a strong potential application in detection of low-concentration biological samples in the THz band. In this paper, a novel THz metamaterial sensor with a double-chain separated resonant cavity structure based on QBIC is designed and fabricated. The process of excitation of the QBIC mode is verified and the structural parameters are optimized after considering the ohmic loss by simulations. The simulated refractive index sensitivity of the sensor is up to 544 GHz/RIU, much higher than those of recently reported THz metamaterial sensors. Sensing sensitivity of the proposed metamaterial sensor is confirmed in experiment by detecting low-concentration lithium citrate (LC) and bovine serum albumin (BSA) solutions. The limits of detection (LoDs) are obtained to be 0.0025 mg/mL (12 μM) for LC and 0.03125 mg/mL (0.47 μM) for BSA, respectively, both of which excel over most of reported results in previous studies. These results indicate that the proposed THz metamaterial sensor has excellent sensing performances and can well be applied to the detection of low concentration biological samples.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

This paper proposes a new dynamic analysis of reasoning. It makes the following contributions. First, the current states of the psychology of reasoning are critically reviewed from theoretical and empirical perspectives. The long-standing controversies between logicians and psychologists are clarified by introducing the gauge theoretic structure. The debates between the mental logic theory and the mental models theory are carefully reviewed by identifying the problem types used in the empirical studies. Certain mental fluctuations are found and a quantum theoretic solution is examined. Second, it solves the measurement problem within the Hamiltonian framework. The modeling structure underlying reasoning dynamics is the electrodynamics. They are both the single-charge dynamic systems sharing the U(1) symmetry. The mental logic theory and the mental models theory are now characterized as wavefunctions in a unified account of the dynamic analysis with the yes/no measurements. The stochastic sampling method is introduced to solve the measurement problem, which has a wide range of applications in social sciences. Third, a novel metatheoretic framework of reasoning dynamics within Lagrangian framework is proposed. It solves the “Man vs. Men” problem via gauge theoretic modeling, by making the distinction between the global level and the local level. At each level, the gauge transformations are applied to achieve the global symmetry and the local gauge symmetry. It explains the meanings of the gauge principle and the Nether theorem. Fourth, the special theory of relativity is applied to address the “Man vs. Men” problem. It explains how to draw the global language cone and the individualized local language cones. Finally, it points out that borrowing modeling methods from physics has a long tradition in psychology. The present work utilizes the conceptual and modeling methods found in modern theoretical physics.

Keywords: methylmercury, ApoE, epididymal white fat, metabolomics, dyslipidemia, oxidative stress, liver steatosis.

Viral tropism is most commonly linked to receptor use, but host cell protease use can be a notable factor in susceptibility to infection. Here we review the use of host cell proteases by human viruses, focusing on those with primarily respiratory tropism, particularly SARS-CoV-2. We first describe the various classes of proteases present in the respiratory tract, as well as elsewhere in the body, and incorporate the targeting of these proteases as therapeutic drugs for use in humans. Host cell proteases are also linked to the systemic spread of viruses and play important roles outside of the respiratory tract; therefore, we address how proteases affect viruses across the spectrum of infections that can occur in humans, intending to understand the extrapulmonary spread of SARS-CoV-2.

The surface velocity of the Amery Ice Shelf (AIS) is vital to assessing its stability and mass balance. Previous studies have shown that the AIS basin has a stable multi-year average surface velocity. However, spatiotemporal variations in the surface velocity of the AIS and the underlying physical mechanism remain poorly understood. This study combined offset tracking and DInSAR methods to extract the monthly average surface velocity of the AIS and obtained the inter-annual surface velocity from the ITS_LIVE product. An uneven spatial distribution in inter-annual variation of the surface velocity was observed between 2000–2022, although the magnitude of variation was small at less than 20.5 m/yr. The increase and decrease in surface velocity on the eastern and western-central sides of the AIS, respectively, could be attributed to the change in the thickness of the AIS. There was clear seasonal variation in monthly average surface velocity at the eastern side of the AIS between 2017–2021, which could be attributed to variations in the area and thickness of fast-ice and also to variations in ocean temperature. This study suggested that changes in fast-ice and ocean temperature are the main factors driving spatiotemporal variation in the surface velocity of the AIS.

The paper presents a proposal for an in-house mathematical model of a liquid flat-plate solar collector. The proposed model is a one-dimensional distributed parameter model enabling simulations of the collector operation under arbitrarily variable boundary conditions. The model is based on the solution of energy balance equations for all components of the collector. The formulated differential equations are solved iteratively using an implicit difference scheme. In order to obtain a stable numerical solution, it is necessary to use appropriate steps of the time and spatial division. These were found by comparing the results obtained from the model with the results of the analytical solution available in the literature for the transient state, which constitutes the novelty of the present study. The accuracy of the results obtained from the model was verified experimentally by comparing the measured and the calculated history of the fluid temperature at the outlet of the collector. The calculation of the collector time constant is proposed in the paper as an example of the model practical application. The results of the time constant calculation were compared with the values obtained experimentally on the test stand. This is another novelty of the presented research. The analysed collector instantaneous efficiency was then calculated for selected outdoor conditions. The presented mathematical model can also be used to verify the correctness of the collector operation. Comparing, on an ongoing basis, the measured and the calculated values of the fluid temperature at the collector outlet, conclusions can be drawn about the process of solar glass fouling or glycol gelling. The model's simplicity and the low computational demands enable such comparisons in an online mode.

