Search Results (139)

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

Uncertainty remains a central challenge in robotic navigation, exploration, and coordination. This paper examines how Partially Observable Markov Decision Processes (POMDPs) and their decentralized variants (Dec-POMDPs) provide a rigorous foundation for decision-making under partial observability across tasks such as Active Simultaneous Localization and Mapping (A-SLAM), adaptive informative path planning, and multi-robot coordination. We review recent advances that integrate deep reinforcement learning (DRL) with POMDP formulations, highlighting improvements in scalability and adaptability as well as unresolved challenges of robustness, interpretability, and sim-to-real transfer. To complement learning-driven methods, we discuss emerging strategies that embed probabilistic reasoning directly into navigation, including belief-space planning, distributionally robust control formulations, and probabilistic graph models such as enhanced probabilistic roadmaps (PRMs) and Canadian Traveler Problem-based roadmaps. These approaches collectively demonstrate that uncertainty can be managed more effectively by coupling structured inference with data-driven adaptation. The survey concludes by outlining future research directions, emphasizing hybrid learning–planning architectures, neuro-symbolic reasoning, and socially aware navigation frameworks as critical steps toward resilient, transparent, and human-centered autonomy. Full article

Downy mildew, caused by the oomycete Pseudoperonospora cubensis, is the most destructive foliar disease of cucumbers. While partially resistant slicer cultivars (with spined fruits) are commercially available, no resistant Beit Alpha cultivars (characterized by smooth, dark green fruit) have been developed to date. Here, we report the successful breeding of downy mildew-resistant Beit Alpha cucumber lines. Resistance was transferred from the wild Sikkim cucumber accessions PI 197088 and PI 330628 (characterized by round fruit, with heavily netted brown rind). The resistance and fruit phenotype were restored through backcrosses to elite commercial susceptible cultivars. Due to the recessive nature of the resistance genes and their distribution across multiple chromosomes, the breeding program required multiple backcrosses and stringent selections for both resistance and fruit type. Full article

After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area of Sikkim’s Pakyong district, which is facing severe water seepage and instability. The problem, intensified by the 2011 seismic event and ongoing local construction, is examined through subsurface fracture mapping using Vertical Electrical Sounding (VES) and profiling techniques. A statistical factor method, applied to interpret VES data, helped identify fracture patterns beneath the lake. Results from two sites (VES-1 and VES-2) reveal significant variations in weathered and semi-weathered soil layers, indicating fractures at depths of 17–50 m (VES-1) and 20–55 m (VES-2). Higher fracture density near VES-1 suggests increased settlement risk and ground displacement compared to VES-2. Contrasting resistivity values emphasize the greater instability in this zone and the need for cautious construction practices. The findings highlight the role of seismic-induced fractures in ongoing water depletion and underscore the importance of continuous dewatering to stabilize the swampy terrain. Full article

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

The Mutanchis, known by their derogatory exonymic term ‘Lepcha’, are autochthonous to Sikkim, India. The name ‘Mutanchi’ derives from the phrase ‘Mutanchi Rumkup Rongkup’, eliciting the response ‘Achulay’, meaning ‘Beloved children of It-bu-mu, who have come from the snowy peaks’. The nomenclature prompts an ontological understanding rooted in the community’s eco-geographical context. Despite possessing a well-developed script categorised within the Tibeto-Burman language family, the Mutanchis remain a largely oral community. Their diminishing, scarcely documented repository of Mutanchi clan narratives underscores this orality. As a Mutanchi, I recognise these narratives as a medium for expressing Indigenous value systems upheld by my community and specific villages. Mutanchi clan narratives embody spiritual and cultural significance, yet their fantastic rationale reveals complex epistemological tensions. Ideally, each Mutanchi clan reveres a chyu (peak), lhep (cave), and doh (lake), which are propitiated annually and on specific occasions. The transmigration of an apil (soul) is tied to these three sacred spatial geographies, unique to each clan. Additionally, clan etiological explanations, situated within natural or supernatural habitats, manifest beliefs, values, and norms rooted in a deep ecology. This article presents an ecosophical study of selected Mutanchi clan narratives from Dzongu, North Sikkim—a region that partially lies within the UNESCO Khangchendzonga Man-Biosphere Reserve. Conducted in close consultation with clan members and in adherence to the ethical protocols, this study examines clans in Dzongu governed by Indigenous knowledge systems embedded in their narratives, highlighting biocentric perspectives that shape Mutanchi lifeways. Full article

