Spherical or hexagonal NaYF4:Yb,Er nanoparticles (UCNPs) with sizes of 25 nm (S-UCNPs) and 120 nm (L-UCNPs) were synthesized by high-temperature coprecipitation and subsequently modified with three kinds of polymers. These included poly(ethylene glycol) (PEG) and poly(N,N-dimethylacrylamide-co-2-aminoethylacrylamide) [P(DMA-AEA)] terminated with an alendronate anchoring group, and poly(methyl vinyl ether-co-maleic acid) (PMVEMA). The internalization of nanoparticles by rat mesenchymal stem cells (rMSCs) and C6 cancer cells was visualized by electron microscopy and the cytotoxicity of the UCNPs and their leaches was measured by the real time proliferation assay. Comet assay was used to determine oxidative damage of UCNPs. In vivo study on mice determined the elimination route and potential accumulation of UCNPs in the body. The results showed that the L- and S-UCNPs were internalized into cells in the lumen of endosomes. Proliferation assay revealed that L-UCNPs were less toxic than S-UCNPs. The viability of rat mesenchymal stem cells (rMSCs) incubated with particles decreased in the order S-UCNP@Ale-(PDMA-AEA) > S-UCNP@Ale-PEG > S-UCNPs > S-UCNP@PMVEMA. Similar results were obtained in C6 cells. Oxidative damage measured by the Comet assay showed that neat L-UCNPs caused oxidative damage to rMSCs more than all coated UCNPs while no difference was observed in C6 cells. An in vivo study indicated that L-UCNPs were eliminated from the body via the hepatobiliary route; L-UCNP@Ale-PEG particles were almost eliminated from the liver 96 h after intravenous application. Pilot fluorescence imaging confirmed the limited in vivo detection capabilities of the nanoparticles.

Recent technical advances in the stem cell field have enabled the in vitro generation of complex structures resembling whole organs termed organoids. Most of these approaches employ culture systems that allow stem cell-derived or tissue progenitor cells to self-organize into three-dimensional (3D)-structures. Since organoids can be grown from various species, organs and from patient-derived induced pluripotent stem cells, they create significant prospects for modelling development and diseases, for toxicology and drug discovery studies, and in the field of regenerative medicine. Here, we report on intestinal stem cells, organoid culture, organoid disease modeling, transplantation, current and future uses of this exciting new insight model to veterinary medicine field.

As the world population grows, fulfilling the need for biomaterials in the healthcare system becomes increasingly important. Biomaterials, which include metals, polymers, ceramics, and composites, play an important role in the creation of medical devices that improve people's lives. The European Society for Biomaterials described biomaterials as non-biological materials used in medical devices to interact with biological systems. This definition has developed to encompass materials that treat, assess, or replace tissues or bodily functions. Biocompatibility, as defined by the FDA, is a key feature of biomaterials, as is the requirement to fulfil design requirements such as mechanical performance, geometry, and electrical control. Over the last decade, advances in regenerative medicine and tissue engineering have boosted biomaterials' usage in medical applications dramatically. Despite its promise, scientists, engineers, and physicians continue to face significant challenges due to the complexity of biomaterials. Biomaterials have various benefits, including biodegradability, non-toxicity, and biocompatibility, making them extremely useful for replacing, sustaining, or improving biological activities in damaged tissues. They are used in a wide range of applications, including tissue engineering implants, human tissue healing and regeneration, medication delivery systems, and biosensors. This study gives insights into the fundamental characteristics, case studies, and ethical issues associated with the usage and use of biomaterials, further examining their potential and significance in healthcare and medicine.

Resveratrol (RSV), a naturally occurring phytoalexin, is the most important stilbenoid synthesized by plants, as a defense mechanism, in response to microbial aggression, toxins or ultraviolet radiation. RSV came to the attention of researchers both as a potential chemopreventive agent and a possible explanation for the low incidence of cardiovascular disease (CVD) in French people with a high-fat diet. RSV is mainly administered as a food supplement, its properties being evaluated in vitro or in vivo, on various experimental models. RSV modulates signaling pathways that limit the spread of tumor cells, protects nerve cells from damage, is useful in the prevention of diabetes, and generally acts as an anti-aging natural compound. It was highlighted that RSV could ameliorate the consequences of an unhealthy lifestyle caused by an exaggerated caloric intake. This paper reviews the evidence supporting the beneficial effect of RSV for various pathological conditions, e.g., neoplastic diseases, neurodegeneration, metabolic syndrome, diabetes, obesity, CVDs, immune diseases, bacterial, viral, and fungal infections. The study also focused on the chromatographic analysis of trans-RSV (tRSV) in Romanian wine samples, providing a comprehensive overview of tRSV content across different types of wine.

The growing demand in industrial and biotechnological settings for more efficient enzymes with enhanced biochemical features, particularly thermostability and thermotolerance, necessitates a timely response. Renowned for their versatility, thermostable enzymes, offer significant promise across a range of applications, including agricultural, medicinal, and biotechnological domains. This comprehensive review summarizes the structural attributes, catalytic mechanisms, and the relationships between structure and function of two major classes of thermostable enzymes: α-amylases and laccases. These enzymes serve as valuable models for understanding the structural basis of protein thermostability. Commercial significance of these enzymes and researchers' interest in further optimization and innovation, this article can greatly contribute to ongoing research on thermostable enzymes and aiding industries in optimizing production processes. It also give insights to the exploration of suitable strategies and factors for enhancing thermostability, resulting in heightened multipronged stability and notable enhancements in the enzymes’ industrial applicability.

Background/Objectives: This study aimed to evaluate compliance and accuracy in comparison with traditional PD diaries for tracking motor symptoms using a new smartphone application (My Parkinson’s) and paper diary strategies. Parkinson's disease (PD) is a neurodegenerative disorder with progressive motor symptoms. Treatment becomes more challenging as PD progresses, motor complications in the form of wearing-off phenomenon and levodopa-induced dyskinesia develop. Traditional paper diaries and clinical scales used to evaluate patients may be inadequate in assessing whether the patient is “on” or “off,” resulting in less-than-ideal treatment changes. Methods: A randomized crossover design was utilized to examine 22 advanced PD patients who underwent symptomatic assessment with both diaries during two separate 24-hour periods seven days apart. The compliance and accuracy of data were assessed by comparing diary entries with the clinical examination notes and WhatsApp queries. Latterly, patients' diary preferences were also evaluated.; Results: The digital diary had significantly better compliance and accuracy than the paper diaries, with substantial/almost perfect levels of agreement (κ = 0·615 to 0·818) between logged symptoms and clinical examination notes. 65% of patients preferred the digital diary for follow-ups, and there was no significant difference in ease of use compared to paper diaries. Conclusions: Digital diaries are helpful in the clinical management of PD patients as they minimize recall bias and reduce data errors in appropriately selected patients. Our study suggests a broader adoption of digital health technologies in PD management. Still, additional research is necessary to improve the tools and assess long-term patient outcomes.

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This study encapsulates the in-depth analysis of different influential factors such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions that affect the processing, properties and determine the resultant applications of graphene hybrid polymer composites. Additionally, it discusses the substantial role of graphene collaboration with other fillers such as carbon nanotubes, silica, nano-clays and metal oxides to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties to offer solutions for heavy industries like aerospace, automotive, electronics, and energy harvesting. The review concludes with some suggestions and outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

This paper aims to review the challenges, toxicity, and routes of synthesis and usage of silver nanoparticles in different applications but also highlighting their sustainability from both medical and environmental issues. Regarding their toxicity, it is known that silver nanoparticles can destroy over 650 microorganisms comparing with antibiotics. Supplementary, will be presented in a comparative manner some conventional synthesis routes (physical and chemical methods) and green synthesis routes using plant extracts. The approach using plant extracts have various advantages comparing with physical, chemical and microbial synthesis methods because there is no need to use chemicals, wasteful purifications and high energy requirements. The paper presents an overview on “green nanotechnology” focused on using either biological micro-organisms or plant extracts as an alternative to the classical chemical and physical methods. An important issue discussed in the paper is an overview of the synthesis routes of silver nanoparticles, some expected applications of silver based active agents and their toxicity and challenges that must be overcome. Also, it needs to focus our attention on the dismissal of silver nanoparticles into the environment and especially in water systems, fact which suggests that this issue must be fully understood and applied the law.

This study investigates the cytotoxicity profile of superparamagnetic Fe3O4-Ag decorated nanoparticles against human fibroblasts (HFF-1) and breast cancer cells (MCF-7). The nanoparticles underwent comprehensive characterization employing scanning electron microscopy (SEM), X-ray diffraction (XRD) analysis, X-ray photoelectron spectroscopy (XPS), and magnetic assays including hysteresis curves and zero-field-cooled (ZFC) plots. Notably, the nanoparticles exhibited superparamagnetic behavior as evidenced by magnetic studies. Remarkably, cytotoxicity assays demonstrated that both HFF-1 and MCF-7 cells maintained nearly 100% viability upon nanoparticle exposure. These results underscore the outstanding biocompatibility of Fe3O4/Ag decorated nanoparticles, suggesting their potential utility in biomedical applications such as drug delivery and magnetic targeting without inducing significant cytotoxic effects. This investigation advances our understanding of the behavior of nanostructured materials in biological settings and highlights their promising prospects in biomedicine.

This study presents a comprehensive review of the development and progression of autonomous underwater vehicles (AUVs) in polar regions, aiming to synthesize past experiences and provide guidance for future advancements and applications. We extensively explore the history of notable polar AUV deployments worldwide, identifying and addressing the key technological challenges these vehicles face. These include advanced navigation techniques, strategic path planning, efficient obstacle avoidance, robust communication, stable energy supply, reliable launch and recovery, and thorough risk analysis. Furthermore, the study categorizes the typical capabilities and applications of AUVs in polar contexts, such as under-ice mapping and measurement, water sampling, ecological investigation, seafloor mapping, and surveillance networking. We also briefly highlight existing research gaps and potential future challenges in this evolving field.

