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For low-permeability reservoirs, water-flooding development is usually adopted, which leads to induced fractures near the wellbore, increasing reservoir heterogeneity, and making water-flooding development more complex. This paper focuses on low-permeability reservoirs, considering the characteristics of induced fractures and elliptic-flow composite, and the well-test model for injection wells is established. The mathematical model in Laplace space is obtained through dimensionless transformation and Laplace transformation. Subsequently, the Mathieu function is introduced to obtain the bottom hole pressure, and the pressure response curve is drawn. The six flow stages of the curve are defined, and the sensitivity of parameters such as half-length of induced fractures, range of lateral-swept area, permeability in unswept area, and outer boundary distance at constant pressure are analyzed. The results show that the half-length of the fracture mainly affects the linear flow of the fracture, the range of the lateral wave-affected area mainly affects the radial flow of the swept area, the permeability of the unswept area mainly affects the radial flow of the unswept area, and the outer boundary distance at constant pressure mainly affects the boundary flow. Based on the production performance of a certain injection well in J Oilfield, a series of key parameters are obtained through analytical solution model inversion, including the induced-fracture half-length of 10.32 m, the lateral-swept range of elliptic partition flow of 128.95 m, the permeability of the swept area of 6.87 mD, and the mobility ratio of 119.92, which show the superiority of the analytical solution model.

The efficacy of underwater laser detection is considerably impacted by the intense attenuation of light resulting from the scattering and absorption effects of water. In this study, we present the simulation and design of the underwater Lidar system that integrates the paraxial multi-channel detection strategy to enhance the dynamic range in subsea environments. To evaluate the performance of the system with multiple detection channels, we introduce a multi-channel underwater Lidar simulation (MULS) method based on the radiative transfer Lidar equations. Experimental validations were conducted under varied water conditions to assess the performance of the prototype and validate the simulation results. The measured range accuracy of each channel in the prototype is better than 0.1085 m, and the simulated and measured waveforms exhibit strong correlations, verifying the reliability and validity of the simulation method. The effects of transceiver configuration and the maximum detectable range of different detection methods were also discussed. Preliminary results indicate that the paraxial multi-channel design effectively suppresses near-field backscattering and substantially enhances the maximum detectable range. The findings presented in this study may provide valuable insights for the design and optimization of future underwater laser detection systems.

Solid-state lithium metal batteries offer superior energy density, longer lifespan, and enhanced safety compared to traditional liquid-electrolyte batteries. Their development has the potential to revolutionize battery technology, including the creation of electric vehicles with extended ranges and smaller more efficient portable devices. The employment of metallic lithium as the negative electrode allows the use of Li-free positive electrode materials, expanding the range of cathode choices and increasing the diversity of solid-state battery design options. In this review, we present recent developments in the configuration of solid-state lithium batteries with conversion-type cathodes, which cannot be paired with conventional graphite or advanced silicon anodes due to the lack of active lithium. Recent advancements in electrode and cell configuration have resulted in significant improvements in solid-state batteries with chalcogen, chalcogenide, and halide cathodes, including improved energy density, better rate capability, longer cycle life, and other notable benefits. To fully leverage the benefits of lithium metal anodes in solid-state batteries, high-capacity conversion-type cathodes are necessary. While challenges remain in optimizing the interface between solid-state electrolytes and conversion-type cathodes, this area of research presents significant opportunities for the development of improved battery systems and will require continued efforts to overcome these challenges.

