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Traditionally, to assess reliability lifetimes and to evaluate reliability performance of semiconductor devices and chips, we test the samples to their failures. This can be called the "Test-to-Fail" scenario, which usually takes a long time (e.g., longer than a week). The Test-to-Failure scenario is required especially at the qualification stage, whose objective is to obtain the lifetimes of, e.g., devices, dielectrics, and metal lines. Due to the long test times, this approach is inadequate for reliability monitors, which need to be completed in a much shorter period of time so the product shipment will not be delayed and, if failed, timely corrective actions can be taken. Therefore, we are in urgent need of a much more efficient method to judge if the monitor meets reliability requirements. The "Test-to-Target" reliability test methodology perfectly matches such demand by only stressing the samples to much shorter times and can be applied on most common reliability tests like NBTI (Negative Bias Temperature Instabilities), HCI (Hot Carrier Injection), TDDB (Time Dependent Dielectric Breakdown), Isothermal EM test, and IMD (Inter Metal Dielectric) Vramp test. The corresponding specs for the Test-to-Target approach are defined based on the baseline records from the former complete Test-to-Fail reliability tests. From practical exercises after a long time, we prove the Test-to-Target methodology a truly useful approach particularly effective for reliability monitors, inline reliability assessments, process change management, nonconformance dispositions, and tool releases.

Reliability assessment is very important in semiconductor manufacturing, and we need to setup and maintain an efficient and effective reliability baseline (BL) management system to, for example, assess reliability risks, to dispose impacted lots, and to release equipment. To achieve these goals, we propose a corresponding flow and pinpoint the critical points to establish and maintain reliability BLs. To provide insights to readers by actual cases on reliability BL analyses and applications, we setup and analyze the BLs for the electric-field acceleration factor and activation energy of gate oxide, the gate voltage exponent of device Negative Bias Temperature Instabilities (NBTI), and the parameters of interconnect model for current density and activation energy. We also find the enriched reliability BL database does benefit line excursion disposition, process optimization, quicker judgment on the reliability impacts of nonconformance, feasibility studies of process changes, faster tool releases, risk assessments of common reliability tests, and earlier root cause identifications of abnormality by narrowing down failure mechanisms.

Reliability is one of the key dimensions of the quality of services and products that should be always evaluated. Growth and development of industries can be achieved by appropriate reliability engineering of products. Companies should evaluate and predict the reliability of products and accordingly find and fix the potential problems. In this regard, early detection of reliability problems based on the parameters of the production line or quality test results can prevent future warranty costs. Early detection of reliability problems based on production process and test data has not gained much attention in the literature. Therefore, an early detection model for predicting the reliability of products according to their quality test results is proposed in this research. For this purpose, hot test and warranty data of car engines manufactured by an automotive company are utilized. This data are prepared to predict engine reliability after preprocessing and removing inefficient data. Then, engines are divided into two homogeneous clusters using particle swarm optimization (PSO) clustering algorithm. Afterwards, the data in these clusters are used to feed the Artificial Neural Network (ANN) to predict the reliability of the engines. The obtained results show that the proposed ANN-based method is able to predict the reliability of the engines based on engine kilometers operated and hot test results. Also, it is shown that the proposed method outperforms the Cox proportional hazards model which has previously been used for early detection of product reliability.

Cytosponge: Trailblazer for Barrett's Oesophagus?

A Glimpse into Healthcare Policies: How Cancer Control and Prevention Programmes Have Evolved Across Asia.

Understanding Healthcare Policies in the Philippines: Cancer Care.

Healthcare Systems and Health Policies in Thailand: Cancer Care.

Cancer Care and Control in Taiwan: An Interview with Dr. Shu-Ti Chiou.

An Interview with Professor Myint Han: Healthcare in Myanmar.

This review paper discusses recent research achievements in medical thermography with concerns about the possibility of early breast cancer detection. With the advancements in infrared (IR) technology, image processing methods, and the pathophysiological-based knowledge of thermograms, IR screening is sufficiently mature to be utilized as a first-line complement to both health managing and clinical prognosis. In addition, it explains the performance and environmental conditions in identifying thermography for breast tumor imaging under strict indoor controlled environmental circumstances. An irregular thermogram is indicated as a significant biological risk marker for the presence or growth of breast tumors. Breast thermography is completely non-contact, with no form of radiation and compression. It is useful for all women of all ages, for pregnant and breastfeeding women, for women with implants, for women with dense or fibrocystic breasts, for women on hormone replacement therapy, and for pre or post menopausal women.

Breast thermography is specifically worthwhile during the early stages of fast tumor growth, which is not yet recognizable by mammography as thermography is a physiological test while mammography is an anatomical one. Often, physiological changes precede anatomical changes. This early detection of irregular tissue liveliness gives breast thermography the potential to be greatly useful and economical as an imaging program and provides the opportunity to apply non-invasive treatment to reform breast tissue activity. The non-radiating nature of thermography also permits repeated images. Thus, changes can be compared over time and the results of protective approaches can be observed to ensure utmost care of breast cells.

Since cancer becomes the most deadly disease to our health, research on early detection on cancer cells is necessary for clinical treatment. The combination of microfluidic device with cell biology has shown a unique method for cancer cell research. In the present review, recent development on microfluidic chip for cancer cell detection and diagnosis will be addressed. Some typical microfluidic chips focussed on cancer cells and their advantages for different kinds of cancer cell detection and diagnosis will be listed, and the cell capture methods within the microfluidics will be simultaneously mentioned. Then the potential direction of microfluidic chip on cancer cell detection and diagnosis in the future is also discussed.

