Search Results (496)

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value. Full article

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning system designed for care facilities. The three-layer architecture integrates sensors for temperature, humidity, light, air quality, and noise; employs ESP-NOW and wireless fidelity mesh for reliable networking; and supports user interfaces with real-time anomaly alerts. Using PCA and Isolation Forest for efficient anomaly detection, the modular, node-based design enhances safety, reduces manpower burden, and enables scalable smart services. Full article

The competency-based medical education (CBME) curriculum was introduced recently for undergraduate courses in medical institutions in India. The program needs a paradigm shift in the teaching and assessment methods. Therefore, challenges at the individual as well as organizational level are expected in the initial years of implementation. We used a mixed-method approach through focus group discussions (FGD) and an online survey to assess the perception and attitude of MBBS phase 1 and 2 teachers towards CBME. Themes were generated from FGD, and quantitative data were collected using a structured questionnaire through an online survey. Nearly 80% of the participating faculty perceived that the CBME curriculum was better than traditional teaching methods. Major challenges were either related to a deficiency of curriculum-optimized learning material (85%), material infrastructure (38%), and manpower (46%), or increased documentation (74%), and time constraints (52%). The faculty felt attitudinal change (63%), better acquaintance with the professional environment (60%), improved participation (58%), and the performance of students (38%) were major commendations of CBME. The CBME curriculum is a welcome change in Indian medical teaching institutes, and faculty intend to improve it through feedback mechanisms. The perceived complexities need to be addressed at different levels through collaborative approaches. Full article

With the explosive growth in the quantity of electrical equipment in distribution networks, traditional manual inspection struggles to achieve comprehensive coverage due to limited manpower and low efficiency. This has led to frequent equipment failures including partial discharge, insulation aging, and poor contact. These issues seriously compromise the safe and stable operation of distribution networks. Real-time monitoring and defect identification of their operation status are critical to ensuring the safety and stability of power systems. Currently, commonly used methods for defect identification in distribution network electrical equipment mainly rely on single-image or voiceprint data features. These methods lack consideration of the complementarity and interleaved nature between image and voiceprint features, resulting in reduced identification accuracy and reliability. To address the limitations of existing methods, this paper proposes distribution network electrical equipment defect identification based on multi-modal image voiceprint data fusion and channel interleaving. First, image and voiceprint feature models are constructed using two-dimensional principal component analysis (2DPCA) and the Mel scale, respectively. Multi-modal feature fusion is achieved using an improved transformer model that integrates intra-domain self-attention units and an inter-domain cross-attention mechanism. Second, an image and voiceprint multi-channel interleaving model is applied. It combines channel adaptability and confidence to dynamically adjust weights and generates defect identification results using a weighting approach based on output probability information content. Finally, simulation results show that, under the dataset size of 3300 samples, the proposed algorithm achieves a 8.96–33.27% improvement in defect recognition accuracy compared with baseline algorithms, and maintains an accuracy of over 86.5% even under 20% random noise interference by using improved transformer and multi-channel interleaving mechanism, verifying its advantages in accuracy and noise robustness. Full article

Asserting program correctness is a longstanding challenge in software development that consumes lots of resources and manpower. It is often accomplished through software testing at various levels. One such level is unit testing, where the behaviour of individual components is tested. In this paper, we introduce the concept of test analysis, which instead of executing unit tests, analyses them to establish their outcome. This is line with previous approaches towards using formal methods for program verification; however, we introduce a middle layer called the test analysis framework, which allows for the introduction of new capabilities. We (briefly) formalize ordinary testing and test analysis to define the relation between the two. We introduce the notion of rich tests with a syntax and semantic instantiated for C. A prototype framework is implemented and extended to handle property-based stubbing and non-deterministic string variables. A few select examples are presented to demonstrate the capabilities of the framework. Full article

Coastal saline-alkali areas represent huge under-exploited land and water resources. Due to the high salinity, there exists a great discrepancy between the benefits derived from the cultivation of agricultural crops and the cost in terms of manpower and material resources. The mud crab Scylla paramamosain can survive across a wide range of salinity, making it an excellent aquaculture species in crop–fish co-cropping in coastal saline-alkali areas. However, detailed research concerning economic and ecological efficiency remains unclear. This study investigated the effect of stocking density of S. paramamosain co-cropping with salt-tolerant rice on the economic benefits, physiochemical parameters, and the microecological changes. By elaborate management of aquaculture and rice cropping, together with the comprehensive investigation of physiochemical influence on paddy water and soil, microbial community alteration, and functional gene dynamics, we found that an appropriate density of 6000 ind/ha generated the highest net profit, which is more than 9-fold higher than the rice monoculture. In addition, nutrient inflow increased the environmental burden of higher stocking densities. Microbial community composition and structure were altered, as shown by the 16S amplicon sequencing of water and soil samples. Functional gene chips confirmed that the carbon, nitrogen, sulfur, and phosphorus cycle genes in the microbial community contributed to the microecological function. This study proposes a new salt-tolerant rice–mud crab integrated culture mode, which is customized for the underdeveloped saline-alkali areas, and will be helpful in promoting aquaculture as well as sustainable development. Full article

