In medical imaging, an X-ray image generated using a flat panel detector (digital) typically has poor image quality, affecting the capability of successful medical diagnosis based on the images. The image enhancement process intends to provide better interpretability of the information contained in the images. The main problems considered for medical images include poor quality and low contrast. Therefore, the general objectives of image enhancement include contrast improvement and noise reduction. This study proposes an upgraded X-ray image enhancement hybrid algorithm that utilizes and consists of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method combined with the Wiener filter. Based on the performance metrics results, including MSE, PSNR, and Entropy, as compared to the existing CLAHE method only, the proposed methodology has a lower MSE signifying lower error, a higher PSNR representing a lower amount of distortion, and higher information entropy which indicates higher obtained information. Furthermore, the implementation of the proposed approach is applied to 6000 X-ray images before deep learning classification modeling, which significantly improved from 50% to 78% validation accuracy. Therefore, the proposed method improves the image enhancement methodology and can substantially assist in diagnosing diseases.

This research works focuses on the co-pyrolysis of coconut coir fiber combined with PET in order to increase its heating value, in addition to solid mass reduction for prolonged shell life and storage issues. Co-pyrolysis is a process of efficiently producing high-quality biofuel from two or more materials. Parameters combinations were identified using the Taguchi optimization methodology model in MINITAB19. Nine samples with three replications were evaluated. Results revealed that changing the temperature, duration, and feedstock blends show a significant effect on solid mass yield and heating value. The biochar with 75:25 (coconut coir fiber: PET) shows that duration and temperature directly affect the solid yield. For biochar, with 25:75 (coconut coir fiber: PET), pyrolysis duration contributed largely to the output. The highest solid mass reduction with an average of 55% solid yield was obtained. Despite a high solid mass reduction, the heating value measured is only 13 MJ/kg. Feedstock blend with PET to coconut coir ratios of 75:25, 25:75, and 50:50 resulted to an average solid yield of 70%, 65%, and 83% respectively. In terms of heating value, for all three replications, the biochar sample subjected to 200°C, 30 minutes, and PET to coconut coir ratio of 75:25, with an average solid yield of 67%, had the highest value with 20.94 MJ/kg, 24.42 MJ/kg, and 23.55 MJ/kg for Trial A, B, and C, respectively. The result shows that the incorporation of PET effectively increases the heating value of the coconut coir fiber from 10 MJ/kg to 24.42 MJ/kg.

The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs’ criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system’s features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student’s applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system’s use case.

The daily maximum multi-annual series including the rainfall frequency analysis, are one of the inputs for the design process for stormwater management, that entails numerous procedures: (a) rainfall data gathering from Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), (b) information gathering, and (c) checking all received datasets for missing or different data. To address these setbacks, 6 rain gauge stations located in Region VIII, Visayas, Philippines were used to first determine whether or not the Gumbel Extreme Value (GEV) was the better suitable method to use in producing Rainfall Intensity Duration Frequency (RIDF) than Log-Pearson Type III (LP3) by performing Chi-square test; secondly, to select the better RIDF values; and lastly, the isohyetal maps should be developed for return periods of 2, 5, 10, 25, 50, and 100 years. GEV was a better fit for the x2 values (27.96, 54.59, 52.82, 87.96, 11.78, 7.66) obtained through chi-square test were close to or smaller than the critical value of 30.144. The RIDFs produced in GEV were used in plotting isohyetal maps. In all return periods, Borongan generated the highest rainfall intensity value.

The electro-mechanical salted egg grading system was developed to support producers by streamlining the cleaning process, delivering a sorted outcome, saving time, decrease human resources needs, labor costs, and minimized egg breakage, consequently boosting production efficiency. OpenCV (Open Source Computer Vision Library) was employed as a development platform and the Raspberry Pi 3 Model B as a microcomputer due to its speedier and more powerful CPU, which is required to operate the system's components and process the acquired images for classification. In addition, a Raspberry Pi camera module V2 was employed to capture the images for scanning, LED bulb for candling, and an SG90 micro servo for sorting. Furthermore, we used B66 and B35 V-belts for the conveyor assembly. An induction motor of 0.125 horse power is used to rotate the conveyor assembly, a chain, and sprocket to reduce its speed. The researchers also used soft bristles brushes which are ideal for cleaning the eggshell. For cleansing, sprinklers were used along with the water PVC pipe that holds pressurized water of 30 psi. The camera's captured images are categorized as clean, dirty, well-pickled, and spoilt eggs. Empirical results exhibited that the detection accuracy achieved 96% and 93% for cleanliness and quality, respectively. It establishes the model and prototype's robustness in cleaning, sorting, and grading salted eggs.

