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New derivatives of carbazole and diphenyl imidazole for potential multiple applications were synthesized and investigated. Their properties were studied by thermal, optical, photophysical, electrochemical, and photoelectrical measurements. The compounds exhibited relatively narrow blue light-emission bands, which is favorable for deep-blue electroluminescent devices. The synthesized derivatives of imidazole and carbazole were tested as fluorescent emitters for OLEDs. The device showed deep-blue emissions with CIE color coordinates of (0.16, 0.08) and maximum quantum efficiency of 1.1%. The compounds demonstrated high triplet energy values above 3.0 eV and hole drift mobility exceeding 10−4 cm2/V·s at high electric fields. One of the compounds having two diphenyl imidazole moieties and tert-butyl-substituted carbazolyl groups showed bipolar charge transport with electron drift mobility reaching 10−4 cm2/V·s at electric field of 8 × 105 V/cm. The synthesized compounds were investigated as hosts for green, red and sky-blue phosphorescent OLEDs. The green-, red- and sky-blue-emitting devices demonstrated maximum quantum efficiencies of 8.3%, 6.4% and 7.6%, respectively.

The formation of Ag nanoparticles on the surface of TiO2 (AgNP/TiO2) to enhance photocatalytic efficiency was studied. The Ag nanoparticles (AgNP) size, form, and distribution dependence on the initial thickness of Ag thin films, annealing temperature, and time were analyzed. The optimal annealing temperature of 400 °C and annealing time of 60 min were chosen to form AgNP from the initial Ag thin films with a thickness of 5, 7.5, and 10 nm. The formation of AgNP was done on amorphous TiO2 (a-TiO2), which crystallized into the anatase phase after the annealing. The photocatalytic efficiency (k–degradation rate constant, Defi–degradation efficiency) was evaluated by the photodegradation of Rhodamine B aqueous solution. The results suggested that the highest photocatalytic efficiency of Rhodamine B aqueous solution was reached where the average diameter (DA) of AgNP was ~38 nm (k38 = 0.017 min−1, Defi_38 = 63.5%), compared to 27 and 82 nm (k27 = 0.012 min−1, Defi_27 = 51.2% and k82 = 0.011 min−1, Defi_82 = 52.1%, respectively). The acquired results did not show clear correlation between the size and distribution of the AgNP on the TiO2 surface and photocatalytic efficiency. Nevertheless, the results suggest that AgNP can enhance the photocatalytic efficiency of TiO2 thin films (kTiO2 = 0.008 min−1, Defi_TiO2 = 36.3%).

Gadolinium-doped ceria (GDC) nanopowders, prepared using the co-precipitation synthesis method, were applied as a starting material to form ceria-based thin films using the electron-beam technique. The scanning electron microscopy (SEM )analysis of the pressed ceramic pellets’ cross-sectional views showed a dense structure with no visible defects, pores, or cracks. The AC impedance spectroscopy showed an increase in the total ionic conductivity of the ceramic pellets with an increase in the concentration of Gd2O3 in GDC. The highest total ionic conductivity was obtained for Gd0.1Ce0.9O2-δ (σtotal is 11 × 10−3 S∙cm−1 at 600 °C), with activation energies of 0.85 and 0.67 eV in both the low- and high-temperature ranges, respectively. The results of the X-ray photoelectron spectroscopy (XPS) and inductively coupled plasma optical emission spectrometer (ICP-OES) measurements revealed that the stoichiometry for the evaporated thin films differs, on average, by ~28% compared to the target material. The heat-treatment of the GDC thin films at 600 °C, 700 °C, 800 °C, and 900 °C for 1 h in the air had a minor effect on the surface roughness and the morphology. The results of Raman spectroscopy confirmed the improvement of the crystallinity for the corresponding thin films. The optimum heat-treating temperature for thin films does not exceed 800 °C.

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches. Medical IoT combines medical devices and AI applications with healthcare infrastructure to support medical diagnostics. The current state-of-the-art approach fails to diagnose breast cancer in its initial period, resulting in the death of most women. As a result, medical professionals and researchers are faced with a tremendous problem in early breast cancer detection. We propose a medical IoT-based diagnostic system that competently identifies malignant and benign people in an IoT environment to resolve the difficulty of identifying early-stage breast cancer. The artificial neural network (ANN) and convolutional neural network (CNN) with hyperparameter optimization are used for malignant vs. benign classification, while the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) were utilized as baseline classifiers for comparison. Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models. We employ a particle swarm optimization (PSO) feature selection approach to select more satisfactory features from the breast cancer dataset to enhance the classification performance using MLP and SVM, while grid-based search was used to find the best combination of the hyperparameters of the CNN and ANN models. The Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used to test the proposed approach. The proposed model got a classification accuracy of 98.5% using CNN, and 99.2% using ANN.

Learning environments ensure successful implementation of the learning process but not always the effective design of the e-learning objects (ELOs) and moreover, search and adaptation. A technological solution for the design of the learning objects, repositories, and the semantic web is needed. There are many open educational resources, but not many platforms assure the possibility to adapt learning objects. The existing developed multifunctional platforms do not ensure the effective ELOs adaptation as well as the process of design and adaptation in the multifunctional environment. They do not have an automatic search of ELOs in the semantic web, which is directly targeted to the specific objects in repositories of open educational resources and do not allow for adaptation of the already developed ELO by automatically assigning reusable objects. The structure of the papers consists of the literature review and overview of existing practices, research methodology, research results description and conclusions provided by authors. The objective of the research is to suggest, to teachers, a model for effective e-learning objects design, automatic search and adaptation processes in the multifunctional environment by developing a platform based on semantic technologies for e-learning objects design and adaptation.

Metal-based nanoparticles with antimicrobial activity are gaining a lot of attention in recent years due to the increased antibiotics resistance. The development and the pathogenesis of oral diseases are usually associated with the formation of bacteria biofilms on the surfaces; therefore, it is crucial to investigate the materials and their properties that would reduce bacterial attachment and biofilm formation. This work provides a systematic investigation of the physical-chemical properties and the antibacterial activity of TiO2 thin films decorated by Ag and Au nanoparticles (NP) against Veillonella parvula and Neisseria sicca species associated with oral diseases. TiO2 thin films were formed using reactive magnetron sputtering by obtaining as-deposited amorphous and crystalline TiO2 thin films after annealing. Au and Ag NP were formed using a two-step process: magnetron sputtering of thin metal films and solid-state dewetting. The surface properties and crystallographic nature of TiO2/NP structures were investigated by SEM, XPS, XRD, and optical microscopy. It was found that the higher thickness of Au and Ag thin films results in the formation of the enlarged NPs and increased distance between them, influencing the antibacterial activity of the formed structures. TiO2 surface with AgNP exhibited higher antibacterial efficiency than Au nanostructured titania surfaces and effectively reduced the concentration of the bacteria. The process of the observation and identification of the presence of bacteria using the deep learning technique was realized.

