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In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. [ABSTRACT FROM AUTHOR]

The digital economy's rise has fueled the growth of digital banking, but concerns linger about customer protection. While offering advantages like financial inclusion, this shift disrupts traditional banking experiences and introduces potential risks. Customer safety in this new landscape is paramount, as dissatisfied users may switch providers and institutions risk reputational damage. To remain competitive, financial institutions must prioritize a secure experience that aligns with customer expectations. This study investigates five key factors influencing customer protection in Pakistan's digital financial services. Analysis reveals all factors positively impact customer protection, with information security holding the most weight. These findings highlight the need for robust information security measures as a critical driver for the Pakistani digital banking industry's success. [ABSTRACT FROM AUTHOR]

This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks. [ABSTRACT FROM AUTHOR]

This study investigates the influence of government effectiveness, health expenditure, and sustainable development goals (SDGs) on life expectancy in Pakistan. To accomplish this, a systematic analysis was conducted on time series data spanning from 2000 to 2020. Cointegration analysis was utilized to evaluate the long-term integration of all variables, while a comprehensive causality test was performed to investigate the short-term links among government effectiveness, health expenditure, SDGs, and life expectancy. The findings of the Johansen Cointegration test definitively confirmed the presence of long-term cointegration among all variables. In addition, the results of the Granger causality test show that there is a one-way causal relationship between government performance, health spending, and SDGs to life expectancy in the short term. The validation of both enduring and immediate connections among these factors emphasizes the crucial significance of healthcare services in Pakistan. Therefore, it is important to push for more healthcare investments and increased national budget allocations by the Pakistani government. Prioritizing the allocation of resources towards healthcare, bolstering the efficiency of the administration, and attaining SDG targets are all crucial for enhancing life expectancy in Pakistan. The study's results also carry significant policy implications, underscoring the necessity of strategically implementing health expenditure and SDG targets to enhance human capital and population well-being, as demonstrated by the increased life expectancy. [ABSTRACT FROM AUTHOR]

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and crucial facet of study anxiety in the university students of Pakistan. Using the Liebowitz Social Anxiety Scale (LSAS), we found a significant increase in the social anxiety level among university students in the past three years after the COVID-19 lockdown. Our data showed that the 'very severe anxiety' level soared up to 52.94% in the year 2023 from just 5.98% in the year 2021, showing a net increase of 47.06%. Statistical analyses demonstrate noteworthy differences in the overall social anxiety levels among the students, reaching significance at the 5% level and a discernable upward trend in the social anxiety levels as study anxiety. We also employed predictive analytics, including binary classifiers and generalized linear models with a 95% confidence interval, to identify individuals at risk. This study highlights a dynamic shift with escalating social anxiety levels among the university students and thus emphasizing its awareness, which is significantly important for the timely intervention, potentially preventing symptom escalation and improving outcomes. [ABSTRACT FROM AUTHOR]

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author's personal information, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic, semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, and marketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French, etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author's age and gender based on Roman Urdu text messages. The dataset used in this study is Fire'18-MaponSMS. This study proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiple linguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model is contrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Naïve Bays (NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable, RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 with Random Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender prediction calculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and 60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguistic features for age and gender prediction. [ABSTRACT FROM AUTHOR]

