Up to now, many studies have been performed on particle radiations before or during earthquakes (EQs). In our previous study, with the help of piezoelectricity relationships and the elastic energy formula, the MCNPX simulation code was applied to find the amount of created atomic/ nuclear particles, the dominant interactions; and the energy of the particles for various sizes of quartz and granite blocks. In this study, using the MCNPX simulation code, we have estimated the flux of the particles (created from under-stressed granitic rocks) at different distances from the EQ hypocenter inside the fractures, filled with air, water, and CO2. It was found that inside a water-filled fracture, the particles do not show the flux far from the EQ hypocenter, but inside the gases like air and CO2 with the normal condition density, different types of particles can have a flux far from the source (more than a kilometer) and they might reach themselves to the surface in the case that the EQ hypocenter is very shallow (0- 5 km). However, for deep EQs, it seems that the most detected nuclear particles on the surface should pass via the vacuum-filled fractures and reach themselves to the surface. Moreover, it was concluded that the more density of the fracture’s filling fluid, the less distance that the particles can have a flux.

Efficient thermal management of modern electronics requires the use of thin films with highly anisotropic thermal conductivity. Such films enable the effective dissipation of excess heat along one direction while simultaneously providing thermal insulation along the perpendicular direction. In this study, we investigate the thermal conductivity of bilayer graphene (BLG) sheets using non-equilibrium molecular dynamics, examining both in-plane and cross-plane thermal conductivities. The in-plane thermal conductivity of 10 nm × 10 nm BLG with zigzag and armchair edges at room temperature is found to be around 204 W/m.K and 124 W/m.K, respectively. The in-plane thermal conductivity of BLG increases with sheet length. For all sizes, BLG with a zigzag edge exhibits higher thermal conductivity than that of the armchair edge, with this difference becoming more pronounced as the lengths of the sheets increase. In addition, increasing temperature from 300 K to 600 K decreases the in-plane thermal conductivity of a 10 nm× 10 nm zigzag BLG by about 34%. Similarly, the application of a 12.5% tensile strain induces a 51% reduction in its thermal conductivity compared to the strain-free values. Armchair configurations exhibit similar responses to variations in temperature and strain, but with less sensitivity. Furthermore, the cross-plane thermal conductivity of BLG at 300 K is estimated to be 0.05 W/m·K, significantly lower than the in-plane results by approximately four orders of magnitude. The cross-plane thermal conductance of BLG decreases with increasing temperatures, specifically, at 600 K, its value is almost 16% of that observed at 300 K.

Early detection of breast cancer, one of the most common cancers in women worldwide, raises survival rates and lowers the cost of treatment. Although breast cancer detection and classification (CAD) tools have improved, some issues and restrictions still require further research. The creation of breast cancer CAD systems, particularly with deep learning models, was significantly impacted by recent advancements in machine learning and image processing techniques. The current deep learning-based CAD system to identify and categorize masses in mammography is presented in a structured manner in this survey. The survey provides the most popular breast cancer CAD system evaluation metrics, publicly available mammographic datasets, and current image modalities. The survey examines the available literature, emphasizing its strengths and limitations. The survey also emphasizes the difficulties and drawbacks of the current methods for classifying and detecting breast cancer. We point out research gaps and make suggestions for further study. This systematic review may be beneficial for clinicians using CAD systems to diagnose breast cancer early on and for researchers looking for knowledge gaps and making more contributions to the field.

This study evaluates literature on the wheel of audit partners in Chinese companies and institutions by examining the impact of relationships before and after the rotation between the input and output partners. We consider the partners in the rotation before the rotation, before the work relationship. We find two different results from the previous working relationships: (a) increasing the likelihood of the outgoing partners after the cooling period, and (b) reducing the quality of the audit and lower accounting after the rotation. These findings ask whether the rotational partners are truly independent of the working relationships.

The quality of the environment affects human behavior, the micro-communities of human beings need creative thinking and ideas. Social environments have a significant impact on collective behaviors and social interactions. Nowadays achieving social indicators in housing with a sustainable approach is one of the goals that have been considered. The most important issue in the field of research is recognizing and examining the value of spaces in residential environments to achieve social life, in which it leads human to be social in public residential spaces. Paying attention to social relations with neighbors and people to create Social relationships are proportionate to the presence of individuals in their realm of life. The concept of neighborhood is being responsible for creating social relationships, influencing people living in the complex and community-based life. In this research, by satisfying the human need to communicate and interact with others, creating collective spaces in different scales such as commercial and recreational spaces in residential complexes, the presence of people in these spaces leads to the socialization of collective space and the factor in which the space achieves success. By explaining and identifying the components of socialization in the collective spaces of residential complexes, such spaces can be prepared for the presence of people in the space.

Based on a large sample of publicly listed and non-listed US commercial banks from 1996 to 2011, we find robust evidence consistent with banks using realized available for sale (AFS) securities gains and losses to smooth earnings and increase low regulatory capital. We also find that (i) banks with positive earnings smooth earnings, and banks with negative earnings generally take big baths; (ii) regulatory capital constrains big baths; (iii) banks with more negative earnings and more unrealized beginning-of-quarter losses (gains) take big baths (smooth earnings); and (iv) banks with low regulatory capital and more unrealized gains realize more gains. Also, banks with negative earnings tak big baths (avoid or reduce the earnings loss) if their unrealized gains are insufficient (sufficient) to offset the negative earnings. Our inferences apply to listed and non-listed banks, which indicates that the earnings management incentives do not derive solely from public capital markets. Our findings reveal that the accounting for AFS securities gains and losses enables banks to manage regulatory capital and earnings in a variety of ways.

Dysferlinopathies comprise a spectrum of muscular dystrophies characterized by progressive muscle weakness and degeneration due to mutations in the DYSF gene, which encodes the dysferlin protein critical for muscle membrane repair. This review delves into the clinical spectra of dysferlinopathies, their molecular mechanisms, and the spectrum of emerging therapeutic strategies. We explore the phenotypic heterogeneity of dysferlinopathies, highlight the incomplete understanding of genotype-phenotype correlations, and discuss the implications of various DYSF mutations. Furthermore, we examine the utility of animal models in elucidating disease mechanisms and the potential of symptomatic, pharmacological, molecular, and genetic therapies in mitigating the disease's progression. We also consider the roles of diet and metabolism in managing dysferlinopathies, as well as the impact of clinical trials on treatment paradigms. By culminating the complexities inherent in dysferlinopathies, this article emphasizes the need for multidisciplinary approaches, precision medicine, and extensive collaboration in research and clinical trial design to advance our understanding and treatment of these challenging disorders.

Fibrodysplasia Ossificans Progressiva (FOP) is an enigmatic, ultra-rare genetic disorder characterized by progressive heterotopic ossification, wherein soft connective tissues undergo pathological transformation into bone structures. This incapacitating process severely limits patient mobility and poses formidable challenges for therapeutic intervention. Predominantly caused by missense mutations in the ACVR1 gene, the disorder has hitherto defied comprehensive mechanistic understanding and effective treatment paradigms. This write-up offers a comprehensive overview of the contemporary understanding of FOP's complex pathobiology, underscored by advances in molecular genetics and proteomic studies. We shed light on targeted therapy, spanning genetic therapeutics, enzymatic and transcriptional modulation, stem cell therapies, and innovative immunotherapies. We also focused on the intricate complexities surrounding clinical trial design for ultra-rare disorders like FOP, addressing fundamental statistical limitations, ethical conundrums, and methodological advancements essential for the success of interventional studies. We advocate for the adoption of a multi-disciplinary approach that converges bench-to-bedside research, clinical expertise, and ethical considerations to tackle the challenges of ultra-rare diseases like FOP and comparable ultra-rare diseases. Overall, this article serves a dual role: as a definitive scientific resource for ongoing and future FOP research and as a call to action for innovative solutions to address methodological and ethical challenges that impede progress in the broader field of medical research for ultra-rare conditions.

Molecular engineering research is the fundamental way and the only way for the development of biomaterials. Based on molecular engineering, the biocompatibility of natural conformation and polymer biomaterials was studied. In this paper, we discuss that natural conformation is the basis of protein biological function, and that the synergistic action of peptide chain and side group is the motive force for protein to construct natural conformation and complete biological function. On the basis of the influence of the adsorption of polymer biomaterials on the natural conformation of proteins, the relationship between biocompatibility of biomaterials and protein conformation is further explained. Studies have shown that bismuth molecular materials can only be applied in the market and have their functionality if they have good biocompatibility. Therefore, the biocompatibility evaluation of new materials has important practical significance.

Nowadays, the wasting of resources is one of the fundamental challenges of the industrial sector. The rectangle packing problem can be very effective in this context. Practical applications of this issue in the timing and designing of the industries and businesses are very remarkable. The purpose of this issue is to arrange a set of rectangles with specific dimensions in a rectangular page with a specific width and unlimited height without overlapping. The fundamental challenge in this issue is that this is an NP-complete issue. Therefore, it is difficult to achieve the best arrangement, which has the maximum rate of resource utilization and also has a linear running time. Many algorithms have been presented to estimate a practical solution for this issue. In the past decades, the best fit method has been one of the most useful methods for this purpose. This study presents a combinatorial algorithm based on two algorithms, including the lowest front-line strategy and the best-fit algorithm. The running results indicate that the suggested algorithm performs well, despite its simplicity. The time complexity of the suggested algorithm is O(nm), in which n is the number of input rectangles and m is the number of the created front lines.

In this research, the antioxidant and antibacterial activities of active films based on potato starch containing Viola odorata extract (VOE) were investigated both in vitro and in chicken fillets. The VOE was added to the starch film formulation at 0, 1, 2, and 3% (w/v). The results showed that by increasing the extract level, the total phenol content and antioxidant and antibacterial activity of the films against Escherichia coli, Staphylococcus aureus, and Salmonella typhimurium improved remarkability. The results of meat tests indicated significant antioxidant and antimicrobial activity of active films containing different levels of VOE in chicken fillets, and a direct relationship was observed between the concentration of the extract and the functional activity of the films, so with the increase in the concentration of the extract in the films, the rate of lipid oxidation and growth of microorganisms in chicken fillets decreased significantly during the storage period, and less volatile nitrogen bases, metmyoglobin, and oxidation products were produced in the fillets. In general, the results of this research demonstrated that active film based on potato starch containing VOE (especially 2 and 3% levels) has the ability to extend the oxidative and microbiological shelf life of chicken fillets during cold storage for at least eight days.

