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Parental behavioral control is one of the most common parenting practices employed by parents in China. It is necessary to identify this practice and how it may be influenced by adolescents’ and parents’ sociodemographic factors in order to develop family intervention strategies. The present study examined whether fathers’ and mothers’ behavioral control was related to adolescents’ gender, age, and sibling status and parents’ age, education level, and income. A sample of 916 adolescents, aged 11–15 years, reported their fathers’ and mothers’ parental behavioral control. In terms of adolescents’ gender, boys perceived higher level of fathers’ behavioral control compared to girls. Regarding parents’ sociodemographic factors, the level of mothers’ behavioral control was higher compared to that of fathers. Mothers’ education levels were reported to have a negative relationship with maternal behavioral control. Regarding adolescents’ gender differences, there was a positive relationship between fathers’ educational levels and behavioral control in adolescent boys. However, there was a positive relationship between fathers’ monthly income and fathers’ behavioral control in adolescent girls rather than boys. In addition, there was a negative relationship between mothers’ age and behavioral control in adolescent girls but not in boys. These findings add to the literature on Chinese mothers’ and fathers’ parenting and offer practical implications for Chinese family interventions.

Diabetic cardiomyopathy is a significant and severe complication of diabetes that affects a large portion of the global population, with its prevalence continuing to rise. Secondary metabolites, including quercetin, have shown promising effects in mitigating the progression of diabetic cardiomyopathy by targeting multiple pathological mechanisms, including impaired insulin signaling, glucotoxicity, lipotoxicity, oxidative stress, inflammation, fibrosis, apoptosis, autophagy, mitochondrial dysfunction, cardiac stiffness, and disrupted calcium handling. Addressing these mechanisms is crucial to prevent left ventricular diastolic and systolic dysfunction in advanced stages of diabetic heart disease. Scientific evidence has highlighted the cardioprotective properties of quercetin at both the myocardial and cellular/molecular levels in diabetic models. Therefore, this review aims to present a comprehensive overview of the proposed mechanisms underlying quercetin’s beneficial effects, providing valuable insights that could inform future drug discovery efforts specific to diabetic cardiomyopathy.

Background/Objectives: Microorganisms such as bacteria, viruses, and fungi are frequently the cause of infections. Antimicrobial agents, such as antibiotics, antivirals, and antifungals, are used to target and eliminate these infectious agents. On the other hand, inflammation is a natural response of the immune system to injury, infection, or irritation. Although herbal remedies have been used to treat these conditions for centuries and can be effective in certain situations, it is crucial to use them with caution. Not all herbal remedies are supported by scientific evidence, and their safety and efficacy can vary. Thus, we conducted this review to determine the potential health benefits of agarwood as an antimicrobial and anti-inflammatory agent. Methods: Three databases (PubMed, Scopus, and Google Scholar) were used to search for original papers submitted between 2013 and 2023, using the Medical Subject Heading (MeSH) terms “agar-wood” crossed with the terms “antimicrobial” and/or “anti-inflammatory”. Synonyms and relevant search terms were also searched. Results: The most-studied agarwood for antimicrobial and anti-inflammatory agents is Aquilaria sinensis. Some studies have shown its potential application as a potent inhibitor of fungi, including Lasiodiplodia theobromae, Fusarium oxysporum, and Candida albicans. Moreover, it is capable of inhibiting Bacillus subtilis and Staphylococcus aureus activities. Several chromones detected in agarwood have been shown to inhibit NF-κB activation, LPS-induced NO production, and superoxide anion generation. In conclusion, more research is needed, particularly regarding future intervention studies, to enhance our knowledge and understanding of agarwood and its isolates. Conclusions: This review reveals that despite the absence of clinical trials, agarwood exhibits antimicrobial and anti-inflammatory properties.

This study investigates the influence of thermoplastic polyurethane (TPU) reinforced with jute cellulose nanofibers (CNFs) on the water absorption and mechanical properties of geopolymer concrete. The integration of TPU/jute CNF nanocomposites into geopolymer concrete is explored as a strategy to enhance both its durability and mechanical performance. Geopolymer concrete, a sustainable alternative to traditional Portland cement concrete, is known for its low carbon footprint, but it suffers from high brittleness and water absorption. The water absorption behavior of the modified concrete was assessed, revealing a significant reduction in water uptake due to the hydrophobic nature of TPU and the reinforcing effect of jute CNFs. Additionally, the mechanical properties, including compressive and flexural strengths, were evaluated to understand the impact of the nanocomposites on the structural integrity of the concrete. The addition of TPU/jute CNFs notably enhanced the splitting tensile strength (63.5%), compressive strength (59%), and water absorption (0.59%) of the composite, indicating a promising route for developing high-performance construction materials. The integration of 6 wt% of TPU/jute CNF nanocomposites was found to be optimal, resulting in a uniform matrix, reduced micro-cracks, and improved compressive strength due to enhanced adhesion between the nanocomposites and the geopolymer matrix. Furthermore, a curing temperature of 100 °C was identified as ideal, minimizing unreacted fly ash and enhancing adhesion strength, while higher temperatures (140 °C) led to material deterioration due to rapid water loss. The findings demonstrate that the addition of TPU/jute CNF nanocomposites not only improves resistance to water penetration but also enhances overall mechanical performance. This supports the development of more sustainable and resilient construction materials, contributing to global efforts to reduce the environmental impact of the construction industry. Future research should focus on the long-term durability of these composites under various environmental conditions to validate their effectiveness in real-world applications.

