Reliable Technology Research Articles

A High‐Efficiency System for Long‐Term Salinity‐Gradient Energy Harvesting and Simultaneous Solar Steam Generation

AbstractThe vast energy stored in the ocean, which receives an average solar power of ≈60 000 TW per year, surpasses human energy consumption by three orders of magnitude. Harnessing even a small fraction of it holds great promise in addressing global energy and water crises. Here, an integrated device that achieves unprecedented power density up to 1.1 W m−2 with excellent stability through a salinity concentration gradient induced by solar evaporation, while simultaneously producing clean water at a rate of 1.25 kg m−2 h−1 under one sun irradiation is presented. The remarkable electricity generation capability stems from the unique interlayer structure of polyaniline‐graphene oxide‐MnO2 (PANI@GO/MnO2) electrodes, enabling the recovery of electrochemical potentials from a wide range of ion salinity concentrations within the device and the additional Donnan potential generated by the anion‐exchange membrane. Furthermore, periodic flipping of the device effectively reactivates the electrodes and suppresses salt accumulation, enabling long‐term operation. Notably, a prototype device of 8 × 25 cm2 exhibits a short‐circuit current of 10 mA and an open‐circuit voltage of 10.2 V, as well as a clean water production rate of 24.8 g per hour. These findings shed light on the reliable technology for power and freshwater supply in marine environments.

Technical characteristics and development trend of third generation solar cells

The future of global sustainable energy systems is anticipated to heavily rely on photovoltaic solar energy (PV). This paper provides an analysis of third-generation solar cells, exploring their underlying working mechanisms, evaluating their performance metrics, and discussing both their advantages and disadvantages. Third-generation solar cells, which include perovskites, dye-sensitized cells, and organic photovoltaics, offer promising advancements over traditional silicon-based cells, such as higher efficiencies and potential for lower production costs. However, challenges like material stability, scalability, and long-term reliability hinder their widespread adoption. In offering a comprehensive examination of these advanced solar technologies, the paper aims to contribute to the understanding and progress of renewable energy solutions. It underscores the importance of continued research and innovation in overcoming the existing limitations of third-generation solar cells. The transition towards a future that relies more heavily on sustainable energy sources hinges on the ability to develop and implement efficient, reliable, and cost-effective solar technologies. The paper identifies these bottleneck issues and suggests pathways for overcoming them.

A DFT Study of Band-Gap Tuning in 2D Black Phosphorus via Li+, Na+, Mg2+, and Ca2+ Ions.

Black phosphorus (BP) and its two-dimensional derivative (2D-BP) have garnered significant attention as promising anode materials for electrochemical energy storage devices, including next-generation fast-charging batteries. However, the interactions between BP and light metal ions, as well as how these interactions influence BP's electronic properties, remain poorly understood. Here, we employed density functional theory (DFT) to investigate the effects of monovalent (Li+ and Na+) and divalent (Mg2+ and Ca2+) ions on the valence electronic structure of 2D-BP. Molecular orbital analysis revealed that the adsorption of divalent cations can significantly reduce the band gap, suggesting an enhancement in charge transfer rates. In contrast, the adsorption of monovalent cations had minimal impact on the band gap, suggesting the preservation of 2D-BP's intrinsic electrical properties. Energetic and charge analyses indicated that the extent of charge transfer primarily governs the ability of ions to modulate 2D-BP's electronic structure, especially under high-pressure conditions where ions are in close proximity to the 2D-BP surface. Moreover, charge polarization calculations revealed that, compared with monovalent cations, divalent cations induced greater polarization, disrupting the symmetry of the pristine 2D-BP and further influencing its electronic characteristics. These findings provide a molecular-level understanding of how ion interactions influence 2D-BP's electronic properties during ion-intercalation processes, where ions are in close proximity to the 2D-BP surface. Moreover, the calculated diffusion barrier results revealed the potential of 2D-BP as an effective anode material for lithium-ion, sodium-ion, and magnesium-ion batteries, though its performance may be limited for calcium-ion batteries. By extending our understanding of interactions between ions and 2D-BP, this work contributes to the design of efficient and reliable energy storage technologies, particularly for the next-generation fast-charging batteries.

