Context: Business Process Automation (BPA) is adopted by organizations to improve efficiency, reduce costs, and increase overall business performance. Traditional Business Process Automation (TBPA) is one of the three approaches employed to develop a BPA. TBPA entails developing BPA in a programming language for integrating the relevant applications in the digital ecosystem to execute a given process. Process variability and practitioner unavailability encumber the requirements specification for TBPA software. Objective: This work proposes a log-based approach for TBPA software to make software more adaptable to process changes and reduce reliance on practitioners, by providing a higher alignment among business process requirements and software architecture, and employing process mining to semi-automatically discover the business process during requirements elicitation. Method: The research conducted a case study in a technology institute to assess the approach and report its results in practice. Results: The results revealed significant improvements in adaptability to business process changes and decreased the time spent with practitioners, and, efficiency in development. The approach also presented limitations, including human intervention to accurately obtain the business process, complexity to trace the process into the architecture, data privacy concerns, and risk of network traffic overload. Conclusion: This research demonstrated the effectiveness of traceability between process requirements and software architecture, as well as the use of logs and process mining. These methods made TBPA software enhanced the software adaptability to changes and minimized the reliance on practitioners during requirements elicitation respectively.

Cyber-physical testing has been applied for a decade in hydrodynamic laboratories to assess the dynamic performance of floating wind turbines (FWT). Rotor loads, computed by a numerical simulator fed with measurements, are applied in real time on the physical floater by using actuators. The present paper proposes a set of short and targeted benchmark tests that aim at quantifying the performance of actuators used in cyber-physical testing of FWT. They aim at ensuring good load tracking over all frequencies of interest, and satisfactory disturbance rejection for large motions. These benchmark tests are exemplified on two radically different 15MW FWT models tested at SINTEF Ocean.

Enzyme catalysis has traditionally been used by various human cultures for creating value, long before its basic concepts have been uncovered, by preparing useful products through the trans-formation of raw materials available from natural resources. Tremendous scientific and techno-logical progress has been accumulated globally in understanding what constitutes an enzyme, what reactions enzymes can catalyze, and how to search, develop, apply and improve enzymes to make desired products. The exquisite properties of enzymes as nature's preferred catalysts, such as high selectivity, diversity and adaptability, enable their optimal work, whether in single reactions or in multiple reactions. Excellent opportunities for resource efficient manufacturing of compounds needed are provided by the actions of enzymes working in reaction cascades and pathways within the same reaction space, like molecular robots along a production line. Enzyme catalysis plays an increasing role for industrial innovation and responsible production in various areas, such as green and sustainable chemistry, industrial or white biotechnology. Sources of inspiration can be current manufacturing or supply chain challenges, the treasure of natural enzymes or the opportunities of engineering tailor-made enzymes. Making best use of the power of enzyme catalysis is essential for changing the way how current products are manufactured, how renewable biobased resources can replace fossil-based resources, and how the safety, health environment aspects of manufacturing processes can be improved towards cleaner and more sustainable production.

Background: Current treatment for Chronic Kidney Disease (CKD) focuses on improving manifestations and delaying progression. Nutritional approaches play a crucial role in CKD management, and various supplements have become available. Ketoanalogues of amino acids (KA), calcum citrate, and inulin have been proposed as suitable supplements, yet their widespread use has been limited due to insufficient evidence. This study aimed to generate general guidance statements on the appropriateness of these supplements through a RAND/UCLA consensus process; Methods: A RAND/UCLA consensus panel was convened to evaluate the appropriateness of these supplements in different clinical scenarios. In this study, we present a subgroup analysis focusing on a panel of eleven clinical nephrologists from among the experts; Results: Supplementation of low protein diets (LPD) and very low protein diets (VLPD) with KA was considered appropriate to reduce manifestations and delay CKD outcomes, supplementation with calcium citrate is considered appropriate to reduce CKD manifestations, and supplementation with inulin is considered appropriate to delay CKD outcomes and manage comorbidities; Conclusions: Based on a combination of clinical experience and scientific evidence , the panel reached a consensus that KA supplementation of LPD and VLPD, calcium citrate, and inulin are appropriate in patients with CKD across various scenarios.

Measuring cell confluence in high-throughput screening of pharmaceutics, cell culture monitoring, cancer research, and industrial applications requires quantitative and standardizable measures to minimize subjective observations and increase throughput1-12 . Over the past half-century, non-invasive specialized programs have been created to quantitatively assess cell confluence, and some software tools exist to meet these needs1,4,5,6,7,8,9 . However, many of these tools are expensive, computationally-demanding, or inaccessible to non-experts1,6. With the assistance of generative artificial intelligence, we have developed the Confluence Analysis Program (CAP), a modern tool designed to be precise, interactive, and user-friendly. CAP is written entirely in Python and runs in any command-line interface (CLI), including Terminal (OS) and Command Prompt (Windows). In addition to fetching all necessary external libraries automatically and prompting drag-and-drop image retrieval, CAP features live visual comparison of cell mass identification to the original image assay. Our results demonstrate robust performance of CAP on fluorescent micrographs of varying confluences ranging from low to high. CAP is provided as a free resource, accessible in the Supplementary Materials.

In response to the increasing importance of knowledge as a key competitive advantage, the role of knowledge management in value creation cannot be overstated. The International Standards Organization (ISO) has recognized this and announced a Management Standard for Knowledge Management Systems (KMS). This initiative is significant because, by viewing knowledge strategically, organizations are seeking ways to enhance their processes and align them to address their challenges. A synergistic effect can be achieved when another management system highly relevant to KMS is joined and later integrated. This new system is Artificial Intelligence Management (AIMS). The paper is structured as follows: Firstly, we explore knowledge and knowledge management (KM), along with the challenges companies face in implementing and operating KM. Next, we examine the ISO 30401:2018 standard as a guide for KM implementation. Finally, we discuss how ISO 30401:2018 and ISO/IEC 42001:2023 can help solve key organizational problems and support machine learning and knowledge extraction through AI. In conclusion, we summarize the key outcomes of the paper. The main purpose of the paper is to determine how these standards contribute to solving practical problems in the creation and development of a knowledge management system supported by artificial intelligence and to demonstrate their synergistic effect when used together.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

Exogenous melatonin, a nutraceutical for maintaining a healthy sleep-wake cycle and managing sleep disorders, requires large, repeated doses due to low bioavailability and short half-life. This necessitates the development of a sustained-release formulation with a longer half-life and sustained plasma concentration. Therefore, exogenous novel 5 mg sustained-release melatonin capsules (Melatonin-SR, test product) were formulated. This open-label cross-over study compared the pharmacokinetics (maximum concentration [Cmax], time to reach Cmax [Tmax], area under the curve [AUC] and elimination half-life [t1/2]) and safety of Melatonin-SR with 5 mg immediate-release melatonin capsules (Melatonin-IR, reference product) after single-dose oral administration in healthy fasting adults. Sixteen participants (aged 18-45 years) were randomized (1:1) to receive either Melatonin-SR or Melatonin-IR in two periods with a 7-day washout period. Melatonin-SR reported lower Cmax (11446.87 pg/mL) compared to Melatonin-IR (22786.30 pg/mL). The mean Tmax of Melatonin-SR and Melatonin-IR was 1.26 h and 0.87 h, respectively. The mean t1/2 of Melatonin-SR (5.10 h) was prolonged by five-fold compared to Melatonin-IR (1.01 h). One adverse event (vomiting) was reported following administration of the Melatonin-IR. Melatonin-SR resulted in higher and sustained plasma melatonin concentrations for an extended period and was well-tolerated. Hence, Melatonin-SR may be a promising nutraceutical for maintaining healthy sleep.

The analysis of social media content plays a crucial role in uncovering intricate user behaviors and trends across diverse digital platforms. Social media is inherently multimodal, incorporating texts, images, audios, and videos, each offering unique insights into user engagement and preferences. Traditional classification methods often focus narrowly on the most prominent modality, typically neglecting the synergistic potential of integrating multiple data types. To address this gap, we introduce the Unified Multimodal Classifier (UMC), a suite of streamlined models that adeptly harness and integrate these varied modalities. UMC leverages a novel architecture that combines a pooling layer with auxiliary learning tasks, facilitating the formation of a robust and shared feature space. This integration allows UMC to not only accommodate but also capitalize on the inherent diversity of social media data. The models are designed to be inherently flexible, adjusting to the availability of data modalities and maintaining high classification accuracy under varied conditions. In emotion classification scenarios, UMC has shown exceptional performance, significantly outperforming traditional methods by effectively synthesizing information across modalities. Its robustness is highlighted in its ability to deliver consistent results, even in the absence of one or more modalities. The simplicity and efficiency of UMC make it a potent tool for social media analytics, enabling it to achieve higher accuracies and maintain effectiveness even with limited data inputs. This adaptive capability ensures that UMC can be deployed in real-world applications where data incompleteness is common, thus broadening the scope of its applicability in various analytical contexts.

With the compelling popularity of integrated sensing and communication (ISAC), WiFi sensing has drawn increasing attention in recent years. Starting from 2010, WiFi CSI-based wireless sensing has enabled various exciting applications such as indoor localization, target imaging, activity recognition and vital sign monitoring. In this paper, we retrospect the up-to-date achievements of WiFi sensing using commodity-off-the-shelf (COTS) devices over the past 5 years in detail. Specifically, this paper first presents the background of CSI signal and related sensing models. Then, recent researches are categorized from two perspectives, namely according to their application scenario diversity and corresponding sensing methodology difference respectively. Next, this paper points out the challenges faced by WiFi sensing including domain dependency and sensing range limitation. Finally, three imperative research directions are highlighted, which are critical for realizing more ubiquitous and practical WiFi sensing in real-life applications.

Conventional farming relies on large-scale, intensive crop production using mechanical tillage and synthetic inputs like chemical fertilizers and pesticides, which, despite being economically beneficial, can be environmentally destructive, whereas organic cropping system offers a more sustainable alternative with less harmful environmental impact. But is organic system more beneficial for soil health than conventional? In this study, we focused on evaluating the functional potential of soil for the conversion of carbon, nitrogen, phosphorus, and sulfur, as well as measuring the actual activity of enzymes involved in these cycles. We used Chernozem soil samples from fields that were managed using organic (OCS) and conventional (CCS) methods as the objects of our study. Another objective of the study was to compare two methods: profiling of functional genes of the microbial community and measurement of actual enzyme activity. The comparison of OCS versus CCS indicates that functional gene profiling and enzymatic activity measurements similarly capture qualitative differences between these systems, but diverge in how they assess quantitative differences. A combination of these two methods for assessing soil conditions appears to be promising. Overall, the study demonstrated that the agronomic practices used in the conventional cropping system not only preserved the functional properties of the soil microbiome but also significantly enhanced its microbiological activity for nutrient bioconversion compared to the organic cropping system. However, the question remains as to how long the conventional system can sustain its high potential.

The intrusion of seawater in coastal aquifers in Greece is a particularly widespread phenomenon. The coastline of Greece has a length of 15,147 km. Once groundwater resources are contaminated, remediation strategies are difficult, costly and time consuming. In this paper three coastal aquifer systems of Peloponnese are studied. Initially the main ions and cations were calculated for the three aquifers. According to the results from hydrochemical analyzes Na+,Mg2+, Ca2+, SO4-2, Cl- have high values and are affected by the intrusion of seawater into the coastal aquifers. It estimates the vulnerability of groundwater to the intrusion of seawater. Using Geographical Information Systems (GIS) software, maps are prepared for the presentation of each parameter of the GALDIT method. The main factors that affect the seawater intrusion are basically presented in the 6 letters of the acronym GALDIT. The analysis of each parameter is as follows: Groundwater occurrence (aquifer type: unconfined, confined, and leaky confined), Aquifer hydraulic conductivity, depth to groundwater Level above the sea, Distance from the shore (inland distance perpendicular from shoreline), Impact of existing status of sea water intrusion in the area and Thickness of the aquifer. The final map that emerged from the study of the three groundwater systems shows the vulnerability of seawater intrusion in Peloponnese. The maps show that the groundwater system of Larissos presents lower vulnerability to the seawater intrusion compared to the other two groundwater systems. This is because the occurrence of flysch in the northern part of the area acts as a barrier to the penetration of seawater.

Background: Strength assessment is one of the main fields in sports performance, physical rehabilitation, physical activity and health. Purpose: To compare maximal voluntary isometric contractions (MVIC) and paired voluntary isometric contractions (VIC) of knee extensors between an isokinetic dynamometer (BIODEX) and a portable and lightweight device (DINABANG). Methods: From 19 volunteers (age: 28.7±7 y; body mass: 72±10 kg and height: 173±7 cm) we obtained 114 paired MVIC measures and, from the force-time curves of these repetitions, 22507 paired VIC measures of knee extensors. Results: We observed “excellent” repeatability for MVIC (ICC:1.00; p<0.001) between BIODEX (247±79.5 Nm) and DINABANG (247±74.8 Nm), with “trivial” effect (mean difference: 0.12 Nm (0.02%); 95%CI: -0.13 to 0.23 Nm; p=0.606; d=0.048). Bland-Altman plots revealed high accuracy for MVIC (bias: 0.12 Nm) and consistent distribution (precision) inside the limits of agreement (-4.81 to 5.06 Nm) and respective 95%CI. “Excellent” repeatability was also observed for VIC (ICC:1.00; p<0.001) between BIODEX (219±84.1 Nm) and DINABANG (218±84.0 Nm), with “trivial” effect (0.24 Nm (0.11%); 0.08 to 0.11 Nm; p<0.001; d=0.100). Bland-Altman plots revealed high accuracy for VIC (bias: 0.24 Nm) and consistent distribution (precision) inside the limits of agreement (-4.5 to 4.9 Nm) and respective 95%CI. Conclusion: DINABANG is accurate, precise and reliable for torque measurement.

