Miracle-dz/UniFAR-FTUs huggingface dataset:
ASSESSMENT OF AIR POLLUTION TOLERANCE INDEX OF CERTAIN PLANTS GROWN ALONGSIDE PARWANOO- SOLAN NATIONAL HIGHWAY OF HIMACHAL PRADESH

The present investigation entitled “Assessment of air pollution tolerance index of cer tain plants grown alongside Parwanoo-Solan National High way of Himachal Pradesh” was conducted during 2011-2012 on Parwanoo to Solan National High way falling in Solan district of Himachal Pradesh. The Four species namely Grewia optiva, Toona ciliata, Melia azedarach and Woodfordia floribunda of uniform size, age, spread and common in occurrence o both sides of the highway were selected for the study. Dust accumulation and biochemical parameter s (pH of leaf extract, relative water content, ascorbic acid content and chlorophyll content) of l eaves of selected plants was estimated during three main seasons (Rainy, Winter and Summer) of the year . Air pollution tolerance Index (APTI) of selected species was worked out. The heavy metal concentrati on on deposited dust was also determined. Samples were collected from two horizontal distances (0-5 m and 5-10 m) on the both sides of road during study . In total there were 24 treatment combinations which were replicated six times in Randomised Block Design (Factorial). The dust accumulation on the le aves ranged from 0.0083 g m -2 in T. ciliata to 0.0597 g m in G. optiva which followed the order of G. optiva > W. floribunda > M. azedarach > T. ciliata. Irrespective of species, season wise trend of speci es was winter > summer > rainy season. Plants grown at a distance of 0-5 m accumulated higher dust on t heir leaves as compared to 5-10 m distance from the road. The highest pH was recorded in M. azedarach (7.66) and lowest in W. floribunda (5.85) and the pH was maximum in rainy followed by winter and summer season. Leaf relative water content was maximum in M. azedarach (59.34 %) and minimum in T. ciliata (49.76 %) and season wise, it was highest in rainy followed by winter and summer seas on. The highest ascorbic acid content was recorded in M. azedarach (8.40 mg g) and lowest in T. ciliata (2.13 mg g). Ascorbic acid content was recorded maximum in winter followed by summer and rainy seas ons. Highest chlorophyll content was found in M. azedarach (7.18 mg g) and lowest in T. ciliata (2.18 mg g) and was recorded highest in rainy followed by winter and summer season. The APTI was found max i um in M. azedarach (18.37) and minimum in T. ciliata (6.82). The APTI also varied with season of the year. The highest APTI was noticed in rain y followed by winter and summer season. The trend of APTI recorded for various species was M. azedarch > G. optiva > W. floribunda > T. ciliata. The present study indicated M. azedarach as most tolerant and T. ciliata as most sensitive species to air pollution. The du st accumulated on leaves had heavy metals, Cu (29.14 mg kg) and Zn (1219.91 mg kg ) above permissible limit of the soil. Signature of Major Advisor Signature of student Countersigned Professor and Head Department of Environmental Science Dr. Y. S. Parmar University of Horticulture and Forestry, Nauni-173 230, Solan (H.P.)
Skylight VIS-NIR Polarization Radiometer: Design, Calibration, and Measurement

Skylight polarization radiation measurement has important application value in atmospheric remote sensing, aerosol retrieval, and polarization navigation. Traditional instruments like the Cimel CE318, while widely used in AERONET and SONET networks, suffer from limitations, such as long measurement time resolution, limited measurement modes, and incomplete calibration. Addressing these gaps, this article designs and implements a Skylight VIS-NIR Polarization Radiometer (SVNPR) for precise spectral polarization measurements, covering a spectral range of 400–1100 nm. The SVNPR, based on ground-based passive, robust, constant-temperature, and multiscan features, is particularly suited for field deployment. Furthermore, a comprehensive and feasible indoor calibration scheme is proposed, including spectral calibration, radiometric calibration, and polarimetric calibration. Finally, field measurements were conducted, and the experimental results show that the SVNPR can achieve effective and accurate skylight polarization measurements in practical applications. The calibrated instrument showed good consistency with CE318-DP, with mean absolute deviation (MAD) and root mean square error (RMSE) both within 0.005.
Hyperspectral remote sensing data analysis

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise, blur, and atmospheric effects. In this talk, I will present an overview cross section of some relevant hyperspectral data analysis methods and algorithms, namely, data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. For each topic, I will summarize the mathematical problem involved, give relevant pointers to state-of-the-art algorithm to address these problems, and illustrate experimentally the potentialities and limitations of these algorithms.
A Framework for Solving the Job Shop Scheduling Problem

