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- Publications by researchers, faculty members, staff, and students of DMMMSU.
- A "lakasa" (storage) for the information materials related to La Union, Ilokano, and Iloko.
Recent Submissions
Psychometric Evaluation of the Filipino Versions of the Duke University Religion Index and the Spiritual Coping Strategies Scale in Filipino Hemodialysis Patients
(Springer New York LLC, 2017) Cruz, Jonas Preposi; Reyes, Ralph Warren P.; Colet, Paolo C.; Estacio, Joel C.; Caldeira, Sílvia; Vitorino, Luciano Magalhães; Koenig, Harold G.
This study evaluated the psychometric properties of the Filipino versions of the Duke
University Religion Index (DUREL-F) and the Spiritual Coping Strategies scale (SCS-F) for
hemodialysis (HD) patients in the Philippines. A convenient sample of 162 HD patients
was included in this descriptive, cross-sectional study. The DUREL-F and SCS-F exhibited
acceptable internal consistency and stability reliability, as well as excellent content
and construct validity. The findings confirmed the soundness of the psychometric
properties of the two scales. Thus, they can be used for timely and accurate assessment
of religiosity and spiritual coping utilization among Filipino patients receiving HD.
Nutrient dynamics and lignocellulose degradation in decomposing Quercus serrata leaf litter
(Ecological Research, 1998-08) Salamanca, Eric F.; Kaneko, Nobuhiro; Katagiri, Shigeo; Nagayama, Yasuhide
The litter mass loss, concentration and mass of some major nutrient elements, degradation of lignin and cellulose in decomposing Quercus serrata Murray leaf litter were monitored for 3 years using the litterbag method. The mobility of elements during the course of the study was in the order of: K > P > C > Mg > Ca > N. Three patterns of nutrient dynamics were observed: (i) concentration increased while mass decreased (N, Mg and Ca); (ii) concentration and nutrient mass decreased (K and C); and (iii) both concentration and mass had fluctuated (P). The C to element ratio tended to increase as the element was released, and decreased as the element was retained. Nitrogen mobility in relation to carbon was characterized by three phases: (i) initial release; (ii) accumulation and (iii) final release. The decay rate (k.) calculated from 0-6 months period was overestimated for an average annual rate while those of 0-36 months fit the negative single exponential model (Adj. r2 = 0.99) better than shorter periods. For lignin, the concentration had increased then decreased but tended to stabilize after 1 year while the lignin mass had continuously decreased throughout the study period. During the first 9 months, both the concentrations and mass of cellulose had fluctuated but declined thereafter. The amounts of N had initially increased but declined after 1 year; P had fluctuated while K, Ca, Mg and C had decreased throughout the study. N and C/N ratio exerted strong influence on mass loss during the first 24 months but the influence of lignin emerged after 24 months.
BIO-N inoculation:
(Journal of Agriculture and Rural Development in the Tropics and Subtropics, Supplement, 2005-08) Abalos, Emilia
Inorganic nitrogen fertilizers are the most utilized but expensive and inefficient agricultural input. Only 47% of the inorganic N applied to rice is utilized by the plant (IRR, 1983). Slow release fertilizers although effective, were not popularly used because of its high cost. Azolla had been extensively explored but its use was not sustained possibly because of the difficulty in culturing azolla in ponds of water prior to its utilization. The air has 78% nitrogen (N2) which could be fixed by nitrogen-fixing bacteria into usable form of N, (NH4+) and (NO3-) ions. Nitrogen fixation by nitrogen-fixing bacteria for nonlegumes such as rice and corn could be harnessed. Nonsymbiotic nitrogen fixing bacteria (Azospirillium sp.) were isolated from "talahib" (Saccharum spontanium, LINN) from "Pinatubo lahar" area. Inoculants were produced by the BIOTECH Institute of UPLB, Laguna from the isolates and were named BIO-N. BIO-N inoculants were tested on rice, corn and some vegetable crops at farmers' field at different locations. The effect of BIO-N on growth, yield and benefit-cost ratio were compared with farmers' practice and inorganic fertilization. Results showed that BIO-N inoculation could supply 1/3 to 1/2 of the nitrogen requirement of rice, corn. BIO-N inoculation plus 1/2 of the recommended rate and BIO-N plus full recommended rates using chemical fertilizer gave a 43.42% to 80.79% increase in yield of Hybrid corn over the control. Similar trend is observed in rice. Comparison on the peso inputs/kg yield showed that the use of BIO-N alone requires only 0.016 peso input as compared to the 0.443 peso input/kg yield. A benefit-cost ratio (BCR) of 3.67 can be realized with BIO-N inoculation plus 1/2 recommended rate as compared to 2.79 from full inorganic fertilization. However, inconsistent results were observed from different locations. BIO-N inoculation could not totally substitute the inorganic nitrogen requirement but can supplement the requirement of crops at minimal cost without harming the environment.
Data collection, processing, and applications for geospatial analysis
(Springer Japan, 2014) Lwin, Ko Ko; Estoque, Ronald C.; Murayama, Yuji
Geospatial data collection is an important task for many spatial information users. Geospatial data collection may include field data collection, remote sensing data processing, and in-house geographical information science (GIS) data conversion. Nowadays, geospatial data are available from various sources. Among these, remote sensing data (i.e., optical, radio detection and ranging (RADAR), light detection and ranging (LIDAR), etc.) are among the primary data sources in many GIS analyses. For example, high-resolution satellite images such as QuickBird, IKONOS, and aerial photographs are the basis for the generation of qualitative land-use maps (i.e., land-use zoning maps) and the delineation of transportation networks. Medium-resolution satellite images such as ALOS, SPOT, and Landsat TM/ETM are used in the generation of quantitative land-use maps (i.e., land cover maps) for regional-scale studies of changes in land use. The shuttle radar topography mission (SRTM) and LIDAR provide topographical characteristics for GIS analysis. Moreover, remote sensing data are important for environmental studies such as deforestation, global warming, and natural resource management. This technology captures the real-world information with various sophisticated sensors and platforms. However, building a GIS database is required for further geospatial analysis and mapping purposes. GIS converts the real-world information into a geodatabase in order to retrieve, analyze, and allow further geocomputations. On the other hand, field data collection is important for spatial information users in order to collect spatially distributed objects with their associated attribute information. In this chapter, we discuss geospatial data collection methods and processing, and their applications in GIS.
A hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus
(American Institute of Physics Inc., 2017) Aquino, Ronald L.; Alcantara, Nialle Loui Mar T.; Addawe, Rizavel C.
The Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.