Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data

The cellular industry faces challenges in controlling the quality of signals for all users, given its meteoric growth in the last few years. The service providers are required to place cellular towers at the optimal location for providing a strong cellular network in a particular region. However, due to buildings, roads, open spaces, etc., of varying topography in 3D (obstructing the signals) and varying densities of settlements, finding the optimal location for the tower becomes challenging. Further, in a bigger area, it is required to determine the optimum number and locations for setting up cellular towers to ensure improved quality. The determination of optimum solutions requires a signal strength prediction model that needs to integrate terrain data, information of cellular tower with users’ locations, along with tower signal strengths for predictions. Existing modeling practices face limitations in terms of the usage of 2D data, rough terrain inputs, and the inability to provide detailed shapefiles to GIS. The estimation of optimum distribution of cellular towers necessitates the determination of a model for the prediction of signal strength at users’ locations accurately. Better modeling is only possible with detailed and precise data in 3D. Considering the above needs, a LIDAR data-based cellular tower distribution modeling is attempted in this article. The locations chosen for this research are RGIPT, UP (45 Acre), and Shahganj, Agra, UP, India (6 km2). LiDAR data and google images for the project sites were classified as buildings and features. The edges of overground objects were extracted and used to determine the routes for transmission of a signal from the tower to user locations. The terrain parameters and transmission losses for every route are determined to model the signal strength for a user’s location. The ground strength of signals is measured over 1000 points in 3D at project sites to compare with modeled signal strengths (an RMSE error 3.45). The accurate model is then used to determine the optimum number and locations of cellular towers for each site. Modeled optimum solutions are compared with existing tower locations to estimate % over design or under design and the scope of improvement (80% users below −80 dB m improves to 70% users above −75 dB m).
The RELIABILITY OF A NEW DEVICE FOR ASSESSING ANKLE JOINT PRORIOCEPTION IN HEALTHY ADULTS

The objective of this study is to evaluate the within-day and between-day reliability of a new device for assessing ankle joint prorioception. Eleven healthy adults, composed of seven males and four females, participated in the study. Each subject completed three sessions, two on the same day and the last one week later. In each session, three successful testing trials for ankle joint plantarflexion, dosiflexion, inversion, and eversion were performed. The mean values in one direction were calculated and analyzed. The within-day ICC values ranged from 0.808 to 0.973. The SEM for the device ranged from 0.118 ° to 0.448 °. The between-day ICC values ranged from 0.628 to 0.884. The SEM ranged from 0.287 °to 0.618 °. The measurements indicate good to excellent reliability of the device.
Tele-ICU in the Unites States: Is a cost-effective model?

ABSTRACT Introduction: The United States Society of Critical Care Medicine has announced a shortage of all critical care intensivists due to the increased need for critical care for the aging population with comorbidities and improved life expectancy. This shortage has led to tele-ICU programs that have allowed intensivists to care for patients simultaneously remotely. Methods: This study aimed to assess the potential for the hospital implementation of tele-ICU to determine its overall healthcare cost-effectiveness. The methodology was a review that followed a systematic search approach utilizing 42 sources. Results: The study findings showed that tele-ICU contributed to reduced hospital LOS by 36% and positive financial outcomes of return of investment and cost-effectiveness. These reduced LOS stay times have allowed for savings up to $11.5 million. Discussion: The Tele-ICU program's implementation has positively impacted financial and clinical areas, reducing mortality and reduced hospital LOS. Furthermore, it has reduced improved patient safety, care quality, and decreased ventilator days, contributing to a cost-efficient technology.
Simultaneous Feasible Local Planning and Path-Following Control for Autonomous Driving

In this paper, a new approach for lane change and double-lane change planning and following for autonomous driving is proposed. Herein, we introduce a novel technique, based on exponential functions, to generate online and feasible lane change maneuvers; these maneuvers satisfy the constraints on the maximum allowable curvature a given vehicle can handle. In addition, a simultaneous local path planning and path-following control framework is adapted. The framework utilizes a multi-threading architecture to run the local planner module concurrently with the control module. The planning module generates parametric reference paths based on the proposed lane change and double-lane change maneuvers. The control module is based on a Model Predictive Path-following Control (MPFC) scheme, which achieves the path following objective while satisfying vehicle’s state and control limits. To validate the proposed framework, several real-time simulation scenarios are designed and tested on CARLA soft real-time vehicle simulator. The results show the effectiveness of the proposed framework in generating and smoothly following lane change maneuvers.
Anxiety, depression, and sleep disorders among healthcare workers during the COVID-19 pandemic

