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RESEARCH PICKS

 * Minting wafer thin defect detection
   
   Research published in the International Journal of Information and
   Communication Technology may soon help solve a long-standing challenge in
   semiconductor manufacture: the accurate detection of surface defects on
   silicon wafers. Crystalline silicon is the critical material used in the
   production of integrated circuits and in order to provide the computing power
   for everyday electronics and advanced automotive systems needs to be as
   pristine as possible prior to printing of the microscopic features of the
   circuit on the silicon surface.
   
   Of course, no manufacturing technology is perfect and the intricate process
   of fabricating semiconductor chips inevitably leads to some defects on the
   silicon wafers. This reduces the number of working chips in a batch and leads
   to a small, but significant proportion of the production line output failing.
   
   The usual way to spot defects on silicon wafers has been done manually, with
   human operators examining each wafer by eye. This is both time-consuming and
   error-prone due to the fine attention to detail required. As wafer production
   has ramped up globally to meet demand and the defects themselves have become
   harder to detect by eye, the limitations of this approach have become more
   apparent.
   
   Chen Tang, Lijie Yin and Yongchao Xie of the Hunan Railway Professional
   Technology College in Zhuzhou, Hunan Province, China explain that automated
   detection systems have emerged as a possible solution. These too present
   efficiency and accuracy issues in large-scale production environments. As
   such, the team has turned to deep learning, particularly convolutional neural
   networks (CNNs), to improve wafer defect detection.
   
   The researchers explain that CNNs have demonstrated significant potential in
   image recognition. They have now demonstrated that this can be used to
   identify minute irregularities on the surface of a silicon wafer. The "You
   Only Look Once" series of object detection algorithms is well known for being
   able to balances accuracy against detection speed.
   
   The Hunan team has taken the YOLOv7 algorithm a step further to address the
   specific problems faced in wafer defect detection. The main innovation in the
   work lies in using SPD-Conv, a specialized convolutional operation to enhance
   the ability of the algorithm to extract fine details from images of silicon
   wafers. Additionally, the researchers incorporated a Convolutional Block
   Attention Module (CBAM) into the model to sharpen the system's focus on
   smaller defects that are often missed in manual inspection or by other
   algorithms.
   
   When tested on the standard dataset (WM-811k) for assessing wafer defect
   detection systems, the team's refined YOLOv7 algorithm achieved a mean
   average precision of 92.5% and had a recall rate of 94.1%. It did this
   quickly, at a rate of 136 images per second, which is faster than earlier
   systems.
   
   Tang, C., Yin, L. and Xie, Y. (2024) 'Wafer surface defect detection with
   enhanced YOLOv7', Int. J. Information and Communication Technology, Vol. 25,
   No. 6, pp.1–17.
   DOI: 10.1504/IJICT.2024.141433

 * Burying the carbon
   
   Odd as it may seem, coal seams that cannot be mined might provide an
   underground storage medium for carbon dioxide produced by industries burning
   coal above ground. Research in the International Journal of Oil, Gas and Coal
   Technology has undertaken controlled experiments designed to simulate the
   deep geological environments where carbon dioxide might be trapped as a way
   to reduce the global carbon footprint and ameliorate some of the impact of
   our burning fossil fuels. Coal seams represent a potential repository for
   long-term storage of carbon dioxide sequestered from flue gases, as they can
   trap a lot of carbon dioxide gas in a small volume.
   
   Major Mabuza of the University of Johannesburg, Johannesburg, Kasturie
   Premlall of Tshwane University of Technology, Pretoria, and Mandlenkosi G.R.
   Mahlobo of the University of South Africa, Florida, South Africa, subjected
   coals to a synthetic flue gas for 90 days at high pressure (9.0 megapascals)
   and a mildly high raised temperatures of 60 degrees Celsius. These conditions
   were intended to replicate the pressures and temperatures found deep
   underground, providing a realistic model for how coal might behave when used
   for carbon dioxide sequestration.
   
   The team then looked at how the chemical structure of coal was changed by
   exposure to flue gas under these conditions using various advanced analytical
   chemistry techniques – carbon-13 solid-state nuclear magnetic resonance
   spectroscopy, universal attenuated total reflectance-Fourier transform
   infrared spectroscopy, field emission gun scanning electron microscopy with
   energy dispersive X-ray spectroscopy, and wide-angle X-ray diffraction.
   
