Effect of natural and artificial surroundings on perceived restorativeness and affective states: Evidence from a satellite image segmentation-based method

Thermal comfort differences between pregnant women at different gestational stages and escort personnel in hospital environments.

Abstract Accurate characterization of cerebral cortex biomechanics is essential for the safety of implantable neural interfaces. The porcine brain is commonly used as a surrogate for human tissue in preclinical studies. However, rigorous comparisons under standardized physiological conditions, especially for pathological states like epilepsy, are lacking. This study directly compared the quasi-static and viscoelastic properties of cortical tissue from pharmacoresistant epileptic patients and healthy pigs. Indentation-relaxation tests were performed in a controlled physiological environment (oxygenated artificial cerebrospinal fluid aCSF, 37 °C) at an implant-relevant rate (0.5 mm s −1 to 1 mm depth). Data were analyzed using a modified Hertzian contact model with a 3rd-order Prony series. Results showed no statistically significant differences in the instantaneous shear modulus, equilibrium modulus, or relaxation times between human epileptic and porcine cortex across gray and white matter. Short-term variations between white and gray matter diminished at equilibrium, consistent with poroelastic behavior. Under stringent environmental control, the macro-scale viscoelastic response of epileptic human cortex closely resembles that of healthy porcine cortex. This validates the porcine model for preclinical testing of neural implants for epilepsy and underscores that standardized testing protocols, rather than assumed pathological softening, are critical for reliable biomechanical comparisons.

Aquaculture production has increased over the last decade; however, field observations suggest that nearly 20% of potential yield is lost in each cycle due to inadequate monitoring and imprecise growth estimation. Although overall production continues to rise, the lack of dependable automated prediction systems restricts operational efficiency in fish farming. Conventional practices, which involve manually handling fish to measure length and weight, are time-consuming, inconsistent, and dependent on operator expertise. Moreover, frequent handling disrupts the aquatic environment, induces stress in fish, and negatively influences their growth patterns. These limitations hinder continuous monitoring, particularly in environments with rapidly fluctuating conditions. Practical aquaculture operations, including adaptive feeding strategies, early identification of growth anomalies, and dynamic environmental control, require accurate and real-time predictive capabilities. Existing solutions predominantly rely on Linear Regression (LR), Ridge Regression (RR), and Lasso Regression (Lasso), which are insufficient for modeling complex, non-linear interactions in aquaculture data. To overcome these challenges, this study introduces ConvETR, a hybrid regression framework that integrates a Convolutional Neural Network (CNN) with an Extra Trees Regressor (ETR). The CNN extracts meaningful temporal and spatial features from continuous sensor inputs, capturing behavioral and environmental variations, while the ETR enhances prediction stability by effectively handling noise and non-linearity. This non-intrusive and scalable approach enables accurate real-time predictions, improves decision-making, and supports efficient and sustainable aquaculture management.

The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to a Plant Factory (PF) for hydroponic vegetable cultivation using the Nutrient Film Technique (NFT). The objective of this study was to develop a system capable of effectively monitoring and controlling the environmental variables that directly influence the microclimate of a closed agricultural environment. The proposed system integrates a four-layer IoT architecture based on a MODBUS RS-485 communication bus, which allows for continuous data acquisition and the operation of multiple sensors and controlled devices. Additionally, user-oriented tools such as a human–machine interface (HMI), a web application, a mobile application and an automatic alert module were incorporated, enhancing accessibility and remote supervision. Experimental results showed stable control performance of ambient temperature (TA), relative humidity (RH), photoperiod, and photosynthetic photon flux density (PPFD), along with continuous monitoring of CO2 concentration. A 30-day validation experiment using Swiss chard (Beta vulgaris L. var. cicla) under controlled conditions was conducted. The results showed progressive plant development, with leaf area increasing from 15.17 cm2 to 690.39 cm2, plant height from 7 cm to 31 cm, fresh weight from 23 g to 171 g, and the number of leaves from 9 to 20. These results support the functional validity of the proposed system as a reliable platform for environmental monitoring and control in controlled-environment agriculture.

