Lion Foundation

El presente escrito resalta la importancia de cuatro figuras jurídicas, clasificadas como instrumentos de política ambiental por la ley general de la materia en México, cuya finalidad es fomentar el respeto a las disposiciones por los gobernados, tratando de crear una conciencia social para que se realicen actividades en diversos órdenes, acordes al medio ambiente, no comprometiendo las condiciones que permiten la vida, presente y futura. Dichos instrumentos son: planeación, económicos, autorregulación y auditorías, investigación y educación.

Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photo-plethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform.

Deep learning plays a vital role in day-to-day applications. Recent studies show that deep learning models are not resilient against adversarial attacks, which is also applicable to Deep Reinforcement Learning (DRL) agents. Considering sensitive use cases of the DRL agents, there is a pressing need to make them robust to adversarial attacks. However, to design an efficient defense, it is imperative that we fully understand the vulnerability of such agents. In this work, we propose statistical and model-based approaches to identify critical states in an episode. Our work shows that by attacking less than 1% of the total number of states, the agent performance can be reduced by more than 40%. Furthermore, we model a long-term impact classifier to identify critical states. This method reduces the average compute time by 80.3% when compared to previous approaches.

現在，ストレス社会により唾液量が減少し口腔乾燥を訴える患者が増えている．唾液量の減少は，歯科口腔疾患だけでなく，嚥下障害にも関連し全身の健康に影響を与える．我々は唾液分泌を促進する簡便で安全な方法としてにおい刺激に着目し，本研究ではパイナップルの香り刺激に対する唾液量と唾液中コルチゾール濃度への影響を検討することを目的とした．健常成人10名を対象に，香りなし試行と香りあり試行の2試行を実施させ，実験中の唾液量と唾液中コルチゾール濃度を測定した．香りあり試行では，パイナップル香料を滴下したムエットを被験者の鼻孔から約3 cmの位置に設置し，鼻呼吸を行わせることにより香り刺激を与えた．その結果，パイナップルの香りにより唾液量は有意に多くなったが，唾液中コルチゾール濃度の有意な差は認められなかった．

Freezing of Gait (FoG) is a common gait deficit among patients diagnosed with Parkinson's Disease (PD). In order to help these patients recover from FoG episodes, Rhythmic Auditory Stimulation (RAS) is needed. The authors propose a ubiquitous embedded system that detects FOG events with a Machine Learning (ML) subsystem from accelerometer signals. By making inferences on-device, we avoid issues prevalent in cloud-based systems such as latency and network connection dependency. The resource-efficient classifier used, reduces the model size requirements by approximately 400 times compared to the best performing standard ML systems, with a trade-off of a mere 1.3% in best classification accuracy. The aforementioned trade-off facilitates deployability in a wide range of embedded devices including microcontroller based systems. The research also explores the optimization procedure to deploy the model on an ATMega2560 microcontroller with a minimum system latency of 44.5 ms. The smallest model size of the proposed resource efficient ML model was 1.4 KB with an average recall score of 93.58%.

This paper proposes a generic architectural design for a microgrid cluster using a smart energy routing scheme resulting in effective utilization of excess energy and mitigated overall cost. The Clustered Microgrid Design (CMD) presents the Storage Agent Replacement Optimization (SARO) technique which uses intelligent throughput estimation to corroborate accurate replacement cycle of the system, a Smart Energy Routing Algorithm (SERA) that efficiently captures and utilises excess solar energy which is shared within the integrated microgrid cluster and an Energy Storage Optimization (ESO) technique that enhances the CMD by resizing the storage agents. The CMD determines the optimised systems for cost and efficiency at various Capacity Shortages (CS). The proposed framework is implemented for a cluster of three interconnected microgrids, where a decrease of 11.29% in Lifetime Cost is observed for a 10% CS and an efficiency gain of 4.99% is discerned for a CS of 20%.

