Search

Dystrophic cervical kyphosis secondary to neurofibromatosis 1 (NF1) is rarely reported. The primary objective is to highlight the clinical presentation and surgical outcomes based on clinical and biomechanical parameters. The secondary objective is to highlight the early and late complications of these surgeries.The hospital records of six patients operated between 2008 and 2018 were retrospectively reviewed with a minimum follow-up of 24 months. Besides demographics information, radiological findings and operative details, the outcome measures reported were neurological (MJOA score, Nurick scale), pain (VAS score) and operative complications.The mean age of patients was 15.1 years (8-32 years). The average kyphotic vertebra involved-3.6 bodies (range 2-5 bodies) with four patients showing intraspinal anomaly-neurofibromas, dural ectasia. Clinically, patients improved postoperatively with-VAS (pre vs. post-: 6.6 vs. 2.6, p - 0.05), MJOA score (pre vs. post: 10.3 vs. 13.3, p - 0.05), Nurick grade (pre vs. post: 3.5 vs. 2.1, p - 0.05). There was significant deformity correction from 66.8° to 20.7° (p value 0.031), mean T1 Slope (pre vs. post - 1.8 ± 20.4 to 0.6 ± 12.8, p value - 0.43). Mean blood loss encountered was 1800 ± 434.6 cc; however, patients with paraspinal neurofibromas reported greater blood loss. Late complications included pseudoarthrosis (1), C5 palsy (1) and junctional kyphosis (1). Vertebral dysplasia and erosions continued in all patients post-operatively.Antero-posterior approach provides circumferential decompression and better sagittal balance correction. Cervical spine must be screened in all NF-1 patients and followed up regularly to assess for dystrophic changes.

The coefficient of consolidation (ch) of clay interpreted based on piezocone penetration test (CPTU) usually deviates from the actual values. This can be due to the inherent variability of ch, model uncertainty in the CPTU-based interpretation model, and test errors. Considering the importance of ch in designing the structures over clays, Bayesian inference approach combined with total probability theorem was proposed in this study to integrate the prior knowledge of ch with the project-specific test data to obtain the site-specific distributions of ch. Subsequently, a large number of equivalent samples were generated from the updated distributions of ch by Markov Chain Monte Carlo simulation. Then, Bayesian model comparison approach was developed to quantitatively compare the model proposed by Torstensson (model M1) with the model proposed by Teh and Houlsby (model M2). Finally, a sensitivity analysis was performed to study the effect of model uncertainty and data quantity on the updated distributions of ch. The results showed that model M2 was significantly better than model M1 after considering the rigidity index (Ir). The updated distributions of ch approximately exhibited the log-normal distribution, especially for model M2, which was less affected by the model uncertainty and data quantity than model M1. The proposed Bayesian approach in this study gives the updated distributions of ch from a limited number of test data and recommends model M2 for calculating ch.

Quite a good number of population-based meta-heuristics based on mimicking natural phenomena are observed in the literature in resolving varieties of complex optimization problems. They are widely used in search of the optimal model parameters of artificial neural networks (ANNs). However, efficiencies of these are mostly dependent on fine tuning algorithm-specific parameters. Rao algorithms are metaphor-less meta-heuristics which do not need any algorithm-specific parameters. Functional link artificial neural network (FLANN) is a flat network and possesses the ability of mapping input–output nonlinear relationships by using amplification in input vector dimension. This article attempts to observe the efficacy of Rao algorithms on searching the most favorable parameters of FLANN, thus forming hybrid models termed as Rao algorithm-based FLANNs (RAFLANNs). The models are evaluated on forecasting five stock markets such as NASDAQ, BSE, DJIA, HSI, and NIKKEI. The RAFLANNs performances are compared with that of variations of FLANN (i.e., FLANN based on gradient descent, multi-verse optimizer, monarch butterfly optimization and genetic algorithm) and conventional models (i.e., MLP, SVM and ARIMA). The proposed models are found better in terms of prediction accuracy, computation time and statistical significance test.

