Character Types in the Tales of the Four Seasons

The Four Seasons Tales, published in 2024, are a rich source for exploring character types that reflect the impact of seasonal changes on human behavior. This study seeks to analyze and classify the character types within these tales, presenting a new model based on the interaction between nature and the psychological characteristics of individuals. The research focuses on how the four seasons shape the nature and roles of characters, and the extent to which they align with psychological traits known in personality science. This article examines character types in Muhammad Jibril's novel *The Four Seasons*, by analyzing the main character (the translator) as a rounded and complex model, embodying an internal struggle between a tired body and a reflective consciousness, oscillating between nostalgia and withdrawal. The article also highlights the role of secondary characters, who are used to perform framing functions without narrative shifts. They contribute to highlighting feelings of loneliness and aging, shaping the overall psychological climate in a style that does not sink into directness or declarativeness. The research uses literary and psychological analysis to study characters in selected tales, drawing on theories of literary psychology, such as Jung's theory of psychological archetypes, as well as studies that address the relationship between seasons and psychological changes in humans. The research relies on an analysis of literary models from different cultures to clarify how characters interact with their environment and the impact this has on their psychological and social development. The study highlights the importance of these archetypes in shaping the dramatic plot and the dynamics of interaction between characters, opening new horizons for studying the influence of nature on human behavior in narrative literature. The study also presents a new insight that can be applied to other studies to understand character archetypes in world literature, enhancing the literary and psychological analysis of folk tales in multiple cultural contexts. The research highlights the importance of classifications in understanding characters within modern novels

Adapting to Remote Work: Emerging Cyber Risks and How to Safeguard Your Organization

The COVID-19 pandemic has rapidly accelerated the shift to remote work, permanently altering organizational dynamics. As businesses and employees adapted to a remote-first environment, they also became exposed to a new set of cybersecurity threats. The traditional cybersecurity measures designed for office environments are no longer sufficient to address the unique risks associated with remote work. These risks include vulnerabilities in home networks, unsecured devices, increased susceptibility to phishing and social engineering attacks, and the rapid adoption of cloud-based collaboration tools. This paper will explore the cybersecurity challenges posed by remote work and suggest proactive steps organizations can take to safeguard their data, assets, and personnel. Key strategies, including enhanced endpoint security, secure communication channels, Multi-Factor Authentication (MFA), security awareness training, and Zero Trust architecture, will be discussed to help organizations minimize their exposure to these emerging risks

Extension of Connecting Formulas on Hypergeometric Function

The Hypergeometric series is the extension of the geometric series and the Confluent Hypergeometric Function is the solution of the Hypergeometric Differential Equation. Kummer has developed six solutions for the differential equation and twenty connecting formulas. The connecting formula consist of a solution expressed as the combination of two other solutions. Further extension was done by Poudel et al. This research work has extended the nine connecting formulas obtained by Poudel et al. to obtain the other nine solutions

Investigating the behavior of dynamic systems based on linear differential equations

Linear differential equations are fundamental tools in the analysis and modeling of dynamical systems that are used in many physical and engineering phenomena. This paper examines the structure, properties, and applications of these linear equations in the analysis of mass-spring systems, RLC circuits, and heat transfer processes. By presenting mathematical models and analyzing the time responses of these systems, it is shown how linear equations can model complex behaviors in a simple, predictable, and robust manner. The results of this research show that linear differential equations, despite their simplicity, are effective tools for the analysis of physical systems, and are used to more accurately model more complex systems over time

A COMPREHENSIVE SURVEY OF MEMORY UPDATE MECHANISMS FOR CONTINUAL LEARNING ON TEXT DATASETS

Over the last several years, there has been a growing focus on the CL field in the context of machine learning and its goal to create models capable of learning new tasks step by step without loss of prior knowledge. Among these, catastrophic forgetting is especially challenging in real-world settings where the data experience changes over time. To this effect, what has become pivotal for models is mechanisms for memory update to enable the models to learn information as well as update what has been previously learned easily. This survey specifically investigates the memory update strategy in the continual learning setup wherein new categories and domains are continuously added in the text datasets including sentiment analysis, named entity recognition, text classification tasks etc. Moving on, three primary memory update strategies of memory replay, memory consolidation, and parameter isolation are discussed; this paper further addresses certain adaptations of the proposed methods for text-based applications. Memory replay means that part of previous data is stored to be replayed when new tasks are learned while memory consolidation strengthens only significant memories. Parameter isolation helps avoid masking previous tasks or overwriting the parameters when the machine learning algorithm is trained to accomplish new tasks. In this paper, we discuss the latest in these techniques and offer a thorough insight into their use in text datasets such as Amazon Reviews and Yelp Reviews.  Further, we outline the primary drawbacks of existing solutions for memory updates such as capacity limitations, domain variation, and continually learning without having access to new task information. In addition, a summary table of literature review identifying the most relevant works within the field is offered. Lastly, we discuss the remaining issues and potential research directions where more focus and development should be given in CL for text data by noting the importance of efficient and adaptive update policies towards the memory

