Search Results (344)

The EVM method is an essential project management technique that compares the work done to the planned values (PVs) to identify project variances. However, the inherent uncertainties and possibilities in the project introduce uncertainty in the input data. Despite this importance, the input data for this method is often considered deterministic in most research, and several researchers have modeled it under fuzzy information. In this research, we developed a novel approach using a combined model of fuzzy numbers and grey numbers called interval grey triangular fuzzy (IGTF) numbers. This model is designed to manage the project’s fuzzy grey value, addressing the two pillars of fuzzy and grey uncertainty. The findings of the study indicate that by combining the grey degree of confidence with the triangular fuzzy number (TFN), the results are closer to the real world and more lenient. Finally, based on the estimation of the actual final cost of the project, managers are encouraged to use certainty in lowering costs concerning the value obtained by using the Z-number for the phases of the project. This research provides practical insights and a new model for managing sustainable project uncertainties using fuzzy and grey theories, offering a potential solution to this challenge. Full article

This paper describes methods for optimal filtering of random signals that involve large matrices. We developed a procedure that allows us to significantly decrease the computational load associated with numerically implementing the associated filter and increase its accuracy. The procedure is based on the reduction of a large covariance matrix to a collection of smaller matrices. This is done in such a way that the filter equation with large matrices is equivalently represented by a set of equations with smaller matrices. The filter we developed is represented by $\mathbf{x}={\sum }_{j=1}^p{\mathbf{M}}_j{\mathbf{y}}_j$ and minimizes the associated error over all matrices ${\mathbf{M}}_1,\dots ,{\mathbf{M}}_p$. As a result, the proposed optimal filter has two degrees of freedom that increase its accuracy. They are associated, first, with the optimal determination of matrices ${\mathbf{M}}_1,\dots ,{\mathbf{M}}_p$ and second, with an increase in the number p of components in the filter. The error analysis and results of numerical simulations are provided. Full article

Quality and consumer satisfaction of meat products could be influenced by endpoint cooking temperatures. Attributes of pork, such as palatability, cooking loss, and internal color, influence consumer acceptability. The degree of doneness was evaluated on pork chop characteristics of texture, cooking loss, consumer acceptability, and electronic nose. Pork loin chops (N = 264) were allocated randomly to one of three endpoint degrees of doneness (63 °C, 71 °C, and 79 °C). Cooking pork chops to an internal temperature of 79 °C caused the cooked color to be darker (p < 0.0001) and less red (p = 0.0057). In addition, chops cooked to a 63 °C degree of doneness had greater moisture and lower shear force values (p < 0.0001). Consumer panel ratings of flavor profiles were greater for juiciness, texture, and tenderness (p < 0.0001) when chops were cooked to a 63 °C degree of doneness. Electronic nose analysis of the changes in cooked volatiles can impact the overall flavor and aroma profiles of pork loin chops. These findings conclude that cooking pork chops to an internal temperature of 63 °C improves the overall eating quality, acceptability, and cooking characteristics of pork loin chops. However, more information on the use of an optimal endpoint cooking temperature is needed to improve consumer awareness of pork chop quality. Full article

High-temperature thermal images enable monitoring and controlling processes in metal, semiconductors, and ceramic manufacturing but also monitor activities of volcanoes or contrasting wildfires. Infrared thermal cameras require knowledge of the emissivity coefficient, while multispectral pyrometers provide fast and accurate temperature measurements with limited spatial resolution. Bayer-pattern cameras offer a compromise by capturing multiple spectral bands with high spatial resolution. However, temperature estimation from color remains challenging due to spectral overlaps among the color filters in the Bayer pattern, and a widely accepted calibration method is still missing. In this paper, the quantum efficiency of an imaging system including the camera sensor, lens, and filters is inferred from a sequence of images acquired by looking at a black body source between 700 °C and 1100 °C. The physical model of the camera, based on the Planck law and the optimized quantum efficiency, allows the calculation of the Planckian locus in the color space of the camera. A regression neural network, trained on a synthetic dataset representing the Planckian locus, predicts temperature pixel by pixel in the 700 °C to 3500 °C range from live images. Experiments done with a color camera, a multispectral camera, and a furnace for heat treatment of metals as ground truth show that our calibration procedure leads to temperature prediction with accuracy and precision of a few tens of Celsius degrees in the calibration temperature range. Tests on a temperature-calibrated halogen bulb prove good generalization capability to a wider temperature range while being robust to noise. Full article