Use of Sustainable Energy Systems in Educational Institutions

Educational institutions like other public and private organizations should try to eliminate their carbon footprint for the achievement of the ambitious 2050 target of net-zero carbon emissions. Several mature, reliable and cost-effective sustainable energy technologies can be used in these institutions replacing the use of fossil fuels and reducing their carbon emissions. Solar energy, wind energy and biomass can be used for heat and electricity generation replacing the use of oil, gas and grid electricity. Additionally, various low-carbon emission technologies including heat pumps, co-generation systems, fuel cells, district heating systems and power storage technologies can be also used in them. The combined use of these technologies in schools, colleges and universities can replace the current use of conventional energy sources reducing or completely eliminating their carbon emissions. The abovementioned benign energy technologies are already used commercially in several sectors proving their reliability and cost-effectiveness. It has been indicated that a plethora of green energy technologies can be used in educational institutions eliminating their carbon footprint achieving the desired global 2050 target of net-zero carbon emissions. Our results could be useful to policy makers and to the authorities of schools, colleges and universities who are willing to promote their environmental sustainability, achieving economic benefits and offering valuable educational opportunities to students.

Interactive Technologies in Online Teacher Education in Africa: A Systematic Review 2014–2024

This systematic review investigates the integration of interactive technologies in online teacher education across Africa from 2014 to 2024. The study aims to identify the types of interactive technologies employed, the leading countries in regard to their adoption, and the outcomes of their usage on teacher training programs. Despite significant advancements, particularly in countries like Kenya and South Africa, the effectiveness of these technologies varies widely due to infrastructural, socio-economic, and policy-related challenges. The findings highlight the critical role of reliable information communication technology (ICT) infrastructure, culturally relevant content, and comprehensive training for educators in optimizing the use of interactive technologies. Additionally, the study emphasizes the need for greater policy coordination and collaboration among African nations to fully leverage these technologies. The review concludes that, while interactive technologies hold substantial potential in terms of transforming teacher education in Africa, sustained efforts are required to overcome the existing barriers and ensure equitable access to quality education across the continent.

Computational Exploration of Adsorption-Based Hydrogen Storage in Mg-Alkoxide Functionalized Covalent-Organic Frameworks (COFs): Force-Field and Machine Learning Models.

Hydrogen is a clean-burning fuel that can be converted to other forms. of energy without generating any greenhouse gases. Currently, hydrogen is stored either by compression to high pressure (>700 bar) or cryogenic cooling to liquid form (<23 K). Therefore, it is essential to develop safe, reliable, and energy-efficient storage technology that can store hydrogen at lower pressures and temperatures. In this work, we systematically designed 2902 Mg-alkoxide-functionalized covalent-organic frameworks (COFs) and performed high-throughput (HT) computational screening for hydrogen storage applications at 111, 231, and 296 K. To accurately model the interaction between Mg-alkoxide sites and molecular hydrogen, we performed MP2 calculations to compute the hydrogen binding energy for different types of functionalized models, and the data were subsequently used to fit modified-Morse force field (FF) parameters. Using the developed FF models, we conducted HT grand canonical Monte Carlo (GCMC) simulations to compute hydrogen uptakes for both original and functionalized COFs. The generated data were subsequently used to evaluate the materials' gravimetric and volumetric storage performance at various temperatures (111, 231, and 296 K). Finally, we developed machine learning (ML) models to predict the hydrogen storage performance of functionalized structures based on the features of the original structures. The developed model showed excellent performance with a mean absolute error (MAE) of 0.061 wt % and 0.456 g/L for predicting the gravimetric and volumetric deliverable capacities, enabling a quick evaluation of structures in a hypothetical COF database. The screening results demonstrated that the Mg-alkoxide functionalization yields greater improvements in volumetric H2 storage capacities for COFs with smaller pores compared to those with larger (mesoporous) pores.