Background and Purpose: Oxidative stress is an important factor in the pathogenesis of neurological problems. One of the compounds causing oxidative stress is diazinon. In the present project, the protective role of gallic acid compound in reducing oxidative stress in brain tissue of mice exposed to diazinon was investigated. Method: In this study, eight groups of six series of mice including the first group (control) of normal saline, the second group of diazinon (40 kg/mg) as IP, three groups of diazinon with gallic acid (50, 100, 200 kg/mg), was prescribed as a single dose and for 24 hours. The atropine + pralidoxime group was also evaluated as a standard treatment group. At 24 hours after the last dose, the animals were sacrificed and brain tissue was examined to assess oxidative stress parameters, including glutathione content, protein carbonyl content, lipid peroxidation, reactive oxygen species (ROS) and mitochondrial function in mitochondria isolated from brain tissue. Results: The results of this research showed that oxidative stress biomarkers increased significantly (P<0.001) in the diazinon group compared to the control group. Oxidative stress was significantly reduced in the groups receiving gallic acid at a dose of 200 and 100 kg/mg of diazinon compared to the diazinon group. Conclusion: The results of the present study show that diazinon causes damage to brain tissue by inducing oxidative stress. Therefore, according to the reduction of oxidative stress by gallic acid compound, it can be concluded that this compound can protect against brain toxicity caused by diazinon in animal model by stimulating antioxidant and radical scavenging pathways.

In this paper, we investigate a financial market in which asset prices evolve based on a multi-dimensional Brownian motion process and a multi-dimensional Poisson process with different credit and deposit rates We proceed to evaluate European options by establishing upper and lower hedging prices through a transition to a suitable auxiliary market. Additionally, we address the minimization of shortfall risk and no-arbitrage price bounds in incomplete markets within this framework.

The aim of this study was to develop sago starch-based bionanocomposite films containing TiO2 nanoparticles and Penganum harmala extract (PE) to increase the shelf life of chicken fillets. First, sago starch films containing different levels of TiO2 nanoparticles (1, 3 and 5%) and PE (5, 10 and 15%) were prepared, and the thickness, barrier properties, and antibacterial activity of the films. Then, the produced films were used for the chicken fillets packaging and the physicochemical and antimicrobial properties of samples were examined during 12-day storage at 4°C. The results showed that the addition of the combination of nanoparticles and extract and increasing their levels in the films increased the antibacterial activity against gram-positive and gram-negative bacteria. The results also demonstrated that during the storage period, the pH, total volatile basic nitrogen (TVB-N), and the number of aerobic mesophilic bacteria, psychrophilic bacteria, coliforms, molds and yeasts in fillets increased (p&lt;0.05). However, coating the fillets with bionanocomposites can significantly reduce the rate of change in chemical properties and microbial growth of the samples. This sample increased the shelf life of the fillets by at least 8 days compared to the control sample.

Background: Qazvin province has suffered a lot of natural disasters (earthquakes) for a long time. so, the establishment of the first industrial city in Iran, has always been a source of concern. One of these problems is the possibility of chlorine gas leaking from the chlorination system of drinking water and treatment plant the existing ones are on the health of the inhabitants of this region.Material and method: This analytical study was carried out in Novorossiysk district of Alborz in December 2012. Initially, chlorine gas leakage scenarios from these facilities were simulated by ALOHA and WISER software, and then using the Emergency Level System, according to the CCPS Consultative Center's Chemical Process Safety Advice, the Emergency Response Program was presented. Results: ALOHA software, in comparison with WISER software, has more precision to identify and address the consequences of chlorine gas leakage hazards and according to the simulation of this software in the event of a leak from the well and water resources unit at 8240 m from the area and at night 7520 meters will be affected. Due to the speed of delivering results through the WISER software, this software can be used at times when the operating speed is considered (incident time).Conclusion: According to the CCPS model, the emergency conditions were classified at levels 3 and 4, therefore, the relevant crisis management agencies (Qazvin Crisis Management Committee, Alborz City Health Center, Red Crescent and Firefighting Authority) should provide training maneuvers At Alborz city level, and informing people of cities and industrial companies, many efforts have been made to reduce human and environmental damage in case of possible leakage.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

This paper is devoted to the study of reproducing kernels on 2-inner product Hilbert spaces. We focus on a new structure to produce reproducing kernel Hilbert and Banach spaces. According to multi-variable computing, this structures can be useful in electrocardiographs, machine learning and economy

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

Digital transformation has become the top priority for 80% of sports companies worldwide, but statistics show that between 70% and 95% of all digital transformation projects fail due to signif-icant and varied challenges that sports companies face during the process of digital transfor-mation. This is because strategy, not digital technology, drives digital transformation, and suc-cess is unlikely without a mature digital transformation model. Moreover, the digital transfor-mation model of developing and emerging markets is not the same and cannot be adopted as a blueprint. Therefore, the aim of this research was to propose a framework for the digital trans-formation maturity of esports businesses in developing and emerging markets. Semi-structured interviews were conducted with a sample of 15 stakeholders of esports businesses in Iran. Thematic analysis was used to analyze the interviews, and three main perspectives were identified: (1) enablers, (2) digital resilience and capabilities, and (3) digital transformation ma-turity stages. The enablers were formed by six sub-themes, including governance and leader-ship, strategy, culture and skills, innovation, technology, and data. Digital resilience and capa-bilities were formed by four sub-themes, including business model and ecosystem, digital cus-tomer experience, digital employee experience, and digital processes. Additionally, it was found that digital transformation maturity stages are based on four stages of digital beginners, digital followers, digital conservatives, and digital leaders. The findings indicate that moving towards digital transformation and achieving digital resilience in esports businesses will be impossible without synergy between enablers and digital resilience and capabilities. This framework can be useful for esports businesses to evaluate their current digital status and effectively guide them towards a desirable digital status. Furthermore, esports businesses can prioritize their efforts and resources for digital transformation by focusing on targeted capabilities and enablers and ensure more effective and efficient allocation of resources towards digital transformation.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

This paper propose an original approach of achieving a Cymatics based visual perception of image-extracted text. In this context, an effective approach for automated text detection and recognition for the natural scene images is proposed. The incoming image is firstly enhanced by employing CLAHE and DWT. Afterwards, the text regions of the enhanced image are detected by employing the MSER feature detector. The non-text MSERs are removed by employing the geometrical and contour based filters. The remaining MSERs are grouped into words or phrases by finding out similarities between them. The text recognition is performed by employing an OCR function. The extracted text is sequentially analysed on character by character basis. Each character is converted into a methodical acoustic excitation. Finally, these excitations are converted into the systematic visual perceptions by using the phenomenon of Cymatics. The system functionality is tested with an experimental setup. For the case of studied natural scenes, the suggested approach achieves 80% precision in text localization and 53% precision in end-to-end text recognition. The devised system principle is novel and can be employed in various applications like visual art, encryption, education, integration of impaired people, etc.

This paper proposes a novel approach, based on the adaptive rate processing and analysis, for the isolated speech recognition. The idea is to smartly combine the event-driven signal acquisition and windowing along with adaptive rate processing, analysis and classification for realizing an effective isolated speech recognition. The incoming speech signal is digitized with an event-driven A/D converter (EDADC). The output of EDADC is windowed with an activity selection process. These windows are later on resampled uniformly with an adaptive rate interpolator. The resampled windows are de-noised with an adaptive rate filter and their spectrum are computed with an adaptive resolution short time Fourier transform (ARSTFT). Later on, the magnitude, Delta and Delta-Delta spectral coefficients are extracted. The Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The system functionality is tested for a case study and results are presented. An 8.2 times reduction in acquired number of samples is achieved by the devised approach as compared to the classical one. It aptitudes a significant computational gain and power consumption reduction of the proposed system over the counter classical ones. An average subject dependent isolated speech recognition accuracy of 96.8% is achieved. It shows that the proposed approach is a potential candidate for the automatic speech recognition applications like rehabilitation centers, smart call centers, smart homes, etc.

The high figure of merit and earth abundance of Cu₁₂Sb₄S₁₃ thermoelectric ma- terials have recently attracted many attentions toward these type of complex compounds. Intrinsic low thermal conductivity, as well as tunable electronic transport properties, make them suitable for thermoelectric power generation. In this study, we perform a comparative theoretical study on the substituted compounds, primarily at the Cu site including known tetrahedrite Cu₁₂Sb₄S₁₃, by means of first-principles calculations. The density functional theory of electric structure is applied to investigate the result of substitution.

Studying various marine biomineralized ultrastructures reveals the appearance of common architectural designs and building blocks in materials with fascinating mechanical properties that match perfectly to their biological tasks. Advanced mechanical properties of biological materials are attributed to evolutionary optimized molecular architectures and structural hierarchy. One example which has not yet been structurally investigated in great detail is the shell of Mytilus edulis L. (Finnish blue mussel) found in the archipelago of SW-Finland. Through a combination of various state-of-the-art techniques such as high-resolution electron microscopy imaging, Fourier-transformed infrared spectroscopy, powder X-ray diffraction, synchrotron wide-angle X-ray diffraction, nanoindentation and protein analysis, both the inorganic mineralized components as well as the organic-rich matrix were extensively characterized. We found very similar ultra-architecture across the shell of M. edulis L. as compared to the widely studied and closely related M. edulis. However, we also found interesting differences, for instance in the thickness and degree of orientation of the mineralized layers indicating dissimilar properties and related alterations in the biomineralization processes. Our results show that the shell of M. edulis L. has a gradient of mechanical properties, with the increase in the stiffness and the hardness from anterior to the posterior region of the shell. The shell is made from distinct and recognizable mineralized layers each varying in thickness and microstructural features. At posterior regions of the shell, moving from dorsal to ventral side, these layers are an oblique prismatic layer, a prismatic layer and a nacreous layer, in which the oblique prismatic layer is found to be the main and thickest mineralized layer of the shell. Probing the calcified rods in the oblique prismatic layer using high resolution SEM imaging revealed opening of channels with a diameters of 40-50 nm and lengths up to a micrometer extending through the rods. The chitin and protein have been found to be the main component of the organic-rich interfacial matrix as expected. Protein analysis showed two abundant proteins with sizes around 100 kD and 45 kD which likely not only regulates biomineralization and adhesion of the crystals but also governing the intrinsic-extrinsic toughening in the shell. Overall, this detailed analysis provides new structural insights into biomineralization of marine shells in general.

