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This study seeks to profile the shoppers' propensity to reuse shopping bags and the type of shopping bags they reuse on the basis of demographics. The study used an observation method to record the consumers' shopping habits and a short questionnaire to record the participants' demographics. A binary regression analysis and a multinomial regression analysis were used to analyse data. The results indicated that the mature aged, women, and low income shoppers were more likely to practice reuse of plastic shopping bags than their respective counterparts. There were no statistically significant differences in the likelihood of young aged shoppers compared to mature aged shoppers and low income shoppers compared to high income shoppers using cotton bags than plastic bags. There were also no statistically significant differences in the likelihood of young aged shoppers compared to mature aged shoppers and male shoppers compared to female shoppers using paper bags than plastic bags. However, the results indicated a statically significant result in how males compared to females differed in their using of cotton bags than plastic bags and a statistically significant result in how low income shoppers compared to high income shoppers differed in their use of paper bags than plastic bags. [ABSTRACT FROM AUTHOR]

The demand for particleboard has been increasing over the years. Currently, most particleboards are produced from wood which may not be sustainable in the long term. Therefore, there is need of exploring alternative materials such as making particleboards from waste materials. This study investigated the mechanical properties of particleboard consisting of waste leather shavings and waste papers blended together by unsaturated polyester. A single-layered particleboards were manufactured using compression method. Different resin contents (60%, 70%, 80%, and 90%) and leather/paper ratios (100:0, 25:75, 50:50, 75:25) were used to determine the effects on the mechanical properties (internal bond, bending strength, compression, and impact strength) of fabricated boards. From the results of this study, it was found that leather shavings and waste papers can be used as alternative raw materials for particleboard production and that mechanical properties were depended on the resin content and the blend ratio. Also, mechanical properties were reduced with resin content increment, except for impact strength, and improved by high paper blend ratio. It could be concluded that the produced particle panels could be used for indoor application or interior equipping. Additionally, it is recommended that further studies can be done on morphological analysis to establish the bonding between the particles and matrix. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the properties of concrete with waste paper ash (WPA) as cement replacing material. The chemical composition of WPA was reported, and Portland cement was partially replaced with 0%, 5%, 10%, 15%, and 20% of WPA to explore its effect on both fresh and hardened properties of C—25 concrete. The result of this study indicated that the chemical compositions of WPA were not classified as Pozzolanic material. Setting times of blended cement paste with WPA were lengthened and its consistency was also increased. The cement paste with replacement up to 10% showed a normal consistency within the standard range. The workability of concrete was tested immediately after preparing the concrete mix, whereas the compressive strength was tested after 7 and 28 days of curing. The results indicated that the workability of concrete containing WPA decreases as the WPA content increases. This study also revealed that there is a significant improvement in the compressive strength of concrete. Replacement of ordinary Portland cement by WPA up to 10% results in better compressive strength than that of the control mix. An improvement of 5.6% & 1.2% was observed, respectively, when compared with the 28-day compressive strength of the control mix. But the compressive strength decreases as the WPA replacement increases over 10%. Maximum compressive strength of 37.89 MPa was obtained for concrete containing 5% of WPA. [ABSTRACT FROM AUTHOR]

The deterioration of fiber properties during recycling processes, especially the loss of tensile strength, raises concerns that paper products made from recycled fibers might not satisfy quality requirements. The purpose of this paper is to estimate the deterioration of tensile strength and the damage in paper sheets made of recycled fibers using the theory of damage mechanics and machine learning methods. Experiments were carried out to recycle wood fibers and nonwood fibers four times, and the physicochemical properties of the handsheets made from these fibers were measured after each recycling. Water retention value and relative bonded area were selected as the features to estimate and predict tensile strength during recycling because they had strong correlations with tensile strength. This paper proposed a damage index to quantitatively express the severity of the damage in paper sheets based on the experimental investigation and the theory of damage mechanics. Thus, the deterioration of tensile strength could be estimated and predicted. To determine the damage index, a curve fitting model based on the hyperbolic theory of pulp properties was developed. The proposed quantitative expression of the damage index is: D = (Ds - h)²/a2 - (Dh - k)²/b2, where the coefficients were determined through the curve fitting model. This paper also developed a long short-term memory recurrent neural network model to determine the damage index according to the sequence of recycling. Both models were trained with the experimental data of water retention value and relative bonded area. The estimation and prediction by the curve fitting model were more accurate than those of the neural network model. The root mean square errors by the curve fitting model were 0.0278 for estimation, 0.1667 for prediction; and by the neural network model were 0.2445 for estimation, 0.2206 for prediction, respectively. After the damage index was determined, the deterioration of tensile strength then could be calculated as T = T0 (1-D). [ABSTRACT FROM AUTHOR]

In cotton yarn bundles and fabric layers, wicking and rate are accounted as crucial indigenous liquid transportation properties, playing a significant role in temperature reduction on their surfaces and being used to extend the food and agricultural storage life. In this article, manual test methods are described to measure water wickability and wicking rate of cotton yarn bundles and plain weave fabric layers. These methods described the water flow through the in-plane surface of yarn bundles and fabric layers oriented in either vertical or horizontal lines without external force. The wicking lengths and wicking growth rates in both untreated and treated cotton yarn bundles and fabric samples in either in-plane vertical or horizontal orientations were compared. The highest to lowest wicking length and rate were found in the treated yarn bundles, treated fabric layers, untreated yarn bundles, and untreated fabric strips, respectively. The wicking height and length obtained in untreated yarn bundles and untreated fabric layers was lower than the Kraft paper. The higher wicking and rate values indicate a considerable potential for liquid water migration. The results indicated that treated yarn bundles and fabric layers that are oriented horizontal direction are the best options for constructing capillary evaporative cooling. [ABSTRACT FROM AUTHOR]