The knowledge of the precise anatomy and dimensions of the pulp chamber in the different teeth allows the odontologist to perform correct diagnosis, as well as treatment planning and moni-toring. The clinical practice has introduced cone-beam computed tomography (CB-CT) as a method to evaluate the morphology and dimensions of the pulp chamber. Nevertheless, micro-computed tomography (micro-CT) is regarded as the gold standard in approaching those topics. Here we have designed an algorithm that takes as input DICOM images from in vivo CB-CT of permanent molars to determine the accuracy CB-CT for evaluation of pulp chamber volume. The values were compared with those from in vivo CB-CT (rough and expert manipulated) and ex vivo micro-CT. The relative errors obtained with the volume calculated by the algorithm vs. the volume measured by micro-CT do not exceed 5.7%; also, no significant differences were found between algorithm volumes vs. manipulated CB-CT volumes while all volumes were different from those obtained with automatic CB-CT software. These data demonstrate that volume algorithm CB-CT based may be a reliable technique for evaluation of the pulp chamber volume in permanent molars and can be useful in the diagnosis of pulp diseases, as well as in the planning and monitoring of their treatments.

In this study we evaluate performance of two American surveillance systems for monitoring the spread of antimicrobial-resistant (AMR) gonorrhea: the Gonococcal Isolate Surveillance Project (GISP) and the enhanced Gonococcal Isolate Surveillance Project (eGISP) which includes the non-urethral isolates in addition to the urethral ones utilized in the original surveillance system. A continuous-time agent-based model of gonorrhea transmission among the US men who have sex with men (MSM) population was developed and used for this purpose. Our results show that the accuracy of eGISP was nearly 10% higher than the accuracy of GISP (97.8% (95% uncertainty interval: 83.5%, 100%) vs. 87.9% (55%, 100%)). Most of time, GISP detects the moment when the resistance to the first-line antibiotic reaches the switch threshold recommended by the WHO later than when it was actually reached. This leads to the additional spread of AMR gonorrhea. We also found that including the extragenital isolates in the surveillance is more important for the accurate monitoring than increasing the number of isolates. Finally, our results demonstrate that rectum and pharynx can serve as a niche for fostering antimicrobial resistance. These findings provide a scientific basis which can be used to inform the decision made by the policymakers on how many GISP and eGISP sites to establish each year.

The development of new turbomachinery designs requires numerous time-consuming and computationally intensive computational fluid dynamics (CFD) calculations. However, most of the generated high spatial resolution data remains unused at later development steps. That is also the case with automated optimization processes that use only a few integral values to determine objectives and constraints. Therefore the development of a data-driven AI model was initiated to ensure the potential of further use of the CFD data which is also used to train the AI model. The presented method subsequently provides a fast approximation of the 3D flow for new designs. In this paper, the structure of the developed AI model is presented and the approximation quality is analysed using a complex, state-of-the-art compressor test case. It is shown that the AI model can reproduce many characteristics of the 3D flow of new designs, and performance measures such as efficiency can be derived from these flow predictions. In addition, the complex test case revealed that greater design variation reduces the AI approximation quality which can lead to undesirable exploratory behaviour in an optimisation setup. Overall, the test case has shown promising results and has provided hints for further improvements of the AI model.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

There are various approaches to managing polyethylene terephthalate waste, including methods for recycling PET. However, at present, only the cleanest wastes that can already be efficiently recycled using known methods are considered in the scientific literature. The study aims to look at other forms of polyethylene terephthalate waste that pose a challenge: low quality PET flakes, polyester tire cord, PET dust and prepolymer waste. The waste was characterized by FTIR spectroscopy, viscosimetry, differential scanning calorimetry, laser diffraction and sieve analysis. The findings describe the composition and properties of various forms of polyethylene terephthalate waste. Low quality PET flake waste and polyethylene terephthalate dust have been shown to be suitable for all chemical recycling methods. When recycling waste polyester tire cord, it is difficult to completely separate the rubber, so its presence in the final product must be taken into account. The most promising way to process a prepolymer is its depolymerization by hydrolysis, alcoholysis or glycolysis to produce monomers for the synthesis of PET, similar in properties to the primary one.

This short report extends our previous paper “Multiplicity of human scent signature” and transfers canine identification to digital processing of data. The question is whether current GCxGC-MS technology and computer-based identification can perform identification as well as canines. This would objectify the identification of persons. Working with digital scent samples enables the use of multiple scent signatures for more exact identification. More differently constructed signatures during the identification process may represent the key point in identification of persons in olfactronics. Several types of scent signatures were considered in this article. The digital scent signatures were constructed based on the relative contents of the chemical compounds in the samples and relative ratios of compounds in the samples. Further on the signatures were made from the parts of the original samples fractioned by the volatility of compounds and by the relative abundance of the compounds in the samples. These results confirm the scent multiplicity phenomenon in digital scent samples. Using differently constructed signatures may be the key point in identification of persons using digital scent samples. This approach enables a transition from subjectivity to objectivity and a change from physical olfactory to olfactronic processing of digital scent samples.

To protect skin from harmful ultraviolet (UV), zinc oxide (ZnO) with tannin from gallnuts was treated on cotton and polyester fabrics in this study. Before the treatments, the fabrics were dyed with shikonin and xanthophyll from Gromwell and Cape Jasmine. First, this study found that xanthophyll was more effective at preventing light fade or discoloration than shikonin. Despite the highest K/S, the untreated cotton in violet faded the most intensely when exposed to UV. The color variation of untreated polyester was narrow, with little change in L, a*, and K/S. Due to the non-photochemical xanthophyll, the yellow-dyed samples treated with ZnO/polyphenol did not vary yellowness (b*: 28.838) significantly, while the violet fabrics displayed a significant decrease in K/S and an increase in b*. The combination of ZnO and polyphenol treatment improved UV absorption at 350 to 250 nm. Cu-mordanting after ZnO/polyphenols treatment caused the violet cotton to turn reddish from blueish (negative to positive b*), with a hue change of 316° to 59° and the highest ΔE (25.90±4.34) after UV exposure. In this study, the combination of ZnO/polyphenol with Cu-mordants contributed the xanthophyll-dyed polyester to achieve a minimum ΔE and keep chroma and hue after UV exposure.