Background: Microvascular disease (MVD) describes systemic changes in small vessels (~100 µm diameter or smaller) that impair tissue oxygenation and perfusion. MVD has been demonstrated to play an independent role in the risk of limb loss. Despite this relevance, MVD is not regularly assessed clinically because tools used to evaluate and quantify the severity of MVD of the foot remain limited. We sought to evaluate if the Semmes-Weinstein 10-g Monofilament (SWM) can be used to stratify clinical MVD severity. Methods: We evaluated a racially diverse cohort of 124 patients (with 248 limbs). SWM testing was performed on the plantar aspect of the feet at 1st, 3rd^, and 5th metatarsophalangeal joints. Clinical MVD was stratified in an ascending order of severity into: no diabetes; type 2 diabetes (DM); diabetes+ neuropathy (DM+N); diabetes + neuropathy + retinopathy (DM+N+R). Logistic regression models were used to examine the association between a patient’s clinical MVD severity and an abnormal SWM test. Results: Sixty-four patients (51.6%) tested had an abnormal sensation. The odds of an abnormal SWM test were significantly higher for patients with DM+N and DM+N+R compared to those with no DM respectively. (DM vs. No DM: OR: 3.58, [0.98–13.09], p = 0.05; DM+N vs. No DM: OR: 30.46, [10.33–105.17], p < 0.001; DM+N+R vs. No DM: OR: 43.00, [9.89–309.17], p < 0.001). Furthermore, we categorized SWM based on the degree of sensation loss and found that the proportion of people with a higher degree of sensation loss increased across the clinical MVD severity spectrum. Conclusions: Abnormal SWM sensation strongly correlates with the severity of clinical MVD. This suggests that a simple, non-invasive, 1-min SWM test that can be done in the clinic is a promising tool in assessing MVD in the feet, which is particularly significant considering MVD involvement in limb loss. Full article

Eccentricity faults are one of the main causes that significantly affect the performance of permanent magnet synchronous machines (PMSMs). Monitoring eccentricity in real time could prevent failures by adapting operation conditions and maintenance schedule when early signs of deterioration are detected. This article proposes making a circuit-type model of a permanent magnet machine with an easily configurable eccentricity for simulations and real-time analysis of signals under different operating conditions. The basis for the construction of the circuit model will be the simulation of the PMSM with 49 different coordinates of the rotor center, using the finite element analysis (FEA). The presence of eccentricity causes a variation in the inductances, the no-load flux and the expansion torque depending on the position of the rotor. The model proposes the use of bilinear interpolation (BI) to estimate the inductance matrix, the no-load flux vector captured by the stator winding and the cogging torque due to the presence of the magnets in the rotor, all of them for each rotor position. The validation is done by comparing the precision in the results of the machine’s self-inductances, the torque and the voltage waveform at the PMSM terminals and the static torque of the PMSM. The circuit model results are validated in two ways: (1) through experimental simulation, comparing the same results obtained using FEA and (2) through practical experimentation, producing a dynamic eccentricity in the machine of 0.3 mm. The results show that the proposed model is capable of accurately reproducing the behavior of the PMSM against eccentricity faults and presents computational time savings close to 99% compared to the response time obtained using FEA. This rapid PMSM model, parameterizable according to the degree of eccentricity, is the basis for the real-time simulation of the main machine waveforms, such as voltage, current and torque. Full article

Electronic evidence is an essential component in most legal trials of criminal activities, and digital forensics is therefore a crucial support for law enforcement investigations. For instance, a wide range of electronic devices contain Not AND (NAND) flash memory chips, and when a criminal leaves digital evidence on non-operational or locked systems, accessing this memory is crucial. Student acquisition of the necessary competences and skills associated with electronic devices, their basic principles, and the associated technologies can be provided by experimental training, as done with the optional Digital Forensics module included in the degree in Criminalistics: Forensic Sciences and Technologies offered by the University of Alcalá (Spain). This module equips students with the appropriate skills to extract, process, and authenticate evidence information using suitable tools. The purpose of this study was to investigate the effectiveness of experimental learning, deployed through laboratory digital forensic tasks. A literature review was conducted of novel data extraction and analysis tools and procedures as a guide to the design of data recovery tasks incorporating experimental learning. Drawing on student feedback, our results highlight positive learning outcomes for the students. It is concluded that powerful forensic image analysis freeware is capable of identifying elements, and practical tests involving JTAG/chip−off extraction and analysis yield favorable results. A proposal for future studies is to reduce the destructiveness of invasive extraction methods. Full article