No single evolutionary event has been identified as the cause for the final emergence of our species. I propose that a mutation on CD155 receptor gene occurred to establish a symbiosis with poliovirus, which exerted its beneficial impact via RNA dependent non-genetic transgenerational inheritance, which caused a qualitative enhancement of cognitive functions. I posit that this mutation occurred in what we call, Anatomically Modern Humans, our immediate ancestor species and that the disruption of this symbiosis causes autism spectrum disorder. Positive selection of CD155 to the extent of becoming a species defining characteristic, the chronology of autism spectrum disorder prevalence increase and continued increase, the multigenerational nature of RNA inheritance, the universal infection of humans by poliovirus and a very low associated mortality rate, and several other factors support this hypothesis. Specific genetic, epidemiological and sperm miRNA content studies are suggested to test this hypothesis.

Gold- and MnO₂- nanozymes are well-known for their enzyme-like activity. In this regard, initially, gold- and MnO₂- nanozymes were synthesized by simple and green methods. Afterward, the kinetic studies were performed using the Michaelis–Menten model for both gold- and MnO₂- nanozymes. The kinetic parameters including K_m and V_max were calculated via the construction of the linear plot of Lineweaver–Burk for both nanozymes. The results showed a V_max and K_m of 185 nM sec^-1 and 47 nM sec^-1 for the gold- and MnO₂- nanozymes, in order. The ratio of V_max(gold)/V_max(MnO₂) was found to be about 4.0 which pointed that the catalytic efficiency of gold-nanozymes is 4.0-fold higher than the catalytic efficiency of MnO₂- nanozymes. The K_m value was found to be 0.72 mM and 1.6 mM for the as-prepared gold- and MnO₂- nanozymes, respectively, and the K_m of MnO₂-nanozymes is 2.2-fold higher than that of gold nanozymes. Since the Km shows the affinity of substrate for binding to nanozyme active nodes (lower K_m=higher affinity), it is consultable that the substrate affinity toward MnO₂-nanozymes is 2.2-fold lower than that of the gold-nanozymes. Considering the above results, the as-prepared gold nanozymes are very stronger peroxidase-like mimics than the metal oxide MnO₂-nanozymes.

The use of nitrogen (N) fertilizers can have detrimental effects on the environment due to nitrate leaching. This study aims to examine the impact of N fertilizer source and application timing on wheat yield and nitrate leaching in various soil textures. A randomized complete block design (RCBD) was employed to conduct the experiment. The treatments consisted of two N fertilizer sources: ammonium nitrate (AN) and urea (NN), applied at three different times throughout the year, with a control treatment. The three application timings were total application before sowing (T₁), total application in spring (T₂), and half the amount before sowing and the other half in spring (T₃). The experiment was conducted in three different soil textures (sandy loam, silty clay loam, and silty clay), with three replications over three growing seasons: 2018-19 (Y₁), 2019-20 (Y₂), and 2020-21 (Y₃). The results indicated significant effects of different soil textures on grain yield. Specifically, the silty clay loam soil differed significantly from the sandy loam and silty clay soils in terms of grain yield. The interaction effects between the N fertilizer source and application timing were also found to be significant for all soil textures (p<0.01). In sandy loam and silty clay soils, the grain yield increased when using the T₃ application timing compared to the silty clay loam soil. Notably, the silty clay loam soil with the T₂ application timing exhibited the highest grain yield, with a yield of 6863.6 kg ha^-1. Furthermore, the study revealed that N fertilizers and their application timing significantly influenced the protein content in the silty clay and sandy loam soils. The protein percentages were 10.76 and 10.93 for the silty clay and sandy loam soils, respectively, following the T₂ treatment. Additionally, ammonium nitrate (AN) demonstrated a noticeable effect on the percentage of grain protein compared to urea (NN). In conclusion, the application timing of N fertilizers had an impact on grain yield and protein content in different soil textures. Moreover, nitrate concentration increased throughout the experiment, with the highest soil nitrate concentration observed in both fertilizer treatments, particularly in the sandy loam soil.

MnO₂ nanoparticles are considered nanozymes with intrinsic peroxidase-like activity and there are several reports on their application in the field of nanozyme-based sensing and detection, however, up to now, there is no report on the investigation of their biochemical and kinetics properties toward enzyme-mediated oxidations. Hence, in this work, the MnO₂ nanozymes were synthesized and characterized with different characterization methods. Thereafter, their biochemical properties including pH stability, thermal stability, and salt stability as well as their kinetics perfromances were evaluated toward n-electron irreversible oxidation of 3,3’-diaminobezedine. The results showed that the as-prepared nanozymes reveal their maximal enzyme-like activity over a wide pH range of 3.0-6.0 at a temperature of 23-25 ℃ toward oxidation of 3,3’-diaminobezedine. Besides, the salt stability studies exhibited that the as-mentioned nanozymes can save their maximal activity over a wide range of high salt concentrations over 3-7 M of NaCl. Moreover, the kinetics studies revealed a K_m as low as 1.6 mM and a V_max as high as 47 nM sec^-1 for the MnO₂ nanozymes toward irreversible oxidation of DAB to produce the brown-colored polyDAB.

In this study, considering the peroxidase-like activity of the SiO₂@AuNPs nanocomposite, their biochemical properties including, pH stability, cycling stability, shelf stability, and kinetic parameters were investigated. The results revealed that the as-mentioned SiO₂@AuNPs nanocomposite reveal its maximal activity at pH=4.0 along with saving 83.3% of its maximal activity at pH=5.0. Besides, the reusability and shelf-storage studies exhibited that the SiO₂@AuNPs nanocomposite retained 90% and 100% of its initial activity after 5 operational cycles and 30 days of storage, in order. The kinetic parameters of the as-prepared nanozymes were calculated using Menten kinetic model, revealing a V_max of 1.35 µM min^-1 and a K_m as low as 0.06 mM for the SiO₂@AuNPs nanocomposite. Based on the results of this work, the as-prepared SiO₂@AuNPs nanocomposite with intrinsic peroxidase-like activity shows high substrate affinity and catalytic efficiency along with excellent cycling- and shelf-stability, making them suitable for application in peroxidase-mediated reactions instead of the native enzyme.

In this contribution, unmodified silver nanoparticles were synthesized and characterized for their size and morphological properties. Thereafter, their peroxidase-like activity as the common catalytic property of silver nanoparticles was investigated by catalyzing the oxidation of 3,3’,5,5’-tetramethyl-benzidine (TMB) as peroxidase substrate, exhibiting, a specific activity as high as 5.4 µM min^-1 for the as-prepared unmodified silver nanoparticles. The stability of the catalytic activity of the as-prepared nanozymes was also checked upon their storage at ambient temperature within 7 days at different storage conditions. The results revealed that the peroxidase-like activity of unmodified silver nanoparticles was approximately retained at about 75%, and 63% after 7 days exposing daylight and air oxygen, in order. The shelf-life (storage stability) of the as-prepared nanozymes was also investigated at usual storage conditions (i.e., 4 ℃ under dark), revealed that the nanozymes saved their activity about 96% of their initial activity after 10 days of storage at 4 ℃ under dark conditions.

It is well-known that the peroxidase enzyme catalyzes the oxidation of different substrates such as 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine, however, the affinity of different substrates to the enzyme and consequently and catalytic performances of the enzyme toward different substrates is not same, resulting in different kinetics indexes. As the BSA-gold nanozymes are considered artificial peroxidase enzymes, hence, the kinetics indexes of these nanozymes should also be varied by varying their substrate. Hence, in this work, a comparative study was performed on the kinetics performances of BSA-gold nanozymes for enzyme-mediated oxidations of 3,3’,5,5’-thetramethylbenzidine, and 3,3’-diaminobenzidine. The results showed that the Km value of BSA-gold nanozymes was 0.03 mM and 0.72 mM toward TMB and DAB, in order, to reveal the higher affinity (lower Km) of TMB for binding to nanozyme active nodes compared to its alternative substrate, DAB. In contrast, the Vmax was found to be 263 nM sec^-1 and 185 nM sec^-1 for nanozmye-mediated oxidation of TMB and DAB, respectively. The higher Vmax of the nanozyme-mediated oxidation of TMB revealed that the catalytic efficiency of BSA-Au nanozymes toward TMB oxidation is higher (about 1.5-fold) than that of the DAB oxidation. The difference between the kinetic indexes of TMB and DAB may be related to their different oxidation pathways and their different reactivity. In fact, the DAB oxidizes via an n-electron irreversible oxidation pathway to produce an indamine polymer. While TMB nanozyme-mediated oxidation has occurred upon a 2-electron reversible mechanism for the production of a cation radical. These different pathways resulted in different kinetic performances.

The fourth Industrial Revolution, well-known as “Industry 4.0”, based on the integration of information and communication technologies, has introduced significant improvements in manufacturing. However, vision systems still experience various impracticalities in dealing with the effect of complex lighting on the systems platform. Therefore, a machine vision system for automatic identification of pen parts under varying lighting conditions at a digital learning factory is proposed. The developed vision system presents a straightforward approach by effectively minimizing the environmental lighting effect on the identification process. First, the obtained information of the designed vision framework is exported to a program, where a reduction of non-uniform illumination is achieved through the implementation of Retinex image enhancement techniques. Then, the color-based Fuzzy C-means (FCM) algorithm, including improved mark watershed segmentation, is employed for pen parts object classification. Finally, the position features of the selected pen part are reported. The process applied to a total number of 210 upper pen parts (caps) and 241 lower pen parts (tubes) images under different lighting scenarios. Results indicate that average parts identification precision for cap and tube parts is different and equals to 98.64% and 95.26%, respectively. The present methodology provides a promising scheme that can be feasibly adapted for other industrial Color-based object recognition applications.

It is well established in literature that the public debt and economic growth bear positive and non-linear relationship. However, in recent literature, evidence of no causal relationship is found when accounted for endogeneity in case of advanced economies (Panizza & Presbitero, 2014). Chudik, Mohaddes, Pesaran, & Raissi, (2017) analyse the data on forty countries and find no evidence of universally applicable threshold effect in the relationship between debt and growth. These advancements in the debt-growth literature provides the motivation to re-explore the relationship between public debt and economic growth under non-linearity and endogeneity in context of developing economies of South Asia including Pakistan, India, Bangladesh and Sri-Lanka for the period 1980-2014. There exists a significant, positive but nonlinear relationship between the public debt and economic growth for the selected set of developing countries when accounted for endogeneity and non-linearity. The negative association between the public debt and economic growth for SAARC region is found when the debt level is higher than 61% of GDP which is quite lower than developed economies (90% of GDP). Individual threshold levels for debt-to-GDP ratio divulge that Sri Lanka, Pakistan and India need to control their public borrowings as their current debt levels are higher and/or around the respective threshold levels.

