The authors propose a proof of concept (PoC) combining Adversarial Machine Learning (AML) for robust local ransomware detection with blockchain to share adversarial threat intelligence (TI) across connectivity networks (air gaps). Using a ransomware dataset in a simulated air gap environment, they show how independent cloud tenants strengthen local models. Achieved through adversarial threat intelligence training and publishing immutable intelligence on new evasion tactics to a shared ledger, Baseline Machine Learning (ML) models are vulnerable to carefully crafted perturbations. Modern Artificial Intelligence (AI) security adversarial TI training greatly enhances robustness. When a tenant adopts blockchain-anchored intelligence produced by another tenant, it gains significant advantages against previously unseen adversarial ransomware. This PoC demonstrates a collaborative defense approach suitable for critical infrastructure, such as permissioned mission critical service operators, and durable knowledge sharing under strict compliance requirements.
The main purpose of this study is to investigate the effect of the generalized demand pattern and the delay in payment conditions within the EOQ (Economic Order Quantity) framework. The EOQ inventory model for a deteriorating item is formulated with incorporation of the following characteristics: 1) the proposed inventory system considers only single type of items; 2) deteriorating items follow the constant deterioration rate; 3) demand is deterministic, continuous, and cubic function of time; 4) delay in payment is accepted; 5) neither shortages nor any form of backlogging is allowed to occur. The EOQ inventory model is derived on the basis of two main circumstances for the account resolution, such as Case 1) the grace period is less than or equal to the inventory cycle, and Case 2) the grace period is greater than the inventory cycle. Results are analyzed and discussed with the help of four numerical examples. Finally, sensitivity analysis of several system parameters of the proposed model from one of the four examples is discussed.
The Handbook of Blockchain Technology, edited by Marinos Themistocleous, offers a clear and engaging overview of blockchain's growing impact across technology, business, and society. The book is organized into four parts, covering blockchain's role in the Metaverse, the rise of NFTs and tokenization, decentralized governance through DAOs, and the challenges of global adoption. With contributions from both academics and industry experts, it blends theory with practical insights, highlighting how blockchain is reshaping ideas of ownership, trust, and digital infrastructure. This handbook is a timely and accessible resource for researchers, professionals, and policymakers seeking to understand the evolving blockchain landscape.
Distribution centers (DCs) run more efficiently if their inbound deliveries are spread out evenly over the days of the week so that volume is aligned with receiving capacity. However, delivery dates are often scheduled at the time of placing purchase orders without an efficient process for spreading deliveries evenly, and there has been little research on how to optimally do this. This paper terms this process Master Purchase Receipt Scheduling (MPRS) and introduces a novel MPRS algorithm developed by New Horizon Soft, LLC using data from a major U.S. restaurant chain, where the algorithm is now in use. The approach optimizes order delivery dates, accounting for vendor shipping calendars, shipment sizes, and transportation uncertainty. Results from a Monte Carlo simulation validate the effectiveness and robustness of the algorithm. By addressing the complexities of DC operations, this innovation promises to reduce receiving bottlenecks, increase efficiency and capacity utilization, and foster additional research in this vital area.
With the development of the major e-commerce platforms in recent years, China's logistics enterprises have entered a stage of rapid development. A large number of logistics enterprises have resorted to external capital to seek development. Related research about website investor relations management arises at the historic moment. Based on the analysis of website content of listed logistics enterprises, this paper constructed an evaluation index of the website investor relationship management. The index includes motivating information factor, health information factor, and information transmission channels factor. Then the authors analyze and test the value effect of the website investor relations management of the listed logistics companies. The results show that health information factor has a positive impact on the current performance of enterprises, and this effect presents inverted u-shaped characteristics. The influence of incentive information on performance is characterized by u font. Information transmission channel factors have no significant impact on company performance.
The objective is to determine the best re-allocations of the stations to easily reach the accident location with both the least cost and time possible, with the best firefighting effective required facilities. This objective required dividing the six governorates into 133 areas served in 6 minutes efficient response time. The final findings of this study were 30 reallocated stations, which managed effectively to cover all 133 required areas. This has been shown on included maps of the six governorates. The goal linear programming model idea was not discussed in the emergency field research of the “State of Kuwait,” specifically in the firefighting emergency service. Moreover, this modeling can be expanded to cover all other types of emergency service topics such as health, paramedics, and police stations.
