Diagnosing Parkinson’s Disease (PD) necessitates monitoring symptom progression. Unfortunately, diagnostic confirmation often occurs years after disease onset. A more sensitive and objective approach is paramount to the expedient diagnosis and treatment of persons with PD (PwPDs). Recent studies have shown that we can train accurate models to detect signs of PD from audio recordings of confirmed PwPDs. However, disparities exist between studies and may be caused, in part, by differences in employed corpora or methodologies. Our hypothesis is that unaccounted covariates in methodology, experimental design, and data preparation resulted in overly optimistic results in studies of PD automatic detection employing sustained vowels. These issues include record-wise fold creation rather than subject-wise; an imbalance of age between the PwPD and control classes; using too small of a corpus compared to the sizes of feature vectors; performing cross-validation without including development data; and the absence of cross-corpora testing to confirm results. In this paper, we evaluate the influence of these methodological issues in the automatic detection of PD employing sustained vowels. We perform several experiments isolating each issue to measure its influence employing three different corpora. Moreover, we analyze if the perceived dysphonia of the speakers could be causing differences in results between the corpora. Results suggest that each independent methodological issue analyzed has an effect on classification accuracy. Consequently, we recommend a list of methodological steps to be considered in future experiments to avoid overoptimistic or misleading results. Full article

This paper examines a new vibrating dynamical motion of a novel auto-parametric system with three degrees of freedom. It consists of a damped Duffing oscillator as a primary system attached to a damped spring pendulum as a secondary system. Lagrange’s equations are utilized to acquire the equations of motion according to the number of the system’s generalized coordinates. The perturbation technique of multiple scales is applied to provide the solutions to these equations up to a higher order of approximations, with the aim of obtaining more accurate novel results. The categorizations of resonance cases are presented, in which the case of primary external resonance is examined to demonstrate the conditions of solvability of the steady-state solutions and the equations of modulation. The time histories of the achieved solutions, the resonance curves in terms of the modified amplitudes and phases, and the regions of stability are outlined for various parameters of the considered system. The non-linear stability, in view of both the attained stable fixed points and the criterion of Routh–Hurwitz, is investigated. The results of this paper will be of interest for specialized research that deals with the vibration of swaying buildings and the reduction in the vibration of rotor dynamics, as well as studies in the fields of mechanics and space engineering. Full article

Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve current challenges in modeling and simulation in production science. Due to the individualization and customization of products and the resulting increase in complexity of production, a need for flexibly adaptable simulations arises. This need is exemplified in the trend towards Digital Twins and Digital Shadows. Their application to production systems, against the background of an ever increasing speed of change in such systems, is arduous. Moreover, missing understandability and human interpretability of current approaches hinders successful, goal oriented applications. The OntologySim can help solving this challenge by providing the ability to generate truly cyber physical systems, both interlocked with reality and providing a simulation framework. In a nutshell, this paper presents a discrete-event-based open-source simulation using multi-agency and ontology. Full article

Recently, the demineralized dentin matrix has been suggested as an alternative material to autologous bone grafts and xenografts for clinical purposes. The aim of this study was to investigate the effect of different times of demineralization on the chemical composition and the surface morphology of dentinal particles. Extracted teeth were ground and divided into 5 groups based on demineralization time (T0 = 0 min, T2 = 2 min, T5 = 5 min, T10 = 10 min, and T60 = 60 min) with 12% EDTA. The analysis was performed using Fourier-Transform Mid-Infrared spectroscopy (FT-MIR) and Scanning Electron Microscopy (SEM) (p < 0.05). The FT-MIR analysis showed a progressive reduction of the concentration of both PO₄³⁻ and CO₃²⁻ in the specimens (T0 > T2 > T5 > T10 > T60). On the contrary, the organic (protein) component did not undergo any change. The SEM examination showed that increasing the times of demineralization resulted in a smoother surface of the dentin particles and a higher number of dentinal tubules. Full article

Bioactive compounds extracted from marine organisms showed several biological activities. The present study is an extension of our earlier studies where we assessed the antiproliferative and pro-apoptotic activities of ethanol, methylene chloride, ethyl acetate, acetone, and chloroform crude extracts of sponges: Negombata magnifica (NmE) and Callyspongia siphonella (CsE) against cancer cells. Herein, we are extending our previous findings on both sponge species depending on an alternative methanol extraction method with more advanced molecular biochemical insights as additional proof for anticancer and antimicrobial activity of N. magnifica and C. siphonella. Therefore, sponge specimens were collected during winter 2020 from the Dahab region at the Gulf of Aqaba. Each sponge was macerated with methanol to obtain the crude extracts; NmE and CsE. GC–MS analysis presented a total of 117 chemical compounds; 37 bioactive, 11 represented previously as constituents for a natural organism, and 69 had no biological activities. NmE dose-dependently inhibited the growth of HepG2, MCF-7, and Caco-2 carcinoma cell lines compared to CsE, which unfortunately has no antiproliferative activity against the same cancer cells. NmE was found to induce G0/G1 cell cycle arrest in HepG2 cells with its inhibition for CDK6, Cyclins D1, and E1 in HepG2, MCF-7, and Caco-2 cells. NmE also activated ROS production in HepG2 cells and induced apoptosis in HepG2, MCF-7, and Caco-2 cells via an increase in pro-apoptotic protein Bax, caspase-3, and cleavage PARP, and a decrease in anti-apoptotic protein BCL2. Unlike its anticancer potential, CsE exhibited clear superior results as an antimicrobial agent with a wider range against six microbial strains, whereas NmE showed a positive antibacterial activity against only two strains. Full article