OBJECTIVE In this study, we examined the level of anxiety and depression, daytime sleepiness, and sleep quality in healthcare workers working during the COVID-19 pandemic. METHODS This study was conducted in a tertiary care university hospital. Socio-demographic information form, Beck Anxiety Scale, Beck Depression Scale, Pittsburg Sleep Quality Index, and Epworth Sleepiness Scale were used as data collection tools. RESULTS A total of 273 healthcare workers were recruited into the study. It was determined that there is an enormous mental health burden on healthcare workers. About 100% and 45.4% of the participants got above-threshold scores from Beck Anxiety Scale, and Beck Depression Scale, and the prevalence of daytime sleepiness and impaired sleep quality was 11.4% and 38.8%, respectively. The female gender was found to have more severe anxiety levels and lower sleep quality (p<0.001). In logistic regression analysis, the female gender was a risk factor for having a level of severe anxiety, and the nursing profession was a risk factor for having severe anxiety and low sleep quality (p<0.05). CONCLUSION Global serious outbreaks cause increased depression and anxiety levels and sleep disorders in healthcare workers. Therefore, we believe that trainings and support which aims to strengthen the psychological well-being of healthcare workers should be implemented.
Synthesis of Marine Cyclopeptide Galaxamide Analogues as Potential Anticancer Agents

In this paper, eight new galaxamide analogues (Z-1~Z-8) were synthesized and evaluated for their cytotoxic activities against five cancer cell lines, MCF-7, MD-MBA-231, HepG2, Hela, and A549, using MTT assays. The modified analogue Z-6 displayed broad spectrum cytotoxic activity toward each tested cell line with IC50 values of 1.65 ± 0.30 (MCF-7), 2.91 ± 0.17 (HepG2), 4.59 ± 0.27 (MD-MBA-231), 5.69 ± 0.37 (Hela), and 5.96 ± 0.41 (A549) μg/mL, respectively. The galaxamides Z-3 and Z-6 induced concentration-dependent apoptosis of the MCF-7 cells after 72 h as evaluated by the flow cytometry experiment. The results showed that these compounds could induce MCF-7 cell apoptosis by arresting the G0/G1 phase of the cell cycle and finally achieving the effect of inhibiting the proliferation of MCF-7 cells.
Reverse-Engineering the Design Rules for Cloud-Based Big Data Platforms

Big Data's 5 V complexities are making it increasingly difficult to develop an understanding of the end to end process. Big Data platforms play a crucial role in many critical systems, combining with Internet-of-Things, Artificial Intelligence and Business Analytics. It is both relevant and important to understand Big Data systems to identify the best tools that fit the requirements of heterogeneous platforms. The objective of this paper is to "discover" a set of design principles and rules for Cloud-based Big Data platforms for complex, heterogeneous environments. The design scope comprises Big Data's significance, challenges and architectural impacts. Using a methodology Reverse Engineered Design Science Research (REDSR), artifacts from leading vendors are used to elicit the design principles and rules with relevant details of Big Data components. We conclude that the findings are relevant and useful for DevOps architects and practitioners in operating complex, heterogeneous Cloud-based Big Data platforms.
10-Year Survival Rates After High Tibial Osteotomy Using Angular Stable Internal Plate Fixation: Case Series With Subgroup Analysis of Outcomes After Combined Autologous Chondrocyte Implantation and High Tibial Osteotomy

Background: Good-to-excellent midterm results after high tibial osteotomy (HTO) to treat medial compartment cartilage defects or osteoarthritis (OA) have been published, but little is known about long-term survival rates in terms of conversion to total knee arthroplasty (TKA) using angular stable internal plate fixation. Purpose: To determine TKA-free survival rates and functional and radiological outcomes at 10 years after HTO. A subgroup analysis of patients who underwent combined HTO and autologous cartilage implantation (ACI) was also performed. Study Design: Case series; Level of evidence, 4. Methods: Included were 125 patients with a mean follow-up of 9.90 ± 2.25 years; 90 patients underwent HTO for medial OA, and 35 patients underwent ACI and HTO for medial focal cartilage defects. Functional outcome measures included visual analog scale (VAS) for pain, Lysholm, International Knee Documentation Committee (IKDC), and Knee injury and Osteoarthritis Outcome Score (KOOS) subscales and KOOS4 (average of 4 KOOS subscales: Pain, Symptoms, Sport, and Quality of Life). Radiological outcomes included lateral distal femoral angle, medial proximal tibial angle, and joint line convergence angle. Results: Overall, 16 patients required conversion to TKA at a mean 86.75 ± 25.73 months (10-year survival rate, 87.2%). Only 2 patients in the HTO+ACI subgroup required a conversion to TKA (10-year survival rate, 94.3%). The complication rate for all patients was 8.8%. In both the HTO and HTO+ACI subgroups, VAS pain levels decreased and Lysholm scores increased significantly from pre- to postoperatively (P < .001). A higher preoperative Tegner score led to a significantly lower risk for conversion to TKA (P = .001), and a preoperative body mass index of ≥35 was associated with a significantly higher risk (P = .019), as was female sex (P = .046). Radiological parameters remained within physiological ranges. The postoperative joint line conversion angle did correlate with postoperative functional outcome but not with TKA conversion. Conclusion: Long-term results of HTO for medial compartment OA or cartilage defects with underlying varus deformity were good to excellent. In particular, patients who underwent HTO+ACI presented excellent long-term survival rates. HTO, therefore, delays or prevents TKA implantation, especially in young, active patients with medial compartment damage.
Simulation of Softwood Lignin Gasification in Supercritical Carbon Dioxide

