The global demand for energy is on the rise, accompanied by increasing requirements for low-carbon environmental protection. In recent years, China’s “double carbon action” initiative has brought about new development opportunities across various sectors. The concept of energy pile foundation aims to harness geothermal energy, aligning well with green, low-carbon, and sustainable development principles, thus offering extensive application prospects in engineering. Drawing from existing research globally, this paper delves into four key aspects impacting the thermodynamic properties of energy piles: the design of buried pipes, pile structure, heat storage materials within the pipe core, and soil treatment around the pile using carbon fiber urease mineralization. Leveraging the innovative mineralization technique known as urease-induced carbonate mineralization precipitation (EICP), this study employs COMSOL Multiphysics simulation software to analyze heat transfer dynamics and establish twelve sets of numerical models for energy piles. The buried pipe design encompasses two types, U-shaped and spiral, while the pile structure includes concrete solid energy piles and tubular energy piles. Soil conditions around the pile are classified into undisturbed sand and carbon fiber-infused EICP mineralized sand. Different inner core heat storage materials such as air, water, unaltered sand, and carbon fiber-based EICP mineralized sand are examined within tubular piles. Key findings indicate that spiral buried pipes outperform U-shaped ones, especially when filled with liquid thermal energy storage (TES) materials, enhancing temperature control of energy piles. The carbon fiber urease mineralization technique significantly improves heat exchange between energy piles and surrounding soil, reducing soil porosity to 4.9%. With a carbon fiber content of 1.2%, the ultimate compressive strength reaches 1419.4 kPa. Tubular energy piles mitigate pile stress during summer temperature fluctuations. Pile stress distribution varies under load and temperature stresses, with downward and upward friction observed at different points along the pile length. Overall, this research underscores the efficacy of energy pile technologies in optimizing energy efficiency while aligning with sustainable development goals. Full article

Self-powered electronic equipment has rapidly developed in the fields of sensing, motion monitoring, and energy collection, posing a greater challenge to triboelectric materials. Triboelectric materials need to enhance their electrical conductivity and mechanical strength to address the increasing demand for stability and to mitigate unpredictable physical damage. In this study, polyaniline-modified cellulose was prepared by means of in situ polymerization and compounded with polydimethylsiloxane, resulting in a triboelectric material with enhanced strength and conductivity. The material was fabricated into a tubular triboelectric nanogenerator (TENG) (G-TENG), and an electrocatalytic pretreatment of mixed office waste paper (MOW) pulp was performed using papermaking white water as the flowing liquid to improve the deinking performance. The electrical output performance of G-TENG is highest at a flow rate of 400 mL/min, producing a voltage of 22.76 V and a current of 1.024 μA. Moreover, the deinking effect of MOW was enhanced after the electrical pretreatment. This study explores the potential application of G-TENG as a self-powered sensor power supply and emphasizes its prospect as an energy collection device. Full article

The literature data emphasize that nanoparticles might improve the beneficial effects of near-infrared light (NIR) on wound healing. This study investigates the mechanisms of the synergistic wound healing potential of NIR light and silver metal–organic frameworks combined with nitrogen- and sulfur-doped carbon dots (AgMOFsN-CDs and AgMOFsS-CDs, respectively), which was conducted by testing the fibroblasts viability, scratch assays, biochemical analysis, and synchrotron-based Fourier transform infrared (SR-FTIR) cell spectroscopy and imaging. Our findings reveal that the combined treatment of AgMOFsN-CDs and NIR light significantly increases cell viability to nearly 150% and promotes cell proliferation, with reduced interleukin-1 levels, suggesting an anti-inflammatory response. SR-FTIR spectroscopy shows this combined treatment results in unique protein alterations, including increased α-helix structures and reduced cross-β. Additionally, protein synthesis was enhanced upon the combined treatment. The likely mechanism behind the observed changes is the charge-specific interaction of N-CDs from the AgMOFsN-CDs with proteins, enhanced by NIR light due to the nanocomposite’s optical characteristics. Remarkably, the complete wound closure in the in vitro scratch assay was achieved exclusively with the combined NIR and AgMOFsN-CDs treatment, demonstrating the promising application of combined AgMOFsN-CDs with NIR light photodynamic therapy in regenerative nanomedicine and tissue engineering. Full article