To better understand effects of agronomic practices on yield-nutrition relationships in wheat (Triticum aestivum L.) grains for Zn biofortification while improving yields simultaneously, effects of different soil fertilization and different drone-based foliar spraying treatments were investigated. The mixture of Zn or increasing N but simultaneously reducing P in compound fertilizers were all practical to increase grain yields and Zn concentrations in calcareous soils tested. However, the overall effects are limited, with a maximal yield increase by only 7.0% and a maximal increase of the grain Zn density from 19.4 to 27.0 mg/kg, which is far below the target biofortification value of 40-50 mg/kg. Unfortunately, there was a negative side effect, which decreased Fe and Mn concentrations and the Fe bioavailability. Notably, grain yields and Zn concentrations were significantly increased by drone-based foliar Zn sprayings from 7.5 to 8.6~8.8 t/ha by 12.0-17.3% and from 33.5 to 41.9~43.6 mg/kg by 25.1~30.1%, respectively, indicating a double-win in yield and nutrition. A spraying of ZnSO4·7H2O increased grain Zn concentrations and accumulation more so than ZnO, indicating the importance of chemical Zn forms in determining the effectiveness of foliar spraying. Moreover, foliar Zn sprayings simultaneously increased grain concentrations and accumulation of Fe, Mn and Cu, demonstrating multiple benefits. There were positive correlations between Zn and Fe, Mn or Cu, indicating synergistic but not antagonistic interactions. Compared to micronutrients, concentrations of grain macro- and medium nutrients (N, P, K, Ca, Mg) were less affected. Thus, a double-win in grain yields and micronutrient (particulaly for Zn) nutrition could be effectively achieved through appropriate soil fertilization and foliar Zn spraying. These results provide a better understanding of the yield-nutrition relationship among wheat grain yields, Zn and other nutrient elements for a better integrated manipulation to achieve a win-win situation in yield and nutrition.

Conventional X-ray therapy (XRT) is commonly applied to suppress cancerous tumors, however it often inflicts collateral damage to nearby healthy tissue. In order to provide a better conformity of the dose distribution in the irradiated tumor, proton therapy (PT) is increasingly being used to treat solid tumors. Furthermore, radiosensitization with gold nanoparticles (GNPs) has been extensively studied to increase the therapeutic ratio. The mechanism of radiosensitization is assumed to be connected with an enhancement of the absorbed dose due to huge photoelectric cross-sections with gold. Nevertheless, numerous theoretical studies mostly based on Monte Carlo (MC) simulations did not provide consistent and thorough picture of dose enhancement and, therefore, radiosensitization effect. Radiosensitization by nanoparticles in PT is even less studied than in XRT. Therefore, we investigate physics picture of GNP-enhanced RT using MC simulation with Geant4 equipped with the most recent physics models, taking into account a wide range of physics processes relevant for realistic PT and XRT. Namely, we measured dose enhancement factors in the vicinity of GNP with diameters ranging from 10 nm to 80 nm. The dose enhancement in the vicinity of GNP reaches high values for XRT, while it is very modest for PT. The macroscopic dose enhancement factors for realistic therapeutic GNP concentrations are rather low for all RT scenarios, therefore other physicochemical and biological mechanisms should be additionally invoked for an explanation of the radiosensitization effect observed in many experiments.

Balanites aegyptiaca del. (Zygophyllaceae) is widely used in traditional medicine, both human and veterinary, throughout Africa for its many properties, including antiparasitic properties. This experimental study aims to optimise the extraction conditions of the seeds of Balanites aegyptiaca del. Aqueous maceration was carried out with mass to volume ratios of 40%, 30%, 20%, 10% and 5% and extraction times of 6, 12, 24, 36 and 48 hours. Extraction yields, phytochemical screening, saponins assay, antioxidant activities (ABTS and FRAP) and antiparasitic tests on Heligmosomoides bakeri were used to compare the different extracts. The pharmaco-chemical study generally showed that aqueous maceration gave the best results, with a mass/volume ratio of 10% after 12 hours of maceration. The yield obtained was 28.03% with a saponins content of 13.81 mg/g. The antioxidant activities were 4.25 ± 0.17µg/mL by the ABTS method and 0.739µg/mL by the FRAP method. The larvicidal activity also showed that the 10%12h extract produced 100% larval mortality from 25ug/mL. These data provide a basis for guiding the choice of extraction process parameters in producing this antiparasitic phytomedicine.

Building energy modelling tools play a crucial role in quantifying and comprehending the energy performance of buildings. These tools demand substantial amounts of data, which can be challenging to acquire and are often associated with significant uncertainties. The incorporation of Sensitivity Analysis represents a relevant step towards developing reliable models, as it identifies the most critical parameters that require a meticulous characterization. In this study, a Sensitivity Analysis based on Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modelled in EnergyPlus. Different number of Morris trajectories and levels were considered to analyse the impact of the user-defined values of r and p when employing Morris method. Convergence was achieved from r=200 and p=12, which are higher than the typically employed values (r=10 and p=4). Setpoint temperatures, orientation, roof solar absorptivity and roof conductance rank among the most relevant parameters affecting thermal loads, however variations are observed for the four case studies.

In a constantly transforming retail financial sector, TCS has established a strategic alliance with Cencosud Scotiabank, expanding its role beyond consultancy to become an essential partner in the journey of growth and transformation. This collaboration reflects a shared commitment to advancing together in the competitive world of retail financial services, driving significant strategic change at Cencosud Scotiabank. As part of this effort, TCS has co-designed a series of practical workshops aimed at fostering and expanding the culture of innovation within the organization. These practical workshops are fundamental in the transformation process, providing deep and practical learning that serves as a solid foundation for the desired change. Each workshop session, carefully designed and executed according to an innovation playbook, aligns visual thinking tools with practical techniques to ensure smooth and effective execution of the work. The goal of these efforts is twofold: on the one hand, to strengthen the culture of innovation at Cencosud Scotiabank through collaboration; on the other, to firmly center the organization around the customer. The workshops are not just meetings; rather, they are collaborative spaces where creativity, deep user understanding, and agility converge to shape innovative solutions.

Generalised Modules for Membrane Antigens (GMMA) are outer membrane vesicles derived from Gram-negative bacteria that can be used to design affordable subunit vaccines. GMMA are highly immunogenic and capable to induce an optimal antigen-specific humoral immune response both in animals and humans. Despite their potent immunogenicity, GMMA are usually formulated with aluminum salts (Alum) to reduce their potential systemic reactogenicity. GMMA, in fact, contain agonists of Toll-Like Receptor 4 (TLR4) and TLR2 that possess pro-inflammatory activity. The adsorption of GMMA onto Alum is believed to reduce their systemic exposure. However, it has been found that GMMA formulated without Alum did not induce concerning signs of systemic reactogenicity in the rabbit model. Here we asked whether GMMA promote local reactogenicity. We immunized mice intramuscularly with GMMA alone or formulated with Alum and we analyzed the injection site during 7 days after treatment. We found that GMMA alone promoted only mild inflammation within the muscle, whereas Alum induced severe muscle inflammation, as expected. Thus, in the mouse model, GMMA demonstrated to possess mild reactogenic potential locally, while Alum is confirmed a major driver of local reactogenicity. Our results further support the idea to investigate the reactogenicity of GMMA formulated without Alum in clinical studies.

Endometrial preparation is crucial in in vitro fertilization (IVF) protocols to create optimal conditions for embryo implantation and pregnancy. Successful embryo nidation requires a receptive endometrium, which is influenced by factors such as endometrial thickness, hormonal environment, autocrine and paracrine growth factors, and the presence of resident and non-resident stem cells within the endometrium basalis. Synchronization between endometrial and embryonic development is also essential. Endometrial preparation, whether achieved through spontaneous cycles, natural cycles with minimal support, ovulation induction, or hormone replacement therapy (HRT), results from a combination of biochemical and biophysical conditions, each independent of the others. These conditions are difficult to pinpoint within a fixed period (Window of Implantation, WOI) using any specific examination due to the variability in estrogen and progesterone production rates, their metabolic clearance rates (MCR) when administered exogenously, and their metabolism when given systemically, topically, or orally. These variations also occur in different phenotypes and over different durations. Moreover, the synchronization of endometrial preparation with incomplete decidualization and the uncertain duration of the implantation window pose challenges. It is also unknown if there are compliance mechanisms similar to diapause seen in other species. This review aims to outline a clinical approach that, while not an absolute clinical recommendation due to the lack of definitive certainties, assists in understanding complementary phenomena and provides a useful orientation for clinical practice.

Adulteration of food products, including honey, is a serious problem in today’s economy. Honey has become the third most counterfeited food product in the world, which requires effective authentication methods. This article presents considerations regarding the development of meteorology to test the quality of honey based on the analysis of bee pollen. The justification for conducting such analyses is the relatively high need for the authentication of honey and its derivatives. It turns out that the honey market and honey itself are the third products in the world in which undesirable, inappropriate, and quality-reducing components may appear, affecting the final quality of a product such as honey. The analyses mentioned above thus come down to issues related to the analysis and processing of images. A certain novelty of the approach to this analysis is an attempt to parameterize them using the so-called Hjorth descriptors, which have been a tool known for almost 50 years from the analysis of electroencephalographic (EEG) signals. In this paper, after an introduction to the issues related to testing honey, the issues related to the method of obtaining bee pollen images, improving their quality by applying appropriate filtration, performing normalization, and finally calculating Hjorth distributors such as: Activity, Mobility, and Complexity will be discussed. In the next stage described in this article, the authors perform a detailed statistical analysis leading to the initial construction of a simple classifier that distinguishes defined gropus of bee pollen images.

This qualitative research explores the impact of Artificial Intelligence (AI) on Customer Relationship Management (CRM), providing a comprehensive analysis of how AI technologies are transforming CRM practices. AI integration in CRM has revolutionized customer interactions, enabling enhanced personalization, efficiency, and customer engagement. By leveraging machine learning, natural language processing, and predictive analytics, organizations can create highly tailored customer experiences, automate routine tasks, and anticipate future customer needs with precision. The study identifies the significant benefits of AI, including improved customer insights, faster response times, and proactive engagement strategies. However, it also highlights the challenges associated with AI adoption in CRM, such as data privacy and security concerns, algorithmic bias, high implementation costs, and resistance to change. The findings emphasize the need for robust data protection measures, strategies to mitigate biases, significant investments in technology and training, and effective change management to overcome these challenges. The thematic analysis reveals key areas where AI is making an impact, including customer service, customer segmentation, and personalized marketing strategies. Additionally, the study discusses the organizational implications of AI adoption, such as enhanced decision-making, increased productivity, and the necessity for staff reskilling. Future directions for AI in CRM suggest further advancements in AI algorithms, greater integration with other business systems, enhanced customer experiences, and a focus on ethical AI practices. This research underscores the transformative potential of AI in CRM and provides insights for organizations aiming to leverage AI for improved customer relationship management.

The elevated environment temperature in summer plays adverse effects on bovine fertility. Antioxidant treatment may have positive impact to enhance oocyte competence could be possible strategy for alleviating the summer effects on bovine fertility. The present study was aimed to investigate the effects of exogenous melatonin treatment on oocyte development competence, blastocyst development rate, activity of antioxidant enzymes, expression profiles of development related genes and pregnancy rates in cattle during summer. The experiment was conducted in 44 non-lactating indigenous Sahiwal cattle divided into control and treatment group. Treatment group received (day 0) inj. Melatonin @ 18mg/50kg body weight while control group received inj. Corn oil, 4ml i.m. On day 4, all the cows were subjected to ovum pickup (OPU) to aspirate oocytes for in-vitro embryo production. Blood samples were used to determine lipid peroxidation (MDA) and antioxidant enzymes concentration (SOD, GSH), and also for plasma hormonal concentration analysis. The results revealed that blastocyst development rate (%) was higher in treatment group (p<0.05). Treatment group had increased (p<0.05) antioxidant enzymes (SOD and GSH), plasma melatonin and estradiol concentrations, and decreased (p<0.05) MDA concentrations than controls. The mRNA expression level of SOD2, HSPB1, BCL2, MT2 and STAR in oocytes, cumulus cells, COCs (immature and mature) and blastocyst were found to be higher, however, BAX was downregulated in melatonin-treated than control group. In conclusion, the results suggesting probable beneficial role of exogenous melatonin administration in reducing heat stress and enhancing blastocyst developmental competence in indigenous cattle.