   The results showed that exposure to synthetic flue gas led to major changes
   to the chemical makeup of the coal. For instance, key functional groups, such
   as aliphatic hydroxyl groups, aromatic carbon-hydrogen bonds, and
   carbon-oxygen bonds, were all weakened by the process and the overall
   physical properties of the coal were also changed.
   
   By clarifying how coal interacts with flue gas under simulated, but
   realistic, conditions, the team fills important gaps in our knowledge about
   the long-term stability and effectiveness of carbon dioxide storage below
   ground and specifically in coal seams.
   
   Mabuza, M., Premlall, K. and Mahlobo, M.G.R. (2024) 'In-depth analysis of
   coal chemical structural properties response to flue gas saturation:
   perspective on long-term CO2 sequestration', Int. J. Oil, Gas and Coal
   Technology, Vol. 36, No. 5, pp.1–17.
   DOI: 10.1504/IJOGCT.2024.141437

 * Cancelling the curse
   
   Research in the International Journal of Computational Science and
   Engineering describes a new approach to spotting messages hidden in digital
   images. The work contributes to the field of steganalysis, which plays a key
   role in cybersecurity and digital forensics.
   
   Steganography involves embedding data within a common media, such as words
   hidden among the bits and bytes of a digital image. The image looks no
   different when displayed on a screen, but someone who knows there is a hidden
   message can extract or display the message. Given the vast numbers of digital
   images that now exist, and that number grows at a remarkable rate every day,
   it is difficult to see how such hidden information might be found by a third
   party, such as law enforcement. Indeed, in a sense it is security by
   obscurity, but it is a powerful technique nevertheless. There are legitimate
   uses of steganography, of course, but there are perhaps more nefarious uses
   and so effective detection is important for law enforcement and security.
   
   Ankita Gupta, Rita Chhikara, and Prabha Sharma of The NorthCap University in
   Gurugram, India, have introduced a new approach that improves detection
   accuracy while addressing the computational challenges associated with
   processing the requisite large amounts of data.
   
   Steganalysis involves identifying whether an image contains hidden data.
   Usually, the spatial rich model (SRM) is employed to detect those hidden
   messages. It analyses the image to identify tiny changes in the fingerprint
   that would be present due to the addition of hidden data. However, SRM is
   complex, has a large number of features, and can overwhelm detection
   algorithms, leading to reduced effectiveness. This issue is often referred to
   as the "curse of dimensionality."
   
   The team has turned to a hybrid optimisation algorithm called DEHHPSO, which
   combines three algorithms: the Harris Hawks Optimiser (HHO), Particle Swarm
   Optimisation (PSO), and Differential Evolution (DE). Each of these algorithms
   was inspired by natural processes. For example, the HHO algorithm simulates
   the hunting behaviour of Harris Hawks and balances exploration of the
   environment with targeting optimal solutions. The team explains that by
   combining HHO, PSO, and DE, they can work through complex feature sets much
   more quickly than is possible with a current single algorithm, however
   sophisticated.
   
   The hybrid approach reduces computational demand by eliminating more than 94%
   of the features that would otherwise have to be processed. The stripped back
   information can then be processed with a support vector machine (SVM)
   classifier. The team says this method works better than meta-heuristic
   (essentially trial-and-error methods) and better even than several deep
   learning methods, which are usually used to solve more complex problems than
   steganalysis.
   
   Gupta, A., Chhikara, R. and Sharma, P. (2024) 'An improved continuous and
   discrete Harris Hawks optimiser applied to feature selection for image
   steganalysis', Int. J. Computational Science and Engineering, Vol. 27, No. 5,
   pp.515–535.
   DOI: 10.1504/IJCSE.2024.141339

 * Brighter days for business with clouds
   
   Cloud computing has become an important part of information technology
   ventures. It offers a flexible and cost-effective alternative to conventional
   desktop and local computer infrastructures for storage, processing, and other
   activities. The biggest advantage to startup companies is that while
   conventional systems require significant upfront investment in hardware and
   software, cloud computing gives them the power and capacity on a
   "pay-as-you-go" basis. This model not only reduces initial capital
   expenditures at a time when a company may need to invest elsewhere but also
   allows businesses to scale their resources based on demand without extensive,
   repeated, and costly physical upgrades.
   
