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The development of tandem photovoltaics and photoelectrochemical solar cells requires new absorber materials with bandgaps in the range of ≈1.5–2.3 eV, for use in the top cell paired with a narrower‐gap bottom cell. An outstanding challenge is finding materials with suitable optoelectronic and defect properties, good operational stability, and synthesis conditions that preserve underlying device layers. This study demonstrates the Zintl phosphide compound CaZn2P2 as a compelling candidate semiconductor for these applications. Phase‐pure, ≈500 nm‐thick CaZn2P2 thin films are prepared using a scalable reactive sputter deposition process at growth temperatures as low as 100 °C, which is desirable for device integration. Ultraviolet‐visible spectroscopy shows that CaZn2P2 films exhibit an optical absorptivity of ≈104 cm−1 at ≈1.95 eV direct bandgap. Room‐temperature photoluminescence (PL) measurements show near‐band‐edge optical emission, and time‐resolved microwave conductivity (TRMC) measurements indicate a photoexcited carrier lifetime of ≈30 ns. CaZn2P2 is highly stable in both ambient conditions and moisture, as evidenced by PL and TRMC measurements. Experimental data are supported by first‐principles calculations, which indicate the absence of low‐formation‐energy, deep intrinsic defects. Overall, this study shall motivate future work integrating this potential top cell absorber material into tandem solar cells. [ABSTRACT FROM AUTHOR]

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly(acetyl L -lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. The hydrodynamic diameters of particles were typically withing the range of 108–190 nm, depending on degree of acetylation of the hydrophobic block. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We also demonstrated the biocompatibility and cellular effects of these materials in the context of drug delivery in different types of anticancer cell lines, such as MIA PaCa-2, PANC-1, cancer like stem cells (CSCs), and non-cancerous HPNE cells. We observed that the release of a model drug (such as a STAT3 pathway inhibitor, Napabucasin) can be loaded into these nanoparticles, with >90% of the dosage can be released over 3 h under the influence of HDAC8 enzyme in a controlled fashion. Further, we conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs. [ABSTRACT FROM AUTHOR]

Extreme fire and related heat exposure that is attributable to natural or man-made events may cause significant damage to concrete members, with associated strength reduction. Rapid and effective on-site post-fire assessment is essential in estimating the associated damage and loss of load carrying capacity, leading to decisions on rehabilitation or replacement. Current knowledge on this subject considers a single nondestructive evaluation (NDE) technique and only qualitative estimations of the damage or destructive methods such as coring. This study fills this critical knowledge gap through the experimental utilization of ground penetrating radar (GPR) and thermography techniques on laboratory heat-affected concrete beam samples. Parameters such as concrete strength, heat level and duration, and presence of fireproofing, steel, and fiber reinforced polymer rebars were considered through experimental design. The thermal imaging method was found to be successful in a qualitative way as supplementary to the more reliable GPR approach. Predictive quantitative models with high reliability relating the parameters to the expected GPR output amplitude and residual flexural capacity of beam samples were developed. User-friendly and rapid in-situ post-fire NDEs of concrete flexural members were proposed based on the availability of information on the considered parameters from as-built plans or actual observations. [ABSTRACT FROM AUTHOR]

Accessory minerals like zircon, rutile and monazite are routinely studied to inform about the timing and nature of geological processes. These studies are underpinned by our understanding of the transfer processes of trace elements and the assumption that the isotopic systems remain undisturbed. However, the presence of microstructures or Pb-bearing phases in minerals can lead to the alteration of the Pb isotopic composition. To gain insight into the relationship between Pb isotopic alterations from inclusions and microstructures, this study focused on inclusions from an ultra-high-temperature metamorphic rutile. The studied inclusions are submicrometer monazites, a common mineral rich in Pb but normally not present in rutile. The sample is sourced from Mt. Hardy, Napier Complex, East Antarctica, an ultra-high-temperature (UHT) metamorphic terrane. By applying correlative analytical techniques, including electron backscatter diffraction mapping, transmission electron microscopy (TEM), and atom probe tomography, it is shown that monazite inclusions are often in contact with low-angle boundaries and yield no preferred orientation. TEM analysis shows the monazite core has a mottled texture due to the presence of radiation damage and nanoclusters associated with the radiation damage defects that are rich in U, Pb, and Ca. Some monazites exhibit a core-rim structure. The rim yields clusters composed of Ca- and Li-phosphate that enclose Pb nanoclusters that are only present in small amounts compared to the core, with Pb likely diffused into the rutile-monazite interface. These textures are the result of two stages of Pb mobility. Initial Pb segregation was driven by volume diffusion during UHT metamorphism (2500 Ma). The second stage is a stress-induced recrystallization during exhumation, leading to recrystallization of the monazite rim and trace element transport. The isotopic signature of Pb trapped within the rutile-monazite interface constrains the timing of Pb mobility to ca. 550 Ma. [ABSTRACT FROM AUTHOR]

