Ready-to-eat fresh fruit have an increasing presence in international markets due to its convenience and health benefits. However, these produces are highly perishable and efficient technologies to increase their shelf life are needed. In the present research, different citrus fruit species and cultivars from organic farming were assayed to obtain enzymatically citrus segments. The best results in terms of segment quality were observed for ‘Hernandina’ clementine which was chosen to make a new ready-to-eat produce based on peeled citrus segments packaged in glass jars with slight syrup made on citrus juice and organic sugar cane. The most appreciated syrup was those containing 50-50 (v/v) of ‘Fino’ lemon juice and ‘Hernandina’ clementine based on the sensory scores given by the panellists. In addition, different pasteurization treatments were assayed to preserve the new produce safety and nutritional and sensory quality properties during storage at cold temperature. Results showed that pasteurization treatment at 50 °C for 45 min could be enough to avoid microbial contamination with mesophilic and psychrophilic aerobic bacteria or yeast and mould and to maintain sensory properties until five weeks of storage at 4 °C. In addition, only a 10 % reduction of vitamin C was observed from fresh segments or syrup until the end of storage period, showing that the new ready-to-eat produce would conserve high bioactive compound content and health benefits after pasteurization and prolonged cold storage.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network LBFNet is proposed in this paper.First, a lightweight convolutional neural network LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning makes the model accuracy reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.

Data could be in many forms like text, image, audio, video and many others. Amongst these data, processing image is of important concern even with sophisticated technologies. Generally, images consume more storage space and processing time. This work aims in attaining a novel image encryption strategy that consumes very less computational time with maximum security. The idea is to split the image into blocks and rearrange the pixels in diagonal fashion to achieve confusion. Next, the blocks are combined to form sub images and one block of the sub image is subjected to Secure Hash Algorithm (SHA 512 bits). The SHA bits are XORed with the remaining blocks of the sub image. Finally, the entire image is encrypted using Advanced Encryption Standard (AES). Decryption process is the exact reverse of encryption process. The computational overhead is very low, and security is efficient. The results are compared with existing encryption techniques.

Two well-known drawbacks in fuzzy clustering are the requirement of assign in advance the number of clusters and random initialization of cluster centers.; the quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of FCM using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption.

Has anyone ever missed an event because he was confused in days and dates? Do we remember the date of any day without looking at a calendar? Is the current Gregorian Calendar efficient enough for use, and does it facilitate our life or make it more complicated? Have you ever thought about a much simpler way to calculate days and dates in a year? All these questions are answered in this paper, in which the author proposes original optimization algorithm that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize and very simple to predict the days and dates in any year.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

Suicide is a major public health concern in South Korea, and self-poisoning by pesticides is one of the common methods of suicide. Pesticide ban policies have been successful for suicide prevention; however, no studies have shown their effect according to occupational groups. The present study analyzed suicide and suicide by pesticide rates among South Korean workers age 15-64 in 2003-2017, their associations with occupational groups, and the impact of three major economic indices on these factors. Workers in the agriculture, forestry, and fishery industries have relative risks of 5.62 (95% CI: 5.54-5.69) for suicide overall and 25.49 (95% CI: 24.46-26.57) for suicide by pesticide. The real gross domestic product (RGDP) has a positive association with suicide overall only in the last five-year period investigated in this study, and the unemployment rate consistently has a positive association. The economic status and policy for suicide prevention affect suicide and suicide by pesticide rates differently among occupational groups and different time periods. Policy addressing suicidal risk for different occupational groups should be of concern in South Korea.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

Objective: The aim of the current study was to examine whether and to what extent mood disorders, comprising major depression and bipolar disorder, are accompanied by structural changes in the brain as measured using voxel-based morphometry (VBM). Methods: We have performed a VBM study using a 3Т MRI system (GE Discovery 750w) in patients with mood disorders (n=50), namely 39 with major depression and 11 with bipolar disorder, compared to 42 age, sex and education matched healthy controls. Results: Our results show that depression was associated with significant decreases in grey matter (GM) volume restricted to regions located in medial frontal and anterior cingulate cortex on the left side and middle frontal gyrus, medial orbital gyrus, inferior frontal gyrus (triangular and orbital parts), and middle temporal gyrus (extending to the superior temporal gyrus) on the right side. When the patient group was separated into bipolar disorder and major depression the reductions remained significant only for the patients with major depressive disorder. Conclusions: Using VBM the present study was able to replicate decreases in GM volume restricted to frontal and temporal regions in patients with mood disorders mainly major depression, as compared with healthy controls.

Reversible protonic ceramic cells (rPCCs) combine two different operation regimes, fuel cell and electrolysis cell modes, which allow reversible chemical-to-electrical energy conversion at reduced temperatures with high efficiency and performance. Here we present novel technological and materials science approaches, enabling a rPCC with symmetrical functional electrodes to be prepared using a single sintering step. The response of the cell fabricated on the basis of P–N–BCZD|BCZD|PBN–BCZD (where BCZD = BaCe_0.5Zr_0.3Dy_0.2O_3–δ, PBN = Pr_1.9Ba_0.1NiO_4+δ, P = Pr₂O₃, N = Ni) is studied at different temperatures and water vapor partial pressures (pH₂O) by means of volt-ampere measurements, electrochemical impedance spectroscopy and distribution of relaxation times analyses. The obtained results demonstrate that symmetrical electrodes exhibit classical mixed-ionic/electronic conducting behavior with no hydration capability at 750 °C; therefore, increasing the pH₂O values in both reducing and oxidizing atmospheres leads to some deterioration of their electrochemical activity. At the same time, the electrolytic properties of the BCZD membrane are improved, positively affecting the rPCC’s efficiency. The electrolysis cell mode of the rPCC is found to be more appropriate than the fuel cell mode under highly humidified atmospheres, since its improved performance is determined by the ohmic resistance, which decreases with pH₂O increasing.

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.

Graph coloring is a manifestation of graph partitioning, wherein, a graph is partitioned based on the adjacency of its elements. Partitioning serves potentially as a compartmentalization for any structural problem. Vertex coloring is the heart of the problem which is to find the chromatic number of a graph. The fact that there is no general efficient solution to this problem that may work unequivocally for all graphs opens up the realistic scope for combinatorial optimization algorithms to be invoked. The algorithmic complexity of graph coloring is non-deterministic in polynomial time (NP) and hard. To the best of our knowledge, there is no algorithm as yet that procures an exact solution of the chromatic number comprehensively for any and all graphs within the polynomial (P) time domain. However, several heuristics as well as some approximation algorithms have been attempted to obtain an approximate solution for the same. Here, we present a novel heuristic, namely, the 'trailing path', which returns an approximate solution of the chromatic number within polynomial time, and, with a better accuracy than most existing algorithms. The ‘trailing path’ algorithm is effectively a subtle combination of the search patterns of two existing heuristics (DSATUR and Largest First), and, operates along a trailing path of consecutively connected nodes (and thereby effectively maps to the problem of finding spanning tree(s) of the graph) during the entire course of coloring, where essentially lies both the novelty and the apt of the current approach. The study also suggests that the judicious implementation of randomness is one of the keys towards rendering an improved accuracy in such combinatorial optimization algorithms. Apart from the algorithmic attributes, essential properties of graph partitioning in random and different structured networks have also been surveyed, followed by a comparative study. The study reveals the remarkable stability and absorptive property of chromatic number across a wide array of graphs. Finally, a case study is presented to demonstrate the potential use of graph coloring in protein design – yet another hard problem in structural and evolutionary biology.

Background/Objectives: The introduction of robotic surgery has revolutionized surgical procedures bringing significant improvements in procedures such as radical prostatectomy. In Poland, robotic technology was first used in public healthcare in 2010 for colorectal surgery in Wrocław. Public funding for robotic urologic surgery began in 2022. The aim of this study is to evaluate the impact of integrating a stable source of funding on the development and utilization of robotic surgery. Methods: Using data from the National Health Fund, a retrospective analysis of these procedures was conducted, focusing on radical prostatectomies from the beginning of the funding period to November 2023. Results: The study showed that in the first year of public funding (2022), 2,149 radical prostatectomies were performed using robotic systems, and this number almost doubled to 4,331 by October 2023. Patients aged 60 years and older accounted for more than 80% of those undergoing robotic surgery. The rise in the popularity of robotic surgery is attributed to its advantages, including high precision, reduced invasiveness in the surgical field, minimized bleeding and pain, and reduced postoperative wound size. The use of robotic surgery also brings environmental benefits. Conclusions: The growing number of robot-assisted surgeries in Poland reflects the increasing role of robotics in medicine. These advances meet the growing demand for healthcare services and demonstrate the precision and effectiveness of robotic technology, potentially improving healthcare access and outcomes. Further implementation and refinement of robotic solutions are necessary to meet the changing needs of medical specialties.

The emergence and re-emergence of pathogens with pandemic potential has been a persistent issue throughout history. Recent decades have seen significant outbreaks of zoonotic viruses from members of the Coronaviridae, Filoviridae, Paramyxoviridae, Flaviviridae, and Togaviridae families, resulting in widespread infections. The continual emergence of zoonotic viral pathogens and associated infections highlights the need for prevention strategies and effective treatments. Central to this effort is the availability of suitable animal models, which are essential for understanding pathogenesis and assessment of transmission dynamics. These animals are also critical for evaluating the safety and efficacy of novel vaccines or therapeutics and are essential in facilitating regulatory approval of new products. Rapid development of animal models is an integral aspect of pandemic response and preparedness; however, their establishment is fraught by several rate-limiting steps, including selection of a suitable species, the logistical challenges associated with sharing and disseminating transgenic animals (e.g., the time-intensive nature of breeding and maintaining colonies), availability of technical expertise, as well as ethical and regulatory approvals. A method for rapid development of relevant animal models that has recently gained traction is the use of gene therapy vectors to express human viral receptors in readily accessible laboratory animals to enable virus infection and development of clinical disease. These models can be developed rapidly on any genetic background making mechanistic studies and accelerated evaluation of novel countermeasures possible. In this review, we will discuss important considerations for the effective development of animal models using viral vector approaches and review the current vector-based animal models for studying viral pathogenesis and evaluating prophylactic and therapeutic strategies.

This research focuses on the use of the HEC-RAS model to estimate flash flood risk. It combines advanced hydraulic modeling techniques and precise data entry. The aim is to contribute knowledge to assess flash flood risk and reduce impacts on communities and infrastructure. The selection of study areas with a history of severe flooding, such as the Lam Saphung Basin, Phrom River Basin, and Lam Chern River Basin Part 1 in Thailand, highlights the importance of thorough flash flood risk assessment. By leveraging the HEC-RAS model, the results of this research inform proactive risk management strategies, improve emergency preparedness and help develop sustainable mitigation measures. The results are important in developing emergency response strategies and overall recovery ability in areas at risk of flash floods. The research combines advanced hydrologic modeling and real-world flash flood data. In addition to the flood condition assessment, the extent of the flood area and associated hazards were assessed using a two-case simulation strategy. The study covers future scenarios related to land use changes and retrospective analysis of past flood events. The results have practical implications for improving emergency response strategies, land use planning, and overall resilience in flash flood-prone areas. The integrated approach emphasizes the research output to providing insights and actionable solutions for communities facing ongoing challenges posed by flash floods.