Three neutral Pt(II) complexes with diphenylamino-modified 2-phenylpyridine derivatives as cy- clometalating ligands and acetylacetone as the ancillary ligand exhibit aggregation-induced phosphorescent emission (AIPE) properties in THF/H2O. The crystal structures of the complexes highlight the contributions of non-covalent Pt···Pt interactions and hydrogen bonds to the AIPE properties. These AIPE-active Pt(II) complexes 1-3 have been successfully applied to detect picric acid (PA) in aqueous media, affording the lowest limit of detection at 70 nM. Furthermore, three Pt(II) complexes are able to detect PA in common water samples. The quenching of luminescence in the detection can be attributed to photo-induced electron transfer.

In the last decade, the integration of robots into agricultural tasks has significantly transformed food production from planting to harvest. The precision and efficiency of a robot allow for specialized crop management in cases such as; plant disease identification, optimization of water and fertilizer use, monitoring of environmental and soil conditions, among others. That is, the adoption of robots in agricultural practices through intelligent automation increases crop yields and decreases environmental impact. Intelligent automation faces contemporary challenges such as climate change and population growth, promoting sustainable food security. Therefore, this article presents a review of the state of the art of modular robots and their applications in agriculture. Modular robots have the ability to reconfigure and adapt to different tasks, promoting versatile solutions for the agricultural sector. These solutions are linked to the robot's control systems, which can be centralized, decentralized, and hybrid. Firstly, centralized control allows for unified management and coordination in high-precision tasks. Secondly, the decentralized approach offers flexibility and robustness in changing environments where adaptability is required. Thirdly, hybrids incorporate features of both control types balancing control and autonomy to increase efficiency and effectiveness in practical applications. In some cases, control systems incorporate bio-inspired motion control techniques, where the robot mimics natural movements and behaviors to improve its adaptability in performing a task. For example, the chemosynthesis model, which is a biological process where bacteria convert an inorganic compound into energy, has been adapted for individual robots to explore and navigate their environment. Therefore, this article presents an overview of modular robots, the incorporation of bio-inspired motion control techniques, and their convergence towards the sustainable solution of contemporary problems in the agro-industry.

In recent years, the demand for natural and synthetic zeolites has surged due to their distinctive properties and myriad industrial applications. This research aims to synthesise crystalline zeolites by co-recycling two industrial wastes: salt slag (SS) and rice husk ash (RHA). Salt slag, a problem-atic by-product of secondary aluminium smelting, is classified as hazardous waste due to its reac-tive and leachable nature, though it is rich in aluminium. Conversely, RHA, an abundant and cost-effective by-product of the agro-food sector, boasts a high silicon content. These wastes were utilised as aluminium and silicon sources for synthesising various zeolites. The study examined the effects of temperature, ageing time, and sodium concentration on the formation of different zeolite phases and their crystallinity. Results indicated that increased Na+ concentration favoured SOD zeolite formation, whereas LTA-type zeolite formation was promoted at higher temperatures and extended ageing times. The formation range of the different zeolites was defined and supported by a crystallographic, microstructural, and morphological analysis. Additionally, the thermal be-haviour of the zeolites was investigated. This work underscores the potential to transform indus-trial waste, including hazardous materials like salt slag, into sustainable, high-value materials, fostering efficient waste co-recycling and promoting clean, sustainable industrial production through cross-sectoral industrial symbiosis.

Histone deacetylase inhibitors (HDACis) are being recognized as a potentially effective treatment approach for peripheral T-cell lymphoma (PTCL), which is a diverse group of aggressive lymphomas with a bleak prognosis. Recent evidence has shown that HDACi are effective in treating PTCL, especially in cases where the disease has relapsed or is resistant to other treatments, and there are few available options. Several clinical trials have demonstrated that HDACi, such as romidepsin and belinostat, can elicit long-lasting positive outcomes in individuals with PTCL, either when used alone or in conjunction with conventional chemotherapy. They exert their anti-tumor actions by regulating gene expression through inhibiting histone deacetylases, which results in the halting of cell division, programmed cell death, and the transformation of cancerous T-cell, as indicated by gene expression profile studies. Importantly, besides clinical trials, real-world evidence indicated that the utilization of HDACi presents a significant and beneficial treatment choice for PTCL. However, although HDACi showed potential effectiveness, they couldn’t cure most patients. Therefore, new combination with conventional drugs as well as new targeted agents are under investigation.

This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.

Form deviation generated during the milling profile process poses challenges to the precision and functionality of industrial fixtures and product manufacturing across various sectors. The inspection of contour profile quality often relies on commonly employed contact methods for measuring form deviation, but these methods frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system (RAM−StarliteTM), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The RAM−StarliteTM vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations when compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. Results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, quality classification.

Background; Cell-released extracellular vesicles (EVs) acting as 'metabolically and proteolytically active machines,' show potential in metabolomic and proteomic analysis of serum EVs. Despite diverse challenges, post-isolation omics characterization EVs offers crucial insights for effective analysis; (2) Methods: The research, involved children with Congenital Zika Syndrome, utilizing mass spectrometry for proteomics and GC-MS for metabolite identification. Vesicles were isolated using Izon qEV columns, quantified, and characterized by NTA and TEM. Data analysis employed Cytoescape/String and MetaboAnalyst, revealing variations in metabolic and proteomic profiles among groups through PCA and volcano plots. Proteins and Metabolite set enrichment analysis provided biologically meaningful patterns to enriched metabolites; (3) Results: Using molecular exclusion chromatography, the EVs were characterized, revealing size variations. Protein analysis identified 13 significantly altered proteins, including upregulated (e.g., AOM8Q6 - IGLC7) and downregulated (e.g., Q8TD86 - CALML6) ones. Metabolite analysis indicated involvement in the PI3K-AKT-mTOR pathway and suggested a role in Angiotensin inhibition in CZS+. Upstream of mTOR, Akt is the central signaling molecule in the PI3K pathway and plays critical roles in brain development as well as synaptic plasticity important for Zika Virus. The study provides insights into molecular mechanisms associated with CZS; (4) Conclusions: The study pinpointed valuable possible biomarkers, specifically proteins and metabolites, in Zika virus (ZIKV) infection. It stresses the necessity for broader investigations with advanced techniques to uncover molecular targets, potentially advancing pharmacological strategies.

Aluminum (Al) toxicity and low phosphorus availability (LP) are the top two co-existing edaphic constraints limiting agriculture productivity in acid soils. Plants have evolved versatile mechanisms to cope with the two stresses alone or simultaneously. However, the specific and common molecular mechanisms, especially those on flavonoids and carbohydrate metabolism, are still unclear. Laboratory studies were conducted on two wheat genotypes, Fielder (Al-tolerant and P-efficient) and Ardito (Al-sensitive and P-inefficient), exposed to 50 μM Al and 2 μM Pi (LP) in hydroponic solutions. After 4 days of stress, wheat roots were analyzed via transcriptomic and targeted metabolomic techniques. A total of 2296 differentially expressed genes (DEGs, 1535 up and 761 down) under Al and 3029 DEGs (1591 up and 1438 down) under LP were identified in Fielder. Similarly, 4404 DEGs (3191 up and 1213 down) under Al and 1430 DEGs (1176 up and 254 down) under LP were identified in Ardito. The GO annotation analysis results that 4079 DEGs annotated to the metabolic processes term. These DEGs were significantly enriched in the phenylpropanoid, flavonoid, flavone and flavonol biosynthesis, and carbohydrate metabolism pathways by performing the KEGG enrichment analysis. The targeted metabolome analysis detected 19 flavonoids and 15 carbohydrate components in Fielder and Ardito under Al and LP. More responsive genes and metabolites were involved in flavonoid metabolism under LP than Al in the Fielder, while an opposite trend in the Ardito. In the carbohydrate metabolism pathway, the genes and metabolites were higher in Fielder than in Ardito. The combined transcriptome and metabolome analysis revealed that the flavonoid and carbohydrate genes and metabolites differed between Fielder and Ardito under Al and LP, which may be an important reason for Fielder’s high Al and LP resistance. The results of this study lay a foundation for pyramiding genes and breeding multi-resistance varieties.

Amphiphilic block copolymers of N-vinyl pyrrolidone (NVP) and various vinyl esters (VEs), PNVP-b-PVEs, namely vinyl butyrate (VBu), vinyl decanoate (VDc) and vinyl stearate (VSt) were synthesized by RAFT polymerization techniques. The sequential addition of monomers methodology was employed starting from the polymerization of NVP followed by the polymerization of the VEs monomer. The reaction was conducted at 80oC in dioxane solutions using AIBN as initiator and O-ethyl S-(phthalimidylmethyl) xanthate as the CTA. The block copolymers were characterized by Size Exclusion Chromatography (SEC) and NMR spectroscopy. The thermal transitions of the copolymers were studied by Differential Scanning Calorimetry (DSC), whereas their thermal stability via Thermogravimetric Analysis (TGA) and Differential Thermogravimetry (DTG).

The risk factors and positive factors of teleworking have been widely analyzed. Emotional exhaustion has been one of the most studied among them. However, the impact of ethical leadership in virtual environments has remained completely unnoticed. There is no clear answer to whether the ethical leader is a factor that buffers or accelerates stress-related processes in teleworking. Furthermore, the role of perceived work autonomy as a theoretical mechanism that protects against specific adverse psychological outcomes requires more scientific evidence because the results obtained so far are confusing and contradictory. Therefore, this study explores how and when ethical leadership influences emotional exhaustion through work autonomy and teleworking intensity (TI). The sample consisted of 448 professionals from six organizations in the Colombian electricity sector. The mediation effect was evaluated through a four-step procedure, and the moderated mediation model was analyzed using statistical tools such as PROCESS and AMOS macros for SPSS. Surprisingly, our results revealed a positive relationship between ethical leadership and emotional exhaustion, with work autonomy potentially mediating this relationship. However, teleworking intensity (TI) was found to moderate this mediation process. Specifically, the negative impact of ethical leadership on emotional exhaustion was nullified when TI was high. Moreover, a high level of TI strengthened the relationship between ethical leadership and work autonomy, while weakening the positive relationship between work autonomy and emotional exhaustion. In conclusion, our study suggests a potential positive relationship between ethical leadership and emotional exhaustion from the follower's perspective. However, the number of teleworking days can influence this trend, potentially even reversing it. In other words, teleworking intensity (TI) is a resource as its intensity increases, and it can alter the direction of work autonomy and reconfigure the relationship between ethical leadership and emotional exhaustion. This study, to the best of our knowledge, is the first to analyze these variables, significantly contributing to our understanding of the attributes of ethical leadership and its impact on employee well-being.

Background and Objectives: In the literature, relationships between being married and having prediabetes or diabetes are inconsistent. We aimed to investigate whether marriage is a protective or risk factor for prediabetes and to uncover new insights into its impact on prediabetes. Materials and Methods: In this cross-sectional observational study, questionnaires were distributed by email to 1,039 staff members who participated in an employee health check from a hospital affiliated with a medical university in Taiwan. Fasting blood glucose and triglyceride (TG) levels were checked and the questionnaires elicited basic demographic characteristics and included the Copenhagen Burnout Inventory and Nordic Musculoskeletal Questionnaire. The chi-square test or Fisher’s exact test, logistic regression, and mediation analysis were conducted for statistical analysis. Results: Among the group aged 20–37 years, married, obesity, neck and shoulder pain, and elevated TG levels were independent risk factors for prediabetes (impaired fasting glucose). For those >38 years old, overweight, obesity, and elevated triglyceride (TG) were the independent risk factors for impaired fasting glucose. Increased TG levels serve as a mediating factor linking marriage to an increased risk of prediabetes for the group aged 20–37 years. Conclusions: TG play a significant role in the association between marriage and prediabetes among the group aged 20–37 years. Therefore, dietary habits, especially those of young adult couples should be considered. Our findings connect marital status to prediabetes, facilitating advances in diabetes prevention.

Cold-water coral (CWC) reefs represent vulnerable marine ecosystems (VMEs) of critical ecological importance. This study presents a novel approach for the automated detection and segmentation of key CWC species, Desmophyllum pertusum and Madrepora oculata, in underwater imagery using the YOLOv8l-seg deep learning model. The model was applied to images collected at two Natura 2000 sites in the Cantabrian Sea: the Avilés Canyon System (ACS) and El Cachucho Seamount (CSM). Results demonstrate the model's high accuracy in identifying and delineating individual coral colonies. Significant variability in coral cover was observed between and within the study areas, highlighting the patchy nature of CWC habitats. Three distinct coral community groups were identified based on percentage coverage composition and abundance, with the highest coral cover group being located exclusively in the La Gaviera canyon head within the ACS. This research demonstrated the potential of deep learning models for efficient and accurate monitoring of VMEs, facilitating the acquisition of high-resolution data essential for understanding CWC distribution, abundance, and community structure, and contributing to the development of effective conservation strategies.

The analysis of aggregate data has received increasing attention in the statistical discipline over the past 20 years. Much of this attention has been focused on estimating the cells frequencies of a 2×2 contingency table given only the marginal totals; the analyses proposed have been received with missed reviews. More recently, the focus has shifted toward analysing the overall association structure rather than on the estimation of the cell frequencies. This article provides some insight int how informative the aggregate data of a single 2×2 contingency table is for assessing the association. The information contained in the margins of the table is quantified using the squared deviation of the expected cell frequencies over the possible range of values, where the row and column totals are known and fixed. The new measure we discuss to quantify this association is referred to as the aggregate informative index (AII) and is shown to be the standardized area under the squared deviation curve.

