Search Results (46)

Ransomware is a group of malware that aims to make computing resources unavailable, demanding a ransom amount to return control back to users. Ransomware can be classified into two types: crypto-ransomware and locker ransomware. Crypto-ransomware employs strong encryption and prevents users’ access to the system. Locker ransomware makes access unavailable to users either by locking the boot sector or the user’s desktop. The proposed solution is an anomaly-based ransomware detection and prevention system consisting of post- and pre-encryption detection stages. The developed IDS is capable of detecting ransomware attacks by monitoring the usage of resources, triggered by anomalous behavior during an active attack. By analyzing the recorded parameters after recovery and logging any adverse effects, we were able to train the system for better detection patterns. The proposed solution allows for detection and intervention against the crypto and locker types of ransomware attacks. In previous work, the authors introduced a novel anti-ransomware tool for Windows platforms, known as R-Locker, which demonstrates high effectiveness and efficiency in countering ransomware attacks. The R-Locker solution employs “honeyfiles”, which serve as decoy files to attract ransomware activities. Upon the detection of any malicious attempts to access or alter these honeyfiles, R-Locker automatically activates countermeasures to thwart the ransomware infection and mitigate its impact. Building on our prior R-Locker framework this work introduces a multi-stage detection architecture with resource–behavioral hybrid analysis, achieving cross-platform efficacy against evolving ransomware families not addressed previously. Full article

This paper presents a new boot loader scheme for embedded devices with file system support built as a hardware module. The work focuses on improving the boot loader hardware and the possibility of carrying out a full boot-up process from the dedicated on-chip hardware, using a light file system to store an operating system kernel. To do so, the new full-hardware boot loader is integrated into two Field-Programmable Gate Array (FPGA) System-on-Chip (SoC), capable of launching a Linux kernel from a formatted removable media. Full article

In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices. Full article

Accurately predicting plant cuticle–air partition coefficients (K_ca) is essential for assessing the ecological risk of organic pollutants and elucidating their partitioning mechanisms. The current work collected 255 measured K_ca values from 25 plant species and 106 compounds (dataset (I)) and averaged them to establish a dataset (dataset (II)) containing K_ca values for 106 compounds. Machine-learning algorithms (multiple linear regression (MLR), multi-layer perceptron (MLP), k-nearest neighbors (KNN), and gradient-boosting decision tree (GBDT)) were applied to develop eight QSPR models for predicting K_ca. The results showed that the developed models had a high goodness of fit, as well as good robustness and predictive performance. The GBDT-2 model ($R_{\mathrm{adj}}^2$ = 0.925, $Q_{\mathrm{LOO}}^2$ = 0.756, $Q_{\mathrm{BOOT}}^2$ = 0.864, $R_{\mathrm{ext}}^2$ = 0.837, $Q_{\mathrm{ext}}^2$ = 0.811, and CCC = 0.891) is recommended as the best model for predicting K_ca due to its superior performance. Moreover, interpreting the GBDT-1 and GBDT-2 models based on the Shapley additive explanations (SHAP) method elucidated how molecular properties, such as molecular size, polarizability, and molecular complexity, affected the capacity of plant cuticles to adsorb organic pollutants in the air. The satisfactory performance of the developed models suggests that they have the potential for extensive applications in guiding the environmental fate of organic pollutants and promoting the progress of eco-friendly and sustainable chemical engineering. Full article

Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption. Full article

The present study investigates the developmental trajectory of abstract representations for syntactic structures in children. In a structural priming experiment on the dative alternation in German, we primed children from three different age groups (3–4 years, 5–6 years, 7–8 years) and adults with double object datives (Dora sent Boots the rabbit) or prepositional object datives (Dora sent the rabbit to Boots). Importantly, the prepositional object structure in German is dispreferred and only rarely encountered by young children. While immediate as well as cumulative structural priming effects occurred across all age groups, these effects were strongest in the 3- to 4-year-old group and gradually decreased with increasing age. These results suggest that representations in young children are less stable than in adults and, therefore, more susceptible to adaptation both immediately and across time, presumably due to stronger surprisal. Lexical boost effects, in contrast, were not present in 3- to 4-year-olds but gradually emerged with increasing age, possibly due to limited working-memory capacity in the younger child groups. Full article

Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children–Fifth Edition (WISC–V^FR) with five standardization sample age groups (6–7, 8–9, 10–11, 12–13, and 14–16 years) using EGA. The primary research questions include (a) how many WISC–V^FR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the “old” perceptual reasoning factor. Results did not support the distinction between visual–spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC–V^FR. Full article

In this work, we carried out the design and synthesis of new chimeric compounds from the natural cytotoxic chalcone 2′,4′-dihydroxychalcone (2′,4′-DHC, A) in combination with cinnamic acids. For this purpose, a descriptive and predictive quantitative structure–activity relationship (QSAR) model was developed to study the chimeric compounds’ anti-cancer activities against human breast cancer MCF-7, relying on the presence or absence of structural motifs in the chalcone structure, like in a Free-Wilson approach. For this, we used 207 chalcone derivatives with a great variety of structural modifications over the α and β rings, such as halogens (F, Cl, and Br), heterocyclic rings (piperazine, piperidine, pyridine, etc.), and hydroxyl and methoxy groups. The multilinear equation was obtained by the genetic algorithm technique, using logIC₅₀ as a dependent variable and molecular descriptors (constitutional, topological, functional group count, atom-centered fragments, and molecular properties) as independent variables, with acceptable statistical parameter values ($R^2$ = 86.93, $Q^2$_LMO = 82.578, $Q^2$_BOOT = 80.436, and $Q^2$_EXT = 80.226), which supports the predictive ability of the model. Considering the aromatic and planar nature of the chalcone and cinnamic acid cores, a structural-specific QSAR model was developed by incorporating geometrical descriptors into the previous general QSAR model, again, with acceptable parameters ($R^2$ = 85.554, $Q^2$_LMO = 80.534, $Q^2$_BOOT = 78.186, and $Q^2$_EXT = 79.41). Employing this new QSAR model over the natural parent chalcone 2′,4′-DHC (A) and the chimeric compound 2′-hydroxy,4′-cinnamate chalcone (B), the predicted cytotoxic activity was achieved with values of 55.95 and 17.86 µM, respectively. Therefore, to corroborate the predicted cytotoxic activity compounds A and B were synthesized by two- and three-step reactions. The structures were confirmed by ¹H and ¹³C NMR and ESI+MS analysis and further evaluated in vitro against HepG2, Hep3B (liver), A-549 (lung), MCF-7 (breast), and CasKi (cervical) human cancer cell lines. The results showed IC₅₀ values of 11.89, 10.27, 56.75, 14.86, and 29.72 µM, respectively, for the chimeric cinnamate chalcone B. Finally, we employed B as a molecular scaffold for the generation of cinnamate candidates (C–K), which incorporated structural motifs that enhance the cytotoxic activity (pyridine ring, halogens, and methoxy groups) according to our QSAR model. ADME/tox in silico analysis showed that the synthesized compounds A and B, as well as the proposed chalcones C and G, are the best candidates with adequate drug-likeness properties. From all these results, we propose B (as a molecular scaffold) and our two QSAR models as reliable tools for the generation of anti-cancer compounds over the MCF-7 cell line. Full article