(1) Background: Neurologic and musculoskeletal diseases represent a considerable portion of the underlying etiologies responsible for the widely prevalent symptoms of pain, weakens, numbness, and paresthesia. Because of the subjective and often nonspecific nature of these symptoms, different diagnostic modalities have been explored and utilized.; (2) Methods: Literature review; (3) Results: Nerve and muscle biopsy remains the gold standard for diagnosing many of the responsible neurological and musculoskeletal conditions. However, the need for invasive tissue sampling is diminishing as more investigations explore alternative diagnostic modalities. Because of this, it is important to explore the current role of neurosurgical intervention for nerve and muscle biopsies and its current relevance in the diagnostic landscape of neurological and musculoskeletal disorders. With consideration of the role of nerve and muscle biopsy, it is also important to explore innovations and emerging techniques for conducting these procedures. This review explores the indications and emerging techniques for neurological intervention for nerve and muscle biopsies; (4) Conclusions: The role of neurosurgical intervention for nerve and muscle biopsy remains relevant in diagnosing many neurological and musculoskeletal disorders. Biopsy is especially relevant as a supportive point of evidence for diagnosis in atypical cases. Additionally, emerging techniques have been explored to guide diagnostics and biopsy, conduct less invasive biopsies, and reduce risks of worsening neurologic function and other symptoms secondary to biopsy.

Abstract: Objective: The aim of the study was to identify dietary patterns and its association with socio-economic, dietary and lifestyle practices among adolescents in Malaysia. Methods: A validated food frequency questionnaire was used to assess dietary patterns. Results: Multivariate analyses show that age and physical activity (PA) levels were emerged as positive determinants of healthy-based food pattern in Malay (All, p<0.001), whereas higher consumption of eating-out from home (EatOut) and fast food (All, p<0.05) were negative determinants. High weekly breakfast skipping (p<0.001) and EatOut (p<0.01) were positively associated with a western-based pattern, whereas age (p<0.001) and household income (p<0.05) were negative determinants. Higher frequency of daily snacking (p<0.05) was emerged as positive determinant of local-based food pattern. For Chinese adolescents, age (p<0.001), PA levels (p<0.001) and maternal education level (p<0.05) emerged as positive determinants for the healthy-based pattern, whereas high EatOut and fast food intakes (All, p<0.01) were negative determinants. Higher weekly consumption of EatOut (p<0.01), fast food (p<0.05) and carbonated beverages (p<0.05), and daily snacking practice (p<0.01) were positively associated with higher western-based food pattern, whereas age (p<0.01) was inversely associated. Conclusion: These findings suggest that unhealthy dietary and lifestyle practices could increase the risk of adherence to unhealthy western-based food pattern that is high in fat, sugar and salt contents, and consequently increase the risk of developing obesity and metabolic-related disorders during these critical years of growth.

After the great success of Mobile wallet, the Internet of Things (IoT) leaves the door wide open for consumers to use their connected devices to access their bank accounts and perform routine banking activities from anywhere, anytime and with any device. However, consumers need to feel safe when interacting with IoT-based payment systems, and their personal information should be protected as much as possible. Unlike as usually done in the literature, in this paper, we introduce two lightweight and secure IoT-based payment protocols based on an identity-based signature scheme. We adopt a server-aided verification technique to construct the first scheme. This technique allows to outsource the heavy computation overhead on the sensor node to a cloud server while maintaining the user's privacy. The second scheme is built upon a pairing-free ECC-based security protocol to avoid the heavy computational complexity of bilinear pairing operations. The security reduction results of both schemes are held in the Random Oracle Model (ROM) under the discrete logarithm and computational Diffie-Hellman assumptions. Finally, we experimentally compare the proposed schemes against each other and against the original scheme on the most commonly used IoT devices: a smartphone, a smartwatch and the embedded device Raspberry Pi. Compared with existing schemes, our proposed schemes achieve significant efficiency in the term of communication and computational overheads

The growing requirements regarding the safety of using polymers and their composites are related to the emergence of more effective, sustainable, and hazardous-limited fire retardants (FR). Significant amounts of FR are usually required to effectively affect the polymer burning behavior, while the knowledge of their recycling potential is still insufficient. At the same time, concerns are related not only to the reduced effectiveness of flame retardancy but, above all, to the potential deterioration of mechanical properties caused by the degradation of temperature-affected additives under processing conditions. This study describes the impact of four-time reprocessing of bio-based polyamide 11 (PA11) modified with an intumescent flame retardant (IFR) system composed of ammonium polyphosphate (APP), melamine cyanurate (MC) and pentaerythritol (PER) and its composites containing additionally short basalt fibers (BF). Composites manufactured by twin-screw extrusion were subjected to four reprocessing cycles by injection molding, comprehensively analyzing structural, mechanical, and fire behavior changes in each cycle. The obtained results confirmed the safety of using the proposed flame-retardant system for polyamide, processed under the recommended process parameters, without the risk of significant changes in the structure despite the partial increase in flammability caused mainly by the degradation of the polymer.

Canada is known to have a complex pathway for new drug approval and reimbursement resulting in delayed access to patients with serious and life-threatening diseases such as cancer. Several recent publications from key stakeholders including patients, physicians and policymakers highlight patient helplessness, physician frustrations and policymakers entangled in a massive network of bureaucracy unable to make headway. Several quantitative assessments using time from regulatory approvals to successful reimbursements confirm long review times and high rejection rates for oncology drugs especially those receiving conditional approvals. A consensus forum of 18 Canadian oncology clinicians recently voiced frustration with the process and inability to deliver guideline-supported efficacious therapies to their patients. This manuscript compares data extracted from publicly available data sources from 2019 to June 2024 to previous publications. Methods: Public databases from Health Canada, the Canadian Agency for Drugs and Technologies in Health (CADTH), which is in the process of changing to Canada’s Drug Agency, and the pan-Canadian Pharmaceutical Alliance (pCPA) were reviewed and data collected were analyzed with descriptive statistics. Results: From the data, three trends emerge, i) an increasing number of oncology drugs are receiving conditional approvals from Health Canada, ii) the percentage of conditionally approved oncology drugs receiving positive reimbursement recommendations from CADTH is still low but appears to be improving, but delays in access are now contingent upon pCPA deciding whether to negotiate price and then the duration of any negotiation, and iii) real-world evidence is no longer part of decision-making for conditional approvals. A slight increase in positive endorsement of RWE used to support CADTH recommendations was observed. Conclusion: Lack of timely access to oncology drugs hurts Canadian patients. While a small trend of improvement appears to be emerging, longer-term data collection is required to ensure sustained patient benefits.

Spent batteries recycling is an important way to obtain low-cost graphite. Nevertheless, the obtaining of crystalline graphite with a rather low density of defects is required for many applications. In the present work, high-quality graphites have been obtained from different kinds of spent batteries. Black masses from spent alkaline batteries (batteries black masses, BBM), and lithium-ion batteries from smartphones (smartphone black masses, SBM) and electric and/or hybrid vehicles (lithium-ion black masses, LBM) were used as starting materials. A hydrometallurgical process was then used to obtain recycled graphites by acidic leaching. Different leaching conditions were used depending on the type of the initial black mass. The final solids were characterized by a wide set of complementary techniques.

The change in both streamflow and baseflow in urban catchments has received significant attention in the latest decades as a result of their drastic variability. In this research, effects of climate variation and dynamics of land use are measured separately and in combination on streamflow and baseflow in the Little Eagle Creek (LEC) watershed (Indianapolis, Indiana). These effects are examined using land use maps, statistical tests, and hydrological modeling. Transition matrix analysis was used to investigate the change in land use between 1992 and 2011. Temporal trends and changes in meteorological data were evaluated from 1980-2017 using the Mann-Kendall test. Changes in streamflow and baseflow were assessed using the Soil and Water Assessment Tool (SWAT) hydrological model using multiple scenarios that varied in land use and climate change. Evaluation of the model outputs showed streamflow and baseflow in LEC are well represented using SWAT; however, comparing the calibration and validation period showed SWAT performs better for the calibration. During 1992-2011, roughly 30% of the watershed experienced change, typically cultivated agricultural areas became urbanized. Baseflow is significantly affected by the observed urbanization; however, the combination of land and climate variability has a larger effect on the baseflow in LEC. Generally, the variability in the baseflow and streamflow appears to be heavily driven by the response to climate change in comparison to variability due to altered land use. The results reported herein expand the current understanding of variation in hydrological components, and provides useful information for management planning regarding water resources, as well as water and soil conservation in urban watersheds in Indiana and beyond.

One of the most used techniques for reducing drag on blunt bodies when they move at speeds above the speed of sound are spikes. An important number of numerical works based on CFD have highlighted the fluid dynamics behind its use, as well as its advantages. However, most of docu-mentation focuses mainly on modified spike physical properties, while keeping constant supersonic or hypersonic flow conditions. In this sense, it is necessary to resort to current numerical techniques as machine learning, which can be obtain the most appropriate technical solution in compared to the costly iterative simulations that can delay the rapid development and testing of new aerody-namic configurations. This investigation postulates a specific hypothesis: that the K-Nearest Neighbours algorithm, renowned for its simplicity and effectiveness in classification problems, can be extrapolated as a robust tool for predicting drag reduction on spike blunt bodies. A wide range of Mach number and aspect ratios between blunt body diameter, spike length, and spike diameter are used for the algorithm dataset. The algorithm presents good predictions of the coefficient with errors of less than 5% percent, applying a multivariable regression method. Even, in the vicinity of the hypersonic speed zone, with a minimum of 3 neighbour nodes. The above validates the flexibility of the method and shows a new area of opportunity for the calculation of aerodynamic properties in a body moving near or above the speed of sound.

A recently proposed single image parasite quantification (SIMPAQ) platform based on a Lab-On-a-Disc (LOD) device has been earlier successfully tested in field conditions demonstrating the efficiency in soil-transmitted helminths (STH) egg detection and analysis. A related study (Micromachines 2021, 12, 1032) revealed the effects that can limit the performance of a SIMPAQ method due to the action of the Euler and Coriolis forces, and the interaction of the moving eggs with the walls of the LOD chamber. Here we propose a new improved design that allows to overcome those limitations and enhance the yield of the SIMPAQ LOD device which is demonstrated in the experiments with synthetic particle model system and real parasite eggs.

Keywords: Antisense Oligonucleotides, Cell Penetrating Peptides, Delivery, DG9 peptide, Phosphorodiamidate morpholino oligomers (PMO), Pip, R6G.

Background: Alzheimer's disease causes great changes, with the prefrontal cortex be-ing frequently damaged zones, affecting physical and cognitive behavioural and compromising autonomy and quality of life. Objective: To evaluate the effectiveness of feedback-based tech-nology on the improvement of physical-cognitive capacities in Alzheimer. Methods: Forty pa-tients with Alzheimer were recruited from the Santa Elena Alzheimer's Association, Seville, of which 15 ended up being pilot participants, divided randomly into controls (8) and experi-mental (7). They were evaluated with: Cognitive Mini-Examination Scale, Oddball Test and At-tention Network, Berg Scale, Tinetti, Timed up and Go and Geriatric Deterioration Scale. The experimental group was treated with feedback-based technology under the supervision of physiotherapists, during 16 thirty-minute sessions, twice a week. The control group performed their usual care. Result: A significant improvement was observed in the experimental group (Cognitive Mini-Examination and Tinetti). In the group of control, a worsening of physical and motor skills was evidenced because of the degenerative disease. Discussion/Conclusion: This treatment could be a strategy to keep the patient in early stages for longer, and improve quali-ty of life delaying dependency. It creates intergenerational bonds and use at home to promote treatment adherence.