In a bid to develop a cheaper alternative material for particleboard, fiber/particulate reinforced cement-paper composites were produced. The paper-cement matrix had 12 wt. % of cement and the matrix was reinforced with 10 wt. % fixed amount of coconut shell powder and varied amount of palm kernel fiber (0, 0.5, 1.0, 1.5, 2.0, and 2.5 wt. %). Two separate groups of samples were produced; one containing untreated fiber (UPKF), in varied proportions of 0–2.5 wt. % and the second containing alkaline-treated fiber (TPKF), in varied proportion of 0–2.5 wt. % fiber. Samples produced were cured for 28 and 49 days and examined for physical, mechanical, and thermal properties as well as microstructural features. The result obtained revealed improvement in properties of samples as fiber proportion increased. Comparison of the properties obtained indicates that alkaline-treated samples gave a better performance than the untreated counterparts, which was reflected in the sum of property ratio for each mix. The value for TPKF samples was higher than the value for UPKF samples. Also, samples cured for 49 days had better performance than the ones cured for 28 days for both UPKF and TPKF. Evaluation of the three experimental variables, fiber addition, alkaline treatment, and curing length proved that curing was more effective in enhancing the properties of the composites developed, especially the mechanical properties. [ABSTRACT FROM AUTHOR]

The intent of the present research work is to explore the prospect of replacing natural soil used in brick making by some extent with industrial waste, that is, lime mud, as obtained from paper mill waste. Extensive adoption of this mill waste as a brick making material will deter the environmental effects of this waste disposal. Moreover, its adoption will not only solve the problem of stocked waste piles but also scale down the usage of natural soil as a brick making material. In consideration of the central intend of this study to utilize industrial waste as an alternate raw material, yet another industrial waste—fly ash, has also been blended to the mix to manufacture unburnt bricks, such that, it may produce cementitious compounds with lime mud. So, the materials used for brick making in this study are: lime mud, collected from the dumping site of M/s Hindustan Paper Corporation Limited, Jagiroad, Assam, India; soil, collected from a brick manufacturing unit also near Jagiroad; and fly ash, collected from National Thermal Power Station, Badarpur, Delhi, India. The materials have been characterized with respect to their chemical and geotechnical properties. Then, experiments have been conducted on hand-moulded bricks of aforementioned mix in distinct proportions. In this study, all the bricks have been dried in the natural condition and then tested for compressive strength conforming to International standards. It has been duly noted that none of the treated unburnt bricks satisfies the requirements of standard codes. Nonetheless, in case of burnt bricks, the compressive strength satisfies the requirements of International standard codes wherein lime mud has been added up to a certain percentage (=20%) in soil-lime mud mix. [ABSTRACT FROM AUTHOR]

One of the uses of particle board is in the design of speaker/woofer casing, but with increasing demand, the cost is skyrocketing, effects of which lead to the high cost of end-use woofer gadgets. Cement–paper composite reinforced with snail shell powder/banana fiber was developed as an alternative. The fiber was grouped into two; untreated banana fiber (UBF) and treated banana fiber (TBF) (treated with 1 M NaOH). Fibers were infused into the composite at 0%, 0.5%, 1%, 1.5%, 2%, and 2.5% by weight of paper pulp while snail shell powder was added at 15 wt.% constant proportion for all samples produced. Curing was carried out for 28 and 56 days, respectively. The composite boards produced were examined for physical, mechanical, and microstructural properties. Results obtained indicated an uptrend in water absorption and thickness swelling at increasing fiber loading. Internal bond strength, screw holding strength, moduli of rupture, and elasticity were observed to be enhanced with increment in fiber proportion. It was further noticed that alkaline TBF performed better than the untreated fiber, hence recommended for paperboard production. Experimental trend and performance analysis engaged in revealed hydration to be the most efficient experimental variable and important for overall property maximization in cement–paper composite. Performance evaluation conducted showed effective property index was noticed to be highest for composite board doped with 2.5 wt.% TBF. [ABSTRACT FROM AUTHOR]

With the maturity of information and communication technology (ICT), numerous innovative applications are proposed in different arenas including smart living environments. Technology-enabled smart living has transformed the traditional living system to an enhanced user satisfaction model by providing a balanced environment, thus, securing the residents from disruptions and risks. Besides these magnified advantages, it is found almost full of faints in emergency situations. The researchers and architects put their full potential towards the development of new applications, but no significant attention is paid to analyze the existing designs to identify flaws and suggest enhanced solutions accordingly. To bridge this gap in the literature, this paper presents a comprehensive review to evaluate the capabilities of available smart home designs to counter any emergency situations. Along with highlighting safety, healthcare, and many other unwanted challenges, we also discussed the key problems that obfuscate the trustworthiness of smart homes for its residents. Moreover, the design limitations to present an early alarming and automatic evacuation mechanism especially for deaf, blind, and other visually impaired people is another big challenge to tackle. Finally, we elaborate on the limitations of available smart home solutions and suggest various open research problems that require further development. [ABSTRACT FROM AUTHOR]

The market of courier, express and parcel services (CEP) is characterized by the presence of requests for the transfer of goods and documents. During the COVID-19 pandemic, the global postal and CEP infrastructure played a key role in enabling governments to respond to the emerging challenges. In this paper, the competitiveness and the rank of four observed CEP service providers are analyzed using ratio indicators, the Ordinal Priority Approach method (OPA) and the Weighted Sum Method (WSM). We use the OPA method because there is no need for a pairwise comparison matrix and no need for normalization. The paper aims to examine the liquidity, economy and profitability of CEP service providers to get a true picture of the situation in the market of postal and CEP services in the Serbia. In the analysis of business performance and the implementation of the OPA method, the opinions of experts were taken into account. This paper yields a key insight: the identification of the top-performing provider within the group of service providers is accomplished through the utilization of both the OPA and WSM methods. Sensitivity analysis is shedding light on the varying impact of the provider's rank on the final ranking of the CEP providers. [ABSTRACT FROM AUTHOR]

This paper proposes a new testing method, phased array ultrasonic testing, for the internal defects of gear teeth, which can be used to inspect the teeth of final products, breaking the limitations of incomplete volume coverage of conventional ultrasonic testing. This paper mainly discusses the inspection process of phased array ultrasonic testing, and verifies the feasibility of phased array ultrasonic testing for gear teeth defects of finished products by using physical and chemical inspection technology. The practical application results show that the phased array ultrasonic testing technology uses sector scanning mode to scan the area comprehensively between the pitch circle and the base circle, and the inspection image displayed by the equipment is intuitive, which can clearly identify defects. The number of defects inspected by phased array ultrasonic testing is consistent with the results of the physical and chemical inspection technology. The above summary shows that phased array ultrasonic testing is feasible, and the test results are reliable. [ABSTRACT FROM AUTHOR]

