Search Results (25)

Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose a deep learning-based framework to automatically detect and quantitatively assess road damage using high-resolution pre- and post-disaster satellite imagery. To achieve this, the study systematically compares three distinct change detection approaches: single-timeframe overlay, difference-based segmentation, and Siamese feature fusion. Experimental results, validated over multiple runs, show the difference-based model achieved the highest overall F1-score (0.594 ± 0.025), surpassing the overlay and Siamese models by approximately 127.6% and 27.5%, respectively. However, a key finding of this study is that even this best-performing model is constrained by a low detection recall (0.445 ± 0.051) for the ‘damaged road’ class. This reveals that severe class imbalance is a fundamental hurdle in this domain for which standard training strategies are insufficient. This study establishes a crucial benchmark for the field, highlighting that future research must focus on methods that directly address class imbalance to improve detection recall. Despite its quantified limitations, the proposed framework enables the visualization of damage density maps, supporting emergency response strategies such as prioritizing road restoration and accessibility planning in disaster-stricken areas. Full article

Purpose: This study explores the impact of take-off distance on hurdling and interval running kinematics in sprint hurdles, recognizing its potential to improve performance. While beginners often use shorter take-off distances, a deeper understanding could inform coaching strategies aimed at improving hurdle technique. Methods: Ten male elite and highly trained hurdlers ran 60 m hurdles under original, short, and long take-off distances (OTD, STD, and LTD, respectively). The sagittal plane kinematics of the fourth hurdle and interval running were obtained using two high-speed cameras at a rate of 120 frames per second. Intraindividual step parameters were compared between conditions. Results: Running speed and step frequency were significantly lower in the STD than in the OTD and LTD. Significant interactions were found for step length with a significantly longer recovery step length in the STD than in the LTD. Furthermore, the hurdling distance was significantly longer in the LTD than in the OTD. In addition, the touchdown distance was significantly shorter in the LTD and longer in the STD compared to the OTD. Therefore, an STD is associated with a shorter acceleration distance between hurdles, whereas an LTD is associated with a longer acceleration distance. Therefore, the take-off distance influenced the distance for acceleration between hurdles, and the recovery step was related to the take-off distance. Conclusions: STD has negative effects on hurdling and interval running, even among elite and highly trained hurdlers. Full article

Systemic lupus erythematosus (SLE) is a systemic autoimmune disease affecting all organ systems. The disease preferentially affects females of childbearing age. It runs a variable course. It may run a mild course that may never lead to severe disease and manifestations from critical organ systems. However, it may also run an undulating course with periods of mild and severe disease. It may run as a mild disease, quickly deteriorating to severe disease and affecting multiple organ systems. Various immune pathways related both to the innate and adaptive immune response are involved in the pathogenesis of SLE. Various drugs have been developed targeting cellular and molecular targets in these pathways. Interferons are involved in the pathogenesis of SLE, and various drugs have been developed to target this pathway. T and B lymphocytes are involved in the pathophysiology of SLE. Various treatment modalities targeting cellular targets are available for the treatment of SLE. These include biologic agents targeting B lymphocytes. However, some patients have disease refractory to these treatment modalities. For these patients, cell-based therapies may be used. Hematopoietic stem cell transplantation involving autologous cells is an option in the treatment of refractory SLE. Mesenchymal stem cells are also applied in the treatment of SLE. Chimeric antigen receptor (CAR)-T cell therapy is a novel treatment also used in SLE management. This novel treatment method holds major promise for the management of autoimmune diseases and, in particular, SLE. Major hurdles to be overcome are the logistics involved, as well as the need for specialized facilities. This review focuses on novel treatment modalities in SLE targeting cellular and molecular targets in the immune system. Full article

The 2019 coronavirus (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has significantly impacted human lives, overburdened the healthcare system, and weakened global economies. The lack of specific drugs against SARS-CoV-2 is a significant hurdle toward the successful treatment of COVID-19. The SARS-CoV-2 Main protease (M^pro) is considered an appealing target because of its role in replication in host cells. Plant-derived natural compounds are being largely tested for their efficacy against COVID-19 targets to combat SARS-CoV-2 infection. To discover hit compounds that can be used alone or in combination with repositioned drugs, we curated a set of 224,205 natural product structures from the ZINC database and virtually screened it against COVID-19 M^pro. Sequential docking protocols involving different levels of exhaustiveness were performed to screen a library of natural compounds. The final 88 compounds were selected and post-processed using the MM-GBSA analysis for the generation of binding free energies. The top four compounds (ZINC000085626103, ZINC000085569275, ZINC000085625768, and ZINC000085488571) showed higher affinity against the COVID-19 M^pro enzyme selected for MD simulation studies. The RMSD, RMSF, and RoG analysis of all four compound–protein complexes indicated absolute stability during a 100 ns MD run. Furthermore, the post-MD simulation binding free energies were calculated for all four compounds and were found to be in the range of −38.29 to −18.07 kcal/mol. The in silico virtual screening results suggested that the selected natural compounds have the potential to be developed as a COVID-19 M^pro inhibitor and can be explored further for experimental research to evaluate the in vitro and in vivo efficacy of these compounds for the treatment of COVID-19. Full article