Cyclospora spp. is a food-borne intestinal protozoan, which is widely distributed in the world and poses the risk of zoonosis. In order to reveal the prevalence of Cyclospora spp. in Holstein cattle in partial areas of the Yunnan Province, 524 fresh fecal samples of Holstein cattle were collected from Dali, Kunming, Chuxiong, and Qujing in Yunnan Province. A nested PCR amplification of the small subunit (SSU) rRNA gene of Cyclospora spp. was carried out, and the products of the nested PCR were further analyzed by restriction fragment length polymorphism (RFLP) using Bsp E Ⅰ. The results of the present study showed that 13 samples were positive for Cyclospora spp., and the total infection rate of Cyclospora sp. was 2.48%. The infection of Cyclospora spp. was detected in Dali, Qujing, and Chuxiong. Chuxiong showed the highest infection rate (5.71%), and infection rate in Dali and Qujing was 2.19% and 3.16%, respectively. Interestingly, the infection of Cyclospora spp. was not detected in Kunming. The infection of Cyclospora spp. showed no significant differences among different regions (p > 0.05). Cyclospora sp. infection was detected in all ages and sexes, but the differences were not significant (p > 0.05). Sequencing and phylogenetic analysis showed that five Cyclospora spp. samples were closely related to the Cyclospora spp. of humans, and the others were closely related to the Cyclospora spp. of bovines. The results of the present study suggested that there was an infection of Cyclospora spp. in Holstein cattle in the Yunnan Province, and the Cyclospora spp. showed a risk of zoonosis. Thus, the prevention and control of Cyclospora spp. should be strengthened in the Yunnan Province, China. The results of this investigation provide data references for the further research of Cyclosporiasis in Holstein cattle in the Yunnan Province.

An evaluation of the measurement uncertainty of on-road NOx emissions using portable emission measurement system (PEMS) based on real local testing data collected in China was carried out as per the type B method defined in the EN 17507 standard. The aim of this work was to quantify the “absolute” measurement uncertainty of PEMSs, which excluded “PEMS relative to laboratory constant volume sampler (CVS)” uncertainty from the calculation of on-road NOx measurement uncertainty using PEMSs. PEMS instruments from three mainstream manufacturers were employed. The zero drift of the NOx analyzers was evaluated periodically during the real driving emissions (RDE) test, and it was noticed that there was neither a linear nor step model of zero drift, with no correlation with the boundary conditions or measurement principle. Additionally, from the 256 valid RDE tests, the zero drift always ranged from 3.8 ppm to −3.8 ppm, and more than 95% of the span drifts were within a range of 1.5%. Based on the laboratory testing of ten vehicles using the worldwide harmonized light-duty vehicle test cycle (WLTC), the type B uncertainty of PEMS NOx measurements corresponding to China-6a and China-6b limits was assessed. An uncertainty of 26.5% for China-6a was found (NOx limit = 60 mg/km over the WLTC), which is very close to the 22.5% from the EU evaluation results (NOx limit = 80 mg/km over the WLTC); the uncertainty with respect to China-6b was found to be 42.8% because the type-I limit was tuned down to 35 mg/km. This result indicates that, with the ever-tightening regulatory limits of vehicle NOx emissions, big challenges will be posed in terms of the reliability of PEMS measurements, which requires PEMS manufacturers to improve the performance of the instruments and policymakers to refine the test procedures and/or result calculation method to minimize the impacts.

In order to defraud state subsidies, some unscrupulous users use improper means to steal photovoltaic (PV) power. This behavior brings potential safety hazards to photovoltaic grid-connected operations. In this paper, a photovoltaic power-stealing identification method based on similar-day clustering and interval prediction of the quantile regression model for long short-term memory neural network (QRLSTM) is proposed. First, photovoltaic data are clustered into three similar days by the similar-day clustering according to weather conditions. Second, compared with the quantile regression neural network (QRNN) prediction method, the good prediction performance of the QRLSTM method is illustrated. Third, using the prediction intervals with different confidence levels on three similar days, according to the time scale (short-term, medium-term and long-term) combined with different electricity-stealing judgment indicators, a three-layer photovoltaic power-stealing screening framework is constructed, and the degree of user power stealing is qualitatively analyzed. Last, the power generation data of eight photovoltaic users in a certain region of northwest China and the data of four groups of artificially constructed power-stealing users are used as an example for simulation. The simulation results prove the feasibility of the proposed method in this paper.