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

In recent years machine learning algorithms have been gaining momentum in resolving subsurface flow issues related to hydrocarbon flows, Carbon capture utilization and storage, hydrogen storage, geothermal flows, and enhanced oil recovery. This paper presents and attempts to solve subsurface flow problem using neural upscaling method. The neural upscaling method, described in the present work, is a machine learning approach to calculate effective properties in each grid block for subsurface flow modeling. This method is intended to be more accurate than traditional analytical upscaling methods (which are only accurate for layered or homogeneous media) and numerical upscaling methods (which are more accurate for heterogeneous media but involve higher computational cost and are dependent on boundary conditions). The neural upscaling method is based on learning from a large number of geological realizations, which allows it to account for uncertainty in geology. It is also computationally fast and accurate. The method is demonstrated through a series of 2D test cases, and its accuracy is compared to that of analytical and numerical upscaling methods.

Background. Sedentary behaviour (SB) is an important risk factor for several health-related outcomes. The prevalence of SB is alarmingly high in older adults, who spend on average 9.4 h being sedentary each day, making them the most sedentary of all age groups. Objectives. The primary objective of this review is to assess the impact of interventions aimed at reducing SB in older adults (aged 60 years and older) living in long-term care facilities (LTCFs). The research question for this systematic review is as follows: in older people living in LTCFs, do interventions aimed at reducing SB, compared to usual care, result in a decrease in SB daily time or a reduction in the length of prolonged and uninterrupted sitting bouts? Data sources. Only peer-reviewed articles will be included in this systematic review, articles will be identified using the PICO method in seven different databases. Participants and interventions. Any primary intervention study (including randomized controlled trials, non-randomized controlled trials, and cohort studies) with the aim to reduce SB daily time or shorten the length of prolonged and uninterrupted sitting bouts in older adults living in LTCFs will be included. After searching databases, abstracts of the studies will be screened, and, after retrieving full text articles, data extraction will be conducted by two independent reviewers. Study appraisal and synthesis methods. The review will adhere to PRISMA reporting guidelines. Risk of bias (RoB) will be assessed using ROBINS-I or the RoB 2.0 tool and will be discussed with a third reviewer. The data will be grouped according to study design, with separate analysis for randomised and non-randomised designs. Results. The primary outcomes will be SB or time spent sedentary, assessed before and after the intervention. For the outcomes with the same measurement units, the pooled mean differences will be calculated. Standardised mean differences will be calculated for the outcomes with different measurement units. The data not suitable in numbers will be synthesised narratively. The strength of evidence of the outcomes will be assessed using GRADE assessment. If the data are suitable for quantitative analysis, we plan to use the Revman software to conduct a meta-analysis. Conclusions and implications of key findings. This protocol can serve as a valuable resource for other researchers interested in conducting similar systematic reviews or meta-analyses in the field of SB and older adult health.

Recurrent occurrence of drought stress in varying intensity has become a common phenomenon in the present era of global climate change, which not only causes severe yield losses but also challenges the cultivation of rice. This raises serious concerns for sustainable food production and global food security. The root of a plant is primarily responsible to perceive drought stress and acquire sufficient water for the survival/optimal growth of the plant under extreme climatic conditions. Earlier studies reported the involvement/important roles of microRNAs (miRNAs) in plants’ responses to environmental/abiotic stresses. A number (738) of miRNAs is known to be expressed in different tissues under varying environmental conditions in rice, but our understanding of the role, mode of action, and target genes of the miRNAs are still elusive. Using contrasting rice [IR-64 (reproductive-stage drought sensitive) and N-22 (drought-tolerant)] cultivars, imposed with terminal (reproductive-stage) drought stress, we demonstrate differential expression of 270 known and 91 novel miRNAs in roots of the contrasting rice cultivars in response to the stress. Among the known miRNAs, osamiR812, osamiR166, osamiR156, osamiR167, and osamiR396 were the most differentially expressed miRNAs between the rice cultivars. In the root of N-22, 18 known and 12 novel miRNAs were observed to be exclusively expressed, while only two known (zero novels) miRNAs were exclusively expressed in the roots of IR-64. The majority of the target gene(s) of the miRNAs were drought-responsive transcription factors playing important roles in flower, grain development, auxin signaling, root development, and phytohormone-crosstalk. The novel miRNAs identified in this study may serve as good candidates for the genetic improvement of rice for terminal drought stress towards developing climate-smart rice for sustainable food production.

Young consumers are often described as innovative and concerned about the environment. However, their practices sometimes are not strong enough, which are described as the attitude-behavior gap and are seen in significant amounts of food waste. The objective of this study is to focus on food waste among young consumers in high-income countries and to outline the main determinants of food waste generation. Qualitative data gathered from nine focus groups in Lithuania, Finland and Denmark (2021-2022) contribute to formulating potential intervention to decrease food waste behavior within this segment. The article provides a substantial literature review on food waste and discusses recommendations for possible interventions and further research to solve the attitude-behavior gap. The findings show four specific fields for potential solutions, related to (1) special occasions, (2) assessing food quality, (3) kitchen habits, and (4) shopping habits. Our contribution is discussed at the end of the article.

Chikungunya virus (CHIKV) is a mosquito-transmitted pathogen that causes chikungunya disease (CHIK); the disease is characterized by fever, muscle ache, rash, and arthralgia. This arthralgia can be debilitating and long-lasting, seriously impacting quality of life for years. Currently, there is no specific therapy available for CHIKV infection. We have developed a despeciated equine polyclonal antibody (CHIKV-EIG) treatment against CHIKV and evaluated its protective efficacy in mouse models of CHIKV infection. In immunocompromised (IFNAR−/−) mice infected with CHIKV, daily treatment for five consecutive days with CHIKV-EIG administered at 100 mg/kg starting on the day of infection prevented mortality, reduced viremia, and improved clinical condition as measured by body weight loss. These beneficial effects were seen even when treatment was delayed to 1 day after infection. In immunocompetent mice, CHIKV-EIG treatment reduced virus induced arthritis (including footpad swelling), arthralgia-associated cytokines, viremia, and tissue virus loads in a dose-dependent fashion. Collectively, these results suggest that CHIKV-EIG is effective at preventing CHIK and could be a viable candidate for further development as a treatment for human disease.

