The decarbonization of the electricity grid as one among the actions to reduce fossil fuel emissions, and thus their impact on global warming in the future, will be achieved mainly via the integration and widespread diffusion of renewable power sources. This is going to be supported also by the shift from the paradigm production-transmission-distribution, where electricity production oversees large-size power plants, to renewable-based distributed/diffused production, where electricity is generated very close or even by the same (group of) user(s) (or prosumers in the latter case). The number of mid-/small-size installations based on renewable energy technologies will therefore increase substantially, and the related renewable generation will be dominant against that from large-size power plants. Unfortunately, this will reduce the reliability of the grid very likely, unless appropriate countermeasures are taken/implemented, hopefully at the same time the paradigm shift is being achieved. To this aim, it is important to identify the anomalies and main fault causes that might determine in renewable-based power systems. This paper surveys the current state-of-the-art on anomalies and faults affecting wind-PV-storage hybrid power systems, i.e., the main renewable technology ensemble that will establish the future grid, and highlights possible research directions that may help to fill the literature gaps.

A low cost, scalable and robust process has been proposed as innovative method for coating solar mirrors with a self-cleaning, transparent in the full solar range and versatile material based on auxetic aluminium nitrides, previously obtained on the laboratory scale. This work presents the scaling-up of fabrication process from laboratory to prototypal scale and pre-liminary results of outdoor self-cleaning solar mirror prototypes testing in the demonstrative CSP plant ENEASHIP located in Casaccia (Rome) ENEA research centre. Prototypes 50x40cm have shown stability in external conditions: no degradation occurs during the test campaign. Their washing restores the initial reflectance affected by soiling and the self-cleaning performance allows the utilization of a reduced quantity of water for cleaning operations with respect to uncoated glass of back surface mirrors. A similar self-cleaning AlN coating could be utilized on other solar components affected by soiling, such as glass envelopes of heat collecting elements, PV panels and everywhere are required self-cleaning performance combined to optical ones.

Phased injection of natural gas into internal combustion marine engines is a promising solution for optimizing performance and reducing harmful emissions, particularly unburned methane, a potent greenhouse gas. This innovative practice distinguishes itself from continuous injection because it allows for more precise control of the combustion process with only a slight increase in system complexity. By synchronizing the injection of natural gas with the intake and exhaust valve opening and closing times while also considering the gas path in the manifolds, methane release into the atmosphere is significantly reduced, making a substantial contribution to efforts to address climate change. Moreover, phased injection improves the efficiency of marine engines, resulting in reduced overall fuel consumption, lower fuel costs, and increased ship autonomy. This technology has been tested on a single-cylinder, large-bore, four-stroke research engine designed for marine applications, operating in dual-fuel mode with diesel and natural gas. Performance was compared with that of the conventional continuous feeding method. Evaluation of the effect on equivalent CO2 emissions indicates a potential reduction of up to approximately 20%. This reduction effectively brings greenhouse gas emissions below those of the diesel baseline case, especially when injection control is combined with supercharging control to optimize the air-fuel ratio.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The multiphase motors due to their reliability find new areas of application in the industry and transportation. Developing of any system with multiphase motors at first requires elaborate model of the system, define control mode and controllers. The model of control system for the six-phase drive is based on its conventional d-q mathematical model and indirect field-oriented control. Simulink model of six-phase drive with indirect field-oriented control and simulated with two types PID and PID fuzzy controllers. The simulation results, reflecting step response and provided speed reference law as well as load application at constant speed, are presented and analyzed. Fuzzy controller with 49 rules is considered and applied. Six-phase electric drive with field-oriented control at well-tuned PID controller has good step response specifications: settling time 0.13 s at starting without load, step response has no overshoot, small about 1 % short time decrease of speed at load of 5 Nm application. The system employing PID fuzzy controller shows slightly better results in response to torque application: the decrease in speed 1 % is eliminated in shorter time. Simulation results indicate the robustness of both systems with six-phase drive to disturbance and provide high quality transient specifications at determined reference speed.

Currently, there is a high interest in smart sensors and integrated composite materials in various industries such as construction, aviation, automobile, medical, information technology, communication, and manufacturing. Here, a new conceptual design for a force and temperature sensor system is developed by integrating fiber optic Bragg grating sensors embedded within composite materials, and a mathematical model is proposed that allows one to estimate strain and temperature based on signals obtained from optical Bragg gratings. This is important for understanding the behaviors of sensors under different conditions and for creating effective monitoring systems. Describing the strain gradient distribution, especially considering different materials with different values of Young's modulus, provides insight into how different materials respond to applied forces and temperature changes. The shape of the strain gradient distribution was obtained, which is a quadratic function with a maximum value of 1500 µ, with a maximum value at the center of the lattice and a symmetrically decreasing strain value with distance from the central part of the fiber Bragg grating. With axial strain at the installation site of the Bragg grating sensor under applied force values ranging from 10 to 11 N, the change in strain was linear. As a result of theoretical research, it was found that the developed system with fiber-optic sensors based on Bragg gratings embedded in composite materials is resistant to external influences and temperature changes.

Background: Gender differences characterize the prevalence and attitudes toward weight management. Despite limited evidence suggesting greater weight loss in women with anti-obesity pharmacotherapy, gender-specific analysis remains underexplored. This retrospective study aimed to evaluate the gender-specific response to liraglutide 3.0 mg treatment in people with obesity without type 2 diabetes (T2D). Methods: Data were collected from 47 patients (31 women, 16 men), with age > 18 years, BMI ≥ 30 kg/m^2, absence of T2D, and with exclusion of prior anti-obesity treatment, comorbidities, or bariatric surgery. Only patients who maintained the liraglutide 3.0 mg dose for at least 6 months were included. Results: Both sexes showed significant reductions in weight and BMI at 3 and 6 months. Men achieved greater weight loss (WL), BMI reduction, % WL, WL >5% and >10% than women, and they also showed more significant improvements in metabolic parameters (total and LDL cholesterol, Fibrosis-4 Index FIB-4). No significant gender differences were observed in glucose metabolism or renal function. Conclusion: This study showed a greater therapeutic effect of liraglutide 3.0 mg in men. Given men's higher risk of cardiovascular disease (CVD), and underrepresentation in clinical weight loss programs, these findings may increase male engagement and improve their CVD risk.

Yttria stabilized zirconia materials are frequently used in mechanical engineering and biomedical applications. Demanding loading conditions require materials combining a high level of strength and fracture toughness. A ready to press alumina doped 2 mol% yttria stabilized zirconia powder was shaped by axial pressing and sintering in air at 1250 - 1500 °C for 2 h. The best combination of strength and toughness value of 7.8 MPa√m. Materials sintered in the middle of the chosen temperature range combine fully density, high transformability and small grain size. Toughness measurements by direct crack length measurements delivered unrealistically high fracture toughness values.

With continuous increase in integration of the semiconductor devices, the requirements on size accuracy and surface quality of the etched lead frame are higher. The etchant is a key factor on etching process and etched surface quality, while effects of the difference in etchants on microscopic etched surface morphology of the Cu alloy have not been directly studied. In this study, the aqua regia, acidic FeCl3 and CuCl2 solutions were used as etchants, and the CuCrSn alloy specimens with different microstructure were etched and characterized. The results show that the etching rate in aqua regia is high, and the grain orientation, grain boundary (GB) and dislocation have significant influence on the local etching rate. The preferential etching of some atomic planes forms steps between grains with different orientations, and the preferential etching around the GB and dislocation group forms grooves, resulting in high surface roughness. For etching of the FeCl3 and CuCl2 etchants, the steps and grooves become blurred, and almost invisible on the surface etched by the CuCl2 etchant, thus the surface roughness is much lower. The CuCrSn alloy surface etched by the aqua regia is clean, with little Cr-rich particles, while high density Cr-rich particles are remained on the surfaces etched with the FeCl3 and CuCl2 etchants. For the same kind of etchant, the ion concentration can affect the etching mechanism and etching surface morphology.