Order picking is referred as a critical process of selecting items requested by a customer in a warehouse. Meeting the demand of every customer is the main objective in this area. Large warehouses pose a challenge since the order-picking process is slowed considerably by the lengthy time it takes to transport items across the warehouse. Throughout the study, the system is hoped to develop proper procedures in the order-picking process. In handling this scenario, the decision-makers need to take any possible action to ensure the warehouses can keep operating and meeting the requirements and satisfaction of the customers. Due to this, the study’s main objective is to determine whether the Floyd–Warshall algorithm or the dynamic programming method will give the most accurate shortest path and minimum travel distance for order pickers. Two data sets (nine nodes and nineteen nodes) are used to determine the optimal path and minimum travel distance for the order picker to meet and satisfy customer orders for the warehouse. The two models were modified and applied to address real-world case studies from the automotive manufacturing company in Malaysia. The results show a big difference between the total distance by 113.48% for 19 nodes. Through this finding, the company may choose which method suits their preferences. Concurrently, this study may also contribute to problem-solving issues in any warehouse operation with a similar procedure.

Among healthcare workers, nurses are considered the core of healthcare auth–info services in healthcare facilities because of their responsibilities and duties toward patients. All healthcare professionals, especially nurses, must be completely knowledgeable about the hazards of ionizing radiation, and the most effective radiation protection techniques. This study assessed the attitude and awareness toward radiation protection among final-year nursing students in the Fatima College of Health Sciences (FCHS) campuses. An online cross-sectional survey was conducted between March and April 2022. A total of 200 out of 224 female participants ranging from 18 to 30 years old agreed to participate in the study. The highest percentage of final-year nursing students did not attend any radiation protection course (52%). The results of the last section of the survey indicate a lack of awareness of basic radiation protection knowledge among final-year nursing students in FCHS campuses (less than 80%). The results showed a lack of knowledge and poor attitude toward radiation hazards and radiation protection from final-year nursing students in the FCHS. Formal education about basic radiation and radiation is recommended in the nursing program for safe clinical practice.

Coronavirus disease 2019 (COVID-19), a highly contagious viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), poses a global health threat. The virus enters host cells by binding with angiotensin-converting enzyme 2 (ACE2), which is then facilitated by the protease activity of transmembrane serine protease 2 (TMPRSS2). It triggers a cytokine storm that eventually leads to cell apoptosis, tissue damage, and organ failure. Therefore, any organs in the human body that have both receptors are highly susceptible to COVID-19 infection, potentially resulting in multiple-organ failure. The prostate has been reported to express high levels of ACE2 and TMPRSS2. While there are limited studies regarding the association between COVID-19 and prostatitis, the possibility that SARS-CoV-2 could cause prostatitis cannot be denied. Thus, through this review, a better insight into the associations of SAR-CoV-2 can be provided.

The use of unmanned aerial vehicles (UAVs), or drones, as mobile aerial base stations (MABSs) in Disaster Response Networks (DRNs) has gained significant interest in addressing coverage gaps of user equipment (UE) and establishing ubiquitous connectivity. In the event of natural disasters, the traditional base station is often destroyed, leading to significant challenges for UEs in establishing communication with emergency services. This study explores the deployment of MABS to provide network service to terrestrial users in a geographical area after a disaster. The UEs are organized into clusters at safe locations or evacuation shelters as part of the communication infrastructure. The main goal is to provide regular wireless communication for geographically dispersed users using Long-Term Evolution (LTE) technology. The MABS traveling at an average speed of 50 km/h visits different cluster centroids determined by the Affinity Propagation Clustering (APC) algorithm. A combination of graph theory and a Genetic Algorithm (GA) was used through mutators with a fitness function to obtain the most efficient flyable paths through an evolution pool of 100 generations. The efficiency of the proposed algorithm was compared with the benchmark fitness function and analyzed using the number of serviced UE performance indicators. System-level simulations were used to evaluate the performance of the proposed new fitness function in terms of the UEs served by the MABS after the MABS deployment, fitness score, service ratio, and path smoothness ratio. The results show that the proposed fitness function improved the overall service of UEs after MABS deployment and the fitness score, service ratio, and path smoothness ratio under a given number of MABS.