In “real life” decision-making situations, inevitably, there are numerous unmodelled components, not incorporated into the underlying mathematical programming models, that hold substantial influence on the overall acceptability of the solutions calculated. Under such circumstances, it is frequently beneficial to produce a set of dissimilar–yet “good”–alternatives that contribute very different perspectives to the original problems. The approach for creating maximally different solutions is known as modelling-to-generate alternatives (MGA). Recently, a data structure that permits MGA using any population-based solution procedure has been formulated that can efficiently construct sets of maximally different solution alternatives. This new approach permits the production of an overall best solution together with n locally optimal, maximally different alternatives in a single computational run. The efficacy of this novel computational approach is tested on four benchmark optimization problems.
Research productivity (teaching, research, and community service) in any university is the totality of academic staff academic achievement within a given period of time which is partly used for university ranking. There has been a renewed interest in the debate about the quality and quantity of research output and the factors which influence the output of university lecturers at the same time. Therefore, this study assessed the level of academic staff research productivity in private universities in Southwestern, Nigeria. The survey design of the correlational type was adopted. Proportional to size and stratified random sampling techniques were used to select 30% of academic staff across the various ranks in the selected universities, making a total of 657. Academic staff research productivity (= 2.02) was low as against the norm test of 3.00. Low research productivity can be overcome if investment in research at private universities is increased and academic staff utilise them in line with the emerging digital trend in universities around the world.
Rising international oil costs and the transport industry's recovery from the effects of Covid-19 resulted in the efficient management of fuel by logistics companies becoming a significant concern. One way of managing this is by analyzing the fuel consumption of trucks so as to better utilize the costly resource. Twenty-three driving data variables were gathered from 210 freight trucks and analyzed this data. Relevant variables that impact truck fuel consumption were extracted from the initial 23 variables gathered using stepwise regression, and then a prediction model was built from the identified relevant variables utilizing a binary logistic regression model. In addition, a back propagation neural network was employed in this study to create a second model of truck fuel use, and comparisons between the two models were made. The outcomes showed that the binary logistic regression model and the back-propagated neural network model prediction accuracy were 68.4% and 77.2%, respectively.
AI technologies have the potential to help deaf individuals communicate. Due to the complexity of sign fragmentation and the inadequacy of capturing hand gestures, the authors present a sign language recognition (SLR) system and wearable surface electromyography (sEMG) biosensing device based on a Deep SLR that converts sign language into printed message or speech, allowing people to better understand sign language and hand motions. On the forearms, two armbands containing a biosensor and multi-channel sEMG sensors are mounted to capture quite well arm and finger actions. Deep SLR was tested on an Android and iOS smartphone, and its usefulness was determined by comprehensive testing. Sign Speaker has a considerable limitation in terms of recognising two-handed signs with smartphone and smartwatch. To solve these issues, this research proposes a new real-time end-to-end SLR method. The average word error rate of continuous sentence recognition is 9.6%, and detecting signals and recognising a sentence with six sign words takes less than 0.9 s, demonstrating Deep SLR's recognition.
The study seeks to explore the history of marketing as a practice and the development of marketing as an academic discipline. The research is set out to decide whether or not marketing theory empowers young marketing practitioners. This literature review and integrative synthesis study considers the impact that neoliberal modes of governance have had on the academic discipline of marketing. The literature synthesis revealed that marketing was built as a discipline at the turn of the 20th century when universities operated under an academic paradigm where faculty were given academic freedom and autonomy. These ideals helped form the professional identity of the academic prior to the shift toward neoliberalism. Marketing is moving toward defining its place in academics as a distinct discipline—and not as a branch of economics. Finally, with a new definition of marketing, the study concludes that marketing has not yet developed an overarching theory—nor has it been able to construct a definition that is not dynamically dependent on economic theory.
Individuals reach decisions at every moment of their daily lives. They become happy as long as the benefit of these decisions is above their cost. If real life worked under a complete information hypothesis, all the individuals would live happily forever. In this situation, correct decision making becomes a very important subject for the individuals. There are many decision-making methods. There is no general consensus as to which one of them gives the best solution. Instead, it is suggested to conduct sensitivity analysis in the issue of the reliability of the MCDM methods or comparison to another method. In this study, it has been aimed to compare EDAS and COPRAS methods. For this, the results obtained from EDAS method have been compared to COPRAS and EFI methods. All three methods give the same performance results. The found results are statistically significant. This result supports the strong relationship existing among MCDM methods
Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed.
