Abstract: Coal bed methane (CBM) reservoirs are complex systems whose properties differ from those of conventional reservoirs. Coal seams are dual-porosity systems that comprise the porosities of the matrix and cleat system. Gas in the coal seams can be stored as free gas in the cleat system and as adsorbed gas in the porous medium. The flow mechanisms of the natural gas through the formation include desorption, diffusion, and Darcy’s flow regimes. The permeability of CBM reservoirs is more sensitive to pressure variations than conventional gas reservoirs. To study the flow behavior of CBM reservoirs it is mandatory to use a model that considers their unique characteristics. The objective of this study was to propose a physical and mathematical model of production performance for horizontal wells in CBM reservoirs whose permeability is dependent on pressure. A solution for the model was obtained by applying Pedrosa´s transformation, perturbation theory, Laplace transformation, the point source method, and Sthefest´s algorithm. The solution to this problem was validated with previous work thoroughly. The type curves of the model were built and the pressure transient behavior of the model was analyzed and discussed. The effects of several parameters on pressure behavior were also discussed.

The mucopolysaccharidoses (MPS) are a group of 11 lysosomal storage diseases (LSDs) produced by mutations in the enzymes involved in the lysosomal catabolism of glycosaminoglycans. Most of the mutations affecting these enzymes may lead to changes in processing, folding, glycosylation, pH stability, protein aggregation, and defective transport to the lysosomes. The use of small molecules, called pharmacological chaperones (PCs), that can restore the folding, trafficking and biological activity of mutated enzymes has been extensively explored in LSDs as a therapeutic alternative. PCs have the advantage of wide tissue distribution, potential oral administration, lower production cost, and fewer issues of immunogenicity. In this paper, we will review the advances in the identification and characterization of PCs for the MPS. These molecules, mainly based in molecules mimicking the enzyme substrates, have been described for MPS II, IVA, and IVB, showing a mutation-dependent enhancement of the mutated enzymes. Although the results show the potential of this strategy, further studies should focus in the development of disease-specific cellular models that allow a proper screening and evaluation of the identified PCs. In addition, in vivo evaluation, both pre-clinical and clinical, should be performed, before they can become a real therapeutic strategy for the treatment of MPS patients.

Urban wind energy has emerged as an attractive source of distributed generation in cities to achieve sustainable development goals. The advance in technologies for the use of urban wind energy has turned this source into an attractive alternative for the decarbonization of cities and the energy transition. The objectives of this work are (1) to identify the potential of wind energy through numerical weather prediction (NWP) data tools and (2) to identify the roles and responsibilities of the stakeholders involved in the decision-making process. A methodology was developed in two phases and applied to a case study in the Dominican Republic. The first phase consisted of estimating the wind energy potential for the 32 provinces at a height of 10 m using open-access geographic reference systems tools provided by NASA. In the second phase, 28 stakeholders were identified through snowball sampling. The Responsible, Accountable, Consult, and Informed (RACI) matrix tool was applied to identify the roles of the 28 institutions addressed at the country level as relevant in the decision-making process for the energy sector. The annual average wind speed and energy potential for each province were determined. It was found 24 provinces have poor potential, below <4.5 m/s. In the northwest and east is where there is the greatest potential, between 4.83 - 6.63 m/s. The population density was established and was observed that the provinces with greater potential are less densely populated. Through 59 interviews 28 institutions were identified and evaluated due to their relevance in decision-making for the implementation of energy projects. The MEM has strongly categorized as “A”, electricity distribution companies as “R”, energy associations and universities as “C”, and educational and justice institutions as “I”.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

This literature review investigates the importance of simulation and measurement techniques in the development of transmission lines and antennas, which are crucial to the reliability and efficiency of communication system. It emphasizes the necessity of exact measurement and computational approaches for simulating complicated electromagnetic process, which help to optimize designs before physical prototypes are built. The review examines a variety of research articles that offer innovative solutions to improving accuracy in antenna design, scalability and cost challenges in antenna measurements, and crosstalk measurement methods. It also explores how these methodologies affect antenna design parameters such as gain, bandwidth, and radiation patterns, emphasizing the importance of correct modeling to ensure the performance of transmission lines and antennas in modern communication system. The review serves as a valuable reference for guiding future research and technological advancements in these fields.

Cutaneous melanoma (CM) is a candidate for screening programs, since its prognosis is excellent when diagnosed in an early disease stage. Targeted screening of those at high risk for developing CM, a cost-effective alternative to population-wide screening, requires valid procedures to identify the high-risk group. Self-assessment of the number of nevi has been suggested as a component of such procedures, but its validity has not yet been established. We analyzed the level of agreement between self-assessments and expert assessments of the number of melanocytic nevi in the area between the wrist and the shoulder of both arms based on 4548 study subjects in whom mutually blinded double counting of nevi was performed. Nevus counting followed the IARC protocol. Study subjects received written instructions, photographs, a mirror, and a “nevometer” to support self-assessment of nevi larger than 2 mm. Nevus counts were categorized based on the quintiles of the distribution into five levels defining a nevus score. Cohen’s weighted kappa-coefficient was estimated to measure the level of agreement. In the total sample, agreement between self-assessments and expert-assessments was moderate (raw agreement: 50.29%, weighted κ=0.596). Self-assessed nevus counts were higher than those determined by experts (mean differ-ence: 3.33 nevi). The level of agreement was independent of sociodemographic and cutaneous factors; however, participants’ eye color had a significant impact on the level of agreement. Our findings show that even with comprehensive guidance, only a moderate level of agreement between self-assessed and expert-assessed nevus counts can be achieved. Self-assessed nevus information does not appear to be reliable enough to be used in individual risk assessment to target screening activities.

Millimeter wave antennas have significance for high-speed, high-capacity wireless communication, delivering substantial advantages and an array of applications. This literature review analyzes the historical development, assessment of performance, difficulties with design, and contemporary applications in telecommunications, radar systems, and imaging. It covers the development of technology, especially within the context of 5G and beyond, and focuses at several antenna designs that deal with technical issues including reduction and impedance matching. The review also studies the effect of design on efficiency and bandwidth, identifying research gaps and potential futures, notably in terms of 5G technology. The commitment to develop millimeter wave antenna technology for next-generation wireless networks is evident, underlining the need of continued research and development.

The apicomplexan parasite, Sarcocystis neurona causes the degenerative neurological equine protozoal myeloencephalitis (EPM) disease of horses. Due to its host range expansion, S. neurona is an emerging threat that requires close monitoring. In apicomplexans, protein kinases (PKs) have been implicated in a myriad of critical functions such as host cell invasion, cell cycle progression and host immune responses evasion. Here, we used various bioinformatics methods to define the kinome of S. neurona and phylogenetic relatedness of its PKs to other apicomplexans. Further, three-dimensional (3D) homology models for selected S. neurona putative PKs were constructed and evaluated for inhibitor docking. We identified 92 putative PKs clustering within the AGC, CAMK, CK1, CMGC, STE, TKL, aPK and OPK groups. Although containing the universally conserved PKA (AGC group), S. neurona kinome was devoid of PKB and PKC, but contained the six apicomplexan conserved CDPKs (CAMK group). The OPK group was represented by ROPKs 19A, 27, 30, 33, 35 and 37, but was devoid of the virulence-associated ROPKs 5, 6, 18 and 38. Two out of the three S. neurona CK1 enzymes had high sequence similarities to T. gondii TgCK1-α and TgCK1-β and the Plasmodium PfCK1. Docking of four inhibitors onto homology models of putative ROP27 and PKA indicated that inhibition of S. neurona PKs is feasible, but needs to be experimentally tested. The essentiality of apicomplexan PKs makes the elucidation of S. neurona kinome a key milestone for development of novel therapeutics for EPM.

Open pit mining is a common activity in the Yucatan peninsula for the extraction of limestone. This mining is known under the generic name of quarries, and regionally as sascaberas (sascab=white soil in Mayan language). These areas are characterized by the total removal of the natural vegetation cover and soil in order to have access to the calcareous material. The present study shows the composition and structure of the vegetation in five quarries after approximately ten years of abandonment, and the conserved vegetation near to each one of the quarries in southeastern Quintana Roo. Using a canonical correspondence analysis (CCA), the distribution of the species was determined in relation to the edaphic variables: soil depth, percentage of organic matter (OM), cationic exchange capacity (CEC), pH and texture. 26 families, 46 genera and 50 species were recorded in the quarries and 25 families, 45 genera and 47 species were recorded in the conserved areas. The dominant species in the quarries belong to the families Poaceae, Fabaceae, Rubiaceae and Anacardiaceae. The quarries with higher values of OM (1.63%), CEC (24.05 Cmol/kg), depth (11 cm) and sand percentage (31.33%) include the following species like Lysiloma latisiliquum, Metopium brownei and Bursera simaruba which are commonly found in secondary forests. On the other hand, quarries with lower values of OM (0.39%), CEC (16.58 Cmol/kg) and depth (5.02), and higher percentage of silt (42.44%) were dominated by herbaceous species belonging to the Poaceae family and by Borreria verticillata, which are typical in disturbed areas of southeastern Mexico. In all cases, the pH was slightly alkaline due to the content of calcium carbonate (CaCO3), characteristic of the soils of the region. The edaphic variables are significantly correlated with the development and distribution of vegetation, and with the structure of the communities.

Examining contemporary developments and cutting-edge approaches in the field, this literature review explores how antenna design for transmission lines is changing. Review findings are compiled from a variety of academic sources, including pertinent technical reports published as recently as 2018, conference papers, journal articles, and reports. A variety of antenna topologies suited for transmission lines are evaluated in this paper, including microstrip, slot, and patch antennas, with a focus on theoretical frameworks and practical implementations. This overview clarifies the complex relationship between antenna design and transmission line characteristics by looking at important factors like impedance matching, bandwidth, and radiation pattern management. Notable developments in materials, fabrication methods, and numerical simulations are also examined and their effects on antenna performance explained. Through a comprehensive analysis of the literature, this review offers valuable insights into the state-of-the-art antenna design practices for transmission lines, facilitating informed decision-making and inspiring future research endeavors in this field.

Dipole antennas, fundamental components in wireless communication systems, have garnered significant attention due to their versatility and efficiency across various applications. This literature review explores the performance analysis of dipole antennas, focusing on recent advancements and methodologies from 2019 to 2024. Employing a systematic approach, the review synthesizes findings from multiple databases, emphasizing peer-reviewed studies that delve into dipole antenna designs and applications. Through meticulous data extraction, key parameters influencing dipole antenna performance, such as material properties and operating frequency, are identified and analyzed. Results highlight the widespread utilization of dipole antennas in wireless communication systems, including indoor positioning, mobile communication, and antenna calibration, among others. Discussions underscore the evolution of dipole antennas in base station environments and their role in filtering applications. The review concludes by emphasizing the continued relevance and evolution of dipole antennas, showcasing their adaptability and potential across diverse wireless communication contexts.