Bio-molecular reagents like antibodies required in experimental biology are expensive and their effectiveness, among other things, is critical to the success of the experiment. Although such resources are sometimes donated by one investigator to another through personal communication between the two, there is no previous study to our knowledge on the extent of such donations, nor a central platform that directs resource seekers to donors. In this paper, we describe, to our knowledge, a first attempt at building a web-portal titled Bio-Resource Exchange that attempts to bridge this gap between resource seekers and donors in the domain of experimental biology. Users on this portal can request for or donate antibodies, cell-lines and DNA Constructs. This resource could also serve as a crowd-sourced database of resources for experimental biology. Further, in order to index donations outside of our portal, we mined scientific articles to find instances of donations of antibodies and attempted to extract information about these donations at the finest granularity. Specifically, we extracted the name of the donor, his/her affiliation and the name of the antibody for every donation by parsing the acknowledgements sections of articles. To extract annotations at this level, we propose two approaches – a rule based algorithm and a bootstrapped relation learning algorithm. The algorithms extracted donor names, affiliations and antibody names with average accuracies of 57% and 62% respectively. We also created a dataset of 50 expert-annotated acknowledgements sections that will serve as a gold standard dataset to evaluate extraction algorithms in the future. Contact: [email protected], [email protected] Database URL: http://tonks.dbmi.pitt.edu/brx Supplementary information: Supplementary data are available at Database online.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

The purpose of this paper is to explore the implementation of Retrieval-Augmented Gen- 14 eration (RAG) technology with open-source Large Language Models (LLMs). For this purpose, a 15 web-based application named PaSSER that integrates RAG with three models - Mistral:7b, 16 Llama2:7b: Orca2:7b, is developed. In its development various technologies are used, including 17 Blockchain for managing and storing assessment results of built-in testing modules. PaSSER em- 18 ploys a set of evaluation metrics for LLMs’ performance - METEOR, ROUGE, BLEU, Perplexity, 19 Cosine similarity, Pearson correlation, and F1 score. The specialized knowledge base is in the smart 20 agriculture domain. As an illustration, the paper presents results and analysis of two tests. First one 21 is assessing the performance of LLMs across different hardware configurations, the other is deter- 22 mining the model that delivers most accurate and contextually relevant responses within the RAG. 23 The paper also discusses the integration of blockchain technology with LLMs to manage and store 24 assessment results within a blockchain environment. The tests revealed that Mistral:7b demon- 25 strates the best results across most of the evaluated metrics. The discussion section comments on 26 technical and hardware considerations that affect the performance of LLMs. Future development 27 will focus in leveraging new larger pre-trained open-source LLMs, exploring different finetuning 28 approaches, and further integration with Blockchain and IPFS.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

The development of one technology can be portrayed by common methods like Gartner’s hype cycle or S-curve, however, there is no method to characterize the feature distributions of multiple technologies within a specific domain. This study proposes a big data-based method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or causing potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach via comparing historical trends with literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, Random Forest), unsupervised learning models (K-means clustering), and neural networks (Multi-Layer Perceptron) to predict and identify ice formation patterns. Experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real-time, optimizes de-icing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance.

Firefighters Protective Clothing consist of several layers of different properties and functions. The layers create barriers against dangerous outer factor. However, they are also barriers to transport of sweat produced by human organism. It causes that very often sweat in the form of vapor is not transferred effectively from the human organism through the FPC to environment. This leads to sweat condensation on the skin of the FPC user causing his discomfort. In order to ensure as good as possible physiological comfort of firefighter while firefighting and other rescue actions it is necessary to take into consideration the transport of condensed sweat in the multilayer textile set creating the FPC. In the presented work four variants of multilayer textile sets for the FPC have been tested in the aspect of liquid moisture transport. Measurement was performed by means of the Moisture Management Tester (MMT290) – innovative instrument manufactured by the SDL Atlas. Obtained results showed that the transfer of sweat in liquid form occurs only in the inner, next to skin layer of the multilayer sets for the FPC.

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

In the drive towards environmental restoration and sustainable energy solutions, effective utilization and management of agricultural crop residues are vital to mitigate greenhouse gas (GHG) emissions and waste accumulation. Converting biogenic wastes into biofuels and bioproducts has the potential to tackle the energy crisis and contribute to environmental remediation. In this investigation, teff husk, primarily generated in Ethiopia during teff production within the agro-industrial sector, is used as the feedstock for the slow pyrolysis process. In Ethiopia, it is estimated that over 1.75 million tons of teff husk are generated annually, with a significant portion being incinerated in the fields, resulting in substantial environmental pollution. This study focuses on assessing teff husk as a potential material for slow pyrolysis, a crucial stage in biochar production, to tap into its biochar-producing potential. To determine the composition of the biomass, the teff-husk was initially analyzed by thermogravimetry. The significant presence of fixed carbon indicates that teff husk is a viable candidate for pyrolytic conversion into biochar particles. The process of slow pyrolysis took place at three varied temperatures—specifically, at 400, 450, and 500 °C. Process parameters of slow pyrolysis such as reaction time, temperature, and heating rate were enhanced to obtain maximum biochar yields. The highest biochar yield of 43.4 wt% was recorded at optimized reaction time, temperature, and heating rate of 120 min, 400 °C, and 4.2 °C/min, respectively. Furthermore, biochar's physicochemical, SEM-EDX, FTIR, and TGA characterization were performed. The thermal stability and carbon content of biochar were found to rise at higher temperatures. Unlike fuel recovery, the results suggest that teff-husk can be used as a feedstock for biochar production.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

Being able to express broad families of equivariant or invariant attributed graph functions is a popular measuring stick of whether graph neural networks should be employed in practical applications. However, it is equally important to learn deep local minima of losses (i.e., with much smaller loss values than other minima), even when architectures cannot express global minima. In this work we introduce the architectural property of attracting GNN optimization trajectories to local minima as a means of achieving smaller losses. We take first steps in satisfying this property for losses defined over attributed undirected unweighted graphs with a novel architecture, called Universal Local Attractor (ULA). The latter refines each dimension of end-to-end trained node feature embeddings based on graph structure to track the optimization trajectories of losses satisfying some mild conditions. The refined dimensions are then linearly pooled to create predictions. We experiment on 10 tasks, from node classification to clique detection, on which ULA is comparable with or significantly outperforms popular alternatives of similar or greater theoretical expressive power.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

The number of physical devices connected to the Internet such as vehicles, household appliances and other "things" has been steadily increasing in recent years, thus forming the basis for the Internet of Things (IoT). IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse. Interference between networks leads to frame collisions and consequent packet loss. Frame collisions occur when two or more packets overlap in time and frequency and use the same LoRa parameters, i.e. the same spreading factor (SF), bandwidth (BW) and carrier frequency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher time on air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influences this situation. The aim of this experimental work is to verify the effects of collisions and interference in LoRaWAN, regarding the periodicity of transmissions and the payload sizes. The hardware used is an Arduino board programmed in C/C++, a LoRa Bee module operating at a frequency of 868 MHz and a LoRaWAN LG02 gateway. Different scenarios were tested in an outdoor environment, with Line of Sight and Non-Line of Sight, and different variables such as: SFs equal to 7, 9 and 12; distances of 20, 40 and 60 meters and different data sizes of 14, 32 and 51 bytes, over the same channel. The packets with different SF’s and different data sizes were transmitted in a (pseudo) random way, one after the other, overlapping different SFs in the same channel and testing this way (theoretical) orthogonality among them. By fixing the bandwidth in 125 kHz, and testing different SFs we intend to increase the probability of cause packet loss by applying to our connection a combination of emitted power/short distance that leaves an interference power received, strong enough [2] for the effect. We validate our conclusions by analyzing ToA according with RSSI and different data sizes, the RSSI for different SFs and different data sizes and finally the packet loss through the PDR-Packet Delivery Ratio for a better understanding of the reliability of packet transmission in our link.