An interaction exists between stress and alcohol in the etiology and chronicity of alcohol use disorders, yet a knowledge gap exists regarding the neurobiological underpinnings of this interaction. In this regard, we employed an 11-day unpredictable, chronic, mild stress (UCMS) procedure to examine for stress-alcohol cross-sensitization of motor activity as well as alcohol consumption/preference and intoxication. We also employed immunoblotting to relate the expression of glutamate receptor-related proteins within subregions of the nucleus accumbens (NAC) to the manifestation of behavioral cross-sensitization. UCMS mice exhibited a greater locomotor response to an acute injection of 2 g/kg alcohol than unstressed controls and this cross-sensitization extended to alcohol intake (0-20 percent), as well as to the intoxicating and sedative properties of 3 and 5 g/kg alcohol, respectively. Regardless of prior alcohol injection (2 g/kg), UCMS mice exhibited elevated NAC shell levels of mGlu1α, GluN2b and Homer2, as well as lower phospholipase Cβ within this subregion. GluN2b levels were also lower within the NAC core of UCMS mice. The expression of stress-alcohol locomotor cross-sensitization was associated with lower mGlu1α within the NAC core and lower extracellular signal-regulated kinase activity within both NAC subregions. As Homer2 regulates alcohol sensitization, we assayed also for locomotor cross-sensitization in Homer2 wild-type (WT) and knock-out (KO) mice. WT mice exhibited a very robust cross-sensitization that was absent in KO animals. These results indicate that a history of mild stress renders an animal more sensitive to the psychomotor and rewarding properties of alcohol, which may depend on neuroplasticity within NAC glutamate transmission.

Methamphetamine (MA) is a widely misused, highly addictive psychostimulant that elicits pronounced deficits in neurocognitive function related to hypo-functioning of the prefrontal cortex (PFC). Our understanding of how repeated MA impacts excitatory glutamatergic transmission within the PFC is limited, as is information about the relationship between PFC glutamate and addiction vulnerability/resiliency. In vivo microdialysis and immunoblotting studies characterized the effects of MA (ten injections of 2 mg/kg, i.p.) upon extracellular glutamate in C57BL/6J mice and upon glutamate receptor and transporter expression, within the medial PFC. Glutamatergic correlates of both genetic and idiopathic variance in MA preference/intake were determined through studies of high vs. low MA-drinking selectively bred mouse lines (MAHDR vs. MALDR, respectively) and inbred C57BL/6J mice exhibiting spontaneously divergent place-conditioning phenotypes. Repeated MA sensitized drug-induced glutamate release and lowered indices of N-methyl-d-aspartate receptor expression in C57BL/6J mice, but did not alter basal extracellular glutamate content or total protein expression of Homer proteins, or metabotropic or α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid glutamate receptors. Elevated basal glutamate, blunted MA-induced glutamate release and ERK activation, as well as reduced protein expression of mGlu2/3 and Homer2a/b were all correlated biochemical traits of selection for high vs. low MA drinking, and Homer2a/b levels were inversely correlated with the motivational valence of MA in C57BL/6J mice. These data provide novel evidence that repeated, low-dose MA is sufficient to perturb pre- and post-synaptic aspects of glutamate transmission within the medial PFC and that glutamate anomalies within this region may contribute to both genetic and idiopathic variance in MA addiction vulnerability/resiliency.

Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient’s health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.

Globally, nations are trying to address environmental issues such as global warming and climate change, along with the burden of declining fossil fuel reserves. Furthermore, countries aim to reach zero carbon emissions within the existing and rising global energy crisis. Therefore, bio-based alternative sustainable feedstocks are being explored for producing bioenergy. One such renewable energy resource is microalgae; these are photosynthetic microorganisms that grow on non-arable land, in extreme climatic conditions, and have the ability to thrive even in sea and wastewater. Microalgae have high photosynthetic efficiencies and biomass productivity compared to other terrestrial plants. Whole microalgae biomass or their extracted metabolites can be converted to various biofuels such as bioethanol, biodiesel, biocrude oil, pyrolytic bio-oil, biomethane, biohydrogen, and bio jet fuel. However, several challenges still exist before faster and broader commercial application of microalgae as a sustainable bioenergy feedstock for biofuel production. Selection of appropriate microalgal strains, development of biomass pre-concentrating techniques, and utilization of wet microalgal biomass for biofuel production, coupled with an integrated biorefinery approach for producing value-added products, could improve the environmental sustainability and economic viability of microalgal biofuel. This article will review the current status of research on microalgal biofuels and their future perspective.