Mine waste rock and drainage pose lasting environmental, social, and economic threats to the mining industry, regulatory agencies, and society as a whole. Mine drainage can be alkaline, neutral, moderately or extremely acidic and contains significant levels of sulfate, dissolved iron, and frequently a variety of heavy metals and metalloids, such as cadmium, lead, arsenic, and selenium. In acid neutralization by carbonate and silicate minerals, a range of secondary minerals can form and possibly scavenge these potentially harmful elements. Apart from the extensively-studied microbial-facilitated sulfide oxidation, the diverse microbial communities present in mine rock and drainage may also participate in the formation, dissolution, and transformation of secondary minerals influencing the mobilization of these metals and metalloids. This article reviews major microbial-mediated geochemical processes occurring in mine rock piles that affect drainage chemistry, with a focus on the role of microorganisms in the formation, dissolution and transformation of secondary minerals. Understanding this is crucial for developing biologically-based measures to deal with contaminant release at the source, i.e., source control.

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

In this paper, the Group Method of Data Handling (GMDH) type of neural networks is used for the inductance calculation of variable inductors. The relation between the inductance of the inductor in the linear and nonlinear regions is investigated, and parameters such as the voltage across the inductor, bias current, and ac current are taken into account. The experimental setup is used for generating the data needed for training the neural network. Over 800 experiments were conducted and were used for training and validation of the neural network results. The results are compared with the reluctance equivalent circuit method, and they show a much better accuracy. The proposed method can be used for the calculation of various magnetic components, and it is not limited to variable inductors.

Although most people are infected with Epstein-Barr Virus (EBV) during their lifetime, only a minority of them develop an EBV-associated malignancy. EBV acts in both direct and indirect ways to transform infected cells into tumor cells. There are multiple ways in which the EBV, host, and tumor environment interact to promote malignant transformation. This paper focuses on some of the mechanisms that EBV uses to transform the tumor microenvironment (TME) of EBV-associated gastric cancer (EBVaGC) for its benefit, including overexpression of IDO1, synergism between H pylori and EBV coinfection, and M1 to M2 switch. In this review, we expand on different modalities and combinatorial approaches to therapeutically target this mechanism.

Steam boiler has a significant role in the industrial sector. The failure in boiler tubes significantly reduces the availability of the plant. Furthermore, deposits on the inner tube wall contribute to such failure by changing the thermal resistance of the wall, which causes a significant increase in the tube wall's temperature and, consequently, lower allowable stress. The presented thermal-fluid model is developed for the water tube. The model is implemented using ANSYS FLUENT to estimate the wall maximum water tube wall temperature considering the deposits layer and then determine the minimum thickness of the tube wall via ASME standards to assure safe operation. The model is verified via a mesh independence test. The minimum tube thickness is recommended and justified given the thermo-mechanical performance of the water tubes at several operating loads of 50, 75, and 100 %. These loads are implemented by different convective heat transfer coefficients of flue gases under different deposit thicknesses, thermal conductivity, and flue gas temperature. The present work contributes to the literature by recommending the minimum thickness of the water wall tubes and highlighting the thermo-mechanical effect of the deposits on water tube resilience under different operating conditions.

The development of biosensors for target detection plays a crucial role in advancing various fields of bioscience. This work presents the development of a genosensor that exploits the colorimetric phenol-sulfuric acid sugar reaction for the detection of DNA, and RNA as specific targets, and DNA intercalator molecules. The biosensor combines simplicity and reliability to create a novel bio-assay for accurate and rapid analysis. A 96-well microplate based on polystyrene polymer was used as the platform for an unmodified capture DNA immobilization via a silanization process and with (3-Aminopropyl) triethoxysilane (APTES). After that, a hybridization step was carried out to catch the target molecule, followed by adding phenol and sulfuric acid to quantify the amount of DNA or RNA sugar backbone. This reaction generates a yellow-orange color on the wells measured at 490 nm that was proportional to the target concentration. Under the optimum conditions, a calibration curve was obtained for each target. The developed biosensor demonstrated high sensitivity, good selectivity, and linear response over a wide concentration range for DNA and RNA targets. Additionally, the biosensor was successfully employed for the detection of DNA intercalator agents that inhibit the hybridization of DNA complementary to the immobilized capture DNA. The developed biosensor offers a potential tool for sensitive and selective detection in various applications, including virus diagnosis, genetic analysis, pathogenic bacteria monitoring, and drug discovery.

Geometallurgy has become an important toll to predict the process behavior of the ores, and to decrease the production risks cause by variability of geological settings. In this paper geometallurgical index for grinding properties of the ore has investigated. In a comprehensive research at Sarcheshmeh porphyry copper mine, geological features, what were supposed to affect the main process responses including product’s grade-recovery, and plant’s throughput, were subjected to investigate as the possible geometallurgical indexes. The rock breakage variability, in terms of a ball mill grinding circuit and its effects on the plant throughput and energy consumption are presented. Ninety samples were collected based on geological features including lithology, hydrothermal alteration, and geological structures. The samples were characterized using XRD, XRF, and electron and optical microscopy. A simulated test method for Bond ball mill work index (BWI) was used to perform the comminution test. The results showed BWI values vary from 5.67 kWh/t to 20.21 kWh/t. Examination of the possible correlations between BWI and the geological features showed that the key geological feature related to comminution variability is lithology. In addition, the hydrothermal alteration would be an effective parameter in the period that the plant is fed with a single lithology

Due to urban sprawls, the arable land is shifting in the settlement areas and towns, while industrial development is making it more vulnerable. The current study was conducted to see the changes in land use and land cover in Kotri area, Pakistan from 1998 to 2020 to establish trends in recent land use development based on available land use data. In this regard, satellite images in the study area were acquired from USGS and processed using ArcMap 10.5 and Environmental for Visualizing Images (ENVI). Results show that; there was a change in the settlement pattern of Kotri city with 40% between 1998 and 2020, 77% decrease in Bare-land was observed in 2020. Natural vegetation in the study area has decreased significantly with 4.4 Sq/ km in 2020, surface water-bodies in 1998 were 1.3 sq/km and in 2020 there is 1.8 Sq/km indicating 32% increase in surface water bodies. It was further revealed that settlement in Kotri has increased due to its geographical location, nearness of the major city of Hyderabad, and the establishment of the industrial zone and educational city near Kotri. From the foregoing, the study concluded that Kotri City will have more changes in its land use and land cover as the expansion in the city is going on and on. It is therefore recommended that local government and provincial government should pay attention towards this development and put up an action plan towards proper provision for such expansion.

This research explores a novel approach to integrating offshore wind farms into smart microgrids through the development of an advanced model predictive control (MPC) strategy for offshore wind turbines. The MPC strategy comprehensively incorporates complex wind dynamics, such as wind speed, direction, and turbulence, ensuring an accurate representation of turbine behavior. Additionally, the research includes models addressing wind farm layout, wake effects, and electrical infrastructure, enhancing the modeling framework's fidelity and applicability to real-world offshore wind energy systems within a smart microgrid environment. The inherent variability of wind power, transmission technology limitations, and the need for seamless transitions between grid-connected and islanded operation modes in smart microgrids necessitate robust control mechanisms. These mechanisms ensure efficient power capture, grid stability, and optimal energy utilization. To tackle these challenges, the research proposes a smart microgrid design incorporating both AC-DC and DC-DC conversion technologies. This design leverages renewable energy sources like solar and wind with AC-DC conversion for efficient power capture, integrates DC energy storage systems to store excess energy during low demand periods, and utilizes power control mechanisms to manage energy flow effectively within the smart microgrid. The study emphasizes the importance of modeling and control strategies for both AC-DC and DC-DC converters within the smart microgrid framework. To enhance the microgrid's cost-effectiveness, priority is given to the utilization of solar PV energy sources. It also highlights the integration of protection systems for DC faults and coordinated control of AC/DC breakers and converters. This comprehensive approach aligns with the core principles of adaptability, reliability, and sustainability in smart grids, aiming to advance smart microgrid technologies and pave the way for a more resilient, efficient, and secure energy future.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

This study compares the effects of lecture-based instruction and process-oriented guided inquiry learning (POGIL)-based instruction on the self-efficacy performance of Grade 12 students. A quasi-experimental, pretest-posttest design was adopted to investigate and assess the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes; Knowing, Applying and Reasoning (KAR). Two government high schools in Alain were selected as research sites, one for the boys and one for the girls. The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys), and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a circular motion unit in physics using POGIL-based instruction, while the control group was taught the unit using lecture-based instruction. The study's findings showed statistically significant differences between students of the control group and the treatment group in favour of the latter regarding their science performance, self-efficacy and science-related attitudes. Moreover, positive correlations were found between participants’ performance at the KAR test, self-efficacy and scientific attitudes towards scientific inquiry after the intervention. In conclusion, it is recommended to shift teaching towards POGIL-based instruction due to its positive impact on students’ performance and self-efficacy. It is also suggested to replicate the study to include government and private schools, elementary and high schools, teachers and advisors.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

In this paper, we study the weighted reproducing kernels. We introduce a generalization of weighted reproducing property and extend it to relative reproducing property on Banach spaces. This generalization will develop the parallel computing and it can be used as a base of parallel learning for machines especially in the case that there are non-homogenous weighted hypothesis. This methods formulate learning and estimation problems in a relative reproducing kernel Banach space (RRKBS) of functions defined on the data domain, expanded in terms of a kernel.

Cadmium(II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic cadmium(II) ions from synthetic solutions. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium(II) ion is obtained at pH 6. The thermodynamic parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd(II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

Abstract:Salmonella species (spp) and Escherichia coli (E. coli) are the most common infectious pathogens in poultry. Antimicrobials were given either for the treatment or growth promoters that can increase the possibility of emergence of bacterial resistance towards antimicrobials. The aim of this study was to determine the prevalence of antimicrobial resistant (AMR) Salmonella spp and E. coli isolated from a sample of broiler farms in East Coast Malaysia from 2018-2019. A total of 384 cloacal swabs were collected from broilers farms in Kelantan, Terengganu, and Pahang. The bacteria were isolated and confirmed by bacteriological and serological methods. Following that, confirmed isolates were subjected to antimicrobial susceptibility test. Salmonella spp and E. coli were recovered from the cloacal swabs samples with the overall prevalence of 6.5% and 51.8% respectively. In Kelantan, Terengganu and Pahang, the prevalence of Salmonella spp were 7%, 6.5% and 5.8% respectively, while the prevalence for E. coli were 50%, 48.3% and 58% respectively. Salmonella spp and E. coli displayed resistance towards the following antimicrobials: erythromycin (100% for both pathogens), chloramphenicol (76.2%, 84.5%), tetracycline (62%, 94.6%), ampicillin (47.7%, 87%), sulfamethoxazole/trimethoprim (42.9%, 83.3%), ciprofloxacin (4.8%, 23.8%), nalidixic acid (9.6%, 60.7%), streptomycin (19%,66%), and kanamycin (28.6%,57%), cephalotin (0%, 11%), gentamicin (0%, 20.2%) respectively. No resistance were recorded towards colistin for both pathogens. Multidrug resistance (MDR) was recorded in 82% of Salmonella spp and 100% of E. coli. These findings demonstrate the high prevalence of MDR Salmonella spp. and E. coli in broiler farms in East coast Malaysia. This could be attributed to the excessive use of antimicrobial agents by the poultry farm owners. Enhanced control measures and a strong monitoring system should be urgently implemented to reduce the emergence of antimicrobial resistance that is harmful to public health.