Atmospheric plasma spraying is used to deposit alumina and alumina-graphite composite coatings. The influence of arc current (plasma temperature) on the microstructure, roughness, elemental composition, and phase composition of the coatings is analyzed by scanning electron microscopy (SEM), surface roughness testing, energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). The tribological properties of the sprayed coatings are analyzed using a tribometer with a ball-on-flat configuration. The roughness of the as-sprayed coatings increases and the size of the granules decreases with the increase in plasma temperature. The results demonstrate that the friction coefficients and wear rates are slightly reduced for Al2O3 coatings sprayed at higher arc currents. The Al2O3-graphite coatings register lower friction coefficient values than the Al2O3 coatings under dry sliding conditions.

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks.

In a constantly and rapidly changing global environment, one of the main priority tasks for every country is preserving, maintaining, and operating an independent and individually robust and stable energy system. This paper aims at researching electrical power systems’ (EPSs) behavior during desynchronization from a synchronous area, its stability in islanded mode, and its synchronization. The analysis of EPS behavior was accomplished utilizing numerical simulations in a widely used programming/simulation package. The sudden tripping of the EPS into an isolated island mode with known generation and load values was simulated, analyzed, and discussed. We investigated the behavior of an isolated EPS in the case of the loss of a certain amount of active power, and determined the maximum power that must be available to ensure the reliable operation of the isolated EPS and the power reserve that must be maintained to prevent the EPS from triggering UFLS. The simulation of the synchronization of an isolated EPS with a synchronous area was accomplished and analyzed. The obtained results were applied to reveal the sequence of actions that will help an EPS to ensure and maintain the stable and reliable operation of electrical installations during desynchronization.

Numerous strategies have been suggested to reduce dependence on synthetic products, such as physical, microbial, and natural methods. Among the natural remedies, plant extracts have emerged as a popular option owing to their eco-friendly character, ease of degradation, and harmless nature to humans. In our study, we used the acetone and hexane extracts of Rhaphiolepis indica fruit to combat two fungal pathogens that were isolated from infected bean plants and showed root rot symptoms. The two pathogens were confirmed to be pathogenic by pathogenicity assays conducted in vivo. The morphological and molecular identification by ITS-region sequencing revealed that the two isolates were Rhizoctonia solani and Fusarium solani, and they were assigned accession numbers OQ880457 and OQ820158, respectively. Our data showed that both hexane and acetone extracts caused a significant decrease in the linear growth of F. solani at all concentrations used (1%, 2%, and 3%), compared to the control. However, at a concentration of 3%, the hexane extract caused much greater inhibition than the acetone extract. For R. solani, the hexane extract, shows a significant inhibition percentage at all concentrations, which further increases to 85.24% at 3% concentration. The HPLC of both extracts indicated the presence and absence of phenolic and flavonoid compounds. The obtained results revealed that five acetonic phenolic extract compounds were ferulic, p-coumaric, gallic, p-OH benzoic, and cinnamic, with concentrations of 5.31, 10.36, 7.24, 6.08, and 0.89 mg/mL, respectively. On the other hand, the five hexanoic phenolic compounds were catechol, caffeic, chlorogenic, p-OH benzoic, and cinnamic acids, with concentrations of 3.66, 5.14, 0.69, 6.31, and 13.47 mg/mL, respectively. The identified acetonic flavonoid extract compounds, namely rutin, chrysin, quercetin, kaempferol, chrysoeriol, 7-OH flavone, and naringin, had respective concentrations of 5.36, 10.23, 4.32, 15.33, 1.06, 0.087, and 0.069 mg/mL, respectively. In contrast, it was observed that the seven hexanoic flavonoid extracts comprised of rutin, quercetin, kampferol, luteolin, chrysoeriol, 7-OH flavone, and catechin exhibited concentrations of 5.36, 7.15, 18.20, 6.04, 2.04, 10.24, and 13.43 mg/mL, respectively. The results of the study suggest that plant extracts may be a useful natural remedy for combating fungal pathogens and reducing dependence on synthetic products.

Current artificial intelligence algorithms can classify melanomas at a level equivalent to that of experienced dermatologists. The objective of this study was to assess the accuracy of a smartphone-based "You Only Look Once" neural network model for the classification of melanomas, melanocytic nevi, and seborrheic keratoses. The algorithm was trained using 59,090 dermatoscopic images. Testing was performed on histologically confirmed lesions: 32 melanomas, 35 melanocytic nevi, and 33 seborrheic keratoses. The results of the algorithm's decisions were compared with those of two skilled dermatologists and five beginners in dermatoscopy. The algorithm's sensitivity and specificity for melanomas were 0.88 (0.71-0.96) and 0.87 (0.76-0.94), respectively. The algorithm surpassed the beginner dermatologists, who achieved a sensitivity of 0.83 (0.77-0.87). For melanocytic nevi, the algorithm outclassed each group of dermatologists, attaining a sensitivity of 0.77 (0.60-0.90). The algorithm's sensitivity for seborrheic keratoses was 0.52 (0.34-0.69). The smartphone-based "You Only Look Once" neural network model achieved a high sensitivity and specificity in the classification of melanomas and melanocytic nevi with an accuracy similar to that of skilled dermatologists. However, a bigger dataset is required in order to increase the algorithm's sensitivity for seborrheic keratoses.

Various machine learning algorithms have been applied to network intrusion classification problems, including both binary and multi-class classifications. Despite the existence of numerous studies involving unbalanced network intrusion datasets, such as CIC-IDS2017, a prevalent approach is to address the issue by either merging the classes to optimize their numbers or retaining only the most dominant ones. However, there is no consistent trend showing that accuracy always decreases as the number of classes increases. Furthermore, it is essential for cybersecurity practitioners to recognize the specific type of attack and comprehend the causal factors that contribute to the resulting outcomes. This study focuses on tackling the challenges associated with evaluating the performance of multi-class classification for network intrusions using highly imbalanced raw data that encompasses the CIC-IDS2017 and CSE-CIC-IDS2018 datasets. The research concentrates on investigating diverse machine learning (ML) models, including Logistic Regression, Random Forest, Decision Trees, CNNs, and Artificial Neural Networks. Additionally, it explores the utilization of explainable AI (XAI) methods to interpret the obtained results. The results obtained indicated that decision trees using the CART algorithm performed best on the 28-class classification task, with an average macro F1-score of 0.96878.