Sixteen long-ripened, high-quality Cabrales cheeses from independent producers underwent a comprehensive biochemical and microbiological characterisation. Significant variations in total microbial counts and specific microbial groups were observed among the cheeses. A metataxonomic analysis identified 249 prokaryotic amplicon sequence variants (ASVs) and 99 eukaryotic ASVs, respectively, which were classified into 52 prokaryotic and 43 eukaryotic species. The predominant species included bacteria of the genera Tetragenococcus, Lactococcus (of which Lactococcus lactis was used as a starter), and Staphylococcus, followed by Brevibacterium and Corynebacterium species. The starter mould Penicillium roqueforti was highly abundant in all cheeses; Debaryomyces hansenii, Geotrichum candidum, and Kluyveromyces spp. constituted the subdominant fungal populations. Glutamic acid (≈20 mg g⁻¹) was the most abundant free amino acid in all samples, followed by lysine, leucine, and valine (≈10–13 mg g⁻¹). Moderate-to-high amounts of the biogenic amines tyramine and ornithine were detected. A large variation between cheeses of the main organic acids (lactic, acetic, or butyric) was detected. Differences between samples were also observed for the majority volatile compounds, which included organic acids, alcohols, esters, and ketones. Positive and negative correlations between bacterial and fungal species were detected, as well as between microbial populations and key biochemical markers. Among the latter, Tetragenococcus halophilus correlated positively with ethyl caprylate and hexanoic acid, and Loigolactobacillus rennini correlated positively with γ-aminobutyric acid. Conversely, Staphylococcus equorum showed a strong negative correlation with ethyl caprylate and capric acid. These microbial and biochemical insights enabled us to propose a microbiota-based starter culture comprising prokaryotic and eukaryotic components to enhance Cabrales cheese quality. Full article

This study assesses the risks of glacial lake outburst floods (GLOFs) from moraine sediment dams around Gurudongmar Lake in the Northern Sikkim Himalayas at an elevation of 17,800 feet. It focuses on three moraine sediment dams, analysing the implications of slope failure on the upstream side and the downstream stability under steady seepage conditions, as well as the risks posed by permafrost thawing. Using a comprehensive methodology that includes geotechnical evaluations, remote sensing, and digital elevation models (DEMs), the research employs finite element analysis via PLAXIS2D for the stability assessment. The main findings indicate a stratification of sediment types: the upper layers are loose silty sand, while the lower layers are dense silty sand, with significant variations in shear strength, permeability, and other geotechnical properties. Observations of solifluctions suggest that current permafrost conditions enhance the dams’ stability and reduce seepage. However, temperature trends show a warming climate, with the average days below 0 °C decreasing from 314 (2004–2013) to 305 (2014–2023), indicating potential permafrost thawing. This thawing could increase seepage and destabilise the dams, raising the risk of GLOFs. Numerical simulations reveal that scenarios involving water level rises of 5 and 10 m could lead to significant deformation and reduced safety factors on both the upstream lateral dams and downstream front dams. The study emphasises the urgent need for ongoing monitoring and risk assessment to address the potential hazards associated with GLOFs. Full article

Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)—indoor office and urban microcellular (UMi)—street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system’s average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of ∼10 dB for both InH—indoor office and UMi—street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in ∼7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least ∼5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by ∼6 dB. Full article

We, a group of native scholars based in the Himalayan region, co-author this article to propose an environmental humanities South—concurrently as an Asia-specific interdisciplinary field and a planetary human–nature epistemology of the Global South inextricably entwined with that of the Global North. Framed in the broader field of planetary health, this article begins with a perspectival shift by reconceptualizing the Global South and the Global North as the Planetary South and the Planetary North for the purpose of laying the epistemological groundwork for two interconnected arguments and subsequent discussions. First, the Planetary South is not merely epistemological, but is at once geographically epistemological and epistemologically geographical. Our debates with the currently dominant epistemologies of the South open up a decolonial conversation with what we call the Australian School of the environmental humanities, the initial seed bank of our interdisciplinary environmental work in Asia’s Planetary South. These multilayered epistemological debates and conversations lead to the second argument that the South and the North relate to one another simultaneously in symbiotic and paradoxical terms. Through these two arguments, the article addresses the conundrum of what we call the “postcolonial continuation of the colonial environmentality” and attempts to interweave the meaningful return of the eroding Himalayan native knowledges of nature with modern scientific findings in a way that appreciates the livingness of the earth and is inclusive of nonwestern environmental worldviews. Full article