Even after debridement and chemical root canal preparation, multispecies bacterial biofilms can still be found in root canals and dentinal tubules. This demonstrates the necessity of using suitable antibacterial endodontic sealers. Due to their well-known biological qualities, calcium silicate based root canal sealers or “bioceramic’ sealers have recently been launched and can be successful in endodontic procedures. This study's objective was to compare the impact of CerasealR, Total Fill BC SealerR, Bio-C SealerR, AH Plus BioceramicR, K-biocerR and SealiteR on the elimination of a multispecies’ endodontic biofilm at 3, 7 and 14 days. Enterococcus faecalis, Pseudomonas Aeruginosa, Candida Albicans and Proteus Mirabilis were cultured on specific agars and later grown in Brain Heart Infusion broth on hydroxyapatite discs pretreated with collagen type 1 and incubated for 18 days. The resulting biofilm was then placed on a 10mm round shaped blotting paper set on a metallic net in a 6cm petri dish with an orthodontic bend filled with the calcium silicate based root canal sealer in direct contact with the biofilm. In all tested groups, the total bacterial count has significantly decreased between day 3 and day 14 (P<0.05) with no statistically significant differences among the different sealers’ groups at all time points. Sealite group was capable of decreasing the Candida Albicans count significantly between day 3 and day 14 (P<0.05) in comparison with the bioceramic groups. All sealers had antibacterial activity against the multispecies biofilm between day 3 and day 14.

In this work we derive a generalized series expansion of the acrtangent function by using the enhanced midpoint integration (EMI). Algorithmic implementation of the generalized series expansion utilizes a simple two-step iteration. This approach significantly improves the convergence and requires no surd numbers in computation of the arctangent function.

Plant-based diets are increasingly popular for health as well as financial, ethical, and religious reasons. The medical literature clearly demonstrates that plant-based diets can be both nutritionally sufficient and medically beneficial. However, any person on an intentionally restrictive but poorly-informed diet may predispose themselves to clinically-relevant nutritional deficiencies. For persons on a poorly-informed plant-based diet, deficiencies are possible in both macronutrients (protein, essential fatty acids) and micronutrients (vitamin B12, iron, calcium, zinc and vitamin D). Practitioner evaluation of symptomatic patients on a plant-based diet requires special consideration of 7 key nutrient concerns for plant-based diets. This article translates these concerns into 7 practical questions that all practitioners can introduce into their patient assessments and clinical reasoning. Ideally, persons on plant-based diets should be able to answer these 7 questions. Each serves as a heuristic prompt for both clinician and patient attentiveness to a complete diet. As such, these 7 questions support increased patient nutrition knowledge and practitioner capacity to counsel, refer, and appropriately focus clinical resources.

Information overload is the biggest challenges nowadays for any website, especially the e-commerce website. However, this challenge arises for the fast growth of information on the web (WWW) with easy access to the internet. Collaborative filtering based recommender system is the most useful application to solve the information overload problem by filtering relevant information for the users according to their interests. But, the existing system faces some significant limitations like as data sparsity, low accuracy, cold-start and malicious attacks. To alleviate the mentioned issues, the relationship of trust incorporates in the system where it can be between the users or items, and such system is known as the trust-based recommender system (TBRS). From the user perspective, the motive of the TBRS is to utilize the reliability between the users to generate more accurate and trusted recommendations. However, the aim of the paper is to present a comparative analysis of different trust metrics in the context of the type of trust definition of TBRS. Also, the study accomplishes on twenty-four trust metrics in terms of the methodology, trust properties &amp; measurement, validation approaches, and the experimented dataset.

After the influenza H1N1 pandemic of 2009, the seasonal A/Brisbane/59/2007 strain was replaced by the A/California/07/2009 strain for the influenza virus vaccine composition. After several seasons with no indications on the occurrence of antigenic drift, A/Michigan/45/2015 was chosen as the H1N1 vaccine strain for season 2017/2018. Since the immune response to influenza is shaped by the history of exposure to antigenically similar strains, the potential cross-protection between seasonal human influenza vaccine strains and the emerging pandemic strains was investigated. Human serum samples were tested by haemagglutination inhibition and single radial haemolysis assays against A/Brisbane/59/2007, A/California/07/2009, and A/Michigan/45/2015 strains. Strong cross-reactions between A/California/07/2009 and A/Michigan/45/2015 strains were observed in 2009/2010, most likely induced by the start of the 2009 pandemic, and the subsequent post-pandemic seasons from 2010/2011 onwards when A/California/07/2009 becomes the predominant strain. In 2014/2015 season, population immunity against A/California/07/2009 and A/Michigan/45/2015 strains increased again, associated with strong cross-reactions. While haemagglutination inhibition assay has a higher sensitivity for detection of new seasonal drift, the single radial haemolysis assay is an excellent tool to determine the presence of pre-existing immunity, allowing a potential prediction on the booster potential of influenza vaccines against newly emerging drifted strains.

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

In this paper, a solenoid based linearly movable armature system is designed using robust control theory in order to improve the performance of the system. Reference track method is the best performance analysis for position control systems. Among the robust controllers, H infinity mixed-sensitivity and Mixed H 2 /H∞ with Regional Pole Placement Controllers are used to improve the performance of the system. Comparison of the proposed controllers for tracking a reference displacement signals (step and sine wave) and a promising simulation result have been obtained.

Sustainability, particularly in construction materials, has been a subject of growing interest. Civil construction is one of the industries where more materials are consumed, which leads to high energy consumption and CO2 emissions. The production of cement, especially clinker is largely responsible for these problems. As a solution, new materials emerge, which do not require much energy for their production, which are the alkaline cements, specifically the geopolymers. Geopolymers are inorganic polymers obtained by the alkaline activation of aluminosilicate precursors. In the present study geopolymers were developed with low grade kaolin (as a precursor) from a Portuguese company. The development of these geopolymers will be, due to their properties, a good solution for rehabilitation of earth buildings, especially in adobe. The development of these geopolymers is also a contribution to the sustainability of kaolin exploitations as it opens new markets for the low grade kaolins, presently not easily commercialized. As mechanical strength of adobe materials ranges in literature from 0.6 to 8.3 MPa, the values obtained for the developed geopolymers (between ~2 to 10 MPa) can be considered as totally adequate.

The paper presents a case study and a model for condition monitoring of Diesel engines’ oil of urban buses, through the accompaniment of the evolution of its degradation, with the objective to implement a predictive maintenance policy. Along time, because the usage, there is some decay in the lubricant properties. However, in normal functioning conditions, the lubricants properties, at the time the manufacturers recommend its changing, regardless of they are within the safety limits. Then, based on the accompaniment of the lubricants’ oil condition, the intervals of oil replacement can be enlarged what implies the availability increasing and the corresponding production increasing of the equipment. The model presented in this paper shows its potential to be spread to other types of equipment and organisations that want can implement similar maintenance policies, to achieve the best availability based on the real equipment health conditioning conditions

Three dimension (3D) Multi-input-multi-output (MIMO) scheme, which exploits another dimension of the spatial resource, is one of the enabling technologies for the next generation mobile communication. As the elevation angle in 3D-MIMO channel model might varies against the height of the base station transmit antenna, it has to be taken into account carefully. In this paper, the impact of antenna height on the channel characteristics of 3D MIMO channel is investigated by using the intelligent ray launching algorithm (IRLA). Three typical street scenarios, i.e., the straight street, the fork road and the cross road, are selected as benchmarks. On the basis of simulations, joint and marginal probability density functions (PDFs) of both the elevation angle of departure (EAoD) and the elevation angle of arrival (EAoA) are obtained. The elevation angle spread (AS) and the delay spread (DS) under various antenna heights are also discussed. Simulation results indicate that the PDFs of EAoD and EAoA vary characteristics under different street scenarios. Moreover, the minimum value of the DS can be achieved when the antenna height is half of the building height.

Twice-exceptional learners face a unique set of challenges arising from the intersection of extraordinary talent and disability. Neurobiology research has the capacity to complement pedagogical research and provide support for twice-exceptional learners. Very few studies have attempted to specifically address the neurobiological underpinnings of twice-exceptionality. However, neurobiologists have built a broad base of knowledge in nervous system function spanning from the level of neural circuits to the molecular basis of behavior. It is known that distinct neural circuits mediate different neural functions, which suggests that 2e learning may result from enhancement in one circuit and disruption in another. Neural circuits are known to adapt and change in response to experience, a cellular process known as neuroplasticity. Plasticity is controlled by a bidirectional connection between the synapse, where neural signals are received, and the nucleus, where regulated gene expression can return to alter synaptic function. Complex molecular mechanisms compose this connection in distinct neural circuits, and genetic alterations in these mechanisms are associated with both memory enhancements and psychiatric disorder. Understanding the consequences of these changes at the molecular, cellular, and circuit levels will provide critical insights into the neurobiological bases of twice-exceptional learning.

Increased CO2 emissions and air pollution impact the environment and human health. In this manner, new functionalized polyaminal-based polymers are synthesized via a one-pot polycondensation reaction of melamine with aldehyde monomers 2-bromo-1-naphthaldehyde and 2-hydroxy-1-naphthaldehyde to yield bromine- hydroxyl- functionalized polymer networks PAN-BrNA and PAN-HNA, respectively. The polyaminals porosity parameters were investigated and applied as an adsorbent for CO2 capture. FTIR, solid-state 13C NMR, and X-ray diffraction verified the PANs structure development. A scanning electron microscope and N2 adsorption-desorption methods were utilized to identify the porous properties of the polymers at 77 K. The Brunauer-Emmett-Teller (BET) surface area of PAN-BrNA and PAN-HNA is (16.6990 m2/g) and (716.7568 m2/g), respectively. At 273 K, PAN-BrNA and PAN-HNA can take CO2 gas up to (4.42786 cm3/g) and (47.55 cm3/g), respectively. The findings show that inserting functional groups has a significant impact on surface area and porosity parameters, as well as CO2 uptake.

Our previous study has found the anti-tumor activity of oleandrin in osteosarcoma cells in vitro, but the signal transduction process of cell apoptosis induced by oleandrin is uncertain, which is explored in this study. Fluorescence staining and flow cytometry (FCM) was performed to detect the cell apoptosis, intracellular reactive oxygen species (ROS) and mitochondrial membrane potential (MMP). Caspase-3 activity was detected using a commercial kit. The protein expression of cytoplasmic cytochrome c, mitochondrial cytochrome c, bcl-2, bax, caspase-9, Fas, FasL, caspase-8 and caspase-3 was detected using western blot. A pan-caspase inhibitor, z-VAD-fmk, was applied to block the apoptotic pathway and the apoptosis status were re-tested. We found that oleandrin significantly induced the increased apoptosis of U2OS cells. Meanwhile, the intracellular ROS was elevated, but the MMP decreased. The cytochrome c in mitochondria was notably decreased but increased in cytoplasm. The caspase-3 activity was also enhanced with the increase of drug concentration and treatment time. Oleandrin also down-regulated the level of bcl-2, but remarkably up-regulated the expression of bax, cleaved caspase-9, Fas, FasL, cleaved caspase-8 and cleaved caspase-3. Furthermore, the pre-treatment with z-VAD-fmk almost completely reverted the oleandrin-induced apoptosis. The results suggested that oleandrin induces the apoptosis of osteosarcoma cells via mitochondrial- and death receptor-dependent pathways.