An Interdisciplinary Review on Application of Graph Theory

Graph theory is a branch of discrete mathematics that is used to model and analyze interconnected systems. This paper presents a review on application of its concepts and algorithms that support diverse applications in computer science, biology, neuroscience, social sciences, engineering and geosciences. It is also reviewedthat graph theory helped in network optimization, clustering, molecular modelling and brain connectivity, it provides powerful tools for solving complex problems. It is reviewed that spectral methods, probabilistic models and graph neural network let the graph theory to evolve continuously as an essential interdisciplinary framework for understanding and optimizing complex networks in the modern world

EYES IN THE SKY: STRENGTHENING PUBLIC AWARENESS AND LAW ENFORCEMENT RESPONSE TO DRONE-DRIVEN INFRINGEMENTS ON PRIVACY RIGHTS IN THE UNITED STATES

The rapid proliferation of drones in the United States has created urgent challenges concerning individual privacy, institutional readiness, and legal enforcement. While drones serve diverse functions, their use in surveilling private spaces without consent has exposed significant regulatory gaps in both federal and local legal frameworks. This research investigates these gaps by analyzing a real-life case from Silicon Valley, where a civilian encountered a drone hovering above their private residence and was unable to obtain meaningful assistance from law enforcement.Drawing on recent scholarship, including Siddiqui and Muniza’s “Regulatory Gaps in Drone Surveillance” [Annals of Human and Social Sciences, 2025] and “The Drone’s Gaze: Religious Perspectives on Privacy and Human Dignity” [Al-Qamar, 2024], this paper reveals how current laws fail to protect against aerial intrusions, especially in residential zones. The findings are further contextualized within broader institutional weaknesses, as previously identified in “Public Funds, Private Gains” [JARSSH, 2022] and “Hybrid Warfare and the Global Threat of Data Surveillance” [PSSR, 2025].Moreover, the paper critiques recent legislative efforts, such as the U.S. Countering CCP Drones Act (H.R.2864), through the lens of Siddiqui and Muniza’s (2025) analysis published in the Social Sciences & Humanity Research Review, and assesses their ineffectiveness against AIpowered foreign-manufactured surveillance drones. Philosophical and constitutional dimensions are explored through works like “Liberalism in South Asia” [CIBGP, 2008], and “Constitutional Vulnerability in the Age of Digital Surveillance” [CRLSJ, 2025] The research proposes a three-pronged regulatory framework: 1. Modernization of legal statutes to close regulatory and constitutional loopholes; 2. Institutional upskilling through integrated AI-based geofencing and centralized FAADHS-local reporting platforms; 3. Public empowerment via education, civic engagement, and participatory complaint channels. The paper concludes that safeguarding privacy and national security in the drone era requires an interdisciplinary approach—bridging law, technology, ethics, and public participation. Only through such coordinated efforts can drone innovation be directed toward public benefit without compromising civil liberties.