Social relationships are a fundamental aspect of human existence. Unsurprisingly, policymakers are incessantly devising strategies that accentuate the benefits of social relationships and diminish the risks of social isolation. The natural manifestation of player-to-player interaction in a video game context poses a unique opportunity to study the effects of co-playing on social relationship formation. However, most studies recruited players with existing relationships (e.g., family and friendship), utilized random commercial video games, or experimented in an online environment. These research gaps warrant further investigation on the utility of video games for promoting social relationships among unacquainted players while in the same physical space. Thus, this study presents the development and evaluation of a couch cooperative video game grounded on sequential team-building mechanics. The findings of this study offer empirical evidence that would have significant practical implications for any organization seeking to increase teamwork and cooperation among its members.

Location-based marketing (LBM) is becoming an integral element of the media mix for making highly personalised offers to the targeted audience at the most opportune time and place. Yet, the literature calls for more usability studies due to the lack of user-centred research. To fill this gap, this study explores the development of PushMapp - a geomarketing tool for launching LBM campaigns - through a user-centred, parallel-iterative approach. Usability analysis shows that this type of application is affected by issues related to security, privacy, advertisement relevancy, and notification overload. Meanwhile, only performance expectancy, effort expectancy, and hedonic motivation appeared to be the significant factors in an LBM mobile application. Experiences from this study provided valuable insights for marketers and business owners who plan to capitalise on LBM strategies by underscoring the importance of integrating users' input, ensuring usability compliance, and conforming to factors of mobile application utilisation.

Recent studies have acknowledged social media as a valuable pedagogical tool for connecting the formal and informal learning gap. However, as a new platform, the literature is sparse on the potential of TikTok as a knowledge source. In this study, we explored programming TikTok videos in the #LearnProgramming webpage in terms of content (programming languages and topics) and characteristics (video styles and types). Although TikTok is principally an entertainment destination, our results show that the platform likewise has informative videos. The 349 videos that we examined received a total of 10,046,000 views, 10,523 comments, 932,871 likes, and 35,095 shares, implying extremely high levels of user engagement. Tiktokers showing tips and tricks are the most recurring content type. From a macro perspective, we noticed that TikTokers do not follow the ethos of the platform (e.g., dancing) when producing educational content. This deviation demonstrates the intent of TikTokers to educate than to entertain. Although it is too early to conclude that TikTok can operate as a nanolearning platform, we discovered a substantial amount of content for and engagement from programming learners. Our results lay a potent foundation for devising actionable scholastic implications, policies, and recommendations concerning TikTok consumption. Future works and research prospects were also discussed to propel the social media and nanolearning literature forward.

With the aggravation of the Russia-Ukraine conflict and the rising involvement of foreign powers, it has become more substantial to identify whether an endorsement or condemnation of war efforts is the universal message. This goal is empowered by the clear literature on the vital linkage between public opinion and international relations. Thus, we investigated the sentiments and emotions of the international community on the Russian invasion of Ukraine. A total of 27,894 tweets posted within the first day in the #UkraineRussia hashtag were analyzed. Results show that "war", "people", "world", "putin", and "peace" were some of the most frequently occurring words in the tweets. There were more negative sentiments than positive sentiments, and sadness was the most salient emotion. To date, this study is the first to examine the Russo-Ukrainian War and one of the few sentiment and emotion analyses for exploring Twitter data in the context of modern war.

Automated medical image analysis driven by artificial intelligence can revolutionize modern healthcare in producing swift and precise diagnostics. Due to doctors’ varying breadths of training and expertise, traditional leukemia screening methods frequently involve considerable subjectivity. Using a 3-stage transfer learning approach and stacks of convolutional neural networks, we constructed an efficient pathway for automatic leukemia identification and classification through various phases. Experimental findings disclosed that our pipeline powered by InceptionResNetV2 architecture decisively affects the accuracy with 99.60% (normal vs. leukemia) and 94.67% (normal to L3). Moreover, it reduces error rates by 1.65% and 6.05%, respectively. A consistent result via the T-test confirms our proposed framework robustness with a significant positive difference of 4.71% over the standard transfer learning mechanism (p-value = 0.0001 & t = 0.85310). This research could aid and support oncologists in early yet reliable prognoses of acute lymphoblastic leukemia types.