Solar pasteurization, as a means of treating milk in remote rural areas without electrical power, is based on using solar energy to thermally inactivate pathogenic microorganisms at temperatures below the boiling point to maintain or improve milk quality. A solar milk pasteurizer of flat‐plate water‐heating glass acting as the solar collector, connected to a stainless-steel cylindrical milk vat, was constructed and evaluated in Navrongo, in the tropical savanna zone of Ghana, West Africa. The novel approach taken in this project is to integrate the solar collection and heat exchanger into a single unit using materials and fabrication techniques readily available in the developing world. The vat comprises a 1.5 mm thick stainless steel cylindrical tank, a 2.2 cm wide hot water jacket, and an outer layer of 5.0 mm thick aluminium foil insulation. Hot water produced by the collector was used for pasteurizing milk. The optimum quantity of milk that this device could pasteurize under the study conditions was 8 L. The device could pasteurize raw milk to a maximum temperature of 74 °C at 14:00 h GMT. The ambient temperature during the pasteurization ranged from 30 °C to 43 °C. The microbial analysis before and after the pasteurization was done, and the result shows that the total bacteria count before pasteurization was 6.6x10⁶ log CFU/mL per unit volume, and the total bacteria count after pasteurization was 1.0x10² log CFU/mL. By acceptable standards, the total bacteria count after pasteurization is within a safe range for consumption, and coliform counts were negative. The solar milk pasteurization system is cost-effective. Hence, it is appropriate for milk producers in arid pastoral areas without electricity to adopt the solar pasteurizer as an alternative to firewood as a fuel source for milk pasteurization. This approach can potentially create a cottage industry that may also play a crucial role as a water treatment technology that will improve the health of rural populations.

Pyrolyzed organic matter – commonly referred to as biochar – generally has positive effects on plant growth and can enhance early seedling development. Biochar-based seed coatings may thus be beneficial, specifically under adverse soil conditions such as high soil salinity. We evaluated germination and early plant development in radish (Raphinus sativa) seeds coated with conifer-feedstock slow-pyrolysis biochar; following the precedent of prior studies, polyvinyl acetate (PVAc) was used as the binding agent. Biochar-based seed coatings reduced germination rates and radicle extension in Petri dish trials; however, leaching of coated seeds largely eliminated this effect. PVAc applied in solution inhibited seed germination to a similar extent, suggesting that toxicity of PVAc or chemical products derived from it is the main mechanism for germination inhibition; a meta-analysis of published literature also indicates negative effects of biochar-based seed coatings using PVAc. In greenhouse trials the same biochar seed coatings improved aspects of plant growth and physiological status relative to controls, specifically enhancing root development. We conclude that biochar-based seed coatings have promise in promoting early seedling growth, and in combating salinity stress in agronomic and restoration applications. However, our findings contraindicate PVAc use as a binding agent. Further research is needed to test non-phytotoxic, readily biodegradable binding agents and to optimize biochars and seed coating processes for specific applications in agronomy and ecological restoration.

Reliable hydrological monitoring is crucial for effective water resource management, but establishing observatories in remote areas with extreme weather presents significant challenges. This study aimed at identifying cost-effective methods for streamflow estimation in such environments. We evaluated the performance of the Soil and Water Assessment Tool (SWAT) model using various precipitation data sources: ground-based rain gauge networks with different densities (one-gauge (1RG), two-gauge (2RG), and five-gauge (5RG) configurations) and satellite-derived Integrated Multi-satellite Retrievals for GPM (IMERG) data. The study focused on the Sahafihitry catchment, a 200 km² area in northeastern Madagascar. The results demonstrated that denser rain gauge networks (5RG and 2RG) captured the spatial variability of rainfall more effectively than a single gauge or IMERG data. This translated into superior SWAT model performance. Denser networks achieved higher statistical metrics (R², slope of regression a, Nash-Sutcliffe efficiency NSE, root mean square error RMSE, Kling-Gupta efficiency KGE) indicating a better fit between simulated and observed streamflow. Specifically, 5RG: R² = 0.84, a = 0.92, NSE = 0.83, RMSE = 3.33, KGE = 0.82; 2RG: R² = 0.85, a = 0.95, NSE = 0.83, RMSE = 3.36, KGE = 0.88; 1RG: R² = 0.73, a = 0.80, NSE = 0.72, RMSE = 4.28, KGE = 0.88; IMERG: R² = 0.48, a = 0.63, NSE = 0.37, RMSE = 6.46, KGE = 0.66. Furthermore, rain gauge data outperformed IMERG in simulating both flood events and low-flow periods. While IMERG offers low cost, readily available data, its lower performance introduces significant uncertainty into hydrological modeling. In contrast, the two-gauge network (2RG) achieved satisfactory streamflow simulations and represents the most cost-effective option for establishing reliable observatories within the specific characteristics of this study area.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.

This research aims to investigate psychological anxiety factors, particularly Music Performance Anxiety (MPA), that influence the sustainability of music learning systems. This study employs an innovative integration of qualitative and quantitative methodologies, marking the first use of a nonlinear system paired with a comprehensive data framework for analyzing questionnaire responses. This approach allows for a detailed examination of the effects of these factors on academic outcomes. Previous research has primarily focused on the fragmented music learning system from the perspective of teachers' instructional strategies, emphasizing phased teaching methods and learning objectives which focus often neglected a significant subset of music learners. Addressing this oversight, the current study specifically centers on the emotional aspects of anxiety within music learning systems, with a particular emphasis on students, who play a crucial role in music education. This perspective enables a deep exploration of how MPA and other related emotional factors affect music learning systems. A notable innovation of this research is the development of a stochastic nonlinear system designed to model the psychological factors impacting student music learning, complemented by a quantitative model that assesses actual learning outcomes. By analyzing this nonlinear system, the research identifies weight values for various negative emotions, particularly those related to MPA, that significantly influence the music learning process. The integration of MPA and other anxiety related emotional factors with practical learning outcomes provides a comprehensive understanding of their combined impact on the music learning system. These insights are invaluable for educators and policymakers aiming to enhance both the effectiveness and emotional well-being within music education. This comprehensive approach offers a novel perspective in understanding and improving the dynamics of music education through a more empathetic and scientifically grounded lens.

In order to study the effects of soybean isoflavones on the growth performance and lipid metabolism of juvenile Chinese mitten crab, six experimental diets were formulated by gradient supplementation 0%, 0.004% and 0.008% soybean isoflavones at different dietary lipid levels (10% and 15%), which were named to NF-0 group (10% fat and 0% SIF), NF-0.004 group (10% fat and 0.004% SIF), NF-0.008 group (10% fat and 0.008% SIF), HF-0 group (15% fat and 0% SIF), HF-0.004 group (15% fat and 0.004% SIF) and HF-0.008 group (15% fat and 0.008% SIF). All crabs with an initial weight of 0.4 ± 0.03 g were fed for 8 weeks. The results showed that diets supplementation with 0.004% or 0.008% SIF significantly increased the weight gain and specific growth rate of crabs. Diets supplementation with 0.004% or 0.008% SIF significantly reduced the content of non-esterified free fatty acids and triglycerides in the hepatopancreas of crabs at 10% dietary lipid level. Dietary SIF significantly decreased the relative mRNA expressions of elongase of very long-chain fatty acids 6 (elovl6), triglyceride lipase (tgl), sterol regulatory element binding protein 1 (srebp-1), carnitine palmitoyltransferase-1a (cpt-1a), fatty acid transporter protein 4 (fatp4), carnitine palmitoyltransferase-2 (cpt-2), Δ9 fatty acyl desaturase (Δ9 fad), carnitine palmitoyltransferase-1b (cpt-1b), fatty acid binding protein 10 (fabp10) and microsomal triglyceride transfer protein (mttp) in the hepatopancreas of crabs. At 15% dietary lipid level, 0.008% SIF significantly increased the relative mRNA expressions of fatty acid binding protein 3 (fabp3), carnitine acetyltransferase (caat), fatp4, fabp10, tgl, cpt-1a, cpt-1b and cpt-2 and significantly down regulated the relative mRNA expressions of Δ9 fad and srebp-1. In conclusion, SIF can improve the growth and utilization of high-fat diet by inhibiting genes related to lipid synthesis and promoting lipid decomposition in juvenile Chinese mitten crab.

Under suitable continuity and uniqueness conditions, solutions of an α order Caputo fractional boundary value problem are differentiated with respect to boundary values and boundary points. This extends well-known results for nth order boundary value problems. The approach used is a standard technique and makes heavy use of recent results for differentiation of solutions of Caputo fractional intial value problems with respect to initial conditions and continuous dependence for Caputo fractional boundary value problems.

The theories of Evolution and Big Bang have brought a major impact upon the scientific and societal perception upon the origins, abilities and roles of mankind on Earth. It is conclusively evident that Mother Nature and the physical realm is autonomous and all phenomena in the world occur without an imposition against the “free will” of such events and patterns. Nevertheless, this extent of freedom does not imply that life on Earth represents an accident and has no purpose. By careful analysis of ancient religious texts, it can be suggested even otherwise, that it is especially free will that is offered as a gift from outside of the physical dimensions of existence, with the purpose of a continuous edification and improvement of the overall life conditions and of the human perception. It may be that the multi-generational focus on improvement and progress have been misattributed as a long-term evolution of species, that humans came from animal ancestors and that there is a blurred line associating humans with animals. Furthermore, it may be that scientists based their suggestions that no outside Creator would have brought the Earth into existence on physical perspectives, when the extra-dimensional process of Creation implies that all elements of the physical existence undergo a process of Creation from the outside, where time, space and physical matter do not exist, in spite of such elements possibly reflecting unseen elements of the meta-physical world. Finally, there is the following example that deserves attention within the scientific community; just because all living cells - human, animal and plant - contain DNA chromosomes, ribosomes, mitochondria and a plasma membrane, it does not indicate that humans are animals, just as this does not indicate that plants are animals. In Science, there is a golden statement, that correlation does not imply causation. This example is in complete agreement with the verses of the Book of Genesis in the Holy Scriptures regarding the origins of life; namely, that animals could have been created by an Author of all life on Earth so that man would experience companionship. Another major accordance with the teachings of the Holy Scriptures is the fact that life emerged from the water, and not from the soil, although the soil constituted the second principal means of the commencement and extension of life. Namely, after life first emerged in the seas and the oceans from bacteria, a few bacteria were passed from the marine water into the nearby soil on islands and surrounding land through the circulatory air above the sea level. Afterward, the first life forms emerged from the soil, via the transferred bacteria. Eastern Orthodox Christianity regards water as the principal environment of both physical and spiritual birth. Overall, an improved scientific perception of the timeline of life on Earth may be reached via an approach that combines molecular biology with quantum physics, given that the three layers of Science - Biology, Chemistry and Physics - together with their foundational layer of Mathematics, are in fact situated in a relationship of complete harmony.

Agroforestry is often seen as a sustainable land-use system for agricultural production providing ecosystem services. Intercropping with food crops leads to equal or higher productivity than monoculture and results in food production for industry and subsistence. Low rubber price and low labor productivity in smallholdings have led to a dramatic conversion of rubber plantations to more profitable crops. The literature analysis performed in this paper aimed at better understanding the ins and outs that could make rubber-based agroforestry more attractive for farmers. A library was set-up consisting of 415 scientific references. Of the 232 journal articles, 141 studies were carried out on rubber agroforestry. Since 2011, the number of studies per year has increased. Studies on rubber-based agroforestry systems are performed in most rubber producing countries, in particular in Indonesia, Thailand, China and Brazil. These studies focus more or less equally on perennials (forest species and fruit trees), annual intercrops and mixed plantations. Of the 47 annual crops associated with rubber in the literature, 20 studies dealt with rice, maize, banana and cassava. Agronomy is the main discipline in the literature followed by socio-economy and then ecology. Only 4 papers are devoted to plant physiology and breeding. The discussion section has attempted to analyze the evolution of rubber agroforestry research, progress in the selection of food crop varieties adapted to agroforestry systems, and to draw some recommendations for rubber-based agroforestry systems associated with food crops.

A comprehensive evaluation system of rural building energy consumption from a innovative composite perspective is established, which is suitable for southwest China. the brainstorming method and Delphi method were used to establish the indicator system, the ANP method was applied to calculate the weights of the comprehensive evaluation model, the scoring criteria of all evaluation indexes are constructed based on fuzzy evaluation theory, the applicability of the model is verified by an example of the countryside around Chengdu. The results showed that the score of some factors is low, whole or part area, just like the percentage of clean energy use(C24), the thermal performance of external walls(E21), the implementation of energy-saving measures(S22), were key factors affecting the target level, which has greater potential for improvement. The distribution of comprehensive indicators and evaluation factors have certain spatial distribution characteristics, the overall spatial distribution shows the characterwastic of "high in the southeast and low in the northwest". The key factors and spatial distribution characteristics found can be used as an important basis for the improvement of green energy-saving promotion measures, energy-saving retrofit programmes were proposed in terms of additional solar room, energy-saving of external walls and air-conditioning temperature setting guidance.

Keywords: membrane electricity, excitability, action potential, ion channels

This work explores state-of-the-art approaches for stabilizing an inverted pendulum on a cart system, which has been used for over a century to demonstrate ideas in nonlinear and linear control theory, owing to its simple practical implementation yet complex system dynamics. We introduce energy-based control methods and propose a simple linear controller to quickly force the system to the downward equilibrium before swing-up. By constraining the system to start from a stable equilibrium configuration, the problem of global stability is reduced to ensuring that the system can follow all heteroclinic orbits that start very close to the downward equilibrium. Multiple Lyapunov functions are employed to ensure non-zero tracking, increasing the system's domain of attraction than is achieved by traditional controllers based on a single Lyapunov function. We stabilize our inverted pendulum system in the upright configuration through pole placement using a linear quadratic regulator known for fast settling times and smooth system response and through sliding mode controllers known for their robustness to disturbances and model uncertainties. While sliding mode controllers exhibit faster stabilization of the pendulum angle, they fail to stabilize the cart position in a reasonable amount of time. The paper concludes by suggesting various controller techniques that can enhance the swing-up and stabilization controller architectures, leaving room for future exploration. To facilitate further research and education, an open-source MATLAB simulation code is provided, encapsulating the system dynamics and controller designs. This resource offers flexibility for customization, making it a valuable tool for both researchers and educators working with inverted pendulum systems.

Background: Radiotherapy in the head-and-neck area is one of the main curative treatment options. However, this comes at the cost of varying levels of normal tissue toxicity, affecting up to 80% of patients. Mucositis can cause pain, weight loss and treatment delay leading to worse outcomes and decreased quality of life. Therefore, there is an urgent need for an approach to predict normal mucosa response in patients prior to treatment. Methods: We here describe an assay to detect irradiation response in patient healthy oral mucosa tissue. Mucosa specimens from the oral cavity were obtained after surgical resection, cut into thin slices, irradiated, and cultured for three days. Seven samples were irradiated with X-ray and three additional samples were irradiated with both X-ray and protons. Results: Healthy oral mucosa tissue slices maintained normal morphology and viability for three days. We measured a dose-dependent response to X-ray irradiation and compared X-ray and proton irradiation in the same mucosa sample, using standardized automated image analysis. Furthermore, increased levels of inflammation inducing factors - major drivers in mucositis development - could be detected after irradiation. Conclusion: This model can be utilized for investigating mechanistic aspects of mucositis development and can be developed into an assay to predict radiation-induced toxicity in normal mucosa.

In paired-associate vocabulary learning task, second language learners employ different cognitive strategies, associated with either elaborative or rote rehearsal, resulting in different recall performance. The current study investigated the role of intelligence, measured with Raven’s Advanced Progressive Matrices, in strategy effects on performance and eye movement parameters. The Keyword method, which is regarded as a more effective cognitive strategy in paired-associate task, was induced on a sample of 32 healthy participants with normal or corrected to normal vision. Effective cognitive strategy use, mentioned in a structured post-hoc report, was found to improve performance after strategy induction. Higher intelligence score was associated with higher recall performance in case the Keyword method was reported, but not in the cases when no rote rehearsal was reported. No effect of cognitive strategy use on eye movement measures after explicit cognitive strategy instruction was revealed, which is supposed to be connected with lack of self-sustained strategy use immediately after strategy instruction. The findings emphasize the role of intelligence in cognitive strategy instruction.

