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As the patent landscape continues to grow, so does the complexity of retrieving relevant “prior art”, “background art”, or “state of the art” from an expanding pool of publicly available patent data, a critical step in establishing novelty. However, retrieving this information presents significant challenges due to its volume and complexity. This systematic literature review surveys patent retrieval techniques over the past decade, focusing on ‘prior art’ and ‘novelty’ searches. Adhering to the PRISMA 2020 guidelines, our research includes 78 pertinent articles selected from a corpus of 1441, providing an in-depth overview of recent advancements, emerging trends, challenges, and future directions in the field of patent prior art retrieval. The review addresses six research questions: defining the current state of the art, evaluating the efficacy of various approaches, examining commonly used patent data collections, exploring the impact of semantic search and natural language processing (NLP) technologies, identifying frequently used components of patent documents, and discussing ongoing challenges in the domain of patent prior art search and retrieval. Our findings highlight the growing use of NLP to enhance the precision and comprehensiveness of patent searches, particularly on the Cross-Language Evaluation Forum for Intellectual Property (CLEF-IP) and the United States Patent and Trademark Office (USPTO) databases. Despite advancements, the specialized and technical nature of patent language continues to pose significant challenges in achieving high accuracy in patent retrieval.

Social background profiling of speakers is heavily used in areas, such as, speech forensics, and tuning speech recognition for accuracy improvement. This article provides a survey of recent research in speaker background profiling in terms of accent classification and analyses the datasets, speech features, and classification models used for the classification tasks. The aim is to provide a comprehensive overview of recent research related to speaker background profiling and to present a comparative analysis of the achieved performance measures. Comprehensive descriptions of the datasets, speech features, and classification models used in recent research for accent classification have been presented, with a comparative analysis made on the performance measures of the different methods. This analysis provides insights into the strengths and weaknesses of the different methods for accent classification. Subsequently, research gaps have been identified, which serve as a useful resource for researchers looking to advance the field.

Given the vast diversity of bee species and the limited availability of taxonomy experts, bee species identification has become increasingly important, especially with the rise of apiculture practice. This review systematically explores the application of machine learning (ML) techniques in bee species determination, shedding light on the transformative potential of ML in entomology. Conducting a keyword-based search in the Scopus and Web of Science databases with manual screening resulted in 26 relevant publications. Focusing on shallow and deep learning studies, our analysis reveals a significant inclination towards deep learning, particularly post-2020, underscoring its ability to handle complex, high-dimensional data for accurate species identification. Most studies have utilized images of stationary bees for the determination task, despite the high computational demands from image processing, with fewer studies utilizing the sound and movement of the bees. This emerging field faces challenges in terms of dataset scarcity with limited geographical coverage. Additionally, research predominantly focuses on honeybees, with stingless bees receiving less attention, despite their economic potential. This review encapsulates the state of ML applications in bee species determination. It also emphasizes the growing research interest and technological advancements, aiming to inspire future explorations that bridge the gap between computational science and biodiversity conservation.

As global urbanisation, industrialisation, and population growth escalate, the production of wastewater also increases, leading to significant water pollution on a global scale. This pollution poses severe threats to environmental health, wildlife, and human communities. In rural areas where centralised sewage systems are often absent, septic tanks play a crucial role in managing wastewater. They separate solids from liquids and facilitate the biological decomposition of organic matter. This paper utilises a Patent Landscape Review (PLR) to analyse the scope and direction of innovations in septic tank technology. Conducted on 23 September 2022, the patent search targeted filings from January 2001 through June 2022 to identify prevailing trends and advancements within this field. Through a detailed examination of 889 patents, categorised by keywords, processes, materials, and designs, this study offers a comprehensive overview of the patent landscape for septic tanks. Key findings indicate that fibreglass cylindrical tanks dominate the market due to their durability and efficiency. This review also highlights a growing trend towards modular septic systems, which offer scalable solutions adaptable to specific environmental conditions. Furthermore, some patents propose the repurposing of various objects as septic tanks, demonstrating a move towards sustainability by reducing waste and enhancing environmental conservation. This paper emphasises the importance of continued innovation in septic tank technology to address the challenges of effective wastewater management in underserved rural communities.