Heat shock protein 16-kDa (HSP 16-kDa) is essential for the survival of Mycobacterium tuberculosis (M. tuberculosis) during the latent period; hence, a peptide–MHC presentation of HSP 16-kDa could be a potential diagnostic and therapeutic target for latent tuberculosis (LTB). This study aimed to generate a TCR-like single-domain antibody (sDAb)-human IgG1 antibody and subsequently investigate its diagnostic and therapeutic potential in LTB, utilizing a model cell presenting the target peptide. A previously generated TCR-like sDAB that can bind to HSP 16-kDa was first fused to a human IgG1 Fc-receptor via a linker. The fusion product, sDAb-IgG1, was expressed with HEK293-F and was subsequently purified. Its diagnostic potential was investigated via cell-based ELISA utilizing MCF-7 cells peptide-pulsed with HSP 16-kDa peptides. Investigation into the antibody-dependent cell-mediated cytotoxicity (ADCC) of MCF-7 cells was also conducted to investigate its therapeutic potential. Finally, TCR-like sDAb-IgG1 was successfully produced transiently with HEK-293F and was purified using protein A chromatography. The generated antibody was tested using cell-based ELISA, which demonstrated the effective binding of the TCR-like sDAb-IgG1 to the 16-kDa peptide–MHC on the cell surface. The ADCC assay also showed that the antibody effectively mediated the ADCC of MCF-7 cells with the help of 16-kDa peptide–MHC. This allows us to hypothesize the possible utility of the said antibody for both diagnostics and therapeutics of latent tuberculosis after more investigations with clinical samples.

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

Following mass vaccinations for the control of the COVID-19 epidemic, a spectrum of cardiac and neurological disorders was reported among vaccinated individuals. This study examined the range of complications documented and factors related to their occurrence. Three electronic databases were searched for case reports and case series with descriptions of cardiac and/or neurological complications in COVID-19 vaccine recipients. A total of 698 vaccinees were included in this review, of which 259 (37.1%) had cardiac and 439 (62.9%) had neurological complications. Inflammatory conditions were the commonest among the cardiac complications; while polyneuropathy, demyelinating diseases and cerebrovascular disorders were the more common neurological complications. The mean age of those with cardiac complications (33.8 years) was much younger than those with neurological complications (49.7 years). There was no notable difference in the gender distribution between these two groups of vaccine recipients. mRNA vaccines (all brands) were associated with almost 90.0% of the cardiac complications, whereas viral vector vaccines were associated with slightly over half (52.6%) of the neurological complications. With regard to the dose, cardiac complications were more common after the second (69.1%), whereas neurological complications were more common after the first dose (63.6%). The majority of the cases had an uncomplicated clinical course. Nevertheless, 5.9% of cases with neurological complications and 2.5% of those with cardiac complications were fatal, underscoring the significance of the consistent surveillance and vigilant monitoring of vaccinated individuals to mitigate these occurrences.

The textile industry is suffering a great challenge regarding wastewater management, primarily due to the implementation of improper systems, specifically for dye wastewater treatment. Photocatalysis is one of approaches that have been used to treat wastewater. Titanium dioxide (TiO2) was immobilized by using the dip-coating technique in this research. Epoxidized natural rubber (ENR) and polyvinyl chloride (PVC) were used as a polymer to bind the TiO2 on the glass substrate. This immobilized TiO2/ENR/PVC underwent a photoetching process at various times to study the crosslink and porosity formations. Reactive red 4 dye was used as a model pollutant for photocatalytic performance. All immobilized TiO2/ENR/PVC samples under 12, 24 and 30 h of photoetching process (TEP12, TEP24 and TEP30 samples, respectively) showed higher photocatalytic activity compared to those without photoetching process (TEP0 sample) due to the intermediate charge in crosslinking reaction after the photoetching process. The TEP24 sample showed the highest photocatalytic degradation; light harvesting; photocatalytic degradation.

In the original publication [...]

This study discusses the effectiveness of T-beams reinforced with steel fibers and 600 MPa longitudinal reinforcements on negative flexural responses based on experimental and finite element analysis (FEA) methods. Four T-beams reinforced with 400 MPa or 600 MPa reinforcements were manufactured and inversely loaded. Two of the beams were fabricated with 0.75% (by volume) steel fibers as per JGJ/T465-2019, while the rest were cast following the same design but without steel fibers for comparison. The failure modes, the cracking moment and width, and the stress of the longitudinal reinforcements and concrete were compared between the four tested T-beams. In particular, deflection–load curves indicated the improved flexural ability of the specimens with steel fibers and high-yield steel bars. Models of T-beams with longitudinal reinforcements measuring 6, 8, and 12 mm in diameter were simulated and compared with the results of validated models. The FEA results further demonstrate the better flexural performance of T-beams reinforced with steel fibers and longitudinal reinforcements. Both materials can increase the overall performance of inverted steel fiber-reinforced concrete T-beams, including cracking load, crack width, ultimate load, and deflection. However, the findings showed that different diameters of longitudinal reinforcements generated different levels of effectiveness.

Microdroplet resonators provide an excellent tool for optical studies of water, but water microdroplets are difficult to maintain outside a carefully controlled environment. We present a method for maintaining a water microdroplet resonator on a 3D-printed hydrophobic surface in an ambient environment. The droplet is maintained through a passive microfluidic system that supplies water to the droplet through a vertical channel at a rate equivalent to its evaporation. In this manner, we are able to create and passively maintain water microdroplet resonators with quality factors as high as 3×108.