This paper describes the impact of hydrothermal conditions on the strength properties and hydration processes of belite cement mortar samples. The belite-rich binder was synthesized by sintering the initial mixture of raw materials (granite cutting waste, the silica-gel waste from AlF3 production, and natural materials) in a high-temperature furnace at a temperature of 1150 & DEG;C for 2 h. The prepared clinker consists of larnite, mayenite, srebrodolskite, ye'elimite, and gehlenite. To control hydration kinetics and optimize the hardening of belite cement mortar, the produced clinker was blended with 7.5% of gypsum. The mechanical properties were assessed by curing the standard prisms (following the EN 196-1 standard, cement/sand = 1:3, W/C= 0.67) under water-saturated conditions in a stainless steel autoclave. The curing process was performed in a temperature range of 90 & DEG;C to 200 & DEG;C at various hydrothermal curing durations (6-48 h). The results indicated that the curing conditions highly influence the compressive strength evolution of belite cement mortar and the formed mineralogy of hydrates. The highest compressive strength value (exceeded 20 MPa) was obtained at 200 & DEG;C, i.e., when the main belite cement mineral was entirely hydrated and recrystallized into 1.13 nm tobermorite. The microstructural evolution and the phase assemblage during the hydrothermal curing were determined by X-ray diffraction analysis and differential scanning calorimetry.

The aim of the study was to develop a universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with normal voices and 86 patients with pathological voices. The developed UPB "Voice Screen" application installed on five iOS and Android smartphones was used for AVQI estimation. The AVQI measures calculated from voice recordings obtained from a reference studio microphone were compared with AVQI results obtained using smartphones. The diagnostic accuracy of differentiating normal and pathological voices was evaluated by applying receiver-operating characteristics. One-way ANOVA analysis did not detect statistically significant differences between mean AVQI scores revealed using a studio microphone and different smartphones (F = 0.759; p = 0.58). Almost perfect direct linear correlations (r = 0.991-0.987) were observed between the AVQI results obtained with a studio microphone and different smartphones. An acceptable level of precision of the AVQI in discriminating between normal and pathological voices was yielded, with areas under the curve (AUC) displaying 0.834-0.862. There were no statistically significant differences between the AUCs (p > 0.05) obtained from studio and smartphones' microphones. The significant difference revealed between the AUCs was only 0.028. The UPB "Voice Screen" application represented an accurate and robust tool for voice quality measurements and normal vs. pathological voice screening purposes, demonstrating the potential to be used by patients and clinicians for voice assessment, employing both iOS and Android smartphones.

Due to the constant increase in the number of plant diseases and the lack of available treatments, there has been a growing interest in plant extracts over the past few decades. Numerous studies suggest that plant extract molecules possess valuable antimicrobial activities, particularly against fungi and bacteria. This suggests that these biomaterials could potentially serve as attractive therapeutic options for the treatment of phytopathogen infections. In the present study, we investigated and analyzed the methanolic extract of Eryngium campestre L. whole plant extract using HPLC. The analysis revealed the presence of several polyphenolic constituents, with benzoic acid, catechol, quercetin, vanillic acid, resveratrol, naringenin, and quinol being the most abundant. The amounts of these constituents were determined to be 2135.53, 626.728, 579.048, 356.489, 323.41, 153.038, and 128.77 mg/kg, respectively. Furthermore, we isolated and identified different plant fungal and bacterial isolates from symptomatic potato plants, which were accessioned as Rhizoctonia solani (OQ880458), Fusarium oxysporum (OQ820156) and Fusarium solani (OQ891085), Ralstonia solanacearum (OQ878653), Dickeya solani (OQ878655), and Pectobacterium carotovorum (OQ878656). The antifungal activity of the extract was assessed using fungal growth inhibitions (FGI) at concentrations of 100, 200, and 300 µg/mL. The results showed that at the lowest concentration tested (100 µg/mL), the extract exhibited the highest effectiveness against R. solani with an FGI of 78.52%, while it was least effective against F. solani with an FGI of 61.85%. At the highest concentration tested, the extract demonstrated the highest effectiveness against R. solani and F. oxysporum, with FGIs of 88.89% and 77.04%, respectively. Additionally, the extract displayed a concentration-dependent inhibitory effect on all three bacterial pathogens. At the highest concentration tested (3000 µg/mL), the extract was able to inhibit the growth of all three bacterial pathogens, although the inhibition zone diameter varied. Among the bacterial pathogens, D. solani exhibited the highest sensitivity to the extract, as it showed the largest inhibition zone diameter at most of the extract concentrations. These findings highlight the potential of the E. campestre extract as a source of natural antimicrobial agents for controlling various plant pathogens. Consequently, it offers a safer alternative to the currently employed protective methods for plant disease management.

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The weighting uses the reciprocal of binary cross-entropy as a base learner's performance metric and estimates its Shapley value to measure the overall contribution of a learner to an equally weighted ensemble of various sizes. Two variants of this strategy were empirically compared with a single monolith model and other static weighting strategies using two large banking-related datasets. A variant that discards learners with a negative Shapley value was ranked as first or at least second when constructing homogeneous ensembles, whereas for heterogeneous ensembles this strategy resulted in a better or at least similar detection performance to other weighting strategies tested. The main limitation being the computational complexity of Shapley calculations, the explored weighting strategy could be considered as a generalization of performance-based weighting.

The application of non-stationary Lamb wave signals is a promising tool in various industrial applications where information about changes inside a structure is required. Phase velocity is one of the Lamb wave parameters that can be used for inhomogeneities detection. The possibility of reconstructing the segment of the phase velocity in a strong dispersion range using only two signals is proposed. The theoretical study is performed using signals of the A0 mode propagating in an aluminium plate at a frequency of 150 kHz, 300 kHz, 500 kHz and 900 kHz. The experiment was carried out at a value of 300 kHz. The studies conducted indicated that the maximum distance between two signals, at which the time-of-flight can be measured between the same phase points, is the main parameter for the two signals technique application. Theoretical and experimental studies were performed, and the mean relative error was calculated by comparing the obtained results with those calculated via the SAFE method. In the theoretical study, the mean relative error of 0.33% was obtained at 150 kHz, 0.22% at 300 kHz, 0.23% at 500 kHz and 0.11% at 900 kHz. The calculated mean relative errors δcph=0.91% and δcph=1.36% were obtained at different distances in the experimental study. The results obtained show that the estimation of the phase velocity in dispersion ranges using only two received signals was a useful tool that saved time and effort.

