Search Results (24)

The mechanisms of aim-setting and decision-making in criminal activity as a four-level hierarchical structure were presented for the Russian criminals known as ‘vory v zakonie’. The first level represents a basic concept of saving one’s own life, borrowed from the Torah. The second level, the ‘Thieves’ Law’, is a set of mental models that has much in common with the adaptation and misinterpretation of old religious and legal systems. The third level is a set of general concepts and ideas about what Good or Evil is in the form of words called ‘Notions’. These levels have no material form; they reflect themselves in models of behaviour and argot as a collective output. The fourth level in the material form of page-long ‘secrete messages’, containing some models of behaviour, are the ‘Frames’ (how to behave in imprisonment), wherein the ‘Vory’s Commandments’ (how to behave at large for young criminals) do not belong to the criminal ideology. This criminal ideology, a discrete antisystem, is enriched by the three ideas found in old religious and legal systems proposed as the ‘fifth feeling of Time’: the memory of the soul in the endless time being awoken after reincarnation, making the past as if it never happened, and knowing the future. Full article

The question, what was a monastic? is a complex issue, whether in the context of China, Korea or even in the homeland of Buddhism, India. Nonetheless, this is especially so in the case of Joseon Korea due to the dramatic historical changes that took place with the Imjin War. This obviously brought about shifts not only in the social status but also in the societal role of the monastics. The most substantive factors in the late-Joseon period (1600–1910) was the loss of state patronage and no longer being under the auspices of the state. Simply put, the discussed materials in this paper evince diverse images and roles of monks that range from being state officials, laborers, soldier-monks, and Seon meditators, to thieving bandits. A single descriptor would be unable to capture the diverse identities of the late-Joseon monks. Moreover, the monastics also presented themselves as highly organized with organizational aims, no different from an organization existing inescapably in everyday socio-economic and political conditions. The shared goal of the Buddhist community, by way of presenting certain images, was to regain social recognition and legitimation, to a position of power and privilege perhaps similar to what it once had during the Goryeo period (918–1392). Full article

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations. Full article

Heterogeneous federated learning, as an innovative variant of federated learning, aims to break through the constraints of vanilla federated learning on the consistency of model architectures to better accommodate the heterogeneity in mobile computing scenarios. It introduces heterogeneous and personalized local models, which effectively accommodates the heterogeneous data distributions and hardware resource constraints of individual clients, and thus improves computation and communication efficiency. However, it poses a challenge to model ownership protection, as watermarks embedded in the global model are corrupted to varying degrees when they are migrated to a user’s heterogeneous model and cannot continue to provide complete ownership protection in the local models. To tackle these issues, we propose a dynamic black-box model watermarking method for heterogeneous federated learning, PWFed. Specifically, we design an innovative dynamic watermark generation method which is based on generative adversarial network technology and is capable of generating watermark samples that are virtually indistinguishable from the original carriers. This approach effectively solves the limitation of the traditional black-box watermarking technique, which only considers static watermarks, and makes the generated watermarks significantly improved in terms of stealthiness and difficult to detect by potential model thieves, thus enhancing the robustness of the watermarks. In addition, we design two watermark embedding strategies with different granularities in the heterogeneous federated learning environment. During the watermark extraction and validation phase, PWFed accesses watermark samples claiming ownership of the model through an API interface and analyzes the differences between their output and the expected labels. Our experimental results show that PWFed achieves a 99.9% watermark verification rate with only a 0.1–4.8% sacrifice of main task accuracy on the CIFAR10 dataset. Full article

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works. Full article

To reduce negative effects of floral visitation by ants, which do not serve as reliable cross-pollinators, some plants have developed a non-floral, stem-based defense mechanism called greasy pole syndrome. In the present study, we examined the effects of two surface features (trichomes and three-dimensional epicuticular wax coverage) on stems of Alliaria petiolata plants on visiting frequencies, travelled distances, and running velocities of Lasius niger ants. The experiments were performed with stem samples prepared from different (apical and basal) stem portions showing different surface morphologies (smooth control, covered by wax and trichomes + wax, respectively). The control, mechanically wiped stem samples lacking any surface features were significantly more often visited by ants, where they travelled significantly longer distances and moved with significantly higher velocities, compared to the intact stems. The apical and basal stem portions showed no significant differences in the measured parameters. Based on data obtained, we conclude about the main contribution of the wax to the greasy pole function of the A. petiolata stem via reduction of ant adhesion to the wax-bearing stem surface, whereas trichomes presumably serve as the first barrier for ants approaching usually from the ground level and protect the fragile wax coverage from an excessive deterioration. Full article

