Batangas State University

Legacy book and stationery retail remains structurally important in the Philippines because it supports access to learning materials, school supplies, and low-friction educational consumption across socio-economic segments. National Book Store, Inc. (NBS)—a long-established market leader—now operates in a rapidly shifting environment marked by accelerating e-commerce penetration, digital content substitution, platform-based price competition, and heightened consumer expectations for convenience and assortment transparency. This study examines NBS’s strategic positioning and adaptive direction under digital disruption, with particular emphasis on its emerging omnichannel transition. A qualitative case study design is employed using secondary data drawn from government and industry reports, market analyses, academic sources, and credible public-facing materials. The analysis applies a structured strategic-analytics sequence combining PESTEL macroenvironment scanning, market and competitive assessment, and SWOT synthesis to surface capability strengths, structural constraints, and actionable opportunity spaces. Findings indicate that NBS’s enduring advantages—brand salience, nationwide retail access, and a diversified learning-and-lifestyle assortment—continue to anchor its relevance. However, exposure to platform competition, operational frictions in product discovery and inventory experience, and category-level shifts toward digital consumption create vulnerabilities that require deliberate capability upgrading. The findings suggest that NBS’s sustainability will depend on strengthening its digital customer experience, tightening omnichannel integration, and building partnership ecosystems with schools, publishers, and content providers to preserve trust and differentiation in a platform-dominated retail landscape.

Technical and Vocational Education and Training (TVET) plays a critical role in enhancing employability and supporting economic development. However, global disparities exist in labor force participation, policy implementation, and system effectiveness, highlighting the need for cross-continental analysis. This study aimed to conduct a systematic meta-analysis of international TVET research to 1) evaluate labor force participation of TVET graduates, 2) identify innovative policies and systemic interventions that strengthen TVET outcomes, and 3) propose strategies suitable for enhancing the Philippine TVET system. A systematic review and meta-analysis were conducted on 60 studies published between 2016 and 2022. Data were sourced from four academic databases: ProQuest, EBSCO, JSTOR, and Elsevier. Inclusion criteria targeted studies focused on TVET programs or graduates reporting employment outcomes or systemic interventions. Qualitative data were analyzed using MAXQDA, while quantitative data were synthesized to determine labor force participation across continents. Labor force participation of TVET graduates varied by region, with Asia reporting the highest rate (27.27%) and Africa the lowest (10.54%). Effective policies identified include strong governance, public-private partnerships, competency-based training, recognition of prior learning, and ICT-supported monitoring. For the Philippines, adopting these strategies can improve alignment with labor market needs and enhance graduate employability. The study demonstrates that context-specific policies, systemic interventions, and strategic partnerships are essential for improving TVET outcomes globally and locally. Evidence-based strategies can strengthen the Philippine TVET system, ensure labor market relevance, and promote workforce readiness.

Abstract Text segmentation is a foundational process in NLP, but Myanmar presents a particularly difficult case due to its non-segmented, syllabic writing system and complex morphophonology. While syllable-level tokenization has seen notable progress, word- or meaningful phrase-level segmentation remains underexplored. Existing approaches largely rely on dictionary-based algorithms that ignore grammatical structure and contextual cues. This research proposes a new text segmentation method that integrates linguistic features such as conjunctions, post-particles, and whitespace usage, which carry implicit syntactic and semantic information. The proposed method produces stable and linguistically motivated segmentation across both formal and informal text styles, achieving an intrinsic boundary-level segmentation F1-score of 0.9 and a token-level segmentation F1-score of 0.8 across three different datasets. Extrinsic evaluation using named entity recognition (NER) as a downstream task demonstrates competitive performance across multiple entity types, with F1-scores of approximately 0.7 for DATE, NUM, LOC, PER, and TIME, even when applied without retraining the NER model. These results highlight the effectiveness and practical applicability of grammar-aware segmentation for Myanmar and underscore its potential for downstream NLP tasks in low-resource settings.

This study explores how perceived risk factors affect the attitude of public market vendors towards mobile payment adoption through ‎performance risk, financial risk, time risk, psychological risk, and social risk. Using a survey of 325 randomly selected public market ‎vendors in Batangas City, data were gathered through a validated questionnaire with strong reliability. Statistical analysis, including Pearson ‎R correlation, ANOVA, and Kruskal-Wallis test, was used to examine the relationship between variables. The findings revealed that ‎respondents had a neutral attitude when it comes to mobile payments adoption. When it comes to the correlation between attitude and ‎perceived risks, the results showed that there is a significant relationship where performance and time risk are observed with the strongest ‎correlation, with attitude and social risk having a weaker relationship but still significant. The study also uncovers that neither the number of ‎years of a business, customer volume, nor the type of products sold significantly affects the openness in the adoption of mobile payments. In ‎contrast, the average monthly sales and awareness of mobile payment tend to show a critical and significant relationship in the mobile payment ‎adoption. The results suggest that perceived risks affect the attitude of the respondents in the adoption of mobile payments. Thus, ‎developing training and support programs, enhancing system performance and connectivity, and expanding for future research are ‎recommended to enhance confidence, accessibility, and trust in mobile payment usage while promoting collaboration among relevant ‎stakeholders‎.

This paper presents the design of a programmable, low-noise, high power supply rejection ratio (PSRR) low drop-out (LDO) regulator. High PSRR is obtained using a method called feed forward ripple cancellation technique. Better noise performance is done by implementing unity feedback configuration to the error amplifier replacing the LDO feedback resistor network. Noise filtering is also done at the input of the error amplifier to lessen the noise contribution of the reference voltage to the total noise of the LDO. Programmability feature of the LDO output is achieved by making the reference voltage to the error amplifier programmable. The proposed LDO implementation will improve the noise performance of the LDO by making the noise less dependent to the output voltage of the LDO. The proposed LDO is designed using the full custom analog design flow and implemented in 180 nm CMOS process.

