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Abstract Successful hydraulic fracturing is critical for hydrocarbon recovery from tight reservoirs. Fracture geometry is one essential quality indicator of the created fracture. The geometry provides information about the size of the created fracture and containment and verifies the pre-job modeling. Different techniques are applied to determine fracture geometry, and each has its own advantages and limitations. Due to its simplicity, the radioactive tracer log is commonly used to determine fracture placement and fracture height. Its main drawbacks include shallow depth of investigation, time dependency, and the requirement for multiple interventions for multistage fracturing operations. The crosswell microseismic technique probes a larger volume and it is potentially capable of providing fracture height, length, and orientation. Operational complexity and long processing turnaround time are the main challenges of this technique. Time-lapse shear slowness anisotropy analysis is an effective method to determine hydraulic facture height and orientation. In this technique, the shear slowness anisotropy is recorded before and after the fracture is created. The observed shear anisotropy difference indicates the intervals where the fractures were created, allowing these intervals lengths to be measured. Combining this analysis with gyroscopic data allows determining the fracture orientations. Compared to a tracer log, the differential casedhole sonic anisotropy (DCHSA) has a deeper depth of investigation, and it is time independent. Thus, the repeated log can be acquired at the end of the multistage fracturing operations. Compared to the microseismic technique, this new technique provides more precise fracture height and orientation. The new generation slim dipole sonic technology of 2.125-in. diameter extends the applicability of the DCHSA technique to smaller casing sizes. The shear differential method was applied to a vertical well that targeted the Athel formation in the south of the Sultanate of Oman. This formation is made of silicilyte and is characterized by very low permeability of about 0.01 md on average. Thus, hydraulic fracturing plays a critical role for the economic oil recovery in this reservoir. Aiming to achieve a better zonal contribution, the stimulation design was changed from a limited number of large fractures to an extensive multistage fracturing design in the subject well. Sixteen hydraulic fracturing stages were planned. The DCHSA was applied to provide accurate and efficient fracture geometry evaluation. The DCHSA accurately identified fracture intervals and their corresponding heights and orientations. This enabled effectively determining the created fracture quality and helped explain the responses of the production logs that were recorded during the well test. This study provided a foundation for the placement and completion design of the future wells in the subject reservoir. It particularly revealed adequate fracturing intervals and the optimum number of stages required to achieve optimum reservoir coverage and avoid vertical overlapping.

This paper presents a fast-switching Transmit/Receive (T/R) Single-Pole-Double-Throw (SPDT) Radio Frequency (RF) switch. Thorough analyses have been conducted to choose the optimum number of stacks, transistor sizes, gate and body voltages, to satisfy the required specifications. This switch applies six stacks of series and shunt transistors as big as 3.9 mm/160 nm and 0.75 mm/160 nm, respectively. A negative charge pump and a voltage booster generate the negative and boosted control voltages to improve the harmonics and to keep Inter-Modulation Distortion (IMD) performance of the switch over 100 dBc. A Low Drop-Out (LDO) regulator limits the boosted voltage in Absolute Maximum Rating (AMR) conditions and improves the switch performance for Process, Voltage and Temperature (PVT) variations. To reduce the size, a dense custom-made capacitor consisting of different types of capacitors has been presented where they have been placed over each other in layout considering the Design Rule Checks (DRC) and applied in negative charge pump, voltage booster and LDO. This switch has been fabricated and tested in a 90 nm Silicon-on-Insulator (SOI) process. The second and third IMD for all specified blockers remain over 100 dBc and the switching time as fast as 150 ns has been achieved. The Insertion Loss (IL) and isolation at 2.7 GHz are −0.17 dB and −33 dB, respectively. This design consumes 145 uA from supply voltage range of 1.65 V to 1.95 V and occupies 440 × 472 µm2 of die area.