This paper presents a data-driven approach to classify and locate the faults occurring in underground distribution cables using a CNN-LSTM-based deep learning architecture. A sliding window method is adopted, using the current and voltage signal patches as inputs. The combination of various system parameters is considered to generalize the performance. Additionally, Gaussian noise is added to resemble and extend the approach to practical scenarios. The trained model is evaluated with the data from a new simulation, and the results confirm the generalizability of the adopted method. A comparison is made with other feasible approaches, which show that the adopted method is preferable to achieve better performance. Further, to ease the extension of the proposed method for different systems with similar configurations, parameters of the trained deep learning networks are exploited with the help of transfer learning.

Hirayama disease (HD) is a relatively uncommon cause of lower cervical myelopathy. A number of surgical approaches have been described in patients with HD in literature. We reviewed the literature and did a systematic review and meta-analysis of the studies which presented the clinical outcome following surgical intervention in HD. A systematic search of literature was performed with the keywords “Surgical treatment in Hirayama Disease”, “Surgical approach in Hirayama Disease” and “Hirayama disease surgery”. Data related to clinical outcome following surgery was pooled to calculate the pooled proportion of clinical improvement following anterior and posterior surgical approach. Thirty-four articles met the inclusion criteria and were included in the final review. Altogether, there were 10 types of surgical procedures performed for Hirayama disease. The most commonly described surgical technique was anterior cervical discectomy and fusion with cervical plating. The pooled proportion of patients experiencing clinical improvement following all cervical approaches was 80% (95% confidence interval 76 to 84%). Pooled proportion was maximum for anterior cervical plating (96% (95% confidence interval 62 to 100%)) and minimum for ACDF without plating (57% (95% confidence interval 20 to 88%)). Subgroup analysis based on different surgical approaches was not significant (p value = 0.61). The pooled proportion of patients experiencing clinical improvement following anterior and posterior cervical approach was 80% (95% confidence interval 76 to 84%) and 81% (95% confidence interval 66 to 91%). The indications of surgical treatment in patients with HD include poor patient compliance for neck collar or rapidly progressing severe disease. Good results with more than 80% chances of clinical improvement have been reported following various anterior and posterior surgical approaches. However, there was no significant difference in the pooled outcome of different surgical approaches. Most common technique used in literature is anterior cervical discectomy and fusion with plating.

The COVID-19 pandemic has forced hospitals to prioritize admissions. Epilepsy surgeries have been postponed at most centers. As the pandemic continues with no definite end in sight in the near future, the question arises until when such patients should be denied appropriate treatment. A 12-year-old child with left-sided Rasmussen’s encephalitis with drug refractory epilepsy (DRE) presented at the height of the pandemic, with worsening of seizure frequency from 4–5/day to 20/day, with new-onset epilepsia partialis continua. She demonstrated features of progressive cognitive decline. The pros and cons of operating during the pandemic were discussed with the parents by a multidisciplinary team. She underwent endoscopic left hemispherotomy. Postoperatively she became seizure free but developed hospital-acquired mild COVID infection for which she was treated accordingly. Chosen cases of severe DRE, as the one illustrated above, who are deemed to benefit from surgery by a multidisciplinary team of physicians, should be re-categorized into the most severe class of patients and scheduled for surgery as soon as possible. The risk benefit ratio of the seizures being mitigated by surgery on one hand and possibility of acquiring COVID infection during hospital stay has to be balanced and a decision made accordingly.

Bitcoin prediction is a recent area of research interest and growing fast. Since its inception, in a short period of time Bitcoin got wide popularity and considered as an investment asset. At present it is the most successful cryptocurrency compared to other altcoins dispersed in the world economy. The Bitcoin prices fluctuate like other stock markets due to inherent volatility. The investors’ confidence on Bitcoin rising fast and have been reflected on its prices. Though few computational intelligence methods are available, sophisticated methodologies for accurate prediction of Bitcoin are still lacking and need to be explored. Functional link neural network (FLN) is a flat network, offers lower computational complexity, and achieves enhanced input–output nonlinearity mapping through functional expansion of input signals. Basis functions such as Legendre, Trigonometric, Laguerre, and Chebyshev are commonly used polynomials in FLN for expansion of input signal dimension. In this article along with the weight and bias vector of FLNs, optimal number of polynomial functions for each category of basis function is selected by genetic algorithm during training process rather fixing them earlier. Therefore, an optimal FLN structure is crafted on fly from exploitation of training data. The optimal FLN models are used to predict the daily, weekly, and monthly closing prices of Bitcoin. A comparative study among optimal FLNs is carried out using evaluation metrics such as MAPE, NMSE, ARV, and U of Theil’s statistics. Finally, outcomes from experimental and comparative studies suggested the superiority of optimal FLN models for Bitcoin closing price prediction.

The SPEEK-PVA-Silica hybrid membranes are prepared by solution casting method. The physical, chemical and electrical properties of the polymer membranes are studied by FESEM, FTIR, XRD, DSC, TGA, DMA, water and methanol uptake capacity, Ion Exchange Capacity (IEC), Degree of Sulfonation (DS), hydration number (λ), void volume fraction (%), methanol permeability and proton conductivity. The void volume fraction and density of the polymer membrane increases with increase in the silica content of the membrane which decreases glass transition temperature. At 30 °C, the maximum proton conductivity is found for SPS-3 membrane (3.8 × 10–2 S/cm) which is much higher than recast SPEEK membrane (2.9 × 10–2 S/cm). The proton conductivity of the polymer membrane is dominated by free water facilitated vehicular mechanism. The improvement of the conductivity of the proton in the silica filled polymer membrane at medium to higher temperature is due to the strong interaction between the silica and water resulting in a reduction in the loss of evaporation of water as well as an improvement in the bound water content of the polymer channel, which is regulated by the Grotthuss mechanism. The reduction of methanol permeability and increment in membrane selectivity clearly confirms that the silica filled polymer have excellent methanol blocking capacity which is beneficial for direct methanol fuel cell design.

The aim of this review was to determine the role of surgery in treating hypothalamic hamartoma (HH) causing isolated central precocious puberty (CPP). Literature review was done according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Patients with isolated CPP due to HH, managed with surgical resection, were included. We found 33 studies, reporting 103 patients (76 pedunculated, 27 sessile). Patients were considered "cured" if the symptoms of PP had regressed and the hormone profile had normalized after surgery. Indications for surgery included hamartoma deemed surgically resectable (n-12), for the purpose of tissue diagnosis (n-3), partial response/failure of preoperative therapy (n-9), and unable to afford/to avoid long-term medical therapy (n-7). The extent of resection was total (TR) (n-39), near total/subtotal (NTR/STR) (n-20), partial (PR) (n-35), or unspecified (n-9). On follow-up (range: 3 months-16 years), 73.6% (56/76) of patients with pedunculated HH were cured, while 17.1% (13/76) had partial relief. Only 3/27 (11.1%) of patients with sessile HH were cured. All patients with a pedunculated hamartoma who underwent TR (n=36) improved, with 88.88% cured of the symptoms. Surgery had no effect in 17/23 (73.9%) patients with sessile HH who underwent PR. Psychological symptoms improved in 10/11 patients. There was no mortality. Permanent complications, in the form of 3rd nerve palsy, occurred in 3.7% (2/54) of the patients. To conclude, in the current era of availability of GnRH analogs, surgical resection in a subset of patients may be acceptable especially for small pedunculated hamartomas.

In the recent times, many studies have been devoted toward rectangular excavations but only a few studies have considered the “corner effect” in the optimized design of soil nail wall-retaining pile-anchor cable supporting systems, especially in large-scale deep foundation pit environments excavated by the central-island technique. Corner effect not only increases the construction costs but also may pose a risk to the safety and stability of such pits. In this paper, changes in the lateral displacement of retaining pile, soil at 1 m away from the foundation pit, crown beam, and the settlement of ground surface and surrounding buildings were extensively investigated based on the field measurements and numerical simulations of a large-scale deep foundation pit in Gaoxin zone, Xi’an, China. In addition, the supporting structure was optimized by considering the lateral influence zone of the corner effect. The optimization scheme proposed in this study not only satisfies the safety requirements of foundation pit supports, but also reduces the construction costs.

Infrastructure forms the backbone of a nation’s growth and economy. The smooth operation of those infrastructure assets depends on many factors including proper use of construction materials under different loading, environmental and durability assessments, and the frequency of monitoring after construction. The performance and maintenance of an infrastructure asset thus depend on the behavior of the system in a built geological environment. Proactive monitoring of infrastructure often leads to preventive maintenance. However, it is not economically feasible to use the current traditional monitoring techniques, especially considering the vastness of the infrastructure networks. In this study, unmanned aerial vehicle–close-range photogrammetry (UAV–CRP) technology is being proposed as a supplemental data collection tool to complement existing traditional monitoring techniques for geotechnical infrastructure. Two case studies covering a pavement structure built over rehabilitated subgrade rich with sulfates and stability of a rock slope adjacent to an old rail line were monitored aerially to understand their state of health conditions. The pavement site had a history of experiencing sulfate-induced heaving and was rehabilitated using extended mellowing after lime stabilization. The rail line under inspection was constructed more than a century ago, and the stability of the weathered rock cut holds the key for safe operations on the rail line. The rock was highly weathered and considered to undergo circular failure. Aerial images were collected and processed to build three-dimensional models to evaluate and assess the condition of these geotechnical assets. This approach not only provides a comprehensive idea through dense point cloud models offering real-field-like view of the asset conditions but also is expected to result in significant savings in data collection time and costs.

Histone deacetylases (HDACs) have been described to have both neurotoxic and neuroprotective roles, and partly, depend on its sub-cellular distribution. HDAC inhibitors have a long history of use in the treatment of various neurological disorders including epilepsy. Key role of HDACs in GABAergic neurotransmission, synaptogenesis, synaptic plasticity and memory formation was demonstrated whereas very less is known about their role in drug-resistant epilepsy pathologies. The present study was aimed to investigate the changes in the expression of HDACs, activity and its sub-cellular distribution in mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) patients. For this study, surgically resected hippocampal tissue specimens of 28 MTLE-HS patients and 20 hippocampus from post-mortem cases were obtained. Real-time PCR was done to analyse the mRNA expression. HDAC activity and the protein levels of HDACs in cytoplasm as well as nucleus were measured spectrophotometrically. Further, sub-cellular localization of HDACs was characterized by immunofluorescence. Significant upregulation of HDAC1, HDAC2, HDAC4, HDAC5, HDAC6, HDAC10 and HDAC11 mRNA were observed in MTLE-HS. Alterations in the mRNA expression of glutamate and gamma-aminobutyric acid (GABA) receptor subunits have been also demonstrated. We observed significant increase of HDAC activity and nuclear level of HDAC1, HDAC2, HDAC5 and HDAC11 in the hippocampal samples obtained from patients with MTLE-HS. Moreover, we found altered cytoplasmic level of HDAC4, HDAC6 and HDAC10 in the hippocampal sample obtained from patients with MTLE-HS. Alterations in the level of HDACs could potentially be part of a dynamic transcription regulation associated with MTLE-HS. Changes in cytoplasmic level of HDAC4, 6 and 10 suggest that cytoplasmic substrates may play a crucial role in the pathophysiology of MTLE-HS. Knowledge regarding expression pattern and sub-cellular distribution of HDACs may help to devise specific HDACi therapy for epilepsy.

Buffer/backfill material is an important engineering barrier in a deep geological repository of high-level radioactive waste (HLW). Its thermo-hydro-mechanical (THM) performance is very important for the safe and stable operation of the HLW repository system. Natural graphite powder mixed with sodium bentonite forms a buffer/backfill material that can dissipate heat quickly and provide strong isolation. In this paper, the THM characteristics of bentonite–sand–graphite–polypropylene fiber (BSGF) mixtures, used as a buffer/backfill material, were studied through a series of laboratory tests. The influence of graphite and polypropylene fiber contents on thermal conductivity, swelling pressure, hydraulic conductivity, and strength properties of BSGF mixtures with different sand contents was analyzed. Experimental results indicated that the graphite content, the maximum graphite mesh number, and the initial dry density of bentonite–graphite mixtures influenced the thermal conductivity of bentonite–graphite mixtures. The addition of polypropylene fiber was found to enhance the shear strength and inhibit cracking without significantly affecting the expansivity, permeability, and thermal conductivity of the BSGF mixtures. This study provides a new buffer/backfill material that can improve the stability, functionality, and thermal efficiency of the HLW repository.

Rescuing Hardware from malware attacks is a great challenge today. Moreover detecting the presence of malicious intrusion using low-cost techniques is very challenging especially when it is believed that hardware Trojans are integrated into the rarely excited nodes. Though logical testing is admitted to be the accurate way to check the functional correctness of the circuit, the method becomes inefficient for circuits requiring humongous number of test patterns to activate the hardware trojan. This paper proposes a probabilistic approach to generate a set of test patterns that activate the Trojan explicitly by observing the incorrect responses to the applied test patterns. Our experimental result in ISCAS’85 benchmark circuits show that the test patterns generated by our approach enclose all nets where there is a high chance for trojan insertion. We stress that though our results are circuit specific, the proposed approach is generic and hence it can be applied to any digital circuit. In fact, we demonstrate that for the C880 circuit, our approach requires only 217 inputs to be tested whereas the naive approach needs 260 test patterns. In addition to the experimental results, we use a game-theoretic framework to show the effectiveness of our approach in generating trojan activating test patterns compared to naive and ATPG testing process.

Hyperglutamatergic activity in the hippocampus is a major feature of patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS). Here we investigated whether tonic α7 nicotinic receptor (nAChR) activity could contribute to enhanced glutamatergic activity in the hippocampus of patients with MTLE-HS. Results showed that frequency and amplitude of glutamatergic events recorded from pyramidal neurons in the hippocampal samples obtained from patients with MTLE-HS were altered by α7 nAChR antagonist, methyllycaconitine, suggesting α7 nAChRs may influence hyperexcitability in MTLE-HS.

Under the combination of heavy loading and high moisture content, the metastable structure of natural loess can easily lead to uneven settlement and damage the overlying infrastructure. Using traditional binder such as cement has harmful impact on the environment, especially due to resource consumption and carbon emission. This research has identified the feasibility of using red mud waste as a partial replacement of cement for loess subgrade treatment in terms of dynamic properties and environmental impact. The performance of loess treated with a combination of waste red mud (RM) and small amount of cement additive (C) is evaluated by considering the complex engineering geological conditions. The results show that dynamic stress (σd) and moisture content (w) have a more significant influence on the dynamic properties of RMC-treated loess compared with confining pressure (σ3) and loading frequency (f). Higher w shows a remarkable reduction in the dynamic load resistance of treated loess, yet the addition of RMC still can improve the microstructure and water sensitivity of loess. Specifically, the failure dynamic stress (σdf) and the maximum dynamic elastic modulus (Edmax) of the treated loess at higher w are found to be 100% and 400% higher than those of untreated loess respectively. RMC treatment also improved the dynamic cohesive (cd) value from 23.2 to 173.6 kPa compared with untreated loess. In addition, the leaching toxicity and radiation of RMC-treated loess indicate that it does not pose any risk to the groundwater. Finally, revised Monismith model has been developed based on the proposed formula for predicting power index b, which can be capable of describing the long-term deformation stability under cyclic loading.

The system of multiple agents working in coordination for a given task has several advantages on faster completion, fault-tolerance, etc. To develop a multi-agent graph exploration technique, the following are defined: 1) the way agents communicate, 2) initial placement of agents and 3) the role of each agent in a systematic search of (unexplored) vertices/edges in the graph. However, the general concepts and requirements are not developed. Hence, we attempt to generalize the multi-agent system for graph exploration. The graph may or may not be known a priori. The proposed representation for the multi-agent system is aimed to provide general conditions for declaring completion and finite time completion applicable to any multi-agent system for graph exploration. Results are proved using linear algebraic concepts on graphs. We also propose modifications in the incidence matrix as a data structure for organized information exchange between the agents. Further, a generic algorithm for the system of multiple agents exploring a graph is developed. The algorithm is based on the proposed generalized framework and modified incidence matrix. Simulation videos show the applicability of proposed generalization in exploring a graph using multiple agents for various combinations of initial placements of agents, search strategy, and interagent communication method. Further, We use the generalization to show the application of the proposed method in multi-robot exploration and map building of an unknown environment represented by a graph.