Since 2018, Al-Ahsa Oasis has become a UNESCO site because of the integration of the natural, agricultural, and cultural elements. The objective of this research is to investigate land cover changes (LCC) in this region and the key sustainability factors that influence their likelihood of occurrence between 2000 and 2020. A two-stage methodology was employed, first estimating the LCC level using USA-ArcGIS 10.3 and USA-ENVI 5.4 on digital data gathered from satellites visualizations (LANDSAT). Second, it evaluates the LCC occurrence variables using a binary logistic model (BLM) based on data from 105 surveyed farmers. The major findings reveal a decline in the vegetation area by 324.35 ha and in the desert area by 1625.81 ha. Meanwhile, the areas of bare ground and the city have increased by 1389.79 ha and 560.37 ha, respectively. According to the BLM findings, climate change, elderly farmers (more than 50 years), and small holding size raised the likelihood of LCC occurrence, with an odds ratio superior to one. Meanwhile, it was negatively impacted by the use of modern irrigation methods (drip and sprinkler), technology, and the availability of scavenger manpower in the oasis. Their odds ratios are inferior to one. The urban sprawl had a non-significant negative effect on the LCC. To preserve the identity of the zone as a sustainable agricultural and UNESCO heritage site, the researchers advocate for awareness and extension efforts aimed at the elderly to improve traditional production practices, enhance plant resilience, increase farm sizes for better earnings, and combat climate change effects to protect native plant species. Full article

Cashew apples, a byproduct of the cashew nut industry with an estimated global production of 38 million tonnes, are rich in several essential nutrients and are widely processed into juice, syrup, wine, pickles, and other value-added products. However, their morphological and physicochemical properties vary significantly across varieties, complicating in-field characterization, maturity assessment, and biochemical analysis. These challenges originate from the reliance on costly chemicals, skilled manpower, limited time, and sophisticated equipment. This study employed a user-developed computer vision-based ImageJ 1.x batch processing plugin to assess 15 physicochemical properties across six diverse cashew apple varieties from the images of slices and whole samples. Five methodologies—color grid, surface morphology, gray level co-occurrence matrix, local binary pattern, and color indices—generated image-based metrics rapidly ($2.87\pm 0.79$ s/image). The correlation of wet chemistry with image-based parameters, linear modeling, and wet chemistry parameters prediction with an independent dataset were successfully performed, and the successfully modeled properties include acidity, antioxidants, carbohydrates, carotenoids, crude fat, flavonoids, pH, phenolics, proteins, tannins, vitamin C, and total soluble solids. The results demonstrated the feasibility of predicting 11 out of 15 physicochemical properties of cashew apples ($R^2>0.5$). This methodology offers a faster, safer, and cost-effective alternative to wet chemistry and can be extended to other horticultural crops. Full article

Wave soldering is a technological process that allows for the simultaneous soldering of multiple locations on the same circuit board. Its major defects, such as tin bridging and insufficient tin filling, continue to challenge manufacturers, resulting in increased rework, labor, and operational costs. Therefore, reducing errors in wave soldering is crucial to ensure the best quality for customers and achieve cost savings for the company. This study aims to enhance wave soldering performance by using an integrated approach that combines Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) and Design of Experiment (DOE) for empirical improvement in an Original Equipment Manufacturer (OEM) factory, a subsidiary of a global OEM company. The results are sound: we eliminated tin till bridge defects by 88%, achieved a 33% reduction in manpower, and increased production volumes by 6%. This proposed framework can be utilized in other electronics manufacturing factories and related industries. Full article

Aquaculture has rapidly become a vital sector for ensuring global food security by meeting the growing demand for animal protein. Bangladesh, one of the world’s leading aquaculture producers, recorded a production of 4.91 million MT in 2022–2023, largely driven by inland farming systems. Despite this remarkable growth, the sector is highly vulnerable to disease outbreaks, which are aggravated by different factors. Pathogens such as bacteria, viruses, fungi, and parasites cause significant losses, while conventional disease diagnosis in Bangladesh still depends mainly on visual assessment and basic laboratory techniques, limiting early detection. This narrative review highlights recent advances in diagnostics as molecular tools, immunodiagnostics, nanodiagnostics, machine learning, and next-generation sequencing (NGS) that are widely applied globally but remain limited in Bangladesh due to infrastructure gaps, lack of skilled manpower, and resource constraints. Current treatment strategies largely rely on antibiotics and aquaculture medicinal products (AMPs), often misused without proper diagnosis, contributing to antimicrobial resistance (AMR). Promising alternatives, including probiotics, immunostimulants, vaccines, and enhanced biosecurity, require greater adoption and farmer awareness. The near-term priorities for Bangladesh include standardized disease and AMR surveillance, prudent antibiotic stewardship, phased adoption of validated rapid diagnostics, and investment in diagnostic and human capacity. Policy-level actions, including a national aquatic animal health strategy, stricter antimicrobial regulation, strengthening diagnostic infrastructure in institution, are crucial to achieve sustainable disease management and ensure long-term resilience of aquaculture in Bangladesh. Full article