Recent discoveries of potential ice particles and ice-cemented regolith on celestial bodies like the Moon and Mars have ushered in new opportunities for developing technologies to extract water, facilitating future space missions and activities on these celestial surfaces. This study delves into the potential for water extraction from regolith through an experiment designed to test water recuperation from regolith simulant under varying gravitational conditions. The resultant water vapor extracted from the regolith is re-condensed on a substrate surface and collected in liquid form. Three types of substrates, hydrophobic, hydrophilic, and grooved, are explored. The system’s functionality was assessed during a parabolic flight campaign simulating three distinct gravity levels: microgravity, lunar gravity, and Martian gravity. Our findings reveal that the hydrophobic surface demonstrates the highest efficiency due to drop-wise condensation, and lower gravity levels result in increased water condensation on the substrates. This experiment aims to provide insights into in-situ water recovery, which is pivotal for establishing economically-sustainable water supplies for future missions.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Urban tourism has witnessed significant growth in recent years, driven by various factors that foster the transformation of urban areas into tourist hotspots. These factors include a rising interest in culture and heritage, ongoing urban redevelopment and conversion processes, and the introduction of new experiences and activities. Two particularly influential factors contributing to this growth are low-cost travel, which facilitates more accessible access to urban destinations, and the emergence of new types of accommodation, such as short-term rental (also known as local accommodation or Airbnb-type accommodation). This sudden and continuous growth of this type of tourist accommodation brings new challenges to cities and local communities. On the positive side, it contributes to job opportunities, the dynamism of the local economy, and urban revitalization. On the other hand, it also represents more difficulty in retaining the local population in city centers, as more and more buildings are converted into hostels and tourist apartments. In this context, it becomes essential to conduct studies evaluating the impacts of these dynamics in urban areas and how they influence residents' and visitors' perspectives. Therefore, this study aims to identify the main transformations resulting from short-term rental (STR) development, focusing on the impacts felt by tourists and residents in Porto. A questionnaire was distributed to tourists and residents in Porto's city center in June 2023. The results reveal differences in opinions between tourists and residents, particularly concerning historical heritage preservation, local population relocation, and reduced housing availability for residents. Nevertheless, there is a similarity in their opinions regarding the stimulus that local accommodation brings to the economy and the rehabilitation of buildings and urban areas. This study reinforces the need to pay more attention to the future development of tourist accommodation in city centers, especially regarding local policies and territorial planning, to balance better tourism development and local communities' quality of life.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

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

Three-dimensional (3D) printing technology, also known as additive manufacturing (AM), has emerged as an attractive state-of-the-art tool for precisely fabricating functional materials with complex geometries, championing several advancements in tissue engineering, regenerative medicine, and therapeutics. However, this technology has an untapped potential for biotechnological applications, such as sensor and biosensor development. By exploring these avenues, the scope of 3D printing technology can be expanded and pave the way for groundbreaking innovations in the biotechnology field. Indeed, new printing materials and printers would offer new possibilities for seamlessly incorporating biological functionalities within the growing 3D scaffolds. Herein, we review the extrusion-based 3D printing modalities emphasizing biosensor applications. We highlighted the application of natural, synthetic, and composite biomaterials as 3D-printed soft hydrogels. Emphasis was placed on the approach by which the sensing molecules were introduced during the fabrication process. Finally, the future perspective was provided.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

In the context of improving aircraft safety, this study focuses on the development and evaluation of a graphene-based ice detection system using an environmental chamber. The research is driven by the need for more accurate and efficient ice detection methods, crucial in mitigating in-flight icing hazards. The methodology employed involves testing flat graphene-based sensors in a controlled environment, simulating a variety of climatic conditions that could be experienced in an aircraft during its entire flight. The environmental chamber enabled precise manipulation of temperature and humidity levels, thereby providing a realistic and comprehensive test bed for sensor performance evaluation. The results were significant, revealing the graphene sensors’ heightened sensitivity and rapid response to the subtle changes in environmental conditions, especially the critical phase transition from water to ice. This sensitivity is the key to detecting ice formation at its onset, a critical requirement for aviation safety. The study concludes that graphene-based sensors, tested under varied and controlled atmospheric conditions, exhibit a remarkable potential in enhancing ice detection systems for aircraft. Their lightweight, efficient and highly responsive nature makes them a superior alternative to traditional ice detection technologies, paving the way for more advanced and reliable aircraft safety solutions.

Heavy metals are dangerous contaminants constituting a threat to human health because they persist in soils and are easily transferred into the food chain, causing damages to human health. Among heavy metals, nickel appears to be one of the most dangerous responsible for different disorders. Public health protection requires nickel detection in environment and food chains; a simple, rapid and sensitive method to detect nickel contamination is represented by biosensors. In this paper we report the setup of a whole-cell based system, in which protoplasts, obtained from Nicotiana tabacum leaves, were used as transducers to detect the presence of heavy metal ions and, in particular, nickel ions. Protoplasts were genetically modified with a plasmid containing the GFP reporter gene under the promoter region of a sunflower small HSP gene control. By this device the presence of heavy metal ions was detected. Thus, the possibility to use this whole-cell system as a novel tool to detect the presence of nickel ions in food matrices was assessed.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Drosophila melanogaster serves as a important research model both for genetic investigations and for the study of metal toxicity, facilitating the elucidation of physiological mechanisms comparable to those of human organisms. In our research, we evaluated the toxicity effect resulting from exposure to various concentrations of lead and copper on the prolificity rates throughout the life cycle (egg-adult) of four genotypes of Drosophila melanogaster: wild-type (control), white, brown, and white-vestigial. During our study, the prolificity rates were examined across three repetitions under the impact of exposure to concentrations of 0.50, 1.00, 2.00, and 4.00 mM of copper (CuSO₄) and lead (Pb(C₂H₃O₂₎₂). Prolificity rates throughout the life cycle exhibited variations as a direct consequence of genetic factors, the concentration of exposure, and the specific type of metal, either copper or lead. The mutant white-vestigial genotype revealed an IC₅₀ concentration for prolificity inhibition at lower doses of 2.00 mM for copper and 4.00 mM for lead, in contrast to the control genotype (wild-type), which exhibited an inhibition concentration rate >IC₅₀ of 4.00 mM only in the case of copper. Our results concluded that the (i) dose influences the prolificity rate in a directly proportional manner, (ii) comparative analyses between copper and lead revealed that copper displayed toxicity across all genotypes within the concentration range of 0.50 mM to 2.00 mM. In contrast, lead exhibited toxicity within the concentration range of 1.00 mM to 4.00 mM, highlighting a (iii) more acute toxicity characteristic in the case of copper. Thus, the results of this research reflect the importance of using Drosophila melanogaster as a genetic model in the comparative study of the interaction between genetic factors and the toxicity of metals, offering significant insights into monitoring their impact and defining the maximum permissible doses on organisms.

A common problem in education has always been to get students to achieve an adequate level of motivation and engagement. Many evidences show that gamification has the potential to make learning more engaging, motivating and effective. However, it is essential to design gamified experiences thoughtfully, ensuring that they align with educational objectives and promote meaningful learning outcomes, and so the lack of established design methodologies can lead to unintended consequences.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

In the whole world, including Mexico, the rising prevalence of diabetes poses significant challenges to healthcare systems, with a notable impact on hospital admissions even being an ambulatory care sensitive condition, that means, hospital admissions are avoidable. Traditional healthcare methodologies have been instrumental in managing diabetes and preventing complications, yet they often face limitations such as merging, cleansing, and outlier analysis of health data to identify and address diseases effectively. This paper aims to address this gap by conducting a comprehensive comparison of the methodologies used in healthcare and data science for this purpose. This work uses hospital diabetes discharge records from 2010 to 2023, a total of 36,665,793 records, which belong to medical units of the Ministry of Health of Mexico. In this work, we seek to provide the arguments why as a data scientist it is mandatory to learn the field of knowledge of the problem and its implications if this is not done, and therefore to disclose insights that can help in policy decisions and reduce the burden of avoidable hospitalizations. The approach is mainly based on the standardization or adjust rates by sex and age groups. This study provides the foundations for a new way of data scientist must deal with health data.

The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

The enterprise software sector is experiencing a significant transformation driven by rapid technological progress and evolving business needs. An effective release management process is crucial for the solution providers and customers, as they incorporate the principles of Industry 4.0 and 5.0. With the growing dependence on software solutions and services, ensuring their quality, reliability, and security is highly necessary. The release management process incorporates strategic planning, regression testing, accurate deployment, and continuous monitoring of software updates. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) create a unique challenge to the release management process. The existing research needs to address the necessary adaptations for Industry 5.0, mainly in product-based software organizations, to the release management process without a change management system. After working with several software solution stakeholders, based on the responses from SaaS project stakeholders, we have presented a revised software release readiness workflow by incorporating a change management module. The preliminary result based on data over a limited period of time indicates that the revised workflow has the potential to improve the management of slippage, particularly by more effectively addressing NFR-related issues, thereby resulting in more predictable and reliable release cycles.