The terahertz (THz) frequency range, often overlooked in the past, is now gaining recognition for its unique properties and diverse applications. THz waves, positioned between microwave and infrared frequencies, offer safety, broad bandwidth, and low energy consumption. This review highlights the burgeoning importance of THz technology in communication systems, spectroscopy, biomedical imaging, and various industries. It emphasizes the significance of novel antenna designs in enhancing THz system performance and functionality, enabling rapid communication, data transmission, spectroscopic analysis, and sensor capabilities.

Research into improving the functionality and capacities of wearable technology has surged as a result of its fast progress. The functioning of wearable technology is largely dependent on antenna design, which provides wireless connectivity and communication. This study of the literature searches many online sources for pertinent research on the various antenna designs for wearable technology. The findings verified that the materials vary based on the design being employed. An analysis was conducted on the gain, frequency, materials, and sizes. The review excels at synthesizing research and literature despite its restricted resources.

Lifestyle health has been recognized as an evidence-based innovation that defines how daily habits and practices impact both the prevention and treatment of disease and provides an important adjunctive component to overall health. Specifically, an approach with small changes over time can have a dramatic impact on the health and well-being of individuals not only, in general, but also can be applied to skin health. However, lifestyle health factors to improve skin well-being have not been discussed extensively and/or well promulgated. The narrative for this overview focuses on providing a summary for topic background information, but more importantly, presents four lifestyle factors that can improve dermal health [i.e., factor 1: nutrition - diet; factor 2: rest (sleep); factor 3: movement/physical exercise, and factor 4: social and community associations]. This was accomplished by identifying previous journal articles and reviews with emphasis over the last five years (from January 2018 through July 2023; 156 out of 190 references cited or 82 %) using scientific search databases. The main conclusions of this overview encourage the concept that lifestyle health factors such as nutrition/diet, rest/sleep, movement/physical exercise, and community/social interactions support enhanced skin health and well-being with aging. Plus, social media interventions that aim to promote dietary, sleep and physical activity changes might be an application to improve skin health in the future.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

For their speed in data processing, low heights and cheapness, patch antennas are important to 5G networks. While there have been improvements in the area of bandwidth and size reduction; however, there are still problems that need to be fixed with regards achieving high data rates as well as wideband operation and compact sizes for 5G applications. In the coming years researchers should concentrate on materials optimization and feeding methods so that they can work efficiently at shorter wavelengths for next-generation systems while still covering all necessary bands.

With widespread resistance to a limited number of insecticides available for medical and veterinary pests, new insecticides and insecticide synergists are desperately needed in this market space. We assessed the topical toxicity of carvone, menthone, and fenchone compared to permethrin and methomyl against the yellow fever mosquito, Aedes aegypti, and the house fly, Musca domestica. We also evaluated the synergistic potential of the monoterpenoids with permethrin and methomyl. Additionally, we assessed the acetylcholinesterase inhibitory potential of each monoterpenoid compared to methomyl. While all three monoterpenoids performed relatively poorly as topical insecticides (LD50 > 4000 ng/mg on M. domestica; > 6000 ng/mg on Ae. aegypti), they synergized both permethrin and methomyl as well as or better than piperonyl butoxide (PBO). Carvone and menthone yielded synergistic co-toxicity factors (23 and 29, respectively), which were each higher than PBO at 24 h. Acetylcholinesterase inhibition did not appear to explain the toxic or synergistic effects of the three monoterpenoids with IC50 values greater than 1 mM for all, compared to the 2.5 and 1.7 µM for methomyl on Aedes aegypti and Musca domestica, respectively. This study provides valuable monoterpenoid toxicity and synergism data on two pestiferous insects and highlights potential for these chemistries in future pest control formulations.

This literature review offers a comprehensive analysis of the design and optimization of microstrip patch antennas for wireless communication systems, drawing insights from a variety of research papers. It examines diverse approaches aimed at enhancing antenna performance and functionality. Key topics explored include optimization of resonant frequency, design of antenna arrays, and bandwidth enhancement. Noteworthy optimization techniques, such as genetic algorithms and artificial neural networks, are discussed, highlighting their effectiveness in achieving superior antenna performance. Application-specific solutions tailored to areas such as military communication and RFID systems are emphasized. The review underscores the significant role played by optimization techniques in achieving desired performance metrics and identifies future research directions, including multi-objective optimization, investigation of robustness and reliability, and integration with advanced technologies. Overall, this literature review provides valuable insights into the state-of-the-art in microstrip patch antenna design and optimization, guiding future research endeavors in this dynamic and rapidly evolving field.

This review explores the potential of Intelligent Reflecting Surfaces (IRS) to address limitations of traditional antennas and empower next-generation wireless networks. IRS technology offers a paradigm shift by passively manipulating radio waves, enhancing signal coverage, network capacity, and energy efficiency. IRS technology utilizes flat surfaces equipped with tiny, adjustable reflectors. Unlike traditional antennas that actively send and receive radio waves, IRS passively manipulates incoming signals by reflecting them in specific directions with controlled adjustments. This approach significantly improves signal coverage and strength, particularly in areas with poor reception. The review reveals the IRS technology offers significant advantages over traditional antennas. It can enhance signal coverage by reflecting radio waves, boost network capacity which allows networks to handle a larger number of users simultaneously, promote lower energy consumption compare, and unlock potential of 6G Networks and beyond. Additionally, IRS enables functionalities such as enhanced localization and ultra-fast data transfer. Integrating IRS with next-generation antennas unlocks new possibilities. This includes reconfigurable antennas that dynamically adjust radiation patterns, compact and low-profile designs, and multi-functional antennas that support various services simultaneously. In conclusion, this paves the way for a new generation of robust and sustainable wireless communication systems.

Underwater communication for Underwater Wireless Sensor Networks (UWSNs) faces challenges due to the unique underwater environment. This review explores these challenges (attenuation, multipath propagation, limited bandwidth) across acoustic, optical, and radio frequency (RF) communication channels. It highlights how innovative antenna designs are crucial to mitigate these limitations and enable efficient data transmission. The review analyzes monopole, dipole, and helical antennas, discussing their trade-offs in radiation pattern, efficiency, and mitigating multipath effects. Advancements in materials, metamaterials, and reconfigurable antennas offer promising solutions for improved signal propagation, reduced attenuation, and better environmental adaptability. Research gaps include material exploration for low-conductivity antennas and optimizing antenna designs. Rigorous performance evaluation remains essential. Overall, advancements in underwater antenna technology are key to unlocking the full potential of UWSNs and enabling breakthroughs in marine applications.

Wearable antennas are increasingly vital in modern healthcare, facilitating continuous monitoring and communication for medical purposes. These antennas are integrated into wearable devices, allowing for the transmission and reception of vital health data, such as heart rate, body temperature, and blood pressure, to medical professionals or monitoring systems in real time. This instantaneous data transmission enables timely interventions, thus improving the quality of healthcare services. This review aims to comprehensively synthesize recent research from reputable databases, focusing on the functionality and effectiveness of wearable antennas in medical applications. The primary objectives of each study were categorized, including antenna design and integration, the performance of medical wearable antenna technology, safety considerations in wearable antenna biomedicine, and the use of antennas in clinical applications and their impact. This synthesis provides valuable insights for researchers and practitioners aiming to develop and deploy wearable antennas in medical settings, ultimately enhancing healthcare delivery and patient outcomes.

The Photovoltaic modules are usually installed on the ground which exposes it to surface deposition of foreign particles. In the Middle East and North Africa region, the primary culprit is dust and sand. They form an insulating and opaque layer on the surface of the glass, which obstructs its heat transfer and optical properties, thereby reducing the overall yield efficiency of the solar panel. Cleaning of this layer is critical to the operation of the solar panel and often requires great effort and energy on a large-scale solar array. In this paper, we propose a novel self-cleaning mechanism for solar panels, with an understanding of the structural integrity of the Photovoltaic laminate and application of external mechanical vibration. By applying an external source of vibration, the solar panels vibrate, excites its fundamental frequencies and cleans by its own. The method is analyzed using finite element analysis method and tested using experiments. Our simulation results based on IEC 61215 show that the maximum principal stress and deformation in the critical layers is within limits. Our experimental results prove the proposed theory is feasible and can be extended to large scale solar arrays. Our proposed method is retrofittable and could save money, energy and effort in cleaning the solar arrays, which can replace current techniques.

We present an innovative method harnessing multistability within a diode-pumped erbium-doped fiber laser to construct logic gates. Our approach involves manipulating the intensity of external noise to regulate the probability of transitioning among four concurrent attractors. In this manner, we facilitate the realization of OR, AND, NOT, and NOR logic operations, aligning with the coexisting period-1, period-3, period-4, and period-5 orbits. Employing detrended fluctuation analysis, we establish equilibrium in the probability distributions of these states. The obtained results denote a substantial advancement in the field of optical logic gate development, representing a pivotal stride toward the seamless integration of an all-optical logic gate within laser oscillator-based systems.

The chicken poultry business is becoming a crucial sector for our nation's sustainable food supply. To increase the health of chickens in coops and maybe increase the output of chicken meat and eggs, several innovations and inventions have been developed. Therefore, the creation of an automated chicken feeding device might be highly beneficial for the expansion of the poultry sector. This equipment may take the role of a worker in the chicken feeding process, solving the industry's labor shortage and introducing a semi-automatic procedure. The main processor of the automated chicken feeding system is a Raspberry Pi 3 Model B+. The machine consists of five main components: a Raspberry Pi with an amplifier that makes noise to draw the attention of free-range chickens, a Raspberry Pi with a pi camera that counts and detects the chickens, a Raspberry Pi with a servomotor that moves the food from storage to a container, a Raspberry Pi with a load cell that controls the number of feeds to avoid waste on foods, and a Raspberry Pi with a temperature sensor that monitors the food's freshness. This enhances the coop's atmosphere while lowering labor costs, conserving food, and ensuring that the chickens are fed on time. It also regulates the temperature sensor to ensure that the chicken food is fresh and prevents food from becoming polluted by insects and chicken excrement.