Results on the Use of an Original Burner for Reducing the Three-Way Catalyst Light-Off Time

Individual road mobility comes with two major challenges: greenhouse gas emissions related to global warming and chemical pollution. For the pollution reduction in the spark ignition engine vehicle, the standard and reliable aftertreatment technology is the three-way catalytic converter (TWC). However, the TWC starts to convert once an optimal temperature, usually known as the light-off temperature, is reached. There are many methods to reduce the warm-up period of the TWC, among which is using a burner. The initial question underlying this study was to see if the use of a relatively straightforward extra-combustion device mounted upstream the TWC, without complex elements, was able to serve the purpose of reducing the light-off time. Consequently, an original burner was designed and investigated numerically via the CFD method and experimentally via measurements of the temperature evolution within a TWC, along with the emissions specific to the burner’s operation. The main findings of this study are: (1) the CFD-based examination is a good way to decide on how to achieve the so-called fit-for-purpose internal aerodynamics of the burner (i.e., to obtain a homogeneous mixture) and (2) to reach the light-off temperature, conventionally taken as 500 K, the burner was operated for 5.2 s, i.e., 3.6 g of gasoline injected, 2.7 g of CO2 and 1.351 g of CO, respectively, emitted. Moreover, this study identified measures for improving the burner’s design as well as an enhanced procedure for the burner’s operating control both aiming to produce a cleaner combustion during the TWC pre-heating.

Test-retest reliability and construct validity of Inosen’s Silat Momentum Test

Problem Statements: The kick in Pencak Silat has a magnitude called momentum, which requires a tool capable of measuring several variables based on the International System of Units (SI) in real-time. Nowadays, there are affordable and reliable sensor technologies to meet these needs. Purpose: The research aimed to design a test instrument that utilizes sensor technology to measure kick momentum in the sparring category of Pencak Silat following the applicable SI, and to test its reliability and validity. The resulting instrument is named Inosen’s Silat Momentum Test. Approach: This study followed Johnson and Nelson’s seven-test construction process. Twenty-four Pencak Silat athletes, consisting of 12 elite and 12 non-elite participants, were involved in the evaluation process. The test’s reliability was assessed by examining the correlation between the test and retest results. The construct validity test was conducted by comparing the results of the front kick tests of elite and non-elite Pencak Silat athletes. Data analysis was performed using Pearson correlation and independent t-tests using Minitab 21.4.1 software. Results: The results of the test and retest indicate a very high correlation (r = 0.923), demonstrating the consistent ability of Inosen’s Silat Momentum Test to measure kick momentum. The study found a statistically significant difference in the Inosen’s Silat Momentum Test scores (p &lt; 0.05) between the elite fighter group (average score of 463.3 kg.m/s) and the non-elite fighter group (average score of 375 kg.m/s). The results indicate that Inosen’s Silat Momentum Test is an effective instrument for differentiating kick momentum based on fighter performance. Conclusions: Based on the findings, it is inferred that Inosen’s Silat Momentum Test is a reliable and valid instrument for measuring Pencak Silat kick momentum according to SI standards. Keywords: biomechanics, kick momentum, pencak silat, performance test, sensor technology.

Enhanced detection of glyphosate with a Co-MOF integrated opto-electrochemical sensor

Abstract This study presents a new method for detecting the organophosphorus pesticide glyphosate using advanced scanning electrodes (SPE) and enhanced fluorescence. Metal-organic frameworks from cobalt ions were synthesized using a solvothermal method. It is characterized using Raman spectroscopy, FT-IR, and X-ray diffraction techniques. The electrocatalytic behavior of the materials was studied using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Differential pulse voltammetry (DPV) examined the positive response of plants to glyphosate over a concentration range of 0.55–5.95 mM with a detection limit of 0.334 mM. The fluorescence enhancement ranges from 0.07 mM to 0.67 mM, and the detection limit is 0.0998 mM. Additionally, the selectivity of the proposed opto-electrochemical sensor was evaluated. This selection demonstrates the sensor's ability to detect glyphosate in complex wastewater matrices. This has important implications for environmental monitoring. By addressing glyphosate contamination, the sensor could significantly advance ecological remediation and monitoring strategies. The selectivity, sensitivity, and ability to operate under harsh conditions represent a significant advance in the development of efficient and reliable glyphosate technology for wastewater treatment and environmental protection. In real-sample matrices, the suggested sensor showed a good recovery of the pesticide that had been spiked.