Abstract Background The folate receptor α (FRα)-targeting antibody-drug conjugate (ADC), IMGN853, shows great antitumor activity against FRα-expressing tumors in vivo, but patient selection and consequently therapy outcome are based on immunohistochemistry. The aim of this study is to develop an antibody-derived immuno-PET imaging agent strategy for targeting FRα in ovarian cancer as a predictor of treatment success. Methods We developed [89Zr]Zr-DFO-M9346A, a humanized antibody-based radiotracer targeting tumor-associated FRα in the preclinical setting. [89Zr]Zr-DFO-M9346A’s binding ability was tested in an in vitro uptake assay using cell lines with varying FRα expression levels. The diagnostic potential of [89Zr]Zr-M9346A was evaluated in KB and OV90 subcutaneous xenografts. Following intravenous injection of [89Zr]Zr-DFO-M9346A (~90 μCi, 50 μg), PET imaging and biodistribution studies were performed. We determined the blood half-life of [89Zr]Zr-DFO-M9346A and compared it to the therapeutic, radioiodinated ADC [131I]-IMGN853. Finally, in vivo studies using IMG853 as a therapeutic, paired with [89Zr]Zr-DFO-M9346A as a companion diagnostic were performed using OV90 xenografts. Results DFO-M9346A was labeled with Zr-89 at 37 °C within 60 min and isolated in labeling yields of 85.7 ± 5.7%, radiochemical purities of 98.0 ± 0.7%, and specific activities of 3.08 ± 0.43 mCi/mg. We observed high specificity for binding FRα positive cells in vitro. For PET and biodistribution studies, [89Zr]Zr-M9346A displayed remarkable in vivo performance in terms of excellent tumor uptake for KB and OV xenografts (45.8 ± 29.0 %IA/g and 26.1 ± 7.2 %IA/g), with low non-target tissue uptake in other organs such as kidneys (4.5 ± 1.2 %IA/g and 4.3 ± 0.7 %IA/g). A direct comparison of the blood half life of [89Zr]Zr-M9346A and [131I]-IMGN853 corroborated the equivalency of the radiopharmaceutical and the ADC, paving the way for a companion PET imaging study. Conclusions We developed a new folate receptor-targeted 89Zr-labeled PET imaging agent with excellent pharmacokinetics in vivo. Good tumor uptake in subcutaneous KB and OV90 xenografts were obtained, and ADC therapy studies were performed with the precision predictor.

The selection of an appropriate third-party logistics (3PL) provider has become an inescapable option for shippers in today's business landscape, as the outsourcing of logistics activities continues to increase. Choosing the 3PL supplier that best meets their requirements is one of the most difficult difficulties that logistics consumers face. Effective decision-making (DM) is critical in dealing with such scenarios, allowing shippers to make well-informed decisions within a restricted timeframe. The importance of DM arises from the possible financial repercussions of poor decisions, which can result in significant financial losses. In this regard, we introduce p, q-spherical fuzzy set (p, q-SFS), a novel concept that extends the concept of T-spherical fuzzy sets (T-SFSs). p, q-SFS is a comprehensive representation tool for capturing imprecise information. The main contribution of this article is to define the basic operations and a series of averaging and geometric AOs under p, q-spherical fuzzy (p, q-SF) environment. In addition, we establish several fundamental properties of the proposed aggregation operators (AOs). Based on these AOs, we propose a stepwise algorithm for multi-criteria DM (MCDM) problems. Finally, a real-life case study involving the selection of a 3PL provider is shown to validate the applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

Understanding climate change through knowledge and researching its level of awareness are critical for building resilience in vulnerable populations. Climate change comprehension is not a gender-neutral construct. The purpose of this paper is to investigate women's perceptions of climate change in both rural and urban Sindh, Pakistan, as it is one of the ten most vulnerable countries to climate change. This study also looks into the sources of local women's climate change awareness and knowledge. The study employed a mixed methodology approach, with 400 women from urban and rural areas polled for quantitative data and subject/field experts interviewed to validate the findings using informed opinion. According to the study's findings, women in Sindh, Pakistan, are aware of climate change, but their sources of awareness are secondary, and their knowledge is based on personal experience. Therefore, the study recommends robust government initiatives to raise climate change awareness among women across the country. [ABSTRACT FROM AUTHOR]

Authorship verification is a crucial task in digital forensic investigations, where it is often necessary to determine whether a specific individual wrote a particular piece of text. Convolutional Neural Networks (CNNs) have shown promise in solving this problem, but their performance highly depends on the choice of hyperparameters. In this paper, we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification. We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms: Adaptive Moment Estimation (ADAM), Stochastic Gradient Descent (SGD), and Root Mean Squared Propagation (RMSPROP). The model is trained and tested on a dataset of text samples collected from various authors, and the performance is evaluated using accuracy, precision, recall, and F1 score. We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model. Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%. Furthermore, we demonstrate that hyperparameter tuning can help achieve significant performance improvements, even using a relatively simple model architecture like CNNs. Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice. Overall, this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations. Our findings have important implications for developing accurate and reliable authorship verification systems, which are crucial for various applications in digital forensics, such as identifying the author of anonymous threatening messages or detecting cases of plagiarism. [ABSTRACT FROM AUTHOR]