Abundance of Phytoplankton and Physical Chemical Parameters as Indicators of Water Fertility in Lekok Coast, Pasuruan Regency, East Java Province, Indonesia

The Lekok Coast is part of Pasuruan Regency which has various community activities. The condition of the waters is influenced by natural and anthropogenic factors which receive a lot of input loads from the mainland. These input load can come from human activities such as aquaculture, industry and domestic waste that enters through rivers and then empties into the coasts. These conditions can affect the fertility of eutrophic, mesotropic or oligotropic waters. This study aims to determine the conditions of the waters based on the abundance of phytoplankton and the physical and chemical parameters of the waters to see the fertility status of the waters. The descriptive method is the method used in this study and the determination of the sampling point uses the purposive sampling method, the research was conducted in April – May 2019. Based on the results of the abundance of phytoplankton in the coastal waters of Lekok, it is classified into waters that have oligotrophic fertility levels. The results of observations and measurements of physical and chemical parameters in the coastal waters of Lekok showed that several parameters that support the growth of phytoplankton are less than optimal, such as temperature, brightness and nitrogen elements.

Novel Drill Bit Analysis to Delay and Mitigate Fixed Cutter Thermal Damage

As oil and gas wells become deeper, drilling longer intervals is becoming a major milestone for drill bit companies, as the process comes with a variety of challenges affecting the durability of drill bits. Among the major challenges are thermal and impact damage in polycrystalline diamond compact (PDC) cutters, which can significantly affect the performance and longevity of a drill bit. While cutter technology development remains an important arena to address said challenges, there exists a need to also address these through the design process. This paper presents the development and deployment of a new drill bit analysis method that addresses thermal damage by optimizing the design, which has been field validated across the globe. The analysis involves estimating the thermal input load and the available cooling rate for every cutter on a drill bit during drilling conditions. The data is then used to optimize and apply changes to the design. The analysis considers all the critical and relevant operational parameters to calculate these indices. The outcome of the so-called thermal index analysis enables the design team to make informed decisions to improve the design of the drill bit and to minimize the extent of thermal damage in cutters. The improvements made in the design include changes in cutting structure to affect cutting forces and, eventually, the thermal input load during the drilling process. This stage in practice can bring down the temperature of the cutting edge by 20%, as calculated analytically. Another major change that can affect the results is hydraulic design of the bit, which includes the location of the nozzles as well as their orientation and size. In test cases, the cooling rate improved by 50% while keeping the same flow rate though the bit. Several field trials have validated the correlation of thermal index analysis to drill bit dulls. This analysis is now in the field evaluation and testing phase, where it is being used during the design process to improve bits with thermal damage. The field-testing phase has been primarily conducted in thermally challenging applications across the Middle East, North Africa region, and in West Texas.

Aeolian Dust Inputs in the Mediterranean and Black Sea Marine System

<p>The aim of this paper is to estimate the amount of aeolian dust, deposited by dry and wet processes, that is deposited to the eleven marine regions of the Mediterranean-Black Sea Marine System (MBMS) and to compare it to the riverine influxes (i.e. suspended and dissolved sediment loads). This research is based on information for aeolian dust deposition at several coastal stations, around the MBMS, following an extended research of the available literature. For data elaboration, processing, and visualization a G.I.S. environment was utilized. The total annual amount of dust input for the whole system has been estimated to 59.9 × 106 tonnes, of which 57.2 × 106 tonnes are deposited in the Mediterranean Sea and only 2.7 ×106 tonnes in the Black Sea. The contribution of dust input (load), corresponding to 6.2% and 0.8% of the total amount of suspended and dissolved load, for the Mediterranean and Black Sea respectively, reveals the significant role of the aeolian dust inputs to the MBMS marine environment, in particular, at its southern Mediterranean domain.</p>

Performance of Full-Scale Thermophilic Membrane Bioreactor and Assessment of the Effect of the Aqueous Residue on Mesophilic Biological Activity

To date, the management of high-strength wastewater represents a serious problem. This work aims to evaluate the performance on chemical pollutants and on sludge production of one of the two full-scale thermophilic membrane bioreactors (ThMBRs) currently operational in Italy, based on monitoring data of the last two and a half years. Removal yields on COD, N-NOx, non-ionic and anionic surfactants (TAS and MBAS), increased with the input load up to 81.9%, 97.6%, 94.7%, and 98.4%, respectively. In the period of stability, a very low value of sludge production (0.052 kgVS kgCOD−1) was observed. Oxygen uptake rate (OUR) tests allowed us to exclude the possibility that mesophilic biomass generally exhibited any acute inhibition following contact with the aqueous residues (ARs), except for substrates that presented high concentrations of perfluoro alkyl substances (PFAS), cyanides and chlorides. In one case, nitrifying activity was partially inhibited by high chlorides and PFAS concentration, while in another the substrate determined a positive effect, stimulating the phenomenon of nitrification. Nitrogen uptake rate (NUR) tests highlighted the feasibility of reusing the organic carbon contained in the substrate as a source in denitrification, obtaining a value comparable with that obtained using the reference solution with methanol. Therefore, respirometric tests proved to be a valid tool to assess the acute effect of AR of ThMBR on the activity of mesophilic biomass in the case of recirculation.