Automatic segmentation is essential for enhancing human activity recognition, especially given the limitations of publicly available datasets that often lack diversity in daily activities. This study introduces a novel segmentation method that utilizes skeleton data for a more accurate and efficient analysis of human actions. By employing an autoencoder, this method extracts representative features and reconstructs the dataset, using the discrepancies between the original and reconstructed data to establish a segmentation threshold. This innovative approach allows for the automatic segmentation of activity datasets into distinct segments. Rigorous evaluations against ground truth across three publicly available datasets demonstrate the method’s effectiveness, achieving impressive average annotation error, precision, recall, and F1-score values of 3.6, 90%, 87%, and 88%, respectively. This illustrates the robustness of the proposed method in accurately identifying change points and segmenting continuous skeleton-based activities as compared to two other state-of-the-art techniques: one based on deep learning and another using the classical time-series segmentation algorithm. Additionally, the dynamic thresholding mechanism enhances the adaptability of the segmentation process to different activity dynamics improving overall segmentation accuracy. This performance highlights the potential of the proposed method to significantly advance the field of human activity recognition by improving the accuracy and efficiency of identifying and categorizing human movements.

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems.

The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a Toyota BZ3 (EV), USD 43,409, is more expensive than a Toyota Aqua (HEV) and Toyota Prius (HEV), but cheaper than a Toyota Axio (ICEV) and Toyota Allion (ICEV). It has been found that about a 25% reduction in the acquisition cost of a Toyota BZ3 would lower its LCC to below others. EVs can be a good choice for those who travel a lot. Changes in electricity prices have little effect upon the LCC of EVs. With the expected decline in the annual price for batteries, which is between 6 and 9%, and the improvement of their capacities, EVs will be more competitive with other vehicles by 2030 or even earlier. EVs will dominate the market since demand for alternative fuel-powered vehicles is growing due to their environmental and economic advantages.

The 3D printing industry faces challenges in ensuring reliable and repeatable processes, as increasing slopes can lead to defects and faults forming layer by layer in fabricated parts. Detecting these defects through post-processing quality inspections is time-consuming and laborious. To address this, a new method is proposed that incorporates a scoring scheme to quantitatively evaluate process performance using clad height data during printing. This approach aims to save time and cost while preserving the structural integrity of the part. The study develops a layer-wise point cloud processing technique to convert the incoming unordered data streams into rasterized points. By transforming raw signals into spatially equidistant points representing clad height in a 2-D Cartesian plane, a heatmap tomography of each layer is generated. Subsequently, a novel Defects-Finder algorithm is developed to locate and cluster surface defects based on the assigned scores. The findings demonstrate the algorithm’s ability to identify the root cause of propagated faults, which can result in severe defects. Additionally, the study employs various statistical measures in the layer-level analysis to evaluate miniature process faults or shifts, which may get overshadowed, including cases of under and over-deposition. Through comparison and validation with physical artifacts, the proposed method proves effective in identifying and assessing process faults. Ultimately, this method enhances big data visualization for cost-effective quality control and boosts the overall productivity of additive manufacturing processes. By streamlining defect detection and performance evaluation, it addresses the challenges faced by the industry in ensuring reliable 3D printing outcomes.

Electric Vehicles (EVs) emerge as a crucial solution for alleviating the environmental footprint of the transportation sector. However, fostering their widespread adoption demands effective, targeted policies. This study introduces a versatile model, amalgamating stakeholders and policies and leveraging local data with broader market applicability. It delineates two key EV adopter groups—innovators and imitators—shedding light on their evolving impact on adoption trends. A pivotal feature of the model is the factoring of EV attractiveness, comprising Life-Cycle Cost (LCC), Driving Range, Charging Time, and infrastructure availability, all of which are expected to improve with the fast technological advancement of EVs. Financial policies, notably subsidies, prove potent in boosting EV adoption but fall short of targeted sales due to imitator lag. In response, a pragmatic solution is proposed: a government-led EV acquisition of 840 EVs, coupled with a 20% subsidy on new EV purchases and a 20% tax on new ICEV purchases, potentially realizing a 30% EV sales target by 2035. Future research avenues may delve into behavioral dynamics prompting imitators’ adoption, optimizing EV infrastructure strategies, and assessing the socio-economic impacts of EVs. Interdisciplinary approaches hold promise for enriched insights for effective EV integration policies.

