Algorithm Research Articles

Analysis on addressing the threats to cloud computing on the basis of security safeguards for sap cloud services

Cloud computing has become an integral part of combination/multi-cloud infrastructures due to its many benefits, including quick deployment, flexibility, cheap costs, and scalability, which have attracted businesses of all sizes. New possibilities and threats in terms of security flaws are cropping up in this field, even if cloud storage provides substantial advantages and inexpensive choices for IT administration and growth. When we talk about safeguarding cloud infrastructure systems, we're referring to a set of rules, regulations, and systematic procedures. This is also known as cloud computing security. The purpose of implementing these security measures is to safeguard cloud-based data, facilitate the enforcement of legislation, ensure the privacy of consumer information, and set standards for encryption in specific devices and applications. Modern cloud computing and security concerns throughout the cloud's many layers are addressed in this study. Within this context, the current study would make a substantial contribution to lowering the frequency of security breaches in cloud computing, paving the way for the continued provision of safe and reliable services. Storage of databases on the cloud is expanding rapidly in the IT industry, including SAP networks. Because enterprises are ultimately responsible for the security of their data, migrating databases to cloud computing environments brings with it a plethora of security risks. To guarantee data integrity, availability, and secrecy, appropriate security measures should be put in place before a company migrates its sensitive data to the cloud. When data is at rest, it is most vulnerable to unauthorized access; when it is in transit, it must be subject to strict supervision.

ABOUT SOME APPROACHES TO THE INTERNATIONAL LAW IN BRITISH SCIENTISTS’ RESEARCHES (PART I)

The purpose of the research. At the present time there is a shortage of scientific researches devoted to the concepts of the international law of foreign countries. At the same time, there is a significant need for such works, since any interstate cooperation, as well as confrontation, requires a detailed knowledge of the approaches to the international law of the relevant country. In this context Great Britain stands out. Positions of Great Britain continue to have a significant impact on the formation of the approaches to the international law of the Anglo-Saxon legal family states. Largely due to the ideas of the British the foundations of the pan-Western system of views on the international law were laid. This article deals with the interpretation of various international law issues by the representatives of scientific schools of several universities in Great Britain. The positions of these scientists concerning the essence and meaning of the international law, its application are of interest from the point of view of their comprehension on the basis of Russian approaches. That is the purpose of this article. Results. The approaches of British scientists to the international law considered in the article demonstrate both similarities and differences in comparison with the domestic international legal doctrine. These include: the vision of the international law as simply a set of rules or as a complex system; the role of normativity in the international law; analysis of some of the weak and strong sides of the international law; international law criticism of the foreign policy courses of certain countries, including Great Britain and the Unites States; consideration of Russia as a country that supports special, far from Western, approaches to the international law.

Nominal GDP growth targeting vs. Taylor rules in a model with financial frictions

Nominal GDP growth targeting has gained favor over the last few decades in monetary policy literature (e.g., Ireland 2020; Garín et al. 2016; Beckworth and Hendrickson 2020). Calibrated, small-scale New Keynesian DSGE models have supported the proposition that nominal GDP growth (NGDP growth) rate targeting will improve welfare as compared with alternative strategies such as inflation targeting and Taylor rules with standard parameter settings. This paper examines that proposition in the context of a medium-scale New Keynesian model having a financial sector with frictions. In that model, NGDP growth rate targeting does poorly as compared with the Optimal Taylor rule and a wide range of Taylor rule settings leading up to the Optimal (representative agent utility maximizing) Taylor rule. The presence of the financial sector reveals that not only is NGDP growth rate targeting the least successful among the alternative targets and rules in creating welfare in this model; NGDP growth rate targeting also creates dramatically higher volatility of the policy rate and most financial variables.

Switching Sides: Regiochemistry and Functionalization Dictate the Photoswitching Properties of Imines.

Photoswitchable imines demonstrate light-dependent dynamic covalent chemistry and can function as molecular ratchets. However, the design of aryliminopyrazoles (AIPs) has been limited to N-pyrazole derivatives with ortho-pyrrolidine motifs. The impact of other functionalization patterns on the photoswitching properties remains unknown. Here, we present a systematic structure-property analysis and study how the photoswitching properties can be tuned through ortho- and para-functionalization of the phenyl ring in N-pyrazole and N-phenyl AIPs. This study establishes the first set of design rules for these AIP photoswitches and reports the most stable Z-isomer of an AIP to date, enabling its crystallization and resulting in the first reported crystal structure of a metastable Z-aldimine. Finally, we demonstrate that the AIPs are promising candidates for photoswitching in the condensed phase.

