Search Results (9)

The rational design of photoanode materials is pivotal for advancing photoelectrochemical (PEC) water splitting toward sustainable hydrogen production. This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. We first delineate the fundamental physicochemical criteria for efficient photoanodes, including suitable band alignment, visible-light absorption, charge carrier mobility, and electrochemical stability. Conventional strategies such as nanostructuring, elemental doping, and surface/interface engineering are critically evaluated. We then discuss the integration of ML techniques—ranging from high-throughput density functional theory (DFT)-based screening to experimental data-driven modeling—for accelerating the identification of promising oxides (e.g., BiVO₄, Fe₂O₃, WO₃) and optimizing key parameters such as dopant selection, morphology, and catalyst interfaces. Particular attention is given to surrogate modeling, Bayesian optimization, convolutional neural networks, and explainable AI approaches that enable closed-loop synthesis-experiment-ML frameworks. ML-assisted performance prediction and tandem device design are also addressed. Finally, current challenges in data standardization, model generalizability, and experimental validation are outlined, and future perspectives are proposed for integrating ML with automated platforms and physics-informed modeling to facilitate scalable PEC material development for clean energy applications. Full article

Screen systems are often neglected in practice. This can lead to local flooding, pollution of receiving watercourses, blockages of channels by debris, and safety problems for children playing. The aim of this case study is therefore to protect below-ground channels and people, prevent flooding, improve water quality, and save personnel costs through a new screen system maintenance, repair, and upgrade methodology. The results show that repairing or enlarging the screens optimizes their functionality and reduces the risk of flooding. A particular focus is on increasing the screen dimension from one- and two-dimensional to three-dimensional screens. The new variable safety priority and the bar spacing increase with the passage area. Screens at large discharges should therefore be prioritized. Cleaning sand traps reduces the risk of pipe blockages and improves the water quality of receiving waters. Fine particles often have too high nutrient and oxygen demand values. The installation of pre-screens can increase the efficiency of the main screens. Optimization of travel routes for maintenance teams can be achieved by better planning maintenance routes. Adapting and maintaining screens to climate change by applying the novel prioritization method is likely to be successful. This should include prioritized inspections, repairs, and adjustments to screen structures. Full article

Using the internet to recruit participants into research trials is effective but can attract high numbers of fraudulent attempts, particularly via social media. We drew upon the previous literature to rigorously identify and remove fraudulent attempts when recruiting rural residents into a community-based health improvement intervention trial. Our objectives herein were to describe our dynamic process for identifying fraudulent attempts, quantify the fraudulent attempts identified by each action, and make recommendations for minimizing fraudulent responses. The analysis was descriptive. Validation methods occurred in four phases: (1) recruitment and screening for eligibility and validation; (2) investigative periods requiring greater scrutiny; (3) baseline data cleaning; and (4) validation during the first annual follow-up survey. A total of 19,665 attempts to enroll were recorded, 74.4% of which were considered fraudulent. Automated checks for IP addresses outside study areas (22.1%) and reCAPTCHA screening (10.1%) efficiently identified many fraudulent attempts. Active investigative procedures identified the most fraudulent cases (33.7%) but required time-consuming interaction between researchers and individuals attempting to enroll. Some automated validation was overly zealous: 32.1% of all consented individuals who provided an invalid birthdate at follow-up were actively contacted by researchers and could verify or correct their birthdate. We anticipate fraudulent responses will grow increasingly nuanced and adaptive given recent advances in generative artificial intelligence. Researchers will need to balance automated and active validation techniques adapted to the topic of interest, population being recruited, and acceptable participant burden. Full article

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain—Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data—all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters. Full article

Nosocomial clusters of fungal infections, whilst uncommon, cannot be predicted and are associated with significant morbidity and mortality. Here, we review reports of nosocomial outbreaks of invasive fungal disease to glean insight into their epidemiology, risks for infection, methods employed in outbreak detection including genomic testing to confirm the outbreak, and approaches to clinical and infection control management. Both yeasts and filamentous fungi cause outbreaks, with each having general and specific risks. The early detection and confirmation of the outbreak are essential for diagnosis, treatment of affected patients, and termination of the outbreak. Environmental sampling, including the air in mould outbreaks, for the pathogen may be indicated. The genetic analysis of epidemiologically linked isolates is strongly recommended through a sufficiently discriminatory approach such as whole genome sequencing or a method that is acceptably discriminatory for that pathogen. An analysis of both linked isolates and epidemiologically unrelated strains is required to enable genetic similarity comparisons. The management of the outbreak encompasses input from a multi-disciplinary team with epidemiological investigation and infection control measures, including screening for additional cases, patient cohorting, and strict hygiene and cleaning procedures. Automated methods for fungal infection surveillance would greatly aid earlier outbreak detection and should be a focus of research. Full article