Scholars in the field of environmental economics have extensively examined the relationship between foreign direct investment (FDI) and economic growth. However, they have often failed to consider the crucial roles played by technological innovation and financial development in determining environmental costs. The rapid economic growth and urbanization witnessed in BRICS countries have led to a substantial increase in energy demands, resulting in significant environmental degradation. This study aims to investigate the impact of financial development (including measures such as broad money, domestic credit to the private sector, FDI, and green technological innovation) on carbon emissions in BRICS countries using data from 2001 to 2023. The results of the study demonstrate a strong cross-sectional dependence among the countries in the panel. The Augmented Mean Group (AMG) estimator reveals a negative and statistically significant long-term relationship between broad money, FDI, green technological innovation, and CO2 emissions. On the other hand, domestic credit to the private sector shows a positive and significant association with carbon emissions. To determine the direction of causality, the study utilizes the Dumitrescu and Hurlin panel causality test. The findings indicate a non-directional long-term causality between financial development and CO2 emissions. However, a unidirectional causality is observed between green technological innovation and carbon emissions. Based on these findings, the study suggests that the development of industries, financial institutions, and technological innovation is crucial for attracting high-quality FDI in BRICS countries. However, it also highlights the significant contribution of these developments to environmental degradation, necessitating urgent policy responses.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

The construction industry generates significant CO2 emissions, and rebar is a major contributor to this environmental impact. Extensive research has been conducted to address this issue. Recent advancements have introduced algorithms to reduce rebar waste and consumption, demonstrating the feasibility of achieving near-zero rebar cutting waste (N0RCW) by considering special-length rebars. However, conventional lap splices, the most common rebar joint method, consistently consume excessive rebar quantities despite extending beyond their mandated zones. Conversely, couplers eliminate rebar lengths required for lapping splices, reducing the usage of rebar. Applying special lengths and couplers in heavily loaded structures like diaphragm walls can significantly reduce rebar usage and cutting waste, consequently reducing CO2 emissions and associated costs. This study aims to optimize rebar consumption and sustainability in diaphragm wall structures by integrating mechanical couplers with a special-length rebar approach. A case study confirmed a substantial reduction in purchased rebar usage (17.95% and 5.38%) and carbon emissions (15.24% and 2.25%) compared to the original design and previous study, respectively. Broadly implementing the proposed method across various buildings and infrastructure projects could further multiply these benefits, fostering the achievement of SDGs adopted by the United Nations for sustainable construction.

Microbial community succession in raw milk determines its quality and storage period. In this study, carbon dioxide (CO2) at 2000 ppm was used to treat raw milk to investigate the mechanism of extending the shelf life of raw milk by CO2 treatment from the viewpoint of microbial colonies and metabolites. The results showed that the shelf life of CO2-treated raw milk was extended to 16 days at 4 ℃, while that of the control raw milk was only 6 days. Microbiomics analysis identified 221 Amplicon Sequence Variants (ASVs) in raw milk, and the alpha diversity of microbial communities increased (P < 0.05) with the extension of storage time. Among them, Pseudomonas, Actinobacteria and Serratia were the major microbial genus responsible for the deterioration of raw milk, with a percentage of 85.7%. A combined metagenomics and metabolomics analysis revealed that microorganisms altered the levels of metabolites, such as pyruvic acid, glutamic acid, 5'- cmp, arginine, 2-propenoic acid, and phenylalanine in the raw milk through metabolic activities, such as ABC transporters, pyrimidine metabolism, arginine and proline metabolism and phenylalanine metabolism, and reduced the shelf life of the raw milk. CO2 treatment prolonged the shelf life of the raw milk by inhibiting the abundance of Gram-negative aerobic bacteria, such as Acinetobacter guillouiae, Pseudomonas fluorescens, Serratia liquefaciens and Pseudomonas simiae.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

The effect of the melt cooling rate on the atomic ordering of austenite and, as a consequence, on the martensitic transformation of a nonstoichiometric alloy of the Ni-Mn-In system has been studied. In-situ TEM observations revealed differences in the mechanism of phase transformations of the alloy subjected to different cooling conditions. It is shown that during quenching a high density of antiphase boundaries (APB) is formed and the alloy is in the austenite-martensitic (10M and 14M) state up to a temperature of 120K. In a slowly cooled alloy, a lower APB density is observed, and a two-stage transformation L21/B2 → 10M → 14M occurs in the range of 150–120 K.

With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are penetrating the field of finance. Digital finance is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally friendly enterprises, thereby promoting green and low-carbon development. This paper investigates the effects of digital finance on green and low-carbon cyclic development and the mechanism. First, the level of green and low carbon cyclic development in China is estimated from multiple aspects based on the panel data of 31 provinces by using the spatio-temporal range entropy weight method. Then, the working mechanism of digital finance on green and low carbon cyclic development is revealed by performing empirical tests based on panel regression models, mediating effect models, and threshold models. Finally, the effects of digital finance and conventional variables on green and low-carbon cyclic development are investigated by using a random forest model and a CatBoost model in based on the machine learning. The results show that digital finance has significant positive effects on green and low-carbon cyclic development, and technological innovation plays a key role in the effects of digital finance on green and low-carbon cyclic development; meanwhile, the driving effect of digital finance on green and low carbon cyclic development shows nonlinear features with increasing “marginal effect”; besides, both digital finance and conventional factors have significant impacts on green and low carbon cyclic development. This study provides an empirical basis and path reference for digital finance to achieve “carbon peak, carbon neutralization” in China.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

Along with the rapid development of technology and industry, the need for energy storage has become fundamental. One promising energy storage devices is the supercapacitor. Several type supercapacitors are available, one of which is the electrical double-layer capacitor made from carbon graphite and multiwalled carbon nanotubes (MWCNTs). In this study, we combined these two materials into a working electrode in a three-electrode system. In the morphological analysis using scanning electron microscope, transmission electron microscope, and X-ray diffraction, the two materials were found to have formed a composite on the surface. In the electrochemical analysis, two types of testing were conducted using cyclic voltammetry and electrochemical impedance spectroscopy. Electrochemical analysis was carried out on five electrolyte concentrations of Na2SO4 from 1 to 6 M. The capacitance produced at concentrations of 1, 2, 3, 4, and 5 M were 34.395, 35.808, 46.284, 49.502, and 76.815 F/g, respectively. At an electrolyte concentration of 5 M Na2SO4, an energy density of 27.312 Wh/Kg and a power density of 343.786 W/Kg were produced. Meanwhile on concentration of 6 M, the surface of the electrode was damaged.

To prolong the fatigue life of a product handled by machine, parametric accelerated life testing (ALT) is mentioned as an organized technique to pinpoint design flaws and minimize fatigue-connected failures. It requires (1) an ALT procedure, (2) fatigue model, (3) parametric ALTs with adjustment, and (4) an approximate of whether present product complete the BX life. The use of a quantum-transported time-to-failure archetype and a sample size are advised. The improvements in the reliability of a refrigerator ice-maker, comprising an auger motor with bearing, were utilized as a case investigation. In the 1st ALT, a steel rolling bearing cracked due to repeated loads under cold circumstances (below −20 C) in the freezer compartment. The bearing was altered by adjusting the matter from AISI 52100 alloy steel with 1.30-1.60% chromium to lubricated sliding bearing with sintered and hardened steel (FLC 4608-110HT) because of its high fatigue strength at lower temperatures. In the 2nd ALT, a helix made of polycarbonates (PCs) fractured. As an adjustment, a reinforced rib of the helix was thickened. As no troubles in the 3rd ALT occur, the lifetime of an ice-maker was proved to be B1 life 10 years.

This study examines the effectiveness of the peer-delivered, school-based intervention Healthy by Design (HbD). Data were collected in two cross-sectional surveys before and after invention implementation. In total 1,177 vocational students (before: 557, after: 620) participated in an online health behaviour survey. Multilevel logistic and multilevel linear models explored the effect of the intervention over time and the effect of the intervention dose received on (determinants of) dietary of physical activity behaviours. A significant positive effect over time was found for moderate intensity physical activity. A high intervention dose was positively associated with increased water, breakfast and fruit consumption and higher levels of moderate and vigorous physical activity compared to no intervention dose received. A moderate and high intervention dose was negatively associated with high calorie snacks consumption compared to no intervention dose received. Effects of HbD on the investigated dietary and physical activity behaviours over time are limited, but these effects may be hard to demonstrate and link to the intervention due to the nature of the intervention design and the natural school-setting of this quasi-experimental study. However, a higher intervention dose showed a strong relation with healthier dietary and physical activity behaviours.

In this study, the changes in mass, compressive strength and length were analyzed to investigate sulfate resistance according to ground granulated blast furnace slag (GGBFS) blending ratio and type of sulfate solution. All alkali activated mortars showed excellent sulfate resistance when immersed in sodium sulfate (Na₂SO₄) solution. However, when immersed in magnesium sulfate (MgSO₄) solution, different sulfate resistance results were obtained depending on the presence of GGBFS. Alkali activated GGBFS blended mortars showed a tendency to increase mass, increase length and decrease compressive strength when immersed in magnesium sulfate solution, but the alkali activated FA mortars did not show any significant difference depending on the types of sulfate solution. The deterioration of alkali activated GGBFS blended mortars in the immersion of magnesium sulfate solution was confirmed by the decomposition of C-S-H which is the reaction product by magnesium ion and the formation of gypsum (CaSO₄·2H₂O) and brucite (Mg(OH)₂).

Coffee is rich in phenolic acids, such as caffeic acid and chlorogenic acid (CGA). Polyphenol-rich diets have been shown to reduce the risk of metabolic syndrome (MeTS). Background and Objectives: This systematic review and meta-analysis discusses the effects of coffee consumption and its dose-response on MeTS parameters. Materials and Methods: PubMed and Scopus® were searched for relevant articles published between 2015 and 2020. This review focused on randomised controlled trials (RCTs) investigating the effect of coffee consumption on anthropometric measurements, glycaemic indices, lipid profiles, and blood pressure. Data from relevant studies were extracted and analysed using random, fixed, or pooled effects models with 95% confidence intervals (CIs). Results: Green coffee extract (GCE) supplementation (180 to 376 mg) was found to reduce waist circumference (weighted mean difference (WMD) = -0.39; 95% CI: -0.68, -0.10), triglyceride levels (WMD = -0.27; 95% CI: -0.43, -0.10), high-density lipoprotein-cholesterol levels (WMD = 0.62; 95% CI: 0.34, 0.90), systolic blood pressure (WMD = -0.44; 95% CI: -0.57, -0.32), and diastolic blood pressure (WMD = -0.83; 95% CI: -1.40, -0.26). Decaffeinated coffee (510.6 mg) reduced the fasting blood glucose levels (WMD = -0.81; 95% CI: -1.65, 0.03). The meta-analysis showed that the intake of GCE containing 180 to 376 mg of CGA (administered in a capsule) and liquid decaffeinated coffee containing 510.6 mg of CGA improved the MeTS outcomes in study participants. Conclusions: The findings of the review suggested that the effect of coffee on MeTS parameters varies depending on the types and doses of coffee administered. A more detailed RCT on specific coffee doses (with adjustment for energy and polyphenol intake) and physical activity is needed to further confirm the observed outcomes.

