Relationship of Risk Premium with Expected Volatility and Unexpected Volatility in Developing and Developed Economies

After application of ARIMA model to monthly risk premium and Threshold GARCH-In-Mean (TGARCH-M) models to daily risk premium of developed economies i.e. USA, UK, Germany, France and Canada and developing economies i.e. Pakistan, India, Malaysia and China over a period from January 2000 to December 2014, this study reported that in monthly data relationship between risk premium and expected volatility is negative in Pakistan: positive in Indonesia and Canada while insignificant in all other countries. The relationship between risk premium and unexpected volatility is negative in all the countries except Pakistan and China where it is insignificant while positive in USA. By the application of asymmetric volatility model on daily data for the same span, the relationship between risk premium and expected volatility is negative and significant in UK and France while this relationship is insignificant in all other countries. The study also indicates that the arrival of bad news has a greater impact on conditional volatility than the arrival of good news in all the economies whether developing or developed

Progressive Class-Wise Attention (PCA) Approach for Diagnosing Skin Lesions

Skin cancer holds the highest incidence rate among all cancers globally. The importance of early detection cannot be overstated, as late-stage cases can be lethal. Classifying skin lesions, however, presents several challenges due to the many variations they can exhibit, such as differences in colour, shape, and size, significant variation within the same class, and notable similarities between different classes. This paper introduces a novel class-wise attention technique that equally regards each class while unearthing more specific details about skin lesions. This attention mechanism is progressively used to amalgamate discriminative feature details from multiple scales. The introduced technique demonstrated impressive performance, surpassing more than 15 cutting-edge methods including the winners of HAM1000 and ISIC 2019 leaderboards. It achieved an impressive accuracy rate of 97.40% on the HAM10000 dataset and 94.9% on the ISIC 2019 dataset

LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation

Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. However, current methodologies often fall short in accurately segmenting delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on repeated convolution and pooling operations can hinder the representation of edge information, ultimately limiting overall segmentation accuracy. In this paper, we propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. Additionally, we have optimized the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimization significantly reduces training time and enhances computational efficiency. To assess the robustness and generalizability of LMBiS-Net, we performed comprehensive evaluations on various aspects of retinal images. Specifically, the model was subjected to rigorous tests to accurately segment retinal vessels, which play a vital role in ophthalmological diagnosis and treatment. By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model. The results of our tests demonstrate that LMBiS-Net is not only robust and generalizable but also capable of maintaining high levels of segmentation accuracy. These characteristics highlight the potential of LMBiS-Net as an efficient tool for high-speed and accurate segmentation of retinal images in various clinical applications

Effects of Organizational Justice on Organizational Commitment

Organizational justice has been considered as an important contributor for commitment of employees towards their organization. Only two dimensions (Distributive and procedural justice) of organizational justice were focused in this study.  This research study is cross sectional in design. For questionnaires distribution a sample size of 500 employees (subjects) were chosen from three higher education institutions of Khyber Pakhtunkhwa, Pakistan.  Data were analyzed by using arithmetic mean, standard deviation, correlation and regression tools and techniques. Results shown that distributive and procedural justice both have noteworthy and positive effects on the dependent variable (organizational commitment) of the employees. Keywords: Organizational Justice, Distributive justice, procedural justice, organizational commitment JEL Classifications: D23, L

Experimental Investigation of Vertical Density Profile of Medium Density Fiberboard in Hot Press

This research investigates the performance of medium density fiberboard (MDF) with respect to hot press parameters. The performance of the board, type of glue, and production efficiency determine the optimum temperature and pressure for hot pressing. The actual temperature of the hot press inside the MDF board determines the properties of the final product. Hence, the optimal hot press parameters for the desired product are experimentally obtained. Moreover, MDF is experimentally investigated in terms of its vertical density profile, bending, and internal bonding under the various input parameters of temperature, pressure, cycle time, and moisture content during the manufacturing process. The experimental study is carried out by varying the temperature, pressure, cycle time, and moisture content in the ranges of 200–220 °C, 145–155 bar, 260–275 s, and 8–10%, respectively. Consequently, the optimum input parameters of a hot-pressing temperature of 220 °C, pressure of 155 bar, cycle time of 256 s, and moisture content of 8% are identified for the required internal bonding (0.64 N/mm2), bending (32 N/mm2), and increase in both the core and peak density of the vertical density profile as per the ASTM standard

Development and Comparative Analysis of Electrochemically Etched Tungsten Tips for Quartz Tuning Fork Sensor

Quartz Tuning Fork (QTF) based sensors are used for Scanning Probe Microscopes (SPM), in particular for near-field scanning optical microscopy. Highly sharp Tungsten (W) tips with larger cone angles and less tip diameter are critical for SPM instead of platinum and iridium (Pt/Ir) tips due to their high-quality factor, conductivity, mechanical stability, durability and production at low cost. Tungsten is chosen for its ease of electrochemical etching, yielding high-aspect ratio, sharp tips with tens of nanometer end diameters, while using simple etching circuits and basic electrolyte chemistry. Moreover, the resolution of the SPM images is observed to be associated with the cone angle of the SPM tip, therefore Atomic-Resolution Imaging is obtained with greater cone angles. Here, the goal is to chemically etch W to the smallest possible tip apex diameters. Tips with greater cone angles are produced by the custom etching procedures, which have proved superior in producing high quality tips. Though various methods are developed for the electrochemical etching of W wire, with a range of applications from scanning tunneling microscopy (SPM) to electron sources of scanning electron microscopes, but the basic chemical etching methods need to be optimized for reproducibility, controlling cone angle and tip sharpness that causes problems for the end users. In this research work, comprehensive experiments are carried out for the production of tips from 0.4 mm tungsten wire by three different electrochemical etching techniques, that is, Alternating Current (AC) etching, Meniscus etching and Direct Current (DC) etching. Consequently, sharp and high cone angle tips are obtained with required properties where the results of the W etching are analyzed, with optical microscope, and then with field emission scanning electron microscopy (FE-SEM). Similarly, effects of varying applied voltages and concentration of NaOH solution with comparison among the produced tips are investigated by measuring their cone angle and tip diameter. Moreover, oxidation and impurities, that is, removal of contamination and etching parameters are also studied in this research work. A method has been tested to minimize the oxidation on the surface and the tips were characterized with scanning electron microscope (SEM)