: The abundant and renewable lignin resource has attracted increasingly attention. Supercritical carbon dioxide has excellent properties in catalytic gasification. In this paper, a molecular model of softwood-lignin was established to as a reactant. ReaxFF molecular dynamics simulation method was used to simulate the gasification of softwood in supercritical carbon dioxide environment with nano-platinum particles as catalysts. The method of slow temperature rise (80K/ps) was adopted, and the system was studied at a constant temperature 1700K. The cleavage of different bond at the initial stage of reaction were studied, and the different gasification production under the action of catalysts in supercritical carbon dioxide were analyzed and compared with the non-catalyst condition. The result shows with the addition of catalyst, the amount of CO 2 cracking increases, the number of H 2 and CO molecules produced was increased, and the reaction rate increases.
Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm

Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system's grid-side difficulties.
Age and Illness Severity: A Case of Irrelevant Utilities?

Abstract Illness severity is a priority setting criterion in several countries. Age seems to matter when considering severity, but perhaps not small age differences. In the following article we consider Small Differences (SD): small differences in age are not relevant when considering differential illness severity. We show that SD cannot be accommodated within utilitarian, prioritarian or egalitarian theories. Attempting to accommodate SD by postulating a threshold model becomes exceedingly complex and self-defeating. The only way to accommodate SD seems to be to accept some form of relevance view, where some age differences are irrelevant. This view can accommodate SD, but at the expense of consistent priority orderings. Severity thus becomes unsuitable for systematic decision-making. We argue that SD should be dismissed and that we should accept a continuous relationship between severity of illness and age.
Design, Synthesis, and Cytotoxic Activity of Novel Natural Arylsulfonamide-Inspired Molecules

Primary arylsulfonamide functional groups feature prominently in diverse pharmaceuticals. However, natural arylsulfonamides are relatively infrequent. In this work, two novel arylsulfonamide natural products were first synthesized, and then a series of novel molecules derived from natural arylsulfonamides were designed and synthesized, and their in vitro cytotoxic activities against A875, HepG2, and MARC145 cell lines were systematically evaluated. The results indicate that some of these arylsulfonamide derivatives exhibit significantly good cytotoxic activity against the tested cell lines compared with the control 5-fluorouracil (5-FU), such as compounds 10l, 10p, 10q, and 10r. In particular, the potential molecule 10q, containing a carbazole moiety, exhibited the highest inhibitory activity against all tested cell lines, with IC50 values of 4.19 ± 0.78, 3.55 ± 0.63, and 2.95 ± 0.78 μg/mL, respectively. This will offer the potential to discover novel drug-like compounds from the sparsely populated area of natural products that can lead to effective anticancer agents.
The Usefulness of Prognostic Tools in Breast Cancer Patients with Brain Metastases