Microgreens (young, edible plantlets) are a remarkable opportunity for both consumers, to benefit from health promoting compounds and for applied research, as such organisms have a very high plasticity related to environmental cues, allowing biotechnology development with low costs. Ocimum basilicum species naturally synthesize valuable, phenolic compounds, among which rosmarinic acid is most prominent. Basil plantlets were grown for 10 days under either full spectrum light (white light) or modulated blue:red:far-red:UV spectrum with an additional factorization, by applying fertilisation. Biomass accumulation reached up to 0.8 g/20 plantlets, while chlorophyll fluorescence was in the 0.75 – 0.78 range and remained uniform across treatments, indicating that no significant stress was exerted under modified light treatment. However, total phenolic contents and, in particular, rosmarinic acid contents were markedly enhanced (up to 7.5 mg/g in the red cultivar) under modulated light treatment and fertilization, compared to full spectrum light. Moreover, gene expression was enhanced, 1.3-6.3 fold for genes coding for enzymes involved in phenylpropanoid synthesis patwhays, such as phenylalanine ammonia lyase (PAL), Catechol-O-methyltransferase (COMT) and renin-angiotensin system (RAS). Overall, light modulation coupled with fertilization led to the production of basil microgreens with up to 10% more total phenolics and up to 25% more rosmarinic acid.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is commonly considered to be a potent driver of non-small cell lung cancer (NSCLC) development and related mortality. A growing body of evidence supports the role of the immune system, mainly played by alveolar macrophages (AMs), as key axes regulating the development of COPD or NSCLC phenotypes in response to harmful agents. MicroRNAs (miRNAs) are small non-coding RNAs that influence most biological processes and interfere with several regulatory pathways. The purpose of this study was to assess miRNA expression patterns in patients with COPD, NSCLC, and ever- or never-smoker controls to explore theirinvolvment in smoking-related diseases. Bronchoalveolar lavage (BAL) specimens were collected from a prospective cohort of 43 sex-matched subjects to determine expression of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p and 106a-5p by RT-PCR. In addition, bioinformatic analysis of miRNA target genes linked to cancer was performed. Distinct and common miRNA expression levels were identified in each pathological group suggesting their possible role as an index of NSCLC or COPD microenvironment. Moreover, we identified miRNA targets linked to carcinogenesis using in silico analysis. In conclusion, this study identified miRNA signatures in AMs, allowing us to understand the molecular mechanisms underlying smoking-related conditions and potentially providing new insight for diagnosis or pharmacological treatment.

Foods are complex products consisting of various ingredients of diverse origins. Along the food supply chains, food products are prone to spoilage and safety issues, which could give rise to different health-related concerns and outcomes. Furthermore, the existing highly centralised food systems generate substantial greenhouse gas emissions and food waste. In order to strengthen the sustainable decentralised "farm-to-fork" food chains, a mindset shift supported by relevant education and policy initiatives is highly required. The ERASMUS+ KA220 project SafeFood4EU was conceived with a crucial mission- to develop an interdisciplinary and innovative toolbox of higher education and lifelong learning courses that reflect various aspects of sustainable agri-food systems. The thematic areas discussed within the SafeFood4EU project have covered food safety and supply chain management, sustainable food systems and design, food production and quality of raw materials, food authenticity and analysis, and digital and soft skills. The main objectives have been to develop a distinctive content package and make it available to the students via an e-learning management system. The project's results, aimed at creating a pool of young and capable experts, can significantly impact future food security, thereby increasing awareness about the future challenges regarding food systems transition, dietary shift and the next generation's food security.

This article delves into the complex landscape of Bitcoin halving events, analyzing the historical context and implications of these pivotal occurrences within the cryptocurrency ecosystem and on its sustainability within the landscape of financial technologies. Through an examination of past halving events in 2012, 2016, and 2020, this study meticulously scrutinizes the conditions and prices of Bitcoin before and after each halving. By synthesizing data and trends from previous events, this article aims to uncover patterns and insights that illuminate the impact of Bitcoin halving on market dynamics and sustainability, movement of the price level, reached peaks and price troughs. Furthermore, this research presents a comparative analysis of past halving events, drawing parallels and distinctions to elucidate the evolving nature of Bitcoin's price movements and market sentiment surrounding these milestone events. Looking towards the future, this study estimates predictions and expectations for the upcoming Bitcoin halving. By unraveling the intricate relationship between Bitcoin halving events and market behavior, this article seeks to provide a comprehensive understanding of the broader implications for Bitcoin's trajectory and the evolving landscape of the cryptocurrency market and its sustainability.

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

Improper disposal of waste plastic has caused serious ecological and environmental pollution problems. Transforming plastics into high value-added chemicals can not only achieve efficient recycling of waste plastics, but also an effective way to control white pollution. The catalyst selectively breaks the C-C bond of polyolefin plastic under heat treatment and converts it into liquid fuel, thus realizing sustainable recycling of plastics and has a good development prospect. This review provides a detailed overview of the current development of catalytic pyrolysis, catalytic hydrolysis, solvent decomposition and supercritical hydrothermal liquefaction for cracking plastics to make fuel oil. The reaction mechanism, influencing factors and promoting effects of catalysts in various degradation technologies are analyzed and summarized and the latest proposed tandem reaction for degrading plastics is briefly introduced. Finally, some optimization paths of waste plastic pyrolysis to fuel oil technology are proposed: synergies between mixed raw materials, in-depth exploration of catalysts, design and manufacture of reactors that match the pyrolysis technology. All these are important research directions for promoting the industrialization of plastic pyrolysis to fuel oil.

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

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

Fast-scan cyclic voltammetry (FSCV) is an electrochemical sensing technique that can be used for neurochemical sensing with high spatiotemporal resolution. Carbon fiber microelectrodes (CFMEs) are traditionally used as FSCV sensors. However, CFMEs are prone to electrochemical fouling caused by oxidative byproducts of repeated serotonin (5-HT) exposure, which makes them less suitable as a chronic 5-HT sensor. Our team is developing a boron-doped diamond microelectrode (BDDME) that has previously been shown to be relatively resistant to fouling caused by protein adsorption (biofouling). We sought to determine if the BDDME exhibits resistance to electrochemical fouling, which we explored on electrodes fabricated with either femtosecond laser cutting or physical cleaving. We recorded the oxidation current response after 25 repeated injections of 5-HT in a flow-injection-cell and compared the current drop from the first to the last injection. The 5-HT responses were compared to dopamine (DA), a neurochemical that is known to produce minimal fouling oxidative byproducts and have a stable repeated response. Physical cleaving of the BDDME yielded a reduction in fouling due to 5-HT compared to the CFME and the femtosecond laser cut BDDME. However, the femtosecond laser cut BDDME exhibited a large increase in sensitivity over the cleaved BDDME. An extended stability analysis was conducted for all device types following 5-HT fouling tests. This analysis demonstrated an improvement in the long-term stability of boron doped diamond over CFMEs, as well as a diminishing sensitivity of the laser cut BDDME over time. This work reports the electrochemical fouling performance of the BDDME when it is repeatedly exposed to DA or 5-HT, which informs the development of a chronic, diamond-based electrochemical sensor for long-term neurotransmitter measurements in vivo.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

This research focusing on the environmental impact of tire and brake pad wear constitutes a significant area of transport-related studies. These two key vehicle components are not only the most frequently worn, but also generate micro and nano pollutants (i.e., rubber, metal oxides) that potentially harm the environment. Over half of the globally produced natural and synthetic rubbers, which amounted to about 30 million tons in 2022, are used for tire production. According to Wagner et al.'s research, over 1.3 million tons of such particles are released due to wear and tear in the European Union alone. This work focuses on examining snow deposits from various locations in an urban agglomeration (Poznan, Poland) in the winter season. FT-IR and NMR spectroscopic studies, optical and electron scanning microscopy SEM-EDS, and particle size studies using the DLS technique were conducted for a detailed characterization of pollutants in the environment. The conducted particle separation methods allowed for the extraction of a fraction mainly containing gum residues with particle sizes less than 2 µm. The results of these studies are crucial for understanding the impact of vehicle part exploitation on the environment.