Companies are increasingly focusing on sustainable business practices. Internal and external stakeholders’ expectations manifest in legal requirements, national and international standards, and market and customer expectations, among other things, must be considered. In addition to profit maximization as the usual target for corporate management, management must consider environmental sustainability aspects such as resource efficiency, greenhouse gas intensity, and a company's emissions behavior. In addition, social aspects relating to the company's employees, the immediate urban environment, the situation in the supply chain, and effects on the market environment must increasingly be considered. Specifically, companies are faced with the chal-lenge of dealing with conflicting objectives regarding the various aspects of sustainability and, if necessary, weighing them up against each other. These trade-offs must be made against the company's socio-economic and ecological environment, corporate strategy, and sustainability goals. This paper provides an overview of current approaches and research gaps on this topic through a literature review. It shows a lack of methods and frameworks to specifically deal with trade-offs and conflicts of goals.

This review aims to comprehensively synthesize the most recent research, collected from reputable databases, focusing specifically on transmission line. It examines advancements and challenges in data transmission. Fiber optics remains dominant, with new fiber designs and signal processing techniques like SDM improving capacity. Researchers are also addressing signal distortion and crosstalk. Beyond fiber optics, the review explores findings in coaxial cables and the promise of graphene for future high-speed transmission. Novel methods for mitigating crosstalk are presented. The findings highlight a trend towards more dependable, effective, and versatile transmission methods, with implications for various industries and electronic systems.

In this literature review, discusses the advancements in antenna design for 5G mm-wave communication, particularly in the 28 GHz frequency band. It highlights the significance of millimeter-wave technology in addressing the increasing demands on wireless networks, emphasizing the potential for achieving unprecedented data rates, ultra-low latency, and enhanced spectral efficiency. The literature review explores the current state of research on mmWave technology, antenna array technology, and potential implications for mobile broadband applications, including 5G and future 6G networks. It presents insights into novel antenna designs, such as a single layer MIMO antenna and a multiple input multiple output dielectric resonator antenna, both aimed at supporting a wide range of frequencies and applications in the mm-wave spectrum. It discusses the challenges associated with mmWave antenna arrays, offering valuable insights into the current state of research in this domain.

This literature review offers a comprehensive analysis of antenna designs tailored for 6G applications and THz communications. Various antenna types, including elliptical lens, WHEMS, grid array, CP horn, Quasi Yagi-Uda, patch, transmit array, Vivaldi patch, SRR slotted waveguide array, RCA, and Cassegrain antennas, are scrutinized in terms of their dimensions, characteristics, frequency ranges, and materials. The study highlights existing works on antenna design for 6G applications, exploring different fabrication techniques like PCB technology, LCP technology, Topas fabrication, Wire-cutting EDM, 3D Printing, Laser-drilling processes, and CNC machining. Notably, PCB technology exhibited promise with antennas such as WHEMS, patch, and Vivaldi patch, achieving significant bandwidth and gain. However, gaps remain in research, urging further exploration into additional fabrication methods and antenna designs to meet the demands of 6G networks. The study underlines the need for advanced, intelligent antennas capable of higher bandwidth and better performance in view of future wireless communications technologies such as 6G.

The electronic nose (e-nose) is an innovative device that mimics the human olfactory system, allowing for the detection and identification of numerous scents. This abstract gives an in-depth examination of the current status of electronic nose technologies, with an emphasis on sensing mechanisms, applications, and recent breakthroughs. The research delves into the many uses of e-nose, such as food quality control, environmental monitoring, medical diagnostics, and industrial operations. It also covers the problems and potential future applications of electronic nose technology. The literature review highlights the importance of this technology in improving our capacity to evaluate complex scents in several domains.

Combined sewer overflow (CSO) is a significant environmental concern and public health (e.g., water contamination, eutrophication, and beach closure). The Environmental Protection Agency (EPA) has introduced the National Pollutant Discharge Elimination System (NPDES) permitting program to regulate and address this matter. This program mandates the control of CSOs for more than 700 municipalities obligated to devise Long-term Control Plans (LTCPs) to curb combined sewer overflows and bring them down to safe levels. LTCP involves diverse strategies, including sewer separation, green infrastructure improvements, and conventional gray infrastructure upgrades. This study investigates several municipalities’ solutions for CSO problems in conventional methods and wireless sensor technology as real-time control. The investigation mainly focuses on a comparative analysis of two cities, Richmond, Virginia, and South Bend, Indiana, such as average rainfall, the frequency of overflows, and the capacity of treatment plants. The findings indicate that integrating sensor technology could significantly enhance modeling endeavors, bolster the capacity of existing structures, and substantially enhance preparedness for storm events. The EPA’s Storm Water Management Modeling (SWMM) software is utilized. Through an analysis of SWMM data, the study suggests the potential for leveraging wireless sensor technology to achieve more robust control over CSOs as a part of LTCPs.

We study the synchronous dynamics of three diffusively coupled erbium-doped fiber lasers (EDLFs) in the unidirectional ring configuration without external pump modulation. The dynamical behavior of the system is analyzed using time series, Fourier spectra, Poincaré sections, bifurcation diagrams, and Lyapunov exponents for different values of the coupling strength. For weak coupling, we observe a well-known route to chaos from a stable equilibrium through a Hopf bifurcation and a series of torus bifurcations as the coupling strength is increased. An interesting result is found for large values of the coupling strength, where the phase locking is close to zero. This allows a significant increase in the peak energy of the EDFLs pulses, i.e., above the coupling strength the lasers switch to a Q-switching mode with large-amplitude short pulses. This result allows us to propose a new method for increasing the laser pulse energy based on the control of the bistability by the rotating wave in the array of three unidirectionally ring-coupled EDFLs as a function of the coupling strength. In our system, we were able to increase the peak laser power by almost 20 times more than a continuous single EDFL.

The review aims to assess fifteen (15) academic literature sources, highlighting the application of machine learning algorithms in the maintenance operations of optical fiber networks. It exhibits the collection of data using PRISMA methodology—Preferred Reporting Item for Systems Review and Meta-Analyses. The application, results, and performance metrics are discussed based on the collected observations, computations, and statistics in the studies, which revealed records of high accuracy degrees ranging from 86% to 98% on average and quality ML models including Neural Networks (NNs), Support Vector Machines (SVMs), and LSTM, as well as deep learning models that disclosed effective results of determining challenges and problems within the optical fiber lines. The review mainly centralized on superior machine learning technologies that surpass traditional techniques in fault detection and localization for improved optical fiber networks’ operations while providing insights into the limitations and challenges encountered in real-world applications of these models, offering a comprehensive perspective on the optical fiber network’s domain.

Fourteen (14) rack-mounted polycrystalline modules installed on the concrete roof of the solar energy applications laboratory at the Kwame Nkrumah University of Science and Technology (KNUST) in Ghana, a hot humid environment were assessed after 19 years of continuous outdoor expodure. The physical state of the modules was documented using a visual inspection checklist. They were further assessed by current-voltage (I-V) characterization and thermal imaging. The modules were found to be in good physical state, except some bubbles on front side and minor discolouration/corrosion at edge of the cells. Compared with reference values, the performance decline of the modules observed over the exposure period was: nominal power (P_nom), 21% to 35%; short circuit current (I_sc), 5.8% to 11.7%; open circuit voltage (V_oc) 3.6% to 5.6% and 11.9% to 25.7% for fill factor (FF). It is hoped that this study will provide some helpful information to project developers, manufacturers and the research community on the long-term performance of PV modules in Ghana.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

Large datasets play a crucial role in the progression of surgical robotics, facilitating advancements in the fields of surgical task recognition and automation. Moreover, public datasets enable the comparative analysis of various algorithms and methodologies, thereby assessing their effectiveness and performance. The ROSMA (Robotics Surgical Maneuvers) dataset provides 206 trials of common surgical training tasks performed with the da Vinci Research Kit (dVRK). In this work, we extend ROSMA dataset with two annotated subsets: ROSMAT24, which contains bounding box annotations for instrument detection, and ROSMAG40, which contains high and low level gesture annotations. We propose an annotation method that provides independent labels for the right-handed tool and the left-handed tool. For instrument identification, we validate our proposal with a YOLOv4 model in two experimental scenarios. We demonstrate the generalization capabilities of the network to detect instruments in unseen scenarios. For gesture segmentation, we propose two label categories: high-level annotations that describe gestures at a maneuvers level, and low-level annotations that describe gestures at a fine-grain level. To validate this proposal, we have designed a recurrent neural network based on a bidirectional long-short term memory layer. We present results for four cross-validation experimental setups, reaching up to a 77.35% mAP.

High-resolution GPS/GSM dataloggers provide spatial information of the highest quality, which outperform previous tracking methods, such as Argos telemetry or conventional VHF ground-tracking. As a result, this has improved our knowledge of home-range behavior and spatial ecology of many species, including large raptors. In this paper, we use high-resolution GPS/GSM dataloggers to assess the home-range size and the role of sex, season (breeding or non-breeding season), and breeding status (reproductive or non-reproductive individuals) on the space use of Bonelli’s eagle (Aquila fasciata). To this end, 51 territorial individuals (25 females and 26 males) were equipped with GPS/GSM transmitters and were tracked over 7 years (2015–2021) in eastern Spain. Overall, we recorded 4,791,080 fixes that were analyzed through kernel density methods (50%, 75%, and 95% fixed kernels). The average individual home-range size according to the 95%, 75%, and 50% kernels was 54.84 ± 20.78 km², 24.30 ± 10.18 km², and 11.17 ± 4.90 km², respectively. Overall, the home-range size of individuals occupying the same territory was similar, mainly due to the cooperative hunting behavior exhibited by the species. We did not find interannual differences in the home-range size (95% fixed kernel) of the majority of individuals, showing a strong territorial fidelity of the breeding pairs. In general, females’ home-range size was slightly smaller than males’ size due to the decrease in activity in the breeding season as a result of laying, incubation, and chick attendance at nests. No seasonal variation in the 95% kernel was found, but it was found in the 75% and 50% kernels. In regard to the breeding status, higher home-range size was recorded in the non-reproductive individuals. Moreover, we found a low neighbor overlap among the territories (4.18% ± 3.06%), which evidences a high level of intraspecific competition in the Bonelli’s eagle. Finally, this study highlights the advantages of the use of accurate telemetry information to improve our understanding of the spatial ecology of the endangered Bonelli’s eagle, which ultimately will serve to better inform management actions for its conservation.