There is less attention to use an environmental perspective in economic valuation and individuals' willingness to pay for recreational sites. Therefore, the aim of this study is to use contingent valuation methods to estimate the recreational value and willingness to pay of visitors to the protected area of Helen forest, considering the scale of the new environmental paradigm (NEP). To achieve this, the sample size for survey was determined using the Cochran method, and then economic valuation and environmental perspective questions were designed and completed through the completion of a dual-dimensional questionnaire and NEP scale. To determine the factors influencing individuals' willingness to pay, the logistic regression model was employed. In this study, two modeling approaches were used. The first utilized a base model containing socio-economic variables, while the second model integrated a NEP into the base, and subsequent estimations were conducted. The results indicated that 83.04% of visitors are willing to pay an amount for the recreational use of the study area. The estimated model results demonstrated that variables such as the proposed amount, frequency of visits, environmental statement, age, gender, education, individual monthly income, residency, and the NEP are among the most influential factors affecting the likelihood of accepting the proposed amount by tourists. The strong and positive correlation between the NEP and willingness to pay indicates that individuals with a favorable environmental perspective tend to exhibit a higher willingness to pay. In this study, the average willingness to pay per household per visit was 199,793 Rials, and the annual recreational value for the entire Helen forest area was 23746832564 Rials. Furthermore, the survey results regarding visitors' perspective on the NEP revealed that the average NEP score for visitors to Helen forest was 36.57, with the statement 'plants and animals have similar rights to humans' receiving the highest average score of 28.4. The result of this study shows the importance of environmental perspective for sustainable management of a recreational site to balance economic development with environmental conservation and social well-being.

The rapid penetration of social media has been redefining every facet of the old marketing and customer engagement tactics, not only for the low-end and mass products but also for luxury brands. In this context, brands are dealing with the challenge of keeping the balance between using mass marketing strategies concurrent with accentuating the exclusivity of their offerings. Social media can be considered a boon if brands employ them to reach the right audience and use the right platform by incorporating the right content. In this work, we propose a sector-specific, integrated, and holistic investigation of the social media strategies of luxury brands, together with the impact they generate in terms of the engagement level of the users as an indicator of their success. We provide empirical validation of the method in the sector of luxury fashion brands in the Italian market, providing qualitative and quantitative analysis of the content shared on social media, considering the type, timing, and modality of the sharing. We evaluate consumer-brand engagement in different contexts, including important live events in the field.

This comprehensive review explores the advancements in processing-in-memory (PIM) techniques for deep learning applications. It addresses the challenges faced by monolithic chip architectures and highlights the benefits of chiplet-based designs in terms of scalability, modularity, and flexibility. The review emphasizes the importance of dataflow-awareness, communication optimization, and thermal considerations in designing PIM-enabled manycore architectures. It discusses different machine learning workloads and their tailored dataflow requirements. Additionally, the review presents a heterogeneous PIM system for energy-efficient neural network training and discusses thermally efficient dataflow-aware monolithic 3D (M3D) NoC architectures for accelerating CNN inferencing. The advantages of TEFLON (Thermally Efficient Dataflow-Aware 3D NoC) over performance-optimized SFC-based counterparts are highlighted. Overall, this review provides valuable insights into the development and evaluation of chiplet and PIM architectures, emphasizing improved performance, energy efficiency, and inference accuracy in deep learning applications.

As formally is known, nanozymes are the nanomaterials or nanoscale materials which reveal enzyme-like properties. These nanomaterilas can be utilzied instead of the natural enzymes for simulation of enzymatic reactions in harsh environmental conditions. Among different nanoscale enzyme-like materials, the majority of them reveal peroxidase-like activity, due to the high catalytic efficiency and stability of peroxidase-like nanozymes, they had been widely utilized for sensing and detection via constructing the colorimetric and fluorescence-based analytical and bioanalytical sensors. There are several high impact nanozymatic sensors which reported in the literature. Although the review articles published in this field, however, the current reviews focus on the introducing nanozyme field not description on the best articles published in this filed. Hence, this article aimed to present a brief introduction on the nanozyme-based chemistry with emphasizing on the historical overview of recent nanozymatic sensors. In this regard the best nanozymatic sensors which published over 2016-2023 were briefly described and overviewed.

Considering higher stability and lower cost of nanozymes than native enzymes, the nanozymatic systems had been utilized for several practical application, especially for sensing and detection. Most of common nanozymatic sensors are single-nanozyme based systems, however, recently a new generation of nanozyme-based systems called “multinanozyme system’ was introduced by Hormozi Jangi et al. (2020). Since the first report of multinanozyme systems, several multinaozyme systems have been developed and utilized for highly sensitive and selective sensing aims. The main advantages of multinaozyme systems compared of common nanozymatic sensors are their impact on simultaneous enhancing selectivity and sensitivity of sensor in a well-designed detection process. Since, the principles of design and detection mechanism of this new generation is not well-described in the literature, the aim of this article is the fast review of the principles of design of this new generation of nanozyme-based sensing and detection.

Influence of reaction time on silver nanoparticles-catalyzed oxidation of 3,3’,5,5’-tetramethylbenzidine (TMB) was evaluated via probing the oxidation process by recording the UV-Visible absorbance of the resulting colored products of TMB oxidation by hydrogen peroxide in the presence of silver nanoparticles as peroxides mimics. The time curve of the oxidation process was constructed, revealed that the concentration of the oxidation product was increased by increasing the reaction time and then levled off because the nanozyme active nodes were saturated after a certain reaction time and consequently the steady-state condition can be observed. The maximal activity of the silver nanozymes was achieved after a very short reaction time as short as 3.0 min and after this time, the color intensity of the oxidation products remained constant, revealing saturation of active nodes of nanoparticles with nanozyme-substrate.

In this contribution, time course studies were performed toward oxidation of different peroxidase substrates in the presence of silver nanozymes. In this regard, two common peroxidase substrates, TMB and DAB, were selected as model chromogens. The process of the oxidation reaction was probed by recording the absorbance of the colored products using UV-Vis spectrophotometer. Thereafter, the nanozymatic activity was calculated and used as an index for comparing the performance of the silver nanozymes against different substrates in a certain reaction time. The time course curves, revealed that the maximal activity of the silver nanozymes were achieved after a very short time of 3.0 min toward TMB oxidation while regarding DAB, the maximal activity was observed after 25.0 min which is 6.5-fold slower than that of the TMB oxidation. The difference between the DAB and TMB can be assigned to their different reaction pathways and different reactivity. In fact, the oxidation of DAB has occurred via n-electron irreversible pathway while the TMB oxidation has occurred through a simple 2-electon reversible pathway. Besides, DAB is a high stable and less relative compound compared of TMB, resulting different oxidation times, different affinity for binding to nanozyme active nodes which lead to different nanozymatic activities.

In this work, a high throughput nanozymatic method was developed for green nanozyme-mediated biodegradation of organic dyes in real water media. The nanozymes were synthesized and characterized for their size and morphology by TEM and DLS analysis. The nanozymatic properties of the as-prepared nanomaterials were evaluated by standard enzyme activity assay, revealing high peroxidase-like performances for the as-prepared nanozymes. Therefore, for exploring more precise, the kinetics behavior of the as-prepared nanozymes was evaluated by the standard Lineweaver-Burk method, revealing a V_max as high as 0.263 µM sec^-1 and a K_m as low as 0.03 mM, revealing high catalytic efficiency and affinity of the as-prepared nanozymes. Hence, the as-prepared nanozymes were utilized for organic dye degradation in real water media using methylene blue as a model organic dye. The effective factors on the dye degradation including pH, ionic strength, degradation time, nanozyme amount, etc. were optimized by the one-factor-at-a-time optimization method. At optimal experimental conditions, the as-prepared nanozymes can degrade about 99.0% of the organic dye within a degradation time as low as 7 min. The developed method was finally employed for the nanozyme-mediated degradation of methylene blue from real water media such as pool, mineral, river, and tap water samples. The results of this study revealed an excellent biodegradation yield of over 94.3%-99.0% for the different real samples, proving the suitability of the developed method for dye degradation in real water media.

How do sensor research and technologies grow over time? This paper applies the network analysis with a new computational approach to map the structure and evolution of sensor research and technologies over a 30-year time frame (1990-2020).The goal of this study is to analyze the evolution of sensor research for forecasting emerging scientific and technological trajectories. Results show that the scientific interaction within ecosystem (represented with networks) of sensor generates a co-evolution of scientific fields supporting the accelerated growth of different technological tra-jectories, such as: wireless sensors, fiber optic and optical sensors, gas sensors and biosensors. These results suggest main theoretical implications that explain the evolution of sensor research with critical aspects of innovation management to support R&D investments towards new technological trajectories having a high potential of growth.

The effects of toxic air in the form of air pollution are known to harm women’s health. New data has shown that air pollution has effects on maternal anemia. Yet the effects of particulate matter 2.5 (PM_2.5) on hemoglobin among reproductive maternal (15-49 years) has been understudied. In this population-based study, we study the region-specific associations between prevalence of hemoglobin among reproductive women and PM_2.5 in 43 countries of Africa from 2000-2019. Using generalized linear regression models, we found that after adjusting for key covariates, PM_2.5 had a slight negative association with hemoglobin among reproductive women. Other studies are needed in other low-income settings to expand our findings.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to increasingly enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimizing it for large-scale applications remains to be a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin-Huxley Model, Izhikevich model, integrate-and-fire model and spike response model. Hardware implementation platforms of the chip follow three approaches: analog, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnects with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarizes the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.