Changes in climatic conditions are expected globally resulting in a higher rainfall intensity and longer duration of rainfall. The increase in the rainwater infiltration into the soil contributes to many geotechnical issues, such as excessive settlement, retaining wall failure and rainfall-induced slope failures. These geotechnical problems could be mitigated by the improvement of the problematic soil with the incorporation of the unsaturated soil mechanic principles. Dual-porosity soils or soils with bimodal water retention curve (WRC) are able to retain more water during prolonged drying and they would be able to drain out water faster during intense rainfall to maintain the slope stability. The objective of this study is to investigate the characteristics of the unsaturated shear strength of soil with bimodal WRC. In addition, the new mathematical equation is proposed to estimate the unsaturated shear strength of soils with a bimodal WRC. The results of the study indicated that the nonlinearity of the unsaturated shear strength is a function of the shape of bimodal WRC limited by the first and second air-entry value (AEV) of dual-porosity soils. The proposed equation agreed well with the experimental data of the unsaturated shear strength for dual-porosity soil. Full article

Guided tissue regeneration (GTR) and guided bone regeneration (GBR) became common procedures in the corrective phase of periodontal treatment. In order to obtain good quality tissue neo-formation, most techniques require the use of a membrane that will act as a barrier, having as a main purpose the blocking of cell invasion from the gingival epithelium and connective tissue into the newly formed bone structure. Different techniques and materials have been developed, aiming to obtain the perfect barrier membrane. The membranes can be divided according to the biodegradability of the base material into absorbable membranes and non-absorbable membranes. The use of absorbable membranes is extremely widespread due to their advantages, but in clinical situations of significant tissue loss, the use of non-absorbable membranes is often still preferred. This descriptive review presents a synthesis of the types of barrier membranes available and their characteristics, as well as future trends in the development of barrier membranes along with some allergological aspects of membrane use. Full article

Recognition of malware is critical in cybersecurity as it allows for avoiding execution and the downloading of malware. One of the possible approaches is to analyze the executable’s Application Programming Interface (API) calls, which can be done using tools that work in sandboxes, such as Cuckoo or CAPEv2. This chain of calls can then be used to classify if the considered file is benign or malware. This work aims to compare six modern shallow learning and deep learning techniques based on tabular data, using two datasets of API calls containing malware and goodware, where the corresponding chain of API calls is expressed for each instance. The results show the quality of shallow learning approaches based on tree ensembles, such as CatBoost, both in terms of F1-macro score and Area Under the ROC curve (AUC ROC), and training time, making them optimal for making inferences on Edge AI solutions. The results are then analyzed with the explainable AI SHAP technique, identifying the API calls that most influence the process, i.e., those that are particularly afferent to malware and goodware. Full article

With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation. Full article

This study discussed the mechanism of Fe-modified biochar (FeBC) derived from rice straw biochar (BC) as an adsorbent for removing Cr(VI) from aqueous solution and assessed its applicability in actual industrial wastewater. The Cr(VI) removal percentage increased with the FeBC dose, which achieved a removal of 99.5% at 8.0 g/L FeBC. Increasing the solution pH from 2 to 10 slightly reduced Cr(VI) adsorption by 6.6%. Coexisting ions such as Ca²⁺, Na⁺ and Cl⁻ inhibited the removal of Cr(VI); the removal rate decreased to 60% at their concentration of 0.25 mol/L. The adsorption isotherm and kinetics were better described by the Langmuir isotherm and pseudo-second-order kinetic models, respectively. Through scanning electron microscopy with energy dispersive X-ray, the Brunauer–Emmett–Teller method, Fourier transform infrared, X-ray diffraction and X-ray photoelectron spectroscopy, the analysis revealed that FeBC with iron oxides loaded onto its surface had more active sites than BC; the surface functional groups changed; the removal of Cr(VI) by FeBC was mainly attributed to electrostatic adsorption; the redox reaction of Cr, and Fe loaded onto BC enhanced Cr(VI) reduction process. FeBC showed a good removal performance on actual industrial wastewater with the concentration of both total Cr and Cr(VI) meeting the integrated wastewater discharge standard of China. Full article