E-readiness Assessment in Public Libraries: A Case Study of Khyber Pakhtunkhwa, Pakistan

Purpose: This study probed e-readiness in public libraries regarding human resources, electronic infrastructure and Network support & IT penetration in services and operations. It also assessed the status of the budget, staff, collection and services of libraries. Method/Design: The study population comprised 14 public libraries of Khyber Pakhtunkhwa. A quantitative approach was used and the questionnaire-based survey was conducted to collect data from the librarians of public libraries. The collected data was analyzed using the Statistical Package for Social Sciences version 23. Results: It was found that all libraries were run by LIS professional staff; however, the libraries had limited financial, human and information resources. The e-readiness of human resources demonstrates that they had limited knowledge and a moderate level of skills of ICT and other technologies. All libraries had integrated library software; however, the ICT infrastructure was poor in almost all libraries. It was also observed that the libraries poorly or moderately implemented the IT in the delivery of services. The key factors affecting e-readiness were lack of IT infrastructure, insufficient financial resources and shortage of IT staff. Implication: The study\u27s findings could be beneficial to the authorities. The key point to be considered includes, sufficient funds should be allocated to procure more resources and ICT apparatus, training be arranged to library staff to enhance their ICT knowledge and skills, libraries should develop proper ICT infrastructure and ICT applications should be used in the library operations and services. Originality: This is the first study in the country to assess the e-readiness of public libraries. It will fill the literature gap and guide the researchers to do similar studies across the country. Keywords: E-readiness, Information and Communication Technologies, ICT, Public Libraries, Khyber Pakhtunkhwa, Pakista

Development and In Vitro Characterization of Diacerein Loaded Chitosan–Chondroitin Sulfate Nanoemulgel for Osteoarthritis

The proficient functions of diacerein and anti-inflammatory polymers have been utilized to develop sustained release transdermal diacerein nanoemulgel for long-term osteoarthritis treatment by overcoming the deleterious outcomes of drugs associated with the oral route. Natural anti-inflammatory and biodegradable polymers like Chitosan (CHS) and chondroitin sulfate (CS) were used to formulate diacerein nanoparticles (DCR-NPs) through the ionic gelation method. Design Expert software was used for preparation of optimized preparation by investigating the impact of polymers and surfactant concentrations on particle size, PDI and entrapment efficiency employing Response Surface Methodology (RSM). DCR-NPs formulated using CHS, CS and Tween 80 in optimized concentrations depicted spherical nanoparticles with particle size of 320.0 ± 3 nm having PDI, zeta potential and entrapment efficiency of 0.3 ± 0.07, 40 ± 0.3 mV and 82 ± 4.16%, respectively. DCR-NPs were further analyzed for confirmation of electrostatic interactions between polymers and drug through Fourier transform-infrared spectroscopy (FTIR). In vitro studies show 95% release of DCR in 72 h exhibiting the Korsmeyer–Peppas model. For transdermal delivery, the nanoemulgel of optimized DCR-NPs was formulated utilizing argan oil as a permeation enhancer with intrinsic anti-inflammatory properties, providing a synergistic effect to the formulation. Nanoemulgel was characterized in terms of visual appearance, spreadability, drug content and rheological behavior providing sustained release of drug up to 4 days following Higuchi model with improved ex vivo permeation, confirmed by fluorescent microscopy. Concisely, DCR-nanoemulgel sustained the release of drug with good penetration and enhanced therapeutic properties owing to the presence of CHS, CS and argan oil possessing anti-inflammatory attributes

Rethinking Interpretation: Input-Agnostic Saliency Mapping of Deep Visual Classifiers

Saliency methods provide post-hoc model interpretation by attributing input features to the model outputs. Current methods mainly achieve this using a single input sample, thereby failing to answer input-independent inquiries about the model. We also show that input-specific saliency mapping is intrinsically susceptible to misleading feature attribution. Current attempts to use `general' input features for model interpretation assume access to a dataset containing those features, which biases the interpretation. Addressing the gap, we introduce a new perspective of input-agnostic saliency mapping that computationally estimates the high-level features attributed by the model to its outputs. These features are geometrically correlated, and are computed by accumulating model's gradient information with respect to an unrestricted data distribution. To compute these features, we nudge independent data points over the model loss surface towards the local minima associated by a human-understandable concept, e.g., class label for classifiers. With a systematic projection, scaling and refinement process, this information is transformed into an interpretable visualization without compromising its model-fidelity. The visualization serves as a stand-alone qualitative interpretation. With an extensive evaluation, we not only demonstrate successful visualizations for a variety of concepts for large-scale models, but also showcase an interesting utility of this new form of saliency mapping by identifying backdoor signatures in compromised classifiers