Most of the current, popular approaches to monitoring real driving NOx emissions are based on direct measurement. However, due to the uncertainty of sensor-based measurements, such methods cannot always be used to accurately screen out the malfunctions of an emission control system. In this paper, a random forest (RF) model which extracts information from on-board diagnostics (OBD) data streams transmitted by a remote emission management vehicle terminal (REMVT) is proposed to provide a specific emission method for the online screening of high NOx emissions. First, two particular forms of modeling, random forest and logistic regression (LR), are laid out as representatives of nonparametric models and specified linear models. These two models were trained, validated and compared using OBD data collected from three China-VI heavy-duty diesel vehicles (HDDVs). The results show that as a data-driven, highly adaptive and robust learning method, the RF model can more accurately identify an abnormal emission state. Finally, a further validation was conducted, in which another China-VI HDDV was tested in two typical states, including a fault state and a normal state. The results indicated that the RF model could clearly distinguish the out-of-control emission condition from the normal operation state. The outcome of this research verifies the feasibility of using a machine learning model to process remote OBD data on HD vehicles and to identify high emissions in the case of an in-use fleet. On this basis, more sophisticated combined models and multi-stage models could be developed.

In response to the emergency management caused by COVID-19, Taiwan began to impose a name-based rationing system for the purchase of face masks by having consumers visit physical stores and preorder them online. By doing so, the risk of face mask shortages caused by panic buying was reduced. To understand consumers’ willingness to switch from buying face masks at physical stores to preordering them online, we used a push-pull-mooring (PPM) model to measure related dimensions. We administered an online questionnaire survey and collected 233 valid responses. In the present study, perceived risk (including time risk, psychological risk and social risk) was treated as a second-order formative indicator, while pull effect was measured by the variables of critical mass and alternative attraction. Mooring effect was measured by switching cost. Through structural equation modeling (SEM), perceived risk, as well as critical mass and alternative attraction, had a significant effect on switching intention, while switching cost had no significant relationship with switching intention. This study investigated whether perceived risk (time risk, psychological risk and social risk), critical mass, alternative attraction and switching cost can serve as references for purchase behaviors amid future emergency management, through the prism of population migration theory, and proposed recommendations for their promotion and implementation.

Element doping is a universal way to improve the electronic and optical properties of two-dimensional (2D) materials. Here, we investigate the influence of group−ⅣA element (C, Si, Ge, Sn, and Pb) doping on the electronic and optical properties of the ZnS monolayer with a tetragonal phase by using first-principles calculations. The results indicate that the doping atoms tend to form tetrahedral structures with neighboring S atoms. In these doped models, the formation energies are all negative, indicating that the formation processes of the doped models will release energy. The formation energy is smallest for C−doped ZnS and gradually increases with the metallicity of the doping element. The doped ZnS monolayer retains a direct band gap, with this band gap changing little in other element doping cases. Moreover, intermediate states are observed that are induced by the sp3 hybridization from the doping atoms and S atoms. Such intermediate states expand the optical absorption range into the visible spectrum. Our findings provide an in-depth understanding of the electronic and optical properties of the ZnS monolayer and the associated doping structures, which is helpful for application in optoelectronic devices.

Over the past decade, neurorobotics-integrated machine learning has emerged as a new methodology to investigate and address related problems. The combined use of machine learning and neurorobotics allows us to solve problems and find explanatory models that would not be possible with traditional techniques, which are basic within the principles of symmetry. Hence, neuro-robotics has become a new research field. Accordingly, this study aimed to classify existing publications on neurorobotics via content analysis and knowledge mapping. The study also aimed to effectively understand the development trend of neurorobotics-integrated machine learning. Based on data collected from the Web of Science, 46 references were obtained, and bibliometric data from 2013 to 2021 were analyzed to identify the most productive countries, universities, authors, journals, and prolific publications in neurorobotics. CiteSpace was used to visualize the analysis based on co-citations, bibliographic coupling, and co-occurrence. The study also used keyword network analysis to discuss the current status of research in this field and determine the primary core topic network based on cluster analysis. Through the compilation and content analysis of specific bibliometric analyses, this study provides a specific explanation for the knowledge structure of the relevant subject area. Finally, the implications and future research context are discussed as references for future research.

