Installation Issues Research Articles

Behaviour of yielding mechanical hybrid rockbolts under complex loading conditions

The original “hybrid bolt” was a pragmatic solution to address installation issues in the use of resin grouted rockbolts in heavily fractured ground and the inadequate capacity of friction rock stabilisers. The original hybrid rockbolt involved installing a resin rebar in a friction rock stabiliser. The development of Yielding Mechanical Hybrid Rockbolts has been driven by efforts to eliminate the use of resin in heavily fractured ground. A typical Yielding Mechanical Hybrid Rockbolt consists of a steel tendon mechanically anchored within a friction unit. The bolt is percussion driven by the rock drill and the mechanical anchor is subsequently activated using rotation. This installation process improves the rockbolt resilience to hole closures and avoids issues associated with the use of resin. Yielding Mechanical Hybrid Rockbolts are used in both squeezing and rockburst prone ground conditions.This paper addresses a significant knowledge gap related to the behaviour of Yielding Mechanical Hybrid Rockbolts under multiple quasi-static conditions. A comprehensive experimental program investigated the complete load displacement performance of Yielding Mechanical Hybrid Rockbolts under axial, shear, and a combination of tensile and shear loads. It was observed that the load capacity increases from axial (0°), combined axial and shear (30°), combined axial and shear (60°), and pure shear (90°). The displacement capacity, however, decreases under the same testing conditions. The results are consistent but there is a slightly greater variability as the loading angle increases from 0° to 90°. It was observed that beyond 60° loading angle there is greater variability in the results as the influence of the shear component manifests itself in greater disintegration of the concrete blocks at the shear interface.

A Contact‐Separation Mode Hybrid Generator Based on Magnetic Springs

The emergence of the intelligent society presents a significant challenge with regard to distributed energy. One potential solution is to harvest energy from the environment to power micro/nano systems. Herein, an electric‐triboelectric hybrid generator (ETMHG) supported by magnetic springs is presented that operates in the contact‐separation mode. The traditional mechanical springs in the triboelectric nanogenerators are replaced by magnetic springs, and solenoid coils are added. This structure can achieve electromagnet‐triboelectric hybrid generation without significantly increasing the volume of the generator, and the use of magnetic springs offers a solution to the issues of difficult installation and mechanical wear and tear that are inherent in mechanical spring‐based triboelectric nanogenerators. The proposed hybrid generation ETMHG is shown to increase the output capacity of the generator, improve the efficiency of environmental energy harvesting, and achieve an instantaneous maximum power of 4.75 mW and instantaneous maximum power density of 95 W m−3. The ETMHG can charge a 10 μF capacitor that improves efficiency by 52.6% compared to an electromagnetic generator. The output surface power density of TENG in ETMHG can reach 1.12 W m−2.

Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

AbstractNovel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

Pendulum-type tuned tandem mass dampers-inerters for crosswind response control of super-tall buildings

In the present study, the pendulum-type tuned tandem mass dampers-inerters (PTTMDI) is proposed to control the crosswind-induced dynamic responses in super-tall buildings. This innovative configuration allows for the placement of both terminals of inerters at the same floor, thereby overcoming the challenge issue of installation associated with the tuned tandem mass dampers-inerters (TTMDI). The equations of motion for a PTTMDI equipped multiple-degree-of-freedom (MDOF) structure is derived using the Euler-Lagrange method. To optimize the primary tuning parameters of the PTTMDI for a 400 m super-tall building, the pattern search algorithm is employed. The control effectiveness and robustness of the PTTMDI device are studied and compared with those of the pendulum-type tuned mass damper (PTMD), pendulum-type tuned tandem mass dampers (PTTMD), and pendulum-type tuned mass damper-inerter (PTMDI). Likewise, the design framework for the PTTMDI equipped super-tall building is established. The results indicate that the PTTMDI has superior acceleration control performance and the stronger robustness compared to the PTMD, PTTMD, and PTMDI. Furthermore, the PTTMDI can significantly reduce the necessary damping coefficients with respect to the PTMDI with the maximum reduction rate reaching about 70%, making it easier to implement in engineering practice.