This paper focuses on presenting an accurate, stable, efficient, and fast pseudospectral method to solve tempered fractional differential equations (TFDEs) in both spatial and temporal dimensions. We employ the Chebyshev interpolating polynomial for g at Gauss–Lobatto (GL) points in the range $[-1,1]$ and any identically shifted range. The proposed method carries with it a recast of the TFDE into integration formulas to take advantage of the adaptation of the integral operators, hence avoiding the ill-conditioning and reduction in the convergence rate of integer differential operators. Via various tempered fractional differential applications, the present technique shows many advantages; for instance, spectral accuracy, a much higher rate of running, fewer computational hurdles and programming, calculating the tempered-derivative/integral of fractional order, and its spectral accuracy in comparison with other competitive numerical schemes. The study includes stability and convergence analyses and the elapsed times taken to construct the collocation matrices and obtain the numerical solutions, as well as a numerical examination of the produced condition number $\kappa \left(A\right)$ of the resulting linear systems. The accuracy and efficiency of the proposed method are studied from the standpoint of the $L^2$ and $L^{\infty }$-norms error and the fast rate of spectral convergence. Full article

Plastic contamination in marketable cotton bales, predominantly from plastic wraps used in John Deere round module harvesters, poses a significant challenge to the U.S. cotton industry. Despite rigorous manual efforts, the intrusion of plastic into the cotton gin’s processing system persists. We have developed a machine-vision detection and removal system aimed at mitigating this problem. This system employs inexpensive color cameras to detect plastic on the gin-stand feeder apron and subsequently removes it, reducing contamination. This system, built around low-cost ARM computers running Linux, comprises 30–50 machines and requires considerable effort to calibrate and tune. Moreover, its operation represents a technological challenge to typical cotton gin workers. This research presents a solution to this calibration operational hurdle by introducing an auto-calibration algorithm that has potential to simplify the system into a plug-and-play device. The auto-calibration system is designed to dynamically track the cotton color and utilizes frequency statistics to avoid plastic images that could compromise the system’s performance if incorporated into the auto-calibration process. We detail the design of the auto-calibration algorithm, which is expected to significantly reduce the setup overhead and facilitate the system’s continuous use. This innovation minimizes the need for skilled personnel and, therefore, is expected to expedite the system’s adoption across the cotton ginning industry. Full article

A longstanding question in science and religion is whether standard evolutionary models are compatible with the claim that the world was designed. In The Compatibility of Evolution and Design, theologian E. V. Rope Kojonen constructs a powerful argument that not only are evolution and design compatible, but that evolutionary processes (and biological data) strongly point to design. Yet Kojonen’s model faces several difficulties, each of which raise hurdles for his understanding of how evolution and design can be harmonized. First, his argument for design (and its compatibility with evolution) relies upon a particular view of nature in which fitness landscapes are “fine-tuned” to allow proteins to evolve from one form to another by mutation and selection. But biological data run contrary to this claim, which poses a problem for Kojonen’s design argument (and, as such, his attempt to harmonize design with evolution). Second, Kojonen appeals to the bacterial flagellum to strengthen his case for design, yet the type of design in the flagellum is incompatible with mainstream evolutionary theory, which (again) damages his reconciliation of design with evolution. Third, Kojonen regards convergent evolution as notable positive evidence in favor of his model (including his version of design), yet convergent evolution actually harms the justification of common ancestry, which Kojonen also accepts. This, too, mars his reconciliation of design and evolution. Finally, Kojonen’s model damages the epistemology that undergirds his own design argument as well as the design intuitions of everyday “theists on the street”, whom he seeks to defend. Thus, despite the remarkable depth, nuance, and erudition of Kojonen’s account, it does not offer a convincing reconciliation of ‘design’ and ‘evolution’. Full article

Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential. Full article

Maintaining power consumption has become a critical hurdle in the manufacturing process as CMOS technologies continue to be downscaled. The longevity of portable gadgets is reduced as power usage increases. As a result, less-cost, high-density, less-power, and better-performance memory devices are in great demand in the electronics industry for a wide range of applications, including Internet of Things (IoT) and electronic devices like laptops and smartphones. All of the specifications for designing a non-volatile memory will benefit from the use of memristors. In addition to being non-volatile, memristive devices are also characterized by the high switching frequency, low wattage requirement, and compact size. Traditional transistors can be replaced by silicon substrate-based FinFETs, which are substantially more efficient in terms of area and power, to improve the design. As a result, the design of non-volatile SRAM cell in conjunction with silicon substrate-based FinFET and Metal Insulator Metal (MIM) based Memristor is proposed and compared to traditional SRAMs. The power consumption of the proposed hybrid design has outperformed the standard Silicon substrate FinFET design by 91.8% better. It has also been reported that the delay for the suggested design is actually quite a bit shorter, coming in at approximately 1.989 ps. The proposed architecture has been made significantly more practical for use as a low-power and high-speed memory system because of the incorporation of high-K insulation at the interface of metal regions. In addition, Monte Carlo (MC) simulations have been run for the reported 6T-SRAM designs in order to have a better understanding of the device stability. Full article

This study focuses on highlighting the major effects and challenges being faced in the implementation of the green roof technique in Riyadh, Saudi Arabia. Green roofs have proven to be energy efficient, environment friendly, and economical in a long run. Due to the increasing global environment temperature, it has become necessary to implement such sustainable methods that help in the achievement of urban sustainability. Saudi Arabia has seen some reluctance in the implementation of green roofs in buildings. The reasons for not adopting this system have not been reported as yet. To study the level of awareness among the public and the challenges they are facing regarding green roofs, this study was taken up. A survey questionnaire was designed with a high level of flexibility covering the key issues, including the related areas that are affected in the daily life of a resident and also the challenges faced by the general public in the installation of such systems in their existing or new buildings. An extensive literature review and a reconnaissance survey were performed before shortlisting the major factors and challenges to be included in the survey questionnaire. An overwhelming response was received from the people of Riyadh City. Almost 94% of people agreed to the fact that green roofs enhance the aesthetics of the building, and the same number of people agreed that they play a role in controlling the air quality. On the other hand, 91% of the respondents identified the climate of the area as the biggest challenge in implementing green roofs on the buildings. The study concludes with strong recommendations for the local authorities to plan quick actions. The study shall help the building owners, city planners, and policy makers in identifying the major hurdles being faced by the residents in adopting green roofs and will help them to provide solutions to these issues. Full article

Objective: We aimed to describe hamstring muscle injury (HMI) history and hamstring specific training (HST) in elite athletes. A secondary aim was to analyse the potential factors associated with in-championships HMI. Methods: We conducted a prospective cohort study to collect data before and during the 2018 European Athletics Championships. Injury and illness complaints during the month before the championship, HMI history during the entire career and the 2017–18 season, HST (strengthening, stretching, core stability, sprinting), and in-championship HMI were recorded. We calculated proportions of athletes with HMI history, we compared HST according to sex and disciplines with Chi2 tests or ANOVA, and analysed factors associated with in-championship HMI using simple model logistic regression. Results: Among the 357 included athletes, 48% reported at least one HMI during their career and 24% during the 2017–18 season. Of this latter group, 30.6% reported reduced or no participation in athletics’ training or competition at the start of the championship due to the hamstring injury. For HST, higher volumes of hamstring stretching and sprinting were reported for disciplines requiring higher running velocities (i.e., sprints, hurdles, jumps, combined events and middle distances). Five in-championship HMIs were recorded. The simple model analysis showed a lower risk of sustaining an in-championships HMI for athletes who performed more core (lumbo-pelvic) stability training (OR = 0.49 (95% CI: 0.25 to 0.89), p = 0.021). Conclusions: Our present study reports that HMI is a characteristic of the athletics athletes’ career, especially in disciplines involving sprinting. In these disciplines, athletes were performing higher volumes of hamstring stretching and sprinting than in other disciplines. Further studies should be conducted to better understand if and how HST are protective approaches for HMI in order to improve HMI risk reduction strategies. Full article

H₂ is a low-impact energy carrier, which the EU hydrogen strategy has positioned as a major component of energy policy. Dark fermentation by psychrophilic bacteria is a promising avenue of H₂ production, though one that requires further study. The aim of this study was to determine the H₂ production performance of a Bacteroides vulgatus strain during fermentation of psychrophilic cattle slurry. The test strain was isolated from an inland water body at a depth of 40 ± 5 m. The experimental fermentation process was run at 15 ± 1 °C and yielded 265.5 ± 31.2 cm³ biogas/g COD removed, including 46.9 ± 2.6 cm³ H₂/g COD removed. CO₂ was the main constituent of the resultant biogas, at 79.8 ± 1.9%. The gas also contained 17.6 ± 1.4% H₂ and 2.3 ± 0.2% CH₄. Organic matter removal and nutrient take-up from the feedstock were low. Our findings show that practical applicability of this process is hampered by multiple operational hurdles and its relatively poor performance. Full article