   A study in the International Journal of Business Information Systems has
   highlighted the role of fuzzy logic in evaluating the cost benefits of
   migrating to cloud computing. Fuzzy logic, a method for dealing with
   uncertainty and imprecision, offers a more flexible approach compared to
   traditional binary logic. Fuzzy logic recognises the shades of grey inherent
   in most business decisions rather than seeing things in black and white.
   
   The team, Aveek Basu and Sraboni Dutta of the Birla Institute of Technology
   in Jharkhand, and Sanchita Ghosh of the Salt Lake City Electronics Complex,
   Kolkata, India, explains that conventional cost-benefit analyses often fall
   short when assessing cloud migration due to the inherent unpredictability in
   factors such as data duplication, workload fluctuations, and capital
   expenditures. Fuzzy logic, on the other hand, addresses these challenges by
   allowing decisions to be made that take into account the uncertainties of the
   real world.
   
   The team applied fuzzy logic to evaluate three factors associated with the
   adoption of cloud computing platforms. First, the probability of data
   duplication, secondly capital expenditure, and finally workload variation. By
   incorporating these different factors into the analysis, the team obtained a
   comprehensive view of the potential benefits and drawbacks of cloud computing
   from the perspective of a startup company. The approach offers a more
   adaptable assessment than traditional models.
   
   One of the key findings is that cloud computing leads to a huge reduction in
   the complexity and costs associated with managing business software and the
   requisite hardware as well as the endless upgrades and IT support often
   needed. Cloud service providers manage all of that on behalf of their
   clients, allowing the business to focus instead on its primary operations
   rather than IT.
   
   Basu, A., Ghosh, S. and Dutta, S. (2024) 'Analysing the cloud efficacy by
   fuzzy logic', Int. J. Business Information Systems, Vol. 46, No. 4,
   pp.460–490.
   DOI: 10.1504/IJBIS.2024.141318

 * A rare take on green metal volatility
   
   Research in the International Journal of Global Energy Issues has looked at
   the volatility of rare earth metals traded on the London Stock Exchange. The
   work used an advanced statistical model known as gjrGARCH(1,1) to follow and
   predict market turbulence. It was found to be the best fit for predicting
   rare earth price volatility and offers important insights into the stability
   of these crucial resources.
   
   Auguste Mpacko Priso of Paris-Saclay University, France and the Open
   Knowledge Higher Institute (OKHI), Cameroon, with OKHI colleague explain that
   the rare earths, are a group of 17 metals* with unique and useful chemical
   properties. They are essential to high-tech products and industry,
   particularly electric vehicle batteries and renewable energy infrastructure.
   They are also used in other electronic components, lasers, glass, magnetic
   materials, and as components of catalysts for a range of industrial
   processes. As the global transition to reduced-carbon and even zero-carbon
   technologies moves forward, there is an urgent need to understand the pricing
   of rare earth metals, as they are such an important part of the technology we
   need for that environment friendly future.
   
   The team compared the volatility of rare earth prices with that of other
   metals and stocks. Volatility, or the degree of price fluctuation, was found
   to be persistent in rare earths, meaning that prices tend to fluctuate
   continually over time rather than reaching a stable point quickly. For
   investors and manufacturers dependent on these metals, such constant
   volatility poses a substantial economic risk. As such, forecasting the price
   changes might be used to mitigate that. It might lead to greater stability
   and allowing investors to work in this area secure in the returns they hope
   to see.
   
   Other models used in stock price prediction failed to model the volatility of
   the rare earth metals well, suggesting that this market has distinctive
   characteristics that affect prices differently from other more familiar
   commodities. Given that the demand and use of rare earth metals is set to
   surge, there is a need to understand their price volatility and to take this
   into account in green investments and development. It is worth noting that
   there is a major political component in this volatility given that China, and
   other nations, with vast reserves of rare earth metal ores, do not
   necessarily share the political views or purpose of the nations demanding
   these resources.
   