This paper presents a two-stage optimization strategy for enhancing irrigation load servicing in existing stand-alone rural Indian microgrids. In this case study, the system design for an operating 8 kW rural microgrid in Bihar, India is taken under consideration. The rural loads are categorized as critical, irrigation and non-critical loads and are serviced by the Default Microgrid System (DMS). Optimization of the DMS is carried out in Intelligent Microgrid System (IMS) which involves two stages - Prioritized Load Management (PLM) and Energy Storage Enhancement (ESE). PLM serves the load demand based on defined priorities resulting in improved irrigation efficiency which plays a pivotal role in the rural context. The ESE optimizes the system storage size which results in maintained system cost. The resultant system increases the irrigation efficiency by 10.24% and improves the overall crop yield by 6.54% for this particular case study. However, this two-stage method is generic and can be employed across any rural Indian microgrid where the microgrid and solar pump operate as a hybrid system.

This paper presents a flexible energy transfer algorithm for the pre-existing Indian rural micro-grid and the irrigation system. Dharnai, a small village in Jehanabad district, Bihar (25.0125N, 84.9792E) uses the Dharnai Microgrid system (DMG) for community electrification and its agricultural load is serviced by Dharnai Irrigation System (DIS). The irrigation load demand for the project lifetime of 20 years is modeled using climatic variations like evapotranspiration, rainfall and crop pattern. Since the DIS and DMG systems are not integrated the excess energy generated is rendered unused. The efficient utilization of excess energy is achieved by interconnecting the two systems as they are less than 2 km apart. In this paper, a smart energy transfer algorithm that can be implemented on pre-existing systems is developed to predict and transfer energy. The algorithm is made flexible and dynamic so that the efficiency of a preferred system can be boosted based on village community requirements. A relative efficiency increase in the overall system of 10.57% is obtained using the algorithm and on implementing preferential energy transfer, a relative efficiency increase of 7.8% and 13.05% is achieved in DIS and DMG respectively.

Activity Recognition (AR) is a subset of pervasive computing that attempts to identify physical actions performed by a user. Previous sensor-based AR systems involve computation and energy overheads incurred by the use of heterogeneous and large number of sensors, however it is possible to arrive at an optimized system where the design involves optimization of energy consumption through number of sensors, computation through minimal set of features and cost through a nominal hardware platform ideally making it a multidimensional optimization. The above mentioned modelling was reflected in the construction of this optimized system as the design employs a single accelerometer and extracts only 7 time-domain features resulting in ease of computation to classify the activities, thus encouraging it to be inherently deployable on an embedded platform. The system was trained and tested on the accelerometer data acquired from three publicly available datasets. The performance of four chosen machine learning based classification models from an initial set of eight was evaluated, analysed and ranked on the grounds of efficiency and computation. The model was implemented on a Raspberry Pi Zero (USD 5) and the average time for feature computation and the maximum time taken to classify an instance of an activity was found to be 0.015 s and 1.094 s respectively, thus validating the viability of the system on an embedded platform and making it affordable to the population in the low-income groups.

The purpose of this study was to investigate the incidence of coronal and root caries in participants aged 20-59 years in Japan. The participants comprised 118 men and 23 women undergoing a re-examination 5 years after an initial examination. Lesions were classified as gingival recession, decayed root caries, or filled surfaces. The rates of incidence of coronal caries over the 5 years were 87% in women aged 20-29 years, 77% in men aged 20-29 years, 62% in men aged 30-39 years, 64% in men aged 40-49 years, and 60% in men aged 50-59 years. The rates for those observed to be at risk for the incidence of root caries were 25% for women aged 20-29 years, 16% for men aged 20-29 years, 11% for men aged 30-39 years, 43% for men aged 40-49 years, and 35% for men aged 50-59 years. The distribution for the number of new coronal and root caries lesions was 68.8% and 24.1%, respectively. Univariate logistic regression analyses for new coronal caries revealed that the risk factors were age group at baseline (OR=0.702, p=0.0440), number of coronal decayed and filled surfaces at baseline (OR=2.893, p=0.0107), and number of sound teeth at baseline (OR=3.381, p=0.0013). Moreover, univariate logistic regression analyses for new root caries revealed that the risk factors were age group at baseline (OR=1.787, p=0.0039), presence or absence of gingival recession at baseline (OR=3.288, p=0.0062), and presence or absence of root decayed and filled surfaces at baseline (OR=11.00, p<0.0001). It was concluded from these results that the target of caries prevention in adults should not only be focused on root surfaces, especially in those aged 40 years or over, but also on the coronal surfaces of the dentition in those in their twenties.