Cadmium (II) contamination in the environment is an emerging problem due to its acute toxicity and mobility, so it is very urgent to remove this species from industrial wastewater before it is discharged into the environment. Thus, a starch-based activated carbon (AC) with a specific surface area of 1600 m2g-1 is used as an adsorbent for the capturing of toxic Cadmium (II) ions from synthetic solution. The sorbent is characterized by BET, SEM, TEM, XRD, FT-IR, TGA, and zeta potential. The maximum uptake (284 mg g-1) of Cadmium (II) ion is obtained at pH 6. The thermodynamics parameters like ∆G, ∆H, ΔS are found to be -17.42 kJmol-1, 6.49 kJ mol-1, and 55.66 Jmol-1K-1 respectively, revealing that the adsorption mechanism is endothermic, spontaneous, and feasible. The experimental data follows the D-R and Langmuir models well. The mass transfer is controlled by pseudo 2nd order kinetics. Furthermore, the density functional theory simulations demonstrate that the activated carbon strongly interacted with the Cd (II) ion through its various active sites. The adsorption energy noted for all interactive sites is highly negative (-0.45 eV to -10.03 eV), which shows that the adsorption process is spontaneous and stable which is in agreement with the experimental thermodynamics analysis.

This paper investigates the contribution of business model innovations in improvement of food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces inducing the farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributors models. E-commerce models and internet of things are reported as the essential factors imposing the retailers to innovate their business models. Furthermore, the consumption demand and the product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.

Background: Diabetes is a common non-communicable disease that is responsible for about 9% of all deaths and 25% reduction in life expectancy and nearly half of the diabetic patients are not aware of their disease. In this regard, diabetes screening to identify un-known diabetic patients is of great importance. Aims: The aims of this study were first to evaluate the performance of two commonly used diabetes screening tests that are currently recommended by the Iranian national screening program for diabetes (NSPD). Methods: The validities of the two diabetes screening tests were measured among 1057 participants older than 30 years. The studied screening tests included Capillary fasting blood glucose (CBG) and glycated hemoglobin (HbA1c). The golden standard for measuring the validity of the tests was venous fasting plasma glucose (VPG). Results: According to the results, the sensitivity of CBG and HbA1c tests were 69.01% and 84.5% and the specificity of the tests were 95.7% and 79.3% respectively. Positive and negative predictive values were 53.84% and 97.72% for CBG and 22.72% and 98.61% for HbA1c respectively. The recommended cut-points for CBG and HbA1c were 116.5 mg/dl and 7.15% respectively. Using these values as the new cut-points, sensitivity and specificity of CBG and HbA1c changed to 80.30% and 89.10%, and 77.50% and 94.20% respectively. Conclusions: Compared to several other countries, the performance of NSPD is relatively higher in Iran. ROC analysis suggested new cut-points for significantly better performance of NSPD.

Background SARS-CoV-2 has generated a life-treating pandemic and is the main challenge of this century. Some untranslated regions (UTRs) in SARS-CoV-2 genome, specifically leader sequence and transcription regulatory sequence (TRS) in 5’UTR, can be considered as Achilles' heel of virus. Leader sequence are found at the 5' ends of all encoded transcripts that highlights its importance. TRS can explain the host range and pathogenicity of coronavirus. However, our knowledge on the evolution and the role of UTRs in SARS-CoV-2 pathogenicity is very limited. This study is a pioneering attempt to unravel the evolution of key regions in 5' UTR of SARS-CoV-2 and discover the inhibitory microRNAs against 5' UTR of virus. Methods Evolution of TRS and leader sequence was compared between human pathogenic (SARS-CoV-2, SARS, and MERS) and non-pathogenic (bovine) coronaviruses. Profiling of microRNAs that can inactive the key UTR regions of coronaviruses, UTR-inhibitory microRNAs, was carried out. Findings We found a distinguished pattern of evolution in leader sequence and TRS of SARS-CoV-2, compared to the other coronaviruses. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with an acceptable thermodynamic binding energy against SARS-COV-2, SARS, MERS, Bat Coronavirus, or Bovine Coronavirus. Multivariate analysis demonstrated a distinguished pattern of binding of leader sequence of SARS-CoV-2 against microRNAs, with a lower binding stability. hsa-MIR-5004-3p was the only human microRNA that can target leader sequence of SARS and SARS-CoV-2. However, its binding stability remarkably decreased in SARS-COV-2 (-19.4 kcal/mol), compared to SARS-COV-2 (-25.9 kcal/mol). We found an insertion-type mutation in leader sequence of SARS-COV-2 that results in lower binding stability and escaping of viral leader sequence from hsa-MIR-5004-3p. Altogether, we suggest lack of innate human inhibitory microRNAs to bind to leader sequence and TRS of SARS-CoV-2 contributes to its high replication in infected human cells. On the other hand, mining of two hundred million deposited human genomic variants led us to discovery of 49 missense and splice-disrupt mutations in genomic structure of hsa-MIR-5004-3p. These mutations can negatively affect hsa-MIR-5004-3p function in preventing SARS-CoV-2 replication. Interpretation This study unravels the evolution of key regions in 5’UTR of SARS-CoV-2. Inducing microRNAs to bind to the leader sequence and TRS regions by drugs or food supplements can reduce virus replication. Enhancing the microRNA defence machinery against TRS and leader of virus has a potential to prevent SARS-CoV-2 infection at the first place. The mentioned strategy is rapidly achievable against COVID-19. Missense variation in genomic sequence of 5’UTR inhibitory microRNAs, such as hsa-MIR-5004-3p, can be considered as risk factor of COVID-19.

Abstract: The gut microbiome refers to microorganisms and their genetic material influencing local and systemic inflammation. Inflammation is known to contribute to cancer development, progression, and treatment. Evidence suggests that modulating the gut microbiome may affect responses to various cancer therapies. The gut microbiota has been suggested to have an impact on immunotherapy efficacy, especially the currently widely used immune checkpoint inhibitors in various malignancies. Microbial Interventions like fecal microbiota transplantation, various probiotics, or even antibiotics can increase or de-crease the tumor's sensitivity to immunotherapy. However, not all tumors react in the same manner, highlighting the tumor microenvironment heterogeneity across tumor types and the influence this has on the crosstalk between the microbiome and therapy outcomes. In this study, we intend to review the association between the gut microbiota and immunotherapy response in cancer patients and the factors regulating this interaction.

Epilepsy is a neurological disorder that affects approximately 1% of the world's population. To diagnose and estimate the occurrence of epilepsy, the analysis of recorded brain activity is performed by a neurologist, which is not only time-consuming and tedious but also occasionally accompanied by human error. Therefore, in recent decades, researchers have aimed to unravel an approach for designing and building an automated method for diagnosing and estimating the occurrence of epilepsy. Accordingly, the present study proposed two new-fangled ways based on brain signals and a convolutional neural network (CNN). Moreover, this research implements a CNN with a sequential three-layer structure. Numerous experiments were performed, and the accuracy of estimating epilepsy using the developed methods was achieved at 95% without feedback and 97% with feedback. The proposed methods were proven to be more accurate than the previous techniques and can be employed as a physician's assistant once entering the field of operation.

The fundamental question in the field of sensor research is new directions of scientific fields, which play a vital role in the progress of science and technology. This study confronts this question here by developing a bibliometric analysis, which endeavors to explain the evolution of sensor research and new technologies that are critical to science and society. The database of Scopus concerning scientific documents and patents is used for statistical and computational analyses in these topics. Results suggest that emerging technological trajectories in sensors are wireless sensor networks, wearable sensors and biosensors. Main characteristics of these growing research fields and technologies in sensors are described for fruitful implications of research and innovation policy directed to science advances and technological change in society.

Various natural disasters such as floods, fires, earthquakes, etc. have affected human life. Detection and classification of large and small earthquakes caused by natural or abnormal events have been always important to Earth scientist. One of the most important research challenges in this field is the lack of an effective method for identifying and categorizing various types of seismic events at less important and important levels. Based on latest achievements of Data Mining international institutions such as Rexer-KDnugget-Gartner and also newest authentic articles, SVM, KNN, C4.5, MLP are from most important and popular and leading classifiers in data world.Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ، C4.5 decision tree and KNN near neighborhood have been utilized in a combined form to detect and categorize micro seismic events. In general, the steps involved in the proposed method are: 1) performing artificial seismic tests, 2) data gathering and analysis, 3) conducting preprocessing and separating training and testing samples, 4) generating relevant models with training samples and detecting and categorizing test samples and 5) extracting a cluster with the maximum candidate using boost learning. After simulations, it was observed that the accuracy of proposed boost method to the best answer was about 6.1% higher compare to other methods and the error rate was 0.082% of recalling. Accuracy of detection and classification to the best answer were also improved compare to other methods up to 2.31% and 6.34%, respectively.

Cable interference in cable-driven mechanisms (CDM) is a major challenge in their applications. The wrench closure workspace (WCW) in a fully constrained mechanism may not be realized unless cable interference is considered. This paper classifies all possible types of cable interferences for planar cable-driven mechanisms and presents an analytical solution to find the boundaries of the constant-orientation interference region. It is shown that interference between cables starts and ends when one end of a cable coincides with another cable which can be thought as lying a point on a line segment. This geometrical view is developed and applied to provide a framework for the analysis of cable interference. By subtracting the interference space form WCW, the interference-free workspace is obtained in which the robot is able to operate.

Along with environmental pollutions, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, the consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and consequently, the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.

In the radar system, detection represents a basic and important stage in the receiver side. The detection process is based on the thresholding criteria; two philosophies of this criteria, constant and adaptive threshold. The constant threshold is simple in design, but it has a mis-detection and does not control the false alarm rate. As for the adaptive threshold, it is powerful in target detection, and better control of the false alarm rate, where it is called Constant False Alarm Rate (CFAR). Lots of research in the CFAR design, but the gap in the previous works is that there is no CFAR algorithm can be working with all or most environmental fields and all or most target situations.In this paper, The CFAR, which can work with the most environment and most of the target situations, has been presented. The producing the design and implementation of the new practical CFAR processor is presented. Where, the new CFAR is a combination of the properties of three different CFAR algorithm (CA, OSGO, and OSSO), and from two different families; averaging and statistical. Where it has overperformed of it's is 97.25% for simulation and 96.25% for the implementable version for different target situations. The simulation analysis is made by using Matlab 2015, while the implementation is done by using Xilinx Spartan 700 3a.

Partial sums of analytic univalent functions and partial sums of starlike have been investigated extensively by several researchers. In this paper, we investigate a partial sums of convex harmonic functions that are univalent and sense preserving in the open unit disk.

In this paper, we introduce two subclasses of analytic and Spirallike functions and investigate convolution properties, the necessary and sufficient condition, coefficient estimates and inclusion properties for these classes .

In the present paper, we introduced and study a new class of harmonic univalent functions on unit disc U. also we obtain coefficient conditions, extreme points, convolution condition for the above class of harmonic univalent functions.

