Miracle-dz/UniFAR-FTUs huggingface dataset:
Carboxymethyl Cellulose/Zn-Organic Framework Down-Regulates Proliferation and Up-Regulates Apoptosis and DNA Damage in Colon and Lung Cancer Cell Lines

A solvothermal technique was used to prepare a Zn–benzenetricarboxylic acid (Zn@BTC) organic framework covered with a carboxymethyl cellulose (CMC/Zn@BTC). Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscope (FESEM), and Brunauer, Emmett, and Teller (BET) surface area were applied to characterize CMC/Zn@BTC. Moreover, the anticancer, anti-migrative, anti-invasive, and anti-proliferative action of CMC/Zn@BTC nanoparticles were assessed on cancer cell lines. Apoptotic markers and DNA damage were assessed to explore the cellular and biological changes induced by CMC/Zn@BTC nanoparticles. The microscopic observation revealed that CMC controls the surface morphology and surface characteristics of the Zn@BTC. The obtained BET data revealed that the Zn@BTC nanocomposite surface area lowers from 1061 m2/g to 740 m2/g, and the pore volume decreases from 0.50 cm3/g to 0.37 cm3/g when CMC is applied to Zn@BTC nanocomposites. The cellular growth of DLD1 and A549 was suppressed by CMC/Zn@BTC, with IC50 values of 19.1 and 23.1 μg/mL, respectively. P53 expression was upregulated, and Bcl-2 expression was downregulated by CMC/Zn@BTC, which promoted the apoptotic process. Furthermore, CMC/Zn@BTC caused DNA damage in both cancer cell lines with diverse impact, 66 percent (A549) and 20 percent (DLD1) compared to cisplatin’s 52 percent reduction. CMC/Zn@BTC has anti-invasive properties and significantly reduced cellular migration. Moreover, CMC/Zn@BTC aims key proteins associated with metastasis, proliferation and programmed cellular death.
Coordination in Different Software Development Lifecycles: A Systematic Review

Coordination plays a significant role in Software Development Lifecycle (SDLC) models. However, coordination varies between SDLC models. Relatively, little research has been conducted to analyze coordination in different SDLC models. This paper presents a Systematic Literature Review (SLR) of coordination in different SDLC models. Among the range of SDLC models, the most important and popular ones are the waterfall, spiral, agile, RAD, V and incremental models, which have been investigated on how coordination works in these models.
The fall and rise of the British mall

festation of the mall implements to ensure the maintenance of the experiential values that are so key to its success. This study, by recourse to direct analysis of precedent, is directed at a questioning of the validity of these values and an analysis of the mechanisms of consumption behind them, unravelling these levels of experiential myth in order that a more accurate and productive understanding of the existential nature of the inhabitation of the shopping mall may be achieved.
Adeno-Associated Viruses for Modeling Neurological Diseases in Animals: Achievements and Prospects

Adeno-associated virus (AAV) vectors have become an attractive tool for efficient gene transfer into animal tissues. Extensively studied as the vehicles for therapeutic constructs in gene therapy, AAVs are also applied for creating animal models of human genetic disorders. Neurological disorders are challenging to model in laboratory animals by transgenesis or genome editing, at least partially due to the embryonic lethality and the timing of the disease onset. Therefore, gene transfer with AAV vectors provides a more flexible option for simulating genetic neurological disorders. Indeed, the design of the AAV expression construct allows the reproduction of various disease-causing mutations, and also drives neuron-specific expression. The natural and newly created AAV serotypes combined with various delivery routes enable differentially targeting neuronal cell types and brain areas in vivo. Moreover, the same viral vector can be used to reproduce the main features of the disorder in mice, rats, and large laboratory animals such as non-human primates. The current review demonstrates the general principles for the development and use of AAVs in modeling neurological diseases. The latest achievements in AAV-mediated modeling of the common (e.g., Alzheimer’s disease, Parkinson’s disease, ataxias, etc.) and ultra-rare disorders affecting the central nervous system are described. The use of AAVs to create multiple animal models of neurological disorders opens opportunities for studying their mechanisms, understanding the main pathological features, and testing therapeutic approaches.
Penalty Function Approaches for Proactive Fault-Tolerant Model Predictive Control

