Modeling and Analysis of Perishable Inventory System with Retrial demands in Supply Chain

In this article, we consider a continuous review perishable inventory system with poisson demands. The maximum storage capacity at lower echelon (retailer) is S and the upper Echelon (Distribution Center) is M (= nQ). The life time of each item is assumed to be exponential. The operating policy is (s, S) policy, that is, whenever the inventory level drops to s, an order for Q = (S - s > s) item is placed. The ordered items are received after a random time which is distributed as exponential. We assume that demands occurring during the stock-out period enter into the orbit. These orbiting demands send out signal to complete for their demand which is distributed as exponential. The joint probability distribution of the inventory level at retailer, inventory level at DC and the number of demands in the orbit are obtained in the steady state case. Various system performance measures are derived and the results are illustrated numerically

Changes in Two Point Discrimination and the law of mobility in Diabetes Mellitus patients

<p>Abstract</p> <p>Background</p> <p>Diabetic neuropathy is a family of nerve disorders with progressive loss of nerve function in 15% of diabetes mellitus (DM) subjects. Two-point discrimination (TPD) is one method of quantitatively testing for loss of nerve function. The law of mobility for TPD is known for normal subjects in earlier studies but has not been studied for diabetic subjects. This is a pilot study to evaluate and plot the law of mobility for TPD among DM subjects.</p> <p>Methods</p> <p>The Semmes Weinstein monofilament (SWMF) was used to measure the loss of protective sensation. An Aesthesiometer was used to find the TPD of several areas in upper and lower extremities for normal and diabetic subjects. All the subjects were screened for peripheral artery occlusive disease with ankle brachial pressure index (0.9 or above).</p> <p>Results</p> <p>TPD of normal and diabetic subjects for different areas of hands and legs from proximal to distal is evaluated for 18 subjects. TPD values decrease from proximal to distal areas. Vierodt's law of mobility for TPD holds good for normal subjects in the hand and foot areas. The law of mobility for TPD in DM subjects holds well in the hand but doesn't hold well in foot areas with or without sensation.</p> <p>Conclusion</p> <p>TPD is a quantitative and direct measure of sensory loss. The TPD value of diabetic subjects reveals that the law of mobility do not hold well for Diabetic subjects in foot areas. The significance of this result is that the TPD of the diabetic subjects could provide direct, cost effective and quantitative measure of neuropathy.</p

A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT

The proliferation of IoT services has spurred a surge in network attacks, heightening cybersecurity concerns. Essential to network defense, intrusion detection and prevention systems (IDPSs) identify malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, infiltration, and Heartbleed. This study focuses on leveraging unsupervised learning for training detection models to counter these threats effectively. The proposed method utilizes basic autoencoders (bAEs) for dimensionality reduction and encompasses a three-stage detection model: one-class support vector machine (OCSVM) and deep autoencoder (dAE) attack detection, complemented by density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. Accurately delineated clusters aid in mapping attack tactics. The MITRE ATT&amp;CK framework establishes a “Cyber Threat Repository”, cataloging attacks and tactics, enabling immediate response based on priority. Leveraging preprocessed and unlabeled normal network traffic data, this approach enables the identification of novel attacks while mitigating the impact of imbalanced training data on model performance. The autoencoder method utilizes reconstruction error, OCSVM employs a kernel function to establish a hyperplane for anomaly detection, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determining cluster count, ensuring scalability, and minimizing false positives and false negatives. Evaluated on standard datasets such as CIC-IDS2017 and CSECIC-IDS2018, the proposed model outperforms existing state of art methods. Our approach achieves accuracies exceeding 98% for the two datasets, thus confirming its efficacy and effectiveness for application in efficient intrusion detection systems

Assessment of Safety and Interference Issues of Radio Frequency Identification Devices in 0.3 Tesla Magnetic Resonance Imaging and Computed Tomography

The objective of this study was to evaluate two issues regarding magnetic resonance imaging (MRI) including device functionality and image artifacts for the presence of radio frequency identification devices (RFID) in association with 0.3 Tesla at 12.7 MHz MRI and computed tomography (CT) scanning. Fifteen samples of RFID tags with two different sizes (wristband and ID card types) were tested. The tags were exposed to several MR-imaging conditions during MRI examination and X-rays of CT scan. Throughout the test, the tags were oriented in three different directions (axial, coronal, and sagittal) relative to MRI system in order to cover all possible situations with respect to the patient undergoing MRI and CT scanning, wearing a RFID tag on wrist. We observed that the tags did not sustain physical damage with their functionality remaining unaffected even after MRI and CT scanning, and there was no alternation in previously stored data as well. In addition, no evidence of either signal loss or artifact was seen in the acquired MR and CT images. Therefore, we can conclude that the use of this passive RFID tag is safe for a patient undergoing MRI at 0.3 T/12.7 MHz and CT Scanning

What doesn't kill you makes you stranger: Dipeptidyl peptidase-4 (CD26) proteolysis differentially modulates the activity of many peptide hormones and cytokines generating novel cryptic bioactive ligands

Dipeptidyl peptidase 4 (DPP4) is an exopeptidase found either on cell surfaces where it is highly regulated in terms of its expression and surface availability (CD26) or in a free/circulating soluble constitutively available and intrinsically active form. It is responsible for proteolytic cleavage of many peptide substrates. In this review we discuss the idea that DPP4-cleaved peptides are not necessarily inactivated, but rather can possess either a modified receptor selectivity, modified bioactivity, new antagonistic activity, or even a novel activity relative to the intact parent ligand. We examine in detail five different major DPP4 substrates: glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP), peptide tyrosine-tyrosine (PYY), and neuropeptide Y (NPY), and stromal derived factor 1 (SDF-1 aka CXCL12). We note that discussion of the cleaved forms of these five peptides are underrepresented in the research literature, and are both poorly investigated and poorly understood, representing a serious research literature gap. We believe they are understudied and misinterpreted as inactive due to several factors. This includes lack of accurate and specific quantification methods, sample collection techniques that are inherently inaccurate and inappropriate, and a general perception that DPP4 cleavage inactivates its ligand substrates. Increasing evidence points towards many DPP4-cleaved ligands having their own bioactivity. For example, GLP-1 can work through a different receptor than GLP-1R, DPP4-cleaved GIP can function as a GIP receptor antagonist at high doses, and DPP4-cleaved PYY, NPY, and CXCL12 can have different receptor selectivity, or can bind novel, previously unrecognized receptors to their intact ligands, resulting in altered signaling and functionality. We believe that more rigorous research in this area could lead to a better understanding of DPP4’s role and the biological importance of the generation of novel cryptic ligands. This will also significantly impact our understanding of the clinical effects and side effects of DPP4-inhibitors as a class of anti-diabetic drugs that potentially have an expanding clinical relevance. This will be specifically relevant in targeting DPP4 substrate ligands involved in a variety of other major clinical acute and chronic injury/disease areas including inflammation, immunology, cardiology, stroke, musculoskeletal disease and injury, as well as cancer biology and tissue maintenance in aging