This paper revisits a classical problem in linkage mechanism design, specifically the design of centric crank-rocker mechanisms for a specified rocker stroke with unit time ratio. Beyond the traditional objective of minimizing the transmission angle's deviation from 90 degrees, this paper uses another objective: maximizing the shortest-to-longest link-length ratio in the linkage. The former objective ensures motion transmission quality in the mechanism, while the latter aims to make the mechanism less sensitive to variations in link lengths. It is shown that these two objectives are inherently conflicting, necessitating a trade-off solution. To address this, a Pareto front is developed by analyzing the governing design equations. The front is further normalized by relating each objective to its limit value. A design example using the normalized front is provided that showcases different optimization options. The TOPSIS method is used to illustrate the selection of a preferred design alternative. [ABSTRACT FROM AUTHOR]

In recent times, there has been a growing focus on multi-micro-grids (MMGs) system, owing to its well-suited structures for efficiently accommodating large-scale integration of distributed energy resources (DER). This attention is driven by the system's cost-effectiveness, enhanced efficiency, stability, and reliability performance, achieved through the collaborative exchange of power flow among individual micro-grids (MGs) and the main grid. Fundamental strategies for attaining optimal energy flow and sharing involve the optimal sizing of MGs and the implementation of an Energy Management System (EMS). These strategies play a crucial role in addressing uncertainties associated with intermittent generation, load fluctuations and energy market dynamics. This paper offers a review of grid-connected MMG topologies, EMS structures, coordination methods and current optimization approaches designed to meet EMS objectives. To address the inherent volatilities, the paper introduces various uncertainty quantification techniques along with current challenges. Additionally, it suggests future directions, emphasizing intelligent and predictive modeling to handle uncertainties, as well as recommending the incorporation of energy storage systems (ESSs) to align with emerging trends. [ABSTRACT FROM AUTHOR]

In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

Although corporations have been propelled to adapt eco-innovation strategies including decent work practices, responsible consumption and production, and transition from linear economy to circular economy model to integrate natural ecosystems, businesses, and waste management, inversely, the actual impact of the circular economy implementation (CEI) in Ghana is yet to be felt. This paper aims to analyse the implications of corporate green culture (CGC) on CEI behaviour, and to design a model that explains the mediating roles of environmental innovativeness (EI) and green intellectual capital (GIC) on the relation between CGC and CEI behaviour from the perspective of a lower resource economy context. Our paper is positioned on positivists' reasoning and quantitative research approach. A survey-based structured questionnaire has been used to collect data from 300 manufacturing firms in Ghana. Our hypotheses have been tested with the aid of SMART-PLS and structural equation modelling. The indicative results have shown that CGC positively affect CEI behaviour. Moreover, EI and GIC have significant and positive effects on CEI behaviour, Besides, GIC serves as a medium through which CGC indirectly impacts on CEI behaviour. The implications of the study include the emergence of baseline model to encourage CEI behaviour in Ghana and other Sub-Saharan African countries. The paper recommends that the newly developed model be used by practitioners to explain coordinated economic, ecological and social development, and restore ecological environment. Policymakers are encouraged to facilitate the reinforcement of environmental policies and subsequent realization of Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

Scheduling problems should not only focus on minimizing completion time, but other performances, such as minimizing energy consumption, are urgent to investigate. Many companies and researchers are competing to develop various methods to solve this problem. One problem that has received much attention lately is the energy-efficient Hybrid Flow Shop Scheduling Problem (HFSP). The HFSP problem is also popularly called the flexible flow shop problem. This research provides a comprehensive review of articles that discuss energy-efficient HFSP scheduling to determine the trend of energy-efficient HFSP research and research gaps for future research interests. This review uses a systematic literature review method. The total articles reviewed in this study are 90 articles from January 2008 - December 2022. The articles are classified by year, country, journal/conferences, publisher, objective function, and method. The analysis results show that the trend of energy-efficient HFSP research continues to increase. In addition, the multiobjective problem is the dominating problem in energy-efficient HFSP scheduling problems. The study also presents a gap research analysis and a review of future research directions. [ABSTRACT FROM AUTHOR]

Detecting source code vulnerabilities is an essential issue today. In this paper, to improve the efficiency of detecting vulnerabilities in software written in C/C++, we propose to use a combination of Deep Graph Convolutional Neural Network (DGCNN) and code property graph (CPG). Specifically, 3 main proposed phases in the research method include: phase 1: building feature profiles of source code. At this step, we suggest using analysis techniques such as Word2vec, one hot encoding to standardize and analyze the source code; phase 2: extracting features of source code based on feature profiles. Accordingly, at this phase, we propose to use Deep Graph Convolutional Neural Network (DGCNN) model to analyze and extract features of the source code; phase 3: classifying source code based on the features extracted in phase 2 to find normal source code and source code containing security vulnerabilities. Some scenarios for comparing and evaluating the proposed method in this study compared with other approaches we have taken show the superior effectiveness of our approach. Besides, this result proves that our method in this paper is not only correct and reasonable, but it also opens up a new approach to the task of detecting source code vulnerabilities. [ABSTRACT FROM AUTHOR]

Concrete, a widely used building material, should minimize its environmental impact to align with the construction sector's Sustainable Development Goals. Researchers worldwide are investigating the possibility of e-waste in building materials incorporating various recyclable elements. This paper conducts a thorough review of research on e-waste as a building material. Various E-waste products are used as fine and coarse aggregate replacements in concrete. The study uses a bibliographic approach with the Scopus database to delve into the literature discussing E-waste in construction materials from 2007 to 2022. Data are extracted specifically from Scopus, which identifies 731 papers based on the keyword "Electronic waste used as building materials". The second step involves scientometric analysis, which focuses on patterns within the articles, such as the most prolific countries, sources, frequently used keywords, and reports containing relevant research. Each research phase contains a summary of the results obtained at various stages. Detailed quantitative and qualitative discussions are also conducted to achieve the three primary goals: a summary of quantitative data, a discussion of the existing application, and identifying future research directions. These findings will be useful for subsequent academic studies on transforming e-waste into building materials. The scientometric review provides a path for researchers from various countries to share new ideas and information while encouraging research collaboration. [ABSTRACT FROM AUTHOR]