In the event of actuator faults in systems, standard control algorithms might not be sufficient for stabilizing the system and keeping the performance at an acceptable level. Because of this, fault-tolerant control methods have been an active area during the last decade, and several significant contributions to the reliability of safety-critical systems have been made. Model predictive control (MPC) has shown to be a powerful control scheme for multi-variable control problems, and provide a natural framework for integrating receding-horizon optimization, while also achieving system reconfiguration in the event of faults. However, almost all the efforts on incorporating fault-tolerance in MPC are focused on reactive fault-tolerance, which aim to handle a fault after it has occurred. In contrast, proactive fault-tolerant control seeks to utilize an estimated, conservative time window between the warning of an incipient fault and the time at which the faulty component is rendered useless to steer the state inside a recoverable region before the fault occurs. As such, a proactive approach circumvents the issues of possible infeasibilities and destabilization often encountered in reactive approaches, while allowing the system to continue operation during the subsequent system repair. Furthermore, economic MPC (EMPC) has received increasing attention in the recent years. Rather than separating real-time optimization and control, an economic MPC scheme merges dynamic economic operations with the feedback properties of conventional MPC. However, there has not been paid much attention to including fault-tolerance and economic optimization in a unified framework. This thesis proposes a proactive EMPC algorithm for handling incipient faults, that also takes economic profits in to account. The scheme utilizes an exact penalty function to steer the system inside an invariant set ensuring stability during the loss of actuation in the system. Additionally, the scheme is extended to be robust in terms of handling unknown disturbances to the system, while still achieving the desired fault-tolerance. Stability for the proposed scheme is proven, both for systems with and without disturbances. The merits and shortcomings of the proposed scheme is demonstrated through several numerical examples.
Colour removal of tannery wastewater using salt tolerant microorganisms in a sequential batch reactor

The sequential batch reactor (SBR) technology is being applied in a large number of treatment processes owing to the operational flexibility it offers. SBR’s capability to carry flow equalization, biological processes, and secondary clarification within a single reactor by differing the time period of different phases and duration of aeration proves it a flexible process for wastewater treatment. In this study SBR was employed to treat the tannery industry wastewater for removing colour. The phase of the SBR was observed over a period of 50 d. Statistical based experiments were performed for optimizing the parameters viz., pH, temperature, inoculum’s concentrations, agitation speed and initial concentration of the substrates on the treatment of wastewater from tanneries by the mixed culture derived from the tannery common effluent treatment plant sludge, in a batch reactor under aerobic condition. At the optimized conditions, experiments were performed by salt tolerant microorganisms such as Pseudomonas aeruginosa, Bacillus flexus, Exiguobacterium homiense and Staphylococcus aureus at various organic loading rate (OLR) by varying tannery wastewater concentration and hydraulic retention time (HRT) for colour removal. A maximum decolouriza- tion 94% was obtained in SBR with the OLR 0.312 kg COD/m 3 d, HRT 5 d for the substrate’s initial concentration of 1,560 mg COD/L.
Group Emotion Detection Based on Social Robot Perception

Social robotics is an emerging area that is becoming present in social spaces, by introducing autonomous social robots. Social robots offer services, perform tasks, and interact with people in such social environments, demanding more efficient and complex Human–Robot Interaction (HRI) designs. A strategy to improve HRI is to provide robots with the capacity of detecting the emotions of the people around them to plan a trajectory, modify their behaviour, and generate an appropriate interaction with people based on the analysed information. However, in social environments in which it is common to find a group of persons, new approaches are needed in order to make robots able to recognise groups of people and the emotion of the groups, which can be also associated with a scene in which the group is participating. Some existing studies are focused on detecting group cohesion and the recognition of group emotions; nevertheless, these works do not focus on performing the recognition tasks from a robocentric perspective, considering the sensory capacity of robots. In this context, a system to recognise scenes in terms of groups of people, to then detect global (prevailing) emotions in a scene, is presented. The approach proposed to visualise and recognise emotions in typical HRI is based on the face size of people recognised by the robot during its navigation (face sizes decrease when the robot moves away from a group of people). On each frame of the video stream of the visual sensor, individual emotions are recognised based on the Visual Geometry Group (VGG) neural network pre-trained to recognise faces (VGGFace); then, to detect the emotion of the frame, individual emotions are aggregated with a fusion method, and consequently, to detect global (prevalent) emotion in the scene (group of people), the emotions of its constituent frames are also aggregated. Additionally, this work proposes a strategy to create datasets with images/videos in order to validate the estimation of emotions in scenes and personal emotions. Both datasets are generated in a simulated environment based on the Robot Operating System (ROS) from videos captured by robots through their sensory capabilities. Tests are performed in two simulated environments in ROS/Gazebo: a museum and a cafeteria. Results show that the accuracy in the detection of individual emotions is 99.79% and the detection of group emotion (scene emotion) in each frame is 90.84% and 89.78% in the cafeteria and the museum scenarios, respectively.
Novel Mutation With Literature Review WW Domain-Containing Oxidoreductase (WWOX) Gene