Though much attention is dedicated to the development of its research and innovation policy, the European Union constantly struggles to match the level of the strongest innovators in the world. Therefore, there is a necessity to analyze the individual efforts and conditions of the 27 member states that might determine their final innovative performance. The results of a scientific literature review showed that there is a growing interest in the usage of artificial intelligence when seeking to improve decision-making processes. Data envelopment analysis, as a branch of computational intelligence methods, has proved to be a reliable tool for innovation efficiency evaluation. Therefore, this paper aimed to apply DEA for the assessment of the European Union’s innovation efficiency from 2000 to 2020, when innovation was measured by patent, trademark, and design applications. The findings showed that the general EU innovation efficiency situation has improved over time, meaning that each programming period was more successful than the previous one. On the other hand, visible disparities were found across the member states, showing that Luxembourg is an absolute innovation efficiency leader, while Greece and Portugal achieved the lowest average efficiency scores. Both the application of the DEA method and the gathered results may act as viable guidelines on how to improve R&I policies and select future investment directions.

When realized on computational devices with finite quantities of memory, feedforward artificial neural networks and the functions they compute cease being abstract mathematical objects and turn into executable programs generating concrete computations. To differentiate between feedforward artificial neural networks and their functions as abstract mathematical objects and the realizations of these networks and functions on finite memory devices, we introduce the categories of general and actual computabilities and show that there exist correspondences, i.e., bijections, between functions computable by trained feedforward artificial neural networks on finite memory automata and classes of primitive recursive functions.

Microgrid systems may employ various combinations of system designs to connect generating units, and the number of different system designs increases exponentially upon adding different brands of inverters to a system. Each of the different microgrid system designs must be set up in a way that it works in balance. An example of an unbalanced microgrid system is given in this paper, with the main issue being the non-predictive excess power, which causes a frequency rise and faulty conditions in the microgrid system. There are many simple options for controlling excess power in a microgrid system; however, none of these options solve the issue permanently while ensuring excess power control without affecting the system’s accumulated energy—the battery state-of-charge (SOC) level. Therefore, there is a need to create a variable average power load (VAPL) device to utilize the excess power at a rate it is changing to avoid a reduction in accumulated energy. The main goal of this study is to review average power control methods for the VAPL device and provide guidance to researchers in selecting the most suitable method for controlling excess power. A key finding of the paper is a suggested optimal average power control method ensuring that the VAPL device is versatile to implement, economically attractive, and not harmful to other devices in a microgrid system.

The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications.

Jute is a ligno-cellulosic natural fiber that ranks second in terms of the volume of cellulosic fibers and is extensively utilized for technical textile applications. The goal of this study is to determine the flame-retardance (FR) properties of pure Jute and Jute-Cotton fabrics treated with Pyrovatex CP New at concentrations of 90% (owf), M:L: 1:7. Both fabrics exhibited a substantial improvement in flame-retardancy. After the ignition period, the recorded flame spread time in both FR treated fabrics was zero seconds; whereas for untreated Jute and Jute-Cotton fabrics, the flame spread time was measured 21 s and 28 s, respectively, to burn their entire length (15 cm). Within these flame-spread times, the length of the char was 2.1 cm and 2.57 cm in the Jute and Jute-Cotton fabrics, respectively. After FR finishing, on both fabrics in the warp and weft directions, the physico-mechanical properties significantly decreased. The deposition of flame-retardant finishes on the fabric surface was determined by Scanning Electron Microscope (SEM) images. According to Fourier Transform Infra-Red Spectroscopy (FTIR) analysis, the flame-retardant chemical had no effect on the inherent properties of the fibers. Thermogravimetric analysis (TGA) analysis revealed that FR treated fabrics had early degradation, resulting in the formation of more char than in the untreated samples. After FR treatment, both fabrics showed a significant improvement in residual mass (more than 50%). Although the formaldehyde content observed in the FR treated samples was significantly greater, it was still within the permitted limit formaldehyde content in textiles intended for outerwear and not worn next to the skin. The results of this investigation demonstrate the potential use of Pyrovatex CP New in jute-based materials.

The building sector accounts for over 40% of global energy consumption, and many buildings are old and inefficient. However, the current pace of building renovation is not sufficient to make a tangible impact. A new strategy is needed to accelerate the renovation process. Renovation at the district level and the use of digital tools, such as a digital twin (DT) of a city district, can provide a solution. This paper proposes a novel approach to city block renovation using renewable energy sources (RES), including photovoltaic (PV) solar panels, heat pumps (HP), and electric heaters (EH), while utilizing a DT of a city district to provide a user-friendly representation of the results and data needed for holistic solutions. The proposed method combines an optimization model of the optimal heating system with a solar PV simulation technique to analyse hybrid RES solutions and potential on-site energy generation and supply. Several scenarios are simulated to evaluate RES solutions in the renovation process of the city block using the DT concept. The simulation results demonstrate that a hybrid RES solution, which includes a PV system and a heating system, is optimal when the on-site generated energy is used not only for domestic electricity consumption, but also for the operation of HPs and EHs for heat generation. This study highlights the importance and significance of a DT approach to city block renovation and provides a new solution to accelerate the renovation process and reduce energy consumption in the building sector. Keywords: deep renovation; city block; photovoltaic; heat pumps; digital twin; renewable energy sources; photogrammetry.