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into subcategories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models' performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA). [ABSTRACT FROM AUTHOR]

Core loss estimation in switched reluctance motor is a complex task, due to non-linear phenomena and non-sinusoidal flux density waveforms. Several methods have been developed for estimating it (e.g. empirical, and physical-mathematic models), each one with merits and limitations. This paper proposes a new method for core losses estimation based on Finite Element Method Magnetics software. The main idea is using the machine phase-current harmonics as input for estimating core losses. In addition, a comparative study is carried out, where the proposed approach is faced up to a different one, based on Fourier decomposition of the flux density waveforms in the machine sections. In order to systematically analyze and compare the applied estimation cores loss techniques, a case study of a three-phase 6/4 SRM for different simulation scenarios is introduced. The outcomes of both methods are discussed and compared, where core loss convergence is found for limited speed and load ranges. [ABSTRACT FROM AUTHOR]

This paper presents an experimental and numerical investigation of RCC (Reinforced Concrete) piles with helical grooves under axial and lateral loads in cohesionless soil. A set of experiments is carried out to assess the performance of RCC piles with helical grooves. To replicate the experimental findings, the Finite Element Method (FEM) is employed using Abaqus software. Three-dimensional (3D) models are created to represent both the pile and the soil using Abaqus. The paper compares settlement characteristics and lateral displacement of plain RCC piles and RCC piles with helical grooves. This study also evaluates the impact of varying the pitch of helical grooves on enhancing pile performance. The results indicate that RCC piles with helical grooves outperform plain RCC piles in terms of both axial and lateral load-carrying capacity, inferred from both experimental and analytical techniques. [ABSTRACT FROM AUTHOR]

In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users' PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data. [ABSTRACT FROM AUTHOR]

This paper presents a simple, low-profile rectangular microstrip patch antenna for applications in the 28 GHz band. A regular rectangular microstrip patch antenna is initially designed. The antenna is then modified to perfectly resonate at 28 GHz and improve antenna performance in terms of S11, radiation gain, and impedance bandwidth. The antenna is optimized using inset feed to enhance the matching between the radiating patch and the feeding line. Then, a rectangular slit on the front edge of the patch is inserted with altering the antenna's dimensions to reduce its size. The suggested antenna uses a Rogers RT/Duroid-5880 substrate with a dielectric constant of 2.2, a thickness of 0.508 mm, and a loss tangent of 0.0009. The final design is small, measuring 8 x 8.489 x 0.508 mm³ (≈ 0.75λ x 0.79λ x 0.05λ), having a maximum S11 value of - 45 dB and an impedance bandwidth of 1.43 GHz, extending from 27.28 GHz to 28.71 GHz. The radiation gain is nearly consistent over the operating band, with above 8 dBi is achieved. Besides, the proposed antenna achieves a radiation efficiency of about 99% across the band. HFSS is employed for the design, simulations, and optimizations, while CST is used to validate the HFSS simulation findings. The simulation results from both simulators indicate a high level of agreement. [ABSTRACT FROM AUTHOR]

Customer demand fulfilment is essential for production systems in the highly competitive market environment. Because of that fact, a reliable production line is necessary for timely demand fulfilment, and the system must maintain an appropriate spare parts inventory to run the production line smoothly. This research aims to study the impact of spare part inventory policy on production line availability and demand fulfilment. This paper considers a steel pipe manufacturing company that runs continuously. This company uses a continuous raw material flow and produces discrete finished items. The company's production system is multi-state with unique characteristics in which the machines can be operated at partial capacity under certain failures or disturbances. Hence, three states of the production system, namely fail, working under failure, and success (denoted as 0, 0.5, and 1, respectively) are considered. In addition, the company follows s,S policy for its spare parts inventory system. To deal with this problem, a discrete-event simulation approach is developed as the problem environment is complex with interdependency and variability. A multi-level reliability block diagram is also used to build the production system model. The proposed simulation model is run for different scenarios with either cost or performance as the objective measure. Based on the analysis of results, the proposed approach can improve the current operation in terms of both production line availability and demand fulfilment. [ABSTRACT FROM AUTHOR]

The reliability and availability of a Jet Unmanned Aerial Vehicle (JUAV) for balloon downing are evaluated in this third part of the paper. The paper delves into the challenges faced by traditional air defence systems in countering high-altitude balloons, leading to the proposal of substituting missiles with Unmanned Aerial Vehicles (UAVs), specifically the Jet Unmanned Aerial Vehicle (JUAV). The best solution found in the previous section is a derivation of the 1:6 scale RC model of the Lockheed F104C. This is designed for balloon downing, and the essay meticulously analyses its reliability, failure probabilities, and maintenance strategies for various subsystems. The engine is replaced with a more powerful micro-jet, and an 84 mm recoilless cannon is added at the base of the tail. The scaled-down model can use most data derived from the full-scale airplane flight manual, corrected for larger thrust-to-weight and lower wing-loading. Several adjustments and additions are made on sensors, autopilot, On-Line Diagnostic System, communication system, and firing system to increase the reliability of the system. Due to the relatively short design life of the RC model, the Time Between Overhaul (TBO) of the UAV is reduced to 25 h. Consequently, the study emphasizes the importance of simplicity, operational reliability, and adaptability in the development and assessment of modern missile defence systems. The reliability of the JUAVI is presented as 98.14% for 50 consecutive fully automatic missions, highlighting its potential significance in bolstering national security. [ABSTRACT FROM AUTHOR]

Leukemia is a type of cancer that affects the body's blood-forming tissue, where the bone marrow produces an excessive amount of abnormal white blood cells (WBCs) that do not function properly. The diagnosis of leukemia is typically done by a trained expert who visually observes unique features and determines the type of cancer. However, digital image processing techniques have been improving in the healthcare system, particularly in diagnosing different types of diseases and helping doctors make treatment decisions. This paper presents a system for detecting leukemia in blood microscopic images and classifying them as normal or abnormal (with leukemia) automatically. Two main techniques were used: counting the number of WBCs around red blood cells (RBCs) and measuring the average area of WBCs around a bounding box around each cell. The classification accuracy was calculated at 91.7 and 88.8% for the two techniques, respectively. These techniques can be used as features in machine learning applications, and the system presented is faster and more efficient than traditional diagnostic processes used in hospitals. [ABSTRACT FROM AUTHOR]

The stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols. [ABSTRACT FROM AUTHOR]

Most of the generations in Bangladesh power system are based on fossil fuels which is 99% of its total generation. Following other countries, Bangladesh is also planning to adopt renewable energy system (RES). It has a plan to incorporate 55 MW wind turbine generator (WT) and 100 MW solar photovoltaic generator (PV) in Western grid of its power system. Though RES is green energy but from stability aspect it needs to be studied as RES has limitations in generating reactive power and maintaining inertia. So, voltage stability and rotor angle stability should be given more attention. However, if any stability problem arises, flexible alternating current transmission system (FACTS) devices can be adopted. With a tuned power oscillation damper, FACTS can reduce the expected rotor angle and voltage stability problem which are associated with RES. This paper examines the stability of the Western grid with the incorporation of hybrid energy of both wind and solar power and shows the positive effect of FACTS devices like thyristor controlled series compensator and static synchronous compensator with respective tuned controller. In present paper, the combination of WT and PV is also examined which shows only incorporation of WT reduces both the steady and dynamic stability more than the only adoption of PV. Inclusion of both WT and PV, reduce the both types of stability more than their individual inclusion. This paper shows that FACTS can improve the stability with individual or both kinds of RES penetration. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has brought about a profound transformation in the educational landscape in recent months. Educators worldwide have been challenged to tackle academic issues they could never have imagined. Among the most stressful situations faced by students and teachers is implementing online assessments. This paper proposes a system that includes exam prototypes for computer architecture modules at the higher education level. This system generates a wide range of questions and variations on the server side, supported by a set of simulators, resulting in many unique examination proposals. This system streamlines the monitoring process for the teacher, as it eliminates the possibility of two students receiving similar exams and reduces student stress by allowing them to practice with a limitless number of exam samples. This paper also highlights several indicators that demonstrate the advantages of this framework. [ABSTRACT FROM AUTHOR]

This paper reviews the work done on science and technology of aircraft seat ejection with history, present, and futuristic interest. It is topic of interest that is receiving significant attention in the recent years due to importance of the pilot life saving during war, mishaps, and emergencies. In the early days, the ejection seats used the concept of compressed air and simple mechanical springs. The escape of the aircrew from high-speed aircraft is a serious drawback during World War (WW) II. The jumping out of aeronauts from high-speed aircraft is extremely difficult because of high blast wind pressure, turbulence around the exposed cockpit, and accelerations during the aircraft maneuvers. All these systems are dangerous and damage to the vertebra column of an aviator. As the speed of the aircraft is low, the pilot has to jump out with his parachute. However, as the speed of the aircraft increases, jumping out from the disabled aircraft with the parachute is merely a matter of luck and does not guarantee the aeronaut’s survival. Therefore, it is highly essential to use an ejection seat when damage is caused due to either the fighter or trainer plane. The ejection of the pilot is of utmost importance to the services. Hence, the lives of the pilots are crucial than saving military planes. Safeguarding the pilots from disabled aircraft plays a significant role in services such as Indian Air Force and Indian Navy since their introduction. Different policies and regulations are enforced to ensure the survival and safety of pilots. These are considered an essential area in the safety programs. Security and safety are always threat to the pilots. The novelty of this research paper is to bring about the historical developmental concepts to advanced concepts through science and technologyelated aircraft seat ejection. This paper is based on oration presented by the author on the occasion of National Science Day in Feb 2021. Therefore, an attempt is also made to highlight the important contributions made by various scientists, researchers in the frontier areas related to science and technology of ejection seat for military fighter aircraft. [ABSTRACT FROM AUTHOR]

In response to Agenda 2030 and the Paris Climate Accord, the Ghana Energy Transition has been inaugurated to ensure a speedy shift from fossil fuel sources to clean energy (Solar, Wind, Thermal, Ocean Energy, Hydro Energy etc.) to avoid the worst of climate change. To facilitate the transition, we have developed an integrated model to explain the relationship between industry forces and investment intentions in renewable energy resources, and the moderating role of sustainable competitive strategy on the relationship between industry forces and investment intention. Cross-sectional survey data from Ghanaian private firms have been collected between January and February, 2022, and used for the study. To test our hypotheses, structural equation modelling approach and SMART-PLS 3.3.1 have been used. The results have shown that industry forces have significant effects on investment intention. The results have further shown that sustainable competitive strategy has significant relationships with investment intention. Moreover, the sustainable competitive strategy dimension of entrepreneurial competency has moderated the relationship between industry forces and investment intention. These results offer theoretical, policy and practical implications and guidance on strategies needed to attract private investment in renewable energy resources in Ghana and beyond. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution, also known as Industry 4.0, stems from the rapid advancement of digital technologies such as the Internet of Things and Cyber-Physical Production Systems. It has the potential to weave positive changes to firms and impact organizational structure layers. Therefore, it provides an impetus for the collaboration of factories, suppliers, and customers. Nevertheless, due to the difference of Industry 4.0 vision among companies, there is a lack of unified perception and approach of its implementation roadmap. Therefore, many firms in both developed and developing countries that step in the way of digital transformation encounter not only organizational, technological, and operational challenges but are also compelled to cope with a large deal of confusion. Hence, this paper aims to identify the main concepts, characteristics, and technology enablers related to Industry 4.0 to provide stakeholders with a clear understanding of this paradigm. It then clusters and matches the derived concepts and characteristics associated with Industry 4.0. Further, the paper provides an analysis of how these clusters are supported by technology enablers of Industry 4.0, as well as managerial implications. [ABSTRACT FROM AUTHOR]