This paper systematically studies the characteristics of building vibration and structural noise caused by train operation in view of the vibration and noise problems specific to the railway canopy complex in the integrated development of stations and cities through field measurement. The results show that the vibration energy of the floor slab is concentrated in the medium and low frequency band of 20-80 Hz and directly leads to the main peak of indoor noise in the 40-80 Hz band, with significant coherence between the two, confirming that the “vibration-sound” coupling mechanism is the main source of noise; Interior decoration can effectively improve the acoustic environment quality by reducing the reverberation time. This study reveals the intrinsic connection between vibration and noise and demonstrates that “vibration control of sound” is the fundamental approach to addressing such problems. The research results can provide key theoretical basis and engineering guidance for the optimal design and environmental control of the superstructure.

In response to the technical bottleneck of the Venlo greenhouse’s inability to achieve year-round production due to the high temperature and humidity in the summer in South China, this study took an existing Venlo-type greenhouse in Guangzhou as the research object and constructed a three-dimensional computational fluid dynamics (CFD) model of the greenhouse by comprehensively considering key factors such as solar radiation, thermal radiation, and crop canopy resistance. After on-site experiments, it was verified that, except for the top area of the greenhouse, the temperature deviation between the model simulation values and the measured values was less than 2 °C, and the error rate was less than 5%, confirming the model’s accurate representation of the temperature field distribution within the greenhouse. Based on the characteristics of the temperature and humidity fields revealed by the CFD simulation (canopy temperature gradient K = 0.144 °C/m, maximum temperature difference between upper and lower layers 20 °C), an optimized scheme of “wet curtain fan + salt bath dehumidification equipment” for local cooling and dehumidification of the crop canopy was proposed, and a non-uniform air duct layout was designed according to the temperature gradient characteristics. Field experiments showed that after optimization, the daytime temperature of the crop canopy was mostly controlled within 30 °C, the relative humidity was stably maintained below 80%, and the maximum temperature difference along the length of the greenhouse was reduced from 7 °C to 2 °C, effectively solving the problem of poor cooling and dehumidification effects of the traditional system. This scheme enabled the stable operation and year-round production of Venlo-type greenhouses in South China during the summer, providing technical support and engineering reference for greenhouse environmental control in high-humidity areas.

Microporous membrane-based polymer gating biosensor for the detection of Staphylococcus aureus and antimicrobial susceptibility testing.

Environmental quality, functional control, and social design as determinants of perceived comfort and psychological well-being among older residents in Chinese continuing care retirement communities

The agricultural sector, particularly horticulture, faces significant challenges due to global climate change, causing unstable crop yields and quality. Traditional greenhouses in tropical regions often suffer from a heat trap effect, which induces plant stress if not properly managed. Current environmental control systems rely heavily on manual labor or simple on/off thresholds, leading to energy inefficiency and delayed responses. This study aims to design and simulate an internet of things (iot)-based smart greenhouse control system utilizing a fuzzy logic controller (flc) with the mamdani method. Unlike previous partial control studies, this research integrates four vital parameters simultaneously: air temperature, air humidity, soil moisture, and light intensity. The hardware architecture incorporates an esp32 microcontroller, dht22 and dht11 sensors, a capacitive soil moisture sensor, an ldr module, and actuators including a dc fan, water pump, and led grow lights. Simulation results using proteus software demonstrate that the fuzzy logic algorithm successfully regulates actuator speeds via pulse width modulation (pwm) based on environmental transitions, effectively preventing component chattering. Furthermore, the system successfully transmits real-time microclimate data to the blynk platform for remote monitoring. This integrated approach offers a more precise, energy-efficient, and adaptive solution for optimizing horticultural microclimates.