The gut-brain axis (GBA) represents a complex, bidirectional communication network that intri-cately connects the gastrointestinal tract with the central nervous system (CNS). Understanding and intervening in this axis opens a pathway for therapeutic advancements. In light of this, the current review assesses the effectiveness of neuromodulation techniques in treating neurological and gastrointestinal disorders by modulating the GBA, involving key elements such as gut micro-biota, neurotrophic factors, and proinflammatory cytokines. Through a comprehensive literature review encompassing PubMed, Google Scholar, Web of Science, and the Cochrane Library, and analyzing 182 relevant articles, this research highlights how neuromodulation can positively impact the management of functional gastrointestinal disorders (FGIDs), chronic pain, inflammation, and a range of GI disorders like Crohn's disease and ulcerative colitis. Despite existing challenges, the ability of neuromodulation to adjust disrupted neural pathways, alleviate pain, and mitigate in-flammation is significant in improving the quality of life for patients, thereby offering exciting prospects for future advancements in patient care.

Overactive bladder (OAB) is characterized by urinary urgency and increased urinary frequency, and can impact quality of life significantly. Tamsulosin and mirabegron combination therapy has been studied as a safe and effective treatment option for patients with OAB. This study evaluated the effects of combining these two drugs on their pharmacokinetics and safety profiles in healthy Korean males. In this open-label, fixed-sequence, 3-period, drug-drug interaction phase 1 study, a total of 36 male participants were administered multiple doses of tamsulosin alone (0.2 mg once daily), mirabegron alone (50 mg once daily), and a combination of both drugs. The results showed that the combination of tamsulosin and mirabegron increased tamsulosin exposure in the plasma by approximately 40%. In contrast, the maximum plasma concentration of mirabegron reduced by approximately 17%, when administered along with tamsulosin. No clinically significant changes in safety profiles, vital signs, or clinical laboratory test results were observed in this study. In conclusion, there were no clinically relevant drug-drug interactions between tamsulosin and mirabegron in terms of pharmacokinetics, safety, and tolerability, suggesting that their combination therapy could be a promising treatment option for patients with OAB.

Lattice structures have emerged as promising materials for aerospace structures applications due to their high strength-to-weight ratios, customizable properties, and efficient use of materials. These properties make them at-tractive for use in anti-ice systems, where lightweight and heat exchanging are essential. This paper presents an extensive experimental investigation into mechanical compression properties of lattice trusses fabricated from AlSi10Mg powder alloy, a material commonly used in casted aerospace parts. The truss structures were manufactured using additive manufacturing Selective Laser Melting technique and subjected to uniaxial compressive loading to assess their performance. The results demonstrate that AlSi10Mg lattice trusses exhibit remarkable compressive strength with strong correlations depending upon both topology and cells’ parameters setup. The finding described highlights the potential of AlSi10Mg alloy as a promising material for custom truss fabrication, offering customizable cost-effective and lightweight solutions for aerospace market. The paper also emphasizes the role of additive manufacturing in producing complex structures with pointwise tailored mechanical properties.

Using pumps operating as turbines (PATs) offers the possibility of increasing the sustainability of water and energy systems by recovering the excess of energy that would be otherwise lost in pressure reducing valves or head loss chambers. In on-grid applications, there are many research works and implementations of PATs. However, there is still limited research for the optimization of efficiency and stability of PATs operating in off-grid systems. This work contributes to the development of stable direct current (DC) off-grid electric systems based on PATs, by optimizing and analysing the energy efficiency and stability for a wide range of operation. A self-excited induction generator (SEIG) coupled to the PAT is provided to cover the conventional type of electric ma-chines typically coupled to the hydraulic pumps. In this context, a methodology is proposed, based on the hydraulic, mechanical and electric subsystems, to define the PAT-SEIG operational area, to maximize energy conversion and the system efficiency. This will guarantee stable electric output quantities when operating in a DC off-grid system. In addition, an analytical model is proposed to estimate the PAT-SEIG operation under specific conditions. With this, water managers are suitable to design and optimize an off-grid PAT-SEIG system and to define the best hydraulic machines, electronic equipment, and all different control elements to maximize energy conversion within the target of operational limits. To develop and verify this new methodology, two micro PAT-SEIG setups were implemented in the hydraulic laboratory of IST/CERIS under typical operating conditions. The proposed solution proved to be resilient under a variable load regime. The system's maximum efficiency can be adapted using different capacitor values for the excitation of the SEIG, and the operational range is limited to the electric components. Considering the nominal efficiencies of the system’s components, the maximum p.u. efficiency obtained for each PAT-SEIG system were between 0.7 and 0.8 p.u.

The growth rate in academic and scientific publications increased very fast. Researchers must choose a good representative and significant literature in publications, which has become challenging worldwide. Usually, the paper citation number indicates this paper's potential influence and importance. However, this standard metric of citation numbers cannot be used as a measurement to judge the popularity and significance of the recently published papers. To address this challenge, this paper presents an effective prediction method called FutureCite to predict the future citation level of research articles. FutureCite combines machine learning with text and graph mining techniques, leveraging their abilities in classification, datasets in-depth analysis, and feature extraction. FutureCite aims to predict future citation levels of research articles applying a multilabel classification approach. FutureCite can extract significant semantic features and capture the interconnection relationships found in scientific articles during feature extraction using textual content, citation networks, and metadata as feature resources. Our goal is to contribute to the advancement of substantial effective approaches impacting the citation counts in scientific publications by enhancing the precision of future citations. We conducted several experiments using a comprehensive publication dataset to evaluate our method and determine the impact of using a variety of machine learning algorithms. FutureCite demonstrated its robustness and efficiency and showed promising results based on different evaluation metrics. Using the FutureCite model has significant implications in improving the researchers' ability to determine targeted literature for their research and better understand the potential impact of research publications.

This comprehensive review is devoted to an under-exploited family of phenolic compounds, the coumarins, and the most relevant strands in which they are involved in some spirit beverages – wine spirit, brandy, whiskey, sugar cane spirits (rum and cachaça) and grape marc spirit – with great importance worldwide in terms of production, trade and consumption. It gathers the key discoveries on the topic considering the production process of each spirit beverage, the related sources of coumarins (different kinds of wood used in the ageing stage) along with the factors that govern them and can influence the sensory properties of the aged beverages. An overview of the analytical methods available for their identification/quantification is also included, as well as the corresponding trends for the advancement of knowledge in this field. Moreover, the remarkable role of coumarins as nutraceuticals, their importance as chemical markers for authenticity purposes, and their relationship with food safety of these spirit beverages are also addressed, highlighting the current gaps and issues, and providing clues for future research.

This article shows how to establish expansion formulas for calculating the mixed operations $(A^{\dag})^{\#}$, $(A^{\#})^{\dag}$, $((A^{\dag})^{\#})^{\dag}$, $((A^{\#})^{\dag})^{\#}$, $\ldots$ of generalized inverses, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of a matrix and $(\cdot)^{\#}$ denotes the group inverse of a square matrix. As applications of the formulas obtained, the author constructs and classifies some groups of matrix equalities involving the above mixed operations, and derives necessary and sufficient conditions for them to hold.

Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% 235U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very little data exists for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there is other data with potential value available for developing into quality benchmarks for use in data and software validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers.

Humankind has yearned for hundreds of thousands of years to explore and truly understand the dynamics of our enigmatic universe. However, we have often been limited by our hindsight here on Earth, or the limitations of human travel to further destinations. Having the ability to send humans to deep space would arguably set us millions of years ahead in our scientific understanding of various physical processes, including insight on some of the key mysteries that we still face. Perhaps a settlement further away from the Sun, Earth, and limiting optical resolution could provide us with greater ability to collect more meaningful data through our telescopes or other instruments. Moreover, one of the most significant abilities of such a settlement could be slightly easier access to sites of research within our own solar system that could be essential in the forthcoming years, such as Jupiter, Titan, the Kuiper Belt, and some Oort Cloud objects. Here we describe a unique option and set of goals to pursue and achieve just that via a mission to an L5 Trojan asteroid of Neptune, 2011 HM102. In this paper, we propose a number of possible challenges presented by traveling to the base, modeling its layout, maintaining the presence of life, and opportunities for research on the asteroid as well as possible solutions to these challenges. We begin our journey by outlining some motivations for creating a settlement on the asteroid, then outline a few key technological goals that must be achieved before we are capable of efficiently sending a colony of people to the settlement. We describe materials necessary for its construction as well as a novel mechanism for artificial gravity utilizing the Roche limit of the asteroid and multiple applications of quark-gluon plasma. From there we explore more aspects of settlement design, including key considerations for the layout of our settlement, sources of power, and protection from high-energy radiation from sources such as galactic cosmic rays. Then, we embark on a detailed discussion on the measures needed to maintain life on the settlement, such as hydroponic crop growth, a cycle for water recovery and oxygen generation, temperature maintenance, and air purification. Some remarks on the sustainability of life on the settlement follow, as well as some recommendations for entertainment, social life, economics, and governmental systems on the settlement.

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a pleiotropic myelopoietic growth factor and proinflammatory cytokine, clinically used for multiple indications and serving as a promising target for treatment of many disorders, including cancer, multiple sclerosis, rheumatoid arthritis, psoriasis, asthma, COVID-19. We have previously shown that dimeric Ca2+-bound forms of S100A6 and S100P proteins, members of the multifunctional S100 protein family, are specific to GM-CSF. To probe selectivity of these interactions, the affinity of recombinant human GM-CSF to dimeric Ca2+-loaded forms of 18 recombinant human S100 proteins was studied by surface plasmon resonance spectroscopy. Of them, only S100A4 protein specifically binds to GM-CSF with equilibrium dissociation constant, Kd, values of 0.3-2 μM, as confirmed by intrinsic fluorescence and chemical crosslinking data. Calcium removal prevents S100A4 binding to GM-CSF, whereas monomerization of S100A4/A6/P proteins disrupts S100A4/A6 interaction with GM-CSF and induces a slight decrease in S100P affinity for GM-CSF. Structural modelling indicates the presence in the GM-CSF molecule of a conserved S100A4/A6/P-binding site, consisting of the residues from its termini, helices I and III, some of which are involved in the interaction with GM-CSF receptors. The predicted involvement of the ‘hinge’ region and F89 residue of S100P in GM-CSF recognition was confirmed by mutagenesis. Examination of S100A4/A6/P ability to affect GM-CSF signaling showed that S100A4/A6 inhibit GM-CSF/S100-induced suppression of viability of monocytic THP-1 cells. The ability of the S100 proteins to modulate GM-CSF activity is relevant to progression of various neoplasms and other diseases, according to bioinformatics analysis. The direct regulation of GM-CSF signaling by extracellular forms of the S100 proteins should be taken into account in the clinical use of GM-CSF and development of the therapeutic interventions targeting GM-CSF or its receptors.