One of the key aspects of digitalization brought by Industry 4.0 is driven by the Internet of Things. Perhaps, the most interesting component of digital transformation is Augmented Reality. Augmented reality is merging the digital and physical worlds in the same experience. This new technology is accepted and applications are initiated in various areas including manufacturing. The main usage area in manufacturing is planning, execution, and verification of the assembly and maintenance operations. Likewise, augmented reality is used in the training of inexperienced people and to guide them by remote expert support. However, the augmented reality application selection, especially for non-technology savvy manufacturing organizations is challenging. Detailed and time-consuming analyses are required to understand the key features to compare and select the most suitable augmented reality application. In this paper, the most critical nine augmented reality software selection criteria are identified. To help organizations to apply such criteria and make a selection that is most suitable for the enterprise, a multi criteria decision-making approach is suggested. The suggested method is based on a fuzzy spherical number. The complex proportional assessment method is used to calculate the rankings. Therefore, the suggested method is called SF-COPRAS. Moreover, the detailed definition of augmented reality features and terminologies is explained. Several augmented reality use-case scenarios are discussed for manufacturing organizations on their Industry 4.0 initiatives. This paper aims to guide decision makers on their augmented reality software selection journey. The offered framework aims to save time for investigating augmented reality solution features systematically and objectively. [ABSTRACT FROM AUTHOR]

This paper aims to provide a model that combines reconfiguration with Unit Commitment (UC) and analytically examine Storage Capacity Increment Effects (SCIEs) in Micro-Grid (MG) operation. The case study includes batteries as the storage system and a conventional 10-bus MG with Wind Turbine (WT) and Micro-Turbines (MTs) as energy sources. The load demand and energy estimation of the Wind Unit (WU) with respect to wind speed changes are considered uncertain parameters. Additionally, a newly introduced algorithm and an objective function based on MG's day-ahead benefit are employed to tackle the problem. According to Monte Carlo Simulation (MCS), a few scenarios are created for modeling uncertainties, and the MG's optimal operation is examined under these situations. This study is based on two cases: the first examines the MG's scheme with just one battery, and the second investigate SCIEs. This paper seeks to maximize MG's benefit and optimize power exchange with increasing storage capacity. The statistical analysis results show that the proposed strategy can offer more cost-effectiveness, reliability, and power quality, though challenges remain. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA's parameters by combining exploration and exploitation strategies while preserving the algorithm's core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm's core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems. [ABSTRACT FROM AUTHOR]

This paper presents the design of a novel trajectory tracking control strategy and the development of a mathematical model for a non-holonomic differential-drive wheeled mobile robot. The proposed control system utilizes a dual-loop approach, where the inner loop controls the dynamics by employing Adaptive Fuzzy Sliding Mode Control (AFSMC), and the outer loop, handles kinematics by utilizing an Adaptive Neuro-Fuzzy Inference System ;(ANFIS). The ANFIS is employed to minimize the error between the actual and desired velocities, providing a desired input for the inner loop. Meanwhile, the AFSMC is used to effectively control the system dynamics. The use of these dual-loop controllers considerably improves the system's overall efficiency. The inner controller compensates for dynamic disturbances, while the outer controller manages velocity errors. We integrate the actuator dynamics and the chopper effect of the wheels in the dynamics modeling, which helps to increase the models accuracy. MATLAB was used to implement the controller, while circular and eight-shaped trajectories were generated to assess the performance of the proposed controller. In addition, a comparative analysis of different controllers such as PID, SMC, AFSMC, and AFSMC with ANFIS was presented. The simulations were conducted under uncertainties, and the proposed controller is better than other controllers at tracking desired trajectories. The Lyapunov stability analysis is employed to verify the stability of the proposed controller. This paper shows that the proposed dual-loop controller is stable and more robust to internal parameter variation and external disturbance for the examined system. In general, the AFSMC with ANFIS is superior in trajectory tracking for the examined system compared to other controllers. [ABSTRACT FROM AUTHOR]

Stroke presents a formidable global health threat, carrying significant risks and challenges. Timely intervention and improved outcomes hinge on the integration of Explainable Artificial Intelligence (XAI) into medical decision-making. XAI, an evolving field, enhances the transparency of conventional Artificial Intelligence (AI) models. This systematic review addresses key research questions: How is XAI applied in the context of stroke diagnosis? To what extent can XAI elucidate the outputs of machine learning models? Which systematic evaluation methodologies are employed, and what categories of explainable approaches (Model Explanation, Outcome Explanation, Model Inspection) are prevalent We conducted this review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search encompassed five databases: Google Scholar, PubMed, IEEE Xplore, ScienceDirect, and Scopus, spanning studies published between January 1988 and June 2023. Various combinations of search terms, including "stroke," "explainable," "interpretable," "machine learning," "artificial intelligence," and "XAI," were employed. This study identified 17 primary studies employing explainable machine learning techniques for stroke diagnosis. Among these studies, 94.1% incorporated XAI for model visualization, and 47.06% employed model inspection. It is noteworthy that none of the studies employed evaluation metrics such as D, R, F, or S to assess the performance of their XAI systems. Furthermore, none evaluated human confidence in utilizing XAI for stroke diagnosis. Explainable Artificial Intelligence serves as a vital tool in enhancing trust among both patients and healthcare providers in the diagnostic process. The effective implementation of systematic evaluation metrics is crucial for harnessing the potential of XAI in improving stroke diagnosis. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive survey of the state-of-the-art use of Artificial Intelligence (AI) and Machine Learning (ML) in the field of cybersecurity. The paper illuminates key applications of AI and ML in cybersecurity, while also addressing existing challenges and posing unresolved questions for future research. The paper also emphasizes the ethical and legal implications associated with their implementation. The researchers conducted a thorough survey by reviewing numerous papers and articles from respected sources such as IEEE, ACM, and Springer. Their focus centered on the latest AI and ML breakthroughs in cybersecurity, while also exploring current challenges and open research questions. The results indicate that integrating AI and ML into cybersecurity systems shows great potential for future research and development. Intrusion detection and response, malware detection, and network security are among the most promising applications identified. According to the survey, 45% of organizations have already implemented AI and ML in their cybersecurity systems, while an additional 35% plan to do so. However, 20% of organizations believe that it is not yet the right time for adopting these technologies. Overall, this paper serves as a reliable reference for researchers and practitioners in the field of cybersecurity, offering a comprehensive overview of the use of AI and ML. It not only highlights the potential applications but also addresses the challenges and research gaps. Additionally, the paper raises awareness about the ethical and legal considerations associated with leveraging AI and ML in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