Using English prison inspectorate reports the article reports a Goffmanesque-informed discourse analysis of official accounts about the younger inmates of London’s Feltham prison and the conditions characterizing their lived experiences in this prison environment. The construction of this prison estate captures dilapidation, unhygienic conditions, and endless social danger. The stigmatizing construction of the child prisoner intimates a pervasive culture of violence and bullying resulting in their aversion to purposive activities. While, at first blush, prison inspec-torate reporting is based on the policy of ensuring a safe and rehabilitative prison experience for youth it is argued that the nature of the reporting of incarceration obviates a critique of the wider political fabric that custodial interventions reproduce. The Inspectorate operates within the state’s dominant class stratified political ideology. The adoption of a generic labelling discourse in the reports minimizes the communication of harms inflicted on children by criminal ‘justice’ that can only worsen their wellbeing and reproduce the harmful intensity of their imported marginality.

Flexible wearable pressure sensors offer numerous benefits, including superior sensing capabilities, a lightweight and compact design, and exceptional conformal properties, making them highly sought after in various applications including medical monitoring, human-computer interaction, and electronic skin. Because of their excellent characteristics, such as simple fabrication, low power consumption, and short response time, capacitive pressure sensors have received widespread attention. As a flexible polymer material, polydimethylsiloxane (PDMS) is widely used in the preparation of dielectric layers for capacitive pressure sensors. The Young's modulus of the flexible polymer can be effectively decreased through the synergistic application of sacrificial template and laser ablation techniques, thereby improving the functionality of capacitive pressure sensors. In this study, a novel sensor was developed through a series of processes including sacrificial template method using NaCl microparticles, CO₂ laser ablation, and sandwiching a porous PDMS dielectric layer between two flexible electrodes to create a capacitive pressure sensor. The sensor demonstrates a sensitivity of 0.694 kPa-1 within the pressure range of 0-1 kPa, and can effectively detect pressures ranging from 3 Pa to 200 kPa. The sensor demonstrates stability for up to 500 cycles, with a rapid response time of 96ms and a recovery time of 118ms, coupled with a low hysteresis of 6.8%. Furthermore, our testing indicates that the sensor possesses limitless potential for use in detecting human physiological activities and signaling emergencies.

Three-dimensional (3D) models with improved biomimicry are essential to reduce animal experimentation and drive innovation in tissue engineering. In the current study, we investigate the use of alginate-based materials as polymeric inks for 3D bioprinting of osteogenic models using human bone marrow stem/stromal cells (hBMSCs). A composite bioink incorporating alginate, nano-hydroxyapatite (nHA), type I collagen (Col) and hBMSCs was developed and for extrusion-based printing. Rheological tests perfomed on crosslinked hydrogels confirm the formation of solid-like structures consistently indicating a higher storage modulus compared to the loss modulus. The swelling behaviour analysis showed that the addition of Col and nHA into an alginate matrix can enhance the swelling rate of the resulting composite hydrogels, which maximizes cell proliferation within the structure. The LIVE/DEAD assay outcomes demonstrate that the inclusion of nHA and Col did not detrimentally affect the viability of hBMSCs over seven days post-printing. PrestoBlueTM revealed a higher hBMSCs viability in the alginate-nHA-Col hydrogel compared to the remaining groups. Gene expression analysis revealed that alginate-nHA-col bioink favoured a higher expression of osteogenic markers, including secreted phosphoprotein-1 (SPP1) and collagen type 1 alpha 2 chain (COL1A2), in hBMSCs after 14 days, indicating the pro-osteogenic differentiation potential of the hydrogel. This study demonstrates that the incorporation of nHA and Col into alginate enhances osteogenic potential and provides a bioprinted model to systematically study osteogenesis and the early stages of tissue maturation in vitro.

This paper presents an analysis of the power flow within the Northern Interconnected Grid of Cameroon. The Newton-Raphson method has been performed, known for its accuracy, under MATLAB software, to model and solve complex power flow equations. This study simulates a series of outage scenarios to evaluate the responsive-ness of the grid. The results obtained underline the crucial importance of reactive power management and highlight the urgent need to consolidate the grid infrastructure of North Cameroon. To increase grid resilience and stability, the paper recommends the strategic integration of renewables and the development of interconnections with other power grids. These measures are presented as viable solutions to meet cur-rent and future energy distribution challenges, ensuring a reliable and sustainable power supply for Cameroon.

The rise in precocious puberty cases can be attributed to a combination of genetic and environmental factors. Genetic factors play a significant role, with studies identifying genetic abnormalities in genes such as KISS1, KISS1R, MKRN3, DLK1, GABRA1, LIN28B, NPYR, TAC3, and TACR3, which are associated with central precocious puberty (CPP). Epigenetic factors like DNA methylation and histone modifications may mediate the relationship between genetic and environmental influences on CPP development. Environmental factors such as exposure to secondhand smoke, cesarean section, and BMI have also been linked to precocious puberty, particularly in girls, highlighting the multifactorial nature of this phenomenon. Understanding the interplay between these genetic and environmental factors is crucial in addressing the increasing prevalence of precocious puberty in children. The published literature in the past five years was systematically reviewed and summarized highlighting the interplay of genetic and environmental factors for the rise in the precocious puberty.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of MS, particularly when coupled with liquid chromatography (LC-MS), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized medicine advancement. MS-based technologies have transformed precision medicine, enabling comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing detection of low-abundance molecules and elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress towards precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

This study presents a comparative analysis of indoor air quality (IAQ) across health centers in Arakale, Iju, Owode, FECA, and Oba Ile, Ondo State, Nigeria. Particle count data were analyzed to identify common trends, similarities, and differences in IAQ parameters, with the aim of informing targeted interventions for improving indoor air quality in healthcare settings. The findings reveal consistent positive skewness in particle count distributions, indicating occasional spikes in pollution levels amidst generally lower concentrations. Fine particles in the size range of PC 2.5 were consistently dominant across all locations, emphasizing their significance in IAQ assessment. Similar variability in particle count data suggests comparable levels of fluctuations in IAQ among the health centers. However, variations in PCA patterns highlight unique factors influencing IAQ in specific locations, such as combustion sources and control strategies. Targeted interventions, including enhancing ventilation systems, controlling indoor pollution sources, and promoting smoking cessation programs, are proposed to mitigate health risks associated with indoor air pollution exposure. Ongoing monitoring and collaboration with environmental experts are crucial for assessing IAQ trends and implementing evidence-based interventions to safeguard the health of patients, staff, and visitors in healthcare facilities.

The computer program "Histropy" is an interactive Python program for the quantification of selected features of two-dimensional (2D) images/patterns (in either JPG/JPEG, PNG, GIF, BMP, or baseline TIF/TIFF formats) by means of calculations based on the pixel intensities in this data, their histograms, and user-selected sections of those histograms. The histograms of these images display pixel-intensity values along the x-axis (of a 2D Cartesian plot), with the frequency of each intensity value within the image represented along the y-axis. The images need to be of 8-bit or 16-bit information depth and can be of arbitrary size. (Up to 1024 pixels maximum on both sides is recommended as larger images tend to significantly slow the program’s performance). Histropy generates an image’s histogram surrounded by a graphical user interface that allows one to select any range of image-pixel intensity levels, i.e. sections along the histograms’ x-axis, using either the computer mouse or numerical text entries. The program subsequently calculates the (so-called Monkey Model) Shannon entropy and root-mean-square contrast for the selected section and displays them as part of what we call a "histogram-workspace-plot." To support the visual identification of small peaks in the histograms, the user can switch between a linear and log-base-10 display scale for the y-axis of the histograms. Pixel intensity data from different images can be overlaid onto the same histogram-workspace-plot for visual comparisons. The visual outputs of the program can be saved as histogram-workspace-plots in the PNG format for future usage. The source code of the program and a brief user manual are published in the supporting materials as well as on GitHub . Instead of taking only 2D images as inputs, the program’s functionality could be extended by a few lines of code to other potential uses employing data tables with one or two dimensions in the CSV format.

This study investigates by hybrid approach of classical calculations and numerical explicit FEM simulation the strain rate sensitive hardening of Polypropylene Sabic 83MF10 under dynamic tension loads. The Cowper-Symonds model is employed to characterize the non-linear increase in yield strength with strain rate, within the range of 0,01 to 1 [1⁄s]. For lower strain rates, a drastic decrease in yield strength is observed, suggesting the significant influence of creep and relaxation phenomena. The study proposes further research incorporating viscoelastic properties for a more comprehensive material model. Additionally, the study observes a disproportionate increase in yield strength for samples with smaller radius, indicating a dependency on triaxiality which suggests further research on the Drucker-Prager model for this material. The study concludes by recommending a broader range of input parameters for future studies, including the Johnson-Cook model and comparison of tensile test results with compression test results.

As it turns out, both isotope scaling and density limits are phenomena closely linked to fluid closure. The necessity to include ion viscosity arises for both phenomena. Thus, we have added ion viscosity to our model. The experimental isotope scaling has been successfully recovered in our fluid model through parameter scans. Although ion viscosity typically exerts a small effect, the density limit is manifested by increasing the density by approximately tenfold from the typical experimental density. In our case, this increase originates from the density in the Cyclone base case. Notably, these phenomena would not manifest with a gyro-Landau fluid closure. The isotope scaling is nullified by the addition of a gyro-Landau term, while the density limit results from permitting ion viscosity to become comparable to the gyro-Landau term. The mechanism of zonal flows, demonstrated analytically for the Dimits upshift, yields insights into the isotope scaling observed in experiments. In our approach, ion viscosity is introduced in place of the Landau fluid resonances found in some fluid models. This implies that the mechanism of isotope scaling operates at the level of fluid closure in connection with the generation of zonal flows. The strength of zonal flows in our model has been verified, particularly in connection with the successful simulation of the nonlinear Dimits shift. Consequently, a role is played by our approach in the temperature perturbation part of the Reynolds stress.

Sedimentation is an undesirable phenomenon that complicates the design of microsystems that exploit dense microparticles as delivery tools, especially in biotechnological applications. It often informs the integration of continuous mixing modules, consequently impacting system footprint, cost, and complexity. The impact of sedimentation is significantly worse in systems designed with the intent of particle metering or binary encapsulation in droplets. Circumventing this problem involves the unsatisfactory adoption of gel microparticles as an alternative. This paper presents two solutions – a hydrodynamic solution that changes the particle sedimentation trajectory relative to a flow-rate dependent resultant force; and induced hindered settling (i-HS), which exploits Richardson-Zaki (RZ) corrections of Stokes’ law. The hydrodynamic solution was validated using a multi-well fluidic multiplexing and particle metering manifold. Computational image analysis of multiplex metering efficiency using this method showed an average reduction in well-to-well variation in particle concentration from 45% (Q = 1 mL/min, n = 32 total wells) to 17% (Q = 10 mL/ min, n = 48 total wells). By exploiting a physical property (cloud point) of surfactants in the bead suspension in vials, the i-HS achieved 58% reduction in the sedimentation rate. This effect results from surfactant phase change, which increases the turbidity (transient increase in particle concentration), thereby exploitingthe RZ theories. Both methods can be used independently or synergistically to eliminate bead settling in microsystems or minimize particle sedimentation.

Photosystem I is a key component of primary energy conversion in oxygenic photosynthesis. Electron transfer reactions in Photosystem I take place across two, parallel, electron transfer chains that converge after a few electron transfer steps, sharing both the terminal electron acceptors, that are a series of three Iron-Sulphur (Fe-S) clusters known as , and , and the terminal donor, . The two electron transfer chains show kinetic differences which are, due to their close geometrical symmetry, mainly attributable to the tuning of the physical-chemical reactivity of the bound cofactors, exerted by the protein surroundings. The factors controlling the rate of electron transfer between the terminal Fe-S clusters are still not fully understood because of the difficulties of monitoring these events directly. Here we present a discussion concerning the driving forces associated to electron transfer between and as well as and , employing a tunnelling-based description of the reaction rates coupled to the kinetic modelling of forward and recombination reactions. It is concluded that the reorganisation energy for oxidation shall be lower than 1 eV. Moreover, it is suggested that the analysis of mutants with altered redox properties can also provide useful information concerning the upstream, phylloquinone, cofactor energetics.

Adverse childhood experiences (ACEs) refer to traumatic life events in childhood that comprise abuse (e.g., psychological, physical, sexual), neglect (psychological and physical), indirect violence or household dysfunctions. Studies have found ACEs to be related to severe short-, medium- and long-term consequences in victims’ health. The current work consists of a real-world, multicentric, prevalence study of health outcomes in children <16 years suspected of ACEs by physicians. It relies on electronic health records and healthcare registries from a Portuguese local healthcare unit between January 2001 and December 2021 (N=40 536). Three health outcome groups were analyzed: (1) traumatic injuries and intoxications (2) mental health disorders, and (3) physical disorders. Keywords and ICD-9, ICD-10, and ICPC-2 codes from were employed. Children suspected of ACEs represent 2% (n=918). Social problems, injuries and intoxications, mental health disorders, and physical disorders arise in higher percentages in suspected victims than in non-suspected individuals. Findings uncover that child victims of ACEs may be underdiagnosed. Given the severe consequences for their current and future health, this should be taken as a critical warning for healthcare professionals. Detection and reports in health units are fundamental for early treatment, aiming to avoid an escalation of damage and prevent re-victimization.