Enhancing skin aging parameter assessment in clinical trials: AI-Driven analysis of ultrasound images

BackgroundHigh-frequency ultrasound (HFUS) imaging offers valuable insights into skin aging parameters in clinical trials. However, manual measurements are time-consuming and operator-dependent, necessitating the exploration of automated approaches. ObjectiveThis study aimed to develop, train, and validate an artificial intelligence model for automated HFUS image parameter acquisition in dermatological assessments. A secondary goal was its application in evaluating data from an anti-aging skin therapy clinical trial. MethodsSupervised machine learning algorithms were used to predict target variables from ultrasound images. The best models were selected through independent validation. A training dataset of 144 ultrasound skin images and a validation set of 40 images were employed. A clinical trial involved 34 female subjects with aging skin signs, randomized into two groups: (1) cosmetic formulation plus therapeutic unfocused ultrasound (TUS) and (2) placebo formulation plus TUS. HFUS imaging was conducted before and after a 4-week and 8-week study period. ResultsOur algorithms successfully predicted echogenicity and thickness variables, showing competitive performance. In the clinical trial, the treatment group exhibited a significant improvement in skin echogenicity, while no significant changes were observed in the placebo group. ConclusionThis study demonstrates the feasibility of automating the prediction of bioimage variables for anti-aging strategy development. Mathematical algorithms reduced HFUS image evaluations to minutes, expediting clinical trial progress.

Framework assessment of sustainability: Methodology for evaluating the conformance of fishery and aquaculture management systems to FAO guidelines

Sustainability is about meeting the needs of current generations without compromising those of future generations. For fished and aquacultured products, sustainability is often gauged on an individual fishery or farm basis but can be driven by a well-designed and implemented regulatory management system, or set of laws, rules, and policies promulgated by a governmental entity. To achieve global coverage via current assessment approaches requires enormous effort and resources due to the sheer number of individual operating units. System assessments are well established in assuring seafood safety, but much less so in regard to sustainability. This paper presents a tool to measure sustainability based on the prevailing regulatory management system and invites other researchers and practitioners to gauge the applicability of the methodology to new contexts. Evaluating how management systems support sustainable practices is key for assessing current reality and planning change. The methodology presented is based on international criteria for sustainability set forth in United Nations Food and Agriculture Organization (FAO) guidelines for the ecolabelling of fish and fishery products as well as aquaculture certification. The assessment tools are the distillation of qualitative criteria found within the FAO guidelines into simple propositions called “Topics of Pertinence” that enable a straightforward cross-checking of whether the requirements and provisions of regulatory management systems reflect broadly accepted sustainability criteria in fisheries and aquaculture sectors. Assessments document what management systems aim to do via laws, regulations, and policies, and verify mechanisms within the systems that support implementation and compliance. While other sustainability assessments exist, the methodology described here is unique in that it is the only process that provides a system-based approach targeting higher management levels, which can increase global coverage of sustainability assessment from the current lower-tiered approach of certifying at individual production entities.

Atomic Insights into the Competitive Edge of Nanosheets Splitting Water.

The oxygen evolution reaction (OER) provides the protons for many electrocatalytic power-to-X processes, such as the production of green hydrogen from water or methanol from CO2. Iridium oxohydroxides (IOHs) are outstanding catalysts for this reaction because they strike a unique balance between activity and stability in acidic electrolytes. Within IOHs, this balance varies with the atomic structure. While amorphous IOHs perform best, they are least stable. The opposite is true for their crystalline counterparts. These rules-of-thumb are used to reduce the loading of scarce IOH catalysts and retain the performance. However, it is not fully understood how activity and stability are related at the atomic level, hampering rational design. Herein, we provide simple design rules (Figure 12) derived from the literature and various IOHs within this study. We chose crystalline IrOOH nanosheets as our lead material because they provide excellent catalyst utilization and a predictable structure. We found that IrOOH signals the chemical stability of crystalline IOHs while surpassing the activity of amorphous IOHs. Their dense bonding network of pyramidal trivalent oxygens (μ3Δ-O) provides structural integrity, while allowing reversible reduction to an electronically gapped state that diminishes the destructive effect of reductive potentials. The reactivity originates from coordinative unsaturated edge sites with radical character, i.e., μ1-O oxyls. By comparing to other IOHs and literature, we generalized our findings and synthesized a set of simple rules that allow prediction of stability and reactivity of IOHs from atomistic models. We hope that these rules will inspire atomic design strategies for future OER catalysts.

