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Knife crime and serious violent crime (SVC) among youth has been growing at an alarming rate in the UK (Harding and Allen, 2021). Community and school-based intervention and prevention services to tackle knife crime are being developed with some evaluation; however, these are independent and of varied quality and rigour. Therefore, the purpose of this study is to record the approaches being developed and synthesise existing evidence of the impact and effectiveness of programmes to reduce knife crime. In addition, the complex factors contributing to knife crime and SVC are discussed.

A systematic approach was used to conduct this knife crime intervention evidence review using two search engines and four databases. Inclusion and exclusion criteria were applied to ensure focus and relevance. The results of searches and decisions by the research team were recorded at each stage using Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA).

Some evidence underpins the development of services to reduce knife crime. Much of the evidence comes from government funded project reports, intervention and prevention services reports, with few studies evaluating the efficacy of intervention programmes at present. Some studies that measured immediate impact in line with the programme’s aims were found and demonstrated positive results.

This systematic review specifically synthesised the evidence and data derived from knife crime and weapon carrying interventions and preventions, integrating both grey and published literature, with a novel discussion that highlights the importance of outcome evaluations and issues with measuring the success of individual level interventions and their contributions to the overall reduction of violence.

This paper explores the relevant fire code requirements and outlines the development of an evaluation tool based on these codes to evaluate fire safety measures in dining properties.

Existing literature was examined to identify the combustible materials, fire causes and factors making these properties prone to fire incidents. An evaluation method, based on code regulations, for ensuring fire safety in dining properties was then developed and tested on a specific dining facility to validate its practicality.

Forty requirements, grouped into seven categories, were identified for ensuring fire safety in dining properties. The case study exposed multiple violations of fire safety, leading to corrective measures for enhancing the fire safety status of the building.

This study introduced a methodical approach for raising awareness, among property managers of dining properties, about fire incidences and their consequences. It presents an evaluation tool for assessing the compliance level with fire codes and standards.

Dining properties are facilities that offer both dine-in and take-out food services. Given the increasing number of fire incidents in dining properties worldwide, there is a substantial demand for a process to audit the adherence to fire safety codes in these properties. This study presents a systematic approach to increase public knowledge of fire events and their effects in dining properties.

The study assesses the most significant architectural core technological system that converges into a Battery Electric Vehicle (BEV).

Conceptually grounded in the convergence phenomenon and utilizing the graph theory-based network construction approach, based on the Betweenness Centrality (BC) metric, core International Patent Classifications (IPCs) have been empirically identified. Based on these IPCs, the ownership structure of the patents was established through assignee analysis.

Analyzing the networks obtained at different IPC levels, we found that multiple technologies have converged in a BEV, from battery chemistry to electrical engineering and thermal management of electrical machines.

The outcome of this work has led to the identification of BEV technologies, which can be further developed to assess the trends of technologies and associated gaps and aid technology management for the selection, acquisition, and exploitation of technology.

The outcome of this work will aid technology management practitioners in better planning the selection, acquisition, and exploitation of technologies associated with BEV.

The paper adds an evidence-based approach to the body of knowledge to identify the built-in technologies that produce a BEV.

This study aims to describe on-site determination of pH in aquaculture using a portable colorimeter.

A basic and inexpensive portable colorimeter having light sources of different wavelength has been optimized for the determination of pH. A chromic reagent is optimized to develop colors depending on different pHs. Then, the color is measured using portable colorimeter in terms of resistance. Using the resistance to absorbance relationship, the resistance values are converted to absorbance. The performance of the developed colorimetric system is compared with commercially available colorimeter and pH meter. Eighty brackish water aquaculture samples were tested using the developed colorimetric system to check its adaptability in aquaculture farms.

A chromic reagent optimized in this study exhibited linear relationship with the pH changes of pH standards. Portable colorimeter having three different light sources is optimized using this chromic agent for determination of pH. Colorimeter possessing yellow light revealed more linear results than the others. The developed colorimetric system determines the pH of aquaculture water with an accuracy of ±0.14 in comparison with commercial colorimeter at the pH range of 6.0−9.0.

pH plays a pivotal role in aquaculture, impacting not only productivity but also profitability. The colorimetric system introduced in this research is promising for pH measurement in the field of brackish water aquaculture. The on-site pH assessment facilitated by this colorimetric system holds significant potential for adoption within the aquaculture sector, owing to its precision, portability, simplicity of use and user-friendly nature.

The purpose of this paper is to track the maximal power of wind energy conversion system (WECS) and enhance the search capability for WECS maximum power point tracking (MPPT).

The hybrid technique is the combination of tunicate swarm algorithm (TSA) and radial basis function neural network.