UV-initiated green synthesis of metal nanoparticles by using plant extracts as photoreducing agents is of particular interest since it is an environmentally friendly, easy-to-maintain, and cost-effective method. Plant molecules that act as reducing agents are assembled in a highly controlled way which makes them suitable for metal nanoparticle synthesis. Depending on the plant species, their application for green synthesis of metal nanoparticles for diverse applications may contribute to the mediation/reduction in organic waste amounts, thus enabling the implementation of the circular economy concept. In this work, UV-initiated green synthesis of Ag nanoparticles in hydrogels and hydrogel’s thin films containing gelatin (matrix), red onion peel extract of different concentrations, water, and a small amount of 1 M AgNO3 have been investigated and characterized using UV-Vis spectroscopy, SEM and EDS analysis, XRD technique, performing swelling experiments and antimicrobial tests using bacteria (Staphylococcus aureus, Acinetobacter baumannii, Pseudomonas aeruginosa), yeasts (Candida parapsilosis, Candida albicans) and microscopic fungi (Aspergillus flavus, Aspergillus fumigatus). It was found that the antimicrobial effectiveness of the silver-enriched red onion peel extract–gelatin films was higher at lower AgNO3 concentrations as compared to those usually used in the commercially available antimicrobial products. The enhancement of the antimicrobial effectiveness was analyzed and discussed, assuming the synergy between photoreducing agent (red onion peel extract) and silver nitrate (AgNO3) in the initial gel solutions leading to the intensification of Ag nanoparticles production.

Mechanical scooter riding is a popular physical activity among children, but little is known about the differences in muscle loading between the dominant and non-dominant sides during this activity. The objective of this study was to identify the muscle activation patterns in children’s dominant and non-dominant legs as they rode scooters on the convenient and inconvenient sides. The study included nine healthy children aged 6–8. The participants rode 20 m on a mechanical scooter at a self-selected pace using both the convenient and inconvenient sides. Electromyography was used to measure the muscle activity in the dominant and non-dominant legs during the pushing and gliding phases. A 20 m sprint run was used as a control exercise to estimate the typical differences in muscle activation between the dominant and non-dominant legs. In the pushing phase, the symmetry index for five of the eight analyzed muscles exceeded 50% (p < 0.05); four of these muscles were more active in the pushing leg, and one was more active in the standing leg. In the gliding phase, four muscles were more active in the standing leg, and one was more active in the pushing leg (p < 0.05). Upon observing children who changed sides while riding a scooter, it was found that the pattern of muscle activation displayed a reverse trend that resembled the initial pattern. Our study indicated notable differences in muscle activity patterns between the dominant and non-dominant sides of individual leg muscles during children’s scooter riding. These patterns were reversed when children switched sides on the scooter. These findings suggest that using both legs and switching sides while riding a scooter may be a viable strategy for promoting balanced muscular development.

Background: The quadriceps tendon (QT) autograft has recently drawn attention for anterior cruciate ligament reconstruction (ACLR). Finding the best autograft option for adolescents after an ACL injury is essential to ensure them a high-quality active lifestyle. There are no studies comparing the all-soft-tissue QT autograft with the hamstring tendon (HT) autograft in such population. Methods: In this study, 68 patients younger than 18 years of age were assigned to the HT (38 patients) or the QT (30 patients) ACLR group. The groups were similar, allowing their comparison. The instrumented knee laxity was evaluated with a Genourob arthrometer. In total, 48 patients (27 HT and 21 QT) were tested 3 months post-op, and 45 patients (26 HT and 19 QT) were tested 6 months post-op. Results: We found that 3 months post-op, the side-to-side anterior tibial translation measurements (GNRB1) were worse in the HT than in the QT group (1.4 (0.2–5.2; 1.715) vs. 0.6 (0.1–2.1; 0.905) mm, p = 0.02). There was no difference in side-to-side anterior tibial translation at 6 months post-op (GNRB2) between the HT and the QT group (1 (0.2–5.3; 1.519) vs. 1.1 (0.3–3.4; 1.279) mm, p = 0.927). At 3 months post-op, the side-to-side anterior tibial translation (GNRB1) was worse in males than in females. irrespective of the graft choice (1.45 (0.1–5.2; 1.696) vs. 0.4 (0.1–3.4; 0.89) mm, p = 0.016). The displacement curve slopes 6 months post-op were better for females (3 (0–13.1; 3.335) vs. 5.3 (0–26.1; 7.848), p = 0.014). At 3 months post-op, the GNRB measurements showed that the side-to-side anterior tibial translation (GNRB1) was better for females when the HT autograft had been used (0.45 (0.2–3.4; 0.942) vs. 2.4 (0.3–5.2; 2.333) mm, p = 0.003). In general, both autografts provided excellent objective outcomes at early follow-up. Conclusions: The all-soft-tissue QT autograft should be considered as a reliable alternative for ACLR in adolescents. This autograft has at least the same properties as the HT autograft in the early stages after ACL reconstruction in adolescent athletes and, in some respects, seems superior to the HT autograft. Research should continue to find the best possible graft choice for the most active and willing-to-return-to-sport population.

The purpose of this research is to correctly model steady-state heat transfer in and around rectangular bus bars installed horizontally in an indoor environment and to estimate the corresponding ampacities, considering the effects of horizontal transverse vibrations caused by electromagnetic forces. This thermo-electro-magneto-mechanical problem is solved analytically using correlations determined experimentally by other researchers, while the accuracy of the obtained results is verified numerically using the finite element method (FEM). The novelties of the developed model are as follows. First, modeling the effects of horizontal transverse vibrations on free convection from the top and bottom surfaces of rectangular bus bars via forced convection for different characteristic lengths. Second, modeling the effects of vibration amplitudes and vibration frequencies on the bus bar ampacity. Third, introducing the existing vibration classes (A, B, and C) into the analytical and FEM-based thermal analyses. The results show that with an increase either in the vibration amplitude or the vibration frequency, there is a greater convection-based dissipation of heat from the bus bars and an increase in their ampacity. Finally, for the standard vibration classes, it is found that the effect of horizontal transverse vibrations on the ampacity can be up to 41.99% for Class C.

The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.

This study investigates the possibility of utilising bottom slag (BS) waste from landfills, and a carbonation process advantageous for the use of artificial aggregates (AAs) in printed three-dimensional (3D) concrete composites. In general, the main idea of granulated aggregates is to reduce the amount of CO2 emissions of printed 3D concrete objects (wall). AAs are made from construction materials, both granulated and carbonated. Granules are made from a combination of binder (ordinary Portland cement (OPC), hydrated lime, burnt shale ash (BSA)) and waste material (BS). BS is a waste material left over after the municipal waste burning process in cogeneration power plants. Whole printed 3D concrete composite manufacturing consists of: granulating artificial aggregate, aggregate hardening and sieving (adaptive granulometer), carbonation of AA, mixing 3D concrete, and 3D printing. The granulating and printing processes were analysed for hardening processes, strength results, workability parameters, and physical and mechanical properties. Printings with no granules (reference 3D printed concrete) were compared to 3D printed concretes with 25% and 50% of their natural aggregate replaced with carbonated AA. The results showed that, theoretically, the carbonation process could help to react approximately 126 kg/m3 CO2 from 1 m3 of granules.