Genetic alterations in the WW domain-containing oxidoreductase (WWOX) gene cause autosomal recessive developmental and epileptic encephalopathy, characterized by the onset of refractory seizures in infants, along with severe axial hypotonia and profoundly impaired psychomotor development. It has also been expanded to include metabolism and endocrine systems. Despite its function as a tumor suppressor gene, genetic alterations in WWOX have been found in several metabolic disorders and neural diseases related to brain development. Whole-exome sequencing (WES) was performed on the patient sample. Genomic DNA was fragmented, and the exons of known genes in the human genome, as well as the corresponding exon-intron boundaries,were enriched using Roche KAPA capture technology (KAPA hyperExome Library, WES identifying the homozygous variant c.406A>G in WWOX (OMIM:605131). This variant of WWOX was also observed in the prenatal WES data, indicating that both parents were heterozygous carriers and the detected variant was homozygous. This study highlighted the importance of the human WWOX gene in brain development and the association between WWOX gene mutations and developmental delay. We recommend performing WES as a primary screening before the final diagnosis, particularly in populations with high rates of consanguinity and in clinically challenging cases.
Climate-Growth Relationships of Chinese Pine (Pinus tabulaeformis Carr.) at Mt. Shiren in Climatic Transition Zone, Central China

Simple Summary Tree rings are widely used in global change research based on the accurate dating capabilities, climate sensitivity and wide distribution of samples. In the context of global warming, the response of tree growths in north–south transition zones to climate change is one of the hot issues in Dendroecology. The research results found that trees’ growth had different responses to May–June temperature and precipitation on the north and south of the mountain. Therefore, we analyzed the relationship between tree ring and a regional hydrothermal composite factor and reconstructed its variation. The variations agree with other drought series and represent the drought variation in central and eastern monsoon regions, and may provide better understanding of drought variation and service for agricultural production. Abstract Tree ring data from the southern boundary of Chinese Pine (Pinus tabulaeformis Carr.) distribution where is the southern warm temperate margin, the paper analyzes the response of climate factors along north–south direction to tree growth. The results show that temperature and precipitation in May–June and relative moisture from March to June are main limiting factors on trees growth; however, the temperature in the south of the mountains and the moisture in the north of the mountains have relatively greater influence on trees’ growth. Additionally, we also found that the regional scPDSIMJ (that is scPDSI in May–June) was the most significant and stable factor limiting tree growth to be used for reconstruction. The reconstructed scPDSIMJ revealed that there were 29 extremely dry years and 30 extremely wet years during 1801–2016, and it could represent the drought variation in central and eastern monsoon region. The variation exists in good agreement with the reconstructed PDSI for Mt. Shennong and the drought/wetness series in Zhengzhou. Further research found that the droughts of May–June in central China were mainly impacted by local temperature and moisture (including precipitation, soil moisture, potential evaporation and water pressure), and then by the northern Pacific Ocean and the northern Atlantic Ocean. These results may provide better understanding of May–June drought variation and service for agricultural production in central China.
Improvement of the sensitivity of chalcogenide-based infrared sensors dedicated to the in situ detection of organic molecules in aquatic environment

The aim of this study was to develop mid-infrared chalcogenide sensor and perform its functionalization by polymers in order to detect various hydrocarbon pollutants in water and to ensure an efficient attenuation of the water absorption bands. Selenide waveguides were fabricated by radiofrequency magnetron sputtering on silicon substrates using two different glass target compositions, for cladding and guiding layers. A hydrophobic polymer was deposited on the surface of zinc selenide prisms to allow its characterization by ATR-FTIR (Attenuated Total Reflectance-Fourier Transform InfraRed) spectroscopy. Benzene, toluene and ortho-, meta- and para-xylenes in solutions of distilled water at concentrations ranging from 10 ppb to 20 ppm were simultaneously detected and the measured limit of detection was determined to be equal to 250 ppb. However, the limit of detection must be improved to meet environmental standards. To achieve this goal, metallic nanostructures were deposited on the surface of the chalcogenide waveguides to increase the sensitivity of the future optical sensor thanks to the plasmon resonance phenomena. Thus, the fabrication of a heterostructure composed of gold nanoparticles deposited by electron beam evaporation was performed on a slab selenide waveguide in order to assess SEIRA (Surface-Enhanced InfraRed Absorption) effect.
Digital Transformation Strategy: A Systematic Literature Review