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges, and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody-couplers or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment further standardization efforts are required. The study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs, and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector.

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

Background: Micrococcus luteus is a commensal bacterial member on the human skin and is essential in keeping the balance among the various microbial flora of the skin. M. luteus strain Q24 or BLIS Q24^TM was isolated from the skin of a healthy human adult and has been identified to produce a unique antimicrobial spectrum that is inhibitory towards pathogenic bacteria associated with many skin diseases such as acne. It has been developed as a probiotic with potential applications in improving overall skin health.Aim: This study aimed to evaluate the perceived changes in skin quality following topical application of a formulation containing skin commensal probiotic BLIS Q24^TM. Materials and methods: A 28-day study involving 100 adult participants with self-reported normal skin with occasional blemishes or breakouts, topically applied a serum formulation containing BLIS Q24^TM to the face. Participants quantitatively evaluated the probiotic serum effect on their skin at baseline Day 0 and then again on Day 10, 20 and 28 of the application. In addition, participants maintained a photo diary to record the perceived changes to their skin.Results: A total of 96 participants completed this research. In comparison to the baseline (Day 0), within 10 days, a significant reduction in blackheads (48%), oiliness (48%), blemishes (57%), dry areas (23%), redness/rosacea (45%) and flaky and rough skin (38%) were reported. The probiotic serum was also effective in a significant reduction in fine lines and wrinkles (38%), whiteheads (41%), pimples (55%), age spots (29%), and acne (21%) by the end of 28 days of probiotic application. Overall, there’s agreement that probiotic serum delivers or exceeds consumer expectations from the concept and performs just as well, or better, than their regular moisturiser.Conclusions: An approach of delivering a live probiotic packaged in an innovative formula to improve several skin conditions in otherwise healthy individuals was found to be successful. This study highlights that a serum formulation containing BLIS Q24^TM has a positive impact on skin health with it being effective topically in reducing the appearance of skin blemishes, oiliness, wrinkles, redness, and dryness. BLIS Q24^TM offers the potential for the prevention of skin health-related issues and routine maintenance of skin microbiome for healthy skin.

The article presents the results of research work devoted to the study of composite materials on polymer binder with the content of modified mineral filler - water-dispersion paint and varnish materials on acrylic polymer with modified filler - diatomite. The aim of the study is to obtain polymer-based composites with modified diatomite (water-dispersion paint on acrylic binder) with improved performance properties. Methods: determination of porosity, adhesion, elasticity, hiding power of the control composition; determination of porosity, adhesion, elasticity, hiding power of the obtained composites with the use of modified filler; investigation of the influence of radiation in the infrared radiation (IR) region of the spectrum of the paint coating surface using FLIRB620 thermal imager. As a result of work it is noticed that modification of water dispersions by silica activated diatomite promotes elimination of the main disadvantages of materials and coatings on the basis of acrylate binder - low water resistance and low physical and mechanical characteristics, introduction of modified diatomite into water emulsion paint on acrylate basis does not lead to deterioration of the main operational characteristics of paint and varnish coatings - porosity, adhesion, elasticity and covering. When analyzing the effect of emissivity in the IR region of the spectrum of the paint coating surface, a significant effect of the quantitative content of modified diatomite on the reduction of emissivity of paint coatings in the IR region of the spectrum was established and it was shown that a significant effect of the reduction of emissivity of paint coatings in the IR region of the spectrum is provided by the introduction of modified diatomite in the amount of 40 wt.h. into the mixture. The influence of the time of full-scale tests in the investigated small range regardless of the quantitative composition of modified diatomite on the performance characteristics of the paintwork was established.

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r>0.90) and peri-urban (HWM, HWA, r>0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

Salinity stress emerges as an important menace to global food security, particularly affecting susceptible crops such as common bean plants (Phaseolus vulgaris L.). The disruption of ion balances caused by salt stress withing plants cells reduces water and nutrient absorption, and hampers growth and productivity. Sacchorhiza polyschides, a brown macroalga rich in essential elements, and macrominerals, has drawn attention as a natural biostimulant, being a potential remedy to bolster crop resilience against salt stress. This study delves into the effect of water extracts of Sacchorhiza polyschides (SPE) across varying concentrations (1%, 2%, and 5%) in common bean plants under salt stress (68.4 mM NaCl) conditions. Two different methods of application were used: by spraying the aerial part of the plant or by irrigation together with the watering solution. The effects of both systems of application were studied on the aerial parts of the plants. SPE exhibited significant enhancements in morphological parameters, chlorophyll content, carbon-nitrogen activities, and antioxidant enzymatic system activities, alongside increasing osmolyte accumulation, compared to salt-stressed plants without biostimulation. Moreover, SPE reduced reactive oxygen species (ROS) levels caused by salt stress, mitigating lipid peroxidation. The concentrations of 2% and 5% SPE applied by spraying showed the most significant results in most of the parameters analyzed. By shedding light on the underlying science behind this biostimulant effect, this research underscores the potential of SPE to enhance crop resilience and augment food production in salt-challenged environments.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

Glycerol represents a useful functional component of heat-transfer fluids. Glycerol is known to be triboelectrically active. We report how the flow of glycerol, stopped in a cylindrical coiled pipe, influences horseradish peroxidase (HRP) enzyme. Namely, glycerol flow was pumped through the coil and then stopped. After that, we incubated the 0.1 µM solution of HRP near the side of the coil, and then adsorbed HRP onto mica. This operation markedly increased the HRP aggregation on mica in comparison with the control sample. We explain this phenomenon by the influence of triboelectrically induced electromagnetic fields. Our results can be useful in the development of flow-based systems for biosensors and bioreactors.

This study investigates the potential benefits of integrating coarser particle size distributions (PSDs) of 45-106 µm into laser-based powder bed fusion of metals (PBF-LB/M), aiming to reduce costs while maintaining quality standards. Despite the considerable advantages of PBF-LB/M in producing intricate geometries with high precision, the high cost of metal powders remains a barrier to widespread adoption. By exploring the use of coarser PSDs, particularly from electron beam-based powder bed fusion of metals (PBF-EB/M), significant cost-saving opportunities are identified. Through comprehensive powder characterization, process analysis, and mechanical property evaluation, this study demonstrates that PBF-LB/M can effectively utilize coarser powders while achieving comparable mechanical properties to those produced with 20-53 µm PSD. Adaptations in process parameters enable the successful processing of coarser powders, maintaining high-relative density components with minimal porosity. Additionally, market surveys reveal substantial cost differentials between PBF-LB/M and PBF-EB/M powders, indicating 40 % cost reduction potential by integrating coarser PSDs into PBF-LB/M. Overall, this study provides valuable insights into the economic and technical feasibility of printing with coarser powders in PBF-LB/M, offering promising avenues for cost reduction without compromising quality, thus enhancing competitiveness and adoption of the technology in manufacturing applications.

The cooling of server components has been continuously developed over the past few years in order to meet the increasing cooling requirements of server component manufacturers such as for the processor chips. The growth in performance and power density increases the cooling demand. These requirements directly influence the energy consumption and performance of the hardware. To gain a better understanding of the historical evolution and possible future technology development paths in the field of data center cooling, a patent analysis method was used. The focus is on previous and current development priorities and possible research gaps within the server and computer room levels. The European patent database was selected as data source. To determine relevant patents, the search terms for various cooling technologies were developed iteratively. After the patent extraction for the period 2000 - 2023 (earliest priority year), the search results were classified and analysed. Most of the patents deal with air or liquid cooling. In the area of liquid cooling, the focus was on direct liquid cooling, followed by total liquid cooling. Since 2015 a technological shift from air to liquid cooling can be identified on the level of patent activities. In this context, the patent groups with focus on “constructional details for different types of electric apparatus” and “electrical digital data processing” have the largest share of patents. In addition, the technology focus of the TOP 10 patent applicants was in the field of liquid cooling. In conclusion from the patent analysis, it can be derived that liquid cooling will continue to gain in importance in the future and could also be combined with other approaches in the form of hybrid cooling. However air cooling may still be relevant, even if the main cooling load is handled by liquid-based approaches. At the same time, the optimisation potential for air cooling seems to have been largely exploited in comparison to liquid cooling, as can be seen from the falling number of the patent applications.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for the automation system. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review study, bone drilling vibration data collected in the University laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone which are processed and classified using several machine learning methods. LSTM is, used in the bone drilling classification study to prove that the layers of the bone drilling are associated with the vibration signal and can be classified and predicted using the machine learning method.

