The Lower Colorado Group (Late Albian-earliest Cenomanian) has been allostratigraphically divided on the basis of regional unconformities and transgressive surfaces, resulting in recognition of an informal Lower Colorado allogroup, comprising the Paddy, Joli Fou, Viking, Westgate and Fish Scales alloformations. The Paddy alloformation forms an eastward-thinning wedge up to 125 m thick, composed of nine allomembers that progressively onlap the basal unconformity PE0 from west to east. Paddy rocks are mainly alluvial in the west, grading into marginal marine in the east and north. Paleo valley-fills are present at the tops of most allomembers. The Joli Fou alloformation transgressively overlies nonmarine Mannville Group rocks, and forms a relatively sheet-like blanket (average 20 m) of marine mudstone that coarsens in the north, where it is assigned to the lithostratigraphic Viking Formation, and in the far south, where it is assigned to part of the lithostratigraphic Bow Island Formation. The Viking alloformation erosively overlies the Joli Fou alloformation at surface VE0 and consists of regional allomembers VA, VB and VD, separated by unconformities VE0, VE1, VE3 and VE4. Allomembers VA and VB (mean 30 m thick) are intensely bioturbated shallow marine silty fine sandstones whereas allomember VD is weakly bioturbated, and includes sandy shoreface deposits in the SW and thick (>50 m) offshore marine mudstone in the north (part of the lithostratigraphic Hasler Formation). Allomember VC is confined to paleovalley deposits below VE3. All regional Viking allomembers can be traced into the lower Bow Island Formation in the south. Marine mudstones of the Westgate alloformation form a wedge thinning from >600 m in the NW to <40 m in the far south, comprising informal units WA, WB and WC from base to top. Westgate strata onlap southward onto VE4 such that only unit WC persists to southern Alberta, where it passes into marginal marine facies of the middle and upper Bow Island Formation. The Fish Scales alloformation (earliest Cenomanian) erosively overlies the Westgate alloformation at surface FE1 and comprises two units FA and FB, the former being confined to a depocentre in the NW. The Fish Scales alloformation is characterized by abrupt introduction of fine sand into the basin and sea-floor erosion which formed uranium-enriched phosphatic lags which give a characteristic highly radioactive log signature. An absence of benthic fauna and high organic content indicate deposition below anoxic water. The top of the Fish Scales alloformation is the Fish Scales Upper (FSU) marker which is a highly radioactive condensed section and downlap surface below prograding clinothems of the early-mid Cenomanian Dunvegan alloformation. Allomember FB is locally coarse-grained in the far south, forming the lower part of the Barons Sandstone, whereas the upper Barons is fine grained and equivalent to allomember C of the Dunvegan Formation.The Paddy alloformation is entirely, or almost entirely older than the Joli Fou alloformation, and henc...

However, the LiNi 1-x-y Co x Mn y O 2 (1 − x − y > 0.9) commercialization is hindered, because it suffers from serious structural instability in the long-term cycling due to the crack formation, structure degradation, and cathode-electrolyte interface film formation. [8][9][10] Notably, both the structure formation during calcination and the structure change during attenuation were directly related to the properties for NCM cathodes. [11,12] Currently, much efforts have been done on the structure evolution in the formation (high-temperature lithiation reaction) and degradation (charging/discharging) of NCM cathodes. For NCM formation, Wang and coworkers revealed that the structure change was actually a topological phase transformation process during the high-temperature lithiation of LiNi 0.77 Co 0.1 Mn 0.13 O 2 and involved multiple phase transformations. [1,[13][14][15] For NCM degradation, the loss of active elements, cation migration, and the lattice stress caused by Li + (de)intercalation led to the destruction of the layered structure and the sharp decay of performance during the charging/discharging. [16][17][18][19][20] Structure reconstruction induced by the migration ofLi, O, and transition metal (TM) ions plays a key role in the performance of Ni-based cathodes, yet their interactions are still poorly understood. This work investigates systematically the structure transformation of the high-temperature lithiation in air and oxygen rich atmosphere, charging process, and long-term storage. Structural and electrochemical characterization of Li-free/Li-containing phases (Ni 0.92 Co 0.04 Mn 0.04 (OH) 2 and Li x Ni 0.92 Co 0.04 Mn 0.04 O y ) and a series of detailed analyses provide an in-depth understanding of the structural reconstruction induced by the interaction of Li, O, and TM ions, i.e., the shift of Li/TM ions in the lattice leads to structural reconstruction via a layered structure with oxygen vacancies.

Wireless localization for mobile device has attracted more and more interests by increasing the demand for location based services. Fingerprint-based localization is promising, especially in non-Line-of-Sight (NLoS) or rich scattering environments, such as urban areas and indoor scenarios. In this paper, we propose a novel fingerprint-based localization technique based on deep learning framework under commercial long term evolution (LTE) systems. Specifically, we develop a software defined user equipment to collect the real time channel state information (CSI) knowledge from LTE base stations and extract the intrinsic features among CSI observations. On top of that, we propose a time domain fusion approach to assemble multiple positioning estimations. Experimental results demonstrated that the proposed localization technique can significantly improve the localization accuracy and robustness, e.g. achieves Mean Distance Error (MDE) of 0.47 meters for indoor and of 19.9 meters for outdoor scenarios, respectively.

No abstract

No abstract

No abstract

No abstract

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.