Bots (social robots) are computer programs that replicate human behavior in online social networks. They are either fully automated or semi-automated, and their use makes online activism vulnerable to manipulation. This study examines the existence of social robots in the #FeesMustFall movement by conducting a scientific investigation into whether social bots were present in the form of Twitter bots and cyborgs. A total of 576,823 tweets posted between 15 October 2015 and 10 April 2017 were cleaned, with 490,449 tweets analyzed for 90,783 unique persons. Three separate approaches were used to screen out suspicious bot and cyborg activity, supplemented by the DeBot team’s methodology. User 1 and User 2, two of the 90,783 individuals, were recognized as bots or cyborgs in the study and contributed 22,413 (4.57 percent) of the 490,449 tweets. This confirms the existence of bots throughout the campaign, which aided in the #FeesMustFall’s amplification on Twitter, complicating sentiment analysis and invariably making it the most popular and lengthiest hashtag campaign in Africa, particularly at the time of data collection. Full article

Environmental sanitation is very essential for healthy living. In our daily livelihood, garbage bins are usually kept without proper monitoring until they are filled to the point of overflowing onto the surroundings and spilling out, resulting in environmental pollution, which has serious health-related issues to human beings and the environment. For smart cities, garbage bins need to be monitored and controlled to ensure a healthy and clean environment. In the present technological advancement, real-time monitoring and control of waste disposal is a challenging area that needs urgent attention by the research community. The traditional approach of monitoring waste in garbage bins placed in strategic locations is a very tedious and inefficient way that consumes time, human effort, and cost, and this is also not in agreement with smart city requirements. This research paper presents the design and implementation of an internet of things (IoT) based Arduino microcontroller working with the ultrasonic sensors that detects the level of waste in the garbage bin placed in garbage locations and constantly at regular intervals display the status information as “filled”, “half-filled”, or “empty” on an LCD screen, as well as send the content level information at those intervals to a central web-server system that displays the garbage bin levels graphically. This is achieved using a microcontroller, a Wi-Fi module, and ultrasonic sensors. The programming of the Arduino Uno microcontroller was done with an Arduino IDE and embedded C programming language. The communication with the web server was done using the hypertext preprocessor PHP scripting programming language. The prototype was designed and simulated using Proteus 8.0 professional simulation software. This process helps to automate garbage bin monitoring and control. Experimental results demonstrate a promising solution to waste management and control. A number of testing runs were performed to evaluate the device workability in real situations. The measured distances from the garbage bins were transmitted to a website; this web page performs analytic and visualization and displays a bar chart showing the levels of the garbage waste, time, and location in real time for viewing. The proposed prototype is an innovative system that will help to keep the smart cities clean and tidy using ultrasonic sensors. Full article

Staphylococcus aureus is an important bacterial pathogen. This study utilized known staphylococcal epidemiology to track S. aureus between patients, surfaces, staff hands and air in a ten-bed intensive care unit (ICU). Methods: Patients, air and surfaces were screened for total colony counts and S. aureus using dipslides, settle plates and an MAS-100 slit-sampler once a month for 10 months. Data were modelled against proposed standards for air and surfaces, and ICU-acquired staphylococcal infection. Whole-cell genomic typing (WGS) demonstrated possible transmission pathways between reservoirs. Results: Frequently touched sites were more likely to be contaminated (>12 cfu/cm²; p = 0.08). Overall, 235 of 500 (47%) sites failed the surface standard (≤2.5 cfu/cm²); 20 of 40 (50%) passive air samples failed the “Index of Microbial Air” standard (2 cfu/9 cm plate/h), and 15/40 (37.5%) air samples failed the air standard (<10 cfu/m³). Settle plate data were closer to surface counts than automated air data; the surface count most likely to reflect pass/fail rates for air was 5 cfu/cm². Surface counts/bed were associated with staphylococcal infection rates (p = 0.012). Of 34 pairs of indistinguishable S. aureus, 20 (59%) showed autogenous transmission, with another four (12%) occurring between patients. Four (12%) pairs linked patients with hand-touch sites and six (18%) linked airborne S. aureus, staff hands and hand-touch sites. Conclusion: Most ICU-acquired S. aureus infection is autogenous, while staff hands and air were rarely implicated in onward transmission. Settle plates could potentially be used for routine environmental screening. ICU staphylococcal infection is best served by admission screening, systematic cleaning and hand hygiene. Full article

There exists a technological momentum towards the development of unobtrusive, simple, and reliable systems for long-term sleep monitoring. An off-the-shelf commercial pressure sensor meeting these requirements is the Emfit QS. First, the potential for sleep apnea screening was investigated by revealing clusters of contaminated and clean segments. A relationship between the irregularity of the data and the sleep apnea severity class was observed, which was valuable for screening (sensitivity 0.72, specificity 0.70), although the linear relation was limited ( $R^2$ of 0.16). Secondly, the study explored the suitability of this commercial sensor to be merged with gold standard polysomnography data for future sleep monitoring. As polysomnography (PSG) and Emfit signals originate from different types of sensor modalities, they cannot be regarded as strictly coupled. Therefore, an automated synchronization procedure based on artefact patterns was developed. Additionally, the optimal position of the Emfit for capturing respiratory and cardiac information similar to the PSG was identified, resulting in a position as close as possible to the thorax. The proposed approach demonstrated the potential for unobtrusive screening of sleep apnea patients at home. Furthermore, the synchronization framework enabled supervised analysis of the commercial Emfit sensor for future sleep monitoring, which can be extended to other multi-modal systems that record movements during sleep. Full article