This study aimed to gain insights into the differences in perceptions of blog writing of two types of writers (i.e., digital natives [DNs] vs. digital immigrants [DIs]). The study focused on the generational literature and Web 2.0 as an online writing platform, investigating the generational differences in DN and DI writers' perceptions on a blog-based writing platform. The "WritingGen" blog was developed for this study to provide a web-based writing place to facilitate writers' writing and editing practices. An empirical study was conducted involving 34 Taiwanese blog writers with five hypotheses to be verified. Data were analyzed using independent samples t tests and logistic regression. The results revealed that the DN writers have significantly more positive attitudes toward blog writing, higher frequencies in blog-based writing behavior, perceived higher satisfaction, and higher knowledge acquisition than the DI writers. Based on these findings, pedagogical implications are provided.

There has been a sharp increase in liver disease globally, and many people are dying without even knowing that they have it. As a result of its limited symptoms, it is extremely difficult to detect liver disease until the very last stage. In the event of early detection, patients can begin treatment earlier, thereby saving their lives. It has become increasingly popular to use ensemble learning algorithms since they perform better than traditional machine learning algorithms. In this context, this paper proposes a novel architecture based on ensemble learning and enhanced preprocessing to predict liver disease using the Indian Liver Patient Dataset (ILPD). Six ensemble learning algorithms are applied to the ILPD, and their results are compared to those obtained with existing studies. The proposed model uses several data preprocessing methods, such as data balancing, feature scaling, and feature selection, to improve the accuracy with appropriate imputations. Multivariate imputation is applied to fill in missing values. On skewed columns, log1p transformation was applied, along with standardization, min–max scaling, maximum absolute scaling, and robust scaling techniques. The selection of features is carried out based on several methods including univariate selection, feature importance, and correlation matrix. These enhanced preprocessed data are trained on Gradient boosting, XGBoost, Bagging, Random Forest, Extra Tree, and Stacking ensemble learning algorithms. The results of the six models were compared with each other, as well as with the models used in other research works. The proposed model using extra tree classifier and random forest, outperformed the other methods with the highest testing accuracy of 91.82% and 86.06%, respectively, portraying our method as a real-world solution for detecting liver disease.

Over the past few decades, the prevalence of chronic illnesses in humans associated with high blood sugar has dramatically increased. Such a disease is referred to medically as diabetes mellitus. Diabetes mellitus can be categorized into three types, namely types 1, 2, and 3. When beta cells do not secrete enough insulin, type 1 diabetes develops. When beta cells create insulin, but the body is unable to use it, type 2 diabetes results. The last category is called gestational diabetes or type 3. This happens during the trimesters of pregnancy in women. Gestational diabetes, however, disappears automatically after childbirth or may continue to develop into type 2 diabetes. To improve their treatment strategies and facilitate healthcare, an automated information system to diagnose diabetes mellitus is required. In this context, this paper presents a novel system of classification of the three types of diabetes mellitus using a multi-layer neural network no-prop algorithm. The algorithm uses two major phases in the information system: the training phase and the testing phase. In each phase, the relevant attributes are identified using the attribute-selection process, and the neural network is trained individually in a multi-layer manner, starting with normal and type 1 diabetes, then normal and type 2 diabetes, and finally healthy and gestational diabetes. Classification is made more effective by the architecture of the multi-layer neural network. To provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. The maximum specificity and sensitivity values of 0.95 and 0.97 are attained by this suggested multi-layer neural network. With an accuracy score of 97% for the categorization of diabetes mellitus, this proposed model outperforms other models, demonstrating that it is a workable and efficient approach.

The need for additive manufacturing (3D printing) to create near net shape components from a wide variety of materials has grown in recent years. There are several additive manufacturing methods to build various parts by different materials. However, it is challenging to construct, the components with incompatible materials combination for high temperature and creep resistance using conventional methods. Consequently, the purpose of this research is to investigate the use of solid state welding (friction welding) in additive manufacturing (SSAM) of incompatible materials, namely alloy Cr18-Ni8 and 42CrMo4 low alloy alternative layers. The interface bonding strength must be strengthened to achieve the desired isotropic characteristics and high strength for the components. Due to the low temperature at the bonding interface, secondary phases cannot develop when solid state welding is used. In order to obtain the highest bonding strength, optimal process parameters were examined using design of experiments (DOE) with Box–Behnken design model and analysis of variance (ANOVA). The major process parameters of upset pressure, friction pressure and burn-off length were varied to obtain the optimal conditions. In addition, the bonded interfaces were examined by the microstructural characteristics as well as mechanical properties such as micro-hardness and bonding strength. The interface is made up of alloys intermixed with different zones such as a dynamically recrystallized zone and a thermomechanical affected zone. The intermixed layers revealed the migration of C and Mo to Cr18-Ni8 alloy and separated the Fe and Ni bands. The fractography analysis revealed ductile and slightly brittle fracture surfaces with a mixed mode. The relationship between bond strength and interface thickness was determined by studying the impact of interface thickness on bond strength.