Digital Transformation and Digital Competences of Urban and Rural Polish Youths

This article explores the level of digital competence of young people in Poland, with the indirect aim being to show the differences in the level of digital competence for adolescents living in rural and urban areas. The research covered a sample of 985 respondents, from 11–18 years old, from Poland. The research was carried out within the EU Kids Online network. The survey tool related to the assessment of digital competences covered issues of installation of software on mobile devices, configuration of internet access as pertains to confidential information, information security awareness, management of information downloaded from the internet, configuration of social networks, e-shopping, verification of costs related to the use of additional software, advanced information search, checking the reliability of information, and editing online content. Descriptive statistics, k-means cluster analysis, one-way analysis of variance (non-parametric test), and correlations were used to show the differences between rural and urban adolescents in the indicated areas. The collected data offer several postulates for education and education policy, being not only diagnostic but also implementational. Based on the analysis of the data, it was noted that: (a) Eleven areas related to basic digital competence strongly differentiate between urban and rural young people; (b) rural young people rate their own digital competence lower than urban young people do; (c) a small percentage of young people from both rural and urban areas have low digital competence; (d) one well-developed area of key competence does not always co-occur with another well-developed area; and (e) the style of using new media among rural and urban young people is similar.

Optimizing Biomass Pre-Treatment Technologies for BBJP Plants in Indonesia: A Multi-Criteria Decision Making Approach

The challenges of energy consumption and environmental sustainability are pronounced in the dynamic landscape of contemporary industries driven by Industry 4.0 technologies. Indonesia, heavily reliant on fossil fuels, charts a course toward a clean energy future with a National Energy Transition Roadmap for Net Zero Emission by 2060. This transition involves innovative strategies such as biomass co-firing and waste utilization in Solid Recovered Fuel (SRF) plants, known as Bahan Bakar Jumputan Padat (BBJP) plants. To optimize these BBJP plants, this study employs Multi-Criteria Decision Making (MCDM) methodologies, specifically the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to evaluate and select pre-treatment technologies. Criteria include capacity, conversion process, waste type, electricity consumption, operational ease, land requirement, and investment cost. Comparing bio-drying, thermal drying, and mechanical drying, AHP ensures consistent criterion weights, with TOPSIS ranking bio-drying as the most favorable, followed by thermal and mechanical drying. The study acknowledges global waste management challenges and introduces a mobile-modular containerized BBJP/SRF plant model, addressing installation, maintenance, scalability, and adaptability issues. While recognizing challenges, especially in pre-treatment processes, the research emphasizes the need for efficient and cost-effective solutions. Practical implications include enhanced decision-making in biomass drying, identification of technology advantages and disadvantages, and a commitment to address challenges for sustainable implementation. The study contributes to Indonesia's energy transition discourse, advocating the pivotal role of BBJP plants in balancing Industry 4.0 demands and environmental protection, providing insights for stakeholders and decision-makers in advancing sustainable waste-to-energy initiatives.

Rationalization of Electrical Energy Consumption in Households through the Use of Cheap IoT Module with Cloud Data Storage

This article explores mechanisms to enhance the efficient utilization of renewable energy sources (RES), with a particular emphasis on photovoltaic installations. One such strategy involves implementing a customized electricity rate system for individual consumers. This paper discusses the potential economic and environmental benefits of transitioning from a flat-rate tariff to a time-of-day tariff. This modification can lead to increased energy consumption during off-peak hours, aligning with occasional periods of photovoltaic installations’ overproduction, which might require their temporary shutdowns. The energy that could be produced by RES is supplied by conventional power plants (mostly coal-fueled). Not only does this have negative effects on the environment, but it also increases energy costs. In order to make an informed decision regarding the change of tariff, the consumer must be aware of its potential benefits and drawbacks. The article introduces an IoT-based, cost-effective system with cloud data storage for monitoring residential electricity consumption, offering various features, including an assessment of the financial viability of switching tariffs. This system has been operational for more than six months in real installation, encouraging homeowners to transition from a flat rate tariff to time-of-day tariff and optimize their use of electrical appliances. The article presents the potential benefits of this action, encompassing both financial aspects for users of the installation and environmental protection issues.

