Visual Merchandising: Does it Matter for Your Brands?

Visual Merchandising is a significant aesthetic practice that helps a company and retailer to create the brand image at POS. Visual Merchandising is an art of presenting products in different ways at the retail store. The purpose of this study is to discuss the importance of merchandising for firms, History of Visual Merchandising and its elements by empirical evidences. This paper will cover theoretical perspective of merchandising, its impact on consumer buying behavior, sales, and on overall brand image. This study concluded that Visual Merchandising is the key to gain the competitive advantage against rivals in the market and it helps the firms to increase the sales, to create brand image, to attract the customers towards the products. So the firms should make strategic long term planning’s to execute the visual merchandising at the POS (Point of Sale). Keywords: Visual Merchandising. Consumer Buying Behavior and Brand Imag

Consensus as a Nash Equilibrium of a Dynamic Game

Consensus formation in a social network is modeled by a dynamic game of a prescribed duration played by members of the network. Each member independently minimizes a cost function that represents his/her motive. An integral cost function penalizes a member's differences of opinion from the others as well as from his/her own initial opinion, weighted by influence and stubbornness parameters. Each member uses its rate of change of opinion as a control input. This defines a dynamic non-cooperative game that turns out to have a unique Nash equilibrium. Analytic explicit expressions are derived for the opinion trajectory of each member for two representative cases obtained by suitable assumptions on the graph topology of the network. These trajectories are then examined under different assumptions on the relative sizes of the influence and stubbornness parameters that appear in the cost functions.Comment: 7 pages, 9 figure, Pre-print from the Proceedings of the 12th International Conference on Signal Image Technology and Internet-based Systems (SITIS), 201

Peripheral Vascular Disease “A Spectrum”

Background: To observe the spectrum of peripheralvascular disease presenting in a tertiary care hospital.Method: This observational, descriptive study wascarried out in Surgical Unit –I at Holy Family Hospital,Rawalpindi over a period of two years. All the patientspresenting with peripheral vascular disorders excludingacute vascular trauma patients, diabetics and varicoseveins patients were included in the study.Results: A total of 49 patients presented to the hospital.The male to female ratio was 2:1.The average age ofpresentation was 50.2 years. About 35% patients presentedwith occlusive disease, 18% with autoimmune disease and14% with embolism. The less common causes of peripheralvascular disorders included femoral pseudoaneurysms in8% patients and true aneurysms of iliac, popliteal andsubclavian arteries in 12% patients. Bilateral gangrene ofboth lower limbs was seen in 4% patients.Conclusion: Peripheral vascular disease is commonerin males and is mostly seen in the sixth decade of life.Atherosclerotic occlusive disease is the commonest causeof peripheral vascular disease followed by vasculitis,embolisms and aneurysms

Prophylaxis of DVT with enoxaparin in patients undergoing total knee replacement

Objective: To evaluate the efficacy and safety of the low molecular weight heparin as prophylaxis against thromboembolism following total knee replacement surgery.Methods: Post-operative bilateral lower extremity colour duplex scan was performed on 55 patients subjected to total knew arthroplasty. The scan was performed 7 days after surgery for detection of DVT. All patients were given Enoxaparin 40mg subcutaneous daily for 2 weeks as prophylaxis against DVT.Results: Two patients were diagnosed as DVT by color duplex scanning and both were distal but only one was asymptomatic. Another patient developed pulmonary embolism and died subsequently. The major and minor wound problems were seen in two and six patients respectively; nearly all complications were seen in obese patients.Conclusion: DVT is not a nonexistent entity in our population. Low molecular weight heparins are safe drugs but apparently the bleeding complications are more as compared to Western literature. Larger case control studies are required to determine the true efficacy and safety of LMWH

Identification of bioflavonoid as fusion inhibitor of dengue virus using molecular docking approach

AbstractDengue virus with four distinct serotypes belongs to Flavivirus, poses a significant threat to human health and becomes an emerging global problem. Membrane fusion is a central molecular event during viral entry into host cell. To prevent viral infection it is necessary to interrupt the virus replication at an early stage of attachment. Dengue Virus (DENV) envelope protein experiences conformational changes and it causes the virus to fuse with host cell. Hinge region movement of domain I and II in envelope protein facilitates the fusion process. Small molecules that bind in this pocket may have the ability to interrupt the conformational changes that trigger fusion process. We chose different flavonoids (baicalein, fisetin, hesperetin, naringenin/ naringin, quercetin and rutin) that possess anti dengue activity. Molecular docking analysis was done to examine the inhibitory effect of flavonoids against envelope protein of DENV-2. Results manifest quercetin (flavonoid found in Carica papaya, apple and even in lemon) as the only flavone that can interrupt the fusion process of virus by inhibiting the hinge region movement and by blocking the conformational rearrangement in envelope protein. These novel findings using computational approach are worthwhile and will be a bridge to check the efficacy of compounds using appropriate animal model under In vivo studies. This information can be used by new techniques and provides a way to control dengue virus infection

Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers

This paper presents a new observer-based approach to detect and isolate faulty sensors in industrial systems. Two types of sensor faults are considered: complete failure and sensor deterioration. The proposed method is applicable to general autonomous nonlinear systems without making any assumptions about its triangular and/or normal form, which is usually considered in the observer design literature. The key aspect of our approach is a learning-based design of the Luenberger observer, which involves using a neural network to approximate the injective map that transforms the nonlinear system into a stable linear system with output injection. This learning-based Luenberger observer accurately estimates the system's state, allowing for the detection of sensor faults through residual generation. The residual is computed as the norm of the difference between the system's measured output and the observer's predicted output vectors. Fault isolation is achieved by comparing each sensor's measurement with its corresponding predicted value. We demonstrate the effectiveness of our approach in capturing and isolating sensor faults while remaining robust in the presence of measurement noise and system uncertainty. We validate our method through numerical simulations of sensor faults in a network of Kuramoto oscillators

Feedback Design for Devising Optimal Epidemic Control Policies

For reliable epidemic monitoring and control, this paper proposes a feedback mechanism design to effectively cope with data and model uncertainties. Using past epidemiological data, we describe methods to estimate the parameters of general epidemic models. Because the data could be noisy, the estimated parameters may not be accurate. Therefore, under uncertain parameters and noisy measurements, we provide an observer design method for robust state estimation. Then, using the estimated model and state, we devise optimal control policies by minimizing a predicted cost functional. Finally, the effectiveness of the proposed method is demonstrated through its implementation on a modified SIR epidemic model

DISMISS: detection of stranded methylation in MeDIP-Seq data

BACKGROUND: DNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukaryotic genomes. Within MeDIP-Seq libraries, methylated cytosines can be found in both double-stranded (symmetric) and single-stranded (asymmetric) genomic contexts. While symmetric CG methylation has been relatively well-studied, asymmetric methylation in any dinucleotide context has received less attention. Importantly, no currently available software for processing MeDIP-Seq reads is able to resolve these strand-specific DNA methylation signals. Here we introduce DISMISS, a new software package that detects strand-associated DNA methylation from existing MeDIP-Seq analyses. RESULTS: Using MeDIP-Seq datasets derived from Apis mellifera (honeybee), an invertebrate species that contains more asymmetric- than symmetric- DNA methylation, we demonstrate that DISMISS can identify strand-specific DNA methylation signals with similar accuracy as bisulfite sequencing (BS-Seq; single nucleotide resolution methodology). Specifically, DISMISS is able to confidently predict where DNA methylation predominates (plus or minus DNA strands – asymmetric DNA methylation; plus and minus DNA stands – symmetric DNA methylation) in MeDIP-Seq datasets derived from A. mellifera samples. When compared to DNA methylation data derived from BS-Seq analysis of A. mellifera worker larva, DISMISS-mediated identification of strand-specific methylated cytosines is 80 % accurate. Furthermore, DISMISS can correctly (p <0.0001) detect the origin (sense vs antisense DNA strands) of DNA methylation at splice site junctions in A. mellifera MeDIP-Seq datasets with a precision close to BS-Seq analysis. Finally, DISMISS-mediated identification of DNA methylation signals associated with upstream, exonic, intronic and downstream genomic loci from A. mellifera MeDIP-Seq datasets outperforms MACS2 (Model-based Analysis of ChIP-Seq2; a commonly used MeDIP-Seq analysis software) and closely approaches the results achieved by BS-Seq. CONCLUSIONS: While asymmetric DNA methylation is increasingly being found in growing numbers of eukaryotic species and is the predominant pattern observed in some invertebrate genomes, it has been difficult to detect in MeDIP-Seq datasets using existing software. DISMISS now enables more sensitive examinations of MeDIP-Seq datasets and will be especially useful for the study of genomes containing either low levels of DNA methylation or for genomes containing relatively high amounts of asymmetric methylation

Secure Set-Based State Estimation for Linear Systems under Adversarial Attacks on Sensors

When a strategic adversary can attack multiple sensors of a system and freely choose a different set of sensors at different times, how can we ensure that the state estimate remains uncorrupted by the attacker? The existing literature addressing this problem mandates that the adversary can only corrupt less than half of the total number of sensors. This limitation is fundamental to all point-based secure state estimators because of their dependence on algorithms that rely on majority voting among sensors. However, in reality, an adversary with ample resources may not be limited to attacking less than half of the total number of sensors. This paper avoids the above-mentioned fundamental limitation by proposing a set-based approach that allows attacks on all but one sensor at any given time. We guarantee that the true state is always contained in the estimated set, which is represented by a collection of constrained zonotopes, provided that the system is bounded-input-bounded-state stable and redundantly observable via every combination of sensor subsets with size equal to the number of uncompromised sensors. Additionally, we show that the estimated set is secure and stable irrespective of the attack signals if the process and measurement noises are bounded. To detect the set of attacked sensors at each time, we propose a simple attack detection technique. However, we acknowledge that intelligently designed stealthy attacks may not be detected and, in the worst-case scenario, could even result in exponential growth in the algorithm's complexity. We alleviate this shortcoming by presenting a range of strategies that offer different levels of trade-offs between estimation performance and complexity

Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems

The design of Luenberger observers for nonlinear systems involves state transformation to another coordinate system where the dynamics are asymptotically stable and linear up to output injection. The observer then provides a state estimate in the original coordinates by inverting the transformation map. For general nonlinear systems, however, the main challenge is to find such a transformation and to ensure that it is injective. This paper addresses this challenge by proposing a learning method that employs supervised physics-informed neural networks to approximate both the transformation and its inverse. It is shown that the proposed method exhibits better generalization capabilities than other contemporary methods. Moreover, the observer is shown to be robust under the neural network's approximation error and the system uncertainties