By utilizing cutting-edge algorithms and machine learning techniques, the incorporation of Artificial Intelligence (AI) in Job Shop Scheduling (JSS) revolutionizes conventional scheduling systems. JSS powered by AI improves scheduling flexibility, reduces makespan, maximizes resource usage, and optimizes production processes. This study examines how AI is used in JSS and demonstrates how it may be used to solve challenging scheduling issues, adapt to dynamic industrial environments, and ultimately increase operational effectiveness and productivity. This paper offers a well-organized and methodical approach for solving the Job Shop Scheduling Problem (JSSP). The goal of the optimization problem-solving framework outlined is to provide a structured and systematic approach for solving complex optimization problems effectively and efficiently. The problem is defined and key components, such as the number of machines, processing durations, and the target function to optimize (such as makespan or total completion time), are specified at the outset of the framework. We place emphasis on developing a solid problem model that includes the construction of an initial schedule and the determination of objective values. The framework also includes goal functions and specifying limitations. A clear representation and a place to start for the search process are made possible by the solution representation and initialization processes, which are essential parts. The Neighborhood Search, which uses a variety of search techniques like Genetic Algorithm, Simulated Annealing, and Ant Colony optimization, is at the core of the framework An extensive visualization and reporting of the ultimate schedule result from the solution’s refinement through termination criteria, iterative enhancements, and optional post-processing. By highlighting the interdependence between problem formulation, solution representation, algorithmic search, and result evaluation, this paradigm provides a tactical method to dealing with JSSP.
A Dataset for Analyzing Crowdsourced Feedback in Usability Testing

Conventional usability evaluation methods despite being widely used usually fall short of producing comprehensive results since they involve testing with few real users/experts in a controlled environment. One possible solution to address this issue is to involve a sufficiently large number of potential end-users to perform usability tests in a completely natural environment. However, this approach generates a massive amount of user feedback, which can be challenging for experts to analyze manually. Integrating machine learning techniques presents a promising solution to address this issue. By leveraging machine learning, the analysis of vast amounts of user feedback can be automated and expedited. However, the effective implementation of machine learning algorithms relies on the availability of relevant datasets. To address this need, we created a comprehensive dataset of 911 usability flaws. This dataset would serve as a valuable resource for researchers and practitioners seeking to train and test machine learning models in the domain of usability testing. The dataset was constructed through a user study involving 800 participants who performed usability testing after getting trained. Additionally, the dataset underwent a thorough validation process conducted by a team of three experts who were carefully chosen based on their expertise in usability evaluation.
The role of Weizmannia (Bacillus) coagulans LMG S-31876 in treating IBS-diarrhea

Introduction Irritable bowel syndrome (IBS) is a common gastrointestinal condition. Some studies have shown the efficacy of probiotics in the treatment of irritable bowel syndrome (IBS). Weizmannia (Bacillus) coagulans LMG S-31876 has been marketed as a dietary ingredient, but to date, its efficacy in diarrhea-predominant irritable bowel syndrome (IBS) condition has not been clinically elucidated. Thus, a double-blind placebo-controlled multi-centered trial was planned to evaluate the safety and efficacy of Weizmannia (Bacillus) coagulans LMG S-31876 in diarrhea-predominant IBS patients. Experimental design Study participants (n = 50) diagnosed with IBS prominent symptoms that include abdominal pain and other GI-related symptoms were treated with ProBC Plus (2 billion CFU) along with a placebo capsule once daily for approximately 8 weeks. Study participants were evaluated for the treatment success determined by the differences in stool consistency and frequency per day between the intervention and placebo groups over the study period. Results The vital signs and the biochemistry parameters were under the normal range; the other parameters showed a significant result as compared to the placebo during the study period. Conclusion This study depicts a significant decline in the clinical symptoms such as abdominal pain, bloating, diarrhea, and frequency of the stool as compared to the placebo. All the parameters such as hematology, lipid profile, and vital signs were in the normal range during the supplementation of ProBC Plus for a period of 8 weeks. Furthermore, the study verified that Weizmannia (Bacillus) coagulans LMG S-31876 and its probiotic product ProBC Plus at a dose of 2 billion/CFU/day has a prominent action in the relief from the clinical symptoms of IBS-D. Therefore, the product is intended safe to utilize for IBS-related symptoms. Clinical trial registration: The clinical study has been registered with CTRI/2023/01/048644 with https://ctri.nic. in/Clinicaltrials/pmaindet2.php? trialid=77708&EncHid=24313.96864&userName=CTRI/2023/01/048644 [CTRI/2023/01/048644].
Impact of Anonymity Indicator "PFDOC Entropy l-Diversity" on Medical Data Utility