Speech samples can provide valuable information regarding speaker characteristics, including their social backgrounds. Accent variations with speaker backgrounds reflect corresponding acoustic features of speech, and these acoustic variations can be analyzed to assist in tracking down criminals from speech samples available as forensic evidence. Speech accent identification has recently received significant consideration in the speech forensics research community. However, most works have utilized long-term temporal modelling of acoustic features for accent classification and disregarded the stationary acoustic characteristics of particular phoneme articulations. This paper analyzes short-term acoustic features extracted from a central time window of English vowel speech segments for accent discrimination. Various feature computation techniques have been compared for the accent classification task. It has been found that using spectral features as an input gives better performance than using cepstral features, with the lower filters contributing more significantly to the classification task. Moreover, detailed analysis has been presented for time window durations and frequency bin resolution to compute short-term spectral features concerning accent discrimination. Using longer time durations generally requires higher frequency resolution to optimize classification performance. These results are significant, as they show the benefits of using spectral features for speaker profiling despite the popularity of cepstral features for other speech-related tasks.

For countries with an abundance of rain, there is definite potential to implement a rainwater harvesting system for different applications. This paper describes feasibility studies of an open-pond rainwater harvesting system for ablution purposes, analysing the quality of harvested rainwater and formulating a rainwater harvesting model with suitable performance measures. The formulated model can be used to analyse the feasibility of the system in any locality by inputting local meteorological data. Quality analysis has shown that the harvested rainwater can be used safely for ablution purposes, albeit with a slightly acidic pH below 6.5. At a depth of 1.0 m and using the current pond configuration of a local mosque, the reliability of the system is 62.5% (228 days per year), and the amount of water saved is 345 m3, which is 60.7% of the water demand. It has been shown that a pond surface area of 60–70 m2 provides optimum reliability and water saving, and more water savings can be expected with a more economical usage of water during ablution. These results indicate the feasibility of implementing a rainwater harvesting system for ablution purposes in religious institutions to help curb the water shortage crisis.

Ankle sprains are among the most prevalent musculoskeletal system injuries. Prevention of ankle sprains is important, given the frequency of occurrence of an ankle sprain, the risk of reinjury, and its long-term effects. A systematic patent review using the World Intellectual Property Organization (WIPO)’s PATENTSCOPE database has been performed to study the current development of ankle sprain prevention methods. Using the PRISMA statement as a basis, a total of 426 patent documents have been selected for review, consisting of 150 granted patents and 276 patent applications. In the past decade, China and the United States of America (43% and 29% of the patent applications, respectively) have shown great interest in developing ankle sprain prevention methods. Approximately 49% (or 74) of the patents from the 150 granted are associated with braces, 46% (or 68) are related to orthosis, 3% (or 5) are related to tape, and the remaining 2% (or 3) are associated with other types of ankle sprain prevention methods. This patent review shows that inventors are leaning towards braces and orthosis as the main prevention methods for ankle sprains, with greater interest in orthosis in recent years. Additionally, patents on smart wearable devices suggest initial commercial interest in the development of smart wearable devices as an ankle sprain prevention method.

With the growth of wireless network technology-based devices, identifying the communication behaviour of wireless connectivity enabled devices, e.g. Internet of Things (IoT) devices, is one of the vital aspects, in managing and securing IoT networks. Initially, devices use frames to connect to the access point on the local area network and then, use packets of typical communication protocols through the access point to communicate over the Internet. Toward this goal, network packet and IEEE 802.11 media access control (MAC) frame analysis may assist in managing IoT networks efficiently, and allow investigation of inclusive behaviour of IoT devices. This paper presents network traffic traces data of D-Link IoT devices from packet and frame levels. Data collection experiment has been conducted in the Network Systems and Signal Processing (NSSP) laboratory at Universiti Brunei Darussalam (UBD). All the required devices, such as IoT devices, workstation, smartphone, laptop, USB Ethernet adapter, and USB WiFi adapter, have been configured accordingly, to capture and store network traffic traces of the 14 IoT devices in the laboratory. These IoT devices were from the same manufacture (D-Link) with different types, such as camera, home-hub, door-window sensor, and smart-plug.