Fault detection in transmission lines plays a role in maintaining the dependability and steadiness of power networks. Traditional methods for identifying faults often struggle to handle the diverse nature of real world fault situations. Machine learning (ML) algorithms offer a data centered approach that can adjust and learn from datasets potentially overcoming the limitations of traditional approaches. This document presents a review of progress in using ML for detecting faults in transmission lines. By drawing insights from a variety of studies we explore how ML algorithms have evolved in fault detection, including techniques like networks, recurrent neural networks featuring Long Short Term Memory and convolutional neural networks. We delve into the spectrum of applications where ML is used for fault detection across fault scenarios and operational settings. Additionally we discuss the obstacles and prospects linked to putting ML based fault detection systems into practice such as challenges with data quality, model interpretability and integration with existing grid monitoring systems. Lastly we outline future research paths focused on pushing forward the boundaries of fault detection, in power transmission systems through approaches and collaborative endeavors involving academia, industry players and policymakers. In general, this review highlights how machine learning has the power to revolutionize fault detection methods enhancing the resilience and dependability of power systems.

The present work studies a swelling porous-elastic system with viscoelastic damping. We establish a general and optimal decay estimate which generalizes some recent results in the literature. Our result is established without imposing the usual equal-wave-speed condition associated with similar problems in literature.

Self-consciousness can be considered as the internal disposition to direct attention to oneself. This dispositional tendency can be focused on private aspects of the self, but also on public characteristics of the individual. We examine self-consciousness in online consumer behavior. This concept has been poorly investigated in consumer research. The main objective of this paper is to analyze the influence of the dimensions of self-consciousness in consumer adoption of online shopping. This study is based on a sample of 725 Spanish undergraduates. Results indicated that public self-consciousness is a significant predictor of the adoption of online shopping.

Coconut is an important multipurpose crop wherein different stages of its maturity are used for different products. Traditional approach of its classification use sound formed by tapping the fruit, and the tapper will base its judgment through the sound he heard. The procedure was highly subjective and attempts had been made by other researchers to automate and classify the adulthood of the coconut objectively. Nowadays, deep learning techniques are being utilized to solve classification problems. One such method is Convolutional Neural Network (CNN) that takes raw pixel data, learns to extract features, and ultimately classifies the input. In this study, a portable device was developed that takes audio signals generated from tapping the fruit mechanically. This audio clip was converted to spectrogram and was used as input to a LeNet CNN architecture. We argue that CNN classifier has improved accuracy compared to the non-deep learning system. Our evaluation confirmed that the use of CNN had improved the accuracy of classifying coconut by about 15%.

In this work we discuss the insights from activity landscape, docking and molecular dynamics towards the understanding of the structure-activity relationships of dual inhibitors of major epigenetic targets: lysine metiltransferase (G9a) and DNA metiltranferase 1 (DNMT1). The study was based on a novel data set of 50 published compounds with reported experimental activity for both targets. The activity landscape analysis revealed the presence of activity cliffs, e.g., pairs of compounds with high structure similarity but large activity difference. Activity cliffs were further rationalized at the molecular level by means of molecular docking and dynamics simulations that led to the identification of interactions with key residues involved in the dual activity or selectivity with the epigenetic targets.

The existence of multiple copies of genes is a well-known phenomenon. A gene family is a set of sufficiently similar genes, formed by gene duplication. In earlier works conducted on limited number of completely sequenced and annotated genomes it was found that size of gene family and size of genome are positively correlated. Additionally, it was found that several atypical microbes deviated from the observed general trend. In this study, we reexamined these associations on a larger dataset consisting of 1484 prokaryotic genomes and using several ranking approaches. We applied ranking methods in such a way that genomes with lower number of paralogs would have lower rank. Until now only simple ranking methods were used; we applied the Kemeny optimal aggregation approach as well. Regression and correlation analysis were utilized in order to accurately quantify and characterize the relationships between measures of paralog indices and genome size. In addition, boxplot analysis was employed as a method for outlier detection. We found that, in general, all paralog indexes positively correlate with an increase of genome size. As expected, different groups of atypical prokaryotic genomes were found for different types of paralog quantities.

The application of adaptive antennas in wireless communication systems is examined in this review of the literature, with particular attention to how well they can transmit and receive signals by dynamically adjusting radiation patterns. The review covers a range of artificial intelligence (AI) approaches that have been used to enhance adaptive antenna performance in beamforming, channel estimation, interference mitigation, and spectrum utilization. Although adaptive antennas have the potential to improve wireless communication systems, the paper also discusses their limitations, including computational complexity, interference sensitivity, and the significance of precise channel state information (CSI) estimates.

Many real complex systems present multilayer structure where high-order metadata on one layer refers to dyadic data on a lower layer. Significant progresses to analyse high-order metadata under the assumption of community organization have been done. However, there are no planted communities in real-world networks, and the necessity of new frameworks to analyze high-order metadata regardless of community organization has been raised.Here, we propose to adopt hyperedge organization. Predicting ‘entanglements’ between a hyperedge and nodes scattered in the rest of the network might suggest structural or functional liaisons, without assumption of any community organization. We introduce a novel concept: hyperedge entanglement (HE), which associates to each hyperedge an entangled hyperedge, by means of a network operator that predicts significant ‘interactions at distance’ between network nodes and existing hyperedges. We also introduce a new challenge termed hyperedge entanglement prediction (HEP), and an algorithm to perform this task. We evaluated HEP performance on social, biological and synthetic data where, given only topology and hyperedges (such as communities or functional modules), the goal is to predict whether nodes not connected to a certain hyperedge might be candidates for a significant entanglement. Finally, as real application in diseasome systems biomedicine, we perform HEP on the human protein interactome to predict unknown gene entanglements with the COPD disease gene hyperedge. HEP predictions are validated by biological experiments, enlarging our understanding of molecular mechanisms behind COPD/aneurysm comorbidity.

Understanding the role of invasive species in ecosystem functioning represents one of the main challenges in ecology. Pteridium aquilinum is a successful cosmopolitan invasive species with negative effects on the ecological mechanisms that allow secondary succession. In this study we evaluated whether P. aquilinum favours the establishment of alternative states, as well as the effect of recovery strategies on the secondary succession. A random stratified sampling was established with three treatments, each one with at least 50 year of fern invasion and with variations on the periodicity of fires and cuttings (chapeos) vs one control without fern bracken We determined the species richness and composition, as well as the relative importance value (IVI) in each treatment. We found that P. aquilinum decreases the action of the mechanisms that allow secondary succession, particularly facilitation. The recovery strategies consist in monthly cuttings and control fires allow to recover the secondary succession and eventually, the regeneration of areas invaded by P. aquilinum. Our study has relevant implications on the ecology of alternative state, and in practical strategies to maintain tropical forests, as well as for the maintenance of environmental services and sustainability.

A class of controlled plants, whose dynamics is governed by a vector system of ordinary differential equations with a partially known right-hand side, is considered. The state variables and their velocities are assumed to be measurable. The aim is to design a controller which minimizes a loss function under certain constraints which arguments is the current state of the controlled plant. The designed control action is admitted to be a function of the current sub-gradient only, which supposed to be measurable on-line. The control design is based on ASG (Average Sub-Gradient method) — version of Integral Sliding Mode (ISM) concept, aimed to minimize on average a given convex (not obligatory strongly convex) cost function of the current state under a set of given constraints. An optimization type algorithm is developed and analyzed using ideas of SDM technique. The main results consist in proving the reachability of the "desired regime" (nonstationary analogue of sliding surface) from the beginning of the process and obtaining an explicit upper bound for the averaged loss function decrement, that is, the averaged in time functional convergence is proven and the rate of such convergence is estimated.

A large diversity of epigenetic factors, such as microRNAs and histones modifications, are known to be capable of regulating gene expression without altering DNA sequence itself. In particular, miR-1 is considered the first essential microRNA in cardiac development. In this study, miR-1 potential role in early cardiac chamber differentiation is analyzed through specific signaling pathways. For this, we performed in chick embryos functional experiments by means of miR-1 microinjections into the posterior cardiac precursors -of both primitive endocardial tubes- committed to sinoatrial region fates. Subsequently, embryos were subjected to whole mount in situ hybridization, immunohistochemistry and RT-qPCR analysis. As a relevant nov-elty, our results revealed that miR-1 increases Tbx5, Gata4 and AMHC1, while this microRNA diminishes Mef2c expression, during early differentiation of cardiac sinoatrial region. Further-more, we observed in this developmental context that miR-1 upregulates CRABPII and RARß, and downregulates CRABPI, which are three crucial factors in retinoic acid signaling pathway. Interestingly, we also noticed that miR-1 directly interacts with HDAC4 and Calmodulin, as well as with Erk2/MAPK1, which are three key factors actively involved in Mef2c regulation. Our study shows, for the first time, a key role of miR-1 as an epigenetic regulator in early differenti-ation of cardiac sinoatrial region through orchestrating opposite actions between retinoic acid and Mef2c, fundamental to properly assign cardiac cells to their respective heart chambers. A better understanding of those molecular mechanisms modulated by miR-1 will definitely help in fields applied to therapy and cardiac regeneration and repair.

As consequence of COVID-19, millions of households suffered mobility restrictions and changes in their lifestyle during several months. The aim of this study was to evaluate the effects of COVID-19 home confinement in food habits, lifestyle and emotional balance of the Spanish population. This cross-sectional study used data collected via an anonymous online questionnaire during the last month before lockdown finished in Spain in a total of 675 participants. 38.8% of the respondents experienced weight gain while 31.1% lost weight during confinement. The increase in body weight was positively correlated with the age (Rs = 0.14, p <0.05) and BMI (Rs = 0.20, p <0.05). We also identified that 39.7% reported poorer quality sleep, it was positively correlated with BMI (Rs = -0.18, p <0.05) and with age (Rs = -0.21, p <0.05). 44.7% of the participants had not performed physical exercise during confinement with differences by sex (p <0.05), by age (p <0.05), by BMI (p <0.05) and sleep quality (p <0.05). According to emotional-eater questionnaire, 21.8% and 11% were classified as emotional eater and very emotional eater, respectively. We emphasize the importance of adopting a healthy lifestyle, as the COVID-19 pandemic nowadays is ongoing.

High electricity demand for consumption at current supply level in Indonesia led to the rising cost of electricity bills. This factor is compounded by the fact that many electric generators in Indonesia still use fossil fuels, which contributes to the high basic generation cost. UBL is one of the universities that aim to be a green campus. Our research explores the possibility of installing motion sensor to contribute on the energy efficiency. Although mostly common in developed countries, the use of motion sensor for energy efficiency is still rare, especially in Indonesia. despite rising cost and supply shortages, Indonesian buildings are still of high energy consumption. our experiment shows that simple installation of commercially available motion sensors can contribute to reduce the electricity bill from the increase of energy efficiency. One of the efforts to lower energy demand in consumer side is to use the electricity efficiently, such as turning off lights in a room when it's not in use. This method can be simply done by turning the light switches for office and classrooms, but difficult to do in public spaces such as toilets and corridors. Our experiment shows that simple installation of commercially available motion sensors can contribute to reduce the electricity bill from the increase of energy efficiency. Automatic light switches experimentally installed in sample toilet room prove that electricity consumption from the lamps can contribute to the reduction of total weekly energy that translates into Green House Gas emission reduction.

