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Calcium nitrate tetrahydrate, used in fertilizers, wastewater treatment, and concrete admixtures, has limited toxicity data despite extensive industrial use. This study evaluated its repeated-dose and reproductive/developmental toxicity in Sprague Dawley rats following OECD TG 422, which combines TG 407 and 421 to extend dosing than TG 407 and reduce animal use compared with separate studies. Rats were administered 0, 100, 300, or 1000 mg/kg/day. Males were treated for 49 days and females from 2 weeks pre-mating to postpartum day 13; the recovery group was observed for an additional 2 weeks. Endpoints included clinical signs, body weight, food consumption, hematology, serum biochemistry, organ weights, histopathology, reproductive performance, and F1 development. No systemic toxicity was observed in F0 males. Minimal prostate atrophy occurred in high-dose males but was considered non-adverse due to limited severity. One high-dose female died on PPD 1, and high-dose F1 litters showed decreased litter size, increased post-implantation loss, and a reduced live-born index. Based on these results, NOAELs were cautiously assigned 1000 mg/kg/day for repeated-dose and male reproductive toxicity and 300 mg/kg/day for female reproductive and developmental toxicity. TG 422 efficiently characterized hazards while reducing animal use, though its limited duration and scope indicate the need for complementary studies.

In this paper, we present the results of a preliminary study on the self-powered autonomous wireless sensor node by using thermoelectric energy generator based on Silicon (Si) thermoelectric legs, energy management integrated circuit (EMIC), Radio Frequency (RF) module with a temperature and humidity sensor, etc. A novel thermoelectric module structure is designed as an energy generator module, which consists of 127 pairs of Silicon legs and this module is fabricated and tested to demonstrate the feasibility of generating electrical power under the temperature gradient of 70K. EMIC has three key features besides high efficiency, which are maximum power point tracking (MPPT), cold start, and complete self-power operation. EMIC achieved a cold start voltage of 200 mV, peak efficiency of 78.7%, MPPT efficiency 99.4%, and an output power of 34 mW through only the Thermoelectric Generator (TEG) source. To assess the capability of the device as a small scale power source for internet of things (IoT) service, we also tested energy conversion and storage experiments. Finally, the proposed sensor node system which can transmit and monitor the information from the temperature and humidity sensor through the RF module in real time demonstrates the feasibility for variable applications.

Open-vocabulary 3D instance segmentation transcends traditional closed-vocabulary methods by enabling the identification of both previously seen and unseen objects in real-world scenarios. It leverages a dual-modality approach, utilizing both 3D point clouds and 2D multi-view images to generate class-agnostic object mask proposals. Previous efforts predominantly focused on enhancing 3D mask proposal models; consequently, the information that could come from 2D association to 3D was not fully exploited. This bias towards 3D data, while effective for familiar indoor objects, limits the system's adaptability to new and varied object types, where 2D models offer greater utility. Addressing this gap, we introduce Zero-Shot Dual-Path Integration Framework that equally values the contributions of both 3D and 2D modalities. Our framework comprises three components: 3D pathway, 2D pathway, and Dual-Path Integration. 3D pathway generates spatially accurate class-agnostic mask proposals of common indoor objects from 3D point cloud data using a pre-trained 3D model, while 2D pathway utilizes pre-trained open-vocabulary instance segmentation model to identify a diverse array of object proposals from multi-view RGB-D images. In Dual-Path Integration, our Conditional Integration process, which operates in two stages, filters and merges the proposals from both pathways adaptively. This process harmonizes output proposals to enhance segmentation capabilities. Our framework, utilizing pre-trained models in a zero-shot manner, is model-agnostic and demonstrates superior performance on both seen and unseen data, as evidenced by comprehensive evaluations on the ScanNet200 and qualitative results on ARKitScenes datasets.