Efficacy of Phthalocyanine‐Based Catalysts in Electrochemical Sensors: A Comprehensive Review

AbstractMetal phthalocyanines (MPcs) are promising materials for electrochemical sensing due to their physicochemical properties, including redox activity, structural versatility, and chemical stability. These materials can incorporate various metals into their central core, ensuring tunable catalytic activity and enhanced sensitivity and selectivity. This makes MPcs valuable for designing advanced electrochemical sensors, which require precise and reliable performance for applications ranging from environmental monitoring to biomedical diagnostics. This review discusses the advancements in MPc‐based catalysts for electrochemical sensors, focusing on their superior catalytic properties, stability under diverse operating conditions, and high functionalization potential. The unique redox behavior of the metal center in MPcs ensures improved detection capabilities of analytes like biomolecules, heavy metal ions, and environmental pollutants, positioning MPc materials as a cornerstone in future sensor technology. MPc‐based sensors have diverse applications across various fields, including environmental sensing, medical diagnostics, and industrial process monitoring. Recent reports highlight the practical relevance and growing importance of MPcs in real‐world applications. Challenges associated with MPc‐based sensors include scalability, environmental stability, and integration into practical devices. The review concludes with a discussion on the future outlook on MPcs in the design and development of next‐generation electrochemical sensors, paving the way for more efficient, cost‐effective, and reliable detection technologies.

Perspectives on Anesthesia and Perioperative Patient Safety: Past, Present, and Future.

During the past 70 years, patient safety science has evolved through four organizational frameworks known as Safety-0, Safety -1, Safety-2, and Safety-3. Their evolution reflects the realization over time that blaming people, chasing errors, fixing one-offs, and regulation would not create the desired patient safety. In Safety-0, the oldest framework, harm events arise from clinician failure; event prevention relies on better staffing, education, and basic standards. In Safety-1, used by hospitals, harm events arise from individual and/or system failures. Safety is improved through analytics, workplace culture, high reliability principles, technology, and quality improvement. Safety-2 emphasizes clinicians' adaptability to prevent harm events in an everchanging environment, using resilience engineering principles. Safety-3, used by aviation, adds system design and control elements to Safety-1 and Safety-2, deploying human factors, design-thinking, and operational control or feedback to prevent and respond to harm events. Safety-3 represents a potential way for anesthesia and perioperative care to become safer.

Design of Underwater Cables Applicable to Submarine(KSS-III Batch-II) Navigation Radar

Since the creation of the first submarine telegraph cable connecting England and France in 1850, today, due to the rapid development of the Internet and Communications fields, the demand for submarine communication cables has increased and continues to develop. Submarine cables are designed and manufactured to be suitable for the maritime environment, and many parts are imported from overseas, and several domestic cable manufacturers are producing them for commercial purposes. Field-proven and reliable commercial submarine communication cable technology is also being applied to the military field, and is especially actively utilized in naval surface ships and submarines. This paper is about the design of an underwater cable applicable to submarine(KSS-III Bach-II) navigation radar and also is to secure the performance of an underwater cable with bending durability in extreme marine environments, emergency diving, and repeated mast raising and lowering. Finally, we hop that the domestic development of underwater cables will help improve the performance of navigation radars for submarines.

Impact of Audio-Visual Complexity on Symptomatology of Laryngeal Dystonia: A Virtual Reality Study.

Laryngeal dystonia (LD) is an isolated focal dystonia characterized by involuntary spasms in laryngeal muscles selectively impairing speech production. Anecdotal observations reported the worsening of LD symptoms in stressful or vocally demanding situations. To examine the impact of surrounding audio-visual complexity on LD symptomatology for a better understanding of disorder phenomenology. We developed well-controlled virtual reality (VR) environments of real-life interpersonal communications to investigate how different levels of audio-visual complexity may impact LD symptoms. The VR experiments were conducted over five consecutive days, during which each patient experienced 10 h of 4100 experimental trials in VR with gradually increasing audio-visual complexity. Daily reports were collected about patients' voice changes, as well as their comfort, engagement, concentration, and drowsiness from using VR technology. After a weekly VR exposure, 82% of patients reported changes in their voice symptoms related to changes in background audio-visual complexity. Significant differences in voice symptoms were found between the first two levels of the audio-visual challenge complexity independent of study sessions or VR environments. This study demonstrated that LD symptoms are impacted by audio-visual background across various virtual realistic settings. These findings should be taken into consideration when planning behavioral experiments or evaluating the outcomes of clinical trials in these patients. Moreover, these data show that VR presents a reliable and useful technology for providing real-life assessments of the impact of various experimental settings, such as during the testing of novel therapeutic interventions in these patients. Level 3 Laryngoscope, 2024.