This work inspects the utilization of all-polymer solar cells (APSCs) in indoor applications under LED illumination, with a focus on boosting efficiency through simulation-based design. The study employs a SCAPS TCAD device simulator to investigate the performance of APSCs under white LED illumination at 1000 lux, with a power density of 0.305 mW/cm². Initially, the simulator is validated against experimental results obtained from a fabricated cell utilizing CD1:PBN-21 as an absorber blend and PEDOT:PSS as a hole transportation layer (HTL), where the initial measured efficiency is 16.75%. The simulation study includes an examination of both inverted and conventional cell structures. In the conventional structure, where no electron transportation layer (ETL) is present, various materials are evaluated for their suitability as the HTL. NiO emerges as the most promising HTL material, demonstrating the potential to achieve an efficiency exceeding 27%. Conversely, in the inverted configuration without an HTL, the study explores different ETL materials to engineer the band alignment at the interface. Among the materials investigated, ZnS emerges as the optimal choice, recording an efficiency of approximately 33%. In order to reveal the efficiency limitations of these devices, the interface and bulk defects are concurrently investigated. The findings of this study underscore the significance of careful material selection and structural design in optimizing the performance of APSCs for indoor applications. Full article

Indoor distance measurement technology utilizing Zigbee’s Received Signal Strength Indication (RSSI) offers cost-effective and energy-efficient advantages, making it widely adopted for indoor distance measurement applications. However, challenges such as multipath effects, signal attenuation, and signal blockage often degrade the accuracy of distance measurements. Addressing these issues, this study proposes a combined filtering approach integrating Kalman filtering, Dixon’s Q-test, Gaussian filtering, and mean filtering. Initially, the method evaluates Zigbee’s transmission power, channel, and other parameters, analyzing their impact on RSSI values. Subsequently, it fits a signal propagation loss model based on actual measured data to understand the filtering algorithm’s effect on distance measurement error. Experimental results demonstrate that the proposed method effectively improves the conversion relationship between RSSI and distance. The average distance measurement error, approximately 0.46 m, substantially outperforms errors derived from raw RSSI data. Consequently, this method offers enhanced distance measurement accuracy, making it particularly suitable for indoor positioning applications. Full article

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m. Full article

High-strength engineered geopolymer composite (EGC) materials exhibit excellent mechanical properties under quasistatic loading, thus showing great potential in military and civilian facilities subjected to impact or explosive loading. However, its dynamic mechanical response under high-speed loading is not fully understood. In this study, dynamic compressive test was performed on EGC with PE fiber contents of 0%, 0.5%, 1.0%, 1.5%, and 2.0% using the Split Hopkinson Pressure Bar (SHPB) test. The results indicated that EGC reinforced with 1.5% fiber exhibited optimal static and dynamic mechanical performance. In the strain rate range of 181 s⁻¹ to 201 s⁻¹, when the fiber content increased from 1.0% to 1.5% and 2.0%, the dynamic compressive strength of the EGC increased by 24.3%, 28.8%, and 44.0%, respectively, compared to the matrix without fiber. Dynamic parameters of the EGC, including dynamic compressive strength, dynamic increase factor, and impact toughness, showed sensitivity to strain rates and increased with strain rate. A modified model, incorporating the fiber bridging effect, was proposed based on the CEB-FIP model, providing important guidance for practical engineering applications. Full article

Mixed-mode high-performance liquid chromatography (HPLC) is increasingly used for the analysis of ionic and highly hydrophilic drugs, which are difficult to separate by conventional single-mode HPLC. In the former case, chromatographic separation is achieved using one of the several commercially available mixed-mode stationary phases, typically combinations of reversed and ion-exchange phases. Moreover, mixed-mode stationary phases can be used as solid-phase extraction (SPE) sorbents. This review focuses on the recent applications of mixed-mode stationary phases in the chromatographic analysis of bioactive compounds, such as drugs, herbicides, and pesticides. Specifically, we briefly summarize HPLC methods utilizing mixed-mode stationary phases and SPE pretreatment procedures utilizing mixed-mode sorbents developed in the last decade, thus providing a reference work for overcoming the difficulties in analyzing ionized or hydrophilic drugs by conventional reversed-phase chromatography. Full article

The feedback stabilization problem for nonlinear discrete-time systems with a reduced-order observer is investigated, in which the nonlinear terms of the systems satisfy the quasi-one-sided Lipschitz condition. First, a discrete-time reduced-order observer for nonlinear systems is designed. Then, a feedback controller with a reduced-order observer is designed for realizing the stabilization of nonlinear discrete-time systems. We prove that the design of a feedback controller and reduced-order observer of systems can be carried out independently in the case of discrete-time with nonlinear terms, which largely reduces the computational complexity of the observer and controller. The introduction of the quasi-one-sided Lipschitz condition simultaneously enhances the robustness and stability of nonlinear control systems. Finally, the feasibility and effectiveness of the proposed design approach is verified by a numerical simulation. Full article