Product development must be continuously done by the chocolate industry to face a high level of competitiveness in the market industry. This study investigates the effect of powdered sappan wood and butterfly pea flower incorporation in milk and white chocolate compounds. Four concentrations of each additional ingredient were used (0, 5, 10 and 15%). The results show that incorporating powdered sappan wood and butterfly pea flower significantly improved the total phenolic and flavonoid content and antioxidant activity of milk and white compounds. This study clearly shows that the selected plant could be an alternative to improve the health-promoting properties of milk and white chocolate compounds. However, supplementation also has some drawbacks, particularly in increasing the moisture content and the degree of colour difference between the milk and white compounds containing additional ingredients and the control. Also, powdered sappan wood and butterfly pea flower caused a higher viscosity of milk and white chocolate compounds. The results obtained in this study create a new strategy for using sappan wood and butterfly pea flower in various food products. Full article

Background: According to epidemiological studies, endometrial carcinoma is one of the most frequently diagnosed malignancies of the female reproductive system, with an increasing incidence. Currently, the risk stratification of this neoplasm takes into account the stage, degree of tumor differentiation, histological type and depth of myometrial invasion. Since the publication of the last International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer in 2009, numerous reports have appeared on the molecular characteristics of different types of endometrial carcinoma. Taking this into account, the FIGO Committee determined in 2023 that changes and updates to the staging system are justified to reflect new information about this tumor. Due to the high prevalence of the disease and mortality from endometrial cancer, an in-depth study of the molecular genetic characteristics of tumor cells is relevant; the results of such studies can be used to improve the efficiency of diagnosis, assess the risk of metastasis and prognosis of the disease. Lymph node assessment is crucial for the choice of treatment strategy for endometrial cancer, since metastatic lymph node involvement is one of the main factors affecting prognosis. At the same time, the criteria for the appropriateness of lymphadenectomy in low-differentiated malignant tumors are not clearly defined. Various molecular methods have been proposed to assess the status of lymph nodes; candidate genes are being studied as potential diagnostic biomarkers, as well as microRNA. The aim of the study was to analyze the literature data on numerous studies of molecular risk factors for progression in endometrioid carcinoma, as well as to preserve the most important marker changes in relation to the prognostic development of this disease. Methods: A literature review was conducted using data from the electronic databases PubMed, Google Scholar, and Wiley Cochrane Library for the period from 2018 to 2023 using the specific keywords. Results: The current scientific genetic studies on metastasis and prognostic factors in uterine cancer were analyzed, and a systematization of the reviewed data from the modern literature was done. Conclusions: To select the most effective treatment - intraoperative, adjuvant or combination therapy, minimize postoperative risks of lymphadenectomy and clearly predict the results - further study of the molecular genetic features of endometrial cancer is necessary. Full article

This work reports the synthesis method and various properties of four rod-like antiferroelectric (R) laterally substituted enantiomers, with or without fluorine atoms used as substituents in the benzene ring. The influence of fluorine substitution on the mesophase temperature range was determined. The synthesized compounds are three-ring rod-like smectics with a chiral center based on (R)-(−)-2-octanol. Their chemical and optical purity was checked using high-performance liquid chromatography (HPLC). Two newly synthesized enantiomers and three previously reported (R) enantiomers were used to formulate two antiferroelectric mixtures. The mesomorphic behavior was characterized by polarizing optical microscopy, differential scanning calorimetry, and X-ray diffraction (XRD). The helical pitch and tilt angle measurements were done using the selective light reflection phenomenon and the electro-optical method, respectively. All the enantiomers exhibit a wide temperature range of the antiferroelectric phase, with a high tilt angle. Furthermore, the enantiomer with lateral fluorine substitution in the ortho position has a very long helical pitch (more than 2.0 µm), relatively low enthalpy of melting point, and a tilt angle close to 45 degrees. The designed (R) enantiomers can be useful for formulating eutectic mixtures for further use in various devices, including photonics and optoelectronics. Full article