Permafrost covers about 23% ~ 25% of the land in the northern hemisphere, mainly in Russia, Canada, Alaska, Northeast and Qinghai Tibet Plateau (QTP) in China. Many engineering especially the transportation has been in the permafrost regions above. Under the action of highways construction and global warming, permafrost under infrastructures is degraded and causes serious damage. Two phases closed thermosyphon (TPCT) is a widely accepted green countermeasure against the problem in permafrost regions. Although it has been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper focused on the thermosyphon technology and application in the permafrost. Moreover, the article highlighted the excellent working performance of the TPCT that improving the stability of the infrastructures and prevent it degrading due its excellent efficiency of heat transfer. The industrial applications of the TPCT were also summarized, along with their limitations. Finally, the results reported in this paper can provide a valuable guidance for the design of the TPCT and construction of the permafrost regions in the future.

Mobile applications, websites and social media networks are nowadays widely used communication tools. With the emergence of communication-related technologies in our lives and, consequently, the rise of social networks and mobile applications, health-related applications, generically encompassed under the term digital health, have become popular among the population. Smartphones and artificial intelligence have become very useful tools for health-related interventions. Because they are very accessible and cost-effective. They are also able to serve a larger number of communities than traditional interventions. Nutrition is not a field that has remained on the sidelines, and numerous mobile applications and technological tools have emerged that are intended to help and support diets or in the process of recovering from disease. However, many of these applications have limitations that are important to consider. For this reason, the aim of this review was to analyze the most widely used tools currently in use, discuss their advantages and disadvantages, and propose hypotheses for improvement.

The aim of this study was to investigate the physicochemical characterization and biological activities of chitosan (CH) based composite films with the incorporation of chestnut flower essential oil (CFEO). The composite films were prepared by casting method and characterized in terms of structural, morphological and mechanical properties via FT-IR, XRD, UV, SEM and TGA. The results proved the significantly increased water vapor permeability (WVP), heat resistance, antibacterial and antioxidant capabilities of CH-CFEO films. The incorporation of CH and CFEO enhanced the UV blocking, which made the film shield almost all UV light. Films with the tensile strength of 6.37 ± 0.41 MPa and elongation at break of 22.57 ± 0.35% were got with 6 mg mL-1 CFEO. Subsequently, the fruit preservation experiments also confirmed that the composite films could effectively extend the shelf life of bananas through their excellent water vapor and oxygen barrier. These desirable performances enable our newly developed composite films to be a remarkable packaging material to solve the fresh fruit preservation dilemma.

Metal nanoframes have gained tremendous attention in the domain of modern research and development due to their distinctive 3D spatial structure, efficient physiochemical properties, and comparatively good activity. Different strategies have been implicated by the researchers to design nano frame of varying chemical nature and shapes. Most of the synthetic protocols being adopted to design a nano framee, mainly consist of two main steps, nucleation and growth of solid particles and secondly excavation of the interiors. In this context, many synthetic methods are overviewed. To show their unprecedented performance or activity, a few applications in catalytic, biomedical, theranostic, SERS, sensing of different materials, reduction of CO2, etc are also discussed.

The medical & the dental field is a never ending field of innovations & developments and each time the reasearchers come up with something new. One such new dimension in the fields of medicine being the incorporation of Artificial intelligence assisted technologies improving diagnosis, treatmemt plan and treatment stategies. This review focusses on the application of different technologies of AI in different fields of medicine.

Chronic obstructive pulmonary disease (COPD) is a condition that significantly impacts both patients and healthcare systems. The management of COPD involves various pharmacological intervention strategies, and addressing the issue of low adherence to these strategies has become a subject of significant interest. In response to this concern, there has been a shift towards utilizing telemedicine and mobile applications. The primary objective of this scoping review is to delineate the usage of mobile applications to enhance pharmacological therapeutic adherence in adult patients with COPD. The study involved a search of databases such as Medline, Google Scholar, Cochrane, and ClinicalTrial.gov, focusing on literature published in English and Spanish over the last decade. The selected studies assessed interventions involving mobile applications (mobile apps) designed to improve pharmacological adherence. Four digital aids were identified, available on online platforms, mobile apps, or both: m-PAC, myCOPD, Wellinks mHealth, and Propeller Health. Pro-peller Health, in particular, is an app that directly measures pharmacological adherence through electronic medication monitors attached to participants' inhalers. Opening the app was associated with higher odds of using control medications compared to participants who did not open the app. The findings suggest that these digital interventions serve as valuable tools to enhance patient adherence to treatment. Future research should focus on evaluating the effectiveness of different digital devices, such as digital inhalers and mobile applications, that directly measure pharmacological adherence.

In the wake of COVID-19, there is an urgent need for a diverse public health work force to address problems presented or exacerbated by the global pandemic. Educational programs that create our work force both train and shape the makeup of access through graduate applications. The Graduate Record Exam has a number of standing issues, with additional barriers created by the pandemic. We trace the GRE waiver movement over several years, focusing on the gradual adoption in CEPH accredited programs and the rapid expansion of temporary waivers as a response to testing access. Going forward, we need to consider gaps in waivers during the pandemic and how this data can be used to shape our future use of the GRE.

The open data market is growing, driven by laws requiring governments to make data publicly available. However, the true value of this market lies in the products and services derived from open data. In Slovenia, the open data market has been growing since 2015, especially in the field of mobility. Platforms such as Avtolog.si facilitate the retrieval of vehicle data via vehicle identification numbers, while tocen.si, ojpp.si, and brezavta.si aggregate mobility data, focusing on sustainable and micromobile public transportation services. These platforms use open government data and private sector open data, and are freely accessible. This study examines the use of open data in the Slovenian mobility applications and similar solutions developed in other countries. By mapping these applications, we gain valuable insights into potential advances in the field, and provide a useful resource for future developers worldwide. By focusing on the socio-economic impact of these innovations, the study identifies methods for evaluating such applications. This approach highlights how transforming open data into user-friendly products and services can deepen our understanding of their broader societal and economic benefits. While open data is valuable in its own right, its full transformative potential is realized through innovative products that support sustainable lifestyles and promote environmentally conscious behavior.

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

Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

3D Printing /Additive manufacturing holds a promising future in healthcare as it aids in visualizing things and making 3D objects which is very important in different areas of medicine. The concept of one size fits all has failed the quality of care delivered by us in healthcare which demands customised needs for patient care to create 3D printed tissues, make personalized prosthetics, implants and physical models of the human body. The materials used for printing models and other healthcare devices for medical education, patient needs, and prototyping are increasing. The application of different materials for use in healthcare is not studied. New materials are also available for the various applications in healthcare industry. This experimental study was conducted to review the nine distinct 3D printing filament materials like PETG, PA, ABS, PLA, HIPS, PMMA, Ceramic, Carbon Fiber, and (PLA + Metal Composite) with the help of a 3D printed physical model of T-12 thoracic vertebrae using all the abovementioned materials. The evaluation of materials was based on diverse mechanical properties, applications across medical fields, performance metrics, costs, accuracy, and quality. The assessment This study also focuses on comparison of mechanical properties such as tensile strength, compressive strength of the abovementioned materials with human bone. The study also reviews the 3D printing process and explores its utilization in biomedical applications. The Insights about the application of various materials holds a promising future for development of newer materials for healthcare industry.

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. In the industrial sector, the greatest need is the decarbonisation of energy-intensive in-dustries, such as producers of iron and steel, aluminum, cement, petrochemicals, paper, and more. The steel industry, in particular, is a hard-to-abate high-emissivity industry that, due to chemical processes and high thermal demands (often above 500 °C), contributed to 7.4% of global CO2 emissions in 2021. This paper explores the potential applications of hydrogen in these hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts, especially as a me-dium to facilitate the transition to green electric energy uses. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing that while there is potential for its inclusion as an element for decarbonization, it is necessary to carefully evaluate the energy balances of the various processes and the methods of hydrogen production, in order to avoid achieving only marginal effects.

The escalating demand for multifunctional and advanced materials for growing industries has turned scientific exploration to graphene-based hybrid filler polymer composites. The introduction of graphene with other fillers into polymer matrices results in astonishing properties and thus has gained significant attention in recent years. This study addresses the significance of graphene-based hybrid filler polymer composites in progressive materials science and engineering. The paper encloses some traditional and current preparation techniques employed in the fabrication of composites and a comprehensive review of successfully prepared graphene hybrid polymer composites with enhanced mechanical, thermal, and electrical properties reported in published research. The role of different factors, e.g., filler type, aspect ratios, filler-matrix compatibility, dispersion effects, homogeneity, filler orientation through polymer matrices, and curing conditions, are summarized. Finally, the applications in heavy industries, e.g., aerospace, electronics, energy storage, and automotive sectors were also analyzed. We hope this study will serve as a practical resource for new researchers and practitioners to understand and harness the full potential of these composites in various scientific and industrial fields.

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring. In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform. Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall. Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods. By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.