Drosera sessilifolia is a carnivorous plant that requires optimal environmental conditions, par-ticularly in terms of light intensity, temperature, and water level. This study aims to utilize the In-ternet of Things (IoT) to implement an environmental control system using Type-2 Fuzzy Logic for monitoring and controlling the environmental parameters of Drosera sessilifolia. The system uses an ESP32 microcontroller supported by several sensors, including a DS18B20 temperature sensor, an LDR light intensity sensor, and a water level sensor, to monitor environmental parameters ac-curately. The acquired data are processed using the Type-2 Fuzzy method to generate control deci-sions for actuators such as a heater, peltier, lamp, peristaltic pump, and fan. The Blynk platform is used to display environmental conditions, including temperature, light intensity, water level, and actuator decision status in real time. Based on the results of a 7-day indoor test conducted in the morning and at night, the average morning temperature was 30.04°C and the average nighttime temperature was 27.91°C, while the average light intensity was 1278 ADC in the morning and 2525.43 ADC at night, and the average water level was 1.16 cm in the morning and 0.99 cm at night. Based on 14 observations over 7 days, the system showed appropriate control responses in all observed scenarios, resulting in a 100% response success rate for the detected conditions. The novelty of this study lies in the implementation of an IoT-based Type-2 Fuzzy system specifically designed for the indoor cultivation of Drosera sessilifolia by simultaneously controlling temper-ature, light intensity, and water level. In addition, this study highlights the novelty of integrating Type-2 Fuzzy Logic and IoT for carnivorous plants, which remains very limited, especially for the cultivation of Drosera sessilifolia in a controlled indoor environment. The test results show that the system is capable of controlling temperature, light intensity, and water level effectively and responsively to environmental changes. The Type-2 Fuzzy method also produces stable and adap-tive control decisions. This system offers a solution for real-time indoor monitoring and environ-mental control of Drosera sessilifolia.

Rapid point-of-need detection of aerosolized pathogens remains a critical need for environmental monitoring and infection control. Traditional filter-based bioaerosol collection methods are time-consuming and nonselective, capturing both infectious and noninfectious viral particles while causing mechanical damage through desiccation and mechanical stress. Furthermore, these approaches require laboratory-based nucleic acid extraction and amplification, which limits their utility for real-time monitoring. Here, we present an integrated aerosol-to-detection platform that couples a three-stage condensation growth tube (CGT) bioaerosol sampler with sensitive electrochemical immunodetection of the SARS-CoV-2 nucleocapsid (N) protein as a model virus antigen. The CGT gently concentrates aerosolized particles into a small liquid volume while preserving biological integrity through a water-based, low-velocity collection. Screen-printed carbon electrodes (SPCEs) were modified with capture antibodies and employed in a sandwich immunoassay format using horseradish peroxidase-linked detection antibodies and 3,3',5,5'-tetramethylbenzidine (TMB) as the electrochemical probe. The electrochemical immunoassay achieved a limit of detection (LOD) of 1.2 ng/mL for spiked N-protein solutions, demonstrating comparable sensitivity to published electrochemical biosensors for SARS-CoV-2 detection. The CGT successfully captured the aerosolized N protein with high efficiency across particle sizes of 50-100 nm with collection efficiency exceeding 95% for the target size range. Electrochemical detection of CGT-captured aerosolized N protein confirmed the successful combination of the sampling and sensing platform, providing average cathodic current signals distinguishable from blank controls. These results demonstrate the feasibility of coupling efficient bioaerosol collection to rapid electrochemical detection for point-of-need pathogen monitoring.

This study presents a capacitive sensor with a zirconium oxide (ZrO2) coating for real-time measurement of component concentration in liquid media. The ZrO2 layer was formed on stainless steel electrodes by magnetron sputtering, and its structural, morphological, and chemical properties were characterized using SEM, EDS, FTIR, and XRD. It was found that increasing coating thickness results in more continuous and highly crystalline layers, while reducing the influence of the substrate on surface properties. The performance of the capacitive sensor was evaluated by analysing the dependence of capacitance on frequency and NaCl concentration. The results show that the thickness of the ZrO2 layer has a significant influence on sensor sensitivity and measurement stability. A thinner layer (~2 µm) provides higher sensitivity but is more affected by parasitic effects, while thicker layers improve measurement stability at the expense of reduced sensitivity. An optimal trade-off between sensitivity and stability is achieved at a ZrO2 layer thickness of approximately 4 µm, ensuring sufficient sensitivity and good measurement repeatability. The results indicate that ZrO2-modified capacitive sensors are a promising technology for monitoring liquid quality, particularly in environmental protection and industrial process control.