The success of supervised learning techniques for automatic speech processing does not always extend to problems with limited annotated speech. Unsupervised representation learning aims at utilizing unlabelled data to learn a transformation that makes speech easily distinguishable for classification tasks, whereby deep auto-encoder variants have been most successful in finding such representations. This paper proposes a novel mechanism to incorporate geometric position of speech samples within the global structure of an unlabelled feature set. Regression to the geometric position is also added as an additional constraint for the representation learning auto-encoder. The representation learnt by the proposed model has been evaluated over a supervised classification task for limited vocabulary keyword spotting, with the proposed representation outperforming the commonly used cepstral features by about 9% in terms of classification accuracy, despite using a limited amount of labels during supervision. Furthermore, a small keyword dataset has been collected for Kadazan, an indigenous, low-resourced Southeast Asian language. Analysis for the Kadazan dataset also confirms the superiority of the proposed representation for limited annotation. The results are significant as they confirm that the proposed method can learn unsupervised speech representations effectively for classification tasks with scarce labelled data.

Organic waste management is a major global challenge. It accounts for a significant portion of waste that ends up in landfills, where it gradually decomposes and emits methane, a harmful greenhouse gas. Composting is an effective method for potentially solving the problem by converting organic waste into valuable compost. Despite many studies focusing on the composting process, no study has reviewed the technological advancements in the composting fields from the perspective of patents. This review paper begins with background information on the composting process, specifically important factors affecting the process, problems associated with it, and the available technologies to facilitate the process. Different technologies are discussed, ranging from manual to automated methods. Subsequently, 457 patents are selected, classified into different categories, and reviewed in detail, providing a patent technology landscape of composting technology. Automatic composters are more prominent than manual ones as managing organic waste at the source has become more crucial in recent years. The need for a domestic composter creates an opportunity for the development of a compact and automated system for organic waste management, which is more suitable for urbanized settings. This technology has the potential to reduce the amount of organic waste that needs to be managed at an already overburdened landfill, as well as the environmental consequences associated with it.

Recent United Nations high-level dialogue on energy, which had emphasized on energy usage and environmental protection, has renewed commitments by different countries on the adoption of electric vehicle (EVs). This paper aims to analyze the economic feasibility of establishing electrical charging stations, which is an important factor for the wide adoption of EVs, using life cycle cost analysis. Although local data have been used, the method can be easily adopted to analyze economic feasibility at different markets. The findings have revealed that an electrical charging station is only feasible when the acquisition cost is kept to a minimum to return 1.47 times the initial investment in terms of life cycle cost. An acquisition cost of BND 29,725 on the electrical charging station represents the threshold below which an electrical charging station is more attractive. In order to promote these charging stations, the government needs to provide multiple incentives, including a subsidy to reduce the acquisition cost, relaxing control on the electric selling price, taxing the establishment of conventional filling stations, and minimally reducing the profit margin on the selling price of fossil fuel. It has been shown that a 40% initial subsidy on the purchase of electrical charging stations, coupled with a slight subsidy of BND 0.018/kWh on electricity, would make electrical charging stations economically competitive. To reach its target of 60% electrification of the transportation sector, Brunei would need to implement a structure program to establish between 646 and 3300 electrical charging stations by the year 2035, to cater for its expected number of EVs.

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.

The pressure pain threshold (PPT) is a useful tool for evaluating mechanical sensitivity in individuals suffering from various musculoskeletal disorders. The aim of this study is to investigate PPT at the heel area in order to assist in the design of orthotic shoes for sufferers of heel pain due to a calcaneal spur. The size and location of the calcaneal spur was determined by x-ray images, with PPT data measured around the spur at five points by using algometer FDIX 25. The pain test experiment was conducted by pressing each point to obtain the pain minimum compressive pressure (PMCP) and its location. The information of shoe size, spur location and dimensions, and the PMCP location for each individual is used to obtain the exact point location for applying a softer material to the shoe in-sole, in order to reduce heel pain. The results are significant as it can be used by designers to design appropriate shoe in-soles for individuals suffering from heel pain.