Phosphorus mass balance and input load estimation from the wet and dry periods in tropical semiarid reservoirs

The dynamics of total phosphorus (TP) in 18 strategic reservoirs of the high-density reservoir network of the Brazilian semiarid was evaluated during the wet and dry periods for the past 12 years. Seasonal TP concentrations presented no significant differences for about 90% of the reservoirs (p > 0.05). This was attributed to a trade-off between the hydrological/limnological processes occurring in the two seasons. Then, a transient complete-mix mass balance model was applied with particular adaptations for the tropical semiarid reservoirs to estimate the TP load for each season. Because of the relatively well mixed conditions and high hypolimnetic dissolved oxygen concentrations during the wet season, the wet load was assumed to represent the external TP load. On the other hand, because of the absence of reservoir inflow during the dry season, phosphorus release under anoxic sediment conditions and wind-induced resuspension under shallow water conditions, the dry load was assumed to reflect the internal TP load. The maximum external loads were related to peak inflows, notably after drought periods. Consistently, the largest internal loads were obtained during the drought periods, when the reservoirs were shallower and more prone to phosphorus release and resuspension. By comparing the impact of the two input load types, the wet period load was predominant in 72% of the reservoirs. The areal phosphorus loads ranged from 0.66 to 7.29 gP.m².yr-1, which were consistent with the literature, despite the very high density of reservoirs. Finally, power-law curves including data for all studied reservoirs were adjusted between the dry period load and volume, dry and wet period loads, wet period load and inflow, and total load and catchment area, resulting in satisfactory R² (0.66–0.82).

Vision-Based Tactile Sensor Mechanism for the Estimation of Contact Position and Force Distribution Using Deep Learning

This work describes the development of a vision-based tactile sensor system that utilizes the image-based information of the tactile sensor in conjunction with input loads at various motions to train the neural network for the estimation of tactile contact position, area, and force distribution. The current study also addresses pragmatic aspects, such as choice of the thickness and materials for the tactile fingertips and surface tendency, etc. The overall vision-based tactile sensor equipment interacts with an actuating motion controller, force gauge, and control PC (personal computer) with a LabVIEW software on it. The image acquisition was carried out using a compact stereo camera setup mounted inside the elastic body to observe and measure the amount of deformation by the motion and input load. The vision-based tactile sensor test bench was employed to collect the output contact position, angle, and force distribution caused by various randomly considered input loads for motion in X, Y, Z directions and RxRy rotational motion. The retrieved image information, contact position, area, and force distribution from different input loads with specified 3D position and angle are utilized for deep learning. A convolutional neural network VGG-16 classification modelhas been modified to a regression network model and transfer learning was applied to suit the regression task of estimating contact position and force distribution. Several experiments were carried out using thick and thin sized tactile sensors with various shapes, such as circle, square, hexagon, for better validation of the predicted contact position, contact area, and force distribution.

DbOBS: Dual Buffered Switch for Variable Optical Bursts in Future Datacenters

Modern data-driven applications pose stringent requirements of high bandwidth, ultra-low-latency, low-powered, and scalable interconnections among switches and routers in data-centers. To address these demands, electronic switching is not a viable choice due to bandwidth and computing bottlenecks. Thus, researchers explored effective optical switch design principles for next-generation data-centers. In optical switches, data aggregates in the form of optical bursts (OB) at ultra-high speeds. In the case of OB contention, solutions are proposed by researchers to store OB as recirculating patterns in fiber delay lines (FDL) with induced optical delay. However, due to variable burst length, it is not possible to measure slot delay length, thus storage of contending bursts is not possible at intermediate core switches. Motivated from the aforementioned discussions, in this paper, we propose a switch design DbOBS, that is capable to store variable OB during contention slots. DbOBS estimates mean burst length, and possible deviation from mean length to minimize burst loss. The considered switch design is validated through parameters like-burst length estimation, over-reservation, and waiting time. For network-layer simulations, poison arrivals of data bursts are considered as packetized units. The packets are sent through Monte-Carlo arrivals and burst loss probability (BLP) is estimated at various input load conditions and buffer sizes. DbOBS achieves a BLP in order of 10-4 at load ≈ 0.8, and buffer-size of 50, and burst length of L = 5, that outperforms the traditional switch designs.

Dynamic Cache Management of Cloud RAN and Multi-Access Edge Computing for 5G Networks

Using a cache to improve efficiency and to save on the cost of a computer system has been a field that attracts many researchers, including those in the area of cellular network systems. The first part of this chapter focuses on adaptive cache management schemes for cloud radio access networks (CRAN) and multi-access edge computing (MEC) of 5G mobile technologies. Experimental results run through CloudSim show that the proposed adaptive algorithms are effective in increasing cache hit rate, guaranteeing QoS, and in reducing algorithm execution time. In second part of this chapter, a new cache management algorithm using Zipf distribution to address dynamic input is proposed for CRAN and MEC models. A performance test is also run using iFogSim to show the improvement made by the proposed algorithm over the original versions. This work contributes in the support of 5G for IoT by enhancing CRAN and MEC performance; it also contributes to how novel caching algorithms can resolve the unbalanced input load caused by changing distributions of the input traffic.