To maximize the performance of energy storage systems more effectively, modern batteries/supercapacitors not only require high energy density but also need to be fully recharged within a short time or capable of high-power discharge for electric vehicles and power applications. Thus, how to improve the rate capability of batteries or supercapacitors is a very important direction of research and engineering. Making low-tortuous structures is an efficient means to boost power density without replacing materials or sacrificing energy density. In recent years, numerous manufacturing methods have been developed to prepare low-tortuous configurations for fast ion transportation, leading to impressive high-rate electrochemical performance. This review paper summarizes several smart manufacturing processes for making well-aligned 3D microstructures for batteries and supercapacitors. These techniques can also be adopted in other advanced fields that require sophisticated structural control to achieve superior properties.

Both the EU and China are evaluating the feasibility of lowering the detection limit of particle number (PN) measurement to 10 nm in future legislations, making it necessary to better understand the sub-23 nm particle emission characteristics from state-of-the-art vehicles. In this study, solid PN emissions with a diameter larger than 10 nm and 23 nm (known as SPN10 and SPN23) were compared over the WLTC, RTS95, and a so-called “worst-case” real driving emission (RDE) cycle (highly dynamic/0 °C) using two certification-level particle number counters (PNCs) employing evaporation tube (ET) and catalytic stripper (CS) as volatile particle remover (VPR). The results show that SPN10 emissions were 31.7%, 27.8%, and 15.2% higher than SPN23 over the WLTC, RTS95, and laboratory RDE cycles. Sub-23 nm particles were almost not identified within the engine cold-start phase and tended to be a hot-running pollutant favored by aggressive driving styles (frequent accelerations and high engine loads), fuel-cut during decelerations, and long idles. Lower testing temperature delayed the light-off of catalyst and, therefore, significantly reduced the formation of sub-23 nm particles within the engine warm-up stage. Lowering the detection limit to 10 nm is deemed to provide more public health protection since it will guide manufacturers to pay more attention to vehicle hot-running emissions.

The current mainstream energy storage systems are in urgent need of performance improvements to meet novel application requirements. In pursuit of a higher energy density in Li-ion and Na-ion batteries, the conventional electrode materials have reached the upper limit of their theoretical specific capacities. Hence, facile methods of reducing irreversible lithium-ion/sodium-ion loss are developed to further boost the battery energy density. Herein, we review studies that use polycyclic aromatic hydrocarbons for wet chemical prelithiation and presodiation. The molecular structures of arenes and solvents used for solution-based prelithiation/presodiation have a substantial impact on the prelithiation/presodiation power and effectiveness. Multiple reports have already shown excellent initial Coulombic efficiency and streamlined processes by using this type of wet chemical prelithiation/presodiation strategy. This review article will cover how to select appropriate polycyclic aromatic hydrocarbon prelithiation/presodiation reagents for various materials/electrodes and provide possible directions and guidelines for future works.

A new SOC (State-Of-Charge)–VOC (Voltage-of-Open-Circuit) mathematical model was proposed in this paper, which is particularly useful in parallel lithium battery modeling. When the battery strings are charged in parallel connection, the batteries can be deemed as capacitors with different capacitances, and the one with larger capacitance always obtains the higher current. According to this mathematical model, the parallel battery charging with different peak capacitances can result in different voltage slew rates on different battery strings during the constant current control. Different parallel battery strings are charged with different currents, of which the battery string under higher current can induce higher power loss and higher temperature. The conventional solution can use this model to switch the constant current charging into the constant voltage charging with the correct timing to avoid overcurrent charging. Other battery pack protection methods including current sense resistor, resettable thermal cutoff device, or resettable fuse can also use this mathematical model to improve the protection. In the experiments, three kinds of batteries including LiFePO4 battery, EV Type-1 battery, and ternary battery were examined. The experiments showed good consistency with the simulation results derived from the mathematical model.

Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs).