ERABiLNet: enhanced residual attention with bidirectional long short-term memory

Alzheimer’s Disease (AD) causes slow death in brain cells due to shrinkage of brain cells which is more prevalent in older people. In most cases, the symptoms of AD are mistaken as age-related stresses. The most widely utilized method to detect AD is Magnetic Resonance Imaging (MRI). Along with Artificial Intelligence (AI) techniques, the efficacy of identifying diseases related to the brain has become easier. But, the identical phenotype makes it challenging to identify the disease from the neuro-images. Hence, a deep learning method to detect AD at the beginning stage is suggested in this work. The newly implemented “Enhanced Residual Attention with Bi-directional Long Short-Term Memory (Bi-LSTM) (ERABi-LNet)” is used in the detection phase to identify the AD from the MRI images. This model is used for enhancing the performance of the Alzheimer’s detection in scale of 2–5%, minimizing the error rates, increasing the balance of the model, so that the multi-class problems are supported. At first, MRI images are given to “Residual Attention Network (RAN)”, which is specially developed with three convolutional layers, namely atrous, dilated and Depth-Wise Separable (DWS), to obtain the relevant attributes. The most appropriate attributes are determined by these layers, and subjected to target-based fusion. Then the fused attributes are fed into the “Attention-based Bi-LSTM”. The final outcome is obtained from this unit. The detection efficiency based on median is 26.37% and accuracy is 97.367% obtained by tuning the parameters in the ERABi-LNet with the help of Modified Search and Rescue Operations (MCDMR-SRO). The obtained results are compared with ROA-ERABi-LNet, EOO-ERABi-LNet, GTBO-ERABi-LNet and SRO-ERABi-LNet respectively. The ERABi_LNet thus provides enhanced accuracy and other performance metrics compared to such deep learning models. The proposed method has the better sensitivity, specificity, F1-Score and False Positive Rate compared with all the above mentioned competing models with values such as 97.49%.97.84%,97.74% and 2.616 respective;y. This ensures that the model has better learning capabilities and provides lesser false positives with balanced prediction

LRH-1 drives colon cancer cell growth by repressing the expression of the <i>CDKN1A</i> gene in a p53-dependent manner

Liver receptor homologue 1 (LRH-1) is an orphan nuclear receptor that has been implicated in the progression of breast, pancreatic and colorectal cancer (CRC). To determine mechanisms underlying growth promotion by LRH-1 in CRC, we undertook global expression profiling following siRNA-mediated LRH-1 knockdown in HCT116 cells, which require LRH-1 for growth and in HT29 cells, in which LRH-1 does not regulate growth. Interestingly, expression of the cell cycle inhibitor p21 (CDKN1A) was regulated by LRH-1 in HCT116 cells. p21 regulation was not observed in HT29 cells, where p53 is mutated. p53 dependence for the regulation of p21 by LRH-1 was confirmed by p53 knockdown with siRNA, while LRH-1-regulation of p21 was not evident in HCT116 cells where p53 had been deleted. We demonstrate that LRH-1-mediated p21 regulation in HCT116 cells does not involve altered p53 protein or phosphorylation, and we show that LRH-1 inhibits p53 recruitment to the p21 promoter, likely through a mechanism involving chromatin remodelling. Our study suggests an important role for LRH-1 in the growth of CRC cells that retain wild-type p53

Is a cooperative approach to seaweed farming effectual? An analysis of the seaweed cluster project (SCP), Malaysia

Seaweed (Kappaphycus spp.) farming has been practised in Malaysia since the late 1970s following government policy incentives (training and farming inputs). However, numerous governance, economic, environmental, technological and sociocultural challenges have limited the industry from achieving its full potential. The Seaweed Cluster Project (SCP) was introduced in 2012 to address some of these challenges. We sought to evaluate the effectiveness of the SCP in delivering its central objectives of increasing seaweed production, optimising the farming area, improving seaweed quality and farming efficiency, raising farmers’ income, and reducing the environmental impact of seaweed farming. Community and industry perceptions of the SCP were obtained from seven communities using a mixed-methods approach based on face-to-face semi-structured interviews, focus group discussions, household surveys, observation and secondary data. Views on the SCP outcomes were generally negative, including low take-up rates by indigenous people, poor stakeholder participation in decision-making, limited acceptance of new technologies, economic vulnerability, a complex marketing system, and low social cohesion of seaweed farming communities. Positive perceptions included recognition that the SCP confers high social status upon a community, reduces operating costs, and facilitates the production of certified seaweed. The SCP’s problems are linked to poor multi-level governance, weak market mechanisms and unintegrated community development. The study concludes with five recommendations to improve the SCP: promote the participation of indigenous people; legalise existing migrant farmers; strengthen local seaweed cooperative organisations; provide entrepreneurship skills to farmers; and fully integrate stakeholders into decision-making