Bolts have the advantages of simple installation and easy removal. They are widely applied in aerospace and high-speed railway traffic. However, the loosening of bolts under mixed loads can lead to nonlinear decreases in pre-loading. This affects the safety performance of the structure and may lead to catastrophic consequences. Existing techniques cannot be used to monitor the bolt performance status in time. This has caused significant problems with the safety and reliability of equipment. In order to study the relaxation law of bolt pre-loading, this paper carries out an experimental analysis for 8.8-grade hexagonal bolts and calibrates the torque coefficient. We also studied different loading waveforms, nickel steel plate surface roughnesses, tangential displacement frequencies, four different strengths and bolt head contact areas of the bolt, the initial pre-loading, and the effects of tangential cyclic displacement on pre-loading relaxation. This was done in order to accurately predict the degree of bolt pre-loading loosening under external loads. The laws are described using the allometric model function and the nine-stage polynomial function. The least squares method is used to identify the parameters in the function. The results show that bolts with a smooth surface of the connected structure nickel steel flat plate, high-frequency working conditions, half-sine wave, and a high-strength have better anti-loosening properties. Taking 5–10 cycles of cyclic loading as a boundary, the pre-loading relaxation is divided into two stages. The first stage is a stage of rapid decrease in bolt pre-loading, and the second stage is the slow decrease process. The performance prediction study shows that the allometric model function is the worst fitted, at 71.7% for the small displacement condition. Other than that, the allometric model function and the nine-stage polynomial function can predict more than 85.5% and 90.4%, which require the use of least squares to identify two and ten unknown parameters, respectively. The complexity of the two is different, but both can by better indicators than the pre-loading relaxation law under specific conditions. It helps to improve the monitoring of bolt loosening and the system use cycle, and it can provide theoretical support for complex equipment working for a long time. Full article

Task batteries mimicking user tasks are of high heuristic value. Supposedly, they measure individual human aptitude regarding the task in question. However, less is often known about the underlying mechanisms or functions that account for task performance in such complex batteries. This is also true of the Multi-Attribute Task Battery (MATB-II). The MATB-II is a computer display task. It aims to measure human control operations on a flight console. Using the MATB-II and a visual-search task measure of spatial attention, we tested if capture of spatial attention in a bottom-up or top-down way predicted performance in the MATB-II. This is important to understand for questions such as how to implement warning signals on visual displays in human–computer interaction and for what to practice during training of operating with such displays. To measure visuospatial attention, we used both classical task-performance measures (i.e., reaction times and accuracy) as well as novel unobtrusive real-time pupillometry. The latter was done as pupil size covaries with task demands. A large number of analyses showed that: (1) Top-down attention measured before and after the MATB-II was positively correlated. (2) Test-retest reliability was also given for bottom-up attention, but to a smaller degree. As expected, the two spatial attention measures were also negatively correlated with one another. However, (3) neither of the visuospatial attention measures was significantly correlated with overall MATB-II performance, nor with (4) any of the MATB-II subtask performance measures. The latter was true even if the subtask required visuospatial attention (as in the system monitoring task of the MATB-II). (5) Neither did pupillometry predict MATB-II performance, nor performance in any of the MATB-II’s subtasks. Yet, (6) pupil size discriminated between different stages of subtask performance in system monitoring. This finding indicated that temporal segregation of pupil size measures is necessary for their correct interpretation, and that caution is advised regarding average pupil-size measures of task demands across tasks and time points within tasks. Finally, we observed surprising effects of workload (or cognitive load) manipulation on MATB-II performance itself, namely, better performance under high- rather than low-workload conditions. The latter findings imply that the MATB-II itself poses a number of questions about its underlying rationale, besides allowing occasional usage in more applied research. Full article