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. Full article

This paper proposes a novel convolution–non-convolution parallel deep network (CNCP)-based method for electricity theft detection. First, the load time series of normal residents and electricity thieves were analyzed and it was found that, compared with the load time series of electricity thieves, the normal residents’ load time series present more obvious periodicity in different time scales, e.g., weeks and years; second, the load times series were converted into 2D images according to the periodicity, and then electricity theft detection was considered as an image classification issue; third, a novel CNCP-based method was proposed in which two heterogeneous deep neural networks were used to capture the features of the load time series in different time scales, and the outputs were fused to obtain the detection result. Extensive experiments show that, compared with some state-of-the-art methods, the proposed method can greatly improve the performance of electricity theft detection. Full article

Nowadays, due to a higher resistance to drugs, antibiotics, and antiviral medicaments, new ways of fighting pathogens are intensively studied. The alternatives for synthesized compositions are natural products, most of which have been known in natural medicine for a long time. One of the best-known and intensively investigated groups are essential oils (EOs) and their compositions. However, it is worth noting that the method of application can play a second crucial part in the effectiveness of the antimicrobial activity. EOs possess various natural compounds which exhibit antimicrobial activity. One of the compositions which is based on the five main ingredients of eucalyptus, cinnamon, clove, rosemary, and lemon is named “five thieves’ oil” (Polish name: olejek pięciu złodziei) (5TO) and is used in natural medicine. In this study, we focused on the droplet size distribution of 5TO during the nebulization process, evaluated by the microscopic droplet size analysis (MDSA) method. Furthermore, viscosity studies, as well as UV-Vis of the 5TO suspensions in medical solvents such as physiological salt and hyaluronic acid, were presented, along with measurements of refractive index, turbidity, pH, contact angle, and surface tension. Additional studies on the biological activity of 5TO solutions were made on the P. aeruginosa strain NFT3. This study opens a way for the possible use of 5TO solutions or emulsion systems for active antimicrobial applications, i.e., for surface spraying. Full article

This study seeks to understand the effects of new hydrogels based on Oregano^® essential oil, Frankincense^® essential oil, and the Thieves^® blend, which includes clove, lemon, cinnamon bark, eucalyptus radiata, rosemary extract, and Frankincense^® essential oil through in vitro cytotoxicity and antimicrobial analysis. Mesenchymal stem cells (D1MSCs) generated from the dental papilla of human wisdom teeth germs were used to test the hydrogels’ cytotoxicity (D1MSCs). The chemical makeup of the tested essential oils (EO) was determined using GC-MS analysis, and their presence in the novel hydrogels was determined using UV-VIS analysis. The primary constituents of the essential oils identified as being present were eugenol, pinene, limonene, carvacrol, and cymene. The primary constituents from essential oils identified by the absorption spectra included eugenol, pinene, limonene, carvacrol, and cymene. The presence of essential oils in the hydrogel composition was also clearly discernible. All of the studied compounds had a reduced cytotoxic effect on cell cultures, proving the lack of in vitro toxicity of the gels. This study demonstrates that hydrogels enriched with pure natural extracts of essential oils have a tangible in vitro antimicrobial potential, especially for Staphylococcus aureus and Bacillus cereus. Full article

A plethora of combinatorial optimization problems can be linked to real-life decision scenarios. Even nowadays, more diverse and complex problems are popping up. One of these problems is the traveling thief problem (TTP), which combines elements from the knapsack and traveling salesperson problems. Hence, it is paramount to keep improving solvers to tackle combinatorial problems. Among recent proposals, hyper-heuristics have proven useful since they seek to combine the strengths of more straightforward solvers. This paper proposes a sequence-based selection hyper-heuristic and assesses its feasibility when solving the TTP. Our proposal can be represented by an array of operators selecting a city or an item. In the first case, the solution moves to a new city and thus advances the tour. In the second one, the thief agent picks an item within the current city and tries to store it in its knapsack. We generate several sets of TTP instances with different parameters to validate our approach and analyze the model’s performance. Our data reveal that the proposed approach outperforms randomly generated sequences. Moreover, our approach finds general sequences that surpass sequences specialized for each instance. We believe this is noteworthy and represents a stepping stone towards achieving a more robust solver for complex problems. Full article

Pollen is a major source of allergies, and its prevalence is rising. Essential oils, primarily composed of terpenes, are easy to diffuse and are becoming more common to use in the home. To test the effect of diffusing essential oils on indoor pollen, pollen was introduced into an enclosed environment containing an ultrasonic diffuser. Lavender, Citrus Fresh™, and Thieves^® were tested against lodgepole pine pollen. Lavender was also tested against pecan pollen. An ultrasonic diffuser, containing either 100 mL of reverse osmosis (RO) water or 100 mL of RO water and 10 drops of an essential oil, was diffused in the environment to see how they would influence the amount of pollen that was deposited on the collection slides, compared to when the diffuser was off. The results show that diffusing RO water and lavender, Citrus Fresh™, or Thieves^® essential oil increased the amount of lodgepole pine pollen deposited on the floor of an enclosed environment. Diffusing either RO water alone or RO water and lavender essential oil increased the amount of pecan pollen found on the floor of an enclosed environment. This suggests that a diffuser may help remove pollen from the air and potentially aid in alleviating allergies. Full article

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box. Full article