Actuated carbon (AC) is utilized in various conditions of uses after its disclosure as a solid and dependable adsorbent. A review on AC is introduced along with returning to the wellsprings of AC age; strategies used to produce AC including pyrolysis enactment; actual actuation; synthetic initiation and steam pyrolysis. The significant variables influencing the AC creation, the potential uses of AC and their future possibilities are likewise examined. AC is applied in water, wastewater and leachate medicines in numerous nations, particularly to clean the shading, eliminate the smell and some substantial metals. Taking into account this, an exhaustive rundown of research on compound, physical and organic change strategies of initiated carbon relating to prevent of foreign substance expulsion from watery arrangements was aggregated and investigated. Additionally, the examination of the actual blending strategy and the impregnation technique in enactment with antacid metals shows that the actuated carbon got through actual blending had a higher porosity than the initiated carbon created by the impregnation technique. The uses of initiated carbon items were quickly surveyed.

Plant-based proteins, like those derived from hemp and rapeseed can significantly contribute to a balanced diet and human daily nutritional requirements by providing essential nutrients such as protein, fiber, vitamins, minerals, and antioxidants. According to numerous recent research papers the consumption of plant-based proteins has been associated with numerous health benefits, including reduced risk of chronic diseases such as heart disease, diabetes, and certain cancers. Plant-based diets are often lower in saturated fat and cholesterol and higher in fiber and phytonutrients, which can support overall health and well-being. Present research investigates the nutritional attributes, functional properties, and potential food applications of hemp and rapeseed protein for a potential use in new food product development, with a certain focus on identifying anti-nutritional factors and bioactive compounds. Through comprehensive analysis, anti-nutritional factors and bioactive compounds were elucidated, shedding light on their impact on protein quality and digestibility. The study also delves into the functional properties of hemp and rapeseed protein, unveiling their versatility in various food applications. Insights from this research contribute to a deeper understanding of the nutritional value and functional potential of hemp and rapeseed protein, paving the way for their further utilization in innovative food products with enhanced nutritional value and notable health benefits.

Tegucigalpa, the capital city of Honduras, has the highest number of landslides recorded in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. The present study focused on assessing the physical vulnerability of houses and the risk linked to landslides in these types of soils. As an input for this assessment, the study generated landslide risk vulnerability functions based on empirical data. The study's findings suggest that the estimated Annual Average Losses associated with landslides in this region is USD 7.26 million. Moreover, the study developed a novel approach for the spatial and temporal estimation of landslide hazard probability, which overcomes some of the limitations identified in previous landslide risk assessment studies.

Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants and soil data provide breeders with useful information about soybean plant health and using these different types of data to predict yield is an active area of research. Furthermore, breeding programs encounter challenges such as data imbalance and external factors like genotype variability across different environments, which present significant hurdles in the development of yield prediction models for large-scale breeding programs. In this work, we perform a comprehensive study of predicting yield using both hyperspectral reflectance and soil data to understand what scenario's offer the best chances of predicting yield with high accuracy. We demonstrate a cluster based ensemble approach for yield prediction using hyperspectral reflectance data that can perform well for large scale breeding programs by efficiently harnessing useful information from data through an unsupervised approach.

This study explores the pivotal biofilm life cycle, essential for comprehending intricate processes in biofilm formation and dispersal. With profound implications for medicine, environmental science, and industrial applications, the life cycle involves initial attachment, irreversible attachment, maturation, and dispersion. Employing bibliographic analysis, the paper unravels critical aspects, unveiling key keywords, prominent research countries/regions, and significant research organizations. This comprehensive approach provides insights into the current biofilm research landscape, identifying gaps for future exploration. Biofilm's influence extends to medical conditions, environmental ecosystems, and industrial challenges, accentuating the urgency of understanding its life cycle. The study also delves into global research distribution and influential organizations, guiding collaborative efforts. As a forward-looking initiative, it proposes future research opportunities, aiming to steer advancements and address challenges in biofilm science. Ultimately, this analysis contributes to the ongoing progress of biofilm research, fostering interdisciplinary collaboration and inspiring future initiatives.

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE₁ = 1.865, ISE₂ = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

Building small populations mortality tables has great practical importance in actuarial applications. In recent years, several works in literature explored different methodologies to quantify and assess longevity and mortality risk, especially within the context of small populations: many models dealing with this problem usually use a two-population approach, modelling a mortality spread between a larger reference population and the population of interest, via likelihood-based techniques. To broaden the tools at actuaries’ disposal to build small population mortality tables, a general structure for a two-step two-populations model is proposed, its main element of novelty residing in a machine-learning based approach to mortality spread estimation. In order to obtain this, Contrast Trees and related Estimation Contrast Boosting techniques have been applied. A quite general machine learning-based model has then been adapted in order to generalize Italian actuarial practice in company tables estimation and implemented using data from Human Mortality Database. Finally, results from the ML-based model have been compared to those obtained from the traditional model.

The invention of nanoscience not only brings a revolutionary change in the field of science but also changed the direction of research. Today the whole world is under the trigger of nano and nanoparticles have multidimensional applications in every aspect of life including environmental point of view. Till today a plethora of nanoparticles have been synthesized and have also been applied for multiple purposes and hence grabbed the attention of researchers all over the world. Among the bunch of NPs discovered to date, we have a particular interest in silver nanoparticles (AgNPs) because of their cost-effectiveness and huge abundance in the earth’s crust. With respect to every passing day, due to various kinds of anthropological activities, the quality of the environment such as air, water and soil is depleting which ultimately hampers the human civilization. To encompass the growing environmental issues, many techniques have been adopted. Among the many strategies, tackling the current issues regarding environment through nanoscience is highly worthy as because of its cost-effectiveness, less time consuming and easy handling process. This article reviews the potential of nanoparticles, particularly silver nanoparticles, for a wide range of environmental applications, such as soil, air, and water remediation.

In the dynamic realm of HPC (High-performance computing) and cloud-native applications, ensuring optimal network performance for Kubernetes Container Network Interfaces (CNIs) is critical. Traditional manual methods for evaluating network bandwidth and latency are prone to errors, time-consuming, and lack consistency. This paper introduces a novel approach that leverages Ansible to automate and coordinate network performance tests across diverse CNIs, performance profiles, and configurations. By automating these processes, we eliminate the potential for human errors, ensure the ability to replicate the process, and significantly reduce the time required for comprehensive testing. The use of Ansible playbooks facilitates the efficient and scalable deployment, configuration, and execution of performance tests, enabling comprehensive and standardized benchmarking across various environments. The set of Ansible playbooks, which we will make available online, has a significant real-world impact by providing DevOps teams with robust and reliable tools for consistently monitoring and enhancing network performance. This, in turn, enhances the stability and efficiency of cloud-native applications, accelerates the development cycle, and ensures that Kubernetes-based infrastructures can meet the demanding performance requirements of modern applications.

Color centers in diamond and nanodiamonds can be utilized as quantum sensors for measuring various physical parameters, particularly magnetic and electric fields, as well as temperature. Due to its small size and possible surface functionalization, fluorescent nanodiamonds are attractive systems for biological and medical applications since they can be used for intracellular experiments. This review focuses on fluorescent nanodiamonds for thermometry with nanoscale spatial resolution for living systems. The current state of the art, possible further development, and potential limitations will be discussed here.

Artificial intelligence (AI) is transforming many healthcare fields including pediatric urology. This in-depth review explores recent advances in AI tools for pediatric urology, emphasizing how state-of-the-art technologies can be used to enhance patient treatment. An initial overview of pediatric urology is given to provide context for this interdisciplinary field and the wide spectrum of problems this entails, before discussing potential uses of AI to improve diagnostic accuracy, treatment planning, and surgical results. AI-powered predictive models are already changing pediatric clinical practice by guiding interpretation of medical images and supporting decision making. In addition to outlining the aims, methods, and conclusions of previous studies on AI applications in pediatric urology, this review also highlights knowledge gaps and priority areas for future study. Particular focus is given to specialized surgeries such pyeloplasty, where AI-guided treatment may help overcome specific technical challenges of this complex procedure. Also highlighted are practical, ethical, and legal aspects of integrating AI into pediatric urology, which will be crucial for responsible innovation in patient-centered treatment. Overall, this article aims to enlighten and inspire clinicians, researchers, and other healthcare stakeholders by offering insight into the future of AI in pediatric urology.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. With reference to the industrial sector, the greatest need is the decarbonisation of industries energy-intensive, such as producers of iron and steel, aluminium, cement, petrochemicals, paper, etc. In particular, the steel industry is a hard to abate high-emissivity industry which due to chemical processes and high thermal demands (often above 500 °C) contributed to 7.4% of global CO₂ in the year 2021. This paper has explored the potential applications of hydrogen in the hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing how although there is potential for its inclusion as an element for the decarbonization, it is necessary to evaluate the energy balances of the various processes and the methods of hydrogen production very carefully, in order not to risk obtaining a marginal effect.

Due to its distinctive chemical and biological properties, pectin has recently drawn much attention in biomedical and tissue engineering applications. Polymers like pectin with cell-instructive properties are attractive natural biomaterials for tissue repair and regeneration. Besides, bioactive pectin and pectin-based composites exhibit improved characteristics to deliver active molecules. Pectin and pectin-based composites serve as interactive matrices or scaffolds by stimulating cell adhesion and cell proliferation and enhancing tissue remodeling by forming an extracellular matrix in vivo. Several bioactive properties, such as immunoregulatory, antibacterial, anti-inflammatory, anti-tumor, and antioxidant activities, contribute to the pectin's and pectin-based composite's enhanced applications in tissue engineering and drug delivery systems.This paper reviews the promising characteristics of pectin or pectic polysaccharides and highlights its potential applications in drug delivery and tissue engineering. Tissue engineering scaffolds containing pectin and pectin-based conjugates or composites demonstrate essential features such as non-toxicity, tunable mechanical properties, biodegradability, and suitable surface properties. The design and fabrication of pectic composites are versatile for tissue engineering and drug delivery applications.