TSA gets input parameters from the rectifier outputs such as rectifier direct current (DC) voltage, DC current and time. From the input parameters, it enhances the reduced fault power of rectifier and generates training data set based on the MPPT conditions. The training data set is used in radial basis function. During the execution time, it produces the rectifier reference DC side voltage that is converted to control pulses of inverter switches.

Finally, the proposed method is executed in MATLAB/Simulink site, and the performance is compared with different existing methods like particle swarm optimization algorithm and hill climb searching technique. Then the output illustrates the performance of the proposed method and confirms its capability to solve issues.

The reliability of solder joints is closely related to the growth of an intermetallic compound (IMC) layer between the lead-free solder and substrate interface. This paper aims to investigate the growth behavior of the interfacial IMC layer during isothermal aging at 125°C for Sn-3Ag-3Sb-xIn/Cu (x = 0, 1, 2, 3, 4, 5 Wt.%) solder joints with different In contents and commercial Sn-3Ag-0.5Cu/Cu solder joints.

In this paper, Sn-3Ag-3Sb-xIn/Cu (x = 0, 1, 2, 3, 4, 5 Wt.%) and commercial Sn-3Ag-0.5Cu/Cu solder were prepared for bonding Cu substrate. Then these samples were subjected to isothermal aging for 0, 2, 8, 14, 25 and 45 days. Scanning electron microscopy and transmission electron microscopy were used to analyze the soldering interface reaction and the difference in IMC growth behavior during the isothermal aging process.

When the concentration of In in the Sn-3Ag-3Sb-xIn/Cu solder joints exceeded 2 Wt.%, a substantial amount of InSb particles were produced. These particles acted as a diffusion barrier, impeding the growth of the IMC layer at the interface. The growth of the Cu₃Sn layer during the aging process was strongly correlated with the presence of In. The growth rate of the Cu₃Sn layer was significantly reduced when the In concentration exceeded 3 Wt.%.

The addition of In promotes the formation of InSb particles in Sn-3Ag-3Sb-xIn/Cu solder joints. These particles limit the growth of the total IMC layer, while a higher In content also slows the growth of the Cu₃Sn layer. This study is significant for designing alloy compositions for new high-reliability solders.

The purpose of this paper is to develop a load detection method for domestic induction cooktops. The solution aims to minimize its impact in the converter power transmission while enabling the estimation of the equivalent electrical parameters of the load. This method is suitable for a multi-output resonant inverter topology with shared power devices.

The considered multi-output converter presents power devices that are shared between several loads. Thus, applying load detection methods in the literature requires a halt in the power transfer to ensuring safe operation. The proposed method uses a complementary short-voltage pulse to excite the induction heating (IH) coil without stopping the power transfer to the remaining IH loads. With the current through the coil and the analytical equations, the equivalent inductance and resistance of the load is estimated. The precision of the method has been evaluated by simulation, and experimental results are provided.

The measurement of the current through the induction coil as a response to a short-time single-pulse voltage variation provides enough information to estimate the load equivalent parameters, allowing to differentiate between no-load, non-suitable IH load and suitable IH load situations.

The proposed method provides a solution for load detection without requiring additional circuitry. It aims for low power transmission to the load and ensures zero-voltage switching and reduced peak current even in no-load cases. Moreover, the proposed solution is extensible to less complex converters, as the half bridge.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

This paper aims to achieve an anti-corrosive coating via uniform dispersion of nanoclay particles (montmorillonite) and polypyrrole (PPy) as a conductive polymer as well as their effects on the anti-corrosion features in the presence of the eco-friendly ionic liquids (ILs).

In this research, PPy with different forms of nanoclay were used. Moreover, ILs additive is used to enhance the better dispersion process of clay and PPy nanoparticles in the resin.

As a result, the IL additive in the formulation of nano-composite coatings greatly improves the dispersion process of clay and PPy nanoparticles in the resin. Due to its high compatibility with polyurethane resin and clay and PPy nanoparticles, this additive contains a high dispersing power to disperse the investigated nanoparticles in the resin matrix.

High polarity of ILs as well as abilities to dissolve both mineral and organic materials, they can provide the better chemical processes compared to common solvents.

IL abilities have not been discovered to a large extent such as catalysts and detectors.

ILs have been emerging as promising green solvents to replace conventional solvents in recent years. They possess unique properties such as nonvolatility, low toxicity, ease of handling, nonflammability and high ionic conductivity. Thus, they have received much attention as green media for various chemistry processes.

The simultaneous existence of clay, PPy and IL additive in the nano-composite coating formulation is responsible for the high corrosion resistance of the coating.