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Utility perceived by individuals is believed to be different from the utility experienced by that individual. System dynamicists implicitly categorize this phenomenon as a form of bounded rationality and traditionally employ a simple smoothing function to capture it. We challenge this generalization by testing it against an alternative formulation of utility perception that is suggested by modern theories of behavioral economics. In particular, the traditional smoothing formulation is compared with the peak-end rule in a simple theoretical model as well as in a medium-size model of electronic health record implementation. Experimentation with the models reveals that the way utility perception is formulated is important and might affect behavior and policy implications of system dynamics models.

The recent commercial availability of stacked dielectric elastomer actuators (SDEAs) has unlocked new opportunities for their application as "artificial skeletal muscles" in rehabilitation robots and powered exoskeletons. Composed of multiple layers of thin, elastic capacitors, these actuators present a lightweight, soft, and acoustically noiseless alternative to traditional DC motor actuators commonly used in rehabilitation robotics, thereby enhancing the natural feel of such systems. Building on our previous research, this study aimed to evaluate the most recent version of commercial SDEAs to assess their potential for mechanizing rehabilitation robots. We quantified the stress and strain behavior and stiffness of these actuators in both single and 1x3 configurations (with three SDEAs connected in series). The actuators demonstrated the capability to generate up to 25 N of force, 103 KPa, a value closely resembling human biceps, with a longitudinal strain measured at about 11%. The significant increase in force generation from 10 N, in the previous version, to 25 N and displacement from 3.3% to 11% substantially enhances the applicability of this actuator rehabilitation robotics. SDEAs' high force generation capability, combined with their strain and stress characteristics comparable to that of human biological muscles make them ideal alternative actuators for biomimetic robots and applications where actuators must operate in the vicinity of the human body.

In this study, the efficiency of coating (alginate and carrageenan) and osmotic dehydration pre-treatment (NaCl and sucrose) on decreasing oil uptake of potato strips was assessed. It was examined how various pre-treatments affected the sample’s texture, morphology, color and density properties as well as their moisture content and effective moisture diffusion coefficient (Deff). Results showed that NaCl osmotic dehydration pre-treatment and sucrose solution decreased oil uptake by 30.76% and 41.75%. This is attributed to moisture removal increasing caused by improving dielectric properties. Osmotic dehydration and coating pre-treatment frequently enhanced product shrinkage, browning index, a* and ∆E. Conversely, density, lightness, breaking force, Deff and b* decreased. In control samples, SEM revealed a severely damaged internal structure in comparison to a tissue homogenous layer produced by coating. For moisture loss and oil uptake, the coefficient of determination (R2) and root mean square error (RMSE) of eight empirical models were compared. Finally, the Dehghannya and Krokida model (moisture loss) and the Movahhed and Naghavi model (oil uptake) were found the best fit to the experimental data. Although coating pre-treatment and osmotic dehydration can be used as an alternate de-oiling approach for potato chips, the mechanism of oil uptake reduction differ.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 &amp; 0.91, p values = 0.001, 95% CI: 0.80-0.92 &amp; 0.87 to 0.94, sensitivity = 0.85 &amp; 0.87, specificity = 0.73 &amp; 0.83, Youden index = 0.58 &amp; 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

Wear usually occurs in the loaded part of hydrodynamic bearings and leads the bearing geometry imperfections. This paper investigates the effects of wear-induced geometric imperfections of tilting pad journal bearings (TPJBs) on the dynamic behavior of the system. Furthermore, the effect of wear on the journal-bearing rub-induced contact pressure severity is investigated. A novel tribo-dynamic model is proposed for a flexible rotor-worn TPJB which integrates a mixed elastohydrodynamic model with a rotor-worn TPJB thermal and dynamic model to assess the effects of the bearing wear progression on the rotor-TPJB behavior. Based on the results, wear changes the temperature distribution of the pads and oil film as well as the dynamic behavior of the system. Dynamic simulations reveal a higher vibration level and contact pressure for the worn TPJBs near the system's critical speed and service speed. Finally, thermal and dynamic condition indicators are suggested to detect TPJB wear severity at its early stages.

The production of concrete and the manufacturing process of cement results in a significant carbon footprint, contributing to a large portion of global emissions in structures such as buildings, bridges, roads, and tunnels. Although concrete is an ideal building material that is durable and long-lasting, it can be susceptible to micro-cracks. These micro-cracks in concrete can allow water and chlorine ions to penetrate the structure, leading to the degradation of concrete and corrosion of the reinforcement, posing an unacceptable level of structural risk. Self-healing concrete is not a new material in the construction industry but can be characterized by the capability of concrete to repair its cracks autogenously or autonomously. Recent advancements in concrete research and technology have given us a better understanding of concrete's healing properties. Self-healing concrete combines durability with sustainability while offsetting the high carbon output of concrete manufacturing and production and associated life-cycle costs. Technologies such as microbially-induced calcite (calcium carbonate) precipitation (MICP), shape memory polymers, encapsulation methods, hydration, and swelling agents can potentially reduce carbon emissions while enhancing resilience and longevity. This paper examines these technologies and their applications in the construction industry by comprehensively reviewing the literature and available case studies. The study concluded that there are promising advancements and innovations in concrete, particularly when improving upon the autogenous healing properties. The recommendations for future research include exploring more ways to bring the concrete industry and cement manufacturing toward net zero carbon emissions.

Plant conciousness is a scenario where people imbibe the culture of engaging plants as a constant part of their day to day activity. The earth supports both plants and animals, but , plants unlike animals are unable to express themselves by shouting, nagging or crying, e.t.c. They are only able to response saliently thus making them oblivious and constantly neglected. Unfortunately, plants play just as much major roles in sustaining the earth as much as people and animals do. The proper handling of plants is much reliant on the ‘’Plant consciousness or obliviousness’’ of people including both the young and the old. Are people well aware of plants, how important they are to the earth, the environment and it sustenance?, the answer is not enough. There is a need for plant knowledge bridges to create more and adequate awareness about plants, their importance, how they should be handled, how they should be cared for, and an understanding that they are partners for the sustaining our dying world. We seek to change this obvious lack of appreciation and understanding of plants, but how can this be done? We discuss methodologies that have and can been used for bridging this knowledge gap to ensure that our world is safe by protecting and caring for plants.

Partial shading on solar photovoltaic (PV) arrays is a prevalent problem in photovoltaic systems that impair the performance of PV modules and is responsible for reduced power output as compared to that in standard irradiance conditions thereby resulting in the appearance of multiple maximas on panel output power characteristics. These maxims contribute to mismatch power losses among PV modules. The mismatch losses depend on shading characteristics together with different interconnected configuration schemes of PV modules. The research presents a comparative analysis of partial shading effects on a 4 x4 PV array system connected in series(S), parallel (P), serries-parallel (SP),total-cross-tied (TCT),central-cross-tied(CCT),bridge-linked(BL),bridge-linked total cross-tied (BLTCT) ,honey-comb(HC), honey-comb total-cross-tied (HCTCT) and ladder (LD) configurations using MATLAB/Simulink. The PV module SPR-X20-250-BLK was used for modeling and simulation analysis. Each module is comprised of 72 number of PV cells and a combination of 16 PV modules was employed for the contextual analysis. Accurate mathematical modeling for the HCTCT configuration under partial shading conditions (PSCs) is provided for the first time and is verified from the simulation. The different configuration schemes were investigated under short-narrow,short-wide,long-narrow,long-wide, diagonal, entire row distribution, and entire column distribution partial shading condition patterns with mathematical implementation and simulation of passing clouds. The performance of array configurations is compared in terms of maximum power generated ), mismatch power loss (∆), relative power loss ) and the fill factor (FF). It was inferred that on average, TCT configuration yielded maximum power generation under all shading patterns among all PV modules interconnection configurations with minimum mismatch power losses followed by hybrid and conventional PV array configurations respectively.

The behavior of convective boundary conditions is studied to delineate their role in heat and mass relegation in the presence of radiation, chemical reaction, and hydromagnetic forces in three-dimensional Powell-Eyring nanofluids. Implications concerning non-Fourier’s heat flux and non-Fick’s mass flux with respect to temperature nanoparticle concentration were examined to discuss the graphical attributes of the principal parameters. An efficient optimal homotopy analysis method is used to solve the transformed partial differential equations. Tables and graphs are physically interpreted for significant parameters

The SARS-CoV-2 virus (COVID-19) has led to an initiative to develop safe and effective vaccines for public health safety. In December 2020, the United States Food and Drug Administration (FDA) administered an Emergency Use Authorization (EUA) for the Pfizer-BioNTech COVID-19 vaccine. Other vaccines from Johnson & Johnson, Moderna, and AstraZeneca shortly followed. This paper aims to examine the EUA process for COVID-19 vaccine approvals, including the requirements for the safety and efficacy of data, along with the role of the FDA and EMA in the approval process. Further, public concerns about vaccine approvals, including issues on the approval process's safety, efficacy, and speed, were also discussed. Using qualitative analysis of the extracted studies, trends and insights into the factors influencing public acceptance of COVID-19 vaccines, such as vaccine hesitancy and misinformation, were discussed. Overall, a transparent and rigorous regulatory process for vaccine approval is critical for education efforts between policymakers, vaccine developers, and the public to address concerns for increased population vaccine uptake.

In this research, a new conceptual framework introduced to analysis of water security. Using the exponential, logistic growth model, and employing the historical population of Tabriz city from 1956 to 2016, the future period (2022-2100) population predicted. Pearson Correlation, Spearman's Rho and Kendall's rank coefficients were used for analyzing correlation between population and water consumption. In addition, the amount of city water demand in the future period predicted using the ordinary least square model. Then, water stress and water scarcity indices calculated for Tabriz city in the future period. Results indicated that the mathematical population growth models are very suitable for prediction of city population. There was a significant positive correlation between the city population and water demand. In addition, the amount of water demand will proportionally increase with rising number of population. Finally, linear and quadratic equations presented for city water demand based on the city population number. It can be concluded that Tabriz city will be faced with severe water stress in the future period. The proposed new method in this study can be used for other different cities of the world. Also, Results will be helpful for decision makers in optimum management of urban water resources.