The integration of engineered nanomaterials (ENMs) in the food and agriculture industry is now common and rampant among giant food manufacturers to enhance the quality, functionality, and physicality of food products. However, consumers have not been consulted and informed on the use of ENMs in food despite various potential safety and health risks associated with oral exposure of ENMs illustrated from scientific studies. In the European Union, the food law was amended to include provisions on nanotechnology after conducting a public consultation to explore public awareness and perception of nanotechnology. In the absence of a specific regulatory framework for nanofood in Malaysia, this study aims to analyse consumers’ awareness and knowledge of nanofood. The result from the study serves as an invaluable input to the regulatory authority in framing any regulatory reform to regulate nanofood. A survey was conducted using a closed-ended questionnaire distributed online. The result indicates that the awareness and understanding of nanofood are still lacking and not satisfactory.
Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment

Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. Thus, estimation using OLS will result in a biased and inconsistent estimator. To overcome this, the dynamic panel data model can be estimated using the GMM Blundell-Bond approach. Based on the discussion, the parameter estimation formula for dynamic panel data regression using the Blundell-Bond GMM approach is as follows:
A Deep Reinforcement Learning Approach to Eco-driving of Autonomous Vehicles Crossing a Signalized Intersection

: This paper outlines a method for obtaining the optimal control policy for an autonomous vehicle approaching a traffic signal head. It is assumed that traffic signal phase and timing information can be made available to the autonomous vehicle as the vehicle approaches the traffic signal. Constraints on the vehicle’s speed and acceleration are considered and a microscopic fuel consumption model is considered. The objective is to minimize a weighted sum of the travel time and the fuel consumption. The problem is solved using the Deep Deterministic Policy Gradient algorithm under the reinforcement learning framework. First, the vehicle model, system constraints, and fuel consumption model are translated to the reinforcement learning framework, and the reward function is designed to guide the agent away from the system constraints and towards the optimum as defined by the objective function. The agent is then trained for different relative weights on the travel time and the fuel consumption, and the results are presented. Several considerations for deploying such reinforcement learning-based agents are also discussed.
Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets

The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic increase of network data and placed a high computation complexity across various connected devices. The IoT devices capture valuable information, which allows the industries or individual users to make critical live dependent decisions. Most of these IoT devices have resource constraints such as low CPU, limited memory, and low energy storage. Hence, these devices are vulnerable to cyber-attacks due to the lack of capacity to run existing general-purpose security software. It creates an inherent risk in IoT networks. The multi-access edge computing (MEC) platform has emerged to mitigate these constraints by relocating complex computing tasks from the IoT devices to the edge. Most of the existing related works are focusing on finding the optimized security solutions to protect the IoT devices. We believe distributed solutions leveraging MEC should draw more attention. This paper presents a comprehensive review of state-of-the-art network intrusion detection systems (NIDS) and security practices for IoT networks. We have analyzed the approaches based on MEC platforms and utilizing machine learning (ML) techniques. The paper also performs a comparative analysis on the public available datasets, evaluation metrics, and deployment strategies employed in the NIDS design. Finally, we propose an NIDS framework for IoT networks leveraging MEC.
A New Computational Deconvolution Algorithm for the Analysis of Forensic DNA Mixtures with SNP Markers

Single nucleotide polymorphisms (SNPs) support robust analysis on degraded DNA samples. However, the development of a systematic method to interpret the profiles derived from the mixtures is less studied, and it remains a challenge due to the bi-allelic nature of SNP markers. To improve the discriminating power of SNPs, this study explored bioinformatic strategies to analyze mixtures. Then, computer-generated mixtures were produced using real-world massively parallel sequencing (MPS) data from the single samples processed with the Precision ID Identity Panel. Moreover, the values of the frequency of major allele reads (FMAR) were calculated and applied as key parameters to deconvolve the two-person mixtures and estimate mixture ratios. Four custom R language scripts (three for autosomes and one for Y chromosome) were designed with the K-means clustering method as a core algorithm. Finally, the method was validated with real-world mixtures. The results indicated that the deconvolution accuracy for evenly balanced mixtures was 100% or close to 100%, which was the same as the deconvolution accuracy of inferring the genotypes of the major contributor of unevenly balanced mixtures. Meanwhile, the accuracy of inferring the genotypes of the minor contributor decreased as its proportion in the mixture decreased. Moreover, the estimated mixture ratio was almost equal to the actual ratio between 1:1 and 1:6. The method proposed in this study provides a new paradigm for mixture interpretation, especially for inferring contributor profiles of evenly balanced mixtures and the major contributor profile of unevenly balanced mixtures.