Biochar (BC) is a carbonaceous material obtained by pyrolysis at 200−1000 °C in the limited presence of O2 of different vegetable and animal biomass feedstocks. BC has demonstrated great potential, mainly in environmental applications, due to its high sorption ability and persistent free radicals (PFRs) content. These characteristics enable BCs to carry out the direct and PFRs-mediated removal/degradation of environmental organic and inorganic contaminants. The types of PFRs possibly present in BC depend mainly on the pyrolysis temperature and the kind of pristine biomass. Since they also can cause ecological and human damage, a systematic evaluation of the environmental behavior, risks, or management techniques of BC-derived PFRs is urgent. PFRs generally could consist of a mixture of carbon- and oxygen-centered radicals, and of oxygenated carbon-centered radicals, depending on the pyrolytic conditions. Here, to promote a more productive and beneficial use of BC and related PFRs and stimulate further studies to make them environmentally safer and less hazardous to humans, we have first reviewed the most common methods used to produce BC, its main environmental applications, the primary mechanisms by which BC remove xenobiotics, as well as the reported mechanisms for PFRs formation in BCs. Secondly, we have discussed the PFRs environmental migration and transformation; we have reported the main PFRs-mediated applications of BCs to degrade inorganic and organic pollutants, the correlated environmental potential risks, and the possible strategies to limit them.

Antimicrobial peptides (AMPs) have emerged as a promising class of bioactive molecules with the potential to combat infections associated with medical implants and biomaterials. This review article aims to provide a comprehensive analysis of the role of antimicrobial peptides in medical implants and biomaterials, along with their diverse clinical applications. The incorporation of AMPs into various medical implants and biomaterials has shown immense potential in mitigating biofilm formation and preventing implant-related infections. We review the latest advancements in biomedical sciences and discuss the AMPs that were immobilized successfully to enhance their efficacy and stability within the implant environment. We also highlight successful examples of AMP coatings for the treatment of surgical site infections (SSIs), Contact lenses, Dental Applications, AMP-Incorporated Bone Grafts, Urinary tract infections (UTIs), Medical Implants, etc. Additionally, we discuss the potential challenges and prospects of AMPs in medical implants, such as effectiveness, instability and implant-related complications. We also discuss strategies that can be employed to overcome the limitations of AMP-coated biomaterials for prolonged longevity in clinical settings.

UHPC is a type of cement-based composite used in new construction and/or rehabilitation of existing buildings to extend service life. It is a novel composite material that can serve as an alternative to concrete construction in hostile climates. Following decades of study and production, a diverse variety of commercial UHPC compositions are now available globally to meet the rising number of applicants and demand for high-quality building materials. Although UHPC offers major benefits over normal concrete, its utilization is restricted due to restrictive design rules and exorbitant costs. As a result, a thorough examination of the durability properties of UHPC is required to give important information for material testing requirements and processes, as well as to broaden its practical uses. This report is aimed at increasing basic understanding of UHPC and supporting more UHPC research and applications.

This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: The introduction in Section 1 provides a brief overview of the field of machine vision pose measurement. Section 2 describes the commonly used algorithms for machine vision pose measurement. Section 3 discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Section 4 presents the applications of machine vision pose measurement in various fields. The paper provides specific examples of how machine vision pose measurement is used in each of these fields. Finally, Section 5 summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.

Heavy metals represent a class of chemical elements that includes metalloids, bases and transition metals, lanthanides and actinides. They are distinguished for their toxicity in small concentrations and for their negative effects on the environment and human health. They are present in various samples, mainly in water, industrial waste and food. The determination of heavy metals is carried out mainly by analytical methods, using spectroscopy, spectrometry and electroanalysis, while studies are being carried out for the development of new and faster methodologies and techniques for their analysis, with particular emphasis on voltammetry. This review deals with the determination of heavy metals mainly in environmental samples (water) using voltammetry. In particular, the methods of their determination are evaluated in terms of sensitivity, selectivity and applicability.

ChatGPT, a groundbreaking natural language processing technology released just three months ago, has attracted significant attention due to its remarkable capabilities. This AI milestone has urged researchers, industry, decision-makers, and governments to examine this technology, including its implications, threats, and benefits. Despite the short period since its release, several researchers have examined ChatGPT from different perspectives. This paper presents a comprehensive review of ChatGPT, highlighting its technical novelties compared to previous models and analyzing existing research from various perspectives. We followed a rigorous methodology to conduct a critical review of existing research on ChatGPT and developed a taxonomy for the different areas of study. Additionally, we identify future challenges and research trends associated with ChatGPT. Our paper is the first critical review of ChatGPT literature, providing valuable insights for practitioners and policymakers. This paper serves as a reference for researchers seeking to advance research on ChatGPT, including its applications and development.

Blockchain innovation stands out enough to be noticed and adopted in various countries and organizations around the world. Many businesses, including finance, medical services, inventory networks, security, libraries, and the internet of things, are currently under attack. For the benefit of the blockchain, many businesses incorporate blockchain technology into their frameworks. Despite its solidarity, blockchain faces a few challenges in security, protection, adaptability, and other areas. This paper examines the forward leap in blockchain innovation, as well as its applications and challenges. While many blockchain papers focus on digital currencies, IoT, and security, this paper focuses on the overall best in a class of blockchain innovation, its new twists and turns, and choices, particularly in areas other than cryptographic forms of money. The investigators' goal is to provide a thorough audit of the cryptography underlying blockchain to better understand the innovation. The examiners also conduct general research on people and venture blockchains, as well as future exploration opportunities and their implications for blockchain innovation.

The new IoT/IoE (internet of things/everythings) paradigm and architecture permits to rethink about the way the Smart City infrastructures are designed and managed, on the other hand a number of problems have to be solved. In terms of mobility the cities that embrace the sensoring era can take advantage of this disruptive technology to improve the quality of life of their citizen, also thanks the rationalization in the use of their resources. In Sii-Mobility, a national smart city project on mobility and transportation, a flexible platform has been designed and here, in this paper, is presented. It permits to setup heterogeneous and complex scenarios that integrate sensors/actuators as IoT/IoE in an overall scenario of Big Data, Machine Learning and Data Analytics. A detailed and complex case-study has been presented to validate the solution in the context of a system that dynamically reverse the traveling direction of a road segment, with all the safety conditions in place. This case study composes several building blocks of the IoT platform, which demonstrate that a flexible and dynamic set-up is possible, supporting off-grid, security, safety, cloud and mixed solutions.

In a software-defined radar (SDR) system, most of the signal processing usually implemented in hardware is implemented by software, thus allowing for higher flexibility and modularity compared to conventional radar systems. However, the majority of SDR demonstrators and proofs-of-concept reported in the open literature so far have been based on simple antenna systems. As a result, the full potentialities of an SDR approach have not been completely exploited yet. In this work, we propose a flexilbe antenna module to be integrated into an active electronically scanning array (AESA) with controlled sidelobe level over a wide angular range, exhibiting polarization reconfigurability with low cross-polarization level and high isolation. For this purpose, analytical and numerically efficient techniques for the synthesis of the aperture distribution and the correct evaluation of the radiating features (e.g., beamwidth, pointing angle, sidelobe levels, etc.) are presented in order to grant a real-time control of the digital beamforming network. A sub-array module demonstrator is fabricated and measured to corroborate the concept.

Recent developments in flexible printed heaters (FPH) for wearable thermal applications, driven by the advancement of printed electronics, show great promise in revolutionizing patient care through the development of wearable flexible heaters for medical applications. Wearable heaters with high thermal stability, heat uniformity, safety, flexibility, comfort, biocompatibility, and power efficiency are desirable for stand-alone medical thermotherapy applications. This paper reviews recent advancements in the design of FPHs for wearable thermal applications. Materials for FPHs, fabrication methods, designs, temperature control mechanisms, medical applications, and performance analysis of specific FPHs are all thoroughly discussed. Materials for FPHs, such as conductive and substrate materials, have received special attention, along with the heater design parameters. Finally, the challenges and future directions for developing FPHs for wearable medical applications are addressed.

Distributed tracing is a cutting-edge technology for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and direct storage and utilization of every trace are impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as simplifying application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of the sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Nonionizing millimeter-waves (MMW) are reported to interact with cells in a variety of ways. Possible mechanisms of the inhibited cell division effect were investigated using 85-105 GHz MMW irradiation within the ICNIRP (International Commission on Non-Ionizing Radiation Protection) non-thermal 20 mW/cm² safety standards. ~1.0 mW/cm² exposure over 5-6 hours treatment on 50 cells/μl samples of Saccharomyces cerevisiae model organism, resulted in 62% growth rate reduction compared to control (sham). The effect was specific for 85-105 GHz range and energy dose and cell density dependent. Irradiation of wild type and Δrad52 (DNA damage repair gene) deletion cells presented no differences of colony growth profiles indicating non-thermal MMW treatment does not cause genetic alterations. Dose versus response relations studied using a standard horn antenna (~1.0 mW/cm²) and compared to that of a compact waveguide (17.17 mW/cm²) for increased power delivery resulted in complete termination of cell division via non-thermal processes supported by temperature rise measurements. Combinations of MMW mediated Structure Resonant Energy Transfer (SRET), membrane modulations eliciting signaling effects, and energetic resonance with biomolecules were indicated to be responsible for the observations reported. Our results provide novel mechanistic insights enabling innovative applications of nonionizing radiation procedures for eliciting targeted biomedical outcomes.