Duchenne muscular dystrophy (DMD) is the most prevalent progressive muscular dystrophy, and the guidelines recommend the regular assessment of respiratory muscle function. This study aimed to assess the relationship between maximum inspiratory pressure (MIP), maximum expiratory pressure (MEP) and sniff nasal inspiratory pressure (SNIP) measurements and upright-supine spirometry parameters in children with DMD, predictability of upright-supine spirometry in terms of diaphragm involvement, and impact of nutrition on muscle strength. This prospective cross-sectional study examined patients with DMD by comparing upright and supine FVC, MIP, MEP, and SNIP measurements. Forty-four patients were included in the study. The mean patient age was 10.8±2.9 years. Twenty-five patients were ambulatory. A significant decrease in FVC, FEV1, and FEF25-75 values was detected in the supine position in both ambulatory and nonambulatory patients (p&lt;0.05). All patients had low MIP, MEP, and SNIP measurements (less than 60 cm H2O). MIP, MEP, and SNIP values were significantly lower in patients with malnutrition than in those without (p&lt;0.05). To accurately assess respiratory muscle strength, supine FVC should be combined with upright FVC, MIP, MEP, and SNIP measurements. It is crucial to regularly screen patients for malnutrition, as this can significantly affect respiratory muscle function during pulmonology follow-up.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

Detection of partial discharge (PD) activities in high-voltage equipment can be done according to the several mechanisms of signal detection, which are electromagnetic wave signal detection, acoustic signal detection, chemical reactions, electrical signal detection, or optical emission detection. Recently, multiple methods of detection and localization of partial discharge activities occurred in power transformers and gas-insulated switchgear (GIS) have been proposed to monitor the health condition of high-voltage equipment, especially when the awareness regarding preventive maintenance has been emphasized at the industrial level and electrical providers. In aligning the needs of industrial and the improvement of PD detection methods, this manuscript focused on reviewing the current practice methods for the detection and localization of PD signals in high-voltage equipment and comparing their efficacy, in summarizing the future direction of research work-related methods of PD detection. The comparative reviews are discussed in terms of the mechanism of PD signal detection, indication parameters, calibration technique, advantages, and limitations of each method of PD measurement in detail.

The study is based on the results of long-term field studies, satellite and aerial data analysis. In the maximum of the Little Ice Age (LIA) 243 glaciers with a total area of 353.4 km2 were reconstructed. By the results of interpretation of Corona images by 1968 the number of glaciers increased (236), the total area reduced to 242 km2. In 2010 glaciation was represented by 237 glaciers with a total area of 201 km2. Thus, from the maximum of the LIA, the glaciation of the Tavan-Bogd mountains decreased by 43%, which is somewhat less than the neighboring glacial centers (Ikh-Turgen, Tsambagarav, Tsengel-Khayrkhan and Mongun-Taiga mountains). The probable cause is the predominance of larger glaciers relatively resistant to warming and higher altitudes. The effect of glacier size on their stability is supported by differences in the relative shrinkage of glaciers in different parts of the Tavan-Bogdo-Ola massif: the smallest decline occurred in the basins of the Tsagan-Gol (31.7%) and Sangadir (36.4%) rivers where the largest glaciers are located. On the contrary, on the lower periphery of the massif, where small glaciers predominate, the relative reduction was large (74-79%). On the background of the general retreat trend large valleys glaciers retreated faster in 1968-1977 and after 2010. In 1990-s the retreat was slow. After 2010 the glacial retreat was extremely fast. The retreat of glaciers in the last 50-60 years was caused by a trend towards a decrease in the amount of precipitation until the mid-1970s and a sharp warming in the 1990s and early 2000s.

In order to fully perform their functions and be durable, mortars for renders and plasters are requested to have a set of characteristics that can vary with the type of exposure to external environmental actions and the type of substrate. Generally, they need moderate strength, high deformability, some water protection capability, good adhesion to the substrate, and compatibility with the pre-existent materials. The presence of water and its movement inside the pore structure of the mortars are among the most significant causes of degradation. Moreover, several authors consider that the main factors for durability and good performance of lime-based mortars are mostly related with the good quality of the binder and the use of adequate aggregates. This paper intends to study the effect of ageing on the properties and durability of air lime mortars, using aggregates of different mineralogy. For this purpose, different mortars compositions are exposed to an accelerated weathering test under defined conditions. The obtained characteristics are discussed and compared with the results obtained with the same mortars tested in laboratory conditions. The effects of the aggregate type on the durability of mortars seem to be linked to their effects on the mortars porous structure.

Background: Hurricanes and other wind events are significant disturbances that affect coastal urban forests around the world. Past research has led to the creation of wind resistance ratings for different tree species which can be used in urban forest management efforts to mitigate the effects of these storms. While useful, these ratings have been limited to species common to one global region (Florida, USA).Methods: Drawing on past ratings and data from a global literature review on tropical storm research, we created a machine learning model to broaden both the geographic coverage and the variety of species currently assessed for their resistance to wind.Results: We were able to assign wind resistance ratings to 281 new species based on the available data and our modelling efforts. The model accuracy and agreement with the original ratings when applied to the testing data set was high with 91% accuracy.Conclusions: The resulting list of wind resistance ratings has been adapted into a spreadsheet-based decision aid for managers, allowing them to assess the overall susceptibility of their urban forest to wind events like hurricanes.

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current “gold standard” DSM/ICD taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by ensembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares (PLS) analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

The novel coronavirus (COVID-19) was first reported in the U.S. on December 29, 2019 and has spread rapidly throughout the country, affecting individuals with varying severity due to their risk status. According to the Centers for Disease Control and Prevention, it is estimated that 45.4% of US adults are at higher risk for complications from coronavirus disease because of cardiovascular disease, diabetes, respiratory disease, hypertension, or cancer. In this study, we developed a mathematical model to assess the impact of a COVID-19 vaccine among low and high risk groups. Numerical simulations shows vaccinating both low and high risk groups simultaneously, rather than prioritizing the vaccine on high risk group only, further reduces the daily mortality. The result supports the need for an aggressive vaccination program, regardless of whether individuals are within the low or high risk population.

The main result of this work is the estimation of the entropy mode accompanying a wave disturbance, observed at the atmosphere heights range of 90-120km. The study is the direct continuation and development of recent results on diagnosis of the acoustic wave with the separation on direction of propagation. The estimation of the entropy mode contribution relies upon the measurements of the three dynamic variables (the temperature, density and vertical velocity perturbations) of the neutral atmosphere measured by the method of the resonant scattering of radio waves on the artificial periodic irregularities of the ionospheric plasma. The measurement of the atmosphere dynamic parameters has been carried out on the SURA heating facility. The mathematical foundation of the mode separation algorithm is based on the dynamic projecting operator technique. The operators are constructed via the eigenvectors of the coordinate evolution operator of the transformed system of balance equations of the hydro-thermodynamics.

CO₂ geological storage in deep saline aquifers was recently developed at industrial scale mainly in sandstone formations. Experiences on CO₂ injections in carbonates aquifers for permanent trapping are quite limited, mostly from US projects such as AEP Mountaineer, Michigan and Williston Basin. The behavior of fractures in carbonates plays a key role in those reservoirs in which porous matrix permeability is very poor, which drives the CO₂ plume migration through the fracture network where hydromechanics and geochemical effects take place due to injection. Hontomín (Spain) is the actual on-shore injection pilot in Europe (EP Resolution of 14 January 2014), whose reservoir is comprised of fractured carbonates. Existing experiences from field scale tests conducted on site have supported to better understand the behavior of this type of reservoirs for CO₂ geological storage. Innovative CO₂ injection strategies are being carried out in ENOS Project (EU H2020 Programme, http://www.enos-project.eu). First results based on field tests conducted at Hontomín, and the advanced modelling developed so far will be analyzed and discussed in this article, as well as, the description of future works. The evolution of operating parameters such as flow rate, pressure and recovery term during the tests confirm the CO₂ migration through the fractures.

Numerical models widely used for hydrocarbon phase behavior and compositional flow simulations are based on assumption of thermodynamic equilibrium. However, it is not uncommon for oil and gas-condensate reservoirs to exhibit essentially non-equilibrium phase behavior, e.g., in the processes of secondary recovery after pressure depletion below saturation pressure, or during gas injection, or for condensate evaporation at low pressures. In many cases the ability to match field data with equilibrium model depends on simulation scale. The only method to account for non-equilibrium phase behavior adopted by the majority of flow simulators is the option of limited rate of gas dissolution (condensate evaporation) in black oil models. For compositional simulations no practical yet thermodynamically consistent method has been presented so far except for some upscaling techniques in gas injection problems. Previously reported academic non-equilibrium formulations have a common drawback of doubling the number of flow equations and unknowns compared to the equilibrium formulation. In the paper a unified thermodynamically-consistent formulation for compositional flow simulations with non-equilibrium phase behavior model is presented. Same formulation and a special scale-up technique can be used for upscaling of an equilibrium or non-equilibrium model to a coarse-scale non-equilibrium model. A number of test cases for real oil and gas-condensate mixtures are given. Model implementation specifics in a flow simulator are discussed and illustrated with test simulations. A non-equilibrium constant volume depletion algorithm is presented to simulate condensate recovery at low pressures in gas-condensate reservoirs. Results of satisfactory model matching to field data are reported and discussed.

Integrating ecosystem services (ESs) to delineate ecological functional zones (EFZs) is a fundamental work in terrestrial spatial planning and ecosystem management. However, existing studies have largely overlooked the refinement of EFZs at local scales, which hinders targeted and multi-scale ecosystem management. This study introduced a "two-step refinement zoning method" to address this gap, first using the self-organizing feature mapping method to delineate EFZs based on ESs at a township scale, and then applying hotspot overlay analysis to identify hotspots of dominant ESs within EFZs at a village scale. By overlaying the outcomes of these two steps, EFZs can be further graded by designating villages with hotspots of dominant ESs as level I and those without as level II. The proposed method was applied in Wuhan City, dividing it into five types of EFZs with different ESs combinations and land use compositions. Furthermore, 5.23% of villages were identified as Level I of EFZs, serving as advantageous areas of dominant ESs in the study area. On this basis, diversified management strategies and conservation priorities were proposed. This study provides a theoretical and methodological reference for terrestrial spatial planning and sustainable ecosystem management.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, with a highly variable expression of phenotypes (restricted interest or activity and repetitive behavior in communication and social interactions), genes (mutation), markers (alteration of transcription) and pathways. Loss of function of the CC2D1A gene appears to primarily affect the brain, leading to a range of behavioral problems in humans. In our study published in 2020; we found that the expressions of miR-19a-3p, miR-361-5p, miR-150-5p, miR-3613-3p, miR-126-3p and miR-499a-5p were downregulated in the serum samples of autistic patients and their families. Here, acquired non-Mendelian hereditary character in a genetically defined mouse model of autism (Cc2d1a +/-) correlates with the transcriptional alteration of five miRNAs. We seek to test the hypothesis that miRNA levels are varying by changes in RNA/DNA structure during development, thereby creating transcription alteration. Behavioral tests were conducted on the offspring of Cc2d1a +/- mutant and control mice, such as novel object, social interaction, marble burying and tail suspension behavior. Two RNA fractions were isolated from mouse hippocampal tissues and sperm cells by standard TRIzol extraction: total RNA and fraction of RNA bound to DNA in the form of a DNA/RNA hybrid (R-loop). The expression levels of miR-19a-3p, miR-361-5p, miR-150-5p, miR-126-3p and miR-499a-5p were investigated by quantitative real-time RT-PCR. We report differences in the distribution of five miRNAs in the hippocampus between male and female mice in RNA fractions, particularly in colonies of Cc2d1a +/- mice. Furthermore, the number of miRNAs engaged in the DNA/RNA hybrid fraction is generally higher in the mutant pedigree than in the control group. On the other hand, in sperm both fractions are at lower levels than in controls. R-loops contribute to the physiology and pathology of organisms including human disease. Here we report a variation in five miRNA levels between gender and tissue. Our results suggest that the transcription levels of these five miRNAs are directly regulated by their RNA.