This research contributes to work on synthesizing zinc oxide nanoparticles (ZnO NPs) at room temperature (RT) and their utilization in flexible gas sensors. RT ZnO NP synthesis with a basicity solution (pH ≈ 13) demonstrates an efficient method for synthesizing well-crystalline ZnO NPs (RT.pH13) comparable to those synthesized by the hydrothermal method (hyd.C). The RT.pH13 achieved a high thermal stability with minimal organic reside impurities (~4.2 wt%), 30–80 nm particle size distribution, and a specific surface area (14 m² g⁻¹). The synthesized pre- and post-calcinated RT.pH13 NPs were then incorporated into flexible sensors for gas sensing applications at ambient conditions (RT and relative humidity of 30–50%). The pre-calcinated ZnO-based sensor (RT.pH13) demonstrated superior sensitivity to styrene and acetic acid and lower sensitivity to dimethyl-6-octenal. The calcinated ZnO-based sensor (RT.pH13.C) exhibited lower sensitivity to styrene and acetic acid, but heightened sensitivity to benzene, acetone, and ethanol. This suggests a correlation between sensitivity and structural transformations following calcination. The investigation of the sensing mechanisms highlighted the role of surface properties in the sensors’ affinity for specific gas molecules and temperature and humidity variations. The study further explored the sensors’ mechanical flexibility, which is crucial for flexible Internet of Things (IoT) applications. Full article

The reliable transfer of bioprocesses from single-use bioreactors (SUBs) of different scales to conventional stainless steel stirred-tank bioreactors is of steadily growing interest. In this publication, a scale-up study for SUBs with volumes of 200 L and 2000 L and the transfer to an industrial-scale conventional stainless steel stirred-tank bioreactor with a volume of 15,000 L is presented. The scale-up and transfer are based on a comparison of mixing times and the modeling of volumetric mass transfer coefficients k_La, measured in all three reactors in aqueous PBS/Kolliphor solution. The mass transfer coefficients are compared with the widely used correlation of van’t Riet at constant stirrer tip speeds. It can be shown that a van’t Riet correlation enables a robust and reliable prediction of mass transfer coefficients on each scale for a wide range of stirrer tip speeds and aeration rates. The process transfer from single-use bioreactors to conventional stainless steel stirred-tank bioreactors is proven to be uncritical concerning mass transfer performance. This provides higher flexibility with respect to bioreactor equipment considered for specific processes. Full article

This study introduces a novel approach to synthesising a three-dimensional (3D) micro-nanostructured amorphous biosilica. The biosilica is coated with cerium oxide nanoparticles obtained from laboratory-grown unicellular photosynthetic algae (diatoms) doped metabolically with cerium. This unique method utilises the ability of diatom cells to absorb cerium metabolically and deposit it on their silica exoskeleton as cerium oxide nanoparticles. The resulting composite (Ce-DBioSiO₂) combines the unique structural and photonic properties of diatom biosilica (DBioSiO₂) with the functionality of immobilised CeO₂ nanoparticles. The kinetics of the cerium metabolic insertion by diatom cells and the physicochemical properties of the obtained composites were thoroughly investigated. The resulting Ce-DBioSiO₂ composite exhibits intense Stokes fluorescence in the violet–blue region under ultraviolet (UV) irradiation and anti-Stokes intense violet and faint green emissions under the 800 nm near-infrared excitation with a xenon lamp at room temperature in an ambient atmosphere. Full article

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction. Full article

Community green spaces (CGSs) constitute a crucial element of urban land use, playing a pivotal role in maintaining the stability of urban ecosystems and enhancing the overall quality of the urban environment. Through the post-occupancy evaluation (POE) of green spaces, we can gain insights into residents’ actual needs and usage habits, providing scientific evidence for the planning, design, and management of green spaces. This ensures that CGSs better meet residents’ needs and improve their quality of life. The POE of CGSs relies heavily on high-precision data support. However, the current POE system for CGSs faces challenges, such as limited data collection methods, incomplete indicator systems, and excessive manual involvement. To address these limitations in data collection, this study proposes a comprehensive, dynamically monitored, objective, and sustainable POE system for CGSs. This system incorporates a multi-dimensional perception system that integrates the Internet of Things (IoT) and sensors to collect data from various sources. It establishes an evaluation framework from the perspectives of policy guidance and usage needs for CGSs, utilizing neural network systems and artificial intelligence techniques to compute the evaluation results. Using the Jiayuan Sanli Community in Beijing as a case study, this paper demonstrates the feasibility of the proposed system. A comparison between the POE results obtained using the multi-dimensional perception technique and those obtained manually reveals an 87% improvement in the accuracy of the evaluation results based on the multi-dimensional perception system. This system bridges the gap between planning perspectives and user experiences, contributing significantly to future urban land planning and land policy formulation. Full article