Abstract Background Insecticide use is an important component of integrated pest management strategies developed for fall armyworm (FAW), Spodoptera frugiperda J.E Smith, control in maize in many African countries. Here, the optimum number of synthetic insecticide and biopesticide applications needed to effectively manage FAW at a minimal cost in maize was studied. Materials and methods A 3 × 4 factorial experiment arranged in a split plot design was used. Insecticides [Neem seed oil (NSO), 3% Azadirachtin); Emastar 112 EC (emamectin benzoate 48 g/L + acetamiprid 64 g/L); Eradicoat (282 g/L Maltodextrin)] were on the main plots, while insecticide spraying regimes [untreated control, spraying once (at VE–V5 maize develoment stage), twice (at VE–V5 and V6–V12 stages), thrice (at VE–V5, V6–V12 and V12–VT stages), four times (at VE–V5, V6–V12, V12–VT and R1–R3 stages)] were on the sub-plots. Results The results showed that larval infestations were generally lower in Emastar 112 EC treated maize than in those sprayed with Eradicoat or NSO. Infestations were higher in the untreated control (no spray) but decreased with increases in number of spray applications in insecticide treated plots. Again, crop damage was low in Emastar 112 EC treated maize. This variable also decreased with an increase in the number of spray applications. Grain yield was significantly affected by the spraying regime only, with this variable being lowest in the untreated control. In both years, yields were at least 1.5-fold higher in maize sprayed twice, thrice or four times compared to the untreated control. Emastar 112 EC had the highest net economic benefits. A single spray of Emastar 112 EC at the VE–V5 maize development stage resulted in maximum profits, while two sprays (i.e., at VE–V5 and V6–V12 stages) were needed for Eradicoat and NSO. Conclusion Hence, synthetic insecticides and biopesticides require different frequency of spray applications for cost effective management of FAW in northern Ghana. These findings are potentially applicable in other sub-Saharan African countries where this pest is present.

This research discusses about the comparison of raw material inventory control CV. Dirga Eggtray Pinrang. It starts with forecasting inventory for the next 12 periods using variations of the time series forecasting method, where the linear regression method provides accurate forecasting results with a Mean Absolute Percentage Error (MAPE) value of 1,9371%. The probabilistic models of inventory control used are the simple probabilistic model, Continuous Review System (CRS) model, and Periodic Review System (PRS) model. The CRS model with backorder condition is a model that provides the minimum cost of Rp. 969.273.706,20 per year compared to another probabilistic model with the largest difference of Rp. 1.291.814,95 per year, with the optimum number of order kg, reorder level kg, and safety stock kg.

Determining the players’ playing styles and bringing the right players together are very important for winning in basketball. This study aimed to group basketball players into similar clusters according to their playing styles for each of the traditionally defined five positions (point guard (PG), shooting guard (SG), small forward (SF), power forward (PF), and center (C)). This way, teams would be able to identify their type of players to help them determine what type of players they should recruit to build a better team. The 17 game-related statistics from 15 seasons of the National Basketball Association (NBA) were analyzed using a hierarchical clustering method. The cluster validity indices (CVIs) were used to determine the optimum number of groups. Based on this analysis, four clusters were identified for PG, SG, and SF positions, while five clusters for PF position and six clusters for C position were established. In addition to the definition of the created clusters, their individual achievements were examined based on three performance indicators: adjusted plus-minus (APM), average points differential, and the percentage of clusters on winning teams. This study contributes to the evaluation of team compatibility, which is a significant part of winning, as it allows one to determine the playing styles for each position, while examining the success of position pair combinations.