Users of escalators and moving walkways with sufficient space to accommodate two lanes of users often follow an implied etiquette for the two lanes: one for walking and one for standing (in the US, China, and many countries, the convention is ``walk left, stand right"). When there is high volume, e.g., when exiting a subway or train station or at the conclusion of an athletic event, the escalators often experience bottleneck congestion that constitutes the primary source of delay. It has been suggested that during such high-congestion periods it would be more efficient if everyone just stood in both lanes, with empirical evidence used to support this counterintuitive finding. Simple deterministic queueing models are used to show under what conditions such results hold and also to provide further insights regarding tradeoffs between performance metrics as a function of the distribution of walkers and standers, which could inform practical implementation policies to increase efficiency.

Escherichia coli is an indicator microorganism in One Health antibiotic resistance surveillance programmes. The purpose of the study was to describe and compare E. coli isolates obtained from pigs and human contacts from a commercial farm in South Africa using conventional methods and whole genome sequencing (WGS). Porcine E. coli isolates were proportionally more resistant phenotypically and harboured a richer diversity of antibiotic resistance genes, as compared to human E. coli isolates. Different pathovars, namely ExPEC (12.43%, 21/169), ETEC (4.14%; 7/169), EPEC (2.96%, 5/169), EAEC (2.96%, 5/169) and STEC (1.18%, 2/169) were detected at low frequencies. Sequence type complex (STc) 10 were the most prevalent (85.51%, 59/169) among human and porcine isolates. Six STcs (STc10, STc86, STc168, STc206, STc278 and STc469) were shared at the human-livestock interface according to multilocus sequence typing (MLST). Core genome MLST and hierarchical clustering (HC) showed that human and porcine isolates were overall genetically diverse, but some clustering at HC2 to HC200 were observed. In conclusion, even though the isolates shared a spatio-temporal relationship, there were still differences in the virulence potential, antibiotic resistance profiles, and cgMLST and HC according to the source of isolation.

Objective: This study aimed to explore potential interventions or preventive measures to reduce the incidence of falls among the elderly in the Madinah region, and to provide recommendations for healthcare professionals, policymakers, and community stakeholders to enhance fall prevention strategies and improve the overall well-being of the elderly population in this area. Methods: The study adopted a cross-sectional study design, and was carried out in the Madinah region using convenient physical assignment among of Saudi and non-Saudi local residents aged 65 years and over. Questionnaire surveys were circulated and demographic characteristics were collected, including participants’ fall experience and health status data over the past year, in order to assess the incidence of falls among the elderly population. This design allows for the collection of data at a single point in time, aiming to evaluate the prevalence, risk factors, and consequences of falls among the target population. Results: A total of 189 participants were recruited to the study, and the data indicated that the prevalence of falls among those aged 65 years and older in the past year was 41.5%. We found that 73% of the participants had experienced falls and post-fall injuries within the past 12 months. There were minimal differences in terms of physical handicapped cases found between those with and without a fall history. Logistic regression analysis revealed a significant link between fall experience and maximum bite force. Conclusion: There is a high prevalence of falls in the elderly population in the Madinah region. Our study reveals that 50.79% of our elderly participants had experienced more than one fall during the preceding year. Some preventable risk factors were identified in an analysis of their daily routines. The impacts of these risk factors have potential consequences for their healthy lives.

Using assembly theory, we investigate the assembly pathways of binary strings of length N formed by joining bits present in the assembly pool and the strings that entered the pool as a result of previous joining operations. We show that the string assembly index is bounded from below by the shortest addition chain for N, and we conjecture about the form of the upper bound. We define the degree of causation for the minimum assembly index and show that for certain N it features regularities that can be used to determine a shortest addition chain. We show that a string with the smallest assembly index for N can be assembled by a binary program of length equal to this index if the length of this string is expressible as a product of Fibonacci numbers. We conjecture that there is no binary program that has a length shorter than the length of the string having the largest assembly index for N that could assemble this string. The results confirm that four Planck areas provide a minimum information capacity that corresponds to a minimum thermodynamic (Bekenstein-Hawking) entropy. Knowing that the problem of determining the assembly index is at least NP-complete, we conjecture that this problem is NP-complete, while the problem of creating the string so that it would have a predetermined largest assembly index is NP-hard. The proof of this conjecture would imply P ≠ NP, since every computable problem and every computable solution can be encoded as a finite binary string.

Background: The surgical approach to treating calcaneal fractures, specifically Sanders types II, III, and IV, is still a subject of controversy. The decision to opt for open reduction and internal fix-ation (ORIF) or minimally invasive osteosynthesis (MIOS) remains uncertain owing to varying complication rates. This study intends to examine each treatment’s pros and cons in relation to the incidence of complications. Methods: In this retrospective study, we examined 70 patients treated between January 2018 and September 2022: 50 received ORIF, and 20 underwent MIOS. We evaluated functional outcomes with the Maryland Foot Score and the Creighton-Nebraska Health Foundation Assessment Scale (CNHFAS). We also assessed the amount of anatomical restoration using Böhler’s and Gissane’s angles, determined by radiographic checks, and the frequency of postoperative complications. Results: A significant reduction in the risk of reinter-vention was observed in patients with high anesthetic risks, polytraumatized individuals, and those with comorbidities who underwent MIOS. The chi-square test analysis of postoperative complications highlighted no substantial differences between the ORIF and MIOS groups in rela-tion to tendinitis, chronic pain and swelling, algodystrophy, and surgical wound infections (p > 0.20 for all). Conclusion: ORIF is more suitable for patients who have high motor function re-quirements, a long-term activity schedule and can endure potential additional procedures. This preference is because ORIF provides better anatomical restoration and features a lower osteoar-thritis occurrence rate.