Late detection of oral/laryngeal cancers or squamous cell carcinoma results in high patient mortality. Therefore, the detection of early-stage disease symptoms and timely medical treatment are important for improving long-term survival rates. Here, three deep learning models (single-shot detector, Yolo V4 and Tiny Yolo) were developed for target detection and binary type classification (normal/suspicious) for four representative oral/laryngeal regions (tongue, epiglottis, vocal cords, and tonsils) with a single-inspection process. The model performance was evaluated quantitatively on desktop and embedded platforms. We collected 1,632 endoscopic still-images and 20 diagnostic videos from the hospital database to train, validate, and test the models. Experimental results demonstrated that implemented models showed F1-scores ranging between 0.74–0.86, 0.86–1.00, and 0.74–0.87, and average precision ranging between 0.60–0.82, 0.92–1.00, and 0.72–0.98 for the tongue, epiglottis, and vocal cords, respectively, on the desktop platform. In addition, the Yolo V4 model showed performances of 0.92, 0.82, and 2.00 frames per second for the F1-score, average precision, and inference speed, respectively, on the embedded platform. Based on these results, we conclude that the implemented deep-learning-based at-home self-prescreening technique may be a reliable tool for personal oral/laryngeal healthcare, which will be especially important in endemic situations.

A novel domain-independent approach to technology trend monitoring is presented in the paper. It is based on the ontology of a technology trend, hype cycles methodology, and semantic indicators which provide evidence of a maturity level of a technology. This approach forms the basis for implementation of text-mining software tools. Algorithms behind these tools allow users to escape from getting too general or garbage results which make it impossible to identify promising technologies at early stages (early detection, weak signals). Besides, these algorithms provide high-quality results in extraction of complex multiword terms which correspond to technological concepts forming a trend. Methodology and software developed as a result of this study are applicable to various industries with minor adjustments and require no deep expert knowledge from a user.

Hypoxia is closely related to many diseases and often leads to death. Early detection and identification of the hypoxia causes may help to promptly determine the right rescue plan and reduce the mortality. We proposed a new multiparametric monitoring method employing mitochondrial reduced nicotinamide adenine dinucleotide (NADH) fluorescence, regional reflectance, regional cerebral blood flow (CBF), electrocardiography (ECG), and respiration under six kinds of acute hypoxia in four categories to investigate a correlation between the parameter variances and the hypoxia causes. The variation patterns of the parameters were discussed, and the combination of NADH and CBF may contribute to the identification of the causes of hypoxia.

A novel approach to trend monitoring and the identification of promising high-tech solutions is presented in the chapter. It is based on the ontology of a technology/market trend, Hype Cycles methodology, and semantic indicators which provide evidence of a maturity level of a technology as well as of emerging user needs (customer pains) in high-tech industries. This approach forms the basis for text mining software tools implemented in Semantic Hub platform. The algorithms behind these tools allow users to escape from getting too general or garbage results which make it impossible to identify promising technologies at early stages (early detection, weak signals). Besides, these algorithms provide high-quality results in the extraction of complex multiword terms which correspond to technological concepts and user pains forming a trend. The methodology and software developed as a result of this study are applicable to various industries with minor adjustments.

Reservoirs in Hong Kong constitute an essential component of local freshwater habitats, but their biodiversity still needs to be better understood. This study adopted environmental DNA (eDNA) technology to assess faunal biodiversity in local reservoirs. This approach enabled the analysis of metazoan diversity in Hong Kong’s reservoirs, which would have been challenging to achieve through conventional, labourintensive, and invasive ecological surveys. We surveyed eight reservoirs of varying sizes and ages, including their catchment areas, to provide baseline information for establishing a long-term monitoring programme for local reservoir habitats. The results revealed a higher success rate in amplifying eDNA fragments from the reservoirs than from catchment areas. The cytochrome oxidase I (COI) and 18S rRNA markers exhibited differential performance in biodiversity assessment. Notably, eDNA detected known naturalised species, such as Caridina cantonensis (bee shrimp), Cryptopotamon anacoluthon (freshwater crab), Parazacco spilurus (predaceous chub), and Liniparhomaloptera disparis (Hillstream loaches). Moreover, potential exotic species, Cherax quadricarinatus (the red-clawed crayfish), Oreochromis niloticus (Nile tilapia), and Paramisgurnus dabryanus (large-scaled loach), were discovered through both eDNA metabarcoding and ecological surveys. The findings suggest that reservoir-associated human activities, such as the release of aquarium pets, are potential sources of species introduction in these freshwater habitats. Regular biodiversity monitoring using eDNA technology can be an effective tool in freshwater ecosystem management and invasive species detection.

With the continued development of the global economy, biological invasion has become a pervasive and costly environmental problem, and it is one of the most critical factors threatening our environment, economy, and biological safety. Biological invasion is an essential element in global change and has been the focus of intensive prevention, management, and research activities worldwide. Prevention is the most effective and cost-efficient way to manage invasive plant species and reduce their negative impacts on ecosystems and the economy. To effectively prevent the introduction and spread of invasive species in new environments, multiple preventive measures can be taken, including conducting a comprehensive survey of biodiversity (biodiversity inventory), formulating strict laws and regulations, carrying out risk assessments, implementing early monitoring and rapid response strategies, and enhancing public education and awareness. Regulation of international trade and freight transportation includes strict biosecurity measures at borders and ports, such as quarantine and inspection, to prevent the unintentional introduction of invasive plant species. Early detection and rapid response involve monitoring and identifying potential invasive species before they become established and implementing swift action to eradicate or control them. Public education and awareness campaigns aim to increase general knowledge and understanding of invasive species and their impacts, encouraging individuals to take action to prevent their introduction and spread. Overall, preventing invasive plant species is a systematic project that requires collaborative efforts between government agencies, enterprises, and the public to implement effective management strategies and reduce the risk of introduction and spread.