Monitoring fetal heart rate during pregnancy is essential to assist clinicians in making more timely decisions. Non-invasive monitoring of fetal heart activities using abdominal ECGs is useful for diagnosis of heart defects. However, the extracted fetal ECGs are usually too weak to be robustly detected. Thus, it is a necessity to enhance fetal R-peak since their peaks may hidden within the signal due to the immaturity of the fetal cardiovascular system. Therefore, to improve the detection of the fetal heartbeat, a novel fetal R-peak enhancement technique was proposed to statistically generate the weighting mask according to the distribution of the neighboring temporal intervals between each pair of peaks. Two set of simulations were designed to validate the reliability of the method: challenges with different levels of (1) noise contamination and (2) R-peak interval changing rate. The simulation results showed that the weighting mask improve the accuracy of the R-peak detection rate by 25% and decrease false alarm rate by 20% with white noise contamination, and ensure high R-peak detection rate (> 80%), especially with mild noise contamination (noise amplitude ratio < 1.5 and noise rate per minute < 25%). For those simulations with continuous R-peak intervals changing, the masking process can still effectively eliminate noise contamination especially when the amplitude of the sinusoidal fetal R-R intervals lower than 50msec. For the real fetus ECGs, detection rate was increasing by 3.498% whereas false alarm rate was decreasing by 3.933%. Next, we implement the fetal R-peak enhancement technique to investigate fractal regulation and multiscale entropy of the real fetal heartbeat intervals. Both scaling exponent (∼0.6 to ∼1 in scale 4-15) and entropy measure (scale 6-10) increase from with gestational ages (22-40 weeks). The results confirmed fractal slope and complexity of fetal heartbeat intervals can reflect the maturation of fetus organism.

Rainwater harvesting is an important step towards maximizing the water availability and land productivity in arid and semi-arid areas. The present study shows that the area of Ghazi Tehsil within Khyber Pakhtunkhwa Province, Pakistan, has great potential for rainwater harvesting due to its feasible climatic and topographic conditions. This area of 348 km² normally receives high rainfall annually, but, due to hilly terrain, the bulk of rainwater is lost in the runoff process. In order to enhance agricultural output for such a large area, the practice of rainwater harvesting is a sustainable and decisive approach. However, the selection of appropriate sites for rainwater harvesting on a large scale presents a critical challenge. In such areas, geospatial technology has proved very decisive in the identification of potential sites. In this study, we have used the HEC-GeoHMS tool (ArcGIS 9.3) to compute a curve number to represent the effects of rainfall against the hydrological soil group and landcover. Subsequently, the curve number was used as an input parameter in the soil conservation service runoff-curve number (SCS-CN) method to estimate surface runoff potential for different combinations of landcover and hydrological soil groups. It was observed that runoff was higher in mountainous areas and relatively low in plain areas. Finally, to identify the potential sites for rainwater harvesting, weighted overlay analysis-based related thematic map layers were further reclassified, and weights were assigned according to the technical guidelines of suggested international standards and under consideration of the study area’s topographic, hydrological, and climatic factors. As a result, about 20% of the area was found suitable, 52% less suitable, and 29% as not suitable. Furthermore, relative suitability was assigned to the results of suitable sites as an input for the identification of potential sites for different rainwater harvesting storage structures. These results show that 10% of the area was suitable for farm ponds, 5.74% for check dams, 21.5% for Nigarims, and 8.9% was found to be suitable for gully plugs. The comparison of our GIS-derived and field-based results spatially affirms that the analyzed results were agreeably overlaid in the context of spatial results for check dams, gully plugs, and Nigarims.

Islamabad is essentially the only well-planned city in Pakistan, but groundwater depletion has become a serious issue there because of the rapid increase in population, poor water management, and deforestation. The current water demand of the city is about 220 million gallons per day, with the Capital Development Authority (CDA) providing up to 70 million gallons per day. The need for water is mostly fulfilled through groundwater sources, such as water bores and commercial tube wells. Hence, identifying recharge sites for natural aquifers is a significant component of groundwater required to overcome the water crisis. Therefore, this study aims to identify potential sites for natural aquifer recharge by using analytical hierarchy process (AHP), weighted linear combination (WLC), and fuzzy logic methods. To achieve the stated objective, seven local influencing factors including soil, slope, water table, population density, land use land cover (LULC), drainage density, and elevation have been utilized in this study. AHP was utilized for the evaluation of the relative importance of the above-mentioned factors, while fuzzy logic was applied for the standardization of these factors. Finally, the AHP-WLC and fuzzy logic approaches were used to merge factor maps in order to identify suitable sites for natural aquifer recharge in Islamabad City. Two different suitability maps were constructed from both techniques, and on each of the resulting maps, the subregions were categorized into five classes: not suitable, less suitable, moderate, suitable, and most suitable. Based on the AHP-WLC results, 5% of the whole study area is deemed most suitable for natural aquifer recharge (NAR), whereas from the fuzzy logic results, 10% of the study area is marked as most suitable. In contrast, 37% and 32% of the whole study area were identified as suitable by the AHP-WLC and fuzzy logic methods, respectively. While both techniques can obtain satisfactory outcomes, the suitability map from fuzzy logic has produced more precise results. Hence, we propose to CDA-Islamabad here different sites for recharge wells based on the results of fuzzy logic. As recommended by this study, to date CDA has constructed twelve recharge wells.

Empirical mode decomposition (EMD) and its variants are adaptive algorithms that decompose a time series into a few oscillation components called intrinsic mode functions (IMFs). They are powerful signal processing tools and have been successfully applied in many applications. Previous research shows that EMD is an efficient algorithm with computational complexity O ( n ) for a given number of IMFs, where n is the signal length, but its memory is as large as (13+ m_imf ) n , where m_imf is the number of IMFs. This huge memory requirement hinders many applications of EMD. A physical or physiological oscillation (PO) mode often consists of a single IMF or the sum of several adjacent IMFs. Let m_out denote the number of PO modes and, by definition, m_out ≤ m_imf . In this paper, we will propose a low memory cost implementation of EMD and prove that the memory can be optimized to (2+ m_out ) n without aggravating the computational complexity, while gives the same results. Finally, we discuss the optimized memory requirements for different noise-assisted EMD algorithms.

We investigate geometric properties of algebraic sets defined by matrix pairs with special commutators. Specifically, we prove that the set consisting of the $n\times n$ matrix pairs whose commutators are diagonal matrices is a complete intersection with two components, one of which is the so called commuting variety that consists of commuting matrix pairs, and that the algebraic set as a scheme is reduced when the base field is of characteristic zero. We also prove that the algebraic set comprising the $n\times n$ matrix pairs whose commutators have vanishing diagonal entries is an irreducible complete intersection, and, as a scheme, is reduced when the base field is of characteristic zero.

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging.