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The International Telecommunication Union (ITU) has recently announced the detailed 5G specifications for International Mobile Telecommunications (IMT-2020). A number of candidate Radio Interface Technologies (RITs) were being evaluated by the independent evaluation groups of the ITU. Meanwhile, the roll out of fifth generation (5G) is now going on, and 5G services are offered by more than 160 mobile network operators (MNO). This paper presents the evaluation of the proponent technologies, including the ones specified as $3^{\text {rd}}$ generation partnership project (3GPP) 5G new radio (NR). The entire 3GPP specifications were examined and evaluated through simulation using Matlab and a custom simulator based on the Go-language. The simulator facilitated the comprehensive evaluation of the 5G NR performance by using the IMT-2020 evaluation framework. Some of the submitted technologies displayed certain discrepancies which were reported to ITU as well as discussed with proponents to improvise the shortcomings. The detailed results and observations are presented in this paper.

Melioidosis caused by Burkholderia pseudomallei has become an important clinical threat, especially in Northern Australia and Southeast Asia. However, the genome information on this pathogen is limited. B. pseudomallei isolates identified from bloodstream infections from inpatients were subjected to whole-genome sequencing by IonTorrent PGM and MinION Oxford Nanopore sequencing technologies. Highly accurate complete genomes of two strains, VB3253 and VB2514, were obtained by a hybrid genome assembly method using both short and long DNA reads. Both isolates carried blaPenI and carbapenemase-encoding blaOXA-57 genes, although the isolates were susceptible to imipenem by E-test method with MIC 1 μg/mL. Multiple IS family transposases specific for all non-fermenting Gram-negative bacteria (NFGNBs)—especially IS3 and IS5, which facilitate mobilization of extended-spectrum β-lactamase (ESBL) and carbapenemase genes—were carried in these genomes. This further adds to the complexity of gene transmission. These IS families were identified only upon hybrid genome assembly and would otherwise be missed. Keywords: blaOXA-57, blapenl, Burkholderia pseudomallei, imipenem, phylogeny, SNPs

The experimental and simulation studies of the radiation performance enhancement of a dipole antenna using metal strip grating are presented in this paper. The subwavelength imaging configuration of the metal strip grating is utilized for enhancing the radiation performance of a dipole antenna working in the S-band. The resultant design shows a gain of 9 dBi and front to back ratio of the design is found to be -23 dB at resonance. The coupling between electric and magnetic resonances provides the necessary impedance matching performance when the antenna is brought in the vicinity of the grating.

Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, derived from the time series using the EEMD, to a detailed analysis to draw information content of the time series. Also, we investigate the influence of random noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behaviour. Here, we extend the Fano factor and Allan factor analysis to the time series of state-to-state transition frequencies of a Markov chain. Our results support not only the usefulness of the intrinsic mode functions in understanding the time series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

Earthquake sequencing studies allow us to investigate empirical relationships among spatio-temporal parameters describing the complexity of earthquake properties. We have recently studied the relevance of Markov chain models to draw information from global earthquake catalogues. In these studies, we considered directed graphs as graph theoretic representations of the Markov chain model and analyzed their properties. Here, we look at earthquake sequencing itself as a directed graph. In general, earthquakes are occurrences resulting from significant stress interactions among faults. As a result, stress-field fluctuations evolve continuously. We propose that they are akin to the dynamics of the collective behavior of weakly coupled non-linear oscillators. Since mapping of global stress-field fluctuations in real time at all scales is an impossible task, we consider an earthquake zone as a proxy for a collection of weakly coupled oscillators, the dynamics of which would be appropriate for the ubiquitous Kuramoto model. In the present work, we apply the Kuramoto model with phase lag to the non-linear dynamics on a directed graph of a sequence of earthquakes. For directed graphs with certain properties, the Kuramoto model yields synchronization, and inclusion of non-local effects evokes the occurrence of chimera states or the co-existence of synchronous and asynchronous behavior of oscillators. In this paper, we show how we build the directed graphs derived from global seismicity data. Then, we present conditions under which chimera states could occur and, subsequently, point out the role of the Kuramoto model in understanding the evolution of synchronous and asynchronous regions. We surmise that one implication of the emergence of chimera states will lead to investigation of the present and other mathematical models in detail to generate global chimera-state maps similar to global seismicity maps for earthquake forecasting studies.

Abstract Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

Andaman and Nicobar Islands support a significant proportion of India’s herpetofauna. We conducted surveys for herpetofauna in the Nicobar Islands during 2008-2009. The Malayan Wolfsnake Lycodon subcinctus was recorded from Nicobar Islands for the first time. We also recorded Tiwari’s Wolfsnake Lycodon tiwarii from Car Nicobar Island for the first time since the original description. A large Colubrid Snake (Coelognathus sp.) of unknown identity was recorded from Great Nicobar Biosphere Reserve. These new records point to the inadequacy of surveys in the Andaman and Nicobar Islands, which may harbor several more unknown species.

Intraplate seismicity occurs in central and northern Canada, but the underlying origin and dynamics remain poorly understood. Here, we apply a graph theoretic approach to characterize the statistical structure of spatiotemporal clustering exhibited by intraplate seismicity, a direct consequence of the underlying nonlinear dynamics. Using a recently proposed definition of "recurrences" based on record breaking processes (Davidsen et al., 2006, 2008), we have constructed directed graphs using catalogue data for three selected regions (Region 1: 45°−48° N/74°−80° W; Region 2: 51°−55° N/77°−83° W; and Region 3: 56°−70° N/65°−95° W), with attributes drawn from the location, origin time and the magnitude of the events. Based on comparisons with a null model derived from Poisson distribution or Monte Carlo shuffling of the catalogue data, our results provide strong evidence in support of spatiotemporal correlations of seismicity in all three regions considered. Similar evidence for spatiotemporal clustering has been documented using seismicity catalogues for southern California, suggesting possible similarities in underlying earthquake dynamics of both regions despite huge differences in the variability of seismic activity.

Occurrence of Hemiphyllodactylus typus in the Andaman archipelago is confirmed based on fresh collections from two different sites namely Mt. Harriet National Park and Long Island. The veracity of an earlier report of this species from the Andaman Islands is discussed and revalidated.

Abstract Introduction Globally, there have been significant declines in HIV incidence over the past two decades, but this decline is slowing, and in some settings, declines have stalled or are growing—particularly where epidemics are concentrated in key populations (KPs). Understanding temporal changes in HIV incidence among KP is critical yet, due to logistical constraints, there are few sources of longitudinal incidence data, particularly among KP. Methods We present HIV incidence rates from June 2014 to December 2022 among cisgender men who have sex with men (MSM) and people who inject drugs (PWID) attending community‐based integrated care centres (ICCs) in 15 Indian cities. ICCs, established between 2014 and 2017, provide HIV testing and other services to MSM (eight sites) or PWID (eight sites). Client HIV testing data were included in the analysis if they had ≥2 tests and were not positive on the first test. We calculated incidence rates per 100 person‐years (PY), stratified by KP, city/site and year. Poisson regression explored associations of incidence with time, age, gender (PWID only) and ICC use. Results From June 2014 to December 2022, 13,501 clients (5722 MSM, 7779 PWID) had ≥2 HIV tests over a median of 1.8 years. There were a total of 1093 incident HIV acquisitions. Overall incidence rates for MSM and PWID were 1.9/100 PY (95% CI: 1.7−2.2) and 4.1 (3.9−4.4), respectively. Among MSM sites, incidence ranged from 0.4 to 3.5 and in PWID sites from 0.6 to 17.9. From adjusted models, incidence increased by 17% annually among MSM. Among PWID, incidence increased by 11% annually up until 2020 and then decreased by 29% after 2020; when excluding the outlier of New Delhi, incidence was stable among PWID. MSM and PWID 21−25 years old had the highest risk of HIV and among PWID, those more consistently engaged in medication for opioid use disorder were at the lowest risk. Conclusions While there was substantial geographic variability, MSM and PWID engaged in a free community‐based clinic experienced persistently high HIV incidence (>2/100 PY). KP in low‐ and middle‐income countries should be a focus when considering novel strategies such as long‐acting pre‐exposure prophylaxis to curtail incidence.

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

The physical parameters like temperature, odor, electrical conductivity, turbidity and pH of Wunna River in Maharashtra (India) at three different stations (upstream region- S,, effluent mixed region• S, and 700 m away from effluent mixed region-S,) were studied during the pre, mid and post• monsoon season. Marginal variations were recorded in temperature and turbidity while rigorous effect of effluent discharge on odor, electrical conductivity and pH was observed at the effluent mixed region as compared to the other stations. The flow of water in the river in different seasons was found to be an important factor responsible for variable impact of pollution. The data analysis confirms a great extent of pollution of the riverine eco-system at the site of release of textile industry effluents

The paper describes the chemical characteristics of Wunna River at Hinganghat, Wardha district of Maharashtra State. Various chemical characteristics such as BOD, COD, total alkalinity, total hardness, total dissolved solids, chloride, sodium, nitrate, chromium and iron were studied at 3 different station of Wunna River viz. upstream region (S,), effluent mixed region (5,) and 700 m away from effluent mixed region (S,). Marginal variations in chemical characteristics were noticed at station 1 and 3. However, higher values were recorded at station 2 i.e. effluent mixed region. Data analysis indicates heavy pollution of eco system of the river at the site of release of textile industry waste water ie. S,

Abstract Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.

The gharial Gavialis gangeticus is a Critically Endangered crocodylian endemic to the Indian subcontinent. The species has experienced a 95% population decline over the past 2 centuries. The largest self-sustaining population inhabits the protected National Chambal Sanctuary (NCS) in north India and represents >80% of extant gharials globally. We developed de novo a panel of polymorphic gharial-specific microsatellites, using whole genome information and microsatellite search tools. These 15 new markers have multiple numbers of polymorphic alleles that are more informative than those obtained from previous studies. Analyses of 93 scute samples collected across age classes from wild gharials residing in the NCS facilitated accurate assessments of genetic diversity and inbreeding coefficient and identified a historical bottleneck event. Estimates of the observed and expected heterozygosities were lower than those reported earlier. The inbreeding coefficient was low, and the population did not deviate significantly from Hardy-Weinberg equilibrium. The calculated M ratio and 2 heterozygosity tests detected a genetic bottleneck, which is consistent with historic sharp declines in population size, followed by recent recovery. These new gharial microsatellite markers are statistically robust and provide an improved means to assess the population genetics of the largest self-sustaining wild gharial population. This study will facilitate additional investigations on the genetic diversity of other extant gharial populations—not only the few remaining wild populations but also those in zoos and rearing facilities. Additional genetic studies of gharial in the NCS are warranted to inform management strategies.

A brain tumour is a malignant condition that spreads extremely quickly and requires rapid detection. In recent years, it has become apparent that deep learning is a promising technique for classifying brain tumours. To identify these tumours, this work proposes a hybrid CNN architecture, implementing InceptionV3, ResNet-50, VGG16, and DenseNet. To evaluate this approach, a total of 3929 images was obtained from Kaggle, including 2556 non-tumorigenic and 1373 tumorigenic specimens. Initially, the method extracts features from MRI pictures, then segments the tumour using the mask images. Subsequently, the segmented tumour image is fused with the original image, and finally the fused image is classified with the assistance of four distinct CNN models, specifically InceptionV3, ResNet, DenseNet, and VGG16. To enhance the performance of the hybrid architecture, these models were further optimized with oneAPI, which allows a comparative analysis of each individual model for classification. To evaluate the effectiveness of medical image classification models, a variety of evaluation criteria have been utilized extensively. We highlight the use of mean Intersection over Union (mIoU) as a more acceptable and intuitive statistic for evaluating the balance between true positives, false positives, and false negatives. This is in response to recent breakthroughs that have been made.

Lower limb amputees encounter considerable challenges in executing fundamental daily tasks, especially in cycling. The lack of natural ankle mobility significantly hinders participation in physical activities that necessitate accurate foot positioning, like biking. This study proposes a groundbreaking system aimed at enhancing the functionality of ankle prosthetics specifically for cycling. This system incorporates cutting-edge technologies, including a gyroscope sensor and RF communication modules, which facilitate real-time, adaptive control of the prosthetic ankle's movements, emulating the natural foot motion required during biking. This system features an MPU6050 gyroscope sensor that monitors the motion of the leg, a servo motor that actuates the prosthetic ankle, and 433 MHz RF transmitter and receiver modules for wireless communication between the sensor and the motor. The gyroscope sensor captures instantaneous changes in the orientation of the residual limb, transmitting this data wirelessly to the servo motor, which adjusts the ankle's position, allowing for precise control over dorsiflexion (lifting the foot upwards) and plantar flexion (pointing the foot downwards), essential movements for cycling, walking, and other lower limb activities. Furthermore, a customdesigned 3D bio-printed prosthetic ankle encases the servo motor, ensuring a natural fit for the amputee while providing a lightweight and durable structure capable of enduring the demands of cycling. The servo motor integrated into the prosthetic ankle enables smooth and responsive movement, facilitating a more natural and fluid pedaling experience. This system not only restores functionality but also improves comfort and stability, thereby enhancing overall mobility and promoting inclusivity in recreational pursuits. This system significantly elevates their quality of life. Its modular architecture facilitates future modifications for various mobility needs. [ABSTRACT FROM AUTHOR]