Currently, Bixa orellana L. extracts are used as a color source in the food, pharmaceutical, and cosmetic industries because they are important as a potential source of antioxidant activity. The extraction is carried out by conventional methods, using alkaline solutions or organic solvents. These extraction methods do not take advantage of the lipid fraction of annatto (Bixa orellana L.) seeds, and the process is not friendly to the environment. In this work, the objective was to obtain an extract rich in nutraceuticals (bixin and tocols) of high antioxidant power from Peruvian annatto seeds as a potential source for a functional food or additive in the industry using supercritical fluid extraction (SFE). Experiments related to extraction yield, bixin, tocotrienols, tocopherols, and antioxidant activity were carried out. The SFE was performed at 40 °C, 50 °C, and 60 °C, and 100, 150, and 250 bar with 0.256 kg/h carbon dioxide as the supercritical solvent (solvent-to-feed ratio of 10.2). Supercritical extraction at 60 °C and 250 bar presented the best results in terms of global extraction yield of 1.40 ± 0.01 g/100 g d.b., extract concentration of 0.564 ± 0.005 g bixin/g extract, 307.8 mg α-tocotrienol/g extract, 39.2 mg β-tocotrienol/g extract, 2 mg γ-tocopherol/g extract, and IC₅₀ of 989.96 μg extract/mL. Economical evaluation showed that 60 °C, 250 bar, and 45 min presented the lowest cost of manufacturing (2 × 2000 L, COM of USD 212.39/kg extract). This extract is a potential source for functional food production. Full article

Rainwater runoff samples from a range of roofing materials were temporally collected from 19 small-scale roof structures and two commercial buildings through simulated and actual storm events, and the concentrations of polycyclic aromatic hydrocarbons (PAHs), phosphorus flame retardants (PFLs), and pyrethroid insecticides and other water quality parameters were analyzed. In Part I of this research, the concentrations of these contaminants in roof runoff and soils receiving runoff from a range of roofing materials were evaluated. In Part II, recommendations have been developed for a first-flush exclusion to improve the quality of water harvesting for nonpotable uses. Recommendations focus on a first-flush diversion based on mass removals of total suspended solids (TSS) and PAHs linked to conductivity measurements throughout a storm event. Additionally, an upper-confidence limit (UCL) was constructed to determine the minimum diversion required to obtain 50, 75, 90, and 95% mass removal of TSS and PAH contaminants. The majority of TSS were produced during the initial 1.2 mm of runoff. Likewise, the majority of PAHs were removed during the initial 1.2 mm of runoff, except for the asphalt shingle roofs, where high PAHs were observed after 6 mm of runoff. The Texas Water Development Board (TWDB)-recommended first-flush diversion of one gallon for every 100 square feet of rooftop was not always adequate for removing 50% of TSS and PAHs from the roofs. Rainwater runoff conductivity decreased drastically between 1.2 to 2.4 mm of rainwater runoff. Diverting the first flush based on conductivity has the potential to also divert the majority of TSS and PAHs in roof runoff. Full article

The emerging heteropathotype shigatoxigenic (STEC) and extra-intestinal pathogenic Escherichia coli (ExPEC) O80:H2 has been the second leading cause of pediatric HUS in France since the mid-2010s. In contrast with other highly pathogenic STEC serotypes, for which ruminants have clearly been identified as the main human infection source, this heteropathotype’s reservoir remains unknown. In this context, we describe for the first time the isolation of seven STEC O80:H2 strains from healthy cattle on a single cattle farm in France. This study aimed at (i) characterizing the genome and (ii) investigating the phylogenetic positions of these O80:H2 STEC strains. The virulomes, resistomes, and phylogenetic positions of the seven bovine isolates were investigated using in silico typing tools, antimicrobial susceptibility testing and cgMLST analysis after short-read whole genome sequencing (WGS). One representative isolate (A13P112V1) was also subjected to long-read sequencing. The seven isolates possessed ExPEC-related virulence genes on a pR444_A-like mosaic plasmid, previously described in strain RDEx444 and known to confer multi-drug resistance. All isolates were clonally related and clustered with human clinical strains from France and Switzerland with a range of locus differences of only one to five. In conclusion, our findings suggest that healthy cattle in France could potentially act as a reservoir of the STEC-ExPEC O80:H2 pathotype. Full article