Close-range photogrammetry (CRP) has proven to be a remarkable and affordable technique for data modeling and measurements extraction in construction management applications. Nevertheless, it is important to aim for making CRP more accessible by using smartphones on-site directly without a pre-calibration procedure. This study evaluated the potential of smartphones as data acquisition tools in comparison with compact cameras based on the quality and accuracy of their photogrammetric results in extracting geometrical measurements (i.e., surface area and volume). Two concrete specimens of regular shapes (i.e., beam and cylinder) along with an irregular-shaped sand pile were used to conduct this study. The datasets of both cameras were analyzed and compared based on lens distortions, image residuals, and projections multiplicity. Furthermore, the photogrammetric models were compared according to various quality criteria, processing time, and memory utilization. Though both cameras were not pre-calibrated, they both provided highly accurate geometrical estimations. The volumetric estimation error ranged from 0.37% to 2.33% for the compact camera and 0.67% to 3.19% for the smartphone. For surface area estimations, the error ranged from 0.44% to 0.91% for the compact camera and 0.50% to 1.89% for the smartphone. Additionally, the smartphone data required less processing time and memory usage with higher applicability compared with the compact camera. The implication of these findings is that they provide professionals in construction management with an assessment of a more direct and cost-effective 3D data acquisition tool with a good understanding of its reliability. Moreover, the assessment methodology and comparison criteria presented in this study can assist future research in conducting similar studies for different capturing devices in construction management applications. The findings of this study are limited to small quantification applications. Therefore, it is recommended to conduct further research that assesses smartphones as a photogrammetric data acquisition tool for larger construction elements or tracking ongoing construction activities that involve measurements estimation. Full article

This study investigated the potential use of dietary fibers (psyllium and pectin fibers added in different proportions of 0–10%) to improve the rheological, textural, and sensory characteristics of vegetable ice cream using vegetable milk (almond and hemp milk). Hemp milk was obtained from the peeled seeds of the industrial hemp plant, which includes varieties of Cannabis sativa, which have a low content of the psychotropic substance tetrahydrocannabinol (THC) and are grown for food. The rheological characteristics of the mix and ice cream were determined by using the Haake Mars rheometer. Compared with the control sample, the viscosities of the mix in all samples analyzed were enhanced with the addition of dietary fibers, due to the occurrence of interactions and stabilizations. The viscoelastic modules G′ G″ were determined on ice cream samples at a temperature of −10 °C. The elastic and viscous modulus showed high values with the increase of the addition of 6% dietary fibers. The textural characteristics were assessed by the shear strength of a layer of ice cream at a temperature of −4 °C. Hardness, firmness, and adhesiveness were influenced by the size of their ice crystals, the fat content, and the percentage of dietary fibers added. The sensory analysis of the ice cream showed higher overall scores for the almond milk ice cream, because the sweet taste was appreciated with a maximum score, while the hemp milk ice cream was evaluated for flavor and taste. Full article

Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs. Full article

The servitisation of the built environment, through the implementation of product–service systems, is considered a promising business strategy to achieve a circular economy transition. This servitisation faces a number of practical challenges, among them the technological readiness and effective integration and application of existing and emerging products, manufacturing processes, and digital monitoring and management tools. The research builds on targeted literature review, and on a research-through-design approach based on full-scale pilot projects developed in an ongoing feedback loop between researchers, planners, and industry partners representing both the demand and supply sides of the façade industry in the Netherlands. The paper analyses the technical implementation challenges currently preventing the façade industry from adopting performance-based contracts. It then proposes the roles that existing and emerging digital design and engineering technologies, manufacturing processes, and asset management systems can play in the development, implementation, and fulfilment of such contracts. The paper proposes a multi-stakeholder, systemic model for the development and application of façade technologies capable of overcoming many of the technical implementation barriers to the delivery of performance-based contracts for integrated facades. From this it concludes that an effective development of building technologies should strategically align with the solving of economic and contractual challenges such as circularity-readiness, profitability, risk distribution, legal demarcation, performance monitoring, and residual value stewardship. The resulting framework provides a strategic and conceptual basis for the development of circularity-enabling façade technologies, accounting for the diverse and sometimes conflicting interests of the multitude of stakeholders involved throughout a project’s lifecycle. The framework aims to support planners, manufacturers, and builders accelerate the circular deep energy renovation of the built environment while also exploring new business opportunities. Full article

High-early strength fiber-reinforced concretes are effective materials for the full depth repair of rigid highway and airfield pavements. A comprehensive study was carried out on the influence of the amount of steel anchor fiber and hardening accelerator on properties that are important for repairing concrete. A two-factor experiment was carried out, in which the influence of the hardening accelerator and fiber dosages on the strength, frost resistance, wear resistance and shrinkage of repaired steel-fiber-reinforced concrete for rigid pavements was studied. The investigated concretes contained 400 kg/m³ of cement and polycarboxylate plasticizer in the amount of 1.2% of the cement content. It has been established that the optimal concrete compositions are with the amount of Sika Rapid 3 hardening accelerator from 1 to 2% of the cement content and the steel fiber amount from 60 to 90 kg/m³. Optimal fiber-reinforced concrete compositions have a reduced shrinkage during hardening, and at the age of 2 days they have a compressive strength of at least 55 MPa and a flexural strength of at least 8.5 MPa. At the design age, the fiber-reinforced concrete compressive strength is 85–90 MPa, its flexural strength ranges from 15.5 to 17.5 MPa, it has a frost resistance of F200 and abrasion not higher than 0.24 g/cm². These properties ensure the high durability of the repair material. Full article