Heart sounds and murmurprovidecrucial diagnosis information for valvular heart diseases (VHD). Phonocardiogram (PCG) combined with modern digital processing techniques, provides a complementary tool for clinicians. This article proposes a benchmark different time-frequency representations, which are spectogram, mel-spectogram and cochleagram for obtaining images, in addition to the use of two interpolation techniques to improve the quality of the images, which are Bicubic and Lanczos. Deep features are extracted from a pre-trained model called VGG16 and for feature reduction the Boruta algorithm is applied. To evaluate the models and obtain more precise results, nested cross-validation is used. The best results achieved in this study were for coclegram with 99.2% accuracy and mel-spectogram representation with the bicubic interpolation technique which reached 99.4% accuracy, both having support vector machine (SVM) as classifier 10 algorithm. Overall, this study highlights the potential of time-frequency representations of PCG 11 signals combined with modern digital processing techniques and machine learning algorithms for 12 accurate diagnosis of VHD.

This literature review delves into the realm of fault detection techniques for transmission lines, aiming to provide a comprehensive overview of recent advancements and key trends in the field. Employing a structured approach, the review synthesizes a plethora of research spanning from 2019 to 2024, sourced from diverse databases including IEEE Xplore, ScienceDirect, ResearchGate, Scopus, Litmaps, and Google Scholar. The methodology encompasses a systematic literature search protocol, stringent inclusion and exclusion criteria, meticulous data extraction, and a multi-dimensional analysis framework. The literature review uncovers a spectrum of fault detection methodologies, ranging from traditional signal processing techniques like Discrete Wavelet Transform and phase angle-based methods to cutting-edge deep learning algorithms such as Capsule Networks and Convolutional Neural Networks. Insights gleaned from the review underscore the critical importance of fault detection in maintaining the reliability, safety, and efficiency of power grids, highlighting its role as a frontline defense against widespread outages and equipment damage. Key findings from the review shed light on the efficacy of different fault detection approaches, showcasing their strengths and limitations across diverse system conditions. Furthermore, the review identifies common trends and challenges, including the need for real-world validation, scalability, adaptability to various network configurations, and cybersecurity considerations. This literature provides valuable insights and recommendations for future research endeavors in fault detection for transmission lines. By embracing advancements in both traditional and emerging techniques, researchers can continue to enhance the resilience and dependability of power transmission systems, ensuring their ability to withstand evolving challenges and safeguard critical infrastructure.

A mathematical model, composed of two non-linear differential equations that describe the population dynamics of CD4 T cells in the human immune system, as well as viral HIV particles, is proposed. The invariance region is determined, classical equilibria stability analysis is performed using the basic reproduction number, and numerical simulations are carried out, in order to illustrate stability results. Later, the model is modified with a delay term, which describes the time that cells require for immunological activation. This generates a two-dimensional integro-differential system, which is transformed into a system with three ordinary differential equations, via auxiliary variable use. For the new model, equilibrium points are determined, their local stability is examined, and results are studied by way of numerical simulation.

There is an urgent need to develop new therapies for cancer treatment due to high rates of resistance and tumor relapse. Moreover, chemotherapy and radiation weaken the immune system and leave patients susceptible to sudden death from infections and organ failure since antineoplastic drugs lack specificity. Hence, scientists worldwide continue the search for natural alternatives with anticancer activities and fewer side effects. In this regard, traditional medicine offers strategies for preventing and treating numerous diseases and nowadays scientific studies worldwide highlight some of the mechanisms underlying their potential as an effective alternative for the management of cancer. In Mexico, Mayan healers have been prescribed mainly medicinal plants for more than 2,000 years. Furthermore, their ethnomedical knowledge has led to drug discovery or the development of herbal remedies. Medicinal plants from the genus of Cissus remain an important element of all indigenous medical systems in Mexico including the Mayan tribes in Yucatan, particularly for the management of gastrointestinal illnesses, sores, cutaneous diseases, and tumors. In this review, we present the reported bioactivities against cancer cells of the extract and compounds from the species of Cissus with a particular focus on recent studies on the phytochemistry of C. trifoliata which is widely distributed in Mexican territory and used for tumor management in traditional medicine.

Six bacterial strains with differing abilities to produce varying concentrations of Indole Acetic Acid were tested individually and in consortia for plant growth promoting and fitness related traits of Cicer arietinum. In all experiments the presence of the nitrogen fixer Mesorhizobium ciceri resulted in increased biomass production. In the absence of this strain, IAA Psedomonas putida and Bacillus megaterium hinder plant growth and fitness related traits. The application of mixes of the three strains always resulted in better plant performance when M. ciceri was present. Whereas P. putida has a noticeable plant growth-promoting effect B. megaterium resulted less effective. The low levels of IAA produced by the selected strains had a significantly greater positive effect on plant biomass accumulation, flower, pods and seed production as well as on total plant nitrogen and nitrogen concentration in seed than high IAA producer strains.

The synthesis of metal nanoparticles using plant extracts is a very promising method in green synthesis. The medicinal value of Moringa oleifera leaves and the anti-microbial activity of metallic copper were combined in the present study to synthesize copper nanoparticles having a desirable added-value inorganic material. The use of a hydroalcoholic extract of M. oleifera leaves for the green synthesis of copper nanoparticles is an attractive method as it leads to the production of harmless chemicals and reduces waste. The total phenolic content in the M. oleifera leaves extract was 23.0 ± 0.3 mg gallic acid equivalent/g of dried M. oleifera leaves powder. The M. oleifera leaves extract was treated with a copper sulphate solution. A color change from brown to black indicates the formation of copper nanoparticles. Characterization of the synthesized copper nanoparticles was performed using UV-Vis spectrophotometer, FT-IR spectrometer, TEM, SEM, and XRD. The synthesized copper nanoparticles have an amorphous nature and particle size of 35.8-49.2 nm. We demonstrate that the M. oleifera leaves extract and the synthesized copper nanoparticles display considerable antioxidant activity. Moreover, the M. oleifera leaves extract and the synthesized copper nanoparticles exert potent anti-bacterial activity against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, and Enterococcus faecalis (MIC values for the extract: 500, 250, 250, and 250 μg/mL; MIC values for the cooper nanoparticles: 500, 500, 500, and 250 μg/mL, respectively). Similarly, the M. oleifera leaves extract and the synthesized copper nanoparticles exert relatively more potent anti-fungal activity against Aspergillus niger, Aspergillus flavus, Candida albicans, and Candida glabrata (MIC values for the extract: 62.5, 62.5, 125, and 250 μg/mL; MIC values for the cooper nanoparticles: 125, 125, 62.5, and 31.2 μg/mL, respectively). Our study reveals that the green synthesis of copper nanoparticles using a hydroalcoholic extract of M. oleifera leaves was successful. In addition, the synthesized copper nanoparticles can be potentially employed in the treatment of various microbial infections due to their potent antioxidant, anti-bacterial, and anti-fungal activities.

The changes in consumer preferences and the increasingly competitive global market have demanded that food entrepreneurs engage in innovative value-added activities. The date is a delicatessen fruit known by its content of active compounds (e.g., dietary fiber and antioxidants) and its biological activity which has a vast potential in the design of new products such as bioactive ingredients, sugar substitutes, dietary supplements, functional foods, among others. In the current paper, innovative approaches to the value addition to date fruits and their processing by-products have been reviewed from recent high-quality scientific works. New processes such as ultrafiltration and hydrothermal treatments are shown as a useful alternative to obtain differentiated date-based ingredients (e.g., fiber concentrates, sap syrups, and date powders). Moreover, the use of date fruits and their byproducts as natural sources of value-added active compounds in the preparation of dairy, meat, and bakery and cereal products is also presented.

Pinus patula is one of the most planted wood conifer species worldwide; however, no foliar nutrient standards exist for this species up to date. The objective of the present study was to generate and verify two sets of foliar nutrient standards for nearly ten-year-old P. patula trees: critical nutrient concentrations and DRIS norms. Nutrients studied were N, P, K, Ca, Mg, Fe, Cu, Zn, Mn, and B. The reference standards were verified experimentally by installing two fertilization trials; one of them located in Huayacocotla, state of Veracruz and the other one in Aquixtla, state of Puebla, Mexico. Nutrient status of each fertilization trial was correctly predicted by critical nutrient values and DRIS as well. Both standards were able to detect the secondary growth-limiting nutrient deficiency in the Huayacocotla trial, where the primary limitation for growth was scarcity of solar radiation within tree crowns. The limiting nutrient in both experimental trials was K.

The use of plant extracts in the synthesis of metal nanoparticles is a very attractive approach in the field of green synthesis. To benefit from the potential synergy between the biological activities of the Moringa oleifera leaves extract and metallic bismuth, our study aimed at synthesizing bismuth nanoparticles using a hydroalcoholic extract of M. oleifera leaves as a means of green synthesis that yields nontoxic products and reduces the production of wasteful material. To this end, the M. oleifera leaves extract was treated with a bismuth nitrate pentahydrate solution. A color change from light brown to dark brown indicates the synthesis of bismuth nanoparticles. The total phenolic content in the M. oleifera leaves extract used was 23.0 ± 0.3 mg gallic acid equivalent/g of dried M. oleifera leaves powder. Antioxidant property of MO synthesised bismuth Nanoparticles was evaluated and in line with the extract used in the synthesis of NPs. The physical properties of the synthesized bismuth nanoparticles were characterized using UV-Vis spectrophotometer, FT-IR spectrometer, TEM, SEM, and XRD. The synthesized bismuth nanoparticles have a size in the range of 40.4-57.8 nm with amorphous morphology. Using DPPH and phosphomolybdate assays, our findings revealed that the M. oleifera leaves extract and the synthesized bismuth nanoparticles possess antioxidant properties. Using resazurin microtiter assay, we also demonstrate that the M. oleifera leaves extract and the synthesized bismuth nanoparticles exert potent anti-bacterial activity against Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus and Enterococcus faecalis, similarly to the inhibition exerted by Moringa extract, especially against Enterococcus faecalis (MIC values for the extract: 500, 250, 250, and 250 µg/mL; MIC values for the bismuth nanoparticles: 500, 500, 500, and 250 µg/mL, respectively). Similarly, the M. oleifera leaves extract and the synthesized bismuth nanoparticles display relatively stronger anti-fungal activity against Aspergillus niger, Aspergillus flavus, Candida albicans, and Candida glabrata (MIC values for the extract: 62.5, 62.5, 125, and 250 µg/mL; MIC values for the bismuth nanoparticles: 250, 250, 62.5, and 62.5 µg/mL, respectively). Thus, the hydroalcoholic extract of M. oleifera leaves was successfully used in the synthesis of bismuth nanoparticles, showing a positive antioxidant, anti-bacterial, and anti-fungal activity. Therefore, the synthesized bismuth nanoparticles can potentially be employed in the alleviation of symptoms associated with oxidative stress and in the topic treatment of Candida infections.

