Search Results (649)

Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, are widely distributed across the planet. The application of spectral technology to underwater environments is useful for wide-area water resource monitoring. Although spectral detection technology is well-established, its underwater application presents challenges, including waterproof housing design, power supply, and data transmission, which limit widespread application of underwater spectral detection. Furthermore, underwater spectral detection necessitates the development of compatible computational methods for sample classification or regression analysis. Focusing on underwater spectral detection, this work involved the construction of a suitable hardware system. A compact spectrometer and LEDs (400 nm–800 nm) were employed as the detection and light source modules, respectively, resulting in a compact system architecture. Extensive tests confirmed that the miniaturized design-maintained system performance. Further, this study addressed the estimation of total phosphorus (TP) concentration in water using spectral data. Samples with varying TP concentrations were prepared and calibrated against standard detection instruments. Subsequently, classification algorithms applied to the acquired spectral data enabled the in situ underwater determination of TP concentration in these samples. This work demonstrates the feasibility of underwater spectral detection for future in situ, high-speed monitoring of aquatic biochemical indicators. In the future, after adding UV LED light source, more water quality parameter information can be obtained. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

The increasing integration of renewable energy has led to power systems characterized by a high penetration of renewable energy sources (RES) and power electronic devices (PEDs). However, wide-band oscillation phenomena caused by RES grid integration have emerged and propagated through transmission networks. Notably, large-scale renewable energy bases located in remote areas are typically connected to the main grid via long-distance AC transmission lines. These lines exhibit an inter-harmonic amplification effect, which may exacerbate the propagation and amplification of wide-band oscillations, posing significant risks to bulk power-grid stability. This paper establishes impedance models of long-distance AC transmission lines and asynchronous motors under wide-band oscillation frequencies, and derives equivalent line parameters mathematically to reveal the oscillation amplification problem of long-distance renewable energy oscillation transmission through AC transmission lines. A transfer coefficient is defined to quantify inter-harmonic current amplification. A single-machine-load model is developed in MATLAB/Simulink to validate the proposed model. Furthermore, the influence of line parameters on oscillation amplification is analyzed, and a suppression strategy is proposed. This study provides valuable insights for the parameter design of long-distance transmission lines in renewable energy integration scenarios, as it helps mitigate potential inter-harmonic amplification risks by reducing the peak values of the transfer coefficient. Full article

This study explores how older Chinese migrants in London engage with climate change discourse using participatory co-design workshops. Although already practising sustainability behaviours such as recycling, this group faces significant barriers—particularly language difficulties and cultural differences—that limit their active participation in broader climate initiatives. The research addresses three key aspects: (1) identifying opportunities for sustainable practices within migrants’ daily routines; (2) understanding their influential roles within families and communities; and (3) examining their trusted sources and preferred channels for climate communication. Results highlight that family and community networks, combined with digital platforms (e.g., WeChat) and visually engaging materials, play essential roles in disseminating climate information. Participants expressed strong motivations rooted in intergenerational responsibility and economic benefits. The findings emphasise the necessity of inclusive and peer-led communication strategies that are attuned to older migrants’ linguistic preferences, media habits, and cultural values—underscoring their significant but often overlooked potential to meaningfully contribute to climate action. Full article

Plants can sense light signals using specific photoreceptors, activating light signaling pathways to precisely regulate photomorphogenesis and shade-avoidance responses. This study examines the molecular responses of Arabidopsis thaliana to the CoeLux^® lighting system, a unique LED-based light source designed to simulate natural sunlight. Previous studies found that the CoeLux^® light type, characterized by a higher blue-to-green ratio and reduced blue light levels, stimulates responses in plants comparable to those displayed in shade conditions. This research compared the effects of CoeLux^® lighting to conventional high-pressure sodium (HPS) lamps, focusing on the expression of critical photomorphogenesis-related genes under both long- and short-term light treatments. Lower HY5 and elevated HFR1 expression levels were observed under the CoeLux^® light type and low-intensity light conditions. On the contrary, the influence of the CoeLux^® light type on COP1 and PIFs expression levels seems more marginal. These responses suggest a complex regulation involving both gene expression and protein-level adjustments. Additionally, mutant plants lacking these essential regulatory genes displayed altered morphologies under CoeLux^® light, underscoring the functional contribution of these genes in the adaptation to light. Our findings are twofold, advancing the understanding of plant–light relationships and plant adaptation to artificial light environments. These may foster strategies for optimizing indoor plant growth under simulated sunlight conditions. Full article