Multidrug-resistant Salmonella spp. is one of the leading causes of worldwide foodborne disease outbreaks. Animal-derived foods, particularly chicken and poultry products, are the most likely source of Salmonella transmission to humans. The increasing demand for chicken meat has raised a global food safety issue. This review aims to determine the prevalence and antimicrobial resistance of Salmonella spp. in chickens from various countries in Asia. The methods for detecting Salmonella will also be discussed in this review. The prevalence of Salmonella spp. in chicken and poultry products is lower in developed countries than in developing countries. In addition, the incidence of Salmonella spp. in chicken and poultry products from fresh markets is higher than those from supermarkets. Furthermore, this review also reported the presence of multidrug-resistant Salmonella strains in various Asian countries. Rapid Salmonella detection based on immunological assays, molecular-based assays, and biosensors can provide more accurate results with high sensitivity and specificity. These methods also require a shorter time than the cultural-based Salmonella detection method. The use of suitable detection methods to determine the presence of Salmonella spp. in chicken and poultry products is important to ensure food safety.

Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.

Breast cancer is considered the second-leading cancer after lung cancer and is the most prevalent cancer among women globally. Currently, cancer immunotherapy via vaccine has gained great attention due to specific and targeted immune cell activity that creates a potent immune response, thus providing long-lasting protection against the disease. Despite peptides being very susceptible to enzymatic degradation and poor immunogenicity, they can be easily customized with selected epitopes to induce a specific immune response and particulate with carriers to improve their delivery and thus overcome their weaknesses. With advances in nanotechnology, the peptide-based vaccine could incorporate other components, thereby modulating the immune system response against breast cancer. Considering that peptide-based vaccines seem to show remarkably promising outcomes against cancer, this review focuses on and provides a specific view of peptide-based vaccines used against breast cancer. Here, we discuss the benefits associated with a peptide-based vaccine, which can be a mainstay in the prevention and recurrence of breast cancer. Additionally, we also report the results of recent trials as well as plausible prospects for nanotechnology against breast cancer.

Antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) are among the top pharmaceutical contaminants that have been often found in the aquatic environment. The presence of these contaminants in the aquatic environment is of great concern since it has a negative impact on both ecology and human health. In contrast to other tertiary treatments, adsorption stands out as a viable treatment approach since it provides benefits such as easier operating conditions with no byproduct formation. Commercial activated carbon is widely researched as a pharmaceutical adsorbent, but its large-scale applicability is constrained by its high cost. Agricultural waste also contains a large amount of various functional groups, which may be adapted to surface modification to increase its adsorption ability. In this regard, this study is designed to review the recent progress of efficient adsorbents derived from various agricultural wastes for the removal of antibiotics and NSAIDs contaminants from water bodies. Adsorbents made from agricultural waste have important benefits over commercial activated carbon for the reduction in waste while controlling water pollution.

This study is based on the premise of investigating antibacterial activity through a novel conjugate of silver nanoparticles (AgNPs) and antimicrobial peptides (AMPs) in line with a green synthesis approach by developing antimicrobial-coated implants to prevent bacterial resistance. The AMPs were obtained from Bellamya Bengalensis (BB), a freshwater snail, to prepare the nanocomposite conjugate, e.g., AgNPs@BB extract, by making use of UV-Visible spectroscopy. The antimicrobial assessment of AgNPs@BB extract conjugate was performed using the Resazurin Microtiter Assay Method (REMA), followed by the use of three biocompatible implant materials (titanium alloys, Ti 6AL-4V stainless steel 316L, and polyethylene). Finally, the coating was analyzed under confocal microscopy. The results revealed a significant reduction of biofilm formation on the surfaces of implants coated with conjugate (AgNPs@BB extract) in comparison to uncoated implants. For the MTT assay, no significant changes were recorded for the cells grown on the AgNPs/AMP++ sample in high concentrations. Staphylococcus epidermidis, however, showed more prominent growth on all implants in comparison to Staphylococcus aureus. It is evident from the results that Staphylococcus epidermidis is more susceptible to AgNPs@BB extract, while the minimum inhibitory concentration (MIC) value of AgNPs@BB extract conjugates and biosynthesized AgNPs was also on the higher side. This study indicates that AgNPs@BB extract carries antibacterial activity, and concludes that an excessive concentration of AgNPs@BB extract may affect the improved biocompatibility. This study recommends using robust, retentive, and antimicrobial coatings of AgNPs@BB extract for implantable biocompatible materials in accordance with the novel strategy of biomaterial applications.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95–96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

We demonstrate a method to effectively 3D print microfluidic devices with high-resolution features using a biocompatible resin based on avobenzone as the UV absorber. Our method relies on spectrally shaping the 3D printer source spectrum so that it is fully overlapped by avobenzone’s absorption spectrum. Complete overlap is essential to effectively limit the optical penetration depth, which is required to achieve high out-of-plane resolution. We demonstrate the high resolution in practice by 3D printing 15 μm square pillars in a microfluidic chamber, where the pillars are separated by 7.7 μm and are printed with 5 μm layers. Furthermore, we show reliable membrane valves and pumps using the biocompatible resin. Valves are tested to 1,000,000 actuations with no observable degradation in performance. Finally, we create a concentration gradient generation (CG) component and utilize it in two device designs for cell chemotaxis studies. The first design relies on an external dual syringe pump to generate source and sink flows to supply the CG channel, while the second is a complete integrated device incorporating on-chip pumps, valves, and reservoirs. Both device types are seeded with adherent cells that are subjected to a chemoattractant CG, and both show clear evidence of chemotactic cellular migration. Moreover, the integrated device demonstrates cellular migration comparable to the external syringe pump device. This demonstration illustrates the effectiveness of our integrated chemotactic assay approach and high-resolution biocompatible resin 3D printing fabrication process. In addition, our 3D printing process has been tuned for rapid fabrication, as printing times for the two device designs are, respectively, 8 and 15 min.