The operation of a variety of natural or man-made systems subject to uncertainty is maintained within a range of safe behavior through run-time sensing of the system state and control actions selected according to some strategy. When the system is observed from an external perspective, the control strategy may not be known and it should rather be reconstructed by joint observation of the applied control actions and the corresponding evolution of the system state. This is largely hurdled by limitations in the sensing of the system state and different levels of noise. We address the problem of optimal selection of control actions for a stochastic system with unknown dynamics operating under a controller with unknown strategy, for which we can observe trajectories made of the sequence of control actions and noisy observations of the system state which are labeled by the exact value of some reward functions. To this end, we present an approach to train an Input–Output Hidden Markov Model (IO-HMM) as the generative stochastic model that describes the state dynamics of a POMDP by the application of a novel optimization objective adopted from the literate. The learning task is hurdled by two restrictions: the only available sensed data are the limited number of trajectories of applied actions, noisy observations of the system state, and system state; and, the high failure costs prevent interaction with the online environment, preventing exploratory testing. Traditionally, stochastic generative models have been used to learn the underlying system dynamics and select appropriate actions in the defined task. However, current state of the art techniques, in which the state dynamics of the POMDP is first learned and then strategies are optimized over it, frequently fail because the model that best fits the data may not be well suited for controlling. By using the aforementioned optimization objective, we try to to tackle the problems related to model mis-specification. The proposed methodology is illustrated in a scenario of failure avoidance for a multi component system. The quality of the decision making is evaluated by using the collected reward on the test data and compared against the previous literature usual approach. Full article

The alarming rise of food waste all over the world due to population and economic growth must be tackled with better waste management and treatment methods. The current practice of landfilling has been scientifically proven to adversely impact environmental and societal health. A relatively new technology called hydrothermal carbonization (HTC) has the potential to solve this problem. It takes in high-moisture-content material, like food waste, and converts it into bio-coal with a heating value similar to normal coal. The present study explored the feasibility of HTC technology and bio-coal production in Malaysia. An in-depth study via desk research was conducted by implementing Porter’s five forces analysis to evaluate the feasibility of the bio-coal production project. A survey involving 215 respondents from different households that represent the average demography of Malaysia was also conducted to understand the behaviors and attitudes of different households towards food waste. The present study found that a typical Malaysian household disposes mostly of meal leftovers, with an average of 926 g of food waste per day. In addition, the 3 highest food categories that were disposed of were rice or noodles or pasta (13.0%), vegetables (12.2%) and curry and soup (10.1%). Meal leftovers such as curry and soup are high in moisture content, which is suitable for HTC. The survey on household waste provided adequate information to support the availability of a sufficient quantity of food waste in the country to sustain the raw material for the bio-coal project in Malaysia. Furthermore, a consumer survey involving seven industrial firms was conducted to determine the potential buyers of bio-coal. The responses from the industrial firms show that a bio-alternative for coal is important, and they are willing to transition to greener technologies. However, five out of seven firms stated that the main hurdle in adopting bio-coal is the high cost of production and incompatibility with existing industrial processes. Finally, interviews were conducted with key players in the industry to evaluate the adoptability of bio-coal into the wider market. The findings from the desk research and the primary research show that the outlook for bio-coal in the market is quite positive. In the long run, HTC is certainly profitable, but for immediate benefits, adequate government support and policy in favour of the use of HTC bio-coal in power plants are required. Full article

The main objective of the study was to assess the pacing strategy of running 400 m hurdles of the world-level female athletes over the past 40 years based on the functional asymmetry -temporal and spatial characteristics. The data were collected from 1983 to 2019 using the review of scientific literature. Over the 35 years of the study, 37 top-level competitions with 283 finalists-competitors were included. The analysis of the 400 m hurdle covered mainly spatial and temporal factors of the run, related to those technical skills, the level of motor skills, and somatic structure. In addition to the basic statistics, the ANOVA analysis of variance, regression analysis, Pearson correlation, the principal component analysis (PCA), and Kaiser’s criterion was used for the multivariate analysis. The final result in the 400 mH run is determined not by the simple sum of the individual temporal and/or spatial characteristics of the run (the number of steps, the type of attacking leg, but their interaction in the area of functional asymmetry. The decisive factor in the 400 mH run strategy is the second curve, where the emphasis is on the optimal setting of the stride pattern in the context of minimizing the loss of running speed. Additionally, the application of multidimensional statistical methods is a valuable tool that allows to significantly deepen the interpretation of the obtained results, and thus optimize a strategy for a 400 mH run. Full article