Reliability analysis of subsea manifold system using FMECA and FFTA

Subsea manifold system is a complex system that occupies a pivotal role in contemporary ocean engineering and has a significant impact on the safety of marine resource exploitation. Reliability technology plays a significant role in ensuring the safe operation of the subsea manifold system. To perform a comprehensive analysis of the reliability of complex systems, a combination method of FMECA-FFTA (Failure Modes, Effects and Criticality Analysis - Fuzzy Fault Tree Analysis) is introduced in this study. Firstly, FMECA is used to accomplish a qualitative analysis of system reliability considering multifactorial failure modes, which included analyzing potential failure modes, causes of system failure, and evaluating the degree of hazard to the system through a risk matrix diagram. Then, FFTA is applied to build a fault tree model to divide the system into “system-subsystem-component” and determine the minimal cut sets for quantitative analysis of system reliability. In addition, fuzzy set theory is incorporated to improve the accuracy of handling uncertainty in quantitative reliability analysis. Finally, a qualitative and quantitative reliability analysis is conducted by using FMECA-FFTA method for subsea manifold system. The failure modes of the subsea manifold system are clearly identified, including high-risk modes such as external leakage, medium-high-risk modes such as fail to close/lock on demand, and medium-risk modes such as leakage of critical location, plugged, and effective measures should be taken to focus on preventive protection and regular testing for the high risk, medium-high risk and medium risk modes.

Experimental study on fluid-Induced vibration caused by backward erosion piping based on acoustic emission

Abstract Double-layer dike foundation is one of the most common stratum types with the highest probability of catastrophic failure in dike engineering, and the most important danger is backward erosion piping. The occurrence and development of piping need to be monitored by reliable technology. Therefore, the influence of different piping outlet forms on acoustic emission monitoring is explored by using a self-designed test device. The results show that the overall evolution process of ringing count and accumulated energy can be divided into two stages according to the time of piping. In the first and middle stages, the particle collision is caused by sand boiling at the outlet, and the measured value is small. In the later stage, piping channels appear, particle migration occurs, and the measured value is large. Moreover, the maximum impact value decreases with the increase of the piping outlet, and the cumulative energy growth rate of the three groups of tests in the early stage is the same, while the head difference required for the rapid growth stage is inversely proportional to the piping outlet.

Plant Leaf Disease Detection System using Machine Learning and Deep Learning: A Survey

Plant diseases are becoming more common, which is a serious threat to profitability of agriculture worldwide and requires the creation of reliable and efficient disease detection technologies. This work uses machine learning (ML) and deep learning (DL) approaches to give an extensive assessment of previous research on plant leaf disease identification. We examine and evaluate the many approaches used in the last several research, emphasizing the advantages, disadvantages, and useful uses of each. The study covers both sophisticated deep learning designs, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), as well as more conventional machine learning techniques, such support vector machines (SVM) and decision trees. According to our research, plant leaf diseases can be more accurately identified and classified using deep learning techniques, especially CNNs, when applied to a variety of crop categories. The study also looks at the field’s obstacles and promising directions, highlighting the use of multifaceted approaches and real-time illness monitoring systems.