IMPERSONATION AND ADMINISTRATIVE MISCONDUCT AT THE ELECTION COMMISSION OF PAKISTAN

This study investigates the abuse of power and institutional vulnerability within Pakistan’s electoral system by analyzing the case of Mr. Karan Kumar, a private citizen summoned by the Election Commission of Pakistan (ECP) despite not being a registered candidate. After complying with the summons, Mr. Kumar faced misconduct by ECP officials, including verbal abuse and procedural manipulation by a Deputy Director, with no meaningful inquiry conducted despite tribunal direction. Although a legal petition was filed, the summons remained unrevoked, and harassment reportedly escalated through informal and extrajudicial means. Methodologically, the paper draws on document analysis, comparative reviews of election oversight mechanisms in the UK and India, interviews with legal experts and civil society members, and prior academic literature. The findings expose a troubling pattern of bureaucratic impunity, overreach, and institutional failure within the ECP, consistent with earlier critiques of Pakistan’s administrative bodies raised in Siddiqui’s publications, including “Public Funds, Private Gains” (2022) [https://doi.org/10.61841/2s3kmv78], “Who Judges the Judges?” (2019) [https://doi.org/10.61841/txq2w096], and “Unlicensed Medical Practice and Institutional Silence” (2024) [https://cibgp.com/au/index.php/1323-6903/article/view/2876]. These works collectively reflect a broader scholarly trend in Siddiqui’s research, which critiques systemic weaknesses in public sector accountability and regulatory enforcement. By extending the analytical scope to Pakistan’s electoral machinery, this paper identifies urgent accountability gaps, including lack of internal oversight, politicized appointments, and procedural opacity. Drawing on civic engagement theory and administrative law frameworks, the research recommends seven core reforms: nullification of the unconstitutional summons; an independent inquiry against the responsible officer; standardized conduct and grievance procedures for ECP officials; integration of public complaint systems; mandatory training in ethics and electoral integrity; digital grievance tracking; and amendment of the Elections Act of Pakistan to restrain unchecked administrative discretion. This paper builds on the author’s prior publications such as “Liberalism in South Asia” [https://cibgp.com/au/index.php/1323-6903/article/view/2870], and the cross-jurisdictional critiques in “Surveillance Overreach and Judicial Apathy in Global Drone Policy” [Russian Law Journal, https://doi.org/10.52783/rlj.v9i2.4997], and “Constitutional Vulnerability in the Age of Digital Surveillance” [CRLSJ, https://doi.org/10.52783/crlsj.449], which address the fragility of state institutions in safeguarding citizens’ rights. Ultimately, this research asserts that without transparency, rule-based enforcement, and civic accountability, democratic institutions in Pakistan risk functioning as instruments of coercion rather than justice. The paper calls for structural reforms that realign the ECP with constitutional principles and democratic norms

Enhancing Healthcare Data Security: Mitigating Ransomware Threats to Network-Attached Storage (NAS) Systems for Real-Time Access and Compliance

Ransomware attacks have become one of the most significant cybersecurity threats in the healthcare sector, particularly targeting Network-Attached Storage (NAS) systems that store and manage critical patient data, including Electronic Health Records (EHRs) and medical imaging. As healthcare organizations increasingly rely on NAS devices to provide real-time access to healthcare data, vulnerabilities within these systems—such as outdated software, weak access controls, and insufficient encryption—have made them prime targets for cybercriminals. The consequences of such attacks include the loss or corruption of patient data, significant downtime, and disruptions in patient care, which can jeopardize lives and lead to non-compliance with regulations such as HIPAA. This paper proposes a comprehensive solution to mitigate ransomware threats to NAS systems in healthcare environments by implementing end-to-end encryption, regular system updates, and advanced intrusion detection systems (IDS). These measures ensure the protection of patient data, preserve real-time access to critical healthcare information, and minimize the operational impact of ransomware attacks. Additionally, this approach offers stronger cybersecurity, reduces downtime, and enhances the resilience of healthcare organizations to cyber threats. By adopting these strategies, healthcare providers can improve data security, ensure compliance with regulations like HIPAA, and safeguard patient care continuity. The implementation of encryption and IDS can not only prevent unauthorized access but also enhance the ability of organizations to detect and respond to ransomware attacks in real-time, ensuring that patient data remains available and intact. This solution provides healthcare organizations with an effective, proactive defense mechanism against evolving ransomware threats, thereby enhancing operational efficiency and improving service delivery

Color Image Compression Using Vector Quantization with Fuzzy Logic

Image compression is a critical method for minimizing digital image size for efficient storage and transmission, particularly in bandwidth-constrained applications. A new approach for color image compression by incorporating vector quantization (VQ) and fuzzy logic is introduced in this paper with the aim of further improving performance. Vector quantization is another commonly used lossy compression technique in which an image is divided into small blocks and mapped to a prechosen set of representative vectors called codewords, thereby compressing the image considerably. In order to solve the problem of maintaining image quality while compressing it, we apply fuzzy logic to increase the accuracy of the codeword selection mechanism. Using the fuzzy logic rules, we define the selection rules adaptively based on the characteristics of the image in order to achieve maximum balance between compression ratio and image quality. Application of fuzzy logic enables smoother movement from one region of images with similar content to another and eliminates quantization errors characteristic for VQ, especially in regions of high variance of pixel intensity. The new hybrid method was applied to several color images and was proven to surpass the traditional VQ method in terms of compression ratio, PSNR, and SSIM measures. The hybrid method offers an efficient plan for high-compression-quality-image with little computational cost