Owing to the recent increase in the number of warning reports and alerts on the dangers of fine dusts, there has been an increasing concern over fine dusts among citizens. In spaces with poor ventilation, the occupants are forced to open the window to initiate natural ventilation via the direct introduction of the outside air; however, this may pose a serious challenge if the external fine-dust concentration is high. The lack of natural ventilation increases the indoor carbon dioxide (CO2) concentration, thus necessitating the installation of mechanical ventilation systems. This study analyzed the frequency of the application of mechanical ventilation systems in the Multi-purpose activity space of elementary schools, which are spaces where children require a higher indoor air quality than adults owing to the rapid increase in the CO2 concentration of the Multi-purpose activity space during activities. In addition, the architectural and equipment factors of the Multi-purpose activity spaces of nine elementary schools were characterized. The results revealed that five out of the nine elementary schools installed mechanical ventilation systems, whereas the remaining four schools installed jet air turnover systems. The indoor air quality of the Multi-purpose activity space of D elementary school, which had the minimum facility volume among the schools investigated in this study (564.2 m3), with up to 32 participants for each activity, was investigated. The results revealed that the ultrafine-dust (PM2.5) concentration of the facility was as high as 4.75 µg/m3 at a height of 1.2 m, and the CO2 concentration was as high as 3183 ppm. The results of the analysis of three elementary schools with different volumes were compared and analyzed using CONTAM simulation. This study determined the required volume per occupant and the optimum number of occupants for a given volume and presented guidelines for the optimum number of occupants, activities, and volume to reduce the high concentration of pollutants in the analyzed Multi-purpose activity space. The guideline proposed in this study is aimed at maintaining the CO2 concentration of the Multi-purpose activity space below 1000 ppm, as prescribed by the Indoor Air Quality Control in Public-Use Facilities, Etc. Act in South Korea.

In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms of distance between the main server and the other server, VM capacity and memory size. Then the VMs with best fitness functions are selected as target VMs for placing the migrated VMs. The proposed algorithms are implemented in Cloudsim and performance results show that PEVM-GSO algorithm attains reduced power consumption and response delay with improved CPU utilization.

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

Identifying the number of oil families in petroleum basins provides practical and valuable information in petroleum geochemistry studies from exploration to development. Oil family grouping helps us track migration pathways, identify the number of active source rock(s), and examine the reservoir continuity. To date, almost in all oil family typing studies, common statistical methods such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) have been used. However, there is no publication regarding using artificial neural networks (ANNs) for examining the oil families in petroleum basins. Hence, oil family typing requires novel, not overused and common techniques. This paper is the first report of oil family typing using ANNs as robust computational methods. To this end, a self-organization map (SOM) neural network associated with three clustering validity indices were employed on oil samples belonging to the Iranian part of the Persian Gulf’ oilfields. For the SOM network, at first, ten default clusters were selected. Afterwards, three effective clustering validity coefficients, namely Calinski-Harabasz (CH), Silhouette indexes (SI) and Davies-Bouldin (DB), were operated to find the optimum number of clusters. Accordingly, among ten default clusters, the maximum CH (62) and SI (0.58) were acquired for four clusters. Likewise, the lowest DB (0.8) was obtained for four clusters. Thus, all three validation coefficients introduced four clusters as the optimum number of clusters or oil families. The number of oil families identified in the present report is consistent with those previously reported by other researchers in the same study area. However, the techniques used in the present paper, which have not been implemented so far, can be introduced as more straightforward for clustering purposes in the oil family typing than those of common and overused methods of PCA and HCA.

Climate change is among the major challenges to sustainable agricultural production in Ethiopia. Production of cereal crops, especially maize, is very responsive to changes in rainfall and temperature, as climatic parameters influencing productivity. This paper analyzes how climatic and other variables affect the supply of maize in Ethiopia. The data were obtained from secondary sources and cover the period 1981–2018. Data were analyzed using the Autoregressive Distributed Lag (ARDL) approach. The Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC), and Hannan-Quinn Information Criterion (HQ) were used to select the optimum number of lags. In order to detect whether unit root is present in the series, Augmented Dickey-Fuller (ADF) and Philips-Perron (PP) tests were carried out. The presence of long-run equilibrium was found between maize output and temperature, rainfall, and other included variables. The results show that, in both the long and shortrun, all included climatic variables had a negative relationship with maize output supply, although temperature showed statistical insignificance (P>0.10). The result showed that maize crops are highly sensitive to extremes of rainfall – both shortage in the initial growing period and excessin the vegetative and fruiting stages. It was concluded that farmers face climate-related risk due to variations, particularly in rainfall. Therefore, farmers should adapt by using short-duration and climate-tolerant varieties of maize, along with engagement with eco-friendly production systems.