Improved TLBO algorithm for optimal energy management in a hybrid microgrid with support vector machine-based forecasting of uncertain parameters

The worldwide need for electrical energy is increasing, and integrating renewable energy sources (RES) into the power grid will enhance the efficient use of clean energy to fulfill the growing demand for energy. However, the uncertain nature of power from the RES like solar and wind, utility price, and load demand, necessitates accurate forecasting of the uncertain parameters (UP) to improve the reliability of the hybrid microgrid. In this work, optimal energy management (EM) of a hybrid AC-DC microgrid (HMG) is proposed which comprises of two phases, forecasting and scheduling. In the former phase, the uncertainties like day-ahead utility price, electrical demand, and power from the RES are forecasted using the support vector machine (SVM) algorithm and the results are compared with the artificial neural network (ANN) algorithm. In the second phase, the improved Teaching and Learning-Based Optimization (ITLBO) algorithm isused to reduce the generation costs over a 24-h period in a hybrid microgrid. The forecasted uncertain parameters are used as input in the second phase. Power trading occurs between the utility grid and the hybrid microgrid based on load demand and bidding costs, aiming to minimize generation costs. The proposed framework's viability and performance are assessed using IEEE standard test systems. The generating cost, as well as the optimal power dispatch of the HMG, is obtained using the ITLBO algorithm, and the results are compared with different meta-heuristic techniques such as the teaching and learning-based algorithm (TLBO), Ant Lion Optimization algorithm (ALO) and the artificial bee colony algorithm (ABC). The results obtained demonstrate the superiority of the SVM algorithm in forecasting and the ITLBO algorithm over other methods in minimizing operating costs.

Simulating the emergence of institutions that reverse freshwater salinization: An agent-based modeling approach

Salt concentration in global freshwater supplies has increased steadily, leading to the Freshwater Salinization Syndrome (FSS). To curb the FSS, stakeholders can self-organize to develop institutions, or a set of rules that limit salt emissions. This research develops an agent-based modeling framework to explore how institutions reverse the FSS. Property owners are represented as agents that apply rules of behavior to apply salt to deice pavement in response to winter weather, vote on institutions, and comply with or defect from institutions. Salt enters the soil-groundwater system through infiltration, which is modeled using a transit time distribution approach. Results demonstrate that stable institutions lead to positive economic outcomes for stakeholders, based on their ability to apply salt during winter events and access high-quality drinking water. Simulations are analyzed to explore institutions, or limits to the application of salt, that emerge based on the interactions of stakeholders as they agree on salt application limits, the intensity of monitoring for defectors, and sanctions. Institutions that emerge effectively limit the concentration of salt in drinking water. The emergence of stable institutions low rates of innovation among stakeholders, and the concentration of salt in groundwater exceeds standards due to high rates of defection among stakeholders. This research demonstrates how self-organized institutions can lead to sustainable application strategies that reverse the FSS.

Religion in the Highway: Qualitative Exploration in Muslim Youths’ Moral Reasoning and Development on Traffic Rules

ABSTRACT As a set of rules and teachings, it is interesting to see how religion plays a role in traffic issues. Thus, this article qualitatively explores Muslim youths’ reasoning and moral development in the context of traffic rules and how they see it from the perspective of Islamic teachings. The study provided informants with dilemmatic case-based questions while driving and open-ended questions that explored their perceptions of traffic rules from Islamic teachings. The results illustrate that Muslim youth have diverse reasoning about traffic rules. Some see it normatively, functionally, as a personal image, and some see it as something flexible. Regarding its relation to religious teachings, almost all participants considered traffic rules closely related to religion as something that regulates order and social conduciveness on the road. Thus, religion as a set of teachings and norms can promote traffic order through the command to behave well, which considers the rights of others and the common good on the road.