Convolutional Neural Networks (CNNs) have performed exemplarily well with image recognition tasks, but their efficiency in image recognition drops tremendously in the event of complex noise. The conventional way to normalize has not done sufficient work in tackling the various noise interference. In order to address this problem, this paper would introduce a CNN robustness noise enhancement strategy that involves adaptive normalization. The design of noise-adaptive perception mechanism, and dynamic parameter adjustment strategy, the approach allows intelligent detection and dynamic processing of different types and levels of noise. It adds noise-aware weighting and local statistic information to normalize parameters of the feature optimization in a dynamic way. The average accuracy rates of the proposed method on CIFAR-10 and ImageNet under the functions of Gaussian noise, salt-and-pepper noise, and Poisson noise respectively are 4.127 %, 3.654 % and 3.892 % higher than that of the traditional approaches, respectively, according to experimental findings on the two datasets. This works by making CNN models much more fitted to harmful environments and generalizing proposed observations.

<span lang="EN-US">This study addresses the challenge of enhancing civic engagement in higher education institutions by examining the predictive relationships between readiness, motivations, and digital civic engagement of tertiary students. Employing the quantitative, correlational research approach, data collected through survey questionnaires were analyzed using Minitab statistical software. Significant insights came from the 2,205 tertiary students selected through multistage random sampling, following strictly ethical standards to ensure data privacy and anonymity. As found, a moderate level of readiness underscores the need for targeted interventions, particularly to improve the civic behavior of students. Understanding and values motives emerged as dominant drivers of engagement, indicating the strong desire of students for personal growth and learning. While respondents occasionally exhibit digital engagement, it leans toward civic activities over political involvement, focusing primarily on information consumption rather than active collaboration. Emerging as a significant predictor of engagement, developing tailored initiatives to enhance readiness is crucial to positively influencing civic participation. Higher education institutions may use these findings to develop digital interventions that serve as catalysts for long-term civic engagement among students and engage in further research on other predictive factors.</span>

Graphs considered in this paper are finite simple undirected graphs. Let $G = (V(G), E(G))$ be a graph with the vertex set $V(G) = \{x_1,x_2,..., x_n\}$, for some positive integer $n$. The vertex space $\mathscr{V}(G)$ of $G$, is a vector space over the field $\mathbb{Z}_2 = \{0,1\}$. The elements of $\mathscr{V}(G)$ are all the subsets of $V(G)$. Vector addition is defined as $A + B = A \:\triangle\: B$, the symmetric difference of sets $A$ and $B$, for all $A,B \in \mathscr{V}(G)$. Scalar multiplication is defined as $1 \cdot A = A$ and $0 \cdot A = \emptyset$, for all $A \in \mathscr{V}(G)$. The subgraph $G \langle S \rangle$ of $G$ induced by a subset $S$ of $V(G)$, is the largest subgraph whose vertex set is $S$. The vertex-uniform set $V_H(G)$ of a subgraph $H$ with respect to $G$, is the set of all elements of $\mathscr{V}(G)$ that induces a subgraph isomorphic to $H$. The span of $V_H(G)$ shall be denoted by $\mathscr{V}_H(G)$. If $V_H(G)$ is a generating set, that is $\mathscr{V}_H(G) = \mathscr{V}(G)$, then $H$ is called a vertex-generator subgraph of $G$. This study determines some vertex-generator subgraphs of complete bipartite graph $K_{m,n}$ and tadpole graph $T_{n,m}$.

In recent years, mobile fitness applications have emerged as accessible tools that support physical activity and health behavior change. This study aimed to determine the relationship between exposure to physical fitness mobile applications and the motivation of student-athletes to maintain a healthy lifestyle. A descriptive-correlational research design was employed, involving 86 student-athletes from Batangas State University The National Engineering University, JPLPC-Malvar Campus, during the academic year 2024–2025. Data were collected through a validated questionnaire assessing both app usage and motivation (intrinsic and extrinsic), complemented by physical fitness observations. Descriptive statistics and Pearson's correlation coefficient were used to analyze the data. Results showed that the respondents were moderately exposed to fitness mobile applications (M = 3.10), and exhibited high levels of intrinsic (M = 3.81) and moderate extrinsic (M = 3.38) motivation. A statistically significant and strong positive relationship (r = 0.614, p = 0.000) was found between app exposure and motivation. The findings suggest that increased engagement with fitness apps contributes to higher motivation for adopting and maintaining a healthy lifestyle. The study highlights the value of integrating mobile fitness technology into athletic training and wellness programs to reinforce motivation and health behaviors among student-athletes.

The researchers introduced an interactive projection platform that enables users to control on-screen content through natural hand gestures, enhancing the way people interact with digital media. This innovative system integrates gesture recognition technology, high-definition cameras, and projectors to detect user movements in real time, allowing seamless navigation of presentations and educational materials. By eliminating the need for traditional remote controls and whiteboards, it fosters a more engaging and dynamic experience in both academic and professional environments. The system's core advantage lies in its precise gesture recognition and smooth performance, powered by advanced machine learning algorithms based on Google MediaPipe and supported by high-quality hardware components. Its modular design allows for quick setup, instant response times, and handheld functionality, ensuring adaptability across various settings. Cost-effectiveness was a key consideration, with extensive testing and refinement addressing detection accuracy, system compatibility, and environmental challenges. Ultimately, this gesture-based interface aims to transform presentation methods by promoting immersive, intuitive, and highly interactive user experiences.