Possibilities of utilization of two frequencies synthetic aperture radar (SAR) operating simultaneously in P-band and S or L-band are considered. Advantages of such system are shown. They are caused by improvement of data interpretation and by decrease of ionosphere influence. Tasks of surface and subsurface sensing by the complex SAR were under investigation for vegetation, soil covers are given. As an example, the investigation results of coniferous forest areas backscattering by the spaceborne P-band SAR together with L-band SAR in the September-February period are presented. Studies have shown that the joint use of the results in L and P bands offer unique opportunities to explore the effects of changes in the backscattering properties of forest surfaces.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.

Despite the recognized potential of nanoparticles, only a few formulations have progressed to clinical trials, and an even smaller number have been approved by the regulatory authorities and marketed. Virus-like particles (VLPs) have emerged as promising alternatives to most explored nanoparticles because of the absence of viral genetic material, their incapacity to replicate, mimicry of viral structure, and tropism conservation. Furthermore, VLPs can be surface functionalized with small molecules to improve circulation half-life and target specificity. Through the functionalization and coating of VLPs, it is possible to optimize the response properties to a given stimulus, such as heat, pH, an alternating magnetic field, or even enzymes. Surface functionalization can also modulate other properties, such as biocompatibility, stability, and specificity, deeming VLPs as potential vaccine candidates or delivery systems. In this review, we address the different types of functionalization of VLPs, their importance, and their consequent biomedical applications.

Micro/Nano Robot is an intelligent and efficient microrobot that can perform specific tasks under the influence of external stimuli. Depending on the application scenarios, the microrobot can adaptively transform into appropriate functional forms under different external stimuli, thus perfectly matching the needs. To date, microbots have been widely used in targeted therapy, drug delivery, tissue engineering, environmental remediation, and other fields. Although the applications of microrobots are promising, there are only a few reviews that can focus on the preparation methods and driving mechanisms. Therefore, it is necessary to outline the current status of the development of these microrobots in order to provide some new insights for the further development of the field. Therefore, this paper reviews the research progress of microrobots in terms of preparation methods, stimulus response mechanisms and applications, and highlights the applicability of different preparation methods and stimulus types. Finally, the current challenges faced by microrobots are highlighted and possible solutions are proposed to facilitate the practical application of microrobots.

Aquaculture assumes a pivotal role in meeting the escalating global food demand, and shrimp farming, in particular, holds indispensable significance for the global economy and food security, providing a rich source of nutrients for human consumption. Nonetheless, the industry faces formidable challenges, primarily attributed to disease outbreaks, and the diminishing efficacy of conventional disease management approaches, such as antibiotic usage. Consequently, there is an urgent imperative to explore alternative strategies to ensure the sustainability of the industry. In this context, the burgeoning field of epigenetics emerges as a promising avenue for combating infectious diseases in shrimp aquaculture. Epigenetic modulations entail chemical alterations in DNA and proteins, orchestrating gene expression patterns without modifying the underlying DNA sequence by utilizing DNA methylation, histone modifications, and non-coding RNA molecules. Exploiting epigenetic mechanisms presents an opportunity to enhance immune gene expression and bolster disease resistance in shrimp, thus revolutionizing disease management strategies and optimizing shrimp health and productivity. Additionally, the concept of epigenetic inheritability in marine animals holds immense potential for the future of the shrimp farming industry. To this end, this comprehensive review thoroughly explores the dynamics of epigenetic modulations in shrimp aquaculture, with a particular emphasis on its pivotal role in disease management. It accentuates the significance of harnessing advantageous epigenetic changes to ensure the long-term viability of shrimp farming while deliberating on the potential consequences of these interventions. Overall, this appraisal highlights the promising trajectory of epigenetic applications, propelling the field toward strengthening sustainability in shrimp aquaculture.

With the increase of the Artificial Neural Network (ANN), machine learning has taken a forceful twist in recent times. One of the most spectacular kinds of ANN design is the Convolutional Neural Network (CNN). The Convolutional Neural Network (CNN) is a technology that mixes artificial neural networks and up to date deep learning strategies. In deep learning, Convolutional Neural Network is at the center of spectacular advances. This artificial neural network has been applied to several image recognition tasks for decades and attracted the eye of the researchers of the many countries in recent years as the CNN has shown promising performances in several computer vision and machine learning tasks. This paper describes the underlying architecture and various applications of Convolutional Neural Network.

Natural polysaccharides, which are described in this study, are some of the most extensively used biopolymers in food, pharmaceutical and medical applications, because they are renewable and have a high level of biocompatibility and biodegradability. The fundamental understanding required to properly exploit polysaccharides potential in the biocomposite, nanoconjugate, and pharmaceutical industries is depended on detailed research of these molecules. Polysaccharides are preferred over other polymers because of their biocompatibility, bioactivity, homogeneity, and bioadhesive. Natural polysaccharides have also been discovered to have excellent rheological and biomucoadhesive properties, which may be used to design and create a variety of useful and cost-effective drug delivery systems. Polysaccharide-based composites derived from natural sources have been widely exploited due to their multifunctional properties, particularly in drug delivery systems and biomedical applications. These materials have achieved global attention and are in great demand because to their biochemical properties, which mimic both human and animal cells. Although synthetic polymers account for a substantial amount of organic chemistry, natural polymers play a vital role in a range of industries, including biomedical, pharmaceutical, and construction. As a consequence, the current study will provide information on natural polymers, their biological uses, food and pharmaceutical applications.Keywords: Polymers Biopolymers, Excipient, Natural source, Food and Pharmaceutical applications.

The COVID-19 is an exponentially growing disease that has intentioned nations to use technologies to detect the coronavirus infection. Several nations are working greatly to fight against COVID-19. Many nations have been using a range of devices to combat the pandemic, seeking information about growth, monitoring as well as the leaking the confidential information of the residents. This research aims to assist infected people online using the Internet of Things (IoT) and Blockchain technologies through smart devices. IoT-based healthcare devices gather useful information, provide additional insight through symptoms and behaviors, allow remote monitoring, and simply give people better self - determination and healthcare. Blockchain allows the secure transfer of patient health information, regulates the medical distribution network. A four-layer architecture is proposed using IoT and Blockchain to detect and prevent individuals to be COVID 19. This research provides a framework for patients with COVID-19 infectious disease and recognizes health issues and diagnoses online. Smart devices such as smartphones can install any mobile apps such as Aarogya Setu, Tawakkalna, and so on. These applications can track COVID-19 patients properly. The installation of mobile apps on smart devices focuses to reduce the time and cost and increase the performance of the infectious patient's condition. A four-layer architecture is proposed using IoT and Blockchain technologies. Many research works focus on investigating, analyzing, and highlighting the affected individuals through guiding the COVID-19 infection. Eventually, various mobile apps are recognized and addressed in this paper.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

Smart livestock farming leverages technology to boost production and meet food demand sustainably. This study delves into smart technologies in animal production, covering opportunities, challenges, and solutions. Smart agriculture employs modern technology to enhance efficiency, sustainability, and animal welfare in livestock farming. It includes remote monitoring, GPS-based animal care, robotic milking, smart health collars, predictive disease control, and other innovations to achieve these goals. While smart animal production holds great promise, it does face challenges related to cost, data management, and connectivity. To address these challenges, potential solutions include remote sensing, technology integration, and farmer education. Smart agriculture offers opportunities for increased efficiency, improved animal welfare, and enhanced environmental conservation. A well-planned approach is crucial to maximize the benefits of smart livestock production while ensuring its long-term sustainability. This study confirms the growing adoption of smart agriculture in livestock production, with the potential to support sustainable development goals and deliver benefits such as increased productivity and resource efficiency. To fully realize these benefits and ensure the sustainability of livestock farming, it is essential to address cost and education challenges. Therefore, this study recommends promoting a positive outlook among livestock stakeholders and embracing smart agriculture to enhance farm performance.

A common requirement of embedded software in charge of safety tasks is to guarantee the identification of those Random Hardware Failures (RHFs) that can affect digital components. RHFs are unavoidable. For this reason, functional safety standards, like the ISO 26262 devoted to automotive applications, require embedded software designs able to detect and eventually mitigate them. For this purpose, various software-based error detection techniques have been proposed over the years, focusing mainly on detecting Control Flow Errors. Many Control Flow Checking (CFC) algorithms have been proposed to accomplish this task. However, applying these approaches can be difficult because their respective literature gives little guidance on the their practical implementation in high-level programming languages, and they have to be implemented in low-level code, e.g., assembly. Moreover, the current trend in the automotive industry is to adopt the so-called Model-Based Software Design approach, where an executable algorithm model is automatically translated into C or C++ source code. This paper presents two novelties: firstly, the compliance of the experimental data on the capabilities of Control Flow Checking (CFC) algorithms with the ISO 26262 automotive functional safety standard; Secondly, by the implementation of the CFC algorithm in the application behavioral model is automatically translated. There is no need to modify the code generator. The assessment was performed using a novel fault injection environment targeting a RISC-V (RV32I) microcontroller.

This study presents a methodology to fabricate Ti6Al4V cylindrical compacts with a highly porous core and dense shell with the purpose to mimic the bone microstructure. Compacts with different core diameters were obtained by conventionally press and sintering. Large pores were created with the aid of pore formers. Sintering kinetics was determined by dilatometry, whereas characterization was performed by X-ray computed tomography. Also, permeability was evaluated on the 3D microstructure and the mechanical strength was evaluated by compression tests. Results indicated that sintering is constraint by the different densification rates of the porous and dense layers. Nonetheless, defectless compacts were obtained due to neck bonding between Ti6Al4V particles. Large pores were located in the designed core with similar pore size distribution. Permeability increased following a power law as a function of the pore volume fraction. Stiffness of bilayer components was driven by the porous core, meanwhile, the strength resulted from the combination of both layers. Bilayer materials obtained showed a permeability and mechanical properties, as well as admissible strain (σy /E) similar to those of human bones.