A key element in ensuring successful immunization is the efficient delivery of vaccines. However, poor immunogenicity and adverse inflammatory immunogenic reactions make the establishment of an efficient vaccine delivery method a challenging task. The delivery of vaccines has been performed via a variety of delivery methods, including natural-polymer-based carriers that are relatively biocompatible and have low toxicity. The incorporation of adjuvants or antigens into biomaterial-based immunizations has demonstrated better immune response than formulations that just contain the antigen. This system may enable antigen-mediated immunogenicity and shelter and transport the cargo vaccine or antigen to the appropriate target organ. In this regard, this work reviews the recent applications of natural polymer composites from different sources, such as animals, plants, and microbes, in vaccine delivery systems.

Natural polymers have received a great deal of interest for their potential use in the encapsulation and transportation of pharmaceuticals and other bioactive compounds for disease treatment. In this perspective, the drug delivery systems (DDS) constructed by representative natural polymers from animals (gelatin and hyaluronic acid), plants (pectin and starch), and microbes (Xanthan gum and Dextran) are provided. In order to enhance the efficiency of polymers in DDS by delivering the medicine to the right location, reducing the medication’s adverse effects on neighboring organs or tissues, and controlling the medication’s release to stop the cycle of over- and under-dosing, the incorporation of Fe3O4 magnetic nanoparticles with the polymers has engaged the most consideration due to their rare characteristics, such as easy separation, superparamagnetism, and high surface area. This review is designed to report the recent progress of natural polymeric Fe3O4 magnetic nanoparticles in drug delivery applications, based on different polymers’ origins.

New formulations are produced for problems associated with multiple cracks in the upper part of thermoelectric-bonded materials subjected to remote stress using hypersingular integral equations (HSIEs). The modified complex stress potential function method with the continuity conditions of the resultant electric force and displacement electric function, and temperature and resultant heat flux being continuous across the bonded materials’ interface, is used to develop these HSIEs. The unknown crack opening displacement function, electric current density, and energy flux load are mapped into the square root singularity function using the curved length coordinate method. The new HSIEs for multiple cracks in the upper part of thermoelectric-bonded materials can be obtained by applying the superposition principle. The appropriate quadrature formulas are then used to find stress intensity factors, with the traction along the crack as the right-hand term with the help of the curved length coordinate method. The general solutions of HSIEs for crack problems in thermoelectric-bonded materials are demonstrated with two substitutions and it is strictly confirmed with rigorous proof that: (i) the general solutions of HSIEs reduce to infinite materials if G1=G2, K1=K2, and E1=E2, and the values of the electric parts are α1=α2=0 and λ1=λ2=0; (ii) the general solutions of HSIEs reduce to half-plane materials if G2=0, and the values of α1=α2=0, λ1=λ2=0 and κ2=0. These substitutions also partially validate the general solution derived from this study.

The peel and core discarded from the processing of MD2 pineapple have the potential to be valorized. This study evaluated the functional and volatile compounds in the extracts of MD pineapple peel and core (MD2-PPC). The total soluble solids, pH, titratable acidity, sweetness index, and astringency index were 9.34 °Brix, 4.00, 0.74%, 12.84, and 0.08, respectively, for the peel and 12.00 °Brix, 3.96, 0.32%, 37.66, and 0.03, respectively, for the core. The fat and protein contents of the peel and core were found to be significantly different (p < 0.05). The total phenolic (TPC) and flavonoid contents (TFC) were significantly higher in the peel. The peel also showed better antioxidant activity, with a half-maximal inhibitory concentration (IC50) of 0.63 mg/mL for DPPH free radical activity compared with the core. The TPC of different phenolic fractions from peel extract was highest in the glycosylated fraction, followed by the esterified, insoluble-bound, and free phenolic fractions. GC–MS analysis identified 38 compounds in the peel and 23 in the core. The primary volatile compounds were 2-furan carboxaldehyde, 5-(hydroxymethyl), and 2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP). The identification of phenolics and volatile compounds provides important insights into the valorization of (MD2-PPC) waste.