Skeletal muscle contraction evokes numerous genetic and biochemical alterations that underpin the beneficial effects of exercise. The present study aimed to elucidate the mechanism and significance for contraction-evoked antioxidant adaptation in C2C12 myotubes using electrical pulse stimulation (EPS). As NQO1 and GSTA2 are the top antioxidants upregulated by Nrf2, dose-response and time-course experiments of EPS were performed to optimize the parameter at 10 volts for 4 days by evaluating their protein expression. Western blot analysis demonstrated that EPS-cells expressed significantly higher Nrf2 and a broad array of downstream antioxidant enzymes involved in multiple antioxidant systems. These effects were completely abolished by pretreatment with a ROS scavenger, N-acetylcysteine. MitoSOX-Red, CM-H2DCFDA, and EPR spectroscopy revealed a significantly higher ROS level in mitochondria and cytosol in EPS-cells compared to non-stimulated cells. Seahorse and Oroboros revealed that EPS significantly increased maximal mitochondrial oxygen consumption rate, along with upregulated protein expression of mitochondrial complexes I/V, mitofusin-1, and mitochondrial fission factor. A post-stimulation time-course experiment demonstrated that upregulated NQO1 and GSTA2 last at least 24 hours following the cessation of EPS, whereas elevated ROS declines immediately. These finding suggest an antioxidant preconditioning effect in the EPS-cells. A cell viability study using the CCK-8 assay suggested that EPS-cells displayed 11- and 36-fold higher survival rate compared to control-cells in response to 2 and 4 mM H₂O₂ treatment, respectively. In summary, we found that EPS upregulated a large group of antioxidant enzymes in C2C12 myotubes via a contraction-mitochondrial-ROS-Nrf2 pathway. This antioxidant adaptation protects cells against oxidative stress-associated cytotoxicity.

Amitriptyline is a tricyclic antidepressant commonly used for depressive disorders and pre-scribed off-label for several neurological conditions like neuropathic pain, migraine and anxiety. Amitriptyline potentiates the monoaminergic transmission by blocking the reuptake of norepi-nephrine and serotonin. However, antidepressants also act on additional targets that contribute to either therapeutic or adverse effects and may suggest new indications for their repurposing. Furthermore, since depression is often a co-morbidity in patients with chronic conditions (i.e. cancer, neurodegenerative diseases) the use of antidepressants interfering with pathways in-volved in cell homeostasis may affect the progression and outcome of these pathologies. Here we investigate the effects of amitriptyline on proliferation and autophagy in human SH-SY5Y neuroblastoma cells. Dose and time-dependent upregulation of the autophagy markers LC3II and autophagy receptor p62, with accumulation of LAMP1 positive compartments, were observed in SH-SY5Y cells exposed to amitriptyline. Alteration of autophagy was accompanied by reduced cell viability and decreased clonogenic capacity, without a significant induction of apoptosis. Decrease viability and clonogenic activity were still observed in autophagy deficient Atg5 -/- MEF and following pre-treatment of SH-SY5Y culture with the autophagy inhibitor chloroquine suggesting that the amitriptyline’s effects on cell proliferation were not caused by the modulation of autophagy. Our findings demonstrate that amitriptyline acts on pathways that are crucial for cell and tissue homeostasis and pose the basis for further studies on the potential application of these effects and the consequences during long-term antidepressant treatment.

The Southern European countries of Spain and Portugal both show a high degree of export spe-cialization in agri-food products compared to the EU-27 as a whole. This sector plays a funda-mental role in their economies, determining the management of natural resources, with the food and beverage industry being the most important manufacturing activity in both countries. Drawing on evidence obtained from Eurostat's database on foreign trade, this study analyzes whether the geographical concentration of agri-food exports from the Iberian Peninsula coun-tries to the EU-27 increases between 2008 and 2022, or, conversely, if there is a process of spatial diversification in favor of non-EU markets. Expansion towards new trading partners, as indicat-ed by estimated concentration indices, is significant in Spain but does not occur in Portugal. However, in both economies, the regional market with European partners, characterized by strong competition and low development expectations, remains decisive in the evolution of their exports in the short and medium term. Consequently, institutions and companies in this sector must adopt a more dynamic approach and implement measures that allow for increased sales orientation towards higher growth markets, thus enhancing the global competitiveness of the national agri-food sector.

Papain-like protease PLpro, a domain within a large polyfunctional protein nsp3, plays key roles in the life cycle of SARS-CoV-2, being responsible for the first events of cleavage of a polyprotein into individual proteins (nsp1-4) as well as for the suppression of cellular immunity. Here, we developed a new genetically encoded fluorescent sensor, named PLpro-ERNuc, for detection of PLpro activity in living cells using translocation-based readout. The sensor was designed as follows. A fragment of nsp3 protein was used to direct the sensor on the cytoplasmic surface of endoplasmic reticulum (ER) membrane, thus closely mimicking the natural target of PLpro. The fluorescent part included two bright fluorescent proteins - red mScarlet I and green mNeonGreen, separated by a linker with the PLpro cleavage site. Nuclear localization signal (NLS) was attached to ensure accumulation of mNeonGreen into the nucleus upon cleavage. We tested PLpro-ERNuc in the model of recombinant PLpro expressed in HeLa cells. The sensor demonstrated expected cytoplasmic reticular network in the red and green channels in the absence of protease, and efficient translocation of the green signal into nuclei in the PLpro-expressing cells (14-fold increase of the nucleus/cytoplasm ratio). Then, we used PLpro-ERNuc in the model of Huh7.5 cells infected with SARS-CoV-2 virus, where it showed robust ER-to-nucleus translocation of the green signal in the infected cells 24 h post infection. We believe that PLpro-ERNuc represents a useful tool for screening PLpro inhibitors as well as for monitoring virus spread in a culture.