We aimed to investigate the relationship between physical fitness and motivation in adolescents and analyze if the associations of physical fitness with volitional exercise intensity in adolescents are mediated by motivation. The participants were 108 adolescents (58 girls 16.0 ± 0.92 years). Cardiorespiratory fitness (CRF) was assessed using the Yo-YoITL1, and the push-up test was used to evaluate strength. Body composition was measured by bioelectrical impedance analysis. The intervention was applied in the first 10–15 min of each Physical Education class (PEC), twice a week, for 16 weeks and ranged from 14 to 20 all-out bouts intervals, adopting a 2:1 work to rest ratio. A cut-point of ≥90% of the maximal heart rate (HR) was used as a criterion for satisfactory compliance with high-intensity exercise. Volition intensity was assessed through a forearm wearable plethysmography heart rate sensor to ensure compliance with the exercise stimulus at the predetermined target HR zone. Motivation was estimated with a validated questionnaire (BREQ-3). Mediation effects were estimated using bootstrapped 95% confidence intervals and were deemed significant if zero was not included in the intervals, and values below 0.05 were considered to indicate statistical significance. The mediation analysis revealed a non-significant indirect effect of physical fitness through motivation on exercise intensity, specifically on CRF (B = −0.0355, 95% BootCI [−0.5838; 0.4559]), muscular fitness (B = −0.7284, 95% BootCI [−2.0272; 0.2219]) and body fat (B = 0.5092, 95% BootCI [−0.4756; 1.6934]). These results suggest that high or low values of motivation did not increase or decrease volitional high-intensity exercise, and lower levels of fitness (CRF, muscular and body fat) were associated with higher volitional exercise intensity. These findings highlight the need for regular moderate-to-vigorous physical exercise for maintaining or improving physical fitness, regardless of motivation regulations, and emphasize the importance of new strategies in PEC with acute vigorous-intensity activities that retain the health-enhancing effects. Full article

In dual-purpose cereal systems, the co-production of fodder and grain can increase farm profitability and reduce farming risks. Our work evaluated shoot and root growth in durum wheat (Triticum durum Desf.) under dual-purpose management in a medium-high rainfall area of southern Italy. We compared a modern variety (Core) with a tall ancient variety (Saragolle lucana) under traditional (NDP) and dual-purpose (DP) management and tested the hypothesis that clipping plants during the vegetative stage would reduce root growth and dewatering before anthesis, which is advantageous in drought-prone environments. Experiments were conducted in Bella (PZ), Basilicata region, southern Italy (40°42′ N, 15°32′ E) on a clay loam soil in 2021 in a split-plot design on 2 × 2 main plots and 1 × 2 split-plots with 6 replicates. The DP treatment consisted of simulated grazing by clipping plants at 5 cm from the ground 3 months after sowing (at first hollow stem). Forage Biomass was not different at p = 0.05 between varieties, with an average of 0.58 t ha⁻¹ DM. Grain yield was not penalized by clipping (p = 0.05) and did not differ significantly between varieties. SPAD was always lower in the Saragolle variety and lowered by clipping. Defoliation delayed phenology in both cultivars but did not reduce the final number of spikes per square meter. Stomatal conductance was correlated to temperature, did not differ between cultivars, and was not influenced by clipping. Soil water depletion was monitored in modern wheat from the booting stage to the beginning of grain filling. Clipping did not result in a reduction in pre-anthesis water depletion, possibly due to evaporative losses. Root density was markedly reduced by clipping in core variety between 0.20 and 0.60 m and much less in Saragolle. Unclipped Saragolle produced thicker roots and higher root masses compared to clipped plants. Defoliated Saragolle shifted to finer roots, reducing root mass more than length. This may have reduced the metabolic cost of soil exploration, thereby increasing root foraging efficiency. Full article

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R² were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R² in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R² in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. Full article

While design modifications present on work boots improve safety, they may not always provide optimal human performance during work tasks. Understanding the impact of these different design features on biomechanical and physiological postural control and locomotion variables can aid in better design modifications that can provide a safe and efficient human performance. This brief review focuses on a series of studies conducted by the current research team, that have tested three different work boots (SB: high-top steel-toed work boots; TB: high-top tactical work boots; SR: low-top slip-resistant work boots). The series of studies included testing of these work boots or combinations of them under acute and chronic simulated occupational workloads, assessing biomechanical variable such as postural stability, gait, slips, and muscle activity, as well as physiological variables such as heart rate, energy expenditure, oxygen consumption, and pain perception. The impact of each of the work boots and their design feature on postural control and locomotion are summarized from these studies’ previously published literature. Finally, work boot design suggestions for optimal human performance are provided for better work boot selection, modification, and design. Full article