Two prototypes with energy efficiency calculations have been developed to enable real-time efficiency assessment and data collection. The results of the experiment demonstrate that the use of microprocessor technology and the Internet of Things can significantly improve the efficiency and accuracy of energy audits in power transformers. The prototypes developed in this study provide real-time efficiency assessment and data collection, enabling more effective energy management and cost savings for industrial users. During the experiment, it was found that resonance can cause the same losses as a poor power factor of the system, highlighting the importance of addressing energy quality issues in addition to energy efficiency. These findings have important implications for energy efficiency policies and practices in the context of climate change mitigation and rising energy prices.

Grease, oil, hydrocarbon residues, heavy metals, and surfactants are all present in car wash wastewater (CWW), which all can have detrimental effects on the environment and human health. This study was designed to assess CWW treatment using an upgraded physical technique combined with a range of conventional and more sustainable coagulants. Physical treatment effectively lowered the oil and grease (O&G) and chemical oxygen demand (COD) of the CWW by 79 ± 15% and 97 ± 1.6%, respectively. Additional treatment was provided using chemical coagulation–flocculation–settling. In jar test studies, humic acid (HA) and alum were found to provide significantly higher turbidity removal, 79.2 ± 3.1% and 69.8 ± 8.0%, respectively, than anionic polyacrylamide (APA), 7.9 ± 5.6% under influent turbidity values from 89 to 1000 NTU. Overall physical/chemical treatment of CWW yielded 97.3 ± 0.8% COD removal, and 99.2 ± 0.4% O&G removal using HA and alum. Due to the numerous problems created when using synthetic coagulants, naturally occurring coagulants that have no impact on human health, such as HA, are highly desirable options. The findings of this study show that treating CWW provides several advantages for sustainable development, health and well-being, and raising public knowledge and support for water reuse.

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks' performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions.

While the last two decades have seen an increased interest in STEAM (science, technology, engineering, arts, and mathematics) in K-12 schools, few efforts have focused on the teachers and teaching practices necessary to support these interventions. Even fewer have considered the important work that teachers carry out not just inside classrooms but beyond the classroom walls to sustain such STEAM implementation efforts, from interacting with administrators to recruiting students and persuading parents about the importance of arts and computer science. In order to understand teachers’ needs and practices regarding STEAM implementation, in this paper, we focus on eight experienced computer science teachers’ reflections on implementing a STEAM unit using electronic textiles, which combine crafting, circuit design, and coding so as to make wearable artifacts. We use a broad lens to examine the practices high school teachers employed not only in their classrooms but also in their schools and communities to keep these equitable learning opportunities going, from communicating with other teachers and admins to building a computer science (CS) teacher community across district and state lines. We also analyzed these reflections to understand teachers’ own social and emotional needs—needs important to staying in the field of CS education—better, as they are relevant to engaging with learning new content, applying new pedagogical skills, and obtaining materials and endorsements from their organizations to bring STEAM into their classrooms. In the discussion, we contemplate what teachers’ reported practices and needs say about supporting and sustaining equitable STEAM in classrooms.

Energy harvesting technologies and material science has made it possible to tap into the abundant amount of surrounding vibrational energy to efficiently convert it into useable energy providing power to portable electronics and IoT devices. Recent investigations show that the piezoelectric effect is created in cellular polymers called ferroelectrets. These cellular-compliant polymers with polarized pores have a piezoelectric response to generate electrical energy when subjected to mechanical strain or surrounding vibration. It is found that there is a significant difference between ferroelectret polarized cellular polypropylene foam and traditional piezoelectric polymers such as polyvinylidene fluoride (PVDF). The former has approximately ten times higher piezoelectric coefficient than the latter. This means that with an acceleration of 9.81 m/s2 force on this material, ferroelectrets generate up to 39 (µW/g/mm3) power output. Designing a polypropylene-based piezoelectric energy harvester based on the d33 mode of vibration can be challenging due to several factors, as it requires balancing multiple factors such as mechanical stability, piezoelectric response, circuit topology, electrode size, spacing, placement relative to the piezoelectric material, and so on. This paper proposes the preliminary experimental investigation of ferroelectret cellular polypropylene foam in harvesting performance. Suggestions of different approaches for the structural design of energy harvesters are provided. The vibration-dependent response and generated output are examined concerning pulse or sinusoidal input excitation. The voltage generated for both excitations is compared and suggestions are provided regarding the suitable kind of excitation for the chosen ferroelectret material. Finally, conclusions and prospects for ferroelectret materials used in energy-harvesting applications are given.

The organizational reflex in relation to the company is considered to be an instinctive reaction of its employees to an external stimulus that has arisen or, in other words, the ability of the employee to spontaneously generate a competitive response of the company based on the information received from the global environment. It is an intentional algorithm that enables employees to know how and when to react as quickly as possible, according to the signal from the global environment received, within the limits of their area of expertise. This response phenomenon comes forth in the context of the functioning of a sustainable organization and highlights the coherence of the performance of a human resource. In order to maintain its competitiveness, each organization learns to react to the ongoing changes in the dynamic environment as quickly as possible and, in this process, develops action algorithms similar to human reflexes in structure. Modern science makes a great contribution to the longevity of the company by creating activity models for the organization and performing simulations in the digital space. In this paper, the simulation model of a company’s competitive human resource response to global changes, which is used for the description of organizational reflex, is present. The model reflects the human reflex arc in a company’s decision-making process, whose effectiveness depends on the coherence of its human resources and its ability to sustain an effective organizational reflex. The purpose of the developed model is to simulate the employee’s response to the signals incoming from the global environment and to describe the development process of the organizational reflex that is leading to the longevity of the company and the sustainability of its market leadership.

