Mindanao, a region in the southern Philippines, is home to a diverse population consisting of Muslims, Christians, and indigenous Lumads. Despite its cultural richness, Mindanao has long experienced conflict rooted in historical injustices, political marginalization, land dispossession, and socio-economic inequality. Over decades, the Philippine government has implemented various peace strategies, from military campaigns to peace agreements and the establishment of autonomous political structures such as the Bangsamoro Autonomous Region in Muslim Mindanao(BARMM). This paper revisits the historical development of the Moro conflict, evaluates governmental peace initiatives, and stresses the importance of cultural pluralism in fostering long-term peace. Additionally, it highlights a critical contemporary element: the increasing role of digital tools, ICT resources, and online platforms in facilitating peacebuilding, good governance, and socioeconomic development in Mindanao. Digital governance, social media–based peace dialogues, countering violent extremism online, GIS-enabled conflict mapping, and ICT-driven economic inclusion are transforming peace efforts across the region. The paper argues that lasting peace in Mindanao requires a multi-dimensional approach integrating historical justice, cultural pluralism, political autonomy, socio-economic development, and digital transformation.

This paper presents the methodological framework and comparative analysis of the spyware applications available on the Google Play store based on the parameters Stealth & Evasion, Privacy and Detectability. The spyware applications are invading privacy of an individual by its covert nature and challenging the digital forensic investigation procedures. These types of apps are used for the parental monitoring, abusing partners and corporate spies this make it used for the illegal purpose to monitor others without their consent and breaching their privacy. The literature review concluded that due to the lack of standardized methodology for the analysis which leads to the loss of data during investigation, inappropriate results and challenging courtroom procedure. The primary aim of this study is to fulfil the gap in the investigation procedure and comparatively analyzing applications based on their behavior. It provides the multi-layered methodology involving Observational Analysis, Static Analysis and Overall risk assessment (based on scoring model). The purpose of this study is to provide the standardized methodology for the forensic analysis of spy applications and comparatively determining the risk level of an application.

India is well known for its rich cultural heritage and diversity. India is divided into different States, and all of the States are endowed with ancient cultural heritage and natural beauty that has always been an attractive tourist destination. Thus India has great potential to become one of the leading tourism destinations in the world. All the Indian states have their fantastic tourism slogans to promote the state. Andhra Pradesh is a land of abundant Tourism potential. The Ministry of Tourism is the nodal agency to formulate national policies and programs for developing and promoting of tourism in the country. Some new schemes were launched in the financial year 2014-15 to 2021-23. The flagship schemes like Swadesh Darshan- Integrated Development of Tourist Circuits on specific themes, development of Iconic Tourist Destinations and National Mission for Pilgrimage Rejuvenation and Spiritual Augmentation Drive (PRASAD). Ministry of Tourism has developed thematic tourist circuits under the Swadesh Darshan scheme. The main focus of this paper is to present important Govt. of India schemes under the implementation of The Ministry of Tourism i.e., Swadesh Darshan 2.0, PRASAD, how far implementation/completion stipulated time and how the benefit to promoting the tourism industry, and also how the Ministry of Tourism had been benefited in the area of policy making for infrastructure tourism development in Andhra Pradesh. Post-bifurcation in 2014, the Andhra Pradesh government brought the tourism sector into the limelight declaring it as one of the growth engines and spelt out a grandiose plan to give the much-needed push to the hitherto neglected sector.

In Wireless Sensor Networks (WSNs), relying solely on Cluster Heads (CHs) for management exposes the system to vulnerabilities from compromised or malfunctioning nodes. Introducing WatchDog (WD) nodes provides critical independent monitoring. They enhance security and ensure data integrity. Previous studies in this area have largely overlooked Heterogenous WSNs (HWSNs), where nodes differ in roles and resources. Furthermore, existing methods on homogenous WSNs fall short of achieving optimal clustering and monitoring efficiency. This paper presents a two-phased novel clustering, routing, and WD selection algorithm designed for HWSNs. In the first phase, normal nodes are assigned to super nodes acting as CHs which facilitates structured data aggregation and intra-cluster management. Additionally, in the same phase, predefined number of super nodes are tasked to act as WDs, charged with independent monitoring tasks such as anomaly detection, data integrity assurance, and verification of task execution. Notably, the roles of CH and WD are mutually exclusive to avoid functional overlap and optimize energy resources usage. In the second phase, a spanning tree is constructed over nonWD super nodes. The proposed method employs Genetic Algorithm (GA) for network configuration. Additionally, two fitness functions that take to the account the required energy for data transmission and monitoring are devised to find the best structure for the network in each phase. Simulation results show the effectiveness of the approach in improving data delivery, network lifetime and minimizing energy consumption, by in average 25%, 59 time slices, and 10% respectively.

The design of standard and aspherical optical surfaces often requires time-consuming iterative calculations and relies on point-source assumptions. Consequently, these standard designs are often unsuitable for high-power LEDs with extended emitting areas due to the mismatch between design assumptions and actual source characteristics. In this study, we performed ray tracing simulations to investigate optical efficiency as a function of the light-emitting source size. First, we modeled systems using commercially available surfaces. Under the simulation conditions, maximum power efficiency on a 0.5 mm × 0.5 mm target plane at z = 100 mm decreased from 0.95 to 0.83 as the source width increases from 0.1 mm and 1.4 mm. However, using our proposed strategy optimized for larger emitting areas, the efficiency remained stable, shifting only from 0.93 to 0.91 for the same source widths. Although the proposed model shows a slight reduction in peak efficiency for near-point sources compared to the standard model, it demonstrates significantly higher efficiency and stability for larger light-emitting sources.