To date, documentation of the diversity of orchids from the limestone hills in the northern part of Peninsular Malaysia, particularly in the states of Kedah and Perak, is still lacking, with limited literature available. There is indeed an urgent need to fill this knowledge gap, so that proper documentation of the diversity of orchids from this unique karst habitat can be prepared. In this study, a series of 12 months of diversity assessments on five limestone hills in Kedah and Perak has resulted in the discovery of 56 orchid species from 37 genera. From this account, 12 species are new records within Kedah and 2 species are new records within Malaysia, namely Bulbophyllum meson J.J.Verm., Schuit. & de Vogel and Luisia brachystachys (Lindl.) Blume. Three species are endemic to Peninsular Malaysia which are Anoectochilus sanguineus P.T.Ong & P.O’Byrne, Cheirostylis goldschmidtiana Schltr. and Phalaenopsis appendiculata Carr. Findings of two keystone species, the long-lost Cheirostylis goldschmidtiana and the endangered snow-white slipper orchid, Paphiopedilum niveum (Rchb.f.) Stein, have catalyzed the need for a more comprehensive study to unveil the species richness and endemism within a limestone habitat. Heat maps using geographical data produced from ArcGIS software have enabled precise determination of the areas with the highest concentration of orchid diversity. Results from this study have shown that Gunung Fakir Terbang houses the greatest number of orchid species, followed by Gunung Batu Putih and Gunung Baling. Meanwhile, lower species occurrences were recorded from Gunung Pulai and Gunung Pong. However, lack of attention and delayed conservation action on this unique karst habitat would only lead to more habitat loss, leading to the decline of limestone orchid populations. Orchids as well as other plants are threatened by anthropogenic activity such as quarrying and forest clearing for agriculture. Thus, the results of this study will serve as baseline data for future work in documenting and conserving limestone orchids and their karst habitat in northern Peninsular Malaysia.

New microfluidic lab-on-a-chip capabilities are enabled by broadening the toolkit of devices that can be created using microfabrication processes. For example, complex geometries made possible by 3D printing can be used to approach microfluidic design and application in new or enhanced ways. In this paper, we demonstrate three distinct designs for microfluidic one-way (check) valves that can be fabricated using digital light processing stereolithography (DLP-SLA) with a poly(ethylene glycol) diacrylate (PEGDA) resin, each with an internal volume of 5–10 nL. By mapping flow rate to pressure in both the forward and reverse directions, we compare the different designs and their operating characteristics. We also demonstrate pumps for each one-way valve design comprised of two one-way valves with a membrane valve displacement chamber between them. An advantage of such pumps is that they require a single pneumatic input instead of three as for conventional 3D-printed pumps. We also characterize the achievable flow rate as a function of the pneumatic control signal period. We show that such pumps can be used to create a single-stage diffusion mixer with significantly reduced pneumatic drive complexity.

The justification for making a measurement can be sought in asking what decisions are based on measurement, such as in assessing the compliance of a quality characteristic of an entity in relation to a specification limit, SL. The relative performance of testing devices and classification algorithms used in assessing compliance is often evaluated using the venerable and ever popular receiver operating characteristic (ROC). However, the ROC tool has potentially all the limitations of classic test theory (CTT) such as the non-linearity, effects of ordinality and confounding task difficulty and instrument ability. These limitations, inherent and often unacknowledged when using the ROC tool, are tackled here for the first time with a modernised approach combining measurement system analysis (MSA) and item response theory (IRT), using data from pregnancy testing as an example. The new method of assessing device ability from separate Rasch IRT regressions for each axis of ROC curves is found to perform significantly better, with correlation coefficients with traditional area-under-curve metrics of at least 0.92 which exceeds that of linearised ROC plots, such as Linacre’s, and is recommended to replace other approaches for device assessment. The resulting improved measurement quality of each ROC curve achieved with this original approach should enable more reliable decision-making in conformity assessment in many scenarios, including machine learning, where its use as a metric for assessing classification algorithms has become almost indispensable.

Probabilistic stimulation maps of deep brain stimulation (DBS) effect based on voxel-wise statistics (p-maps) have increased in literature over the last decade. These p-maps require correction for Type-1 errors due to multiple testing based on the same data. Some analyses do not reach overall significance, and this study aims to evaluate the impact of sample size on p-map computation. A dataset of 61 essential tremor patients treated with DBS was used for the investigation. Each patient contributed with four stimulation settings, one for each contact. From the dataset, 5 to 61 patients were randomly sampled with replacement for computation of p-maps and extraction of high- and low-improvement volumes. For each sample size, the process was iterated 20 times with new samples generating in total 1140 maps. The overall p-value corrected for multiple comparisons, significance volumes, and dice coefficients (DC) of the volumes within each sample size were evaluated. With less than 30 patients (120 simulations) in the sample, the variation in overall significance was larger and the median significance volumes increased with sample size. Above 120 simulations, the trends stabilize but present some variations in cluster location, with a highest median DC of 0.73 for n = 57. The variation in location was mainly related to the region between the high- and low-improvement clusters. In conclusion, p-maps created with small sample sizes should be evaluated with caution, and above 120 simulations in single-center studies are probably required for stable results.