Today with enhancement in technology, sciences, there is also an increase in global heating rate. There is an urgent need of any alternate efficient source to reduce the wastage of energy and to utilize it efficiently. The advanced preparation of Expanded graphite ,lauric acid, stearic acid as shape stabilized phase change material deals with different energy harvesting applications. The main reason behind the need for synthesis of this matrix is to prepare a material that can be used in low temperature energy storage applications. Mixture of lauric acid , stearic acid impregnated in expanded graphite through vacuum impregnation followed by Vacuum Drying and Microwave acid treatment serves as novel shape stabilized phase change material of its type. The microwave acid treatment was done in order to increase the removal of moisture from the sample thus initiating proper bonding of its constituents. The mixture was produced in 1:1:1 ratio where all expanded graphite, lauric acid , stearic acid has one proportions of each other. The product obtained after microwave acid treatment was subjected to SEM, DSC analysis

Gold nanoparticles (AuNPs) are promising candidates in various biomedical applications such as sensors, imaging, and cancer therapy. Understanding the influence of AuNPs on lipid membranes is important to assure their safety in the biological environment and to improve their scope in nanomedicine. In this regard, the present study aims to analyze the effects of different concentrations (0.5, 1, and 2 w %) of dodecanethiol functionalized hydrophobic AuNPs on the structure and fluidity of zwitterionic 1-stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC) lipid bilayer membranes using Fourier transform infrared (FTIR) spectroscopy and fluorescent spectroscopy. The size of AuNPs was found to be 2.2 ± 1.1 nm using transmission electron microscopy. FTIR results have shown that the AuNPs induced a slight shift in methylene stretching bands, while the band positions of carbonyl and phosphate group stretching were unaffected. Temperature-dependent fluorescent anisotropy measurements have shown that the incorporation of AuNPs up to 2 w % did not affect the lipid order in membranes. Overall, these results indicate that the hydrophobic AuNPs in the studied concentration did not cause any significant alterations in the structure and membrane fluidity, which suggests the suitability of these particles to form liposome-AuNP hybrids for diverse biomedical applications including drug delivery and therapy.

In this work, an analog front-end (AFE) circuit for Electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter- all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the $g_m/I_D$ methodology. Both the pre-layout and post-layout simulation were carried out. The layout consumes an area of 0.0058 mm$^2$ without the resistors and capacitors. Analysis of various simulation results were carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz with a variable gain between 44 to 58 dB, a notch depth of -56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of -59.65 dB (less than 1\%), an input referred noise spectral density of $

Magnesium is a promising material. It has a remarkable mix of mechanical and biomedical properties that made it suitable for a vast range of applications. With alloying, many of these inherent properties can be further improved. Today, it is primarily used in the automotive, aerospace, and medical industry. However, magnesium has its own set of drawbacks which the industry and research community are actively addressing. Magnesium’s rapid corrosion is its most significant drawback, and it dramatically impeded magnesium’s growth and expansion into other applications. This article will review both the engineering and biomedical aspects and applications for magnesium and its alloys. It will also elaborate on the challenges the material faces, how they can be overcome, and its outlook.

Background:Boom in nanotechnology in current era has sketched unforeseen transformations in number of fields, such as medicine, health care, food, space, agriculture, etc. The synthesis of nanoparticles with different chemical compositions, sizes, shapes and controlled disparities is an important area of research in this field. Over the last decade, the biosynthesis of metal nanoparticles has received considerable attention due to their unusual and fascinating properties, with various applications, over their bulk counterparts.Hypothesis: The nanoparticle can have huge application in the field of food, pharmaceutical, and cosmetic industries and thus become a major area of research. Green synthesis of zinc oxide nanoparticles (ZnO NPs) using plant extracts offers an eco-friendly and promising substitute to the conventional methods of chemical synthesis. Conclusion: In the arena of nanoparticle phytosynthesis, novel materials have been produced that are eco-friendly, cost-effective and stable. In the current situation, nanotechnology inspires progress in all spheres of life, and therefore the phytosynthetic path of nanoparticle synthesis has emerged as a safe and best alternative to conventional methods. This review summarizes the recent work in the field of zinc nanoparticle phytosynthesis and critically discusses the mechanism proposed behind it.

Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.

The indole nucleus is an important element of many natural and synthetic molecules with significant biological activity. Nonetheless, the co-presence of transitional metals in organic scaffold may represent an important factor in the development of effective medicinal agents. This review covers some of the relevant and recent achievements in the biological, chemical and pharmacological activity of important indole-based metal complexes in the area of drug discovery.

Polymer hydrogels are 3D networks consisting of hydrophilic crosslinked macromolecular chains, able to swell and retain water. Since their invention in the 1960s, they have become an outstanding pillar in the design, development, and application of engineered polymer systems suitable for biomedical and pharmaceutical applications (such as drug or cell delivery, regeneration of hard and soft tissues, wound healing, and bleeding prevention, among others). Despite several well-established synthetic routes of polymer hydrogels based on batch polymerization techniques, about fifteen years ago academia started to look for alternative methods involving simpler reaction paths, shorter reaction times, and lower energy consumption. In this context, frontal polymerization (FP) undoubtedly has become an alternative and efficient reaction model that allows for converting monomers into polymers via a localized and propagating reaction, by exploiting the formation and propagation of a “hot” polymerization front, able to self-sustain and propagate throughout the monomeric mixture. Therefore, the present work aims to summarize the main research outcomes, achieved during the last years, concerning the design, preparation, and application of FP-derived polymeric hydrogels, demonstrating the feasibility of this technique for the obtainment of functional 3D networks, and providing the reader with some perspectives for the forthcoming years.

In this paper we exploit concepts from Information Theory to improve the classical Chvatal’s greedy algorithm for the Set Covering Problem. In particular, we develop a new greedy procedure, called Surprisal-Based Greedy Heuristic (SBH), incorporating the computation of a “surprisal” measure when selecting the solution columns. Computational experiments, performed on instances from the OR-Library, show that SBH yields a 2.5% improvement in terms of the objective function value over the Chvatal’s algorithm while retaining similar execution times, making it suitable for real-time applications. The new heuristic was also compared with Kordalewski’s greedy algorithm, obtaining similar solutions with much lower times on large instances, and Grossmann and Wool’s algorithm for unicost instances, where SBH obtained better solutions.

Background: Process mining (PM) exploits event logs to obtain meaningful information about the processes that produced them. As the number of applications developed on cloud infrastructures is increasing, it becomes important to study and discover their underlying processes. However, many current PM technologies face challenges in dealing with complex and large event logs from cloud applications, especially when they have little structure (e.g., clickstreams). Methods: Using Design Science Research, this paper introduces a new method, called Cloud Pattern API – Process Mining (CPA-PM), that enables discovering and analyzing cloud-based application processes using PM in a way that addresses many of these challenges. CPA-PM exploits a new application programming interface (API), with an R implementation, for creating repeatable scripts that preprocess event logs collected from such applications. Results: Applying CPA-PM to a case with real and evolving event logs related to the trial process of a Software-as-a-Service cloud application led to useful analyses and insights, with reusable scripts. Conclusion: CPA-PM helps producing executable scripts for filtering event logs from clickstream and cloud-based applications, where the scripts can be used in pipelines while minimizing the need for error-prone and time-consuming manual filtering.

In this paper, a prototype of an automatic lab on PCB for agarose preparation and electrophoresis is developed. The dimensions of the device are 38×34 mm2 and includes a conductivity sensor for detecting the TAE buffer (Tris-Acetate-EDTA buffer), a microheater for mixing, a NTC thermistor for controlling the temperature, a LDR sensor for measuring the transparency of the mixture, and two electrodes for performing the electrophoresis. The agarose preparation functions are governed by a microcontroller. The device requires a PMMA structure to define the wells of the agarose gel, and to release the electrodes from the agarose. The maximum voltage and current that the system requires are 40 V to perform the electrophoresis, and 1 A for activating the microheater. The chosen temperature for mixing is 80ºC, with a mixing time of 10 min. In addition, the curing time is about 30 min. This device is intended to be integrated as a part of a larger lab on PCB system for DNA amplification and detection. However, it can be used to migrate DNA amplified in conventional thermocyclers. Moreover, the device can be modified for preparing larger agarose gels and performing electrophoresis in an automatic manner.

An image consisting of (m, n) pixels can be seen as a matrix of m × n size. Based on the formula of angles between two subspaces, a pair of angles can be defined between two matrices, by utilizing column spaces and row spaces of the two matrices. The singular values of each matrix can be used to calculate distances. Thus, a distance representation is obtained, and a pair of angles between 2 matrices of the same size.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

ABSTRACT Surfactants are a broad category of tensio-active biomolecules with multifunctional properties applications in diverse industrial sectors and processes. Surfactants are produced synthetically and biologically. The biologically derived surfactants (biosurfactants) are produced from microorganisms with Pseudomonas aeruginosa, Bacillus subtilis Candida albicans and Acinetobacter calcoaceticus as dominant species. Rhamnolipids, sophorolipids, mannosylerithritol lipids, surfactin, and emulsan are well known in terms of their biotechnological applications. Biosurfactants can compete with the synthetic surfactants in terms of performance with established advantages over the synthetic ones including eco-friendliness, biodegradability, low toxicity, and stability over a wide variability of environmental factors. However, at present, the synthetic surfactants are a preferred option in different industrial applications, because of their availability in commercial quantities, unlike the biosurfactants. Usage of synthetic surfactants introduce new species of recalcitrant pollutants to the environment and lead to undesired results where a wrong selection of surfactants is made. Substituting synthetic surfactants with biosurfactants resolves these drawbacks, thus, interest has been intensified in biosurfactant applications in a wide range of industries hitherto considered as experimental fields. This review, therefore, intends to offer an overview of diverse applications where biosurfactants have found useful, with emphases in petroleum biotechnology, environmental remediation and in the agriculture sector. Application of biosurfactant in these settings would lead to industrial growth and environmental sustainability.