Electric vehicles are a leading candidate in the clean energy market. This paper aims to analyse the feasibility of the deployment of electric buses (EB) based on the existing bus routes in Brunei, by the use of life cycle cost analysis and the analysis of the parameters that influence the overall life cycle cost. The findings from the study revealed that EB are significantly more expensive than diesel buses (DB), with their acquisition and maintenance costs contributing substantially to their overall life cycle cost. In order to promote EB deployment, the government needs to look simultaneously into providing subsidies for EB and imposing taxes on DB, the provision of charging infrastructure, and ensuring maintenance capability, as well as increasing the current subsidised diesel price. It was also shown that increasing the cost of diesel to the average US diesel price of USD$3.101/L, an initial subsidy of USD$67,586 towards the purchase of EB, and a tax of USD$67,586 for the purchase of DB would allow EB to compete in the market, with the amount of tax and subsidy being gradually reducible over time, as EB and battery technology becomes more mature. From an environmental perspective, the emissions from EB come out higher than the emissions from DB. The efficiency of electric power generation needs to be enhanced, and renewable energy sources and the adoption of carbon capture technology need to be explored in order to exploit the full benefit of EB and ensure more environmentally sustainable bus operation.

A simple hexagonal lattice photonic crystal fiber model with liquid-infiltrated core for different liquids: water, ethanol and benzene, has been proposed. In the proposed structure, three air hole rings are present in the cladding and three equal sized air holes are present in the core. Numerical investigation of the proposed fiber has been performed using full vector finite element method with anisotropic perfectly match layers, to show that the proposed simple structure exhibits high relative sensitivity, high power fraction, relatively high birefringence, low chromatic dispersion, low confinement loss, small effective area, and high nonlinear coefficient. All these properties have been numerically investigated at a wider wavelength regime 0.6–1.8 μm within mostly the IR region. Relative sensitivities of water, ethanol and benzene are obtained at 62.60%, 65.34% and 74.50%, respectively, and the nonlinear coefficients are 69.4 W−1 km−1 for water, 73.8 W−1 km−1 for ethanol and 95.4 W−1 km−1 for benzene, at 1.3 µm operating wavelength. The simple structure can be easily fabricated for practical use, and assessment of its multiple waveguide properties has justified its usage in real liquid detection.

A porous-core photonic crystal fiber based on a cyclic olefin homopolymer (Zeonex) is proposed; it shows high birefringence, high core power fraction, low losses, and near-zero flat dispersion. The fiber’s core was designed with quad-elliptical (QE) air holes with its center occupied by bulk background material. The superiority of the QE design over the commonly adopted tri- and penta-elliptical (TE and PE) core designs is demonstrated. The presence of the bulk material at the core center and the geometrical configuration cause a broad contrast in phase refractive indices, thereby producing high birefringence and low transmission losses. A high birefringence of 0.096 was obtained at 1.2 THz, corresponding to a total loss of 0.027 cm−1 and core power fraction of approximately 51%. The chromatic dispersion and effective area of the reported fiber were also characterized within a frequency range of 0.4−1.6 THz. The QE air holes were then filled with chemical warfare agents, namely, tabun and sarin liquids. Then, the relative sensitivity, confinement loss, fractional power flow, and effective material loss (EML) of the sensor were calculated. Nearly the same relative sensitivity (r = 64%) was obtained when the QE core was filled with either liquid. Although the obtained EML for tabun was 0.033 cm−1 and that for sarin was 0.028 cm−1, the confinement loss of the fiber when it was immersed in either liquid was negligible. The proposed fiber can be fabricated using existing fabrication technologies. Moreover, it can be applied and utilized as a THz radiation conveyor in a terahertz time domain spectroscopy system for remote sensing of chemical liquids in the security and defense industries.