Zingiber officinale rhizome is widely cultivated in the central region of Sudan (Gezira) and data on the biological properties of this variety grown in the Sudan climate is scarce. The study aims to comprehensively analyze the antibacterial, antioxidant, phytochemical, and GC-MS properties of Zingiber officinale (Ginger rhizome) to explore its potential applications. The in vitro antibacterial assessment of the aqueous extract of the Sudanese ginger revealed moderate activity against Staphylococcus aureus, Salmonella typhi, Pseudomonas aeruginosa, Escherichia coli , and Klebsiella pneumonia, as resulted from the disc-diffusion which ranged between 12.87±0.11 mm to 14.5±0.12 mm at 30 µg/disc. The minimum inhibition concentration ranged between 6.25 to 25 µg/ml, the MBC were between 25 and 50 µg/ml, and the MBC/MIC exhibited its bactericidal effect against all tested bacteria. The phytochemical screening revealed the presence of various chemical constituents such as saponins, flavonoids, glycosides, alkaloids, steroids, terpenoids, and absence of tannins in the Sudanese ginger rhizome. Furthermore, the GC-MS study of ginger rhizome identified 22 chemical compounds with retention times ranging from 7.564 to 17.023 minutes. The identification of 22 chemical compounds through GC-MS analysis further underscores the prospect of harnessing ginger rhizome for the development of novel medications. The computational analyses showed that ginger compounds bind the 1JIJ and 2QZW with high binding affinities, which reach –9.5 kcal/mol. Ginger compounds also established promising molecular interactions with some key residues, that satisfactorily explain the in vitro results and support the pharmacokinetic and experimental findings. This study lays the groundwork for future research and pharmaceutical exploration aimed at harnessing the beneficial properties of ginger rhizome for medicinal and therapeutic purposes, particularly its antimicrobial potential.

This article explores the application of machine learning techniques, specifically focusing on ensemble methods like Random Forests, for detecting fraudulent activities in digital financial transactions. Highlighting the evolution from traditional statistical approaches to modern machine learning models, it underscores the effectiveness of Random Forests in handling the inherent challenges of imbalanced datasets typical in fraud detection scenarios. Using a Kaggle dataset of credit card transactions, the study optimizes Random Forest parameters through rigorous parameter tuning, achieving significant improvements in model performance metrics such as Area Under the Curve (AUC). The findings underscore the critical role of machine learning in enhancing fraud detection capabilities, emphasizing the ongoing evolution and future potential of these methodologies in financial risk management.

The design of highly efficient sensitizers is one of the most significant areas in dye-sensitized solar cell (DSSC) research. We have studied a series of benzothiadiazole-based D-π-π-A organic dyes, putting emphasis on the influence of the donor moiety on the DSSC’s efficiency. Using linear response time-dependent density functional theory (TDDFT) with the CAM-B3LYP functional different donor groups were characterized in terms of electronic absorption spectra and key photovoltaic parameters. As a reference a dye has been considered which has a benzothiadiazole fragment linked via thiophene rings to a diphenylamine donor and a cyanoacrylic acid acceptor. The different systems are first studied in terms of individual performance parameters, which are eventually aggregated into the power conversion efficiency. Here only the amino-substituted species shows a modest increase, whereas the dimethylamino case even shows a decrease.

Hydrogen and carbon monoxide over-equilibria have been found computationally in kinetic dependencies of methane reforming catalytic reactions (steam and dry reforming) using conditions of the conservatively perturbed equilibrium (CPE) phenomenon, i.e. at the initial equilibrium concentration of hydrogen or carbon monoxide.  The influence of pressure, temperature, flow rate and composition of the initial mixture on the position of the CPE point (the extremum point) was investigated in a wide domain of parameters. It is demonstrated that the CPE phenomenon significantly increases the product concentration (H2 and CO) at the reactor length which is significantly less than the reactor length required to reach the equilibrium. The CPE-point was interpreted as the “turning point” in kinetic behavior.  Recommendations on the temperature and pressure regimes are different from the traditional ones related to the Le Chatelier’s law. Obtained results provide valuable information on optimal reaction conditions for complex reversible chemical transformations, offering potential applications in chemical engineering processes.

(1) Background: Heat stress detrimentally restricted the economic in dairy farming production, therefore, this study was designed to investigate how automatic spraying method on alleviating heat stress on cows. (2) Methods: A total of 1208 multiparous dairy cows were randomly allocated into 6 barns, three barns were equipped with automatic sprinklers (SP), each barn was considered as a replicate. The other three barns were considered as control treatments (CON). Each barn was considered as a replicate. (3) Results: body temperature and milk somatic cell count were significantly decreased, while DMI, milk yield, and milk fat content significantly increased with spraying treatment. ICR was improved from 30% to 35%. Rumen fermentation was enhanced by the increased levels of total VFA, acetate, propionate, and butyrate with the spraying treatment. Rumen microbiota results showed at the phylum level, rumen Fibrobacters abundance significantly increased by the spraying treatment. At the genus level, Eubacterium, Shuttleworthia, Saccharofermentans, Lachnospira, Pseudobutyrivibrio, Selenomonas, and Succinivibrio abundance were significantly increased, while Ruminococcaceae and Rikenellaceae abundance were significantly decreased in the rumen with spraying treatment. (4) Conclusion: this study demonstrated automatic spraying modulated the rumen microbiota composition and fermentation function, and effectively meliorated side-effects of heat stress on dairy cows.

Nanostructured lipid carriers (NLC) are typically composed of liquid lipids, solid lipids, and surfactants, enabling the encapsulation of lipophilic drugs. Butamben is a Class II anesthetic drug according to the Biopharmaceutical Classification System (BCS), it has a log P 2.87 and is considered a 'brick dust' (poorly water-soluble and poorly lipid-soluble) drug. This characteristic poses a challenge for the development of NLCs, as they are not soluble in the liquid lipid present in the NLC core. In a previous study [1] we have developed a NLC core consisting of solid lipid (CrodamolTM CP), liquid lipid of medium polarity (SRTM Lauryl lactate), and a hydrophilic excipient (SRTM DMI) that allowed the solubilization of 'brick dust' type of drugs including butamben. In this study, starting from the NLC core formulation previously developed we carried out the optimization of the surfactant system and evaluated their performance in aqueous medium. Three different surfactants (CrodasolTM HS HP, SynperonicTM PE/F68, and CroduretTM 40) were studied and, for each of them, a 2³ factorial design was stablished, with total lipids, % surfactant, and sonication time (min) as the input variables and particle size (nm), polydispersity index (PDI) and zeta potential (mV) as the response variables. Stable NLCs were obtained using CrodasolTM HS HP and SynperonicTM PE/F68 as surfactants. Through a comparison between NLCs developed with and without SRTM DMI, it was observed that besides helping the solubilization of butamben in the NLC core, this excipient helped stabilizing the system and decreasing particle size NLCs containing CrodasolTM HS HP and SynperonicTM PE/F68 presented particle size values in the nanometric scale, PDI lower than 0.3 and zeta potential above |10|mV. Concerning NLCs stability, SBTB-NLC with SynperonicTM PE/F68 and butamben demonstrated stability over a 3-month period in aqueous medium. The remaining NLCs showed phase separation or precipitation during the 3-month analysis. Nevertheless, these formulations could be freeze-dried after preparation, which would avoid precipitation in aqueous medium.

Time estimation was investigated in 24 healthy adults, 12 women and 12 men, before and after an exhaustive exercise. We compared the capability of estimating time intervals in the range from 1 to 5 s using tasks requiring mental count and tasks that did not allow the mental count. Time estimation and blood lactate levels were evaluated before and at the end of the exercise. We found that the perception of time intervals between 1 and 5 s was affected at the end of the exercise. The observed effects, associated with a significant increase in blood lactate levels, were different in the two types of time estimation used in the present study. In experimental conditions in which participants had to evaluate duration of the time interval by mental counting, at the end of exercise a significant reduction in the overestimation of time made at rest was observed. On the other hand, when participants had to assess the difference in duration between two events without the possibility to mentally count, at the end of the exercise a significant deterioration in performance was observed. In both cases, no gender differences were seen.

Dietary intake of omega-3 polyunsaturated fatty acids (eicosapentaenoic acid, EPA) exerts antiarrhythmic effects, although the mechanisms are poorly understood. Here we investigated the possible beneficial actions of EPA on saturated fatty acid-induced changes of the L-type Ca2+ channel in cardiomyocytes. Cardiomyocytes were cultured with oleic acid/palmitic acid mixture (OAPA) in the presence or absence of EPA. Beating rate reduction of cardiomyocytes caused by OAPA were reversed by EPA. EPA also retrieved a reduction of Cav1.2-L-type Ca2+ current, mRNA and protein caused by OAPA. Immunocytochemical analysis revealed a distinct downregulation of the Cav1.2 channel caused by OAPA with a concomitant decrease in the phosphorylated component of a transcription factor adenosine-3’, 5’-cyclic monophosphate (cAMP) response element binding protein (CREB) in the nucleus, which were rescued by EPA. A free fatty acid receptor 4 (FFAR4) agonist TUG-891 reversed expression of Cav1.2- and CREB-mRNA caused by OAPA, whereas an FFAR4 antagonist AH-7614 abolished the effects of EPA. Excessive reactive oxygen species (ROS) accumulation caused by OAPA decreased Cav1.2- and CREB-mRNA expressions, which were reversed by an ROS scavenger. Our data suggest that EPA rescues cellular remodeling caused by OAPA lipotoxicity and oxidative stresses via both free fatty acid receptor 4-dependent and -independent pathways.

Rivers across the globe experience and respond to changes within the riparian corridor. Disturbance of the riparian corridor can affect warmwater, intermediate, and coldwater streams, which can negatively influence instream physical structure and biological communities. This study focused on assessing the influence of riparian habitat on instream structure within the Whitewater River, a coldwater stream system within an agricultural watershed in southeastern Minnesota, USA. Twenty variables (riparian, n = 9; instream, n = 11) were measured at 57 sites across three forks of the Whitewater. Canonical correlation detected significant associations between riparian and instream variables across the river forks, and indicated that wider riparian buffers, more bank grass and shrubs, longer overhanging vegetation, limited bare soil and more rocks on banks were significantly associated with increased instream cover, high levels of coarse substrates with reduced embeddedness, increased pool habitats, and reduced fine sediments. In contrast, excessive fine sediments, lack of riffle habitat, reduced coarse substrates, and high width to depth ratios indicative of impaired instream habitat were associated with narrow riparian buffers and high percentages of bare soil on banks. Riparian corridors have the capacity to enhance and protect physical instream habitat and overall ecosystem health when managed properly. Wide, grassy riparian corridors with stable banks, overhanging vegetation, and limited shade from trees should protect and/or enhance instream physical habitat, providing the structural diversity favored by aquatic communities.