The next generation network requires not only ultra high data rate, global coverage and connectivity, but also reducing network deployment costs and energy consumption. The emergence of reconfigurable intelligent surface (RIS) technology provides an effective way to improve efficiency and reduce cost, while the passive elements bring new challenges of channel estimation (CE) and beam tracking. For a RIS-aided multiple-input and single-output (MISO) system, in this paper, to achieve the channel state information (CSI), we propose a principle component analysis (PCA) based staged channel estimation method. Based on the estimated channel, we propose a deep learning (DL) based beam tracking scheme to realize low-complexity RIS reflection coefficient design, which effectively improves the signal-to-noise ratio (SNR) at the user side. Simulation results verify our proposed channel estimation scheme based on PCA and beam tracking scheme based on DNN for the semi-active RIS-aided MISO systems can obtain approximate performances with much lower computational complexity.

Cellular senescence is a permanent condition of cell cycle arrest caused by a progressive shorten-ing of telomeres defined as replicative senescence. Stem cells may also undergo an accelerated se-nescence response, distinct from telomere shortening, known as premature senescence as a re-sponse to different stress agents. Various treatment protocols have been developed based on epi-genetic changes in cells throughout senescence, using different drugs and antioxidants, senolytic vaccines, or reprogramming of somatic senescent cells using Yamanaka factors. Even with all the recent advancements, it is still unknown how different epigenetic modifications interact with ge-netic profile and how other factors such as microbiota physiological conditions, psychological states and diet influence the interaction between genetic and epigenetic pathways. The aim of this review is to highlight the new epigenetic modifications that are involved in stem cell senescence. Here, we review recent senescence-related epigenetic alterations such as DNA methylation, chro-matin remodeling, histone modification, RNA modification, and non-coding RNA regulation outlining new possible targets for therapy of aging-related diseases. The advantages and disad-vantages of the animal models used in the study of cellular senescence are also briefly presented.

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Chronic pancreatitis is a progressive fibroinflammatory disorder with no currently satisfactory treatment. Emerging evidence suggests an association between gut microbial dysbiosis and chronic pancreatitis. Although direct causative evidence is lacking, it is hypothesized that the gut microbiota may play a pivotal role in modulating pancreatic function via the gut-pancreas axis. Thus, modulating the gut microbiota through the administration of probiotics or prebiotics may alleviate pancreatic disorders. In this review, we first propose the potential mechanisms by which specific probiotics or prebiotics may ameliorate chronic pancreatitis, including the alleviation of small intestinal bacterial overgrowth (SIBO), facilitation of short-chain fatty acids (SCFAs) production, activation of the glucagon-like peptide 1 receptors (GLP-1Rs) in the pancreas. Since there are currently no probiotics or prebiotics used for the treatment of chronic pancreatitis, we have discussed research in other disease models that use probiotics or prebiotics to modulate pancreatic endocrine and exocrine functions and prevent pancreatic fibrosis. This provides indirect evidence for their potential application in the treatment of chronic pancreatitis. We anticipate that this research will stimulate further investigation into the gut-pancreas axis and the potential therapeutic value of probiotics and prebiotics in chronic pancreatitis.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

The disease of transthyretin (TTR) amyloidosis (ATTR) has been known since the 1960s, and during these past 60 or so years, there has been a sustained peri-od of steady discoveries that have led to the current model of ATTR pathogene-sis. More recent research has achieved major advances in both diagnostics and therapeutics for ATTR, which are having a significant impact on ATTR patients today. Aiding these recent achievements has been the remarkable ability of cryo-electron microscopy (EM) to determine high-resolution structures of amy-loid fibrils obtained from individual patients. Here, we will examine cryo-EM structures of transthyretin amyloid fibrils to explore the structural basis of re-cent monoclonal antibody therapies for ATTR, including ALXN-2220 and Co-ramitug; as well as to point out potential applications of this approach to other systemic amyloid diseases.

The existing collaborative channel perception suffered from unreasonable data fusion weight allocation, which mismatched the channel perception capability of node devices. This often led to significant deviations between the channel perception results and the actual channel state. To solve this issue, this paper integrated the data fusion algorithm from evidence fusion theory with data link channel state perception. It applied the data fusion advantages of evidence fusion theory to evaluate the traffic pulse statistical capability of network node devices. Specifically, the typical characteristic parameters describing the channel perception capability of node devices were regarded as evidence parameter sets under the recognition framework. By calculating the credibility and falsity of the characteristic parameters, the differences and conflicts between nodes were measured to achieve a comprehensive evaluation of the traffic pulse statistical capabilities of node devices. Based on this evaluation, the geometric mean method was adopted to calculate channel state perception weights for each node within a single-hop range, and a weight allocation strategy was formulated to improve the accuracy of channel state perception.

System xc-, the cystine/glutamate exchanger, is a membrane transporter that plays a critical role in the antioxidant response of cells. Recent work has shown that System xc- localizes to the plasma membrane during oxidative stress, allowing for increased activity to support production of glutathione. In this study, we used site-directed mutagenesis to examine the role of C-terminal lysine residues (K422, K472, K473) of xCT (SLC7A11) in regulating System xc-. We observed that K473R exhibits loss of transporter activity and membrane localization and is 7.5 kD lower in molecular weight, suggesting that K473 regulates System xc- trafficking and is modified under basal conditions. After ruling out ubiquitination and neddylation, we demonstrated that unlike WT xCT, K473R lacks N- and O-glycosylation and is sequestered in the endoplasmic reticulum. Next we demonstrated that K473Q, a constitutively acetylated lysine mimic, also exhibits loss of transporter activity, decreased membrane expression, and a 4 kD decrease in molecular weight, however, it is N- and O-glycosylated and localized to the endoplasmic reticulum and Golgi. These results suggest that acetylation and deacetylation of K473 in the endoplasmic reticulum and Golgi, respectively, serves to regulate the progression of the transporter through the biosynthetic pathway.

Background and Objectives: Proteomics encompasses the exploration of protein composition, regulation, function, and pathways. Its influence spans diverse clinical fields and holds promise in addressing various women’s health conditions, including cancers, osteoporosis, and cardiovascular disorders. However, no comprehensive summary of proteomics and menopausal health exists. Our objective was to summarize proteomic profiles associated with diseases and disorders in peri and postmenopausal women. Materials and Methods: We conducted a comprehensive search of databases including PubMed, Google Scholar, Cochrane database, Elsevier, and ScienceDirect until 2022. A total of 253 studies were identified, 41 studies met the inclusion criteria to identify data of interest. These included study design, disease, and proteomics/proteins of significance as described by the authors. Results: The 41 studies, covered diverse areas including bone disorders (10 studies), cardiovascular diseases (5 studies), oncological malignancies (10 studies), and conditions such as obesity, nonalcoholic liver disease, the effects of hormone replacement therapy, and neurological diseases (16 studies). Results of our study indicate that proteomic profiles correlate with heart disease in peri- and postmenopausal women, with distinct sex differences. Furthermore, proteomic profiles significantly differed between women with and without osteoporosis. Additionally, patients with breast, ovarian, and endometrial cancer exhibited notable variations in proteomic profiles compared to those without these conditions. Conclusions: Proteomics has the potential to enhance risk assessment and disease monitoring in peri- and postmenopausal women. By analyzing unique protein profiles, clinicians can identify individuals with heightened susceptibility to specific diseases or those already affected by established conditions. This review suggests that there is sufficient preliminary data for proteomics in peri- and postmenopausal women for early identification of cardiovascular disease, osteoporosis, and cancers, in disease monitoring, and in tailoring individualized therapies. Rigorous validation studies involving large populations are essential before drawing definitive conclusions regarding the clinical applicability of proteomic findings.

Tsunami is a type of infrequent geohazards with devastating consequences, which makes accurate computer simulations of tsunami waves particularly desirable for hazard assessment and mitigation purposes. The evolution of a typical tsunami involves the generation, propagation and inundation phases. In this survey, we briefly describe the most common types of tsunamigenic sources and give a detailed review of some most widely used mathematical models for describing tsunami propagation. We emphasize the approximations adopted in each mathematical model and point out their most important advantages and disadvantages. Tsunami propagation modeling is computationally demanding, involving a wide span of spatial and temporal scales. While some of the approximate models are sufficiently accurate for describing wave behaviors at certain scales, they might not be applicable at other scales. Effective and efficient tsunami propagation simulations will likely depend upon the proper coupling of different models for different scales.

The use of natural chloride in the salt fuel has been sometimes promoted for the chlorine based molten salt fast reactor using for example NaCl-UCl4- UCl3. Most of the chemistry based R&D has been carried out on natural chloride, while in reactor physics most of the calculations are based on enriched chloride-37. In any case, the use of natural chloride (75.77% 35Cl and 24.23% 37Cl) induces specific ecological and neutron economical issues. An ecological issue is the production of 36Cl by neutron activation of 35Cl. Chlorine-36 is a soluble long live nuclide that makes it a ‘first’ nuclide in the safety assessment of the repository. We explore the build-up of 36Cl with burnup for various 37Cl (activation product) enrichment. The neutron economical aspect is gained by achieving reactor criticality with a lower amount of fissile material achieved through the use of enriched chloride-37. The criticality (k) of a core is much larger for enriched chloride than for the natural one. The reactor with 99% 37Cl would have a k99/knat of 1.04346 which would allow to reduce the required uranium enrichment and thus the fuel cost. This reactivity gain is evaluated for various 37Cl enrichment factors to estimate uranium enrichment gain achievable through 37Cl enrichment. However, from economical point of view it could be of interest to use as some kind of compromise solution natural NaCl, which will be analysed, too. Production/enrichment of 37Cl can be performed by physical separation e.g. gas centrifugation of 1H35/37Cl or by chemical separation using anion partitioning and elution of 23Na35/37Cl on solid adsorbent columns or in liquid – liquid extraction systems. These techniques require however extensive investigations including chemical thermodynamics and hydrodynamics. Comparisons are suggested on the basis of the separation factor α. Recommendations are finally given for utilising 37Cl in the fast molten salt reactor. The enrichment work for 37Cl is smaller than the work needed for 235U enrichment.

Many definitions of cancer have been proposed over the years to explain cancer’s origins and pathophysiology, but the disease is too variable to allow any single description to completely encompass all of the myriad traits. A true, unifying theory of cancer must not only reveal how all known carcinogens contribute toward malignancy, but must also elucidate why cancers occur predominately in older patients, why they express so many similar behaviors, as well as account for each cancer’s individual variations. A conceptual reorganization of decades of cancer data may reveal the fundamental process that underlies the malignant transformation of cells, which not only provides a new understanding of the etiology of cancers, but also helps to interpret the subsequent events that occur after transformation, which is of significant clinical interest. The revised concepts of initiation, promotion, and progression suggest that cancer is a failed or incomplete attempt at regeneration that occurs when ordinary cellular events unintentionally coincide over time to force the use of a DNA repair pathway that is normally restricted for use in germ cells.

Background: Menopause signifies the end of a woman's reproductive phase, characterized by the cessation of ovarian hormone production and the termination of menstrual cycles, typically occurring between the ages of 45 and 55. The transition involves a range of physical and psychological symptoms manifesting before, during, and after menopause. Method: This cross-sectional analytical study assessed the level of awareness regarding menopausal symptoms among women in Bharatpur-9, Chitwan. A non-probability sampling technique was used to select 257 respondents. Data were collected using a semi-structured interview schedule and analyzed with SPSS version 20.0. Results: Among the respondents, 53.3% were aged 40-49 years (Mean ± S.D.; 48.84 ± 6.27), and 69.3% experienced menarche between ages 13- 15. A majority (59.9%) had not yet reached menopause. Knowledge of menopausal symptoms was as follows: joint pain (90.7%), sleep problems (90.7%), irritability (90.7%), hot flushes (22.2%), anxiety (22.2%), sadness (22.2%), vaginal dryness (22.6%), loss of interest in sex (25.7%), palpitations (18.7%), and decreased visual acuity (9.3%). Statistical significance was found between awareness levels and factors such as religion (p=0.00), ethnicity (p=0.00), educational status (p=0.006), family type (p=0.043), cessation of menstruation (p=0.013), and cause of menopause (p=0.00). Overall, 53.7% of respondents had average knowledge about menopausal symptoms. Conclusion: The study concluded that over half of the respondents possessed average awareness of menopausal symptoms. Hence, there is a need for women to receive correct and comprehensive information regarding menopausal symptoms and their management.

Antimicrobial peptides (AMPs) are short, usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi, and other pathogens. Bacterial AMPs, or bacteriocins, can be produced from Gram-negative and Gram-positive bacteria via ribosomal synthesis to eliminate competing organisms. Bacterial AMPs are vital in addressing the increasing antibiotic resistance of various pathogens, potentially serving as an alternative to ineffective antibiotics. Bacteriocins have a narrow spectrum of action, making them highly specific antibacterial compounds that target particular bacterial pathogens. This review covers the two main groups of bacteriocins produced by Gram-negative and Gram-positive bacteria, their modes of action, classification, sources of positive effects they can play on the human body, and their limitations and future perspectives as an alternative to antibiotics.