In various countries of the global south, school feeding programs are implemented in order to alleviate short-time hunger in children, improved their nutrition and provide employment for food vendors. The impact of these programs is important not only in terms of pupils’ nutrition but also in improving farmers’ livelihoods. This study analyzes the impact of the school feeding program on smallholders' food security based on data collected in 2021 from 240 farmers surveyed in northeast Nigeria. Differently from other studies, several econometric methods are used to analyze the data, namely, binary probit regression, propensity score matching, inverse probability weighted adjusted regression and endogenous switching regression. The results show that about 40% of the beneficial smallholder farmers are food secure compared to 20% of the non-beneficiary households. Findings revealed that the Homegrown school feeding program (HGSF) positively improved smallholder farmers improve household food security status. Results provide evidence for the need to expand school feeding programs vis-a-vis interventions in facilitating farmers’ access to capital and capacity building for better integration in the supply chain.

Poso regency, Central Sulawesi, Indonesia, has a coastal area that has marine tourism potential to be developed. It is expected that marine tourism can bring socio-economic impact to the com-munity. This research was conducted with the objective of assessing the suitability of the area to be developed as a marine and coastal tourism site to provide benefits to the coastal community. Hydrodynamic model will be used in this research as coastal area mapping. As an approach, Analysis Hierarchy Process (AHP) is utilized whose parameters consist of depth, coast type, coast width, brightness, current speed, water base materials, observation of dangerous biota and availability of fresh water. Based on the overall mapping area of 98,644 ha, the research results show that the area that can be utilized is 7,979 ha with a very suitable category, while there is an area of 1,045 ha which can still be classified in the appropriate category.

Herein, we report a stimuli responsive hydrogel with inhibitory activity against Escherichia coli prepared by chemical crosslinking of carboxymethyl chitosan (CMCs) and hydroxyethyl cellulose (HEC). The hydrogels were prepared by esterification of chitosan (Cs) with monochloroacetic acid to produce CMCs which was then chemically crosslinked to HEC using citric acid as the crosslinking agent. To impart a stimuli responsiveness property to the hydrogels, polydiacetylene-zinc oxide (PDA-ZnO) nanosheets were synthesized in-situ during the crosslinking reaction followed by photopolymerization of the resultant composite. To achieve this, ZnO was anchored on carboxylic groups in 10,12-pentacosadiynoic acid (PCDA) layers to restrict movement of alkyl portion of PCDA during crosslinking CMCs and HEC hydrogels. This was followed by irradiating the composite with UV radiation to photopolymerize the PCDA to PDA within the hydrogel matrix so as to impart thermal and pH responsiveness to the hydrogel. From the results obtained, the prepared hydrogel had a pH dependant swelling capacity as it absorbed more water in acidic media as compared to basic media. Incorporation of PDA-ZnO resulted in a thermochromic composite responsive to pH evidenced by a visible colour transition from pale purple to pale pink. Upon swelling, PDA-ZnO-CMCs-HEC hydrogels had inhibitory activity against E. coli attributed to the slow release of the ZnO nanoparticles as compared to CMCs-HEC hydrogels.

The possibility of using graphane monolayer crystals as the electrode material is becoming popular. Graphane is stable at room temperature and has large surface area, but its chemical inertness hinders its direct interactions with Li ions. In this study, we performed density functional theory calculations to study the energetic stability, structural and electronic properties of Li on graphane with various CH divacancy configurations (v12, v13 and v14 ). The results show that adsorption of Li atom reduces the formation energy of the CH divacancy configurations. The Li-v12 is most stable with the highest binding energy of 3.25 eV/Li and relaxes to in-plane with other C atoms. Altering the Li charge state to have Li−1-v12 or Li+1 -v12 affects the energetic stability and electronic characters of Li-v12 . The Li−1-v12 (Li +1-v 12 ) enhances (reduces) the binding force between Li and v 12 configuration, and furthermore it improves (deteriorates) the conductivity of the structure. Further investigation of graphane with vacancies is encouraged due to these intriguing observations, as it holds promise for potential utilization as an electrode material.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

Development of load-bearing polymer composite constructions in civil engineering, such as industrial chimneys and gas ducts, requires filling some research gaps. These gaps are related to long-term behavior under mechanical and temperature loads, cyclic ones as well. Such conditions mean that viscoelasticity should be considered. First of all, viscoelastic parameters should be calculated, based on the used inner structure model. Then, due to the high working temperatures, thermal aging should be considered as well. This research is devoted to effects of thermal aging on viscoelastic behavior of polymers. Two sets of experiments were conducted: creep tensile tests and cyclic heating in constrained state. The Kelvin-Voigt viscoelasticity model was used to determine the rheological parameters of binder from experimental creep curves. Thermally-aged binder turned out to lose its viscous properties and became elastic. Cyclic heating was used to compare the behavior of normal and thermally-aged binder and to evaluate the possibility of temperature stress accumulation. Similar to creep tests, cyclic heating showed that aged binder lost viscosity and worked almost perfectly elastic. Non-aged binder was prone to stress accumulation much more. As a result, the rheological behavior of aged and non-aged binder was compared and showed a significant difference. In addition, the influence of temperature stress accumulation on stress—strain state was evaluated and turned out to be significant as well. The obtained results can be further used to predict the mechanical behavior of structures made of polymer composites operating for a long time at elevated temperatures, including those exceeding the glass transition temperature.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

Stairs are among the key elements in architectural composition, both aesthetically and spatially. They are also one of the main innovations in architecture and building, allowing pedestrians to bridge considerable height differences with relative efficiency. It is therefore surprising that, in spite of all stair regulations in building codes, stairs are responsible for a huge number of accidents – second only to motorcars. The extent of safety failures suggests that user interaction with stairs is poorly understood by designers and policy makers. This is not unrelated to the lack of research into the design and use of stairs. Templer’s seminal work is the exception but it dates from 1992 and since then little has been done to understand the relation between architectural design and stair performance, including safety. To redress this, we can build on affordance-based analyses of stair climbability, which establish a clear connection between the form of a stair and the perception of action possibilities and dangers by all kinds of users. By doing so affordances establish a comprehensive and consistent framework for the analysis of architectural designs, which utilizes both domain and psychological knowledge, including as a foundation for computational applications.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

Addressing nautical bottlenecks is crucial to optimize the utilization of inland waterways and maximize economic benefits of transports. To maximize economic benefits, a study was conducted to validate a Key Performance Indicator (KPI)-based framework. This framework offers a structured approach to assess the impact of resolved nautical bottlenecks on the economic benefits of inland waterway transport (IWT). To validate the applicability of the KPI framework, interviews with eleven experts were conducted. The goal was to prioritize each KPI based on their insights. The results of the interviews shed light on the relevance and coherence of both the individual KPIs and the overall KPI framework. The experts confirmed the importance of measures related to transportation efficiency, such as reduced transit times, increased vessel throughput, and enhanced reliability. The validated KPI-based framework serves as a valuable tool for policymakers, industry stakeholders, and researchers. It enables the assessment of the effects of resolving nautical bottlenecks in inland waterway systems. Future research should focus on quantifying the multifaceted impacts, making this framework even more useful for decision-making processes concerning investments in infrastructure upgrades and maintenance.

Machine learning (ML) is growing in popularity for various particle accelerator applications including anomaly detection such as faulty beam position monitor or RF fault identification, for non-invasive diagnostics, and for creating surrogate models. ML methods such as neural networks (NN) are useful because they can learn input-output relationships in large complex systems based on large data sets. Once they are trained, methods such as NNs give instant predictions of complex phenomenon, which makes their use as surrogate models especially appealing for speeding up large parameter space searches which otherwise require computationally expensive simulations. However, quickly time varying systems are challenging for ML-based approaches because the actual system dynamics quickly drifts away from the description provided by any fixed data set, degrading the predictive power of any ML method, and limits their applicability for real time feedback control of quickly time-varying accelerator components and beams. In contrast to ML methods, adaptive model-independent feedback algorithms are by design robust to un-modeled changes and disturbances in dynamic systems, but are usually local in nature and susceptible to local extrema. In this work, we propose that the combination of adaptive feedback and machine learning, adaptive machine learning (AML), is a way to combine the global feature learning power of ML methods such as deep neural networks with the robustness of model-independent control. We present an overview of several ML and adaptive control methods, their strengths and limitations, and an overview of AML approaches. A simple code for the adaptive control algorithm used here can be downloaded from: https://github.com/alexscheinker/ES_adaptive_optimization

We study the Kelvinons: monopole ring solutions to the Euler equations, regularized as the Burgers vortex in the viscous core. There is finite anomalous dissipation in the inviscid limit. However, in the anomalous Hamiltonian, some terms are growing as logarithms of Reynolds number; these terms come from the core of the Burgers vortex. In our theory, the turbulent multifractal phenomenon is similar to asymptotic freedom in QCD, with these logarithmic terms summed up by an RG equation. The small effective coupling does not imply small velocity; on the contrary, velocity is large compared to its fluctuations, which opens the way for a quantitative theory. In the leading order in the perturbation theory in this effective coupling constant, we compute running multifractal dimensions for high moments of velocity circulation, in good agreement with the data for quantum Turbulence and available data for classical Turbulence. The logarithmic dependence of fractal dimensions on the loop size comes from the running coupling in anomalous dimensions. This slow logarithmic drift of fractal dimensions would be barely observable at Reynolds numbers achievable at modern DNS.