Effect of molecular structure on membrane diffusion: Triphenylmethanes across Escherichia coli studied by second harmonic light scattering

Understanding how the structure of molecules affects their permeability across cell membranes is crucial for many topics in biomedical research, including the development of drugs. In this work, we examine the transport rates of structurally similar triphenylmethane dyes, malachite green (MG) and brilliant green (BG), across the membranes of living Escherichia coli (E. coli) cells and biomimetic liposomes. Using the time-resolved second harmonic light scattering technique, we found that BG passively diffuses across the E. coli cytoplasmic membrane (CM) 3.8 times faster than MG. In addition, BG exhibits a diffusion rate 3.1 times higher than MG across the membranes of liposomes made from E. coli polar lipid extracts. Measurements on these two molecules, alongside previously studied crystal violet (CV), another triphenylmethane molecule, are compared against the set of propensity rules developed by Lipinski and co-workers for assessing the permeability of hydrophobic ion-like drug molecules through biomembranes. It indicates that BG’s increased diffusion rate is due to its higher lipophilicity, with a distribution coefficient 25 times greater than MG. In contrast, CV, despite having similar lipophilicity to MG, shows negligible permeation through the E. coli CM on the observation scale, attributed to its more hydrogen bonding sites and larger polar surface area. Importantly, cell viability tests revealed that BG’s antimicrobial efficacy is ∼2.4 times greater than that of MG, which aligns well with its enhanced diffusion into the E. coli cytosol. These findings offer valuable insights for drug design and development, especially for improving the permeability of poorly permeable drug molecules.

Vanillic Acid Protects PC12 Cells from Corticosterone-Induced Neurotoxicity via Regulating Immune and Metabolic Dysregulation Based on Computational Metabolomics.

Vanillic acid is widely used in the food industry and exhibits an excellent neuroprotective effect. Nevertheless, the mechanisms underlying them are largely unexplored, especially the interactions between the neuroprotection effects of vanillic acid and inflammation-immunity-metabolism. A cell metabolomics-based mathematics algorithm was reported to interpret the potential mechanism of vanillic acid on corticosterone-induced PC12 cells by regulating immune and metabolic dysregulation. Our results showed that vanillic acid markedly inhibited the level of inflammatory factors in corticosterone-induced PC12 cells. Cell metabolomics results suggested that vanillic acid regulated the abnormality of corticosterone-induced PC12 cell metabolic profiles and markedly regulated 11 differential metabolites. Our designed scoring model base entropy weight algorithm showed that the core targets (IL2RB, IFNA13, etc.) and metabolites (lactate, ethanolamine, etc.) regulate the immunity-metabolism of vanillic acid. Furthermore, we demonstrated that vanillic acid inhibited IL2RB expression and modulated the related pathway, JAK1/STAT3 signaling. The JAK inhibitor ABT-494 was further applied to validate the effect of vanillic acid on the JAK/STAT pathway. Results indicate that vanillic acid regulates the abnormal interactions of inflammation-immunity-metabolism by repressing the IL2RB-JAK1-STAT3 pathway. Methodologically, this study contributes to the decoding of vanillic acid's antidepressive effect from the metabolism perspective combined with computer algorithms and mathematics models.

Towards smooth mobile robot deployments in dynamic human environments

AbstractRecently, there has been great interest in deploying autonomous mobile robots in airports, malls, and hospitals to complete a range of tasks such as delivery, cleaning, and patrolling. The rich context of these environments gives rise to highly unstructured motion that is challenging for robots to anticipate and adapt to. This results in uncomfortable and unsafe human–robot encounters, poor robot performance, and even catastrophic failures that hinder robot acceptance. Such observations have motivated my work on social robot navigation, the problem of enabling robots to navigate in human environments while accounting for human safety and comfort. In this article, I highlight prior work on expanding the classical autonomy stack with mathematical models and algorithms designed to contribute towards smoother mobile robot deployments in complex environments.

Efficient branch-and-bound algorithms for finding triangle-constrained 2-clubs

In the Vertex Triangle 2-Club problem, we are given an undirected graph G and aim to find a maximum-vertex subgraph of G that has diameter at most 2 and in which every vertex is contained in at least ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell $$\\end{document} triangles in the subgraph. So far, the only algorithm for solving Vertex Triangle 2-Club relies on an ILP formulation (Almeida and Brás in Comput Oper Res 111:258–270, 2019). In this work, we develop a combinatorial branch-and-bound algorithm that, coupled with a set of data reduction rules, outperforms the existing implementation and is able to find optimal solutions on sparse real-world graphs with more than 100,000 vertices in a few minutes. We also extend our algorithm to the Edge Triangle 2-Club problem where the triangle constraint is imposed on all edges of the subgraph.

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.