The current concerns of both society and materials industries about the environmental impact of thermoset composites, as well as new legislation, have led the scientific sector to search for more sustainable alternatives to reduce the environmental impact of thermoset composites. Until now, to a large extent, sustainable reinforcements have been used to manufacture more sustainable composites and thus contribute to the reduction of pollutants. However, in recent years, new alternatives have been developed such as thermosetting resins with bio-based content and/or systems such as recyclable amines and vitrimers that enable recycling/reuse. Throughout this review, some new bio-based thermoset systems as well as new recyclable systems and sustainable reinforcements are described and a brief overview of the biocomposites market and its impact is shown.

Among other applications, radar-rainfall (RR) and QPE (Quantitative Precipitation Estimation) based on radar reflectivity, dual polarization variables, and multi-sensor information, provide important information for land surface hydrology, such as flood forecasting. Therefore, we developed a flood alert system using rainfall-runoff model forced with RR and QPE, and tipping-bucket observations to forecast river water levels (using rating-curves). In this study, we used an hourly dataset from an S-Band dual-polarimetric radar with two tropical R(Z) relations based distrometer data, a polarimetric R(Z,ZDR) algorithm from the literature and a multi-sensor approach using radar, satellite and rain gauge. Two hydrological models were used and calibrated using observed discharge time-series. Although our previous studies indicated accurate RR-based simulations, in some cases floods were not detected when using catchment-lumped rainfall derived from multi-sensor QPE. In this study, we advance further in this subject using improved R(Z,ZDR) relations and QPE for the period of 2016-2017 and flood event-based rainfall-runoff calibration. Thus, we focused on the development (and timing) of floods in the Marrecas River can be complex and strongly related to storms spatiotemporal distribution. To explore this aspect, we also perform a first analysis in using RR in rainfall-runoff model with a nested catchment discretization.

In the dynamic landscape of 6G and smart cities, Visible Light Communication (VLC) assumes critical significance for IoT (Internet of Things) applications spanning diverse sectors. The escalating demand for bandwidth and data underscores the need for innovative solutions, positioning VLC as a complementary technology within the electromagnetic spectrum. This paper focuses on the relevance of VLC in the 6G paradigm, shedding light on its applicability across smart cities and industries. The paper highlights the growing efficiency of lighting LEDs in infrastructure, facilitating the seamless integration of VLC. The study then emphasizes VLC’s robustness in outdoor settings, demonstrating effective communication up to 10 meters. This resilience positions VLC as a key player in addressing the very last meter of wireless communication, offering a seamless solution for IoT connectivity. By introducing an a freely available open-source simulator combined to an alternative waveform, UFMC, the study empowers researchers to dimension applications effectively, showcasing VLC’s potential to improve wireless communication in the evolving landscape of 6G and smart cities.

The suspension cross-linking technique has been utilized to create the magnetic chitosan nanoparticles, which were then applied to the magnetic carrier method at 80°C. First the FeCl2 and FeCl3 solution co-precipitated and synthesized Fe3O4 for utilization in the formation of magnetic chitosan nanoparticles, it has been characterized by utilizing DLS (Dynamic light scattering), XRD (X-ray diffraction spectrometer), FTIR (Fourier transform infrared spectroscopy), which identified structure, size, FE-SEM (Field Emission Scanning Electron Microscope) and HR-TEM (High-resolution transmission electron microscopy) which identified structure and particle size whereas magnetic behavior of chitosan nanoparticles was determined by VSM (Vibrating sample magnetometer). According to the results collected, the magnetic chitosan nanoparticles have been spherical in form and size ranging from around 250 to 400 nm.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

Among earth’s microbial populations are the genes, functional capacities, generational memory and sentient cognition that enable microorganisms to adapt and thrive in the full range of ecological conditions found on planet earth. In prior sequential publications, we considered fundamental properties (e.g., use of and responses to sound, light, vibrations) and features of microorganisms that cause them to be critical co-partners of human and other holobionts as well as the potential benefits for humanity that can be gained by fully applying quantum-related and novel capacities of microbial life. In this narrative review we: 1) discuss key concepts concerning a microbially-enhanced and sustainable future, and 2) focus on electric- and magnetic- based features of microorganisms that make them pivotal for myriad benefits ranging from beyond pharma-based health and wellness to free- and/or renewable energy and restorative agriculture to improved human networking. While the benefits are many, the risks posed by hazardous electric or magnetic field exposures to both holobionts and microbial communities are significant. This review concludes that microbes and their remarkable capacities offer both humanity and the planet a much brighter future if we reverse the demonization of microbes and wanton microbiome degradation that has predominated much of the past century.

Graphene is a remarkable material with numerous applications. Due to its thin and lightweight design, it is ideal for a variety of applications. The synthesis of high-quality graphene in a cost-effective and environmentally friendly manner continues to be a significant challenge. Chemical reduction is considered to be the most advantageous method for preparing reduced graphene oxide (rGO). However, this process necessitates the use of toxic and harmful substances, which can have a detrimental effect on the environment and human health. Thus, to accomplish the objective, the green synthesis principle has prompted researchers worldwide to develop a simple method for green reduction of graphene oxide (GO), which is readily accessible, sustainable, economical, renewable, and environmentally friendly in nature. For example, the use of natural materials such as plants is generally considered safe. Furthermore, plants contain reducing and capping agents. The current review will focus on the discovery and application of rGO synthesis using extracts from a variety of different parts of the plant. The review aims to aid current and future researchers in their search for a novel plant extract that acts as a reductant in the green synthesis of rGO. The review aims to assist current and future researchers in their research for a novel plant extract that acts as a reductant in the green synthesis of rGO as well as their potential applications in a variety of industries.

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out Of Distribution (OOD) recognition. The term "Out of Model Scope" (OMS) detection provides a clearer perspective, although the original problem remains unsolved. The ability to detect OMS inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting OMS situations, we not only enhance the system's robustness but also prevent it from operating in unknown and unsafe scenarios. In this paper, we introduce a novel approach for OMS detection that combines three sources of information: 1) the original input, 2) the latent feature space extracted by an encoder, and 3) the synthetic data generated from the encoder's latent feature space. We demonstrate the effectiveness of combining original images and synthetically generated images against adversary attacks in the computer vision domain. We achieve results comparable to other state-of-the-art methods and demonstrate that any Encoder can be integrated into our pipeline in a plug-and-train fashion. Our experiments further evaluate which combination of the Encoder's features works best in order to discover OMS samples and highlight the importance of a compact feature space for the training of the Generator.

Enzymes are biological molecules produced by living entities for carrying out biological processes. The application of enzymes for waste treatment has been gaining pace commercially to solve concerns related to agricultural residues, wastewater, replacement of synthetic processes with natural ones, etc. The application of enzymes for waste management has been an environmentally reliable and sustainable process. Treatment of waste with enzymes such as xylanase, proteases, hydrolases, cellulose, peroxidases, chitinases, laccases, etc. has been studied to be effective. These enzymes act upon the waste products and transform them into biodegradable forms that can be recycled, reused and converted to value-added products. They have wide applications and utility as it has been an effective approach, economically cheaper and sustainable techniques. Application of such enzymes for waste management would be beneficial for reducing the quantity of waste, diminishing the negative effects of waste and pollution on the environment, and would be beneficial in bio-converting the waste products into alternate sources of energy. The current chapter focuses on different types of enzymes, their applications for waste management, and their limitations. This chapter also emphasizes the usage of some prominent microorganisms, their secreted enzymes and their proposed mechanisms of action involved with the degradation of the waste products.

The application of three-dimensional (3D) printing/bioprinting technologies and cell therapies has garnered significant attention due to their potential in the field of regenerative medicine. This paper aims to provide a comprehensive overview of 3D printing/bioprinting technology and cell therapies, highlighting their results in diverse medical applications, while also discussing the capabilities and limitations of their combined use. The synergistic combination of 3D printing and cellular therapies has been recognised as a promising and innovative approach, and it is expected that these technologies will progressively assume a crucial role in the treatment of various diseases and conditions in the foreseeable future. This review concludes with a forward-looking perspective on the future impact of these technologies, highlighting their potential to revolutionize regenerative medicine through enhanced tissue repair and organ replacement strategies.

Surfactants are amphiphilic molecules and one of the most versatile products of the chemical industry. They can be absorbed at the air-water interface and can align themselves so that the hydrophobic part is in the air while the hydrophilic part is in water. This alignment lowers the surface or interfacial tension. Gemini surfactants are a modern variety of surfactants with unique properties and a very wide range of potential applications. Hexamethylene-1,6-bis(N-dodecyl-N,N-dimethylammonium bromide) is one such representative compound that is a better alternative to a single analogue. It shows excellent surface, antimicrobial, and anticorrosion properties. With a highly efficient synthetic method and a good ecological profile, it is a potential candidate for numerous applications, including biomedical applications.

This study aimed at assessing the extent to which the Saudi general population has embraced digital health medical applications to meet their health-related needs so that the Saudi Ministry of Health and government can appropriately be guided on scaling up of digital health across the country. As such, this study was guided by the question of to what extent do the Saudi people use digital health mobile-based applications?. This was a cross-sectional study utilizing snowballing sampling approach. Frequencies, Chi-square, and Spearman rank correlation statistics were used to offer descriptive and inferential analysis of the variables. The majority of the participants were economically able to afford smart devices that have medical apps, had at least an app on such devices, and highly regarded the benefits of the apps. Unfortunately, their understanding of how to use such apps was limited and this posed a barrier to embracing digital health alongside difficulty downloading apps, and medical ethical concerns. Although there is a willingness, extra effort is needed from the Saudi Ministry of Health and government to promote the uptake of digital health in Saudi Arabia.