The creative potential of homo sapiens is the biological basis of his spirituality. To take into account factor of spirituality in analysis of global problems of demography and ecology used analogies of chemical kinetics. Stable in the historical time, population of people was modeled by open thermodynamic system, equilibrium state of which depends on climate and geophysics. The demography of creative potential was divided into two geographical zones - north and south. Process of society sapientation was formalized by introducing into logistic equation of Verhulst, in addition to a couple of parents, at least one more teacher from among educated people. Stationary solution of the modified kinetic equations determines optimal demography for sustainable development of population in accordance with its education index. The solution of the demographic, energy and environmental problems of mankind is determined by the level of world creative potential, the growth of which is currently limited by the consumption paradigm.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

This paper considers two types of Protestant ethnic groups of some areas of Urals and Western Siberia. The first type consist of representatives of members of different ethnic groups consisting of well-educated professionals, incorporated into industrial society and associated with the intellectualism of Protestantism. The second type is represented by the indigenous peoples of the Polar Urals and Western Siberia, who use the Protestant religious organizations as tool for restoring life-sustaining elements of the native peoples’ traditional economy. I employed the inductive approach and the comparison method; during the fieldwork I used ethnographic participant observations, sociological structured interviews and closed-ended questionnaires. The empirical data have been collected in the Southern, Middle and Polar Urals and Western Siberia.

The diagnosis and treatment of autoimmune diseases are major challenges. Their signs, symptoms, and their clinical severity change throughout the course of the disease. The available treatments for these conditions do not result in complete remission, and their long-term use generates several side effects. The main challenges in this field are the development of more sensitive diagnostic tools, and effective treatments. Studies in patients and animal models reported alterations in monocyte phenotype, activation, and function. These cells play an essential role in the pathogenesis of chronic inflammatory states and have become a target for extracorporeal monitoring and specific intervention. Nanoparticles (NPs) are ultrafine particles that can be used as contrast agents in diagnostic imaging techniques to detect specific cells in inflammatory infiltrates in tissues that are not easily accessible by biopsy. In addition, NPs can be designed to deliver drugs to a cell population or tissue. This review describes several experimental pieces of evidence demonstrating that monocytes and macrophages can specifically uptake various types of NPs, thus becoming detectable in vivo by magnetic resonance imaging and susceptible to be modulated by therapeutic agents. Hence, nanoparticles targeting mononuclear phagocytes represent promising tools for diagnosing and treatment.

The use of probiotics in reproductive-related dysbiosis is an area of continuous progress due to the growing interest from clinicians and patients suffering recurrent reproductive microbiota disorders. An imbalance in the natural colonization sites related to reproductive health: vaginal, cervicovaginal, endometrial and also pregnancy-related altered microbiota could play decisive role in reproductive outcomes. Oral and vaginal administrations are in continuous discussion regarding the clinical effect pursued, but probiotics as oral supplement therapy is the route administration better studied. To complement and summarise with qualitative and quantitative information of vaginal probiotics clinical studies, the main objective of this work was to retrieve the standardised protocols commonly used and their microbiota modulation capacities. The studies selected were related to treat bacterial vaginosis (BV) as the most commonly disorder, few studies on vulvovaginal candidiasis (VVC) and also administration to stabilise microbiota before in vitro fertilisation (IVF). Probiotic doses administered were similar to oral probiotics protocols, ranging from ≥ 107 CFU/day to 2.5 x 1010 CFU/day, and variable regarding posology duration from 1 day to 12 weeks, being 1 week commonly applied. Moderate modulation was achieved regarding the relative abundance decrease of abnormal microbiota, coinciding with parallel increase in Lactobacillus species.

This research is completed in development of researches devoted to relations between elastic modulus (MoE) and thermal expansivity (CTe) of different materials. This study experimentally confirmed the relation of MoE and CTe for thermosetting polymers and FRPs both under normal and heating temperatures. As the thermosetting polymers, the epoxy compositions were used and glass fiber and mineral additives for FRP. The experiment was based on dilatometric and elastic deformations’ testing. Also, the relaxation model of MoE/CTe was proposed, that is based on physical properties of polymers’ supramolecular structures. The comparative analyzing of MoE/CTes’ different models is done. The most accurate models are determined, and the relaxation model has demonstrated the high accuracy.

The purpose of this study is to analyse the feasibility of using waste from granite gang saws (GCW-GS) to manufacture ultra-high performance, steel-fibre reinforced concrete (UHPFRC). These machines cut granite blocks by abrasion using a steel blade and slurry containing fine steel grit. The waste generated by gang saws (GCW-GS) contains up to 15% Fe2O3 and up to 5% CaO. This is the main difference from the waste produced by diamond saws (GCW-D). Consequently, the second objective of this study is to compare the results of the waste obtained with gang saws with that from diamond saws, in order to determine the influence of iron and calcium oxides. The waste from cutting granite with gang saws was used in different percentages to replace micronized quartz powder of natural origin in the manufacture of UHPRFC. All the test specimens were analysed to determine their compressive strength, elasticity modulus, flexural strength and indirect tensile strength. The final conclusion is that wastes from both gang saws and diamond saws can be used to manufacture UHPFRC with an improvement in the mechanical properties up to a 35% replacement. The results for GCW-GS are better, mainly due to the pozzolanic effect of the iron dioxide. For higher percentage replacements the mechanical properties are close to the control concrete with small decreases.

In the work, the nuclei structure was modeled by a system of shells consisting of combinations of spherical and annular vortices. Having connected the spin and the magnetic moments of the nuclei with the dynamics of the outer shell of the nucleus, we determined its type and carried out a semiempirical calculation of the parameters of its structure for stable and unstable nuclear isotopes in the sequence of the order number Z from 1 to 37. Comparison of the obtained nuclear sizes with the radii of the electron shells of atoms made it possible to reveal a definite correlation. On the basis of it, it was concluded that the periodic law of Mendeleyev can determine the patterns in the change in the structure of the atomic nucleus.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

Artificial Intelligence (AI) has emerged as a disruptive technology in various areas, and medicine is no exception. In the last decade, there has been rapid progress in the application of AI in healthcare, from diagnosing and prognosticating diseases to improving operational efficiency in hospitals. This literature review explores the advances of artificial intelligence in the field of medicine, discusses its benefits, and discusses the ethical and regulatory challenges that need to be addressed to maximize its potential.

Biopolymers based on plant and animal proteins are interesting alternatives in the development of films with future prospects as food packaging. Considering that in recent years there has been an increasing interest in the valorization of agro-industrial residues and by-products and that the blending of polymers can lead to materials with improved properties, in this work, keratin-rich feather fibers and gliadins were blended at different ratios in order to develop sustainable and biodegradable films. Control gliadin G100, feather F100 films and their blends at 3:1 (G75F25) , 2:2 (G50F50) and 1:3 (G25F75) ratios were successfully developed through thermoprocessing. The physical properties were differentiated as a function of the concentration of both polymeric matrices. Although gliadins showed higher hydrophilicity confirmed by its highest swelling degree, films with high gliadin ratios exhibited lower water permeability values at low and medium relative humidities. On the other hand, the feathers displayed the highest Young´s modulus values and provided oxygen barrier to the blends, principally at the highest relative humidity. In conclusion, the blend of these protein-based polymers at different ratio resulted on interesting composites whose physical properties could be adjusted.

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

Transient receptor potential canonical (TRPC) channels are calcium channels with diverse expression profiles and physiological implications in the retina. Neurons and glial cells of rat retinas with photoreceptor degeneration caused by retinitis pigmentosa (RP) exhibit basal calcium levels that are above those detected in healthy retinas. Inner retinal cells are the last to degenerate and are responsible for maintaining the activity of the visual cortex even after complete loss of photoreceptors. We considered the possibility that TRPC1 and TRPC5 channels might be associated with both the high calcium levels and the delay in inner retinal degeneration. TRPC1 is known to mediate protective effects in neurodegenerative processes, while TRPC5 promotes cell death. In order to comprehend the implications of these channels in RP, the co-localization and subsequent physical interaction between TRPC1 and TRPC5 in healthy retina (Sprague-Dawley rats) and degenerating (P23H-1, a model of RP) retina were detected by immunofluorescence and proximity ligation assays. There was an overlapping signal in the innermost retina of all animals where TRPC1 and TRPC5 physically interacted. This interaction increased significantly as photoreceptor loss progressed. Both channels function as TRPC1/5 heteromers in the healthy and damaged retina, with a marked function of TRPC1 in response to retinal degenerative mechanisms. Furthermore, our findings support that TRPC5 channels also function in partnership with STIM1 in Müller and retinal ganglion cells. These results suggest that increase in TRPC1/5 heteromers may contribute to slowing of the degeneration of the inner retina during the outer retinal degeneration.

We propose a promising model of discrete spacetime based on nonassociative geometry and complex networks. Our approach treats space as a simplicial 3-complex (or complex network), built from "atoms" of spacetime and entangled states forming n-dimensional simplices (n=1,2,3). At large scales, a highly connected network is a coarse, discrete representation of a smooth spacetime. We show that for high temperatures, the network describes disconnected discrete space. At the Planck temperature, the system experiences the phase transition, and for low temperatures, the space becomes a triangulated discrete space. We show that the cosmological constant depends on the universe’s topology. The "foamy" structure, analogous to Wheeler’s "spacetime foam," significantly contributes to the effective cosmological constant, which is determined by the Euler characteristic of the universe.

A hallmark of Alzheimer’s disease (AD) are the proteinaceous aggregates formed by the amyloid-beta peptide (Aβ) that is deposited inside the brain as amyloid plaques. The accumulation of aggregated Aβ may initiate or enhance pathologic processes in AD. According to the amyloid hypothesis any agent that has the capability to inhibit Aβ aggregation and/or destroy amyloid plaques represents a potential disease modifying drug. In 2023, a humanized IgG1 monoclonal antibody (lecanemab) against the Aβ soluble protofibrils was approved by US FDA for AD therapy thus providing compelling support to the amyloid hypothesis. To acquire a deeper insight on the in vivo Aβ aggregation, various animal models, including aged herbivores and carnivores, non-human primates, transgenic rodents, fish and worms are widely exploited. This review is based on the recent data obtained using transgenic animal AD models and presents experimental verification of the critical role in Aβ aggregation seeding of the interactions between zinc ions, Aβ with the isomerized Asp7 (isoD7- Aβ) and the α4β2 nicotinic acetylcholine receptor.