   Mpacko Priso, A. and Doumbia, S. (2024) 'Price and volatility of rare
   earths', Int. J. Global Energy Issues, Vol. 46, No. 5, pp.436–453.
   DOI: 10.1504/IJGEI.2024.140736
   
   *Rare earth metals: cerium, dysprosium, erbium, europium, gadolinium,
   holmium, lanthanum (sometimes considered a transition metal), lutetium,
   neodymium, praseodymium, promethium, samarium, scandium, terbium, thulium,
   ytterbium, yttrium

 * Shipping included: boosting port efficiency
   
   Container ports are important hubs in the global trade network. They have
   seen enormous growth in their roles over recent years and operational demands
   are always changing, especially as more sophisticated logistics systems
   emerge. A study in the International Journal of Shipping and Transport
   Logistics sheds new light on how the changes in this sector are affecting
   port efficiency, the focus is on the different types of container activities.
   
   Fernando González-Laxe of the University Institute of Maritime Studies, A
   Coruña University and Xose Luis Fernández and Pablo Coto-Millán of the
   Universidad de Cantabria, Santander, Spain, explain that container ports
   handle cargo that is packed in standardized shipping containers, the big
   metal boxes with which many people are familiar commonly transported en masse
   on vast sea-going vessels, unloaded port-side, and loaded on to trains and
   road transporters for their onward journey. The increasing size of ships used
   for transporting these containers, some of which can carry up to 25000 TEUs
   (twenty-foot equivalent units, the containers), means there is increasing
   pressure on ports to increase their capacity. As such, there is a lot of
   ongoing effort to automate processes and optimize port operations to allow
   the big container ports to remain viable and competitive.
   
   The team used Data Envelopment Analysis (DEA) to evaluate the efficiency of
   container ports by comparing the input and output of their operations. The
   focused on ten major Spanish container ports – among them the major ports of
   Algeciras, Barcelona, and Valencia – in order to understand how various types
   of container activities – import/export, transshipment, and cabotage (coastal
   shipping) – influence port performance.
   
   One of the key findings from the study is the relationship between port
   efficiency and the types of container activities handled. The team found that
   there is an inverted U-shape relationship: ports that balanced transshipment
   (transferring containers between ships at intermediate points) with
   import/export activities tended to perform better than those that specialized
   in only one type of activity. This suggests that a diversified approach to
   container activities may enhance port efficiency.
   
   The work suggests that by adopting a balanced approach to their activities,
   container ports could boost efficiency and reinforce their role in the global
   supply chain.
   
   González-Laxe, F., Fernández, X.L. and Coto-Millán, P. (2024) 'Transhipment:
   when movement matters in port efficiency', Int. J. Shipping and Transport
   Logistics, Vol. 18, No. 4, pp.383–402.
   DOI: 10.1504/IJSTL.2024.140429

 * AI learns elephant talk
   
   Dr Dolittle eat your heart out! Researchers writing in the International
   Journal of Engineering Systems Modelling and Simulation demonstrate how a
   trained algorithm can identify the trumpeting calls of elephants,
   distinguishing them from human and other animals sounds in the environment.
   The work could improve safety for villagers and help farmers protect their
   crops and homesteads from wild elephants in India.
   
   T. Thomas Leonid of the KCG College of Technology and R. Jayaparvathyof the
   SSN College of Engineering in Chennai, India, explain how conflicts between
   people and elephants are becoming increasingly common, especially in areas
   where human activity has encroached on natural elephant habitats. This is
   particularly true where agriculture meets forested land. These conflicts are
   not just an environmental concern, they pose a thread to human life and
   livelihoods.
   
   In India, wild elephants are responsible for more human fatalities than large
   predators. Their presence also leads to the destruction of crops and
   infrastructure, which creates a heavy financial burden on rural communities.
   Of course, the elephants are not to blame, they are wild animals, doing their
   best to survive. The root causes lie in habitat destruction due to human
   activities such as mining, dam construction, and increasing encroachment into
   forests for resources like firewood and water.
   