Introduction: CT scan of the skull in patients with head trauma play an important role in identifying patients' injuries; In moderate to severe brain injury; CT scans are arguably the best way to get information about the intracranial complications of a head injury. Researchers have cited a number of reasons for why doctors are increasingly asking for CT scans of patients. In this study; pathological findings in CT scan of patients with mild cranial trauma and level of consciousness 13-15 are examined. Method: In this study, the treatment process of 445 patients who had referred to the emergency department due to skull trauma was examined as research units; and samples were taken by simple random sampling among patients for whom CT scan of the brain was requested. In this study; the reasons for requesting a CT scan of the skull from the requesting physician were questioned through an interview at the time of request. Results: According to the tables; it can be seen that most of the patients referred to the hospital due to skull trauma for CT scan were men and were discharged in less than the first 24 hours. The age group of 21 to 30 years had the highest and the age group over 70 years had the lowest frequency and the highest percentage of request for CT scan was by doctor and the biggest injury that caused patients with mild cranial trauma to go to hospital was fall. Conclusion Based on the findings of this study, cranial trauma is one of the life-threatening factors that can cause minor or severe injuries Its range varies from superficial injuries to hematoma and intracranial hemorrhage, which is a reason to perform CT scans of patients with skull trauma. In order to carefully examine the extent of the damage to the patient and to avoid possible risks and to take the necessary measures for treatment. On the other hand, the lack of clear protocols as well as the lack of clarity and certainty of physicians in their diagnoses to perform graphs of qualified people has increased the number of unnecessary CT scans

Objectives: Isolated mild neutropenia is a common clinical problem. The current study aims to validate our previous findings regarding the high prevalence of isolated neutropenia in Southern and Southwestern Saudi Arabia and explore the effect of altitude or regional differences. Methods: In this retrospective cross-sectional study, laboratory results of a commercial laboratory were screened over a period of 5 years (2016–2020) in seven different cities in South and southwestern Saudi Arabia. Participants’ laboratory investigations were reviewed and excluded for any ab-normal complete blood count, renal profile, liver profile, lipid profile, thyroid function test, fasting blood glucose, or HbA1c findings. Descriptive analysis and 95th percentile range were calculated using standard statistical methods. Results: A total of 91,880 complete blood count results were included in the final analysis. isolated neutropenia is common laboratory finding, with a prevalence ranging from 11-23%. The 2.5th percentile of the neutrophil count was lower than 1.5×109/L in all seven cities. Conclusions: Mild to moderate neutropenia is common in Southern and Southwestern Saudi Arabia. Benign ethnic neutropenia (BEN) may explain this high prevalence. Since BEN has no clinical significance, the reference range for normal neutrophil counts needs to be adjusted to reflect the effect of BEN.

In the past decade, considerable efforts have been devoted to the development of semitransparent organic solar cells. Numerous materials and architectures have been proposed for the future commercialization of these devices. Among these, the use of ternary active layers demonstrated a great promise for the development of efficient semi-transparent organic solar cells with the potential for future applications including self-powered greenhouses and powered windows. Researchers seek alternative solutions to the trade-off between device power conversion efficiency (PCE) and average visible transmittance (AVT), with photoactive materials being the key parameters that govern both (PCE) and (AVT) as well as device stability. Several new organic materials, including polymers and small molecules, were synthesized, and used in conjunction with a variety of techniques to achieve semitransparent conditions. In this review paper, we will look at the working principle and key parameters of semi-transparent organic solar cells, as well as the methods that have been used to improve the performance of ternary-based semi-transparent organic solar cells. The main approaches were concluded to be spectral enhancement and transparency increment of the active layer through bandgap tuning, utilizing novel organic semiconductors, and design architecture of the active layers.

This study examines the wind shear coefficient (WSC) values at three coastal wind sites located in the southern region of Balochistan, Pakistan: Pasni, Ormara, and Jiwani. These WSC values were obtained using 10-minute measured wind speed data at heights of 20, 40, and 60 meters above ground level (AGL). Since wind measurements are typically recorded at lower heights due to cost and resource constraints, extrapolation techniques were employed to estimate wind speeds at higher altitudes. However, using a constant WSC value for extrapolation may lead to significant errors between extrapolated and actual wind speed measurements, impacting the energy output of wind turbines. To evaluate the effect of WSC on energy yield, the study employed power curves and frequency distributions for 2MW and 1.5MW wind turbines. Additionally, wind power density was calculated using air density derived from measured air temperature and surface pressure data, covering two years period from November 2016 to August 2018. The overall mean WSC values were found to be 0.076 at Pasni, 0.094 at Jiwani, and 0.053 at Ormara. The study further investigated the seasonal, monthly, and diurnal variations of WSC. For assessing wind resources at a height of 60m, the study utilized Wind Roses, wind power density, and Weibull parameters. Comparing the actual WSC values presented in this paper with those obtained using the 1/7 power law and measured data at 60m AGL, the energy yield from the wind turbines showed reduced output and capacity factor.

With the objective of studying the influence of elicitors on growth, yield and quality of kalmegh we carried out an investigation for two consecutive years. Nine treatments with three replications were laid out in CRD design. Chitosan, Yeast extract, Jasmonic acid and Salicylic acid were evaluated at different concentrations. CHT 1000 ppm exhibited highest plant height (73.91 cm) and secondary branches (29.07) at the time of harvest. Primary branches and number of leaves per plant were highest in CHT 1000 ppm (26.36; 88.32) which were on par with SA 200 ppm (26.28; 81.51). Plant spread was highest in SA 200 ppm (35.46 cm2) which was on par with CHT 1000 ppm (35.11 cm2). CHT and SA sprays were recorded with on par results for yield parameters but highest fresh (42.34 g) and dry (18.30) herbage yield per plant were exhibited with SA 200 ppm. Highest total chlorophyll (4.459 mg g-1) and total andrographolides (3.494%) content were recorded in SA 200 ppm spray. Significant and positive improvement in growth, yield and quality of kalmegh was noticed with Salicylic acid spray @ 200 ppm at 30 and 60 DAS signifying its use in cultivation of kalmegh for high productivity, quality and better returns for farmers.

Deep learning-based question answering systems have transformed the discipline of natural language processing (NLP) by automating the extraction of answers from textual data. This survey paper provides a captivating overview of these systems, exploring methodologies, techniques, and architectures such as recurrent neural networks (RNNs), BERT model, and transformer models. Extractive and generative approaches are examined, alongside the challenges of handling complex questions, managing noisy input, and addressing rare or unseen words. This survey serves as a stimulating reference, offering valuable insights to researchers and practitioners, fueling innovation and advancement in question answering systems within NLP.

In document forensic, Ink mismatch relays very important information about forgeries in this way we can find out the authenticity of documents. Finding out and distinguishing these unique inks from the multispectral document is very challenging task. In this paper we proposed the method to identify the inks using clustering. We used K-Mean clustering instead of widely known Fuzzy C-Means Clustering (FCM) and successfully identity the number of inks. For the purpose of optimizing and improving our results we used two optimization techniques such as Elbow and silhouette optimization techniques.

In this paper, we generalize the problems of finding simple polygons with the minimum area, maximum perimeter and maximum number of vertices so that they contain a given set of points and their angles are bounded by $\alpha+\pi$ where $\alpha$ ($0\leq\alpha\leq \pi$) is a parameter. We also consider the maximum angle of each possible simple polygon crossing a given set of points, and derive an upper bound for the minimum of these angles. The correspondence between the problems of finding simple polygons with the minimum area and maximum number of vertices is investigated from a theoretical perspective. We formulate the three generalized problems as nonlinear programming models, and then present a Genetic Algorithm to solve them. Finally, the computed solutions are evaluated on several datasets and the results are compared with those from the optimal approach.

In this paper Brinkman type fluid over an infinite plate between side walls is being investigated. The flow is generated by oscillating shear stress of the bottom plate and the solutions are obtained by using Fourier integral transformation. The obtained results are presented in steady and transient states for both sin and cos shear stresses. The general solutions are reduced to some special cases corresponding, namely to the Brinkman type fluid over an infinite plate and flow of a Newtonian viscous fluid. Graphical illustrations are carried out to have in depth analysis of the involved physical parameters

Natural events such as floods, fires, tsunamis, earthquakes and others have nowadays caused serious damage to human beings and nature. The precise detection of these natural events and especially the earthquake has nowadays become the focus of many computer and geoscientific researchers. Computer science and machine learning algorithms have revolutionized early detection and prediction of these events. Hence, a fuzzy method has been initially used in this article to enhance the authenticity of data based on application of effective variables and then combination of neural network algorithms of the MLP perceptron and radial network of RBF in form of a collective learning system in order to more accurately identify seismic events on a small scale. It was observed after simulating the proposed method that the proposed method has significantly improved based on actual error and root-mean-square error (RMSE) criteria compared to basic methods.

We present observations from evaluation of internal environmental quality of industrial halls with priority on daylighting in combination with the integral lighting. The physical parameters related to indoor lighting in large industrial halls in winter and summer periods were analyzed using in situ measurements and computational methods. These are part of a comprehensive research on indoor environmental quality of industrial halls with the aims of saving energy and providing a comfortable environment for the workers while improving the productivity. The results showed that the procedures used for evaluation of residential or office buildings may not be used for industrial buildings. We also observed that the criteria of occupants’ comforts for indoor industrial buildings may differ from those of other kinds of buildings. Based on these results, an adequate attention is required for designing the industrial buildings. For this reason, appropriate evaluation methods and criteria should be created. We found the measured values of daylight factor very close to the skylight component of the total illumination. The skylight component was observed on average 30% that of the measured daylight factor values. Although the daylight is not emphasized when designing the industrial buildings and its contribution is small, but it is very important for the workers psychology and physiology. The workers must feel a connection with the exterior environment; otherwise, their productivity decreases.

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.

Background: Cognitive for patients with neurocognitive disorders are mostly measured by Montreal Cognitive Assessment (MoCA) and Visual Cognitive Assessment Test (VCAT) as screening tools. These cognitive scores are usually left skewed and the results of association analysis might not be robust. This study aims to study the distribution of the cognitive outcomes and to discuss potential solutions. Materials and Methods: The inverse transformed cognitive outcomes are modelled using different statistical distributions. Robustness of the proposed models are checked under different scenarios: with intercept only, models with covariate, with and without bootstrapping. Results: Main results were based on VCAT score, and validated via MoCA score. Findings suggested that the inverse transformation method improves modelling the cognitive scores compared to the conventional methods using the original cognitive scores. Association of baseline characteristics (age, gender and years of education) and the cognitive outcomes were reported as estimates and 95% confidence intervals. Bootstrap methods improved the estimate precision and the bootstrapped standard errors of the estimates are more robust. Cognitive outcomes are widely analyzed using linear regression models with default normal distribution as a conventional method. We compared the results of our suggested models with the normal distribution under various scenarios. Goodness-of-fit measurements were compared between proposed models and conventional methods. Conclusions: The findings support the use of the inverse transformation method to model the cognitive outcomes instead of the original cognitive scores for early stage neurocognitive disorders where the cognitive outcomes are left skewed.