This is the second paper in a cycle investigating the exact solution of loop equations in decaying turbulence. We perform numerical simulations of the Euler ensemble, suggested in the previous work, as a solution to the loop equations. We designed novel algorithms for simulation, which take a small amount of computer RAM so that only the CPU time grows linearly with the size of the system and its statistics. This algorithm allows us to simulate systems with billions of discontinuity points on our fractal curve, dual to the decaying turbulence. For the vorticity correlation function, we obtain \textbf{quantum fractal laws} with regime changes between universal discrete values of indexes. The traditional description of turbulence with fractal or multifractal scaling laws does not apply here. The measured conditional probabilities of fluctuating variables are smooth functions of the logarithm of scale, with statistical errors being negligible at our large number of random samples $T = 167,772,160$ with $N = 200,000,000$ points on a fractal curve. The quantum jumps arise from the analytical distribution of scaling variable $X = \frac{\cot^2(\pi p/q)}{q^2}$ where $\frac{p}{q}$ is a random fraction. This distribution is related to the totient summatory function and is a discontinuous function of $X$. In particular, the energy spectrum is a devil's staircase with tilted uneven steps with slopes between $-1$ and $-2.03$. The logarithmic derivative of energy decay $n(t)$ as a function of time is jumping down the stairs of universal levels between $1.50$ and $1.53$. The quantitative verification of this quantization would require more precise experimental data.

Long-range interactions are relevant for a large variety of quantum systems in quantum optics and condensed matter physics. In particular, the control of quantum-optical platforms promises to gain deep insights in quantum-critical properties induced by the long-range nature of interactions. From a theoretical perspective, long-range interactions are notoriously complicated to treat. Here, we give an overview of recent advancements to investigate quantum magnets with long-range interactions focusing on two techniques based on Monte Carlo integration. First, the method of perturbative continuous unitary transformations where classical Monte Carlo integration is applied within the embedding scheme of white graphs. This linked-cluster expansion allows to extract high-order series expansions of energies and observables in the thermodynamic limit. Second, stochastic series expansion quantum Monte Carlo which enables calculations on large finite systems. Finite-size scaling can then be used to determine physical properties of the infinite system. In recent years, both techniques have been applied successfully to one- and two-dimensional quantum magnets involving long-range Ising, XY, and Heisenberg interactions on various bipartite and non-bipartite lattices. Here, we summarise the obtained quantum-critical properties including critical exponents for all these systems in a coherent way. Further, we review how long-range interactions are used to study quantum phase transitions above the upper critical dimension and the scaling techniques to extract these quantum critical properties from the numerical calculations.

The evolution of manual grooming and its implications have received little attention in the quest to understand the origins of simian primates and their social and technical intelligence. All simians groom manually, whereas prosimians groom orally despite comparable manual dexterity between some members of the two groups. Simians also exhibit a variable propensity for the manipulation of inanimate, non-food objects, which has culminated in tool making and tool use in some species. However, lemuriform primates also seem capable of tool use with training. Furthermore, lemuriforms appear to understand the concept of a tool and use their own body parts as “tools”, despite not using inanimate objects. This suggests that prosimian primates are pre-adapted for proprioceptive object manipulation and tool use, but do not express these cognitive abilities by default. This essay explores the paleontological, anatomical, cognitive, ethological, and neurological roots of these abilities and attempts to explain this behavioural divide between simians and prosimians. Common misconceptions about early primate evolution and captive behaviours are addressed, and chronological inconsistencies with Machiavellian Intelligence are examined. A “licking to picking” hypothesis is also proposed to explain a potential link between manual grooming and object manipulation, and to reconcile the inconsistencies of Machiavellian Intelligence. Bayesian decision theory, the evolution of the parietal cortex and enhanced proprioception, and analogies with behavioural changes resulting from artificial selection may help provide new insights into the minds of both our primate kin and ourselves.

Guessing effects frequently occur in testing data in educational or psychological applications. Different item response models have been proposed to handle guessing effects in dichotomous test items. However, it has been pointed out in the literature that the oftenemployed three-parameter logistic model poses implausible assumptions regarding the guessing process. The four-parameter guessing model has been proposed as an alternative to circumvent these conceptual issues. In this article, the four-parameter guessing model is compared with alternative item response models for handling guessing effects through a simulation study and an empirical example. It turned out that model selection for item response models should be rather based on the AIC than the BIC. However, the RMSD item fit statistic used with typical cutoff values was found to be ineffective in detecting misspecified item response models. Furthermore, sufficiently large sample sizes are required for sufficiently precise item parameter estimation. Moreover, it is argued that the criterion of statistical model fit should not be the sole criterion of model choice. The item response model used in operational practice should be valid with respect to the meaning of the ability variable and the underlying model assumptions. In this sense, the four-parameter guessing model could be the model of choice in educational large-scale assessment studies.

The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses extensions of invariance alignment and Haberman linking concerning the choice of linking functions for comparisons of many groups. Robust linking functions are proposed for invariance alignment and robust Haberman linking that are particularly suited to item response data under partial invariance. In a simulation study, it is shown that both linking approaches have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment.

The results of measuring the capacity of a thin-film solid-state lithium-ion battery (SSLIB) Ti|Si@O@Al|LiPON|LiCoO₂|Ti at different charge currents are reported. It is shown that the dependence of the capacity on the current density follows the Peukert's law, which is characterized by a low value of the Peukert’s exponent in the region of low currents and a high value of the exponent in the region of high currents. A capacity model for a SSLIB based on the balance of diffusion and migration currents is proposed and the dependence of the capacity on the charge current is derived. The obtained dependence is well approximated by Peukert's law with an exponent of 1.39, which is intermediate between the exponents for lithium-ion batteries with liquid electrolyte and SSLIBs.

The paper proposes a game theory model of price oligopoly with a heterogeneous product, where total demand depends linearly on the minimum market price. This model develops the Bertrand oligopoly for the case of imperfect price elasticity of demand. The most interesting result is an asymmetric Nash equilibrium with different prices and sales in the situation of symmetric oligopolists. The asymmetry is explained by the fact that a decrease in the minimum market price leads not only to a reallocation of consumers among firms, but also to an increase in total demand. Modifications of a two-stage game with a leader and followers and the price discrimination model are also considered. Microeconomic substantiation of the model is given on the basis of the spatial differentiation approach.

In the first part of this article, we discuss and generalize the complete convergence introduced by Hsu and Robbins (1947) to the r-complete convergence introduced by Tartakovsky (1998). We also establish its relation to the r-quick convergence first introduced by Strassen (1967) and extensively studied by Lai (1976). Our work is motivated by various statistical problems, mostly in sequential analysis. As we show in the second part, generalizing and studying these convergence modes is important not only in probability theory but also to solve challenging statistical problems in hypothesis testing and changepoint detection for general stochastic non-i.i.d. models.

By analyzing the urban landscape, this investigation focuses on commercial typologies in historical urban areas and its relationship with the urban landscape and its heritage values. Trade plays an essential role in historical urban areas, both in the past and in the present, since it is part of the urban landscape—creating it and modifying it, but also preserving it. Historical protected urban areas contain diverse elements reflecting the impacts of commercial activities that have existed in cities throughout history. At present, the urban landscape of commercial activity is made up of a multiplicity of typologies and formats which interact with the historical landscape and its values, using them to strengthen its strategies of attraction, differentiation, and sales. Shop owners contribute to the preservation of historic urban areas by maintaining the commercial functions within them. Therefore, we affirm that the role of commercial activity in the preservation of urban protected areas is essential. However, further research is needed because this aspect has not been addressed in depth by the scientific community specializing in the management of cultural heritage.

The windowpane oyster Placuna placenta, lives in brackish coastal waters and has long been economically important to the Philippines because of its durable and translucent shell, which is used as a glass substitute and material for making windowpanes and handicrafts. There has been considerable degradation of the windowpane oyster fishery in past decades. Moreover, there are waste and under-exploited by-products such as the meat, which though nutritious and edible, currently has a very low value; its use being confined to dishes consumed by families in the fishing community. Historically there are instances of excellent practice in terms of regulating the windowpane oyster fishery, while in recent times there have been local initiatives to restore this and to develop high value food products from the meat. These initiatives have, however, never been followed through at a provincial or national level. Research on other molluscs and marine organisms highlights that these also contain high value pharmaceutical products. This review compiles evidence to establish the groundwork for an essential and comprehensive multidisciplinary research program centred around the windowpane oyster, which would ensure a high value for all parts of the oyster, including those currently discarded. By fostering a sustainable and circular economy within this fishery sector and its associated industry, its economic value would be amplified. This is particularly important for oyster harvesters in the Philippines, who often find themselves at the economic ladder's lower rungs. By integrating the principles of a circular economy, this initiative would not only aim to uplift the economic prospects of these harvesters but in doing so would drive the restoration of the windowpane oyster to its former range.

This paper presents one of the approaches to solution of the problem of the repulsion origin in gravity. The approach is based on the property of compactness characteristic for a self-gravitating object in General Relativity. Here we understand compactness as estimation of the upper boundary for mass such an object in a static two--dimensional sphere. Repulsion originates when this boundary is violated. The main hypothesis is formulated in the form of the principle of maximal mass within a two-dimensional static sphere. It is demonstrated that the principle is true for Schwarzschild black holes on absorption of the matter in the process of accretion, both in the classical case and with due regard for quantum-gravitational corrections. The results have been extended to black holes with the Schwarzschild-de Sitter metric in the early Universe. The applicability of the principle suggested is analyzed for the early and for the present Universe.

The invariant mass of free particles is used to derive a bound-state equation for several particle atomic systems at rest. This relativistic-kinematic bound-state equation is applied to the hydrogen and helium atoms. The derived equation has the well-known solutions for the single-electron bound states of the hydrogen atom, and the two-electron states of the helium atom. For the hydrogen atom, existence of the two-particle bound states, for which the electron and the proton kinetic energies are of the same order of magnitude, is predicted. The three-particle bound states with the same feature of the kinetic energies can exist in the helium atom. Radiative operators for processes involving the hydrogen two-particle bound states, are obtained. It is discussed that these new two- and three-particle bound states should be optically inactive.

We consider the Dirac electron as a nonperturbative particle-like solution consistent with its own Kerr-Newman (KN) gravitational and electromagnetic field. We develop the earlier models of the KN electron regularized by Israel and López, and consider the non-perturbative electron model as a bag model formed by Higgs mechanism of symmetry breaking. The López regularization determines the unique shape of the electron in the form of a thin disk with a Compton radius reduced by 4π. In our model this disk is coupled with a closed circular string which is placed on the border of the disk and creates the caused by gravitation frame-dragging string tension produced by the vector potential of the Wilson loop. Using remarkable features of the Kerr-Schild coordinate system, which linearizes the Dirac equation, we obtain solutions of the Dirac equation consistent with the KN gravitational and electromagnetic field, and show that this solution takes the form of a massless relativistic string. Parallelism of this model with quantum representations in Heisenberg and Schrodinger pictures explains remarkable properties of the stringy electron model in the relativistic scattering processes.