This paper proposes three different models for investigating the long-term performance of routine maintenance works using the discrete-time Markov model. The three models are called (M1, M2, and M3), which represent specific forms of the transition probability matrix. The (M1) model only incorporates the routine maintenance variables (Mi,i-1) under the assumption of only one-state upgrade. The (M2 and M3) models incorporate both routine maintenance variables (Mi,i-1), and major rehabilitation variables (Qi,1) under the assumption of upgrade to condition state (1). Two different modeling methods are proposed for pavement long-term performance prediction in the presence of maintenance and rehabilitation (M&R) variables. The first one is the project-level approach as applied to small pavement networks which assumes that all M&R works can be performed in a short-time period. The second one is the network-level approach which requires the M&R works to be spanned over the entire year as applied to large pavement networks. A model cost-effectiveness index is proposed to evaluate the long-term performance of potential M&R schedules under an unconstrained annual budget. In contrast, M&R variable cost-effectiveness indices are proposed as useful parameters for yielding optimal M&R schedules under a constrained annual budget. The sample results indicated the unrealistic performance of the (M1) model and the usefulness of the (M2 and M3) models in yielding reliable long-term M&R schedules. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to utilizing open-source legal databases in academic education, especially in the fields of law and police investigations. Our framework provides a way to organize and analyze this data and extract reports that are associated with crime scenes, addressing the challenge of classifying unstructured legal documents by using text mining, natural language processing, and machine learning techniques. We developed a supervised machine learning model capable of accurately classifying court documents based on two classifiers: one identifies the documents containing crime scenes, and the other classifies them into five types of crimes. The experimental results were promising, as the random forest algorithm achieved an accuracy of 91.07% for the first classifier and support vector machines achieved an accuracy of 82.46% for the second classifier. What distinguishes our work is the creation of a crime dictionary that includes 70 crime tools and 151 related terms extracted from various forensic sources. It is considered relatively small, but it contributed to giving good classification results. The proposed crime dictionary can be generalized, developed, used in advanced searches, and integrated with police databases to improve crime scene analysis. Overall, the research highlights the use of court databases in police academic education and attempts to utilize them in a more effective manner. [ABSTRACT FROM AUTHOR]

Most manufactured electrical cables suffer from reductions in their physical, mechanical and electrical properties. These setbacks are mainly attributed to the improper control of wire tension during the cable manufacturing process. Hence, this paper systematically reviewed different control algorithms involved in controlling tension in moving webs, which include conventional control, advanced control, observer-based control, artificial intelligence-based control and hybrid control techniques. Thus, the review provided information about existing tension control techniques in moving webs, including their strengths and weaknesses. It was observed in this review that although a significant research effort has been made on web tension control systems, a thorough literature review is still lacking. It was concluded that controller optimisation using hybrid control algorithms is gaining popularity in web tension control due to their improved control response. Hence, its application in wire tension control can better help cable manufacturers improve the quality of manufactured cables. [ABSTRACT FROM AUTHOR]

The intense competition in the field of new shipbuilding business today requires shipyards to increase their productivity in terms of quality, cost, on-time delivery performance, and flexibility. Product-oriented Work Breakdown Structure (PWBS)-based steel ship production technology has played a vital role in the shipyard's success in competition. A block and modular assembly approach using the PWBS method was prepared to guide shipyards. This paper presents a study on the technology capability of four selected Indonesian medium-sized shipyards and one leading shipyard as the benchmark in using the PWBS method. The technology capability in shipbuilding is measured using the technometric assessment model by the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), which consists of four indicators; technoware, humanware, infoware, and orgaware. The results show that the shipyards' Technology Coefficient Contribution (TCC) is between 0.5 and 0.7 (TCC maximum 1.0). This value indicates that the four shipyards in this case study are capable of building ships based on PWBS method. However, some aspects have to be improved to ensure the method is implemented properly, including: better design software that could prepare comprehensive production engineering documents, production lane with more automation, and more crane capacity. Of course, large additional investments must be supported by continuous orders for new ships with relatively the same type and size of ships, so that they are more productive and gradually able to compete in the global market. [ABSTRACT FROM AUTHOR]

This paper gives an investigation into the morphological analysis of boron carbide and the mechanical properties of the Jutton-Glass hybrid polymer nanocomposites. As reinforcements, hybrid nanoparticles comprising boron carbide and graphene nanoplatelets were used. For laying the composites, the vacuum bagging technique was used with the composition of hybrid nanomaterial taken as 0, 0.25 and 0.5 wt.% of Gr and B4C, and the samples were tested for mechanical properties as per ASTM standards. The samples prepared have been subjected to various tests, and the results are reported. Before the preparation of composites, the polymer was mixed with surface-modified nanomaterial and stability tests were conducted to assess uniform dispersion with the aid of UV spectroscopy, and the outcomes showed that the samples had been exceptionally uniform over some time. It is determined that the mechanical behavior of hybrid composites, which include B4C with Gr, shows better properties than base, B4C, and Gr. However, the multi-layered samples with graphene nanoplatelets also showcased encouraging mechanical properties like hardness and tensile strength. [ABSTRACT FROM AUTHOR]

This paper describes deep neural network (DNN) models based on hyperparameter optimization for the prediction of the compressive strength of concrete. The novelty of this research lies in the implementation of optimized hyperparameters to train the DNN models with the aim to enhance their predictive accuracy. Utilizing the Keras Tuner library, the most effective hyperparameters for the DNN models were identified. These models are then trained and evaluated using an experimental dataset of 1030 instances, encompassing nine quantitative variables that include various concrete mix ingredients and age. The target variable for all models is the compressive strength of concrete. The assessment of model performance, based on metrics like mean squared error (MSE) and R² values, was conducted on previously unseen test data. The optimal configuration for the hidden layers in the model was identified as five layers, containing 12, 16, 16, 40, and 26 neurons, respectively. The optimal learning rate for the model was 0.001. With this set of optimal hyperparameters, the best DNN model achieved an MSE of 28.76 and an R² value of 0.89 on the testing data. The results demonstrate a significant improvement in the DNN models' performance when trained with the optimized hyperparameters. In comparison to the regression model, the performance of the DNN models was significantly better. Adopting the predictive models developed in this research offers the potential for substantial cost and time savings by circumventing the need for labour-intensive and time-consuming laboratory tests. [ABSTRACT FROM AUTHOR]