In clinical research articles, statistics about the patients under the study are presented in the data format called "patient characteristics". Patient characteristics provide insights for interpreting the study results. However, disclosing patient characteristics with low l of "Patient Family Detect on Overall Category (PFDOC) entropy l-diversity", an indicator of patient characteristics anonymity, raises privacy concerns.In this paper, we examine a scenario in which medical institutions withhold publication of their articles that include low l of PFDOC entropy l-diversity patient characteristics based on the existing patient characteristics publication model. In this scenario, the number of published medical articles would decrease, and this reduction may impact data utility. Therefore, we check two quantitative utility indicators. The first utility indicator is the number of published papers without being withheld. The second utility indicator is the degree of difference between the average of the published population data contained in patient characteristics and the true population ratio, such as the prevalence of diabetes in a particular patient group. According to the numerical results, when articles are excluded by 1 ≦ l ≦ 1.5, the data loss is more than 5% and the average difference is more than 0.5% if a category with a population ratio of 15% or less or 85% or more is included in the patient characteristics. This study provides guidance on the adoptable l of PFDOC entropy l-diversity with consideration of utility.
Surface Chlorophyll Patchiness across Sepanggar Bay: Relationships with Turbidity and Depth

The prevalence of harmful algal blooms (HABs), poses a considerable risk to public health and the livelihood of local fishers centred on Sepanggar Bay (Sabah). While HABs appear ostensibly during the NE Monsoon, there is no information on what may control their mesoscale distribution and production across Sepanggar Bay. For this study, we hypothesize that shallow sediment resuspension of a viable microphytobenthic population, along with potential germination of encysting harmful algae, control the mesoscale surface patchiness of chlorophyll-a across Sepanggar Bay. A ‘snapshot’ over the NE Monsoon, of surface chlorophyll-a, together with turbidity and oxygen concentrations was produced from of 34 sampling stations chosen randomly across the bay’s regions. For rapid processing and measurement, chlorophyll-a, and turbidity were taken from surface colour reflectivity, using the phone app HydroColor™. The remaining variables surface temperature, salinity, and oxygen concentrations came from a probe. Both turbidity and chlorophyll-a showed considerable structure across the bay with depth. In general, there were good correlations between depth (inverse), turbidity (positive), and surface oxygen concentrations (positive) with chlorophyll-a across the bay, but with low chlorophyll-a outliers near a water village. The reasons behind the structure and the above correlations are elaborated in the paper.
Therapeutic factors and biomaterial-based delivery tools for degenerative intervertebral disc repair

Intervertebral disc degeneration (IDD) is the main cause of low back pain (LBP), which significantly impacts global wellbeing and contributes to global productivity declines. Conventional treatment approaches, encompassing conservative and surgical interventions, merely serve to postpone the advancement of IDD without offering a fundamental reversal. Consequently, there is an urgent demand for an effective approach to prevent the progression of IDD. Recent investigations focusing on the treatment of IDD utilizing diverse bioactive substances integrated within various biomaterials have exhibited promising outcomes. Various bioactive substances, encompassing conventional small molecule drugs, small molecule nucleic acids, and cell therapies, exhibit distinct capacities for repairing IDD. Additionally, various biological material delivery systems, such as nano micelles, microspheres, and hydrogels, possess diverse biological and release characteristics. Consequently, these diverse materials and drugs hold promise for advancing the treatment of IDD. This article aims to provide a concise overview of the IDD process and investigate the research advancements in biomaterials and bioactive substances for IDD treatment, delving into their mechanisms.
Developing a Flying Explorer for Autonomous Digital Modelling in Wild Unknowns

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain.
The role of cell-matrix adhesion and cell migration in breast tumor growth and progression