Application of unmanned aerial vehicles (UAVs) for civil infrastructure monitoring has gained impetus owing to the advancement of aerial platforms paralleled with the development of sophisticated sensors. Photogrammetry is the science of measuring distances from two or more images, and close-range photogrammetry (CRP) is a part of photogrammetry that involves calculating measurements of an object within a maximum distance of 305 m away from the inspecting sensors. Geotechnical problems including differential heaving and related cracking of expansive soils cause extensive damage to pavement infrastructure. Feasibility of using UAV–CRP technology in health monitoring of pavement infrastructure constructed over problematic soils has been comprehensively studied, and this paper presents an overview of these results. Pavement performance data including longitudinal and transverse slopes, as well as distress conditions such as pavement surface cracking, pothole formation, and rutting or excessive deformation, are monitored via UAV–CRP technology, and these data sets are comprehensively analysed. UAV–CRP-interpreted performance indicators showed a very good agreement with those obtained from traditional methods surveys and profiler studies. With the further research, the UAV–CRP technology will play an important engineering role in safe, inexpensive and comprehensive health monitoring of infrastructure built over problematic soil conditions.

The aim was to study surgical outcomes in hangman’s fractures in paediatric and adolescent patients and to demonstrate evolution in posterior surgery from C1-C2-C3 fusion to C1 sparing techniques. Patients (aged ≤ 18 years) operated at a tertiary level centre between September 2011 to February 2018 with more than 1 year of follow-up were included. Neurological status, type of fracture, operating time, blood loss, follow-up, and complications were assessed. Nine patients were included, with mean age mean of 16.45 years, with a mean follow-up of 42.78 months. Six patients having neurological deficit showed improvement. Two patients, one having undergone C1-C3 lateral mass screw rod fixation (LMSF) and other had C2 pedicle screw with C3 LMSF, developed kyphosis for which fixation was further extended caudally. One patient with an old hangman’s fracture with reabsorbed axis pedicle underwent C2 body screw along with C3-C4 pedicle screw rod fixation and C2 pedicle reconstruction. All patients showed evidence of postoperative fusion. Hangman’s fractures in young patients can be successfully managed via posterior fixation. In our centre, we have evolved in the direction of motion preservation at C1 C2 joint, along with 3 column stable fixation of the C2 pedicle. C2 pedicle reformation has allowed motion preserving surgery in complex fracture types. Extension of construct till C4 in selected cases is important to prevent postoperative kyphosis.

Intense cyclones, severe heat waves, droughts, floods, and storms are part of life in Odisha, but more robust systems and advanced technologies are improving outcomes. Intense cyclones, severe heat waves, droughts, floods, and storms are part of life in Odisha, but more robust systems and advanced technologies are improving outcomes.

Pressuremeter test (PMT) is conducted to obtain effective soil parameters such as limit pressure (PL) and pressuremeter modulus (Ep) that are frequently used in calculating foundation bearing capacity, settlement, and foundation behavior. However, the application of PMT in China was limited due to higher cost and time. There is a need for identifying a suitable method and establish models to predict reliable PL and Ep for interpreting or cross-checking soil parameters. Piezocone test (CPTU) offers an ideal test method to develop correlation models since it is widely adopted for geotechnical investigations in China. In this study, artificial neural networks (ANN) have been used to develop CPTU-PMT correlations. A total of 92 sets of sandy soil and 65 sets of clayey soil data from four testing sites were collected using CPTU and PMT. ANN was employed to develop 4 models, half of them considering effective overburden stress ( $$ {\sigma}_{v0}^{\hbox{'}} $$ ), for predicting PL and Ep from CPTU data. The obtained ANN models were validated using the measured values of PL and Ep from pressuremeter tests and also the predicted values based on previous correlations. The comparison results show that PL and Ep values predicted by ANN models proposed in this study are more consistent with the measured values at testing sites. Additionally, foundation settlements were measured from a load test and compared with predictive settlements obtained using PL and Ep estimated by the proposed ANN correlation models. The results have shown that the CPTU results can be used to accurately predict PMT parameters and derive settlements.