For semiconducting two-dimensional transition metal dichalcogenides, carrier transport proper-ties of the material are affected by strain engineering. In this work, we investigate the carrier mo-bility of monolayer hafnium disulphide (HfS2) under different biaxial strains by first-principles calculations combined with Kubo-Greenwood mobility approach and compact band model. The decrease/increase in effective mass of conduction band of monolayer HfS2 caused by biaxial ten-sile/compressive strain is the major reason for the enhancement/degradation of its electron mobil-ity. Lower hole effective mass of valence bands in the monolayer HfS2 under biaxial compressive strain results in the better hole transport performance of monolayer HfS2 than the one with biaxial tensile strain. In summary, biaxial compressive strain decreases both effective mass and phonon scattering rate of monolayer HfS2, resulting in an increase in carrier mobility. When the biaxial compressive strain reaches 4%, for the conduction band of monolayer HfS2, electron mobility en-hancement ratio is ~90%. For the valence band of monolayer HfS2, the maximum hole mobility enhancement ratio appears ~13% at biaxial compressive strain of 4%. Our results indicate that the carrier transport performance of monolayer HfS2 can be greatly improved by the strain engineer-ing.

Variable flow emitters are a new type of emitter for underground drip irrigation that are used in locations where soil moisture is difficult to transport to the surface, which leads to crop emer-gence difficulties. The flow channel structure parameters of emitters affect the hydraulic per-formance of the emitter and its anticlogging ability. In this study, the variable flow emitter at the conventional low–flow water supply stage is taken as the research object. A combination of CFD numerical simulation of a two–phase flow of water and sand and clear water testing of test samples is applied to study the influence laws of 10 groups of variable flow emitter structural combinations on the effluent flow rate (q), flow index (x), particle passage rate and flow field inside the flow channel. The results show that the Realizable k–ε turbulence model can be used to simulate the flow field inside the variable flow emitter flow channel. The nRMSE between the measured and simulated values of q is 11.23%, and the relative error between the measured and simulated values of x is 4.66%, which gives a high simulation accuracy. The polar analysis shows that the tooth angle A has the smallest effect on the effluent flow rate q0.1 under 0.1 MPa, x, and particle passage rate of the variable flow emitter. The depth of the flow channel D, the spacing of the teeth B, and the height of the teeth E have a different order of precedence in the influence of the three indices, which are D > B > E > A, B > E > D > A, and E > B > D > A, respectively. The value of particle passage rate is positively correlated with the mean flow velocity (v) and the mean turbulent kinetic energy (k) in the flow channel, and particle passage rate tends to increase and then decrease with the increase of x. The retention time (t) of the particles in the flow channel is closely related to the magnitude of v and k. Three multivariate linear regression equations (R2 = 0.883 ~ 0.995) were constructed for q0.1, x, and particle passage rate versus the flow channel structural parameters. Using the scipy.optimize.minimize function in Python, we de-termined the three optimal combinations of flow channel structural parameters corresponding to better hydraulic performance: q0.1 is 1.5 or 2 L/h (objective function y = min x), better clogging resistance (y = max particle passage rate), and both (y = min (0.5 x - 0.5 particle passage rate)). The research results can provide a ref-erence for the optimal design of variable flow emitters.

This study investigates the performance and environmental impact of n-butanol blended with Jet-A fuel in turbo-engines, aiming to assess its viability as a sustainable aviation fuel (SAF). The research involves experimental testing of various blends, ranging from low to high concentrations of methanol, to determine their effects on engine performance and emissions. The experimental setup includes comprehensive measurements of engine parameters such as thrust, fuel consumption rates, and exhaust gas temperatures. Emissions of nitrogen oxides (NOx), particulate matter (PM), carbon monoxide (CO), and unburned hydrocarbons (HC) are also analysed to evaluate environmental impacts. Key findings indicate that n-butanol/Jet-A blends can significantly enhance combustion efficiency and reduce emissions compared to conventional Jet-A fuel. Higher n-butanol concentrations lead to improved thermal efficiency and lower NOx and CO emissions. However, PM emissions slightly increase with higher methanol content. The study underscores the potential of n-butanol as a SAF for turbo-engines, highlighting its ability to mitigate environmental impacts while maintaining or improving engine performance. This research supports the feasibility of integrating n-butanol into Jet-A blends for turbo-engine applications, emphasizing its role in achieving more environmentally friendly aviation operations.

Technological progress brings new tools to help society's development. In this context, the Internet of Things (IoT) helps to advance modern society. By connecting everyday objects and devices to the Internet, IoT enables benefits such as efficiency and automation, healthcare, environmental sustainability, safety, security, and data-driven decision-making. One of the possible technologies for devices' communication is LoRa, a wireless technology designed for the IoT that allows devices to communicate over long ranges using little battery power. This makes it useful for situations where it is needed to connect many devices spread out over a large area, like in smart cities or industrial settings. This paper aims to verify and evaluate the LoRa communication signal loss in a building environment. For this evaluation, we collected and processed data through experiments with LoRa links in the Instituto Federal de Pernambuco (IFPE) Campus.

This study presents a numerical analysis of rock deformation and damage behaviors induced by dynamic loadings, specifically focusing on the fracturing of tight oil reservoirs. An elastoplastic model based on finite element methods was utilized to simulate the effects of varying loading and perforation parameters. Three distinct scenarios were modeled: a single perforation, boundary loading on three sides, and multiple perforations, each providing unique insights into the mechanical responses of the rock material. The analysis revealed that dynamic effects are highly localized around perforations and loaded boundaries across all scenarios. Acceleration magnitudes are captured with rapid attenuation observed as the distance from the perforation increased. This indicated that the highest stress concentrations and deformation occurred close to the points of loading or perforation. Strain rate analysis in the x direction showed significant fluctuations near the perforations, reflecting intense stress redistributions and compressive deformation, especially prominent in the scenario with multiple perforations. Plastic strain, representing irreversible damage, was found to concentrate near perforation tips and loaded boundaries. However, the pattern of plastic strain accumulation varied: in the small domain scenario with boundary loading, plastic strain did not immediately accumulate next to the loading boundary, whereas in scenarios with single and multiple perforations, damage developed from the loading boundaries. These findings highlight the need for precise control in perforation and dynamic loading processes to manage stress and deformation effectively. It is concluded that optimizing perforation patterns and loading strategies can enhance reservoir stimulation efficiency while minimizing unintended damage to the surrounding formation.

White adipose tissue (WAT) and gut are involved as inducers of neuroinflammation when they detect injury and trigger inflammation. The autonomous nervous system innervates both tissues, although the roles of the sympathetic, parasympathetic and enteric nervous systems have not been fully elucidated. Here, we revisited the participation of both types of inflamed peripheral tissue in neuroinflammation. We first analyzed how inflamed peripheral WAT plays a key role in neuroinflammation once metainflammation is installed. Second, we described the impact of insulin resistance (IR) on hypothalamic dysfunction, T2DM and neurodegenerative diseases. Finally, we analyzed the gut-brain axis, examining cell interactions, soluble factors, the sensing of microbes and the role of the intestinal microbiota and mucosal barriers in neuroinflammation. Since intestinal mucosa and secondary lymphoid organs are densely innervated by the sympathetic, parasympathetic, and enteric nervous systems, we analyzed bidirectional crosstalk between different tissues, neurons, and immune systems in healthy and pathological circumstances to restore normal tissues functions, and consequently homeostasis.

High levels of heavy metal contamination in soil present substantial threats to human health and the environment, leading to severe health problems such as neurotoxicity, cancer, kidney issues, chronic obstructive pulmonary disease, and reduced life expectancy. This research aims to identify and analyze heavy metals in soil samples collected from Superfund sites in North Birmingham, Alabama, specifically in affected areas with Zip codes 35207, 35217, and control area 35214. These affected areas were previously used for mining, coal-fired power plants, coke furnaces, smelting, and other potential sources of heavy metal pollution. Laser-Induced Breakdown Spectroscopy (LIBS) was employed to study 60 soil samples systematically collected from affected and control areas. We found that using LIBS, we could detect arsenic (As), lead (Pb), and manganese (Mn) in all soil samples from the affected areas. The limit of detection (LoD) was 16 ppm for Pb, 43 ppm for As, and 33 ppm for Mn using specific parameters of the detection system and/or argon gas purging at atmospheric pressure. The results were compared with ICP-MS measurements to validate the accuracy of the LIBS findings. Data showed a good linearity for all calibration data at relatively low concentrations and a good correlation between ICP-MS measurements.

We recently demonstrated the effectiveness of long-term treatment with rifaximin and the probiotic VSL#3 at improving urogenital and gastrointestinal symptoms in patients with both chronic inflammatory prostatitis (IIIa prostatitis) and diarrhea-predominant irritable bowel syndrome (IBS-D), relative to patients with IBS-D alone. Because low-grade inflammation of the intestine and prostate may be one of the reasons for co-developing both IIIa prostatitis and IBS-D, we designed the present study to once again evaluate the efficacy of combined rifaximin and VSL#3 treatment in patients affected by IIIa prostatitis plus IBS-D, but we also measured seminal plasma pro-inflammatory (IL-6) and anti-inflammatory (IL-10) cytokines before and after treatment. We consecutively enrolled 124 patients with IIIa prostatitis and IBS-D (diagnosed using the Rome III criteria). Patients were randomized into two groups: group A (n=64) was treated with rifaximin (seven days per month for three months) followed by VSL#3, and group B (n=60) was treated with a placebo. At the beginning of the study and after three months of treatment, all patients completed NIH-CPSI and IBS-SSS questionnaires to score the severity of their urogenital and gastrointestinal symptoms, and we measured seminal plasma levels of IL-6 and IL-10 using ELISA. We defined patients who improved following the treatment as those with a ≥6-point reduction in their NIH-CPSI score or ≥50-point reduction in their IBS-SSS score. By the end of the intervention, 68.7% and 62.5% of patients from group A reported improved NIH-CPSI and IBS-SSS scores, respectively, compared to only 3.3% and 5% of the placebo group. Group A patients also had significantly lower mean seminal plasma levels of IL-6 (11.3 vs 32.4 pg/mL) and significantly higher mean levels of IL-10 (7.9 vs 4.4 pg/mL) relative to baseline, whereas levels of IL-6 and Il-10 did not change in the placebo group. The co-occurrence of two clinical phenotypes (IBS-D plus IIIa prostatitis) result in increased intestinal inflammation and altered immune activation, both of which can be balanced by treatment with rifaximin and VSL#3.

Corporate governance has received increasing attention from practitioners and politicians after the collapse of large companies due to accounting errors and failure to disclose information that reflects the true financial condition of companies. Voluntary risk disclosure includes several aspects, including financial risks, including risks of interest rate, exchange rate, liquidity as well as risks of operations. In Jordan, the most common type of business is family businesses, where boards of directors usually serve the interests of the family that owns the majority stake in the company.This study identifies the moderate effect of family in the link joining the audit committee with the voluntary risk disclosure of financial institutions in Jordan. It's population is the Jordanian commercial banks registered and operating in Jordan during the period extending from 2017 to 2023.Significantly, it concluded with revealing that the characteristics of the audit committee; namely, independence, experience, and committee size, obviously impact disclosure of voluntary risk in the selected banks. However, the results made it obvious that the number of audit committee meetings did not affect the degree of voluntary risk disclosure. In addition, the results view family ownership as moderately affecting the relationship joining some audit committee characteristics with voluntary risk disclosure.

In dementia, particularly Alzheimer's Disease (AD), communication challenges are evident, especially in vocabulary and pragmatic aspects. Affected individuals often use vague, non-specific words, and their speech lacks informative nouns and verbs, leading to imprecise communication. However, aspects like sentence structure, phonology, and articulation are believed to remain intact until later stages, though this view is debated in the research community. The rise of Large Language Models (LLMs) has made significant strides in various domains, including sentiment analysis and question-answering. These advancements have been applied to dementia research, with studies using LLMs to analyze textual data. Some research incorporates pauses in text to enhance performance, while others utilize transfer learning techniques. However, limited datasets for dementia detection pose challenges in training LLMs. Our research presents a novel approach to measuring the impact of in-text encoding strategies by embedding special characters within the text to enhance model performance and incorporating sequences and summaries of their frequency. Our best model achieves 0.88 and 0.86 in f1-score and accuracy, respectively, whereas the baseline has 0.42 and 0.56 in f1-score and accuracy.

This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates tech-niques such as the Nolte method, tangent method, and compliance method. The investigation re-lies on observing changes in fluid flow regimes from pre-closure to post-closure, using fluid flow equations to examine the post-closure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to post-closure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. The paper recommends assessing natural fractures' characteristics and perme-ability to identify the source of leak-off before selecting a technique. It proposes integrating vari-ous methods for a comprehensive understanding of subsurface formations, combining their strengths for accurate fracture closure identification and better understanding of subsurface formations. The new proposed workflow employs the Continuous Wavelet Transform (CWT) technique for fracture closure detection, avoiding physical model pre-assumptions or simplifica-tions to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques.