   As such, finding effective solutions to mitigate human-elephant encounters is
   becoming increasingly urgent. The team suggests that a way to reduce the
   number of tragic and costly outcomes would be to put in place an
   early-warning system. Such a system would recognise elephant behaviour from
   their vocalisations and allow farmers and others to avoid the elephants or
   perhaps even safely divert an incoming herd before it becomes a serious and
   damaging hazard.
   
   The researchers compared several machine learning models to determine which
   one best detects and classifies elephant sounds. The models tested included
   Support Vector Machines (SVM), K-nearest Neighbours (KNN), Naive Bayes, and
   Convolutional Neural Networks (CNN). They trained each of these algorithms on
   a dataset of 450 animal sound samples from five different species. One of the
   key steps in the process is feature extraction, which involves identifying
   distinctive characteristics within the audio signals, such as frequency,
   amplitude, and the temporal structure of the sounds. These features are then
   used to train the machine learning models to recognise elephant calls.
   
   The most accurate was the Convolutional Neural Network (CNN), a deep learning
   model that automatically learns complex features from raw data. CNNs are
   particularly well-suited for this type of task due to their ability to
   recognise intricate patterns in sound data. The CNN had a high accuracy of 84
   percent, far better than the models. This might be improved, but is
   sufficiently accurate to have potential for a reliable, automated system to
   detect elephants on the march that might be heading towards homes and farms.
   
   Leonid, T.T. and Jayaparvathy, R. (2024) 'Elephant sound classification using
   machine learning algorithms for mitigation strategy', Int. J. Engineering
   Systems Modelling and Simulation, Vol. 15, No. 5, pp.248–252.
   DOI: 10.1504/IJESMS.2024.140803

 * A leap towards an emotion detector
   
   Research in the International Journal of Biometrics introduces a method to
   improve the accuracy and speed of dynamic emotion recognition using a
   convolutional neural network (CNN) to analyse faces. The work undertaken by
   Lanbo Xu of Northeastern University in Shenyang, China, could have
   applications mental health, human-computer interaction, security, and other
   areas.
   
   Facial expressions are a major part of non-verbal communication, providing
   clues about an individual's emotional state. Until now, emotion recognition
   systems have used static images, which means they cannot capture the changing
   nature of emotions as they play out over a person's face during a
   conversation, interview or other interaction. Xu's work addresses this by
   focusing on video sequences. The system can track changing facial expressions
   over a series of video frames and then offer a detailed analysis of how a
   person's emotions unfold in real time.
   
   However, prior to analysis, the system applies an algorithm, the "chaotic
   frog leap algorithm" to sharpen key facial features. The algorithm mimics the
   foraging behaviour of frogs to find optimal parameters in the digital images.
   The CNN trained on a dataset of human expressions is the most important part
   of the approach, allowing Xu to process visual data by recognizing patterns
   in new images that intersect with the training data. By analysing several
   frames from video footage, the system can capture movements of the mouth,
   eyes, and eyebrows, which are often subtle but important indicators of
   emotional changes.
   
   Xu reports an accuracy of up to 99 percent, with the system providing an
   ouput in a fraction of a second. Such precision and speed is ideal for
   real-time use in various areas where detecting emotion might be useful
   without the need for subjective assessment by another person or team. Its
   potential applications lie in improving user experiences with computer
   interactions where the computer can respond appropriately to the user's
   emotional state, such as frustration, anger, or boredom.
   
   The system might be useful in screening people for emotional disorders
   without initial human intervention. It could also be used in enhancing
   security systems allowing access to resources but only to those in a
   particular emotional state and barring entry to an angry or upset person,
   perhaps. The same system could even be used to identify driver fatigue on
   transport systems or even in one's own vehicle. The entertainment and
   marketing sectors might also see applications where understanding emotional
   responses could improve content development, delivery, and consumer
   engagement.
   
   Xu, L. (2024) 'Dynamic emotion recognition of human face based on
   convolutional neural network', Int. J. Biometrics, Vol. 16, No. 5,
   pp.533–551.
   DOI: 10.1504/IJBM.2024.140785

 * Cyber shields up!
   
   As computer network security threats continue to grow in complexity, the need
   for more advanced security systems is obvious. Indeed, traditional methods of
   intrusion detection have struggled to keep pace with the changes and so
   researchers are looking to explore alternatives. A study in the International
   Journal of Computational Systems Engineering suggests that the integration of
   data augmentation and ensemble learning methods could be used to improve the
   accuracy of intrusion detection systems.
   