Critical equipment failures in a meat processing plant can shut down the business unit, resulting in a loss of output and financial loss. Furthermore, many stakeholders face risks because of equipment failures. The purpose of this study is to model the failures of the critical equipment which will in turn aid in the analysis of failure patterns and the production of various reliability estimations to prevent such losses. In a meat processing plant, the blood centrifuge is important equipment as failure will render the plant non-operational. This case study conducted the failure analyses of the ‘Keith' centrifuge at the meat processing plant in Windhoek, Namibia. Both parametric and nonparametric models were conducted with the help of computer software. In addition, sensitivity analysis was carried out on both models to improve the reliability of the equipment. The preventative maintenance model was also constructed and tested in the meat processing plant, and it provided good results.
Artificial intelligence (AI) enables the diabetic patient's symptoms and biomarkers to be monitored. People with diabetes are weak, and if a COVID-19 infection is present, the patient must be managed optimally, with a focus on fighting the virus while simultaneously maintaining homeostasis and glycemic control. This study examines the present state of knowledge and limitations in using AI to prevent and manage individuals with diabetes and COVID-19 infection. Furthermore, patient engagement in diabetes care is improved by media and online. These innovative technological advancements have improved glycemic management by lowering fasting and by tracking postprandial glucose levels and glycosylated haemoglobin. In this pandemic period, glycemic management and the implementation of suitable interventions are crucial considerations for diabetic patients, particularly those with an active illness. More research is needed in the future to provide care for diabetic patients' psychological and nutritional well-being as well as to reduce their healthcare costs by building focused AI systems.
To detect breast cancer in the early stages, microcalcifications are considered a key symptom. Several scientific investigations were performed to fight against this disease for which machine learning techniques can be extensively used. Particle swarm optimization (PSO) is recognized as one among several efficient and promising approach for diagnosing breast cancer by assisting medical experts for timely and apt treatment. This paper uses weighted particle swarm optimization (WPSO) approach for extracting textural features from the segmented mammogram image for classifying microcalcifications as normal, benign, or malignant, thereby improving the accuracy. In the breast region, tumor part is extracted using optimization methods. Here, artificial intelligence (AI) is proposed for detecting breast cancer, which reduces the manual overheads. AI framework is constructed for extracting features efficiently. This designed model detects the cancer regions in mammogram (MG) images and rapidly classifies those regions as normal or abnormal. This model uses MG images obtained from hospitals.
The transportation problem is a one of the principal topics in operational research where goods are initially stored at different sources and need to be livered to destination in such a way the total transportation cost is minimum. In this paper, we consider the transportation problem in a trapezoidal fuzzy environment and we introduce the column-row heuristic Dhouib-Matrix-TP1 to solve it in just p iterations (where p is the maximal number between the total number of sources and destinations). The Dhouib-Matrix-TP1 heuristic is enhanced with the robust ranking function and with a new operation for selection based on mean and min metrics. To justify the proposed method, several numerical experiments are given to show the effectiveness of the new technique in solving the trapezoidal fuzzy transportation problems.
The adoption of Electric Vehicles (EVs) has been examined in various settings, yet the issue has rarely been addressed for less developed settings in terms of transport institutions, policies and practices. Turkey, with its rapidly growing emerging economy, presents such a setting for the adoption of EVs. There are various reasons for why the adoption of EVs is still considerably limited in Turkey. A multi-dimensional and multi-actor analysis of the EV landscape can help us better understand the dynamics of transition to EVs. In this paper, a Multi-Level Perspective (MLP) framework is used to examine the current state of EV adoption in Turkey and to interpret the prospects of a possible transition to EVs. Our study shows that a potential transition to EVs in Turkey presents many socio-technical challenges to overcome including current policies, institutions, market dynamics, technological infrastructure, and social limitations. The insights from this review can be used for settings where policies and institutions are not developed enough to achieve a transition to EVs.
The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.
Product return processes are managed by Reverse Logistics (RL) which is a systematic way of putting recovery activities into action successfully. One of the research topics in the RL field is “outsourcing” that has a strategic role in achievement of profitability and compatibility. It mainly includes three decisions: (1) make all operations by the firm itself, (2) all reverse logistics operations are outsourced by the firm, and (3) operations are partially outsourced. This paper proposes a multi phased new approach that combines cloud based design optimization (CBDO) with Analytical Hierarchy Process (AHP) in order to select the best outsourcing alternative under uncertainties. As a novel approach, CBDO is good at dealing with high uncertainty, in computationally satisfactory way. Because the perceptions and ideas of managers can change across time, traditional multi criteria decision making (MCDM) techniques such as AHP is strengthened by a robust technique such as CBDO in our study. In order to verify the performance of the proposed model, an illustrative study is conducted.