The endometrium, the inner mucosal lining of the uterus, undergoes complex molecular and cellular changes across the menstrual cycle in preparation for embryo implantation. Transcrip-tome-wide analyses have mainly been utilized to study the endometrial receptivity, the prerequisite for successful implantation, with most of the studies, so far, comparing the endometrial transcriptomes between (i) secretory versus proliferative endometrium or (ii) mid-secretory versus early-secretory endometrium. In the current study, we provide the complete transcriptome description of the endometrium across the entire menstrual cycle and, for the first time, comprehensively characterize the proliferative phase of the endometrium. Our temporal transcriptome analysis includes five time points: mid-proliferative, late-proliferative (peri-ovulatory phase), early-secretory, mid-secretory, and late-secretory phases. Thus, we exhaustively unveil the transitions between the consecutive proliferative and secretory phases, highlighting their unique gene expression profiles and possible distinct biological functions. Transcriptome analysis reveals many differentially expressed genes (DEGs) across the menstrual cycle, most of which are phase-specific. As an example of the coordinated gene activity, the expression profile of his-tone-encoding genes within the HIST cluster on chromosome 6 showed an increase in cluster activity during the late-proliferative and a decline during the mid-secretory phase. Moreover, numerous DEGs are shared among all phases. In conclusion, in the current study, we delineate the endometrial proliferative phase-centered view of transcriptome dynamics across the menstrual cycle. Data analysis highlights significant transcriptomic and functional changes occurring during the late-proliferative phase – an essential transition point from the proliferative to the secretory phase. Future studies should explore how the biology of the late-proliferative phase endometrium impacts the achievement of mid-secretory endometrial receptivity or contributes to molecular aberrations leading to embryo implantation failure.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

The integration of Wireless Power Transfer (WPT) technologies with Artificial Intelligence (AI) is poised to revolutionize multiple industries, including consumer electronics and industrial applications. This study delves into the latest advancements in WPT, with a focus on AI-enhanced efficiency, range, and reliability. AI-driven algorithms optimize power transfer, improve device alignment, and predict maintenance needs, leading to smarter and more sustainable power solutions. Machine learning and computer vision are employed to achieve precise alignment, while predictive maintenance minimizes downtime and costs. Additionally, AI-powered power management systems adapt to varying demands, optimizing energy usage. This research highlights the transformative potential of AI in enhancing WPT technologies, with significant implications for next-generation applications. The findings emphasize the importance of ongoing research in this interdisciplinary field to address existing challenges and unlock new opportunities for innovation.

This paper undertakes a comprehensive review to underscore the central role of sustainability in the evaluation and innovation of jet grouting technologies. It meticulously traces the evolution of evaluation methodologies, emphasizing the imperative of sustainable practices throughout history. Traditional evaluation paradigms are examined alongside contemporary trends, with a particular focus on sustainability metrics and visualization techniques. Through an examination of innovative jet grouting technologies, this paper illustrates how sustainability principles have driven advancements aimed at improving the efficiency and environmental impact of jet grouting methods. Central to this review is an assessment of sustainability across multiple dimensions, including method applicability, installation and operational compatibility, and the performance of the resulting jet grouted piles. By emphasizing sustainability considerations, the paper highlights the intrinsic link between environmental integrity and technological innovation in the field of jet grouting. In addition, it discusses analytical evaluation methods designed to proactively address sustainability concerns prior to construction, thereby promoting a more environmentally responsible approach. Finally, this paper presents a pioneering evaluation framework that integrates real-time monitoring and visualization tools, allowing stakeholders to track sustainability milestones throughout the construction process. By emphasizing sustainability as a guiding principle, this review advocates for a paradigm shift toward more ecologically sound and resilient jet grouting practices.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

Currently, health tourists primarily rely on independent facilitators to manage and book their medical appointments and vacation plans. There is a notable absence of dedicated booking applications for health tourism packages. This paper proposes HealthTourismHub, an application designed to provide personalized packages that include medical appointments, accommodation options and recommended tourism activities. It also serves as a platform for medical experts and accommodation providers, allowing health tourists to discover and connect with them. To incorporate personalization, HealthTourismHub uses an ontology that organizes medical and tourism data, along with a reasoner that generates new knowledge. This approach enables the application to offer customized packages and identify the most suitable providers for each user. Providers are strategically paired and located in close proximity, with the package also including personalized tourism recommendations. A survey was conducted to assess the usability of the application and general perspectives towards health tourism, including motivations, concerns, and preferences. The results revealed an above-average SUS score, indicating that users found the application user-friendly and effective. Some areas for improvement were identified, such as error handling and additional functionalities. Nonetheless, HealthTourismHub shows great potential as a pioneer in the field of health tourism applications.

Probiotics in cosmetics is a new and burgeoning area of research attracting attention from cosmetic manufacturers and customers looking for natural or additional alternatives in their skincare regimens. Probiotics not only promote beneficial microbiomes to improve skin health but can also modulate the visual, topographical, and mechanical attributes to improve skin quality. This study was conducted in healthy adults to determine the effect of a topical serum formulation containing the live probiotic Micrococcus luteus Q24 on several key cosmetic skin quality parameters and its beneficial impact on the modulation of the skin microbiome. Quantitative changes in various skin parameters were measured using a skin analyzer device and skin swabs were analysed using whole genome sequencing to monitor microbial diversity and levels of probiotic colonisation. Compared to the baseline, a significant reduction in the pore, spots, wrinkles, and impurities scores; and an increase of 101% in the hydration scores were observed. Additionally, 45-80% of participants showed a decrease in pores, spots, wrinkles, and impurities and 90% of participants showed an increase in hydration after 25 days of probiotic serum application. The WGS analysis of the skin swab samples showed a significant increase in the relative abundance of M. luteus Q24. The results from this study indicated that topical application of serum-containing probiotic M. luteus Q24 offers the benefit of improving skin health quality.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

This article develops algorithms for the characterization and the visualization of micro-scale features by using a small number of sample points, and with a goal to mitigate for the measurement shortcomings which are often destructive or time consuming. We implement the algorithms to rapidly examine the microscopic features of a Microelectromechanical System (MEMS) surface. Such images are highly dense; therefore, traditional image processing techniques might be computationally expensive. The contribution of this research include first, we develop local and global algorithm based on modified Thin Plate Spline (TPS) model to reconstruct high resolution images of the micro-surface’s topography, and its derivatives by using low resolution images. Second, we obtain a bending energy algorithm from our modified TPS model, and use it to filter out image defects. Finally, we develop a computationally efficient Windowing technique, which combines TPS and Linear Sequential Estimation (LSE), to enhance the visualization of images. The Windowing technique allows rapid image reconstruction based on the reduction of inverse problem.

The studied solar pot is a recent invention, which gained a utility model protection in 2021 from the Hungarian Intellectual Property Office under patent number 5489. The device is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary.

This paper constituted an extension of two previous studies concerning the mathematical development of the grain boundary grooving in polycrystalline thins films in the cases of the evaporation/condensation and diffusion taken separately. The thermal grooving processes are deeply controlled by the various mass transfer mechanisms of evaporation–condensation, surface diffusion, lattice diffusion, and grain boundary diffusion. This study proposed a new original analytical solution to the mathematical problem governing the grain groove profile in the case of simultaneous effects of evaporation-condensation and diffusion in polycrystalline thin films submitted to thermal and mechanical stress, and fatigue effects; by resolving the corresponding fourth-order partial differential equation ∂y∂t=C∂2y∂x2-B∂4y∂x4 obtained from the approximation y'2≪1. The comparison of the new solution to that of diffusion alone proved an important effect of the coupling of evaporation and diffusion on the geometric characteristics of the groove profile. A second analytical solution based on the series development was also proposed. It was proved that change of the boundary conditions of the grain grooving profile largely affected the different geometric characteristics of the groove profile.