The present paper explored the possibility of discriminating between different malignant degrees of Chondrosarcoma (CS), a diffused bone tumor, and Enchondroma (EC), its benign version, through an approach based on the coupling between Confocal Raman Microscopy (CRM) and Machine Learning (ML) techniques. Recently, Raman Spectroscopy has proven to be a powerful tool for a complete grading of CS by distinguishing between grade 1, 2 and 3, and to distinguish CS from EC. In this paper, we tested some models, belonging either to ML or to the sub-type called Deep Learning (DL), showing excellent classification performances, especially the DL algorithms, with classification accuracy approaching to 100%, making the models promising for future implementations of Raman spectroscopy when applied to oncology diagnosis. In turn, we highlighted how some proper ML models result in worse classification performances but better resolution of specific chemical target compounds, possible candidate to become malignant markers, with relevant implication for a correct diagnosis.

Sentiment analysis is a technique used to understand the publics’ opinion towards an event, product, or organization. For example, positive or negative opinion or attitude towards electric vehicle (EV) brands. This provides companies with valuable insight about the public's opinion of their products and brands. In the field of natural language processing (NLP), transformer models have shown great performances over the traditional machine learning algorithms. However, these models have not been explored extensively in the EV domain. EV companies are becoming signif-icant competitors in the automotive industry and are projected to cover up to 30% of the United States light vehicle market by 2030 [1]. In this study, we present a comparative study of large language models (LLMs) including bidirectional encoder representations from transformers (BERT), robustly optimized BERT approach and generalized autoregressive pretraining for language un-derstanding using Lucid motors and Tesla motors YouTube datasets. Results evidenced LLMs like BERT and her variants are off-the-shelf algorithms for sentiment analysis, specifically, when fi-ne-tuned. Furthermore, our findings presents the need for domain adaptation whilst utilizing LLMs. Finally, the experimental results showed that RoBERTa achieved consistent performance across the EV datasets with a F1 score of at least 92%.

This study explores the development and characterization of ceramic rod reinforced TWIP (Twinning Induced Plasticity) steel matrix composites, fabricated using a lost foam casting process. The incorporation of ceramic rods aims to enhance the mechanical properties of TWIP steel, which is known for its excellent plasticity. Mechanical testing demonstrated a significant improvement in flexural strength and ductility, with the composite exhibiting over double the strength of the unreinforced TWIP steel. Heat treatment at 500°C for 60 minutes further enhanced these properties, increasing the composite's flexural strength to 1023 MPa and improving its ductility. The observed improvements are attributed to the synergistic effects of the hard ceramic reinforcement and the ductile matrix, as well as the microstructural modification conferred by heat treatment. These findings suggest that ceramic rod reinforced TWIP steel composites are promising materials for applications requiring a combination of high strength and ductility.

This paper investigates the energy dissipation level of a nonlinear vehicle suspension system. The suspension system is modelled as a mass-spring-damper system, which is designed using the output frequency response function (OFRF) method. This method enables the derivation of an explicit relationship, between the system output spectrum (or Performance metric) and the system nonlinear parameters of interest. The OFRF polynomial, of the energy dissipation level, was derived in terms of the nonlinear parameters of interest. The results obtained show that the OFRF model, of the energy dissipation level, could be used to estimate the amount of heat dissipated, for a set of design parameters. This has potentials of providing some insights, in the design of vibration-based energy harvesters, as some of the wasted heat energy could be converted to electrical energy.

The inability to delineate a unified physical ontology that accounts simultaneously for the laws of special relativity and the results of quantum experiments has been a defining problem in physics for more than 100 years. This analysis addresses the problem by positing a novel mixed ontology composed of a “discrete” 4D spacetime and a physical, ultra-high dimensional (3 x N) “Planck Space.” Together, Planck Space and the three spatial dimensions of 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the “Dual Ontology”).Critically, the Dual Ontology’s structure replaces 1) the continuous, differentiable manifold of 4D spacetime with a discrete, background-independent 4D spacetime that is intrinsically curved and 2) mathematical spaces with a physical (3 x N) Planck Space. Moreover, the Dual Ontology is structurally and dynamically predicated on the one-to-one mapping and identity between the discrete spatial units that simultaneously form 4D spacetime and Planck Space. The one-to-one mapping and identity of the discrete spatial units support an integrated quantum dynamics based upon the dynamic evolution of single and N-body quantum states in 4D spacetime in full compliance with the laws of special relativity and the instantaneous collapse of all quantum states in an ontic Planck Space, where special and general relativity and more generally, 4D spacetime’s laws of physics, do not apply.

Sodium borohydride-closo-hydroborate Na3(BH4)(B12H12) exhibits the high room-temperature ionic conductivity and high electrochemical stability. To study the dynamical properties of this mixed-anion compound at the microscopic level, we have measured the 1H, 11B, and 23Na nuclear magnetic resonance spectra and nuclear spin-lattice relaxation rates over the temperature range of 8 – 573 K. Our 1H and 11B spin-lattice relaxation measurements have revealed two types of reorientational jump motion. The faster motional process attributed to reorientations of the [BH4]− anions is characterized by the activation energy of 159 meV, and the corresponding reorientational jump rate reaches ~10^8 s^-1 near 130 K. The slower process ascribed to reorientations of the larger [B12H12]− anions is characterized by the activation energy of 319 meV, and the corresponding reorientational jump rate reaches ~10^8 s^-1 near 240 K. The results of the 23Na nuclear magnetic resonance measurements are consistent with the fast long-range diffusion of Na+ ions in Na3(BH4)(B12H12). The diffusive jump rate of Na+ is found to reach ~10^4 s^-1 at 300 K and ~8 × 10^8 s^-1 at 530 K. Comparison of these jump rates with the ionic conductivity data suggests the importance of correlations between diffusing ions.

Sustainable soil quality management is one of the most important approaches in ensuring high agricultural productivity and safe quality produce. With the current rising application of inorganic fertilizers and chemical pesticides, degradation of agricultural soils is becoming a global phenomenon, attracting attention of many researchers. The present study hypothesized that with the increasing chemical pesticide and fertilizer application under various cropping systems, there is potential for increased toxicity of heavy metals residues above background concentrations and their associated complexes and intermedia transfer between environmental compartments in large-scale farms in Kenya. Using standard methods, 204 soil samples collected from five distinct cropping systems were analyzed for PH, Electrical Conductivity, exchangeable base cations (Na+, Mg2+, K+, Ca2+), Cu, Pb, Zn, Cd, Fe, Cr, Total Organic Carbon and Total N and data analyzed using Genstart, version 23.1. The concentrations of Zn, Cu, Pb and Fe were lower than the globally acceptable levels hence less soil pollution threat (NPI&lt;1). However, Cd concentration exceeded permitted levels (NPI&gt;1). Among the cropping systems considered, Maize and sugarcane cropping systems were the most susceptible to heavy metal pollution. In conclusion, the rank order of the pollution potential associated with heavy metal contamination in the study area is Cd&gt;Zn&gt;Pb&gt;Cu&gt;Cr&gt;Fe.

Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactor (MBR). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of membrane fouling. To solve this problem, this paper proposes a compressible diagnosis model (CDM) based on transfer entropy to facilitate the fault diagnosis of the root cause for membrane fouling. Firstly, a framework of CDM between membrane fouling and causal variables is built based on a feature extraction algorithm and mechanism analysis. The framework can identify fault transfer scenarios following the changes in operating conditions. Secondly, the fault transfer topology of CDM based on transfer entropy is constructed to describe the causal relationship between variables dynamically. Thirdly, an information compressible strategy is designed to simplify the fault transfer topology. This strategy can eliminate the repetitious affiliation relationship, which contributes to the root causal variables speedily and accurately. Finally, the effectiveness of the proposed CDM is verified by the measured data from an actual MBR.

Global growth and sustainability highly depend upon the output of industries, corporations, governments, and non-governmental organisations requiring comprehensive energy transition in the face of a worldwide crisis demanding carbon neutralisation. The carbon neutralisation challenges required for energy transition could be overcome by detailed surveying, recording and the analysis of the carbon dioxide emissions. It is crucial to study innovative materials like metal-organic frameworks for their transdisciplinary applications towards carbon capture to have a sustainable strategy to capture harmful emissions. In addition, the CO₂ solubility, adsorption and absorption capacity, and several reinforcing characteristics and factors for carbon dioxide capture using MOFs are discussed in detail. Further, for various multidisciplinary applications towards commercialisation, MOFs solubility, adsorption and absorption parameters were considered. Moreover, the essential organic qualities of MOFs and their organo-derivatives are considerably elaborated. Overall, to align with the current sustainable development goals, it is significant that carbon dioxide emissions must be reduced along with its capture in a scientific manner. Relevant projections for carbon capture through MOFs have been reviewed industrially and universally in alignment with the United Nations Sustainable Development Goals (UNSDGs).

In Brain-Computer Interface Motor Imagery (BCI-MI) systems, Convolutional Neural Networks (CNNs) have traditionally dominated as the deep learning method of choice, demonstrating significant advancements in state-of-the-art studies. Recently, Transformer models with attention mechanisms have emerged as a sophisticated technique, enhancing the capture of long-term dependencies and intricate feature relationships in BCI-MI. This research investigates the performance of EEG-TCNet and EEG-Conformer models, which are trained and validated using various hyperparameters and bandpass filters during preprocessing to assess improvements in model accuracy. Additionally, this study introduces the EEG-TCNTransformer, a novel model that integrates the convolutional architecture of EEG-TCNet with a series of self-attention blocks employing a multi-head structure. The EEG-TCNTransformer achieves an accuracy of 82.97% without the application of bandpass filtering. The source code of EEG-TCNTransformer is available on GitHub.

Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA) is a clinical entity characterized by the absence of significant coronary artery obstruction in epicardial arteries (&lt; 50%) on coronary angiography in the setting of acute myocardial infarction (AMI). This article aims to provide a narrative review of the pathophysiological mechanisms, diagnostic challenges, and prognosis associated with MINOCA focusing only on cases associated with myocardial ischemia but of non-atherosclerotic origin. Etiological factors, including thromboembolism, coronary artery spasm, spontaneous coronary artery dissection, coronary microvascular disease, and supply-demand mismatch are addressed. Imaging modalities such as echocardiography, advances in coronary angiography like intravascular ultrasound (IVUS) and optical coherence tomography (OCT), cardiac magnetic resonance (CMR), and coronary computed tomography angiography (CCTA) are also analyzed. MINOCA patients have a better short-term prognosis compared to those with obstructive coronary artery disease but face significant long-term risks, underscoring the need for precise diagnosis and management strategies. Elevated inflammatory markers and specific genetic predispositions are also associated with adverse outcomes in MINOCA. This review focused on MINOCA from the pathophysiological perspective on ischemic, non-atherosclerotic causes, the diverse underlying mechanisms, the challenges in achieving accurate diagnosis, the importance of a tailored therapeutic approach and the necessity for further investigation of clinical outcomes.

Catheter-associated urinary tract infections (CAUTI) are a recurring problem for individuals with neurogenic bladder who rely on frequent intermittent catheterization. Dysbiosis of the urine microbiome, characterized by the presence of pathogenic organisms and insufficient levels of Lactobacilli, is common in this population. We propose that introducing beneficial Lactobacilli via a catheter lubricant could act as a prophylactic "live barrier" to enhance beneficial bacteria in the urethra. The IC TF1 Catheter Lubricant gel discussed in this study is a freshly prepared viscous gel composed of sterile gelling components and a dry Lactobacillus Rhamnosus spp. powder. The gel prepared on- site by simple mixing, is usable for 24 hours with predictable and precise viability counts. Our investigation focuses on the design, physical attributes, manufacturability with quality control criteria, stability of probiotic viability, antimicrobial properties, cellular toxicity, and tissue compatibility of the IC TF1 gel. The gel demonstrates promising physical properties and biocompatibility, leading to the need for further pre-clinical and human trials to assess its safety and effectiveness in reducing pathogenic urethral colonization.The localized delivery of probiotic supplementation through catheter lubrication has the potential to enhance the well-being of individuals with neurogenic bladder and aid in the management of CAUTI.

Metal additive manufacturing (AM), commonly referred to as 3D printing with metals, is an innovative technology with significant potential to transform industries such as aerospace, automotive, and healthcare. Despite its promise, the field faces several challenges that impact both its technical feasibility and economic viability. This paper explores the primary technical challenges, including material properties and processing complexities, such as the need for precise control of thermal gradients and the mitigation of defects like porosity and residual stresses. Additionally, it addresses the economic barriers, including high initial equipment costs, the need for specialized knowledge, and the relatively slow build speeds compared to traditional manufacturing methods. By examining these challenges, this paper aims to provide a comprehensive overview of the current state of metal additive manufacturing and suggest potential pathways for overcoming these obstacles to facilitate wider adoption and technological advancement in the field.

Inherited cardiomyopathies are a diverse group of heart muscle diseases caused by genetic mutations that result in structural and functional abnormalities of the myocardium. Understanding genotype-phenotype correlations in these conditions is vital for personalized cardiac medicine, enabling targeted therapeutic strategies and predictive diagnostics. This review explores the major types of inherited cardiomyopathies—hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), and restrictive cardiomyopathy (RCM)—and provides detailed insights into how different genetic mutations manifest as clinical features. The integration of multi-omics approaches and advanced disease modeling techniques has enhanced our ability to dissect these correlations. The review also discusses the implications of these findings for personalized medicine, including tailored therapeutic strategies, predictive diagnostics, and future research directions.