This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on the information security and smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies, and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smarty city areas and makes it possible to assist new research that may contribute to further advances. The smart city topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart city research productivity to assist researchers in better understanding the current emerging of the area.

Respiratory allergic diseases, affect over 500 million people globally and pose a substantial burden in terms of morbidity, mortality, and healthcare costs. Restrictive factors such as geographical disparities, infectious pandemics, limitations in resource, and shortage of allergy specialists in underserved areas, impede effective management. Moreover, the well-established suboptimal adherence in patients with respiratory allergies, contribute further to the accelerated loss of disease control. Telemedicine encompasses real-time visits, store-and-forward options triage, and computer-based technologies for establishing and efficient doctor-patient communication. Recent advances in digital technology, including designated applications, informative materials, digital examination devices, wearables, digital inhalers, and integrated platforms, facilitate personalized and evidence-based care delivery. The integration of telemonitoring in respiratory allergy care has shown beneficial effects in disease control, adherence, and quality of life. While the COVID-19 pandemic accelerated the adoption of telemedicine, certain concerns regarding technical requirements, platform quality, safety, reimbursement, and regulatory considerations remain unresolved. Challenges in telemonitoring implementation further include ensuring effective doctor-patient collaboration, addressing privacy concerns, and establishing cost-effective models. The integration of artificial intelligence (AI) in telemonitoring applications holds promise for data analysis, pattern recognition, and personalized treatment plans. Striking the balance between AI-enabled insights and human expertise is crucial for optimizing the benefits of telemonitoring. This overview reports the up- to- date telemedicine approaches in managing patients with respiratory allergies. While telemonitoring exhibits potential in enhancing patient care and healthcare delivery, critical considerations have to be addressed in order to ensure the successful integration of telemonitoring into the healthcare landscape.

This study investigated the potential of electrospun collagen/Hyaluronic Acid nanofibers in biomedicine and skincare. Needleless electrospinning technology employed in the production process resulted in nanofibers with consistent fiber diameters and improved mechanical properties. The composite nanofibers demonstrated impressive tensile strength and elongation capabilities, indicating a favorable interaction between collagen and HA. Cellular assays revealed that the collagen/HA scaffolds facilitated superior cell adhesion and proliferation compared to the control scaffolds, indicating their biocompatibility and potential for tissue engineering. This biomaterial offers an environment-friendly alternative to conventional skincare products by eliminating the need for preservatives and reducing waste. Although the initial costs are high, the long-term benefits of material durability and active ingredient efficacy provide a compelling case for industrial adoption. Our findings suggest that collagen/HA nanofibers have the potential to be next-generation materials for regenerative medicine and skincare, and further research is needed to optimize their production and assess the economic feasibility of skincare and tissue engineering applications.

Device-to-device (D2D) communication is a promising paradigm for the fifth generation 2 (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, 3 including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot 4 of technical challenges such as network architecture, and neighbor discovery, etc. The complexity 5 of configuring D2D links and managing their interference, especially when using millimeter-wave 6 (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address 7 these problems towards boosting the performance of D2D networks. In this paper, a comprehensive 8 survey about recent research activities on D2D networks will be explored with putting more 9 emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions 10 accompanied with their existing conventional solutions, we will show how different ML techniques 11 can be applied to enhance the D2D networks performance over using conventional ways. Then, still 12 open research directions in ML applications on D2D networks will be investigated including their 13 essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool 14 to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks 15 will be presented. This case study will put emphasis on the high potency of using ML solutions 16 over using the conventional non-ML based methods for highly improving the average throughput 17 performance of mmWave NDS.

The explosion of data has transformed the world since much more information is available for collection and analysis than ever before. To extract valuable information from the data in different dimensions, various deep learning models have been developed in the past years. Although these models have demonstrated their strong capability on improving products and services in various applications, training them is still a time-consuming and resource-intensive process. Presently, cloud, one of the most powerful computing infrastructures, has been used for the training. However, how to manage cloud computing resources and to perform the training efficiently is still challenging current techniques. For example, general resource scheduling approaches, such as spread priority and balanced resource schedulers, actually do not work well with deep learning workloads. Besides, the resource allocation problem on a cluster can be divide into two subproblems: (1) local resource optimization: improve resource configuration for a single machine; (2) global resource optimization: improve the cluster-wide resource allocation. In this thesis, we propose two novel container schedulers, FlowCon and SpeCon, that are designed to address these two subproblems respectively and specifically to optimize performance of short-lived deep learning applications in the cloud. FlowCon focuses on resource configuration of single-node in a cluster, as show that it efficiently improves deep learning tasks completion time and resource utilization, and reduces the completion time of a specific job by up to 42.06\% without sacrificing the overall system time. SpeCon targets on cluster-wide resource configuration that speculatively migrate slow-growing models to release resources for fast-growing ones. Based on our experiments, SpeCon improves makespan for up to 24.7\%, compared to current approaches.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Chiroptical responses have been an essential tool over the last decades for chemical structural elucidation due to their exceptional sensitivity to geometry and intermolecular interactions. In recent times, there has been an increasing interest for the search of more efficient sensing by the rational design of tailored chiroptical systems. In this Review article, advances on chiroptical systems towards their implementation in sensing applications are summarized. Strategies to generate chiroptical responses are illustrated. Theoretical approaches to assist in the design of these systems are discussed. Development of efficient chiroptical reporters in different states of matter, essential for the implementation in sensing devises, is reviewed. In the last part, remarkable examples of chiroptical sensing applications are highlighted.

Metal nanoparticles are extensively studied due to their unique chemical and physical properties, which differ from the properties of their respective bulk materials. Likewise, the properties of heterogeneous bimetallic structures are far more attractive than those of single-component nanoparticles. The incorporation of a second metal into the nanoparticle structure alters the surface plasmon resonance and magnetic properties of the bimetallic composite. This review focuses on the enhanced optical and magnetic properties offered by bimetallic nanoparticles and their corresponding impact on biological applications. We summarize the predominant structures of bimetallic nanoparticles, outline their synthesis methods, and highlight their use in biological applications, both diagnostic and therapeutic, which are dictated by their various optical and magnetic properties.

We propose a quantum version of the well known minimum distance classification model called "Nearest Mean Classifier" (NMC). In this regard, we presented our first results in two previous works. In [34] a quantum counterpart of the NMC for two-dimensional problems was introduced, named "Quantum Nearest Mean Classifier" (QNMC), together with a possible generalization to arbitrary dimensions. In [33] we studied the n-dimensional problem into detail and we showed a new encoding for arbitrary n-feature vectors into density operators. In the present paper, another promising encoding of n-dimensional patterns into density operators is considered, suggested by recent debates on quantum machine learning. Further, we observe a significant property concerning the non-invariance by feature rescaling of our quantum classifier. This fact, which represents a meaningful difference between the NMC and the respective quantum version, allows to introduce a free parameter whose variation provides, in some cases, better classification results for the QNMC. The experimental section is devoted to: i) compare the NMC and QNMC performance on different datasets; ii) study the effects of the non-invariance under uniform rescaling for the QNMC.

The advent of graphene opens up the research into two-dimensional (2D) material, which is considered as a revolutionary material in the future. Due to its unique geometric structure, graphene exhibits a series of exotic physical and chemical properties. Besides, single-element-based 2D materials (Xenes) have garnered tremendous interest. At present, 16 kinds of Xenes (silicene, borophene, germanene, phosphorene, tellurene, etc.) have been explored, mainly distributed in the third, fourth, fifth and sixth main groups. The current methods to prepare monolayer or few-layer 2D materials include epitaxy growth, mechanical exfoliation, and liquid phase exfoliation. Although two Xenes (aluminene and indiene) have not been synthesized due to the limitations of synthetic methods or stability of Xenes, other Xenes have been successfully realized by elaborately artificial design and synthesis. Focusing on elemental 2D materials, this review mainly summarizes the recently reported work about tuning the electronic, optical, mechanical, or chemical properties of Xenes via surface modifications achieved by controllable approaches (doping, adsorption, strain, intercalation, phase transition, etc.) to broaden the applications in various fields, including spintronics, electronics, optoelectronics, superconducting, photovoltaics, sensors, catalysis, and biomedicines. These advances in surface modification of Xenes have laid a theoretical and experimental foundation for the development of 2D materials and their practical applications in diverse fields.

Diagnosis and prognosis of failures for aircrafts’ integrity are some of the most important regular functionalities in complex and safety-critical aircraft structures. Further, development of failure diagnostic tools such as Non-Destructive Testing (NDT) techniques, in particular, for aircraft composite materials, has been seen as a subject of intensive research over the last decades. The need for diagnostic and prognostic tools for composite materials in aircraft applications rises and draws increasing attention. Yet, there is still an ongoing need for developing new failure diagnostic tools to respond to the rapid industrial development and complex machine design. Such tools will ease the early detection and isolation of developing defects and the prediction of damages propagation; thus allowing for early implementation of preventive maintenance and serve as a countermeasure to the potential of catastrophic failure. In this paper, following a short introductory summary and definitions, this paper provides a brief literature review of recent research on failure diagnosis of composite materials with an emphasis on the use of NDT techniques in aerospace industry. In addition to this, within a some of significant NDT application extents, prognosis of composites is also briefly discussed.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.