Multi-source wavefield reconstruction combining interferometry and compressive sensing: application to a linear receiver array

SUMMARY Seismic interferometry (SI) is a technique that allows one to estimate the wavefields accounting for the wave propagation between seismometers, any of which can act as a virtual source (VS). Interferometry, particularly noise interferometry, has been applied to several geophysical disciplines such as passive monitoring and distributed acoustic sensing. In practice, one requires long recordings of seismic noise for noise interferometry. Additionally, one can have missing seismic interferometric traces because some receivers in seismic arrays may be absent or inoperative due to issues of receiver installation and malfunction. Thus, filling the gap of seismic interferometric profile requires wavefield reconstruction and regularization techniques. Compressive sensing (CS) is one such method that can reconstruct seismic interferometric wavefields and help mitigate the limitations by exploiting the sparsity of seismic waves. In our work, we use CS to reconstruct missing seismic interferometric wavefields. One can interpolate interferometric wavefields using correlograms provided by one VS. We call this method of reconstructing an individual VS gather single-source wavefield reconstruction. We propose an alternative technique called multi-source wavefield reconstruction, which applies CS to reconstruct multiple interferometric wavefields using a volume of VS gathers provided from all available VSs. Using numerical examples, we show that one can apply CS to recover interferometric wavefields resulting from interferometry of a linear seismic array. To exploit the sparsity of interferometric wavefields, we apply the Fourier and Curvelet transforms to the two reconstruction schemes. Using the signal-to-noise ratio (SNR) to compare reconstruction of interferometric wavefields, the Fourier multi-source method improves the recovery of interferometric wavefields by approximately 50 dB compared to the Fourier and Curvelet single-source wavefield reconstructions.

Moving beyond the desktop: prospects for practical bioimage analysis via the web.

As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science.

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. This paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

Design Framework for Polymer Nanocomposite Flexible Strain Sensor Adhesive Tape

In the emerging flexible electronics field, flexible and wearable sensors play a crucial role. Though many reports focused on developing flexible sensors with novel materials, translational research is scarce. The present research focuses on a design framework for flexible strain sensors considering the design scalability, packaging feasibility and end users’ issues along with performance. To be specific, a flexible strain sensor tape framework is designed and developed using poly (vinylidene chloride-co-vinyl chloride) (PVDC-co-VC) and polystyrene sulfonate functionalized carbon nanotube (PSS-CNT) polymer nanocomposite with underneath adhesive coating and a copper contact pad atop the sensor. At the outset, numerical simulation is also carried out to understand the impact of uneven adhesive thickness profile on the sensor performance and emphasis the necessity of uniform adhesive coating underneath the sensor. Further, the PVDC-co-VC/PSS-CNT polymer nanocomposite is systematically optimized to find its percolation threshold to achieve its maximum performance. Later, the scaled-up long flexible strain sensor tape is developed and validated by measuring the gauge factor in multiple positions along the length of the sensor tape; it shows the uniform gauge factor value of 15.2±0.8 which ensures the sensor’s repeatability and reproducibility. As a testimony, the developed flexible strain sensor is demonstrated for wearable electronics applications with an indigenously developed IOT-enabled portable interface device. The present flexible strain sensor tape design framework supports scalability, and packaging feasibility and solves end users’ installation issues.

Optimization of helical soil nailing behaviors by response surface methodology and hybrid Coot optimization

AbstractSoil nailing is one of the effective methods of slope stabilization, and its stability mainly depends on the properties of both the soil and soil nails. Optimizing soil nailing parameters is an effective task to avoid installation issues. This work uses four input parameters: different shaft diameters, surcharge pressure, helical pitch, and inclination angle. The design plan for the experimentation is conducted using Box Behnken design (BBD) of response surface methodology (RSM), performed in design expert software. In addition, RSM is used to determine the optimal design combinations. A total of 25 experimental runs are taking place, and the significance of the developed quadratic model is determined using the analysis of variance (ANOVA) test. Hybrid deep belief network‐based Coot optimization (DBN‐CO) is also used to optimize the pull‐out capacity and the safety factor during installation for more precised prediction. When compared to others the proposed hybrid DBN‐CO predictive model represents the better performance with regression R = 0.99. Also least MSE & RMSE and highest R & R2 values obtained by hybrid DBN‐CO. The obtained experimental outcomes from RSM are 1.46 of safety factor, and 7.55 kN pull‐out capacity. The predicted results from the proposed hybrid DBN‐CO for the safety factor are 1.44 and pull‐out capacity is 7.77 kN. As a result, the optimal results of DBN‐CO are 3% greater than RSM, DBN, and artificial neural network (ANN). Based on prediction approaches the proposed hybrid DBN‐CO results are in perfect agreement with experimental values and are additional superior to the RSM, DBN, and ANN.