Simple Summary Due to the variability of an individual’s prognosis and the variety of treatment options available to breast cancer (BC) patients with brain metastases (BM), establishing the proper therapy is challenging. Since 1997, several prognostic tools for BC patients with BM have been proposed with variable precision in determining the overall survival. The majority of prognostic tools include the performance status, the age at BM diagnosis, the number of BM, the primary tumor phenotype/genotype and the extracranial metastases status as an outcome of systemic therapy efficacy. It is necessary to update the prognostic indices used by physicians as advances in local and systemic treatments develop and change the parameters of survival. Free access to prognostic tools online may increase their routine adoption in clinical practice. Clinical trials on BC patients with BM remains a broad field for the application of prognostic tools. Abstract Background: Determining the proper therapy is challenging in breast cancer (BC) patients with brain metastases (BM) due to the variability of an individual’s prognosis and the variety of treatment options available. Several prognostic tools for BC patients with BM have been proposed. Our review summarizes the current knowledge on this topic. Methods: We searched PubMed for prognostic tools concerning BC patients with BM, published from January 1997 (since the Radiation Therapy Oncology Group developed) to December 2021. Our criteria were limited to adults with newly diagnosed BM regardless of the presence or absence of any leptomeningeal metastases. Results: 31 prognostic tools were selected: 13 analyzed mixed cohorts with some BC cases and 18 exclusively analyzed BC prognostic tools. The majority of prognostic tools in BC patients with BM included: the performance status, the age at BM diagnosis, the number of BM (rarely the volume), the primary tumor phenotype/genotype and the extracranial metastasis status as a result of systemic therapy. The prognostic tools differed in their specific cut-off values. Conclusion: Prognostic tools have variable precision in determining the survival of BC patients with BM. Advances in local and systemic treatment significantly affect survival, therefore, it is necessary to update the survival indices used depending on the type and period of treatment.
Nonlinear Time Series Fuzzy Regression for Developing Explainable Consumer Preferences’ Models Based on Online Comments

In modeling of consumer preferences based on online comments, nonlinearity and fuzziness exist in the relationship between the product design attribute and the consumer preference. On the other hand, consumer preferences are not static and changing over the time. Previous studies have proposed few approaches to model the variational consumer preferences based on online comments. However, the obtained models have black box problems and are not easy to be understood by humans as explicit models cannot be shown, which give rise to the research area of explainable artificial intelligence. Therefore, it is necessary to develop understandable and accurate consumer preferences’ models. In this article, a nonlinear time series fuzzy regression method is proposed to model the variational consumer preference based on online comments, which can generate a fuzzy dynamic consumer preference model with interactive terms, second-order and/or higher order terms. The datasets are first extracted from online comments using the sentiment analysis. Then, the polynomial structure of the fuzzy dynamic consumer preference model is established by using multiobjective chaos optimization algorithm. Then, the fuzzy regression method is used to allocate the fuzzy coefficients of each item of the model. Using sweeping robot as a case study, the validation results from the proposed approach are compared with those from fuzzy least squares regression, time series fuzzy least squares regression, fuzzy regression, and time series fuzzy regression, and it is found that the proposed approach performs better than the other four approaches in terms of mean relative errors and mean system credibility.
Cyclodextrin Stabilized Freeze-Dried Silica/Chitosan Nanoparticles for Improved Terconazole Ocular Bioavailability

This research assesses the beneficial effects of loading terconazole, a poorly water-soluble antifungal drug in silica/chitosan nanoparticles (SCNs) for ocular delivery. Nanoparticles were fabricated by the simple mixing of tetraethyl ortho silicate (TEOS) and chitosan HCl as sources of silica and nitrogen, respectively, along with alcoholic drug solution in different concentrations. Freeze-dried nanoparticles were fabricated using cyclodextrins as cryoprotectants. SCNs were assessed for their particle size, PDI, yield, drug loading and in vitro release studies. A 23.31 full factorial experimental design was constructed to optimize the prepared SCNs. DSC, XRD, FTIR, in addition to morphological scanning were performed on the optimized nanoparticles followed by an investigation of their pharmacokinetic parameters after topical ocular application in male Albino rabbits. The results reveal that increasing the water content in the preparations causes an increase in the yield and size of nanoparticles. On the other hand, increasing the TEOS content in the preparations, caused a decrease in the yield and size of nanoparticles. The optimized formulation possessed excellent mucoadhesive properties with potential safety concerning the investigated rabbit eye tissues. The higher Cmax and AUC0–24 values coupled with a longer tmax value compared to the drug suspension in the rabbits’ eyes indicated the potential of SCNs as promising ocular carriers for poorly water-soluble drugs, such as terconazole.
Heat stress transcription factor DcHsfA1d isolatedfrom Dianthus caryophyllus enhances thermotoleranceand salt tolerance of transgenic Arabidopsis

Higher plants may encounter a variety of environmental stresses, such as extreme temperatures, drought and salinity, and therefore have formed an effective defence mechanism to adapt to the unfavourable conditions (Scharf et al. 2012). High temperatures (heat stress) negatively influence plant growth and development as well as compromise crop yield (Mittler et al. 2012, Ding et al. 2020). Plants express heat shock proteins (Hsps) in response to various abiotic stresses. Heat shock transcription factors (Hsfs) act as a terminal component of the signal transduction pathway and regulate the expression of Hsps and other heat-responsive transcripts (Ohama et al. 2017).