Persons with drug refractory TLE have the option of being managed by surgery. They may develop memory impairment with specific etiology of mesial temporal sclerosis and anterior temporal lobe resection (ATLR). The study evaluated the semantic verbal memory outcomes in pre- and post-surgery temporal lobe epilepsy (TLE) patients using functional MRI and voxel morphometric methods. Twenty consecutive persons with drug-resistant epilepsy (DRE) and 20 healthy controls were recruited after obtaining the institute ethics approval. The fMRI scans were performed on a 1.5 T MR Scanner using standardized semantic verbal memory tasks using a native Hindi paradigm, before and after an anterior temporal lobectomy (in cases). A task-based functional connectivity (FC) was estimated using a conn toolbox. Data analysis was carried out using the statistical parametric imaging (SPM12) and CAT12 toolbox. Post-surgery TLE group showed increased robust FC in the right middle and posterior temporal regions as compared to pre-surgery session. A significant reduction in grey matter volume was observed in the left temporal lobe post-operatively as compared to presurgery and healthy control groups. In the post-surgery TLE group, neuropsychological scores were reduced in specific PGI domains such as visuospatial, working memory, and executive functioning. Our results may help in understanding of memory reorganization in TLE post-operatively.

A pyridylhydrazone incorporating an anthracene moiety, designated as HLAnc, has been synthesized in order to examine its coordination behaviour towards rhodium(III). The complex [RhLAnc(PPh3)2Cl2], incorporating a four-membered metallacycle, has been isolated and authenticated by a single-crystal X-ray diffraction study. The complex shows interesting redox and optoelectronic properties, and to better understand these, theoretical investigations have been performed using density functional theory (DFT) and time-dependent DFT. The visible excitation for the complex arises from primarily mixed singlet-manifold 1ILCT and 1LLCT transitions.

A comparative life cycle assessment of CO2 capture potential of microalgae, Scenedesmus dimorphus, during autotrophic cultivation in closed airlift photobioreactor and open raceway pond under Indian conditions has been carried out. The LCA inventory inputs for the both the systems were entirely based on primary experimental data obtained by algal cultivation in 1000-L pilot-scale raceway pond and 5-L airlift photobioreactor, respectively. The primary energy demand, environmental impacts and Net CO2 capture efficiency of both the systems were evaluated. The results indicated that the primary energy demand and GWP of airlift photobioreactor were 3.7-fold higher than raceway pond. The NERCO2 (defined as net emission ratio of CO2 and estimated as ratio of CO2 sequestered/CO2 emitted) of raceway pond was fourfold higher as compared to airlift photobioreactor; however, both the cultivation systems were observed to be net negative in terms of CO2 sequestration. However, the process can become net CO2 positive, with up to fourfold and 7.5-fold enhancement in biomass productivities (wrt base value) for raceway pond and airlift photobioreactor, respectively. Further, LCA predictions indicated that utilization of energy-efficient motors with lower specific energy consumptions for microalgae cultivation would facilitate algal cultivation systems to achieve net CO2-positive sequestration.

An indoor wireless network comprises of multiple devices connected wirelessly in a small geographical area, giving mobility to the connected devices. RF signal associated with wireless networks suffers from many propagation losses. It becomes essential to estimate losses at different locations, in the intended coverage area, to attain the desired performance seamlessly. It helps in the optimal device placement in the wireless networks. In the application, in addition to the propagation losses, there is a need to estimate the loss induced by the presence of smoke and fire, individually, in an indoor environment. This paper first deals with the design of a heatmap generating tool, in MATLAB, which gives the approximate signal strength at different locations on a given floor for a given transmitter. Then the paper discusses the formation of plasma in fast flaming fires and presents the path loss due to it. This path loss is incorporated in the heatmap generating tool and a heatmap is generated for our office ground floor for the case of a flaming fire. The paper also describes a testbed, setup in our office premises, to study the scattering loss caused by smoke particles.

Uncertainty and complexity associated with the stock data make the exact determination of future prices impossible. Successful prediction of a stock future price requires an efficient prediction system. This paper proposes an artificial chemical reaction optimization based functional link network termed as ACFLN for stock market forecasting. The efficiency of the proposed model has been evaluated by forecasting five real stock market prices such as BSE, DJIA, NASDAQ, TAIEX and FTSE. Different experiments are conducted to evaluate the performance of the proposed model such as forecasting the stock price 1 day ahead, 1 week ahead, and 1 month ahead. Data is obtained for all the working days in a year and for each data the said experiments are conducted. From simulation studies, it is revealed that the proposed model achieves better forecasting accuracies over others.