Low-frequency sound insulation of honeycomb membrane-type acoustic metamaterials with different interlayer characteristics

The membrane-type structure that can achieve excellent sound insulation characteristics under lightweight conditions is widely concerned, but it generally involves a small mass block attached to a thin membrane. Although this kind of design can obtain better acoustic insulation performance, the mass block will cause installation and manufacturing issues in industrial applications. This paper presents a multilayer honeycomb membrane-type acoustic metamaterial with an interlayer structure. Similar to the mass block, the interlayer structure makes less sound energy transmitted by varying the eigenmode characteristics of the membrane. Considering the integrated design of the interlayer structure, the issues of installation and manufacturing are solved and the sound transmission loss is enhanced on the fixed overall dimension and small additional mass. The results indicate that the design of interlayer structures can break the bottleneck of ultrathin and lightweight requirements. Meanwhile, the symmetry of interlayer position can lead to the shift in transmission loss valley to a higher frequency, achieving high sound insulation in a wide frequency range.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis.

Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

Analysis of Current Situation, Problems and Potential Solutions of Electric Vehicle Industry

The onset of the 21st century has seen remarkable developments in almost every industry in our modern-day world. Technology develops the quality of life and grows at an unprecedented rate. During the long run, newly type of vehicles is innovated, and new energy vehicles are an important development direction. Electronic vehicle (EV) not only is a production of the new era, but also a process of the evolution. The first EV appeared in late of 1830s which also be the prototype of the EV of nowadays. Currently, EVs are commonly used by individuals, the reason that EVs are more environment friendly, with the fuel less costly, and get government’ beneficially. However, EVs still have issues in nowadays, i.e., installation, short range, and battery handling. To solve these issues, the public could offer more installation charging piles, and EV companies could improve the battery capacity and maintenance. Overall, the reports will illustrate the current status and current problems that EVs faced, also showing the solutions and the recommendations to help the EV industry to develop.

Design islanded hybrid micro-grid and analyzing its socio-economic technical and environmental aspects for off-grid electrification in developing countries

Developing countries are facing many electricity crises such as low production, worst power outages, blackouts, and poor inadequate supplies. Most of their rural areas have no basic power infrastructure as it requires long transmission lines, which causes high installation, operation, maintenance costs and line losses issues. The people of these unconnected rural areas belong to low-income families and use kerosene oil, candles, small solar panels, and batteries to meet their basic electricity needs. As an alternative, renewable-based hybrid energy systems have enormous potential. It is also environmentally friendly and low-cost, sustainable, and promising solution to electrify rural off-grid areas. This research work focuses on the economic and optimal design of a renewable-based hybrid energy system for the rural off-grid area of Pakistan. HOMER (Hybrid Optimization of Multiple Energy Resources) software is used to perform techno-economic and environmental analysis. The optimization results show that the most economical and optimal configuration is PV/diesel generator with batteries having lower harmful emissions. Furthermore, the sensitivity analysis is performed to refine the results which show that the most optimal system will remain sustainable if variations occur in the sensitivity variables in the future. Moreover, the results and findings of this study can help the government to make effective policies and planning to provide reliable and affordable electricity to the off-grid areas, to improve the electrification rate in the country.

Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments

The transmission and distribution systems are essential in facilitating power flow from the source multiple loads over large distances with high magnitudes of voltages and currents. Hence, the monitoring and control of various components of these structures are crucial. Traditionally, this was implemented by sensing only the grid current and grid voltage parameters through coils, clamps, or instrument transformers. However, these have bulky structures that restrict them to the substation and have installation and maintenance issues due to their direct contact with high voltage conductors. Currently, the power grid is undergoing various developments e.g., penetration of renewable energy sources, remote control, and automation, bidirectional power flow, etc. These developments call for compact and energy-efficient sensors to sense multiple grid parameters such as the magnetic field data, temperature, humidity, acoustics, etc., to enable real time, wide area monitoring and the predictive maintenance of the power grid. The goal of this paper is to summarize the advancements in sensing technologies on transmission and distribution systems over a decade and to explain their role in the forthcoming expansion of the power grids. This paper aims to outline the current state-of-affairs of sensor technology as well as to fill research gaps by exploring their limitations.

Reliability-based optimization of multiple nonlinear PTMDs for seismic retrofitting of buildings

Recent Probabilistic Seismic Hazard Analysis (PSHA) for Chile has indicated a significant increase in PGA values currently adopted in the country. Thus, assessing the reliability of existing buildings and developing tools for seismic retrofitting purposes have become relevant. Adding external passive energy dissipation systems to attenuate seismic oscillations is usually cost-effective. Among the options, pendulum-tuned mass dampers (PTMDs) are attractive due to their performance and simplicity for installation and maintenance issues. Even so, a comprehensive literature survey reveals that reliability-based optimization (RBDO) studies of single and multiple PTMDs cannot be found, to the best of the authors’ knowledge. Hence, this paper aims at performing the RBDO of single and multiple PTMDs of a steel building based on Chilean seismicity. For this purpose, uncertainties in both ground motion and parameters of the mechanical model are considered through seismic vulnerability analysis, which is integrated into an optimization problem. Because strong ground motion must be included, leading the oscillators to sway at large angles, the PTMDs’ non-linear behavior is considered. The results show that for single PTMDs, the classical closed-form expressions can also be successfully employed, but only for higher mass ratios. Furthermore, the optimization for double PTMDs scenario leads to similar performance (as for the single PTMD), but with the associated advantages in installation and maintenance processes, due to their lower masses.

Application of the Universal Quench Detection System to the Protection of the High-Luminosity LHC Magnets at CERN

The Universal Quench Detection System (UQDS) has been primarily developed to detect quenches in various superconducting magnets of LHC’s High Luminosity upgrade (HL-LHC). The functionality of the system, which comprises insulated high-resolution digitizing front-end channels and a central processing element, is mainly defined by the configuration of the central FPGA (Field Programmable Gate Array). Additional elements such as redundant power supplies, configurable interlocking capabilities and a communications controller are completing the functionality of the system. The system’s architecture is designed to be flexible enough to detect quenches in all superconducting elements of HL-LHC. The application-specific digital signal processing and quench detection algorithms are implemented in the firmware of the FPGA and thus can be changed according to the required specifications. To facilitate this process, the firmware has been structured accordingly and automated code generation techniques are used. The strategy of testing prototypes of the quench detection system with prototypes of the magnets allows an early evaluation of the system, minimizing operational issues in the final installation. This strategy has been already applied at CERN to the 11 T dipole magnets in previous years and extended as well to other magnet families of the HL-LHC Project. We give a system description and focus on the specific configurations for three different magnet families. Prototypes of these magnets have been recently tested at CERN. For each of these, specific features and detection methods have been developed, implemented and evaluated during the magnet tests.

Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data

Heat pumps play an important role in the energy transition. They can extract renewable energy from the air or ground and increasingly replace fossil heating systems in buildings. In operation, however, heat pumps often consume more electricity than necessary due to incorrect settings and installation deficiencies. Although many setting and installation issues are easy to fix, problems often go unnoticed, and the saving potential from quick fixes remains unclear. In a study with 297 Swiss households (41 treatment, 256 control) running for four years, we investigated an energy efficiency campaign in which the treatment group received a professional heat pump inspection and user training. We found considerable heterogeneity with respect to the savings achieved. We derived two criteria based on smart meter data that enable utilities to identify relevant households and thus boost the impact of such efficiency campaigns: For example, pre-selecting half of the households based on available information results in average savings of 1,805 kWh (15.2%) per year and household in the high-potential group compared to no savings in the low-potential group. Thus, heat pump inspections among pre-selected households can lead to large, cost-effective electricity savings, and we show that common smart meter data makes such pre-selection feasible.