We introduce succinct and objective definitions of the various classes of objects in the solar system. Unlike in the formal definitions adopted by the International Astronomical Union in 2006, group separation is obtained from measured physical properties of the objects. Thus, this classification scheme does not rely on orbital/environmental factors subject to debate—the physical parameters are intrinsic properties of the objects themselves. Surface gravity g is the property that single-handedly differentiates (a) planets from all other objects (and it leaves no room for questioning the demotion of Pluto); and (b) the six largest (g&gt;1 m s−2) of the large satellites from dwarf planets. Large satellites are separated from small satellites by their sizes and masses/densities, which may serve as higher-order qualifiers of class membership. Size considerations are also sufficient for the classification of (i) main-belt asteroids (except possibly Ceres) as small solar-system bodies similar in physical properties to the small satellites; and (ii) a group of large Kuiper-belt objects as dwarf planets similar in physical properties to the large (but not the largest) satellites in our solar system. The selection criteria are simple and clear and reinforce the argument that body shape and environmental factors need not be considered in stipulating class membership of solar as well as extrasolar bodies.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

The use of Physical Unclonable Functions (PUFs) linked to the manufacturing process of the electronic devices supporting applications that exchange critical data over the Internet has made these elements essential to guarantee the authenticity of said devices, as well as the confidentiality and integrity of the information they process or transmit. This paper describes the development of a configurable PUF/TRNG module based on Ring Oscillators (ROs) that takes full advantage of the structure of modern programmable devices offered by Xilinx 7 Series families. In this work, the increase in hardware efficiency of the proposed architecture is used with a double objective. On the one hand, perform an exhaustive statistical characterization of the results derived from the exploitation of RO configurability. On the other hand, undertake the development of a new version of the module that requires a smaller amount of resources while considerably increasing the number of output bits compared to other proposals previously reported in the literature. The design as a highly parameterized Intellectual Property (IP) module connectable through a standard interface to a soft- or hard-core general-purpose processor greatly facilitates its integration into embedded solutions, while accelerating the validation and characterization of this element on the same electronic device that implements it. The studies carried out reveal adequate values of reliability, uniqueness and unpredictability when the module acts as a PUF, as well as acceptable levels of randomness and entropy when it acts as a True Random Number Generator (TRNG). They also illustrate the ability to obfuscate and recover identifiers or cryptographic keys of up to 4 096 bits using an implementation of the PUF/TRNG module that requires only a 4×4 CLB array to accommodate the RO bank.

The persistent security challenges in Industry 4.0 due to resource-constrained IoT devices necessitate innovative solutions. Addressing this, a study introduces the ASCON algorithm for lightweight Authenticated Encryption with Associated Data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission, bolstering system resilience against cyber threats. Practical validation using MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era

The complex network is an abstract modeing of complex systems in the real world, which plays an important role in analyzing the function of complex systems. Community detection is an important tool for analyzing network structure. In this paper, we propose a new community detection algorithm (RWBS) based on different seed nodes aims to understand the community structure of the network, which provides a new idea for the allocation of resources in the network. RWBS provides a new centrality metric ($MC$) to calculate node importance, which calculates the ranking of nodes as seed nodes. Furthermore, two algorithms are proposed for determining seed nodes on networks with and without ground-truth, respectively. We set the number of steps for the random walk to 6 according to the six degrees of separation theory to reduce the running time of the algorithm. Since some traditional community detection algorithms may detect smaller communities, e.g., two nodes become one community, this may make the resource allocation unreasonable. Therefore, modularity ($ Q $) is chosen as the optimization function to combine communities, which can improve the quality of detected communities. Final experimental results on real-world and synthetic networks show that the RWBS algorithm can effectively detect communities.

Good communication skills are essential for prospective workers, particularly college students, to clearly convey their abilities and understanding during job interviews. Despite the high demand for strong communication skills among employers, many students struggle with inadequate experience and skills, leading to reduced confidence and lower chances of securing desired jobs. Existing tools and resources for improving communication skills are limited, especially free ones, leaving students underprepared and less confident in interviews. To address this issue, a comprehensive solution is needed that provides free, customizable tools to enhance students' interview communication skills. Invisor is a web-based self-interview training application that leverages AI and Machine Learning to analyze users' behavior, responses, and facial expressions during simulated interviews. Accessible via desktop, laptop, or mobile devices, Invisor offers interactive and voice-recorded feedback to help students prepare for interviews effectively. By employing AI technologies, Invisor generates personalized interview questions based on job descriptions, enhancing the realism of the interview experience and providing tailored feedback to improve users' responses. Developed using agile methodologies, Invisor has undergone iterative enhancements, integrating user and stakeholder feedback promptly to improve usability and functionality. The combination of AI-driven simulations and agile development practices enables Invisor to meet current user needs and anticipate future demands, providing a robust tool for interview preparation in a competitive job market. This paper outlines the development and implementation of Invisor, demonstrating its effectiveness in enhancing interview skills and confidence among students and job seekers.

In the full load operating range, wind turbines typically use pitch control to maintain rotor speed and output power at their rated values to guarantee the whole system’s safety. Blade pitch actuators are subject to physical limitations and safety requirements, which imposes strict constraints on pitch angle’s amplitude and rate of change. These constraints will bring windup problem to the integral part of the pitch controller. It leads to deterioration of the pitch control system’s performance or even the instability problem, especially for large-scale turbines operating in extreme uncertain wind conditions or suffering from cyber attacks. This paper designs an anti-windup robust pitch angle control strategy to deal with pitch rate constraint issue to enhance the security of control system. First, to facilitate controller design, a filtered tracking error technique is employed to transform the nonaffine form into an affine one. Subsequently, a feedback robust controller based on UDE is developed to handle the model’s uncertainty and external disturbances. To address the issue of integral saturation in the pitch system and guarantee its safety, an elliptical bounded constraint is integrated into the designed UDE strategy. This bounded UDE controller can improve the stability of power generation quality, reduces the mechanical loads on components and enhance the whole system’s safety. Finally, the effectiveness of the proposed scheme is verified on the Wind Turbine Blockset platform in Matlab/Simulink. It can achieve better performance than the traditional method.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited because fixed recipes are usually applied to run the processes. Therefore, a digital twin has been developed to optimize an industrial brownfield DCF plant. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation of selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

To ensure the security of highly sensitive remote sensing images (RSI) during their transmission, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on encoder-decoder network architecture with Parallel Attention Mechanisms (PAM) by adversarial training was proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN) and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSI and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected.

The paper illustrates a general framework in which a neural network application can be easily integrated and proposes a traffic forecasting approach that uses neural networks based on graphs. The method minimizes the communication network (between vehicles and the database servers) load and represents a reasonable trade-off between communication network load and forecasting accuracy. Traffic prediction leads to the choice of less congested routes and therefore to the reduction of energy consumption. The traffic is forecasting using a LTSM neural network with a regression layer. The inputs of the neural network are sequences - obtained from graph that represent the road network - at specific moments of time that are read from traffic sensors or the outputs of neural network (forecasting sequences). The input sequences can be filtered to improve the forecasting accuracy. This general framework is based on Contiki IoT operating system that ensure support for wireless communication and efficient implementation of processes in a resource constrained system and it is particularized to implement a graph neural network. Two cases are studied: one case in which the traffic sensors are periodically read and the other case in which the traffic sensors are read when their values changes are detected. A comparison between the cases is made and the influence of filtering is evaluated. The obtained accuracy is very good, very close to the accuracy obtained in infinite precision simulation, and the computation time is low enough and the system can work in real time.

Autonomous target recognition (ATR) plays a crucial role in maintaining situational awareness during environmental monitoring. Unmanned aerial vehicles (UAVs) equipped with autonomous target recognition technology can gather and analyze real-time information about targets, including their locations, sizes, and types. However, UAV-captured images in complex real-world environments often display significant variations in perspective and scale due to changes in UAV altitude and distance. Existing methods for autonomous target recognition on UAVs struggle to capture targets from large field-of-view and multi-scale images, resulting in low recognition accuracy and high false-positive rates. This paper introduces two novel Slimmable neural network models, namely SE-YOLOv5s and ST-YOLOv5s, which are based on the YOLOv5s architecture. These models incorporate the Squeeze and Excitation and Swin-Transformer mechanisms to enhance the ability to extract features from large field-of-view and multi-scale images. To evaluate their performance, experiments were conducted on the Visdrone19 aerial dataset. Compared to the state-of-the-art YOLOv5s algorithm, the utilization of SE-YOLOv5s and ST-YOLOv5s for autonomous target recognition on low-altitude drones resulted in improvements in both accuracy and false-positive rates. These proposed methods combine Slimmable neural network design with feature enhancement mechanisms, addressing the challenges posed by complex real-world environments in UAV missions. The advancements in autonomous target recognition on low-altitude drones will significantly contribute to enhancing situational awareness in future environmental monitoring.

This work focuses on addressing the dynamic positioning and trajectory tracking problem for a 4 degree-of-freedom (DOF) unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. To tackle this problem, a disturbance observer-based control (DOBC) scheme is proposed. The scheme is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the EAOB has the unique ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC (DOBMPC) is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms a robust adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and Robot Operating System (ROS) environment. The results show that it can effectively reject unpredictable disturbances and improve the UUV’s control performance.

Snakebite envenoming can be a life-threatening medical emergency that requires prompt medical intervention to neutralise the effects of venom toxins. Each year up to 138,000 people die from snakebite and 3-fold more victims suffer life-altering disabilities. Current treatment of snakebite relies solely on antivenom – polyclonal antibodies isolated from the plasma of hyperimmunised animals – which is associated with numerous deficiencies. The ADDovenom project seeks to deliver a novel snakebite therapy, through use of an innovative protein-based scaffold as a next-generation antivenom. The ADDomer is a megadalton-sized, thermostable synthetic nanoparticle derived from the adenovirus penton base protein with 60 high-avidity binding sites to neutralise venom toxins. Here, we outline our experimental strategies to achieve this goal using state-of-the-art protein engineering, expression technology, mass spectrometry, as well as in vitro and in vivo venom neutralisation assays. We anticipate that the approaches described here will produce antivenom with unparalleled efficacy, safety and affordability.

In several Indonesian tropical cities, such as Makassar City, frequent flooding during the peak rainy season poses a significant challenge. This study seeks to address this issue by devising effective policies to reduce flood exposure. The main obstacle lies in accurately determining the flood inundation area. To overcome this, the study utilizes multiple satellite data sources on a cloud platform, integrating them with land use data, and DEM data, aligning these with the local government's urban land use plan and existing drainage networks. The research aims to quantify the affected area, identify the predominant inundated land cover, and assess the efficiency of Makassar's drainage system and urban land use plan. The study reveals that an uncoordinated drainage system in the Tamalanrea, Biringkanaya, and Mangala sub-districts results in severe flooding, encompassing a total area of 35.28 km². The most affected land use type is cultivation, constituting approximately 43.5% of the flooded area. Furthermore, 82.26% of the urban land use plan, covering 29.02 km², is submerged. It is imperative for the local government and stakeholders to prioritize the enhancement of drainage systems and urban land use plans, particularly in low-lying and densely populated regions

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.