The COVID-19 outbreak has led to the closure of educational institutions, which may prevent children from attaining skills essential for learning, such as visual–motor integration (VMI) and visuospatial constructional ability (often reflected with the Block Design Test, BDT). This study compares VMI and BDT performance between a pre-pandemic cohort (children who attended preschool in late 2019) and a post-pandemic cohort (those physically attending preschool for the first time at the end of 2021). Participants were children attending government preschools with similar syllabi catered for low-income families. The pre-pandemic cohort was part of an earlier study (n = 202 for VMI and n = 220 for BDT) before lockdowns commenced in March 2020. The post-pandemic cohort comprised 197 children who completed the Beery-VMI and 93 children who completed the BDT. Compared to the pre-pandemic cohort, the post-pandemic cohort had significantly lower mean Beery-VMI scores (t(397) = 3.054, p = 0.002) and was 3.162-times more likely to have a below average Beery-VMI score (OR = 3.162 (95% CI 1.349, 7.411)). The post-pandemic cohort also had significantly lower BDT scores than the pre-pandemic cohort (t(311) = −5.866, p < 0.001). In conclusion, children with disrupted conventional preschool education due to the COVID-19 lockdowns were more likely to have below-average VMI and lower BDT scores.

This study explores the opinions of Malaysian clinical specialists on the antibiotic prophylaxis against infective endocarditis (IE) as described in the 2008 National Institute for Health and Care Excellence (NICE) guideline. This cross-sectional study was performed from September 2017 to March 2019. The self-administered questionnaire comprised two sections: background information of the specialists and their opinions on the NICE guideline. The questionnaire was distributed to 794 potential participants, and 277 responded (response rate of 34.9%). In general, 49.8% of the respondents believed that clinicians should adhere to the guideline, although the majority of oral and maxillofacial surgeons (54.5%) actually disagreed with this view. The dental procedures that were perceived as presented moderate-to-high risk for IE were minor surgery for an impacted tooth with a recent episode of infection, dental implant surgery, periodontal surgery and dental extraction in patients with poor oral hygiene. The cardiac conditions that were strongly recommended for antibiotic prophylaxis were severe mitral valve stenosis or regurgitation and previous IE. Less than half of Malaysian clinical specialists agreed with the changes in the 2008 NICE guideline, contributing to their insistence that antibiotic prophylaxis is still needed for high-risk cardiac conditions and selected invasive dental procedures.

Due to the limitations of sensor devices, including short transmission distance and constrained energy, unmanned aerial vehicles (UAVs) have been recently deployed to assist these nodes in transmitting their data. The sensor nodes (SNs) in wireless sensor networks (WSNs) or Internet of Things (IoT) networks periodically transmit their sensed data to UAVs to be relayed to the base station (BS). UAVs have been widely deployed in time-sensitive or real-time applications, such as in disaster areas, due to their ability to transmit data to the destination within a very short time. However, timely delivery of information by UAVs in WSN/IoT networks can be very complex due to various technical challenges, such as flight and trajectory control, as well as considerations of the scheduling of UAVs and SNs. Recently, the Age of Information (AoI), a metric used to measure the degree of freshness of information collected in data-gathering applications, has gained much attention. Numerous studies have proposed solutions to overcome the above-mentioned challenges, including adopting several optimization and machine learning (ML) algorithms for diverse architectural setups to minimize the AoI. In this paper, we conduct a systematic literature review (SLR) to study past literature on age minimization in UAV-assisted data-gathering architecture to determine the most important design components. Three crucial design aspects in AoI minimization were discovered from analyzing the 26 selected articles, which focused on energy management, flight trajectory, and UAV/SN scheduling. We also investigate important issues related to these identified design aspects, for example, factors influencing energy management, including the number of visited sensors, energy levels, UAV cooperation, flight time, velocity control, and charging optimization. Issues related to flight trajectory and sensor node scheduling are also discussed. In addition, future considerations on problems such as traffic prioritization, packet delivery errors, system optimization, UAV-to-sensor node association, and physical impairments are also identified.

Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel’s intermittency issues were partially solved by maintaining the UE’s connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques.

The System identification (SI) black box method is used in this study to obtain the mathematical model of a fiber braided bending actuator (FBBA) using MATLAB Simulink. Data from the system input and output are used by the black box method. Thus, the voltage supplied to the electro-pneumatic regulators and the position (angle) of the FBBA system are used to collect input–output data in this study. In the system, PRBS generators are used to generate an input signal for the electro-pneumatic valve. The auto-regressive with exogenous input (ARX) model is chosen. As the controller for the FBBA position system, PID with the Genetic Algorithm (GA) tuning method and auto-tuned tuning method is proposed. The reference angle, simulation, and actual test are compared. The mathematical model gained from the SI method is verified through the simulation and test result of the position control. It was found that the model obtained through SI able represent the actual plant.

Magnetorheological elastomer (MRE), which is capable of exhibiting magnetostriction in the presence of a magnetic field, has a great potential to be used for the development of sensor devices. Unfortunately, to date, many works focused on studying low modulus of MRE (less than 100 kPa) which can hamper their potential application in sensors due to short lifespan and low durability. Thus, in this work, MRE with storage modulus above 300 kPa is to be developed to enhance magnetostriction magnitude and reaction force (normal force). To achieve this goal, MREs are prepared with various compositions of carbonyl iron particles (CIPs), in particular, MRE with 60, 70 and 80 wt.% of CIP. It is shown that both the magnetostriction percentage and normal force increment are achieved as the concentration of CIPs increases. The highest magnetostriction magnitude of 0.075% is obtained with 80 wt.% of CIP, and this increment is higher than that of moderate stiffness MRE developed in the previous works. Therefore, the midrange range modulus MRE developed in this work can copiously produce the required magnetostriction value and potentially be implemented for the design of forefront sensor technology.