Due to its advantages, remote sensing monitoring has been used in various applications and made noteworthy contributions to understanding soil and plant processes, as well as in the agriculture sector. The aim of the work is to compare the return of durum wheat crops in conservative agricultural practices in Mediterranean climate conditions by analysing the data from the Sentinel2 satellite through three spectral indices. The analysed spectral indices have different interpretations and therefore have been studied in different periods: (i) NDVI (normalized difference vegetation index) for the evaluation of the vegetative vigour from January to June; (ii) NDWI (normalized difference water index) for the moisture of covered soil from January to June and of bare soil after harvesting from June to August; and (iii) NMDI (normalized multi-band drought index) for the variability of bare soil moisture from June to August. With reference to the machines used in cultivation practices, a further purpose of the study is to investigate the effects of automatic guidance versus manual guidance on production yields and on the spectral indices considered. The first results show that the NDVI follows crop phenological stages by reaching the maximum values in correspondence with the stem elongation and booting stages. Additionally, the NDWI showed the same trend as the NDVI during the current crop. After harvesting, the NDWI showed higher values in the plots cultivated under conservation tillage practices. In the same period, the NMDI showed the same results as the NDWI and a positive correlation, confirming that tillage practices could imply a lower ability to retain water in drought time. Full article

Reading and analyzing data from sensors are crucial in many areas of life. IoT concepts and related issues are becoming more and more popular, but before we can process data and draw conclusions, we need to think about how to design an application. The most popular solutions today are microservices and monolithic architecture. In addition to this choice, there is also the question of the technology in which you will work. There are more and more of them on the market and in each of them it is practically possible to achieve similar results, but the difference lies in how quickly it will be possible and whether the approach invented will turn out to be the most optimal. Making the right decisions at the beginning of application development can determine its path to success or failure. The main goal of this article was to compare technologies used in applications based on microservice architecture. The preparation of a book lending system, whose server part was implemented in three different versions, each using a different type of technology, helped to achieve this goal. The compared solutions were: Spring Boot, Micronaut and Quarkus. The reason for this research was to investigate projects using sensor networks, ranging from telemedicine applications to extensive sensor networks collecting scientific data, or working in an environment with limited resources, e.g., with BLE or WIFI transmitters, where it is critical to supply energy to these transmitters. Therefore, the issue of efficiency and hence energy savings may be a key issue depending on the selected programming technology. Full article

Short-term, automated car rental services, i.e., car sharing, are a solution that has been improving in urban transportation systems over the past few years. Due to the intensive expansion of the systems, service providers face increasing challenges in their competitiveness. One of them is to meet the customer expectations for the fleet of vehicles offered in the system. Although this aspect is noted primarily in the literature review on fleet optimization and management, there is a gap in research on the appropriate selection of vehicle models. In response, the article aimed to identify the vehicles best suited for car-sharing systems from the customer’s point of view. The selection of suitable vehicles was treated as a multi-criteria decision-making issue; therefore, the study used ELECTRE III—one of the multi-criteria decision-making methods. The work focuses on researching the opinions of users who rarely use car-sharing services in Poland. The most popular car models in 2021, equipped with internal combustion, hybrid, and electric engines, were selected for the analysis. The results indicate that the best suited cars are relatively large, spacious, and equipped with electric drive and represent the D segment of vehicles in Europe. In addition, these vehicles are to be equipped with a powerful engine, a spacious boot, and a fast battery charging time. Interestingly, small city cars, so far associated with car sharing, ranked the worst in the classification method. In addition, factors such as the warranty period associated with the quality of the vehicles, or the number of car doors, are not very important to users. The results support car-sharing operators in the process of selecting or modernizing a fleet of vehicles. Full article