ABSTRACT The teaching profession involves significant emotional dimensions, particularly in contexts demanding substantial professional development. This narrative case study examines one Chinese foreign language teacher’s emotional experience and development within a professional learning community over 2 years. Through analysis of 20 reflective journals, the study traced how this teacher navigated four main challenges: professional capability-aspiration discrepancies, identity transitions, pedagogical innovation implementation, and performance anxiety. In this teacher’s case, emotional labour appeared to progress from reactive coping mechanisms to more sophisticated regulatory approaches across three identifiable phases: early phase surface-level strategies (environmental control, preparation), middle phase deeper approaches (cognitive restructuring, social identification), and advanced phase proactive strategies (meta-cognitive awareness, identity reconstruction). Analysis suggests that emotional labour may be better understood as an evolving process wherein regulatory strategies become increasingly sophisticated as a teacher’s integration into professional communities deepens, rather than as static skills. The study demonstrates how this teacher employed diverse regulation strategies – cognitive reappraisal, social identification, problem-focused coping, and reflective practice – as constitutive mechanisms of emotional labour. Findings suggest that professional learning communities may benefit from scaffolding teachers’ emotional labour development through structured reflection and differentiated support, potentially enhancing both teacher well-being and professional effectiveness.

Sericulture is a specialized agro-based industry that depends heavily on the cultivation of mulberry and the rearing of silkworms. Maintaining consistent leaf quality and optimal rearing conditions is essential for improving cocoon production and silk quality. Recent technological developments have introduced precision agriculture as an effective approach to enhance productivity and resource efficiency in agriculture. Precision agriculture integrates digital tools such as sensors, geographic information systems, artificial intelligence and Internet of Things (IoT)–based monitoring systems to support data-driven management practices. In sericulture, these technologies can assist in precise nutrient management in mulberry fields and automated environmental control in silkworm rearing units. The present review discusses the role of precision agriculture in sericulture with particular emphasis on mulberry cultivation and silkworm rearing. It highlights how modern technologies can improve productivity, reduce resource wastage and support sustainable silk production. Furthermore, the review also explores recent advancements, practical applications, challenges and future prospects of precision sericulture in the context of climate variability and increasing demand for high-quality silk.

In indoor farms, urban agriculture setups, research laboratories, and commercial hydroponic facilities, there is a critical requirement for systems that can maintain optimal growing conditions and ensure efficient resource utilization. These environments demand automated, real-time monitoring solutions capable of regulating water, nutrients, and environmental parameters to maximize crop yield. Traditional farming methods rely heavily on soil-based cultivation and manual monitoring, which often result in inefficient water usage, inconsistent nutrient supply, and reduced productivity. Furthermore, conventional systems lack automation, remote accessibility, and precise environmental control, limiting their effectiveness in modern agricultural practices. To address these challenges, the proposed IoT Enabled Smart Hydroponic System for Indoor Farming utilizes the ESP32 microcontroller integrated with solar power and IoT technology to develop an intelligent and sustainable farming solution. The system incorporates sensors for temperature, humidity, water level, and nutrient concentration to continuously monitor plant growth conditions. An AC water pump is automatically controlled to regulate irrigation and nutrient delivery, while a 16x2 LCD provides realtime system updates and a buzzer generates alerts during abnormal conditions. The system operates in both manual and automatic modes, allowing users to control operations remotely via IoT platforms or enable autonomous regulation based on sensor data. Solar power integration ensures energy efficiency and eco-friendly operation. This smart system enhances crop productivity, optimizes resource utilization, reduces manual effort, and supports the development of sustainable indoor farming solutions

Traditional agricultural greenhouse environmental monitoring systems often lack effective anomaly detection mechanisms, which can lead to inaccurate environmental regulation and negatively affect plant growth. To address this issue, this paper proposes a greenhouse monitoring system integrating Zigbee and 4G communication technologies, combined with a CNN-LSTM-POT anomaly detection algorithm. The system employs a Convolutional Neural Network (CNN) to extract local spatial features from multi-source sensor data and a Long Short-Term Memory (LSTM) network to model long-term temporal dependencies. To accurately identify anomalies, the Peaks Over Threshold (POT) method from extreme value theory is applied to prediction residuals, enabling adaptive dynamic threshold determination. Experimental results show that the proposed algorithm substantially improves anomaly detection precision, prevents erroneous data from disrupting greenhouse control decisions and reduces the volume of data transmitted to the cloud platform, thereby lowering computational overhead. This work provides a reliable and efficient solution for data monitoring and precise environmental control in smart agricultural greenhouses.