Piezoelectric c-axis oriented zinc oxide (ZnO) thin films, from 1.8 up to 6.6 µm thick, have been grown by radio frequency magnetron sputtering technique onto fused silica substrates. A delay line consisting in two interdigital transducers (IDTs) with wavelength λ = 80 µm was photolith-ographically implemented onto the surface of the ZnO layers. Due to the IDTs split finger con-figuration and metallization ratio (0.5), the propagation of both the fundamental, third and nineth harmonic Rayleigh waves is excited; also, three leaky surface acoustic waves (SAWs) were detected travelling at velocity close to that of the longitudinal bulk wave in SiO2. The acoustic waves propagation in ZnO/fused silica was simulated by using 2D finite-element method (FEM) tech-nique to identify the nature of the experimentally detected waves. It turned out that, in addition to the fundamental and harmonic Rayleigh waves, also high-frequency leaky surface waves are ex-cited by the harmonic wavelengths; such modes are identified as leaky-Sezawa (LS) waves under cut-off. The velocity of all the modes was found in good agreement with the theoretically calcu-lated values. The existence of a low-loss region for the Sezawa wave below the cut off was theo-retically predicted and experimentally assessed.

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

Nowadays circular economy trend drives researchers in the recovery of various bioactive compounds from agri-food by-products. Enzyme-assisted extraction (EAE) is shown as an innovative green technology for the effective extraction of various phytochemicals from agri-food section by-products, therefore this study aimed to evaluate the application of EAE as green technology to obtain extracts from olive leaves (Olea europaea) for potential industrial production. EAE was conducted under various enzyme dose combinations, and incubation time (120 min). Obtained extracts were characterized in terms of total polyphenols (TP) and Total Antioxidant Activity (AA). Firstly, the enzyme synergistic effect in enzymatic extraction of polyphenols was evaluated. TP optimal extraction conditions (468.19 mg GAE/L) were achieved after EAE (50-50%) and for AA (69.85 AA%). According to the above results, a second experimental part occurred for the incubation time (min.) and enzyme dose (mL) of optimal extraction conditions of olive leaves. Results indicated EAE as an excellent choice for green extraction of polyphenols from the olive leaves.

The literature offers limited information on the effect of obesity on the rate of force development, a critical parameter for mobility in older adults. The objectives of this study were; to explore the influence of obesity on RFD in older adults and to examine the association between this neuromuscular parameter and walking speed. 42 older adults were classified into two groups: the control group (CG, n=22; age=81.13±4.02 years; Body mass index (BMI)=25.13±3.35 kg/m²), and the obese group (OG, n=20; age=77.71±2.95 years; BMI=34.46±3.25 kg/m²). Walking speed (m/s) was measured using the 10-meter walking test. Neuromuscular parameters of the plantar flexors were evaluated during a maximal voluntary contraction test using a dynamometer. The RFD was calculated from the linear slop of the force – time curve in two phases: from the onset of the contraction to 50 ms (RFD50-100) and from 100 to 200 ms (RFD100-200). The gait speed was lower in the OG compared to the CG (p

The manuscript presents the use of three novel technologies for the implementation of wireless green battery-less sensors that can be used in agriculture. The three technologies, namely additive manufacturing, energy harvesting, and wireless power transfer from airborne transmitters carried from UAVs, are considered for smart agriculture applications. Additive manufacturing is exploited for the implementation of both RFID based sensors and passive sensors based on humidity sensitive materials. A number of energy-harvesting systems at UHF and ISM frequencies are presented which are in position to power platforms of wireless sensors, including humidity and temperature IC sensors used as agriculture sensors. Finally, in order to provide the wireless energy to the soil-based sensors with energy harvesting features, wireless power transfer (WPT) from UAV carried transmitters is utilized. The use of these novel technologies can facilitate the extensive use and exploitation of battery-less wireless sensors which are environmentally friendly and thus “green”. Additionally, it can potentially drive precision agriculture in the next era, through the implementation of a vast network of wireless green sensors which can collect and communicate data, to airborne readers so as to support with data the Artificial Intelligence and Machine Learning based, decision making platforms.

Cyberattacks are rapidly evolving both in terms of techniques and frequency, from low-level attacks through to sophisticated Advanced Persistent Threat (APT). There is a need to consider how testbed environments such as cyber ranges can be readily deployed to improve examination of attack characteristics and the assessment of defences. Whilst cyber ranges are not new, they can often be computational expensive, require significant setup and configuration, or may not provide full support for areas such as logging or ongoing learning. In this paper, we propose GoibhniUWE, a container-based cyber range that provides a flexible platform for investigating the full lifecycle of a cyber attack. Adopting a modular approach, users can seamlessly switch out existing, containerised vulnerable services, deploying multiple different services at once allowing for the creation of complex and realistic deployments. The range is fully instrumented with logging capabilities from a variety of sources including Intrusion Detection Systems (IDS), service logging, and network traffic captures. To demonstrate the effectiveness of our approach, we deploy the GoibhniUWE range under multiple conditions to simulate various vulnerable environments, reporting on and comparing key metrics such as CPU and memory usage. We simulate complex attacks which span multiple services and networks, with logging at multiple levels, modelling an Advance Persistent Threat (APT) and their associated Tactics, Techniques, and Procedures (TTPs). We find that even under continuous, active and targeted deployment GoibhniUWE averaged a CPU usage of less than 2 processors and memory usage of less than 4.5 GB.

The domain of construction engineering and management is evolving rapidly. Optimizing resource positioning is crucial for meeting the challenges of efficiency and productivity. This research presents a novel methodology that incorporates Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies, augmented with a Client Early Warning System (CEWS) and a Decision Support System (DSS). What makes this study unique is that it provides a real-time methodology for resource allocation and project management, tailored to address the complexities of modern construction projects. The application of this integrated technological framework marks a significant leap in construction management, empowering data-driven decision-making and significantly improving project efficiency. Empirically validated through a case study at Helwan University's College of Engineering and substantiated by expert surveys, this research showcases a transformative model for resource management. The findings not only underscore the practical applicability of this approach but also open new horizons for its adoption in global construction practices, offering insights for future research endeavors focused on expanding these methodologies to a wider range of resources.

Leptin, acting centrally or peripherally, has complex effects on cardiac remodeling and heart function. We previously reported that central leptin exerts an anti-hypertrophic effect in the heart via cardiac PPARβ/δ activation. Here, we assessed the impact of central leptin administra-tion and PPARβ/δ inhibition on cardiac function. Various cardiac properties, including QRS du-ration, R wave amplitude, heart rate (HR), ejection fraction (EF), end-diastolic left ventricular mass (EDLVM), end-diastolic volume (EDV), and cardiac output (CO) were analyzed. Central leptin infusion increased cardiac PPARβ/δ protein content and decreased HR, QRS duration and R wave amplitude. These changes induced by central leptin suggested a decrease in the ventricu-lar wall growth which was confirmed by MRI. In fact, the EDLVM was reduced by central leptin, while increased in rats co-treated with leptin and GSK0660, a selective antagonist of PPARβ/δ ac-tivity. In summary, central leptin plays a dual role in cardiac health, potentially leading to ven-tricular atrophy and improving heart function when PPARβ/δ signaling is intact. The protective effects of leptin are lost by PPARβ/δ inhibition, underscoring the importance of this pathway. These findings highlight the therapeutic potential of targeting leptin and PPARβ/δ pathways to combat cardiac alterations and heart failure, particularly, in the context of obesity

Recent interest in intestinal hormones has risen with the idea that they modulate glucose tolerance and food intake through a variety of mechanisms, and such hormones like peptide YY (PYY), ghrelin, glucagon-like peptide (GLP)-1 and 2, and cholecystokinin (CKK) are therefore excellent therapeutic candidates for the treatment of diabetes and obesity. Furthermore, in the recent years, multiple studies suggest that the microbiota is critically important for normal host functions, while impaired host microbiota interactions contribute to the pathogenesis of numerous common metabolic disorders. In this study, we considered the nutraceutical effects of β-glucans added to pasta at the concentration of 6g\100g. Ten participants have been recruited and hematochemical analyses and intestinal hormones tests have been performed before and after 30 days of pasta intake. Stool specimens have been studied for Lactobacillus Fermentum, Lactobacillus acidophilus, Lactobacillus salivarius, Bifidobacterium longum, and Enterococcus faecium presence before and after 30 days of nutritional intervention. After 30 days of regular intake of pasta enriched by β-glucans results have been evaluated. In conclusion, pasta prepared from barley flour enriched with β-glucans at 6% exhibit promising responses on glucose metabolism, on intestinal hormones responses and on microbiota modification.