The Importance of Sharia Business Ethics in Creating a Tolerant and Peaceful Economic Environment

This study explores the importance of implementing Sharia business ethics in creating a tolerant and peaceful economic environment, especially in the context of a multicultural global society. Sharia business ethics offers universal principles such as justice, transparency, and fair conflict resolution, which focus not only on achieving economic profit but also on promoting social harmony and common welfare. By emphasizing the values of tolerance and diversity, which are integral parts of Islamic teachings, this study shows that implementing Sharia principles can have a significant positive impact on economic development. This implementation, if supported by education, training, and strengthening individual capacity, has the potential to create a stable, trustworthy, and inclusive business environment. In addition, Sharia business ethics can serve as a comprehensive guide in building a just, harmonious, and tolerant society. Thus, this study confirms that Sharia business ethics is not just a set of rules, but a comprehensive approach to achieving economic and social justice in a global society.

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models-Faster R-CNN, YOLO V2, and SSD-using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter's classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter's classification criterion. This criterion characterizes the third molar's position relative to the second molar's longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications.

Typical daily scenario extraction method based on key features to promote building renewable energy system optimization efficiency

Given its intrinsic volatility and randomness, the effective design of a building renewable energy system (BRES) necessitates long-period, short-step boundaries. Nevertheless, this poses computational burdens. In this context, time-series aggregation (TSA) emerges as a valuable approach, which involves obtaining a set of typical scenarios to substitute original time series. Numerous studies have focused on mathematical algorithms to minimize the deviation between typical scenarios and original time series. However, the premise of enhancing model-solving efficiency by extracting typical scenarios is to ensure the optimality of planning. Henceforth, identifying and quantifying key features in the original time series that significantly influence the planning, and further extracting need to be addressed. This paper proposes a typical day extraction method based on key features, named the feature-based method. Key features are summarized through an extensive literature review and then quantitatively expressed through different indicator models. Finally, a feature-based mixed integer linear programming (MILP) model is established for extraction. To evaluate the effectiveness of the feature-based method, it is applied to design a BRES and compared with traditional TSA methods such as Averaging Method (AM) and k-means. Results indicate that the feature-based method can effectively retain key features, thereby achieving more efficient and accurate designs.

Advanced Model Consistency Restoration with Higher-Order Short-Cut Rules

Sequential model synchronisation is the task of propagating changes from one model to another correlated one to restore consistency. It is challenging to perform this propagation in a least-changing way that avoids unnecessary deletions (which might cause information loss). From a theoretical point of view, so-called short-cut (SC) rules have been developed that enable provably correct propagation of changes while avoiding information loss. However, to be able to react to every possible change, an infinite set of such rules might be necessary. Practically, only small sets of pre-computed basic SC rules have been used, severely restricting the kind of changes that can be propagated without loss of information. In this work, we close that gap by developing an approach to compute more complex required SC rules on-the-fly during synchronisation. These higher-order SC rules allow us to cope with more complex scenarios when multiple changes must be handled in one step. We implemented our approach in the model transformation tool eMoflon. An evaluation shows that the overhead of computing higher-order SC rules on-the-fly is tolerable and at times even improves the overall performance. Above that, completely new scenarios can be dealt with without the loss of information.

Dynamic monitoring of open-surface water bodies in the middle reaches of the Tarim River based on the GEE cloud platform

ABSTRACT This study innovatively utilizes the Google Earth Engine (GEE) cloud platform, combined with a multispectral index rule set, to address the challenges of extracting open-surface water bodies in the middle reaches of the Tarim River Basin (MROTR). To overcome the limitations of traditional index methods, particularly the reduced extraction accuracy caused by mixed pixels in arid environments, this research integrates indices such as MNDWI, NDVI, and EVI for automated water extraction, significantly enhancing the precision of water body delineation. By analyzing the spatiotemporal changes in open-surface water area (SWA) from 1990 to 2022, a notable increase in SWA was observed following the implementation of ecological gate-controlled water management, while a decreasing trend was identified within an 8-km range from the riverbanks. The results show a 93.7% accuracy in surface water identification and an SWA growth rate of 12.47 km2/year, with 96% of this growth attributed to seasonal water areas. Runoff loss decreased by 8.53% in the S1 and S2 regions but increased by 7.76% in the S3 and S4 regions. The mixed index rule method proved effective for large-scale water detection, offering new insights for managing arid region water resources.