The increasing reliance on interconnected devices, particularly through the Internet of Things (IoT), necessitates the development of technically enforceable data protection frameworks. This study presents a systematic meta-review of five major global data protection frameworks: The General Data Protection Regulation, the National Institute of Standards and Technology, the Brazilian general data protection law, China's Personal Information Protection Law, and India's DPDP Act and India IT Act with SPDI Rules. The results show that GDPR and PIPL offer comprehensive technical mandates, while NIST provides a robust technical blueprint for operational privacy management. Brazil's LGPD lacks detailed mandates for certain technical protections, while India's IT Act and SPDI Rules require improvement due to lack of technical specificity and IoT alignment. The study contributes to the global data protection discourse by offering a comparative benchmarking tool and highlighting critical gaps in technical enforceability, especially for developing economies. It recommends an integrated, technically operable framework to ensure privacy, resilience, and interoperability in a hyper-connected digital age.

Food spoilage is a significant threat to human health as well as the economy. Traditional methods of detection of spoilage by human senses are slow and inaccurate. To address this, an automatic real-time solution has been obtained through the Internet of Things (IoT). This project survey paper proposes an IoT based food spoilage detection system with a Raspberry Pi as the controller. The system comprises a range of sensors, like a DHT11 temperature and humidity sensor, a toxic gas sensor, and a pH sensor for acidity level checks. A Pi Camera is also used for checking through visual observation of the food for evidence of spoilage like mold or discoloration. Both sensor measurement and results of visual inspections appear on an LCD screen for in-situ visibility as well as on a webbased dashboard for remote visibility. The system provides real-time alerts whenever there is spoilage, thus offering an automatic, reliable, and cost-effective means of improving food safety at home and along the food chain.

Pharmacy education has undergone significant transformation in recent years, driven by the growing demand for practice-ready pharmacists capable of navigating the complexities of modern healthcare systems. Among the numerous innovations reshaping the field, mock pharmacy classrooms have proven to be invaluable tools for bridging the gap between theoretical knowledge and practical application. These simulated environments are modelled after real-world pharmacy settings, providing students with a safe and controlled environment in which to improve their clinical reasoning, decision-making, and communication skills. Mock pharmacy classrooms provide experiential learning opportunities, which are consistent with accreditation standards that emphasize practice-based education. These classrooms incorporate case-based scenarios, role-playing, and problem-solving exercises, allowing students to work on simulated patient care tasks. The incorporation of technology into mock pharmacy classrooms represents a paradigm shift, redefining experiential learning. AI applications personalize learning by offering real-time feedback and adaptive learning paths that reflect the complexities of clinical scenarios. As pharmacy education evolves, the ethical and practical implications of integrating emerging technologies must be considered. Educational institutions that embrace these advancements will not only improve academic performance, but will also produce competent, confident pharmacists who are prepared to thrive in the changing healthcare landscape. Mock pharmacy classrooms are helping to shape the future of pharmacy education thanks to these innovations. Mock classrooms use technology and artificial intelligence to provide immersive and personalized learning experiences that reflect the complexities of pharmacy practice. Furthermore, mentorship in these settings promotes professional identity, reduces anxiety, and develops important interpersonal skills like communication and teamwork, all of which are essential in community and hospital pharmacy practice. Incorporation of mock pharmacy classrooms and experiential learning will remain indispensable in preparing students to face the challenges.

This study aims to develop a mechanism for extrapolating the values of determinants (Return on Equity (ROE), Return on Sales (ROS), Turnover Ratio (Y), Interest Burden (Ib), and Tax Burden (Tb)) for subsequent integration into an author-developed lag model of capital structure. This approach enables the forecasting of corporate capital structure in an impersonal manner, eliminating reliance on expert assessments of future values for ROE, ROS, Y, Ib, and Tb. The identification of the ROE observation series using the Box-Jenkins methodology within the ARIMA model framework as a first-order autoregressive model AR(1) allows for the interpretation of the corollary from the formula for increments of multivariate functions as an autoregressive model concerning the equity multiplier M. Consequently, this facilitates the calculation of the future period value M₁ based on the actual value M₀ from the preceding period and the relationship between known and predicted values of the lag model's determinants (ROE, ROS, Y, Ib, Tb). The proposed algorithm for calculating capital structure is tested using Magnitogorsk Iron and Steel Works (MMK) as a representative agent. Time series identification based on a 25-year dataset (2000–2024) yielded the following results: Return on Equity and Return on Sales are described by first-order autoregressive models; the Interest and Tax Burden coefficients correspond to stationary «white noise» processes; and the behavior of the Turnover Ratio is described by a Seasonal Autoregressive Integrated Moving Average (SARIMA) process. The justification for the applicability of lag capital structure models and the definition of their determinant values for future periods based on time series identification is presented here for the first time.