Abstract Fabry-Pérot (FP) etalon is an important instrument for high-precision wavelength calibration. Combined with white light, the FP etalon can generate dense and nearly equally spaced frequency peaks, having an advantage in short-term calibration over thorium-argon lamps. However, owing to variations in the cavity’s physical parameters, its resonant frequency will undergo slow drift, resulting in a significant decrease in long-term calibration precision, even strict environmental control cannot completely eliminate this effect. Therefore, equipping drift tracking systems for the FP etalon represents a valuable approach to improve long-term accuracy. FP cavities with meter-level lengths have been fabricated using Anti-Resonant Hollow Core Fiber (AR-HCF), which facilitates the realization of long cavity lengths while maintaining low temperature sensitivity. The drift tracking system is constructed with the External Cavity Diode Laser (ECDL), rubidium absorption cell, and AR-HCF FP cavity. The experimental results show that when the length of the AR-HCF FP cavity reaches above 1 m, it can effectively capture the local characteristics of laser frequency scan and achieve an accuracy of MHz-level.

The Qianlong Stone Classics are the largest and best-preserved ensemble of officially commissioned stone inscriptions of Confucian classics extant, yet their stele bases are currently threatened by salt efflorescence. Fluctuations in ambient temperature and humidity contribute significantly to this deterioration. Taking Stele 17 as a representative case, this study assesses the risks of surface condensation and moisture-induced salt phase transitions through integrated temperature–humidity monitoring, infrared thermography, and soluble salt analysis. The risk of condensation remains low under typical conditions, as the stele base surface temperature exceeds the dew point by at least 0.5 °C. However, risks of salt deliquescence and hydration are substantial. The stone surface contains elevated levels of soluble salts, including four highly soluble species (sodium sulfate, calcium nitrate, sodium nitrate, and sodium chloride) and one moderately soluble species (calcium sulfate). Deliquescence phase transition humidities are approximately 50.5% for calcium nitrate, 74.3% for sodium nitrate, and 75.4% for sodium chloride, while sodium sulfate exhibits a hydration phase transition near 81%. Exhibition Hall humidity fluctuates around these critical thresholds, driving repeated dissolution–crystallization and hydration–dehydration cycles that progressively erode the stone microstructure. These hygrothermal cycles exhibit pronounced seasonal patterns, with frequent air-conditioning operation in summer amplifying thermal and humidity impacts. This study elucidates an air-moisture-driven salt deterioration mechanism distinct from classical capillary rise, clarifies the persistent progression of efflorescence in transitional seasons, and provides a scientific basis for optimizing environmental control strategies.

The significant post-harvest deterioration of fresh produce in rural areas, which lacks cold chain infrastructure, a major obstacle to maximizing farmer profitability and ensuring food security. The quality retention of vegetables is intrinsically linked to rate of moisture loss, which is quantified by physiological weight loss. This rate is dramatically accelerated due to high temperatures during summer season. To address this challenge, a study was conducted utilizing the Zero Energy Cool Chamber (ZECC), a low-cost structure independent of non-renewable energy sources. The objective was to evaluate the comparative storage duration (in days) and quality retention of five diverse summer vegetable. Okra, Radish, Cauliflower, Spinach and Oyster mushroom stored in a ZECC and typical rural open basket practice. The trial showed that ZECC effectively moderated the storage environment during the summer month of April, achieving a temperature reduction of up to 5.8°C (from 35.80 °C ambient to 30.050 °C internal) and raising relative humidity from 55.0% to 95.5%. This environmental control drastically reduced moisture loss. Okra stored in the ZECC for three days exhibited only 7.0% PLW, whereas the open basket comparator showed 23% PLW after the same period. For highly perishable Oyster Mushroom, the ZECC maintained marketable quality for two days with 9.75% PLW, significantly preventing the rapid desiccation and total spoilage (58% PLW) observed in ambient conditions after two days. The findings confirm that ZECC technology provides advantage in minimizing post-harvest losses, enhancing freshness, and extending the marketable life of diverse vegetables in constrained environments.