COVID-19 Pandemic and Transformation of Activity Space: Implications for Urban Area Planning in the Yogyakarta Metropolitan Area

Abstract Travel patterns and spatial behavior of people in the Yogyakarta Metropolitan Area during the COVID-19 pandemic have experienced adjustments due to restrictions on out-of-home activities. Spatial behavior has information on the coordinates of the location of the activity visited, and the area that represents this behavior is called activity space. Exploration of activity space may play an essential part in urban area planning. This study utilizes travel activity diary data before and during the pandemic for change analysis with a quantitative approach. Data before the pandemic used the Probability Proportional to Size method and random selection of respondents. While re-measurement during the pandemic, respondents were collected using convenience sampling on 109 individuals willing to re-engage in the survey while still considering population size. The generalization of findings applies to workers and part of middle-income families. A literature review strengthens the empirical findings and explores their implications. The findings indicate that the coverage area for activities has shrunk due to the pandemic, with most activity places falling within a short distance of one’s residence and from one another. The consistency of these findings in the future has implications for providing more flexible, multifunctional, and decentralized spaces equipped with safe and comfortable paths for active transportation modes and integrated with reliable communication technology services.

Plant Leaf Disease Detection System Using Machine Learning And Deep Learning: A Survey

Plant diseases are becoming more common, which is a serious threat to profitability of agriculture worldwide and requires the creation of reliable and efficient disease detection technologies. This work uses machine learning (ML) and deep learning (DL) approaches to give an extensive assessment of previous research on plant leaf disease identification. We examine and evaluate the many approaches used in the last several research, emphasizing the advantages, disadvantages, and useful uses of each. The study covers both sophisticated deep learning designs, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), as well as more conventional machine learning techniques, such support vector machines (SVM) and decision trees. According to our research, plant leaf diseases can be more accurately identified and classified using deep learning techniques, especially CNNs, when applied to a variety of crop categories. The study also looks at the field’s obstacles and promising directions, highlighting the use of multifaceted approaches and real-time illness monitoring systems.

Rehabilitation of steel structures using innovative brace members equipped with YSPD damper

Earthquakes are serious risks to human life and infrastructure. An earthquake's influence on human life and its socioeconomic consequences highlight the need to investigate and create the most efficient and easily adaptable ways to minimize losses. The basic concept of different control mechanisms used for protecting structures from the destructive impacts of earthquakes is to dissipate the energy efficiently, therefore ensuring that the structure remains undamaged or sustains minimum damage. To achieve this purpose, the passive control mechanism is a particularly suitable and reliable technology as it doesn't rely on external power to actively dissipate energy. The present study proposes using a passive energy dissipation device called the Yielding Shear Panel Device (YSPD) for strengthening the current steel structures. The YSPD consists of a square hollow section (SHS) that houses a diaphragm plate. The fundamental concept of the YSPD involves harnessing the lateral deformation of a steel plate to absorb and disperse the seismic energy. The Bouc-Wen-Baber Noori (BWBN) material has been used for simulating the hysteresis force-deformation relationship of YSPDs by pinching. YSPDs are simulated as spring components that connect between the beam and the V-brace. A nonlinear time history dynamic analysis was performed to assess the alteration in the structural capacity with the setting up of YSPDs. The performance of the tested structure was assessed considering story drift, story displacement, story shear, column shear, and YSPD hysteresis loops. The results indicated that YSPD installation has improved the structure's capacity. However, the use of dampers resulted in significant drift in all stories, a reduction in total floor displacement, and a decrease in the shear force of the stories. However, the results demonstrated that the YSPD dampers effectively absorbed energy and exhibited stable hysteresis loops.

Effect of Al precursor’s properties on interactions with self-assembled monolayers for area selective deposition

Area selective deposition (ASD) is a high-precision atomic-level manufacturing technology that enables the development of bottom-up manufacturing methods in the future semiconductor field. The area selective deposition behavior of Al precursors on 1-octadecylthiol (ODT) passivated Cu/SiO2 surfaces was studied through experimental and theoretical analysis. The relationship between precursor steric hindrance, symmetry, penetration depth in ODT, and adsorption energy was elucidated. The loss of selectivity caused by different penetration depths of the precursor in ODT can be post-treated with acids or H2 plasma to remove the physisorption of precursor molecules between ODT chains, thereby improving the selectivity. Reliable ASD technology has been successfully applied to Cu/SiO2 patterns. Dimethylaluminum isopropoxide can selectively deposit about 10 nm of Al2O3 on SiO2 without detectable defects on the Cu area. This provides important insights into the choice of precursors in the ASD process and can extend its application to a wider range of device manufacturing schemes.