The structural and interfacial stability of silicon-based and lithium metal anode materials is essential to their battery performance. Scientists are looking for a better inactive material to buffer strong volume change and suppress unwanted surface reactions of these anodes during cycling. Lithium silicates formed in situ during the formation cycle of silicon monoxide anode not only manage anode swelling but also avoid undesired interfacial interactions, contributing to the successful commercialization of silicon monoxide anode materials. Additionally, lithium silicates have been further utilized in the design of advanced silicon and lithium metal anodes, and the results have shown significant promise in the past few years. In this review article, we summarize the structures, electrochemical properties, and formation conditions of lithium silicates. Their applications in advanced silicon and lithium metal anode materials are also introduced.

Stepping into the 21st century, “graphene fever” swept the world due to the discovery of graphene, made of single-layer carbon atoms with a hexagonal lattice. This wonder material displays impressive material properties, such as its electrical conductivity, thermal conductivity, and mechanical strength, and it also possesses unique optical and magnetic properties. Many researchers see graphene as a game changer for boosting the performance of various applications. Emerging consumer electronics and electric vehicle technologies require advanced battery systems to enhance their portability and driving range, respectively. Therefore, graphene seems to be a great candidate material for application in high-energy-density/high-power-density batteries. The “graphene battery”, combining two Nobel Prize-winning concepts, is also frequently mentioned in the news and articles all over the world. This review paper introduces how graphene can be adopted in Li-ion/Li metal battery components, the designs of graphene-enhanced battery materials, and the role of graphene in different battery applications.

Employing maintenance threshold plays a critical step in determining an optimal maintenance policy for an offshore wind system to reduce maintenance costs while increasing system reliability. Considering the limited works on this topic, we propose a two-stage procedure to determine the optimal maintenance thresholds for multiple components of an offshore wind power system in order to minimize maintenance costs while achieving the highest possible system reliability. First, using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds of individual components where the cost of maintenance and the rate of failure are critical. Then, fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold considering all components. A variety of factors including weather conditions, system reliability, power generation losses, and electricity market price are carefully considered to enhance the system’s reliability and reduce the costs of maintenance. When maintenance threshold results are compared, component-wise versus system-wise, an average system savings of 1.19% for maintenance cost is obtained while the system reliability is increased by 1.62% on average.

Nitrogen oxides (NOx) emissions from diesel vehicles are major contributors to increasing fine particulate matter and ozone levels in China. The selective catalytic reduction (SCR) system can effectively reduce NOx emissions from diesel vehicles and is widely used in China IV and V heavy-duty diesel vehicles (HDDVs). In this study, two China IV HDDVs, one with SCR system failure and the other with a normal SCR system, were tested by using a portable emissions measurement system (PEMS). Results showed that the NOx emission factors of the test vehicle with SCR system failure were 8.42 g/kW∙h, 6.15 g/kW∙h, and 6.26 g/kW∙h at loads of 0%, 50%, and 75%, respectively, which were 2.14, 2.10, and 2.47 times higher than those of normal SCR vehicles. Emission factors, in terms of g/km and g/kW∙h, from two tested vehicles were higher on urban roads than those on suburban and motorways. The NOx emission factor of the vehicle with failed SCR system did not meet the China IV emission standard. The time-weighted results for normal SCR vehicle over the three road types show that, except for NOx emission factor 12.17% higher than the China IV limit at 0% load, the emission values are 16.21% and 27.54% below the China IV standard limit at 50% load and 75% load, respectively. In general, with higher load, NOx emissions (in terms of g/kW∙h) from the tested vehicle decreased. Furthermore, NO/NOx concentrations of both vehicles with normal and failed SCR systems showed a decreasing trend with the increase in load.