The Border Gateway Protocol (BGP) is the backbone of inter-domain routing on the internet, but its susceptibility to both benign and malicious anomalies creates substantial risks to both network reliability and security. In this study, we present a new approach for deep learning-based BGP anomaly detection utilizing duplicate announcements, which are known to be a symptom of routing disruptions. We developed our methodology based on public BGP data from RIPE and Route Views. We used the number of duplicate announcements as a baseline against which we checked for sporadic and time-based anomalies. Here, we propose a deep learning framework based on the Exponential Moving Average (EMA) model in combination with Autoencoder for anomaly identification. We also apply the Temporal-oriented Synthetic Minority Over-Sampling Technique (T-SMOTE) to overcome data imbalance. Comparative evaluations show that the Autoencoder model is significantly better than LSTM and that existing state-of-the-art methods have higher accuracy, precision, recall, and F1 scores. This study proposes a reliable, scalable, and rapid framework for real-time BGP adversary detection, which improves network security and resilience. Full article

Downy mildew caused by the oomycete Pseudoperonospora cubensis is a devastating disease of cucurbits. Cucumis species are attacked by pathotype 3 (clade 2) of the pathogen, while Cucurbita species are attacked by pathotype 6 (clade 1). The Sikkim-type cucumbers PI 197088 and PI 330628 express high levels of resistance against both pathotypes (clades) of the pathogen but no green-fruit cucumber cultivars resistant to the disease are available on the market. Here we report on several non-Sikkim accessions of cucumber that show resistance against downy mildew in four consecutive seasons. Mean % foliage attacked with downy mildew in the susceptible controls Ilan and SMR-18 was 93% and 71%, respectively, as against 0.2% and 1.8% in the Sikkim-type resistant controls PI 197088 and PI 330628, respectively. Twenty-four green fruit accessions were significantly more resistant than the susceptible cucumber controls. Five accessions showed less than 10% infected leaf area with downy mildew as follows: PI 432870—5%, PI 390266—7.5%, PI 418964—8.5%, PI 390258—8.8%, and G12—10%. PI 390258 and PI 390266 were susceptible to race 1 of powdery mildew but resistant to race 2, whereas PI 418964 was resistant to both races. These accessions may be used in breeding programs to accelerate the production of green-fruit, disease-resistant cucumbers. Full article

To understand the impact of climate change on water resources, this research investigates long-term rainfall trends and anomalies across Northeastern India (NEI), covering Assam and Meghalaya (A&M); Nagaland, Manipur, Mizoram, and Tripura (NMMT); and Sub-Himalayan West Bengal and Sikkim (SHWB&S) using different statistical tests including innovative trend analysis (ITA). The study scrutinizes 146 years of rainfall statistics, trend analyses, variability, and probability distribution changes to comprehend its implications. Furthermore, the change in the assured rainfall probabilities was also worked out to understand the impact on rainfed agriculture of Northeastern India. Comparative analysis between all India (AI) and NEI reveals that NEI receives nearly double the annual rainfall compared to AI (2051 mm and 1086 mm, respectively). Despite resembling broad rainfall patterns, NEI displays intra-regional variations, underscoring the necessity for region-specific adaptation strategies. Statistical characteristics like the coefficient of skewness (CS) and coefficient of kurtosis indicate skewed rainfall distributions, notably during the winter seasons in NMMT (CS~1.6) and SHWB&S (CS~1.5). Trend analyses reveal declining rainfall trends, especially conspicuous in NEI’s winter (−1.88) and monsoon (−2.9) seasons, where the rate of decrease was higher in the last three decades. The return periods of assured rainfall at 50% and 75% probability levels also increased sharply during the winter and monsoon seasons by over 30% during the recent half, posing challenges for rainfed upland hill farming. Furthermore, this study highlights increasing variability and negative anomalies in monsoon rainfall over NEI, exacerbating decreasing rainfall trends and significantly impacting agricultural productivity. These findings underscore the urgency for adaptive measures tailored to evolving rainfall patterns, ensuring sustainable agricultural practices and efficient water resource management. Full article

Salt stress is a major abiotic stressor that limits plant growth, development, and agricultural productivity, especially in regions with high soil salinity. With the increasing salinization of soils due to climate change, developing salt-tolerant crops has become essential for ensuring food security. This review consolidates recent advances in plant genetics, transcription factors (TFs), and next-generation sequencing (NGS) technologies that are pivotal for enhancing salt stress tolerance in crops. It highlights critical genes involved in ion homeostasis, osmotic adjustment, and stress signaling pathways, which contribute to plant resilience under saline conditions. Additionally, specific TF families, such as DREB, NAC (NAM, ATAF, and CUC), and WRKY, are explored for their roles in activating salt-responsive gene networks. By leveraging NGS technologies—including genome-wide association studies (GWASs) and RNA sequencing (RNA-seq)—this review provides insights into the complex genetic basis of salt tolerance, identifying novel genes and regulatory networks that underpin adaptive responses. Emphasizing the integration of genetic tools, TF research, and NGS, this review presents a comprehensive framework for accelerating the development of salt-tolerant crops, contributing to sustainable agriculture in saline-prone areas. Full article