   Xiaoli Zhou of the School of Information Engineering at Sichuan Top IT
   Vocational Institute in Chengdu, China, has focused on a Wasserstein
   Generative Adversarial Network with Gradient Penalty (WGAN-GP). This is an
   advanced version of the standard machine learning model and can create
   realistic data through a process of competition between two neural networks.
   Conventional GANs often suffer from unstable training and pattern collapse,
   where the model fails to generate diverse data. The WGAN-GP variant mitigates
   these issues by incorporating a gradient penalty, according to the research,
   this helps to stabilize the training process and improve the quality of the
   generated data. It can then be used effectively to simulate network traffic
   for intrusion detection with a view to blocking hacking attempts.
   
   There is the potential to enhance the WGAN-GP data quality still further by
   combining it with a stacking learning module. Stacking is an ensemble
   learning technique that involves training multiple models and then combining
   their outputs using a meta-classifier. In Zhou's work, the stacking module
   integrates the predictions from several WGAN-GP models to allow them to be
   classified as normal or intrusive.
   
   The approach was tested against well-established data augmentation methods,
   including the Synthetic Minority Over-sampling Technique (SMOTE), Adaptive
   Synthetic Sampling (ADASYN), and a simple version of WGAN. The results showed
   that the WGAN-GP-based model had an accuracy rate of almost 90%, which is
   better than the scores for the other techniques tested. The model can thus
   distinguish between legitimate and potentially harmful network activity
   effectively. Optimisation might improve the accuracy and allow the system to
   be used to protect governments, corporations, individual, and others at risk
   from network security threats.
   
   Zhou, X. (2024) 'Research on network intrusion detection model that
   integrates WGAN-GP algorithm and stacking learning module', Int. J.
   Computational Systems Engineering, Vol. 8, No. 6, pp.1–10.
   DOI: 10.1504/IJCSYSE.2024.140760

 * Science-based spin-offs
   
   Science-based university spin-offs, especially in the biotech sector, play an
   important role in transforming cutting-edge academic science into marketable
   technological products. However, such start-ups face lots of challenges that
   can be very different from those encountered by conventional startups.
   Research in the International Journal of Technology Management has looked at
   the complexities and potential of such spin-offs and sheds new light on the
   role played by the academic scientists involved in the process and how launch
   timing can make all the difference.
   
   Andrew Park of the University of Victoria, Canada, and colleagues explain
   that unlike typical start-ups, which might bring a product to market
   relatively quickly, new biotechnology companies often have long periods of
   financial investment and require lengthy development, testing, and regulatory
   periods for their products. This is particularly true in drug development,
   where the path from the laboratory bench to the marketplace can span a decade
   or more, not least because of the need for extensive clinical trials and the
   completion of regulatory requirements. As such, there is often a greater need
   to plan strategically and to use resources more effectively even before the
   spin-off company is officially launched.
   
   Many laboratory scientists make the leap from bench to business, some with
   much greater success than others. The successful scientist-entrepreneurs
   bring with them their research acumen and intellectual property, but also
   various intangible assets that can make or break a spin-off company. Among
   those intangibles might be research publications and patents, networks of
   contacts and collaborators, and access to funding opportunities that might be
   unavailable to companies with no direct academic links.
   
   The paper's case studies of three biotechnology spin-offs within the British
   Columbia innovation ecosystem suggests that the value of intangible assets is
   usually only realised when strong entrepreneurial capabilities are available
   to the start-up company. These capabilities are not just about business
   acumen but also an understanding of how to align the technology with market
   needs, protect intellectual property effectively, and mentor the founding
   team to reach biotech commercialization successfully. Critically, the timing
   of a company launch can correlate strongly with success or failure, the
   researchers found.
   
   Park, A., Goudarzi, A., Yaghmaie, P., Thomas, V.J. and Maine, E. (2024) 'The
   role of pre-formation intangible assets in the endowment of science-based
   university spin-offs', Int. J. Technology Management, Vol. 96, No. 4,
   pp.230–260.
   DOI: 10.1504/IJTM.2024.140712

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