During breast cancer progression, there is typically increased collagen deposition resulting in elevated extracellular matrix rigidity. This results in changes to cell-matrix adhesion and cell migration, impacting processes such as the epithelial-mesenchymal transition (EMT) and metastasis. We aim to investigate the roles of cell-matrix adhesion and cell migration on breast tumor growth and progression by studying the impacts of different types of extracellular matrices and their rigidities. We embedded MCF7 spheroids within three-dimensional (3D) collagen matrices and agarose matrices. MCF7 cells adhere to collagen but not agarose. Contrasting the results between these two matrices allows us to infer the role of cell-matrix adhesion. We found that MCF7 spheroids exhibited the fastest growth rate when embedded in a collagen matrix with a rigidity of 5.1 kPa (0.5 mg/mL collagen), whereas, for the agarose matrix, the rigidity for the fastest growth rate is 15 kPa (1.0% agarose) instead. This discrepancy is attributable to the presence of cell adhesion molecules in the collagen matrix, which initiates collagen matrix remodeling and facilitates cell migration from the tumor through the EMT. As breast tumors do not adhere to agarose matrices, it is suitable to simulate the cell-cell interactions during the early stage of breast tumor growth. We conducted further analysis to characterize the stresses exerted by the expanding spheroid on the agarose matrix. We identified two distinct MCF7 cell populations, namely, those that are non-dividing and those that are dividing, which exerted low and high expansion stresses on the agarose matrix, respectively. We confirmed this using Western blot which showed the upregulation of proliferating cell nuclear antigen, a proliferation marker, in spheroids grown in the 1.0% agarose (≈13 kPa). By treating the embedded MCF7 spheroids with an inhibitor or activator of myosin contractility, we showed that the optimum spheroids’ growth can be increased or decreased, respectively. This finding suggests that tumor growth in the early stage, where cell-cell interaction is more prominent, is determined by actomyosin tension, which alters cell rounding pressure during cell division. However, when breast tumors begin generating collagen into the surrounding matrix, collagen remodeling triggers EMT to promote cell migration and invasion, ultimately leading to metastasis.
MTCAM: A Novel Weakly-Supervised Audio-Visual Saliency Prediction Model With Multi-Modal Transformer

Although various video saliency models have achieved considerable performance gains, existing deep learning-based audio-visual saliency prediction models are still in the early exploration stage. The major challenge is that there are relatively few audio-visual sequences with real human eye fixations collected under the audio-visual circumstance. To this end, this paper presents a novel multi-modal transformer-based class activation mapping (MTCAM) model in a weakly-supervised training manner to effectively alleviate the need of large-scale datasets for audio-visual saliency prediction. In particular, by using only video category labels in the video classification task, we propose to employ the class activation mapping based on multi-modal transformer, which follows a two-stage training methodology to extract the most discriminative regions. Such regions with strong discriminative ability are highly consistent with real human eye fixations. Meanwhile, we further devise an efficient feature reuse mechanism to reduce redundant computation and enable previously obtained features can provide effective guidance for downstream model learning. It is particularly noteworthy that this work is the first attempt to exploit the cross-modal transformer to focus on cross-modal interaction at the entire video and predict human eye fixations in a weakly-supervised training strategy. We conduct extensive experiments on several benchmark datasets to demonstrate that the proposed MTCAM model significantly outperforms other competitors. Furthermore, detailed ablation experiments are also performed to validate the effectiveness and rationality of each component in our proposed model.
PUBLIC MANAGEMENT OF THE GHANA'S METRO MASS TRANSIT LIMITED

Ownership and management of state bus public transport has been a source of intense debate, the world over. Using the New Public Management (NPM) framework, we examine Ghana’s flag post urban public transit – the Metro Mass Transit – to ascertain its prospects to thrive despite prior evidence of several state mass transits failures. The focus here is primarily on the shortterm practical issues of management, operations, and funding state bus transit in Ghana. The paper employed exploratory case study research design as a methodological approach. Findings show that owing to NPM-inspired reforms, inter alia; ICT based innovations and other internal controls, the Metro Mass Transit exhibits high resiliency in the Ghanaian urban transit industry.
Interactive Activities Initiation through Retrieving Hidden Social Information Networks

The rise of social platforms based on online social networks has greatly enriched people’s lives, resulting in various applications. Traditional research mainly focuses on users but pays less attention to the edges between users, and they all assume the topology of social networks is known in advance. Indeed, obtaining the network topology is challenging because of privacy protection and business competition. In this paper, we propose an activity initiation problem inspired by real business applications, such as Pinduoduo and Tencent, where each edge can be abstracted as an activity in which both ends (users) of the edge participate together, and the edge can be initiated by one of them. At this time, we hope to select as few users as possible to initiate activities and make the users of the whole network participate together. This problem can be reduced to the classic vertex cover problem, but the network information is hidden by social platforms as much as possible. To address this challenge, we put forward a solver-detector model. In each round of interaction, the solver uses a detector to obtain a small amount of edge information and achieves vertex coverage. This is a model in which a solver has limited access to input, but still gives a 2-approximation that is as good as the conventional model. Finally, we can cover the whole network with very few edge samples, which is a brand-new research perspective.