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

This paper reports the regulated, unregulated, and particle number emissions from six high-mileage, China-4 compliant, dedicated methanol taxis over the new European driving cycle. Compared to new vehicles, carbon monoxide, total hydrocarbons, and nitrogen oxides emissions from in-use methanol taxis increased by 76.1%, 40.2%, and 179.8%, respectively. Still, they managed to meet China-4, indicating good in-service conformity. In the test fleet, the test vehicles with longer mileage inclined to emit higher carbon monoxide and total hydrocarbons emissions. Formaldehyde emissions from these field-aged taxis ranged from 1.06 to 2.33 mg/km, which were similar to or lower than those from previously reported pre-Euro-5 gasoline vehicles. One of the six test vehicles produced extraordinarily high unburned methanol emissions, which was about ten times higher than the rest of the properly operating vehicles due to possible misfire, suggesting that unburned methanol will be the primary stress for future methanol applications. Compared to the regulated emissions, formaldehyde and unburned methanol emissions deteriorated at faster rates along with catalyst aging. Particle number emissions from these methanol taxis remained low even after high-mileage driving, suggesting the compatibility of methanol fueling in future particle number compliance.

From traditional face-to-face courses, asynchronous distance learning, synchronous live learning, to even blended learning approaches, the learning approach can be more learner-centralized, enabling students to learn anytime and anywhere. In this study, we applied educational data mining to explore the learning behaviors in data generated by students in a blended learning course. The experimental data were collected from two classes of Python programming related courses for first-year students in a university in northern Taiwan. During the semester, high-risk learners could be predicted accurately by data generated from the blended educational environment. The f1-score of the random forest model was 0.83, which was higher than the f1-score of logistic regression and decision tree. The model built in this study could be extrapolated to other courses to predict students’ learning performance, where the F1-score was 0.77. Furthermore, we used machine learning and symmetry-based learning algorithms to explore learning behaviors. By using the hierarchical clustering heat map, this study could define the students’ learning patterns including the positive interactive group, stable learning group, positive teaching material group, and negative learning group. These groups also corresponded with the student conscious questionnaire. With the results of this research, teachers can use the mid-term forecasting system to find high-risk groups during the semester and remedy their learning behaviors in the future.

The anti-neuroinflammatory capacities of raw and steamed garlic extracts as well as five organosulfur compounds (OSCs) were examined in lipopolysaccharide (LPS)-stimulated BV2 microglia. According to those results, steaming pretreatment blocked the formation of alliinase-catalyzed OSCs such as allicin and diallyl trisulfide (DATS) in crushed garlic. Raw garlic, but not steamed garlic, dose-dependently attenuated the production of LPS-induced nitric oxide (NO), interleukin-1β (IL-1β), tumor necrosis factor (TNF)-α, and monocyte chemoattractant protein-1 (MCP-1). DATS and diallyl disulfide at 200 and 400 μM, respectively, displayed significant anti-neuroinflammatory activity. Meanwhile, even at 1 mM, diallyl sulfide, S-allyl cysteine and alliin did not display such activity. Inhibition of nuclear factor-κB activation was the mechanism underlying this protective effect of raw garlic and DATS. Analysis results indicated that the anti-neuroinflammatory capacity of raw garlic is due to the alliin-derived OSCs. Importantly, DATS is a highly promising therapeutic candidate for treating inflammation-related neurodegenerative diseases.

The world-to-chip interface is an essential yet intriguing part of making and employing microfluidic devices. A user-friendly connector could be expensive or difficult to make. We fabricated two ports of microfluidic chips with easily available materials including Teflon blocks, double adhesive films, coverslips, and transparency films. By using a mini grinder, coverslips were drilled to form small holes for the fluid passages between port and chip. Except for the double adhesive films, the resultant ports are durable and re-useable. The DK1 port, contains a mini three-way switch which allows users to handle fluid by a tube-connected pump, or by a manual pipette for the sample of trace amount. The other port, the DK2 port, provides secured tube-connections. Importantly, we invented a bridge made of craft cutter-treated transparency films and double adhesive films to mediate liquid flow between DK2 port and chip. With the use of a bridge, users do not need to design new ports for new chips. Also, individual chips could be linked by a bridge to form a chip array. We successfully applied DK1 port on a microfluidic chip where green fluorescent protein was immobilized. We used DK2 port on an array of fish chips where the embryos of zebra fish developed.