In recent years, with the continuous advancement of technology and the expansion of application scenarios, AR has become a highly regarded field. However, AR still faces several challenges in practical usage. Notable shortcomings include inadequate image uniformity, low diffraction efficiency. Among these, the insufficient image uniformity stands out as a significant issue directly affecting user experience. The analysis of uniformity improvement in this study is limited to the simulated scenario of monochromatic blue light (LED light source), aiming to optimize the insufficient uniformity of the image output of the diffractive optical waveguide-based AR technology scheme. We improve the details of the input grating in the waveguide, such as the morphological characteristics of the grating, the detail parameter, etc. In addition, we propose to incorporate a photonic crystal film in the waveguide as an innovative study and find that the incorporation of the photonic crystal thin film significantly improves the uniformity of the output image in the diffractive optical waveguide scheme. In order to further verify the effect of the photonic crystal film on the uniformity of its image output, we also compare different types of coupled gratings and find that they all have a positive effect. Thus, the photonic crystal film demonstrated effective control over the diffraction optical waveguide scheme. This research offers new insights and design approaches for enhancing the output image uniformity based on diffraction optical waveguide technology, providing a new path for improving image uniformity in AR displays. Full article

This study investigated the effects of LED versus fluorescent task lighting on sleep quality and daytime functioning among office workers in a windowless environment. Using a within-subjects crossover design with 32 full-time employees, participants were exposed to both 4000 K LED and 4100 K fluorescent lighting conditions over two one-week periods. Subjective sleep quality and alertness were assessed through the Pittsburgh Sleep Quality Index (PSQI) and daily sleep diaries. Results indicated significantly better global sleep quality, improved subjective sleep assessments, and reduced daytime dysfunction under LED lighting conditions. While sleep duration did not significantly differ, a positive trend was observed favoring the LED condition. These outcomes are likely due to differences in spectral power distribution between the two light sources, particularly the continuous, blue-enriched spectrum of the LED lamp, which supports circadian regulation. The findings suggest that biologically supportive lighting—such as continuous-spectrum LEDs—can positively impact sleep and daytime performance, even in the absence of natural daylight. This research contributes to the growing field of circadian lighting and offers practical implications for architects, designers, and workplace managers aiming to enhance employee well-being and productivity in enclosed office environments. Full article

The end of the conflict in Sierra Leone between 1991 and 2002 provided an important context for exploring the role of peace education in restoring governance, rebuilding institutions, and promoting socio-economic development. Following the civil war, a variety of externally designed peace education programmes were introduced across the country. These included formal programmes integrated into the school curriculum and non-formal initiatives led by stakeholders. This study evaluated these programmes by examining their goals, implementation approaches, and effectiveness in fostering sustainable peace alongside development. The evaluation employed a social constructionist framework, recognising that understandings of peace and education are shaped by historical, social, and cultural factors. Data were gathered through the analysis of documentary sources and semi-structured online interviews with 12 key informants involved in peace education. The findings reveal important insights regarding both the potential of peace education as a developmental tool and its limitations in achieving lasting peace. Key lessons emphasise the critical role of local ownership, cultural relevance, and ongoing support for post-conflict educational interventions. These insights offer valuable guidance for enhancing future peacebuilding and reconstruction efforts in Sierra Leone and other similar post-conflict settings. Full article

LED strips are increasingly used as lighting sources in public and private spaces. However, traditional photometric methods, such as integrating spheres, are unsuitable for measuring their light parameters, often resulting in significant errors and requiring expensive instrumentation or calibration. These errors are typically caused by non-uniform illumination of the internal surface or improper internal geometry, especially when measuring LED sources. This article presents the development of a low-cost integrating device specifically designed to measure LED strips’ light parameters. The device is a compact cube with a volume of less than 1.0 m³. It was tested against alternative methods using an integrating sphere and a goniophotometer in a professional photometric laboratory. The verification results confirmed its effectiveness. The device showed the maximum relative error of luminous flux measurement to be around 5% compared with the accurate, expensive goniophotometric method. For colorimetric measurements, the maximum Correlated Color Temperature (CCT) absolute error was about 35 K for an LED strip with a CCT of 4000 K, indicating a difference imperceptible to the human eye. These results demonstrate the device’s proper relevance in the research and development of LED strip-based lighting equipment to improve lighting equipment quality and control processes. The device is easy to replicate, significantly reducing production and transportation costs, making it an excellent solution for companies and research units seeking a cost-effective method for LED strip measurements. Additionally, the device can measure other light sources or luminaires with reasonably small sizes emitting light in only one hemisphere. The device is the basis of a patent application. Full article

Microscopy is a fundamental tool in biological research. However, conventional microscopes require manual operation and depend on user and equipment availability, limiting their suitability for continuous observation. Moreover, their size and complexity make them impractical for in situ experimentation. In this work, we present a novel, compact, affordable, and portable microscope that enables continuous in situ monitoring by being placed directly on biological samples. This chip-sized lensless holographic microscope (CLHM) is specifically designed to overcome the limitations of traditional microscopy. The device consists solely of an ultra-compact, state-of-the-art micro-LED display and a CMOS sensor, all enclosed within a 3D-printed housing. This unique light source enables a size that is markedly smaller than any comparable technology, allowing a resolution of 2.19 μm within a 7 mm distance between the light source and the camera. This paper demonstrates the CLHM’s versatility by monitoring in vitro models and performing whole-organism morphological analyses of small specimens. These experiments underscore its potential as an on-platform sensing device for continuous, in situ biological monitoring across diverse models. Full article