Biomaterials can be defined as materials that interact positively with living tissues, restoring compromised functions, or enhancing tissue regeneration. Currently, biomaterial research often relies on a “trial-and-error method”, involving numerous experiments driven largely by experience. This strategy leads to a substantial waste of resources, such as manpower, time, materials, and finances. Optimizing the process is therefore essential. A recent and promising approach to this challenge involves artificial intelligence (AI), as demonstrated by the growing number of studies in this field. AI algorithms rely on data and empower computers with decision-making capabilities, mimicking aspects of the human mind and solving complex tasks with little to no human intervention. Due to their potential, AI and its derivatives are now widely used both in everyday life and in scientific research. In biomaterials science, AI models enable data analysis, pattern recognition, and property prediction. The aim of this review article is to highlight the key results achieved through the application of AI in the field of polymers for biomedical applications and, more broadly, in the development of advanced biomaterials. An overview will be provided on how an AI algorithm works, the differences between traditional programming and AI-based approaches, and their main limitations. Finally, the core topic will be addressed by categorizing biomaterials according to material class. Full article

With the advancement of medical care technology, the aging population has become a serious problem, and long-term care for the elderly is a major concern facing today’s society. Long-term care institutions take care of people with dysfunction or difficulties and provide them with continuous assistance. However, the shortage of specialists and the relative increase in costs have affected the burden on families. Long-term care has developed from traditional approaches to advanced ones at well-equipped facilities. We combine network technology with long-term care service with sensors that have alarm functions according to diverse needs, so that the elderly can receive complete care. Full article

Background/Objectives: The stability of caregiver manpower plays a crucial role in the operation of long-term care institutions. This study adopts Social Exchange Theory as the theoretical foundation to construct the psychological mechanism through which organizational commitment and work value influence retention intention via job involvement. Against the backdrop of Taiwan’s intensifying aging society and the increasing service demands of the veterans’ support system, Veterans Homes have gradually become indispensable within the long-term care system. Therefore, the primary objective of this study is to explore the formation mechanism of retention intention among caregivers in Veterans Homes. Methods: Data analysis was conducted using structural equation modeling, with 447 valid samples collected from caregivers across 16 Veterans Homes in Taiwan. Results: The results indicate that, in the process of forming retention intention, job involvement serves as a mediator between organizational commitment and work value on retention intention and demonstrates significant mediating effects. Conclusions: These findings suggest that when caregivers perceive value realization and organizational identification in their work, they are more likely to exhibit active engagement, thereby strengthening their tendency to remain employed. Furthermore, the study reveals that the effect of organizational commitment on job involvement is stronger than that of work value, indicating that exchange motives triggered by emotional bonds carry greater implications for retention. In conclusion, organizational support and personal value perceptions stimulate emotional engagement, which further influences caregivers’ decisions to remain in long-term service and ultimately shape their retention behavior. Full article

The measurement accuracy and equipment stability of superposition-type force sensors are primarily influenced by the layout and number of individual force sensors. Analyzing this impact effect through experimental testing for each configuration would consume significant manpower, material resources, and financial costs. To efficiently analyze the influence of the number of paralleled individual sensors and their layout within a superposition-type force measurement instrument on overall device stability and force measurement accuracy, this paper employs SolidWorks to establish models of force instruments based on common superposition schemes. Subsequently, ANSYS is utilized to perform finite element analysis on models of different schemes, obtaining corresponding data on total deformation, stress, and simulated force values. The analysis results indicate that a relatively sparse sensor layout with symmetric arrangement around the center point of the base plate enhances overall stability, and the force measurement error can be controlled within several ten-thousandths. Furthermore, the more stable and higher-accuracy schemes identified through simulation analysis were compared with practical experimental results to analyze theoretical versus actual errors. The test results showed that when the three single force sensors are placed in a “Pin font” shape, the sum of the forces measured by each individual sensor differs from the sum of the forces measured by the superimposed sensors by only a few ten-thousandths, which is within the acceptable range. Full article

In the face of growing demand for emergency treatment in mass casualty incidents involving acute hemorrhagic shock, disaster sites often suffer from limited search and rescue manpower and inadequate medical detection capabilities. With the rapid development of robot technology, the deployment of robots provides greater flexibility and reliability in disaster emergency response and search and rescue work, which can effectively address the shortage of search and rescue forces and medical resources at disaster sites. This paper introduces a snake-like robot designed for the rapid triage of casualties with hemorrhagic shock. Through a structural design combining active wheels and orthogonal joints, the robot integrates the advantages of high-speed mobility of wheeled robots with the high flexibility of jointed robots so as to adapt to the complex environments typical of search and rescue scenarios. Meanwhile, the end of the robot is equipped with a visible light camera, an infrared camera and a voice interaction system, which realizes the rapid triage of casualties with hemorrhagic shock by collecting visible light, infrared and voice dialog data of the casualties. Through Webots software simulation and outdoor site simulation experiments, seven indicators of the designed snake-like search and rescue robot are verified, including walking speed, minimum passable hole size, climbing angle, obstacle-surmounting height, passable step size, ditch-crossing width and turning radius, as well as the effectiveness of collecting visible light images, infrared images and voice dialog data of the casualties. Full article