Gliomas have displayed significant challenges in oncology due to their high degree of invasiveness, recurrence, and resistance to treatment strategies. In this work, the key hub genes mainly associated with different grades of glioma, which were represented by pilocytic astrocytoma (PA), oligodendroglioma (OG), anaplastic astrocytoma (AA), and glioblastoma multiforme (GBM), were identified through weighted gene co-expression network analysis (WGCNA) of microarray datasets retrieved from the Gene Expression Omnibus (GEO) database. Through this, four highly correlated modules were observed to be present across the PA (GSE50161), OG (GSE4290), AA (GSE43378), and GBM (GSE36245) datasets. The functional annotation and pathway enrichment analysis done through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that the modules and hub genes identified were mainly involved in signal transduction, transcription regulation, and protein binding, which collectively deregulate several signaling pathways, mainly PI3K/Akt and metabolic pathways. The involvement of several hub genes primarily linked to other signaling pathways, including the cAMP, MAPK/ERK, Wnt/β-catenin, and calcium signaling pathways, indicates potential interconnectivity and influence on the PI3K/Akt pathway and, subsequently, glioma severity. The Drug Repurposing Encyclopedia (DRE) was used to screen for potential drugs based on the up- and downregulated hub genes, wherein the synthetic progestin hormones norgestimate and ethisterone were the top drug candidates. This shows the potential neuroprotective effect of progesterone against glioma due to its influence on EGFR expression and other signaling pathways. Aside from these, several experimental and approved drug candidates were also identified, which include an adrenergic receptor antagonist, a PPAR-γ receptor agonist, a CDK inhibitor, a sodium channel blocker, a bradykinin receptor antagonist, and a dopamine receptor agonist, which further highlights the gene network as a potential therapeutic avenue for glioma. Full article

Parkinson’s disease is one of the major neurodegenerative diseases that affects the postural stability of patients, especially during gait initiation. There is actually an increasing demand for the development of new non-pharmacological tools that can easily classify healthy/affected patients as well as the degree of evolution of the disease. The experimental characterization of gait initiation (GI) is usually done through the simultaneous acquisition of about 20 variables, resulting in very large datasets. Dimension reduction tools are therefore suitable, considering the complexity of the physiological processes involved. The principal Component Analysis (PCA) is very powerful at reducing the dimensionality of large datasets and emphasizing correlations between variables. In this paper, the Principal Component Analysis (PCA) was enhanced with bootstrapping and applied to the study of the GI to identify the 3 majors sets of variables influencing the postural control disability of Parkinsonian patients during GI. We show that the combination of these methods can lead to a significant improvement in the unsupervised classification of healthy/affected patients using a Gaussian mixture model, since it leads to a reduced confidence interval on the estimated parameters. The benefits of this method for the identification and study of the efficiency of potential treatments is not addressed in this paper but could be addressed in future works. Full article

Chronic ankle instability (CAI) is a common injury that can occur in daily life or sporting events. Injuries to the anterior talofibular, posterior talofibular, and calcaneofibular ligaments are common, and the core of rehabilitation training involves strengthening the peroneus muscle. Many studies on rehabilitation training have focused on strengthening the peroneus brevis muscle, and few studies have focused on specific training to strengthen the peroneus longus muscle. Therefore, this study aims to investigate changes in the symptoms and functions of patients by applying training to strengthen the peroneus longus and peroneus brevis muscles. Home-based training and mobile monitoring were utilized for 12 weeks, divided into peroneus brevis training (PBT) and peroneus longus training (PLT), in 52 adult males with CAI. Participation was voluntary, with enrollment done through a bulletin board, and intervention training allocation was randomly assigned and conducted in a double-blind manner. This study was registered as a trial protocol (KCT 0008478). Foot and ankle outcome scores (FAOS), isokinetic ankle strength tests, and Y-balance tests were performed before and after the intervention. Both PLT and PBT significantly improved in FAOS, inversion, and eversion at angular velocities of 30°/s and 120°/s and in the anterior and posterolateral directions of the Y-balance test (p < 0.05). Interaction effects by time and group were not significant for the FAOS (p > 0.05). However, PLT improved eversion muscle strength and muscle power to a greater degree, compared with PBT, in the anterior and posterolateral directions of the Y-balance test (p < 0.05). In conclusion, both PLT and PBT were effective for CAI patients; in addition, PLT had greater potential for improving strength and balance. Full article