Spent coffee grounds (SCG) have great potential as a useful, value-added biological material. In this context, activated carbon (AC) was prepared from SCG by an activation process using H3PO4 at 600°C in air and used as an adsorbent for the azo dye AO7, a model molecule for dye colorants found in textile industry effluents. X-ray diffraction, SEM and BET revealed that the AC was predominantly amorphous, consisting of a powder of 20-100 µm particles with mesopores averaging 5.5 nm in pore size. Adsorption kinetics followed a pseudo-second-order law, while the Langmuir model best fitted experimental isotherm data (maximum capacity of 119.5 mg AO7 per AC g). Thermodynamic parameters revealed that adsorption was endothermic and spontaneous. All the characterizations indicated that adsorption occurred by physisorption via mainly - interactions. The best experimental removal efficiency optimized by means of a Box-Behnken design and response surface methodology was 98% for an initial AO7 concentration of 20 mg.L-1 at pH 7.5 with a dose of 0.285 g.L-1 of AC and a contact time of 40 min. These results clearly show that activated carbon prepared from SCG can be a useful material for efficiently removing organic matter from aqueous solutions.

This work examines the beneficial role of an unsteady stenosis, operating without the use of any external energy source, as a means for augmenting the flow rate of a valveless pump in a hydraulic loop, including an open tank. In contrast to our previous work that the loop was horizontal, here gravity was taken into account. The stenosis neck cross-sectional area was controlled by the fluid pressure and an opposing force applied externally by a spring of adjustable tension. The pincher compressed and decompressed periodically a part of a flexible tube with frequencies from 5 Hz to 11 Hz and compression ratios Ab from 24% to 65%. The presence of the stenosis increased the net flow rate 19 times for Ab = 24%, and 6.3 times for Ab = 38%, whereas for Ab = 65%, the flow rates were comparable. The volumetric efficiency varied from 30% to 40% under the presence of the stenosis, and from 2% to 20% without the stenosis. The role of the stenosis role was to cause a unidirectional flow, opening during tube compression and closing during decompression. The pressure amplitudes along the flexible tube increased towards the rigid-flexible tube junction (as a result of the wave reflections) which were found to be significantly attenuated by the presence of the stenosis.

Cigarette smoke (CS) is a major driver of many respiratory diseases including chronic obstructive pulmonary disease (COPD) and non-small cell lung cancer (NSCLC). Tobacco causes oxidative stress, impaired phagocytosis of alveolar macrophages (AMs), and alterations in gene expression in the lungs of smokers. MicroRNAs (miRNAs) are small non-coding RNAs that influence several bi-ological processes and interfere with several regulatory pathways. The purpose of this study was to assess the effect of active CS on miRNAs expression in AMs obtained from bronchoalveolar lavage (BAL) of ever- or never-smoker subjects and patients with COPD or NSCLC. BAL specimens were collected from 43 sex-matched subjects to determine the expression of has-miR-34a-5p, 17-5p, 16-5p, 106a-5p, 223-5p, and 20a-5p before and after in vitro CS exposure by RT-PCR. In addition, bioin-formatic analysis of miRNAs target genes linked to inflammation was performed. Distinct and common miRNA expression profiles were identified in response to CS, suggesting their possible role in smoking-related diseases. It is noteworthy that, following exposure to CS, the expression levels of hsa-miR-34a-5p and 17-5p in both ever- or never-smokers, 106a-5p in never-smokers and 20a-5p in ever-smokers, shifted towards those found in individuals with COPD, suggesting them like a risk factor in developing this lung condition. Moreover, we identified miRNA targets involved in the immune system or AMs property regulation using in silico analysis. In conclusion, our study iden-tified miRNA signatures in AMs exposed to CS, indicating that CS is an important driver of epige-netic changes that contribute to the onset of various lung diseases.

Rotation motion in a three-dimensional physical world refers to an angular displacement of an object around a specific axis in $\mathbb{R}^3$. It is typically formulated as a non-linear and non-convex process due to the nonlinearity and nonconvexity of $\mathbb{SO}(3)$. However, this paper proposes a new perspective that the 3D rotation motion can be expressed by the linear equation without dropping any constraints and increasing any singularities. Moreover, two frequent cases, i.e., $\angle\left(\mathbf{R}\boldsymbol{x},\boldsymbol{y}\right)=0(\pi)$ and $\angle\left(\mathbf{R}\boldsymbol{x},\boldsymbol{y}\right)=\frac{\pi}{2}$, in computer vision and robotics that can be expressed linearly are deeply discussed in this paper.

Background/Objectives: The aim of the project is a longitudinal assessment of body image and mental health in scoliosis females pre-, postoperatively and in a follow-up, and implementation (one group-CBT scoliosis sample, CBTSS) or not (second group-control scoliosis sample, CSS) of CBT interventions. A healthy female sample (HFS) was also selected for comparative purposes. Methods. 36 in total AIS patients participated in the 1st and 2nd study phases, whereas 23 were eligible in the follow-up. Regarding HFS, 18 participants were included in the study. The CBT intervention took place pre- and postoperatively, during the patients' stay in hospital. Participants completed the Spinal Appearance Questionnaire (SAQ), Body Esteem Scale (BES) and Strengths and Difficulties Questionnaire-25 (SDQ-25). Body image was also assessed using a virtual reality-based application “Avatar Scoliosis 3D”. Results: The difference between the desired and objective, actual body shape is significant preoperatively both in the CBTSS and CSS at p