Studies reviewed in this paper show anomaly for temperature pertaining to streamflow and rainfall showing different trends, especially in Ethiopia to support the research findings and interpretation. There are many hydrological models, including 54 physically distributed, lumped, and conceptual hydrological models, of which 28 have been used in Ethiopian river basins. The models include the most adaptable and commonly used SWAT model applicable from small areas up to large basins. It is indeed a challenge to use a single hydrological model as the data rely on consistency, limitation-free, and exactly fitted output. The overall performance of individual physically-based, conceptual, and machine learning (ML) models varied at different watersheds. Reasonably, ML performs very well, up to 0.99 for R² and NSE and up to 0.001 for PBIAS. Inopportunely, using a single hydrological model has its limitations; ensemble multi-individual models, coupling or hybridization of physical or conceptual models with machine learning, combining evolutionary optimization algorithms with ML, and also comparisons of multi-single hydrological models, and selecting the best one are recommended options. No single hydrological model is indispensable and can be termed as better than the other for any watershed. Somewhat, ML outperforms SWAT but cannot be considered an absolute substitute. The size of the watershed, the number of data used, and the ratio between calibrations year to validation year do not have a clear correlation with the performance, particularly for the SWAT model accounted for in this review. Optimization algorithms explore multiple options and choosing the right one is a tedious task before a final decision is taken. [ABSTRACT FROM AUTHOR]

In this paper, some exact solutions of the stochastic generalized nonlinear shallow water wave equation are investigated. This equation is important in fluid mechanics' fields since it can model the propagation of disturbances in water and other incompressible fluids. Opposite to what is usually considered in the literature, the two dispersion coefficients of the nonlinear terms are considered dependent random quantities as a more realistic case. The modified extended-tanh function (METF) method is combined with the random variable transformation (RVT) technique to get full probabilistic solutions of the problem via computing the probability density functions (PDFs) of the solution processes. Based on the probability density function, any statistical moment of the solution can be evaluated. Through two different applications for the input random variables (dispersion coefficients), my findings are applied efficiently. Finally, numerical results are presented graphically along the spatial dimension at a certain wave speed and time. The obtained results ratify that the proposed technique is efficient and powerful for obtaining analytical probabilistic solutions for the problem. [ABSTRACT FROM AUTHOR]

Deepfakes are a rapidly growing technological trend that is revolutionizing many digital processes, from media production to digital access. They offer various advantages for both users and creators alike. For example, they can provide entertainment value by allowing users to create humorous or satirical versions of existing media content without having to film new scenes. They can also automate production workflows by generating background characters or extras for movies. Additionally, deepfakes could be used in healthcare applications, such as virtual avatars for doctors who require assistance during consultations with patients located far from hospitals or clinics. However, deepfakes also pose a threat to society as they can be used for malicious purposes, including spreading false information or exploiting the likenesses of others for financial gain. To address these concerns, various types of regulations have been introduced across jurisdictions worldwide. These laws prohibit doctored audio recordings and visual materials aimed at influencing election outcomes. Some countries have even gone as far as requiring companies producing deepfake materials to identify themselves as such, to make audiences aware that what they are seeing may not always be authentic representations of events. In this paper, the author will discuss what deepfakes are, their potential benefits and risks, the various types of deepfakes that exist, and how the USA has attempted to regulate them. The author will also suggest some policy recommendations aimed at mitigating the risks associated with deepfakes while still preserving their creative potential. [ABSTRACT FROM AUTHOR]

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable. [ABSTRACT FROM AUTHOR]

A drastic decline in the number of students that are enrolled for Engineering is now being experienced in developed as well as developing countries. Learning is becoming boring as a generation brought up on technology is losing the ability to pay attention in traditional classes for a long stretch of time. This has led to the idea that other teaching methods do exist and can be applied in teaching/learning. This work leveraged the use of Digital Game-Based Learning (DGBL) in Engineering classrooms to develop a serious game named LogicHouse Version 1 (LogicHouse-V1 or LogicHouse for short). The game, is a web-based serious game prototype that targets selected topics in Digital Electronics course. This course is a core component of undergraduate curriculum in Electrical & Electronics Engineering, Computer Engineering, Information Technology, Communication Engineering, Computer Science and other related Science Technology Engineering and Mathematics (STEM) courses. LogicHouse involves a virtual broken house where the player has to play the various levels to fix the house and gain points along the way. This game was designed based on the Learning Mechanics-Game Mechanics (LM-GM) model and the Unified Modeling Language (UML). Furthermore, the design was implemented using Adobe Illustrator, Procreate, Unity Engine, C#, Microsoft Azure Playfab and deployed on itch.io. Preliminary evaluation results indicate that students are interested in the game and its application. Based on this, there is a prospect of improved performance in any course in which the game is implemented. [ABSTRACT FROM AUTHOR]

The non-centrifugal cane sugar (panela) subsector stands out in Colombia as one of the most important agro-businesses, with economic growth and projection for the export of its products. For its part, the application of the Lean-Kaizen philosophy has proven to be effective for organizations to achieve continuous improvement in quality and productivity. This paper exposes the implementation of the pillars of Kaizen for the improvement of the panela manufacturing process applied in an artisan mill (Trapiche) in the Santander region, Colombia. Through the case study methodological approach, a Trapiche was selected and its production process was studied in terms of waste, order and cleanliness, and standardization. The results evidenced the types of waste that occur throughout the panela production process, the current state of order and cleanliness, and the way in which the operations are carried out to obtain the finished product. With the implementation housekeeping program (5S) and the standardization of the operational procedures, the main waste, sources of dirt and the reduction of the time used for the manufacture of panela were eliminated. The Kaizen actions improved the order and cleanliness of the Trapiche by 63% and the panela manufacturing time by 10%, complying with product quality standards. [ABSTRACT FROM AUTHOR]