Data Availability Statement: The research data used in the article are based on end-of-course feedback from students, staff meetings, and emails from university management. Therefore, they are confidential and were used with anonymization for data protection. Copyright © 2023 by the authors. Online and distance learning classes have been touted for the last several years as an innovation in higher education that should help improve the entrepreneurial growth mindset of students. However, the reported negative online learning experience of many college students worldwide during the COVID-19 epidemic has shown that many opportunities remain to improve the sustainable development and growth of online visual instruction practices. In this study, we outline and investigate a set of hypotheses related to the perceived usefulness and perceived ease of use (from TAM) of online video instruction in higher education courses during the pandemic. We employ grounded theory using autoethnographic case studies as a data source. We found that (a) synchronous broadcast lectures improve participant attitude (H1) and motivation (H2) toward online instruction, (b) prerecorded video instruction increases participant perceived “ease of use” (H3) and perceived behavioral control (H4) of online instruction, but (c) indicators of recorded dates on pre-recorded video instruction decreases participant perceived “usefulness” (H5) and “certainty” (H6) of online instruction. We enrich the insights of popular motivation models for organizations and the higher education industry by outlining a set of emotional elements originating in neuroscience leadership research (SCARF) that might either amplify or diminish the perceived the ease of use and perceived usefulness to technology usage relationships when participations engage in online learning situations. This research received no external funding.

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.

In the western United States, snow accumulation, storage, and ablation affect seasonal runoff. Thus, the prediction of snowmelt is essential to improve the reliability of water supply forecasts to guide water allocation and operational decisions. The current method used at the Colorado Basin River Forecast Center (CBRFC) couples the SNOW-17 temperature-index snow model and the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model in a lumped approach. Limitations in parameter transferability and calibration requirements for changing conditions with the temperature-index model motivated this research, in which new avenues were investigated to assess and prototype the application of an energy-balance snow model in a distributed modeling approach. The Utah Energy Balance (UEB) model was chosen to compare with the SNOW-17 model because it is simple and parsimonious, making it suitable for distributed application with the potential to improve water supply forecasts. Each model was coupled with the SAC-SMA model and the Rutpix7 routing model to simulate basin snowmelt and discharge. All the models were applied on grids over watersheds using the Research Distributed Hydrologic Model (RDHM) framework. Case studies were implemented for two study sites in the Colorado River basin over a period of two decades. The model performance was evaluated by comparing the model output with observed daily discharge and snow-covered area data obtained from remote sensing sources. Simulated evaporative components of sublimation and evapotranspiration were also evaluated. The results showed that the UEB model, requiring calibration of only a snow drift factor, achieves a comparable performance to the calibrated SNOW-17 model, and both provided reasonable basin snow and discharge simulations in the two study sites. The UEB model had the additional advantage of being able to explicitly simulate sublimation for different land types and thus better quantify evaporative water balance components and their sensitivity to land cover change. UEB also has a better transferability potential because it requires calibration of fewer parameters than SNOW-17. The majority of the parameters for UEB are physically based and regarded as constants characterizing spatially invariant properties of snow processes. Thus, the model remains valid for different climate and terrain conditions for multiple watersheds.

Caffeic acid is a widely distributed phenolic acid. It is described in the scientific literature that caffeic acid has poor solubility. The aim of this study was to improve the solubility of caffeic acid for better dissolution kinetics when administered orally. During the study, oral capsules of different compositions were modeled. The results of the disintegration test revealed that the excipients affected the disintegration time of the capsules. The excipient hypromellose prolonged the disintegration time and dissolution time of caffeic acid. The dissolution kinetics of caffeic acid from capsules depend on the chosen excipients. P407 was more effective compared to other excipients and positively affected the dissolution kinetics of caffeic acid compared to other excipients. When the capsule contained 25 mg of β-cyclodextrin, 85% of the caffeic acid was released after 60 min. When the capsule contained 25–50 mg poloxamer 407, more than 85.0% of the caffeic acid was released from capsules after 30 min. The research results showed that in order to improve the dissolution kinetics of caffeic acid, one of the important steps is to improve its solubility.

A nonlinear static analysis of a circular/annular nanoplate on the Winkler–Pasternak elastic foundation based on the nonlocal strain gradient theory is presented in the paper. The governing equations of the graphene plate are derived using first-order shear deformation theory (FSDT) and higher-order shear deformation theory (HSDT) with nonlinear von Karman strains. The article analyses a bilayer circular/annular nanoplate on the Winkler–Pasternak elastic foundation. HSDT while providing a suitable distribution of shear stress along the thickness of the FSDT plate, eliminating the defects of the FSDT and providing good accuracy without using a shear correction factor. To solve the governing equations of the present study, the differential quadratic method (DQM) has been used. Moreover, to validate numerical solutions, the results were compared with the results from other papers. Finally, the effect of the nonlocal coefficient, strain gradient parameter, geometric dimensions, boundary conditions, and foundation elasticity on maximum non-dimensional deflection are investigated. In addition, the deflection results obtained by HSDT have been compared with the results of FSDT, and the importance of using higher-order models has been investigated. From the results, it can be observed that both strain gradient and nonlocal parameters have significant effects on reducing or increasing the dimensionless maximum deflection of the nanoplate. In addition, it is observed that by increasing load values, the importance of considering both strain gradient and nonlocal coefficients in the bending analysis of nanoplates is highlighted. Furthermore, replacing a bilayer nanoplate (considering van der Waals forces between layers) with a single-layer nanoplate (which has the same equivalent thickness as the bilayer nanoplate) is not possible when attempting to obtain exact deflection results, especially when reducing the stiffness of elastic foundations (or in higher bending loads). In addition, the single-layer nanoplate underestimates the deflection results compared to the bilayer nanoplate. Because performing the experiment at the nanoscale is difficult and molecular dynamics simulation is also time-consuming, the potential application of the present study can be expected for the analysis, design, and development of nanoscale devices, such as circular gate transistors, etc.

Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results. In this paper, we explore the use of the Unity ML-Agents toolkit to train intelligent agents to navigate a racing track in a simulated environment using RL algorithms. The paper compares the performance of several different RL algorithms and configurations on the task of training kart agents to successfully traverse a racing track and identifies the most effective approach for training kart agents to navigate a racing track and avoid obstacles in that track. The best results, value loss of 0.0013 and a cumulative reward of 0.761, were yielded using the Proximal Policy Optimization algorithm. After successfully choosing a model and algorithm that can traverse the track with ease, different objects were added to the track and another model (which used behavioral cloning as a pre-training option) was trained to avoid such obstacles. The aforementioned model resulted in a value loss of 0.001 and a cumulative reward of 0.068, proving that behavioral cloning can help achieve satisfactory results where the in game agents are able to avoid obstacles more efficiently and complete the track with human-like performance, allowing for a deployment of intelligent agents in racing simulators.