Biological systems have a unique ability inherited through their genome. It allows them to build, operate, and manage a society of cells with complex organizational structures where autonomous components execute specific tasks and collaborate in groups to fulfill systemic goals with shared knowledge. The system receives information from various senses, makes sense of what is being observed, and acts using its experience, while the observations are still in progress. We use the General Theory of Information (GTI) to implement a digital genome, specifying the operational processes that design, deploy, operate, and manage a cloud-agnostic distributed application that is independent of IaaS and PaaS infrastructure, which provides the resources required to execute the software components. The digital genome specifies the functional and non-functional requirements that define the goals and best-practice policies to evolve the system using associative memory and event-driven interaction history to maintain stability and safety while achieving the system’s objectives. We demonstrate a structural machine, cognizing oracles, and knowledge structures derived from GTI used for designing, deploying, operating, and managing a distributed video streaming application with autopoietic self-regulation that maintains structural stability and communication among distributed components with shared knowledge while maintaining expected behaviors dictated by functional requirements.

This study reports rapid hydrolysis, acylation, and lipolysis with lipase B from Candida antarctica (CalB) and nanofructosome encapsulated CalB (CalB@NF) enzymes immobilized on silica-coated magnetic nanoparticles (Si-MNPs). Enzymatic hydrolysis was confirmed for conversion to p-nitrophenol (p-NP, λex=400 nm) from p-nitrophenyl butyrate (p-NPB, λex=270 nm), and various catalytic parameters such as Km, Vmax, and Kcat were calculated from Michaelis-Menten and Lineweaver-Burk plot. Efficiency of enzymatic hydrolysis was in the order of CalB > Si-MNPs@CalB > CalB@NF > Si-MNPs@CalB@NF, and reusability was much better with the enzyme immobilized on Si-MNPs than with the native enzyme. Enzymatic acylation was confirmed by the formation of benzyl benzoate (λex=229 nm) from benzoic anhydride (λex=240 nm), and efficiency exceeded 99% in all samples. Finally, enzymatic lipolysis was confirmed by the decomposition of olive oil to fatty acid such as oleic acid (λex=230 nm) using thin-layer chromatography (TLC), column chromatography, and proton nuclear magnetic resonance (1H -NMR). The lipases immobilized on Si-MNPs have the advantage of good thermal stability and reusability and are thus applicable to a wide range of industries.

Understanding the relationship between urban morphology and sustainability can inform strategies for facing the contemporary challenges of cities since urban fabrics embed information regarding the performance of different urban systems and their spatial heterogeneity. This paper assesses the variability of urban sustainability indicators for urban fabric typologies in Cuenca, Ecuador. We assessed 30 sustainability indicators for each cell in a hexagonal regular grid covering the study area and explored their spatial patterns and relationships with urban fabric typologies through spatial and statistical analysis. Results show that indicators vary significantly among urban fabrics, while built environment indicators dominate overall sustainability levels. A marked spatial heterogeneity is revealed by high sustainability for inner-core areas but much lower sustainability for fringe expansion areas – although transition areas are also identified. Spatial clusters of urban fabrics are found to be homogeneous in terms of sustainability only for urban fabrics with high/low sustainability levels, while heterogenous for fabrics in the mid-range levels. The quantitative evidence on the relation between urban morphology and sustainability unveils otherwise hidden patterns, which can support and enhance evidence-based decision-making for urban planning. Open-source data and tools developed allow full replication for further research in similar urban areas.

Background: Vaccine hesitancy, identified by the World Health Organization as a top ten global health threat, presents a significant barrier to public health efforts, exacerbated by the politicization of vaccines during the COVID-19 pandemic.1 Motivational interviewing (MI) has been shown to be effective in addressing difficult topics, including vaccine hesitancy, emphasizing the need for its integration into medical education.2 Objective: This study explores ways to enhance the medical student curriculum on MI, specifically targeting the management of conversations with vaccine-hesitant patients. These authors seek to evaluate the impact of different teaching approaches on the efficacy of MI training within clinical education. Methods: A blinded study design was implemented, comparing two groups of medical students trained using distinct MI teaching methods: Learning Through Experience (LTE) and Traditional Teaching (TT). The LTE group engaged with a Vaccine Hesitant Standardized Patient (VHSP) before and after an instructional module on the use of MI to combat vaccine hesitancy. In contrast, the TT group interacted with a control Standardized Patient (SP) before the same module and then with the VHSP. Any medical student above the age of 18 was eligible for this study. All students in a vaccine elective at RVUCOM on the Colorado and Utah campuses were enrolled, as well as student volunteers. We excluded any students who opted out of the study. The effectiveness of each method was assessed through confidence and performance scores derived from participant self-evaluations and standardized patient feedback, respectively. Results: Participants in the TT group demonstrated superior mastery of MI skills as taught in the didactic session based on statistically significant findings across more tested variables. The LTE group exhibited enhanced ability in practical applications with standardized patients based on statistically significant findings. Both groups showed increased confidence in MI techniques, with the LTE group reporting greater confidence in vaccine knowledge and MI abilities. That said, the LTE method did not drastically outperform the TT approach in overall efficacy. Conclusions: The study indicates that while traditional TT training effectively imparts MI skills, the LTE approach may offer benefits in applying these skills in clinical scenarios. These findings suggest the potential for integrating multiple teaching methods into the medical curriculum to better prepare students for engaging with vaccine-hesitant patients. Further research is needed to explore the long-term effects of these teaching strategies on student performance and patient outcomes.

We report on the role of synergistic polarization of Ba0.9Sr0.1TiO3 (BST) nanoparticles in sulfur cathodes to enhance the redox kinetics of polysulfides for high-capacity Li-S batteries. Ferroelectric nanoparticles are known to significantly improve the electrochemical performance of Li-S batteries due to their inherent self-polarization and high adsorption capacity towards polysulfides. X-ray diffraction spectra confirmed the tetragonal symmetry (c/a=1.0073), while Raman spectroscopic analysis validated the presence of tetragonal orientation Raman modes in BST-modified composites. Scanning electron microscope (SEM) images showed a homogeneous distribution of BST in the sulfur cathode system, with grain sizes ranging from 1 to 1.5 μm. Notably, the BST-coupled S50BST30CB10PVDF10 composite cathode achieved a capacity of approximately 820 mAh/g at 100 mA/g, maintaining stability over 100 cycles, demonstrating improved electrochemical performance. Two distinct plateaus between 2.3 V to 2.0 V and 2.0 V to 1.5 V further underscore the superior performance of BST ferroelectric nanoparticles in enhancing the redox kinetics of Li-S batteries. By leveraging the favourable affinity of polar substances towards polysulfides, we aimed to create a more stable reactive environment within the cathodic site, effectively trapping polysulfide intermediates through the synergistic polarization of BST nanoparticles. The synergistic polarization induced by the asymmetric crystal structure of ferroelectrics is anticipated to generate internal electric fields, enhancing chemisorption with heteropolar reactive. The observed high cyclic stability further validates the efficacy of these composite cathodes in mitigating the polysulfide shuttle effect, offering promising prospects for advancing Li-S battery technology.

Objective: Breast cancer (BC) is the most common cause of death among females. There are many prognostic systems, the most recent of which are the molecular subtypes. Recently, it was included that thyroid disease is a favorable factor for the patients with BC and that the thyroid hormones have an effect on the disease. Thyroid stimulating hormone (TSH), however, is thought to be unrelated to the cancer aggressiveness. In the current study we aimed to examine the status of the postoperative TSH levels, as well as the impact of the thyroid disease on the cancer aggressiveness. Methods: A retrospective comparative analysis of invasive BC patients was performed between 2017 and 2019 year. 111 patients were selected. They were divided into group A (n=62) and group B (n=49), based on the presence or absence of thyroid disease, respectively. The data was processed with SPSS version 25. Results: There is a significant difference between group A and group B concerning the molecular subtypes, ki-67, and estrogen receptor. The current study supports the fact that TSH is responsible for the aggressiveness of the invasive BC and not the thyroid hormones themselves. Moreover, based on the correlation analyses calcium levels preoperatively are linked to cancer aggressiveness and could be used as future prognostic factors. Conclusion: Indeed, the thyroid disease appears to have more favorable prognosis based on the molecular subtype frequency but more emphasis in future studies should be given to the TSH and its relation to the overall survival of the patients.

Cestodes are etiological agents of neglected diseases such as echinococcosis and cysticercosis, which are major public health problems. Antiparasitic treatment relies on a small number of approved drugs, which are often only partially effective, poorly tolerated and require prolonged administration. Thus, the discovery of novel potential treatments is critical. The Stevia genus (Asteraceae) includes species that are associated with medicinal uses and are considered a source of bioactive compounds. Here, we analyzed four South American Stevia species that have previously demonstrated antiprotozoal properties. The effect of dichloromethane extracts of these species was determined by motility assays on Mesocestoides vogae, a laboratory model of cestodes. Stevia alpina showed the highest anthelmintic potential followed by S. maimarensis and S. multiaristata, while S. aristata was the least active. The sesquiterpene lactones estafietin and eupatoriopicrin were purified from S. alpina and S. maimarensis, respectively. Estafietin showed cestocidal activity, inhibiting parasite viability in a dose-dependent manner, even from the first day of incubation. Consistent with the motility effects, the extract of S. alpina and estafietin induced major alterations on the morphology of the parasite. The results of this report show that Stevia species represent a source of new molecules with potential for the treatment of neglected tropical diseases caused by cestodes.

The existing PDC(Polycrystalline Diamond Compacts) -cone hybrid bit bearing adopts a unilateral support structure, which is prone to stress concentration in the journal area, resulting in fracture and wear failure of the bearing, thus reducing the service life of the hybrid bit. In this paper, a new type of double supported bearing hybrid bit is proposed. The static strength analysis of unilateral and bilateral support bearing structures is carried out by finite element simulation, and the stress and strain distribution of the two structures under the load of 20KN-100KN is obtained. Experimental devices for unilateral and bilateral support bearing structures are designed and manufactured to complete 50KN-100KN static pressure loading experiments. The results showed that the stress and strain of unilateral and bilateral support bearing increased linearly with the increase of load. Compared with unilateral bearing, when the load was 100KN, the maximum Mises stress of bilateral bearing decreased from 358.80MPa to 211.10MPa, with a decrease of 41.16%. The maximum contact stress decreased from 415.20MPa to 378.10MPa, decreased by 8.94%, and the maximum principal strain decreased from 11.01e-4 to 9.71e-4, decreased by 11.81%. The axial strain in the danger zone was reduced by 14.68% and 17.35%, respectively. It is found that the contact stress of the simulation data is highly correlated with the bearing life, and the service life of the bilateral bearing bit is increased by 8.94%. The simulation data and experimental results provide data support for the production of hybrid bits with bilateral bearing support.

The study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, the researchers developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a PCP method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

Segregating one space from another is a rudimentary act of architecture and the basic principle of this act is creating differentiation between outside and inside. Thresholds, in their expanded or summed form, also produce spaces. They establish the staging for threshold areas in conjunction with space-defining elements. Threshold spaces are typically complicated spatial formations that serve as transition points. They pose a challenge to the interacting human senses in terms of perception. In this research threshold areas have been studied under a specific typology of architecture i.e the traditional housing in historic cities of India. The traditional dwelling settlements have evolved and grown over multiple layers of history, influenced by the culture, society, economy, climate and topography. The purpose of this research is to examine the characteristics of these transition spaces of the traditional dwelling units in relation to the symbolic reference and social meanings assigned to them that influence the architectural and urban spaces attached to them. It seeks to describe transition space in terms of its nature, character, morphology, and social significance. Transparency and material articulation, as well as hierarchy and enclosure, will be investigated. The importance and quality of the nature of "transition" will be examined from the inside to the outside. The research reveals that the space has great functional and socio-cultural importance. It allows multiple activities, provides protection from sun and rain, defines boundaries, connects people within the neighborhood, enhances security and gives an identity to the owner. The contributions made by this research have the potential to improve design considerations for transition areas between the dwelling units and the street.

This study investigates the impact of vertical greenery systems (VGS) applied to heavyweight walls on indoor thermal conditions in a building module situated in the Mediterranean climate of Catania, Italy. Using dynamic simulations in TRNSYS, the research compares the thermal behavior of walls constructed from hollow clay blocks (Poroton) and lava stone blocks with a lightweight wall setup already in place at the University of Catania. The primary focus is on evaluating the VGS’s effect on reducing peak inner surface temperatures and moderating heat flux fluctuations entering the building. The findings indicate that adding a vertical greenery layer to heavyweight walls can decrease the peak inner surface temperature by up to 1.0°C compared to the same wall without vegetation. However, the greenery’s impact on mitigating heat flux fluctuations is less pronounced than in the case of the lightweight wall. This research underscores the potential of green facades in enhancing indoor thermal comfort in regions with climates similar to the Mediterranean, providing valuable insights for sustainable building design and urban planning.

Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. Spiking Neural Networks (SNNs) can utilize biological temporal coding features, such as noise-induced stochastic resonance and dynamical synapses to increase a model’s performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified Convolutional Neural Network analog, this paper demonstrates two key results: (1) SNNs solve the problem linearly inseparable in ANN with over 10% improvement in the optimal setting, and (2) in SNNs, the synaptic noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results.