We present a comprehensive study of LED-based optical sensing systems for the classification of waste materials, analyzing recent developments in the field. Accurate identification of materials such as plastics, glass, aluminum, and paper is a crucial yet challenging task in waste management for recycling. The first approach uses short-wave infrared reflectance spectroscopy with commercial Germanium photodetectors and selected LEDs to keep data complexity and cost at a minimum while achieving classification accuracies up to 98% with machine learning algorithms. The second system employes a voltage-tunable Germanium-on-Silicon photodetector that operates across a broader spectral range (400–1600 nm), in combination with three LEDs in both the visible and short-wave infrared bands. This configuration enables an adaptive spectral response and simplifies the optical setup, supporting energy-efficient and scalable integration. Accuracies up to 99% were obtained with the aid of machine learning algorithms. Across all systems, the strategic use of low-cost LEDs as light sources and compact optical sensors demonstrates the potential of light-emitting devices in the implementation of compact, intelligent, and sustainable solutions for real-time material recognition. This article explores the design, characterization, and performance of such systems, providing insights into the way light-emitting and optoelectronic components can be leveraged for advanced sensing in waste classification applications. Full article

Background: Chronic disease self-management (CDSM) programmes are widely recommended for heart failure with reduced ejection fraction (HFrEF), yet evidence on their effectiveness remains mixed. This systematic review synthesises the evidence and critically appraises the findings from multiple systematic reviews on CDSM for congestive heart failure (CHF) with a focus on the impact of nurse-led and multidisciplinary CDSM interventions in adults with HFrEF. Design: Systematic review using PRISMA 2020 and AMSTAR-2 guidelines. Data Sources and Eligibility: We searched MEDLINE, Embase, CINAHL, Cochrane Library, and other sources for reviews published from 2012 to 2024. Included were systematic reviews of CDSM interventions for adults diagnosed with HFrEF, focusing on mortality, hospital readmissions, quality of life, and self-management behaviours. Results: A total of 1050 studies were screened, with 60 studies being counted in the final analysis, including 22 reviews of high quality. Evidence for mortality benefit was limited and inconsistent across reviews. However, moderate-to-high-certainty evidence showed that nurse-led CDSM interventions improved hospital readmission rates and health-related quality of life (HRQoL). Improvements in self-management behaviours such as medication adherence and symptom monitoring were also frequently reported. Conclusions: While evidence for a mortality benefit remains inconclusive, this review highlights consistent benefits of nurse-led CDSM interventions in reducing readmissions and improving HRQoL for HFrEF patients. Future research should prioritise standardised outcome reporting, incorporate economic evaluations, and explore patient-centred and culturally tailored approaches to intervention design. PROSPERO registration number CRD42023431539. Full article

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article

In the 6G era, the proliferation of smart devices has led to explosive growth in data volume. The traditional cloud computing can no longer meet the demand for efficient processing of large amounts of data. Edge computing can solve the energy loss problems caused by transmission delay and multi-level forwarding in cloud computing by processing data close to the data source. In this paper, we propose a cloud–edge–end collaborative task offloading strategy with task response time and execution energy consumption as the optimization targets under a limited resource environment. The tasks generated by smart devices can be processed using three kinds of computing nodes, including user devices, edge servers, and cloud servers. The computing nodes are constrained by bandwidth and computing resources. For the target optimization problem, a genetic particle swarm optimization algorithm considering three layers of computing nodes is designed. The task offloading optimization is performed by introducing (1) opposition-based learning algorithm, (2) adaptive inertia weights, and (3) adjustive acceleration coefficients. All metaheuristic algorithms adopt a symmetric training method to ensure fairness and consistency in evaluation. Through experimental simulation, compared with the classic evolutionary algorithm, our algorithm reduces the objective function value by about 6–12% and has higher algorithm convergence speed, accuracy, and stability. Full article

Data visualization is a key activity in data-driven decision making and is gaining momentum in many organizational contexts. However, the role and contribution of both end-user development (EUD) and artificial intelligence (AI) technologies for data visualization and analytics are still not clear or systematically studied. This work investigates how effectively AI-supported EUD tools may assist visual analytics tasks in organizations. An exploratory case study with eight interviews with key informants allowed a deep understanding of data analysis and visualization practices in a large Italian company. It aimed at identifying the various professional roles and competencies necessary in the business context, understanding the data sources and data formats exploited in daily activities, and formulating suitable hypotheses to guide the design of AI-supported EUD tools for data analysis and visualization. In particular, the results of interviews with key informants yielded the development of a prototype of an LLM-based EUD environment, which was then used with selected target users to collect their opinions and expectations about this type of intervention in their work practice and organization. All the data collected during the exploratory case study finally led to defining a set of design guidelines for AI-supported EUD for data visualization. Full article