The vast array of interconnected microbes across earth’s ecosystems and within holobionts has been called the “Internet of Microbes.” Research into the fundamental microbial properties has demonstrated that bacteria and archaea are masters of energy and information collection, storage, transformation, and dissemination using both “wired” and wireless (at a distance) capacities. These features suggest that microbes are quantum entangled within and between earth’s holobionts, and that specific tools affecting energy and information functions could provide an effective strategy for managing microbial populations. Microbes use four critically important microbial networking functions (sound, light, electricity, and magnetism). This narrative review focuses on microbial management based on two of the four: sound and light (as well as related vibrations). These are examined as: 1) tools for managing microbial populations, 2) tools to support new technologies, and 3) tools for healing humans and other holobionts. Given microbial sensitivity to sound, light, and vibrations, it is critical that microbial populations and microbe-laden holobionts are protected from potentially damaging exposures. We conclude that sound, light, and vibrations are useful therapeutic tools that can help support useful microbial populations and help to address ongoing challenges of holobiont disease.

The superconductivity of CaC6 as a function of pressure and of abundant Ca isotopes is revisited using DFT calculations on a 2c – double hexagonal superlattice. The introduction of superlattices is motivated by previous synchrotron absorption and Raman spectroscopy results on other superconductors that show evidence of superlattice vibrations at low (THz) frequencies. A 2c superlattice for CaC6 is illustrative of atomic orbital symmetry and periodicity, including bonding and antibonding s-orbital character implied by cosine modulated electronic bands. Inspection of the cosine band reveals that the cosine function has a small (meV) energy difference between bonding and antibonding regions relative to a midpoint non-bonding energy. Fermi surface nesting is apparent in an appropriately folded Fermi surface using a superlattice construct. Nesting relationships identify phonon vectors for conservation of energy and for phase coherency between coupled electrons at opposite sides of the Fermi surface. Detailed analysis of this Fermi surface nesting provides accurate estimates of the superconducting gaps for CaC6 with change in applied pressure. Recognition of superlattices within a rhombohedral or hexagonal representation provides consistent mechanistic insight on superconductivity and electron−phonon coupling in CaC6.

The main sources of intrinsic noise in excitable cells at the microcircuit and network levels are the stochastic characteristics of ion channel gating and activation of the synaptic conductance. Studies using in vivo, in vitro, and in silico methods to examine the effects of synaptic background activity were not adequately investigated in non-neuronal excitatory cells, where neurotransmitter-based innervation also occurs. We created a mathematical model to replicate the background synaptic noise dynamics in a non-neuronal cell. We utilized the stochastic Ornstein-Uhlenbeck process to represent excitatory synaptic conductance, which was incorporated into a whole-cell model to produce spontaneous and evoked cellular electrical activities. The single-cell model includes many biophysically detailed ion channels represented by a set of ordinary differential equations in Hodgkin-Huxley and Markov formalisms. This paradigm effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potential (sAP) resembling in vitro-like electrical activity in the cells. The input resistance decreased by multiple factors, and the spontaneous action potential firing rate elevated. The potential to reach the action potential threshold is altered. Background synaptic activity can alter the input/output characteristics of non-neuronal excitatory cells. Suppressing these baseline activities would facilitate the discovery of new pharmaceutical targets for different clinical diseases.

Incisional hernias have a particular dynamic related both to the repair method and to the absence or presence of a prosthetic material. The incidence of incisional hernias is estimated at 9.8% after 21 months and increases to 18.7% after ten years. This study presents a surgical method for the repair of recurrent incisional hernias, through open surgery and the use of existing mesh. The selection criteria regarding the use of this method are: the prosthesis is well integrated, without granulomas, suppurations, defect appearing on the same prosthetic abdominal area. The analyzed factors were the location of the incisional hernias, the duration of the operation, the duration of hospitalization, identified complications, the size of the parietal defect. The retrospective study carried out focused on recurrences of incisional hernias from a group of 631 patients operated on for incisional hernias in a general surgery department of Dr. Constantin Andreoiu County Emergency Hospital, Ploiești, Romania, during January 2018 - December 2022. Of the total of 18.22% relapses, 13.6% were relapsed eventrations after prosthetic procedures. Following the application of the selection criteria, several 8 patients with recurrent eventrations were operated by the open method using the existing mesh. In the case of recurrences after prosthetic procedures of incisional hernias, the recurrence can be solved by open surgery using the existing well-integrated prosthesis, without tension, absence of suppuration in the wound, and the peritoneal cavity. For the presented case, the return to daily activities occurred after 22 hours of hospitalization.

The main characteristics of blockchains, such as security and traceability, have enabled their use in many distinct scenarios, such as the rise of new cryptocurrencies and decentralized applications (dApps). However, part of the information exchanged with the typical blockchains is public, which can lead to privacy issues. To avoid or mitigate these issues, some blockchains are applying mechanisms to deal with data privacy. Trusted Execution Environments, the basis of confidential computing, and secure Multi-party Computation are two technologies that can be applied in that sense. In this paper, we analyzed seven blockchain technologies that apply mechanisms to improve data privacy. We defined seven technical questions related to common requirements for decentralized applications and, to answer each question, we reviewed the available documentation and gathered information from chat channels. We briefly present each blockchain technology and the answers to each technical question. Finally, we present a table summarizing the information and showing which technologies are more prominent.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

Active ageing is defined as the process of optimizing opportunities for physical, social and mental health to enable older people to take an active part in society without discrimination and to enjoy an independent and good quality of life. The World Health Organization assumed this as a process for increasing and maintaining an individual’s participation in activities to enhance his/her quality of life. In this survey, the authors addressed the following question: “Is assistive technology (AT) for mobility contributing to enhancement of lifelong capacity and performance?”. From June 2015 until February 2016, 96 community dwelling adults, AT users for mobility (powered wheelchairs, manual wheelchairs, lower limb prostheses, walkers, crutches and canes), aged 45-97, mean 67.02 +/- 14.24 years old, 56.3% female, were interviewed using the Psychosocial Impact of Assistive Devices Scale (P-PIADS), the Activities and Participation Profile related to Mobility (APPM) and demographics, clinical and questions about AT use and training. The participants’ profiles revealed moderate limitation and restrictions in participation, measured by the APPM (2.03). Most participants showed positive impact of AT; average scores obtained from the P-PIADS subscales were: Self-esteem 0.62, Competency 1.11 and Adaptability 1.10. P-PIADS total was 0.96, with the powered wheelchair users scoring the highest (1.53) and the walker users scoring the lowest (0.73). All subscales and P-PIADS total were positively correlated with the activities and participation profile. There was no relation between age and the psychosocial impact of AT or activities and participation profile. These results encourage the authors to follow these participants up for a lifelong intervention. To accomplish that aim, currently, the protocol is implemented at the AT prescribing centers in Coimbra, Portugal in order to assess the impact of AT on participation in society, one of the domains of the Active Ageing Index, a new analytical tool to help policy makers in developing policies for active and healthy ageing.

The development of biofuels and biodiesel industry specifically, has resulted in the generation of large amounts of glycerol as a surplus. The conversion of glycerol, into chemicals with indus-trial interest, through anaerobic fermentation using E. coli as a biocatalyst is an attractive alter-native to improve economic performance in the biodiesel industry, for example through the bi-otransformation of residual glycerol into ethanol. For 2019, the size of the global ethanol market was valued at $ 89.1 billion US dollar. Biodiesel can be produced from several materials like rapeseed, palm, soya, corn, palm kernel, frying oil or olive-pomace oil (OPO). The last, can be extracted from alperujo, byproduct of olive oil process. However, olive-pomace oil converting into biodiesel is not enough established to achieve a global olive-oil/biodiesel derived process with minimized waste generation. Traditionally biodiesel production process produces an equivalent of 10% by mass of glycerol as a waste or by-product. This work explores the conver-sion of residual glycerol into chemicals like ethanol and hydrogen with a focus on projecting raw material potential availability from OPO, contributing to the promotion of the use of biofu-els and biodegradable compounds.

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