The government enacted the Movement Control Order (MCO) to curb the spread of the COVID-19 pandemic in Malaysia, restricting movement and shutting down several commercial enterprises around the nation. The crisis, which lasted over two years and featured a few MCOs, had an impact on Malaysians’ mental health. This study aimed to understand the context of using the word “suicide” on Twitter among Malaysians during the pandemic. “Suicide” is a keyword searched for on Twitter when mining data with the NCapture plugin. Using NVivo 12 software, we used the content analysis approach to detect the theme of tweets discussed by tweeps. The tweet content was then analyzed using VADER sentiment analysis to determine if it was positive, negative, or neutral. We conducted a spatial pattern distribution of tweets, revealing high numbers from Kuala Lumpur, Klang, Subang Jaya, Kangar, Alor Setar, Chukai, Kuantan, Johor Bharu, and Kota Kinabalu. Our analysis of tweet content related to the word “suicide” revealed three (3) main themes: (i) criticism of the government of that day (CGD) (N = 218, 55.68%), (ii) awareness related to suicide (AS) (N = 162, 41.44%), and (iii) suicidal feeling or experience (SFE) (N = 12, 2.88%). The word “suicide” conveyed both negative and positive sentiments. Negative tweets expressed frustration and disappointment with the government’s response to the pandemic and its economic impact. In contrast, positive tweets spread hope, encouragement, and support for mental health and relationship building. This study highlights the potential of social-media big data to understand the users’ virtual behavior in an unprecedented pandemic situation and the importance of considering cultural differences and nuances in sentiment analysis. The spatial pattern information was useful in identifying areas that may require additional resources or interventions to address suicide risk. This study underscores the importance of timely and cost-effective social media data analysis for valuable insights into public opinion and attitudes toward specific topics.

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu’s thresholding with the Chan–Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu’s thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan–Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan–Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan–Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan–Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu’s thresholding and the Chan–Vese method enhanced the segmentation of ultrasound images.

Produced water (PW) is a by-product of oil and gas extraction, of which it is deemed as the primary contributor of wastewater stream in oil production. Conventional treatment such as membrane separation is favoured due to its sustainability and cost effectiveness. On the other hand, oceanic litters such as abandoned fishing nets endangered the marine life ecosystem, despite of its potential to be raw material for fabrication of nanofiber membrane (NFM). This study explores the potential usage of electrospun nylon 6,6 waste NFM for treatment of real PW. In terms of modelling, it is found that feed concentration is the dominant factor with R2 of 0.94 for permeate concentration response and 0.91 for average flux response. Moreover, the optimized system with average flux of 216.5 L/m2h with low specific power consumption of ca. 0.09 kWh/m3 is proven to be economically feasible with less than 5% error from predicted model. As for technoeconomic analysis, it is found that permeate flux plays the major role in controlling total capital cost (CAPEX) and operating cost (OPEX) of the system. The lowest total CAPEX and OPEX to achieve 10 ppm of permeate concentration, also was found to be RM 3.7 M and RM/year 1660, hence proving the economic feasibility of the proposed system.

For the first time, the interfacial microstructures and hardening mechanisms of a multi-material (MM) 316L stainless steel/Inconel 718 (316L SS/IN 718) interface fabricated by a novel multi-material selective laser melting (MM SLM) additive manufacturing (AM) system have been investigated in this study. MM 316L SS/IN 718 parts were successfully built with high densification levels (>99%) and low porosity content (average: ~0.81%). Microscopy analysis indicates that the interfacial microstructures are characterised by dense dislocation tangling networks, NbC and TiC, and very small amounts of Laves phase (

Blockchain introduces challenges related to the reliability of user identity and identity management systems; this includes detecting unfalsified identities linked to IoT applications. This study focuses on optimizing user identity verification time by employing an efficient encryption algorithm for the user signature in a peer-to-peer decentralized IoT blockchain network. To achieve this, a user signature-based identity management framework is examined by using various encryption techniques and contrasting various hash functions built on top of the Modified Merkle Hash Tree (MMHT) data structure algorithm. The paper presents the execution of varying dataset sizes based on transactions between nodes to test the scalability of the proposed design for secure blockchain communication. The results show that the MMHT data structure algorithm using SHA3 and AES-128 encryption algorithm gives the lowest execution time, offering a minimum of 36% gain in time optimization compared to other algorithms. This work shows that using the AES-128 encryption algorithm with the MMHT algorithm and SHA3 hash function not only identifies malicious codes but also improves user integrity check performance in a blockchain network, while ensuring network scalability. Therefore, this study presents the performance evaluation of a blockchain network considering its distinct types, properties, components, and algorithms’ taxonomy.