Empathy is a crucial component to infer and understand others’ emotions. However, a synthesis of studies regarding empathy and its neuronal correlates in perceptual tasks using event-related potentials (ERPs) has yet to occur. The current systematic review aimed to provide that overview. Upon bibliographic research, 30 studies featuring empathy assessments and at least one perceptual task measuring ERP components in healthy participants were included. Four main focus categories were identified, as follows: Affective Pictures, Facial Stimuli, Mental States, and Social Language. The Late Positive Potential was the most analyzed in Affective Pictures and was reported to be positively correlated with cognitive and affective empathy, along with other late components. In contrast, for Facial Stimuli, early components presented significant correlations with empathy scales. Particularly, the N170 presented negative correlations with cognitive and affective empathy. Finally, augmented N400 was suggested to be associated with higher empathy scores in the Mental States and Social Language categories. These findings highlight the relevance of early perceptual stages of empathic processing and how different EEG/ERP methodologies provide relevant information. Full article

Background/Objectives: The newly emergent COVID-19 pandemic involved primarily the respiratory system and had also major cardiovascular system (CVS) implications, revealed by acute myocardial infarction (AMI), arrhythmias, myocardial injury, and thromboembolism. CVS involvement is done through main mechanisms—direct and indirect heart muscle injury, with high mortality rates, worse short-term outcomes, and severe complications. AMI is the echo of myocardial injury (revealed by increases in CK, CK-MB, and troponin serum markers—which are taken into consideration as possible COVID-19 risk stratification markers). When studying myocardial injury, physicians can make use of imaging studies, such as cardiac MRI, transthoracic (or transesophageal) echocardiography, coronary angiography, cardiac computed tomography, and nuclear imaging (which have been used in cases where angiography was not possible), or even endomyocardial biopsy (which is not always available or feasible). Two-case-series presentations: We present the cases of two COVID-19 positive male patients who were admitted into the Clinical Department of Cardiology in “Sfântul Apostol Andrei” Emergency Clinical Hospital of Galați (Romania), who presented with acute cardiac distress symptoms and have been diagnosed with ST elevation AMI. The patients were 82 and 57 years old, respectively, with moderate and severe forms of COVID-19, and were diagnosed with anteroseptal left ventricular AMI and extensive anterior transmural left ventricular AMI (with ventricular fibrillation at presentation), respectively. The first patient was a non-smoker and non-drinker with no associated comorbidities, and was later discharged, while the second one died due to AMI complications. Conclusions: From this two-case series, we extract the following: old age alone is not a significant risk factor for adverse outcomes in COVID-19-related CVS events, and that the cumulative effects of several patient-associated risk factors (be it either for severe forms of COVID-19 and/or acute cardiac injury) will most probably lead to poor patient prognosis (death). At the same time, serum cardiac enzymes, dynamic ECG changes, along with newly developed echocardiographic modifications are indicators for poor prognosis in acute cardiac injury in COVID-19 patients with acute myocardial injury, regardless of the presence of right ventricular dysfunction (due to pulmonary hypertension). Full article

In this study, a compact and low-power-consumption quantum random number generator (QRNG) based on a laser diode and a hybrid chip with integrated silicon photonics is proposed and verified experimentally. The hybrid chip’s size is 8.8 × 2.6 × 1 ${\mathrm{mm}}^3$, and the power of the entropy source is 80 mW. A common-mode rejection ratio greater than 40 dB was achieved using an optimized 1 × 2 multimode interferometer structure. A method for optimizing the quantum-to-classical noise ratio is presented. A quantum-to-classical noise ratio of approximately 9 dB was achieved when the photoelectron current is 1 $\mathsf{\mu }$A using a balance homodyne detector with a high dark current GeSi photodiode. The proposed QRNG has the potential for use in scenarios of moderate MHz random number generation speed, with low power, small volume, and low cost prioritized. Full article

This paper investigates the influence of the concentration and particle size of h-BN nanoparticles in a nanofluid on the surface integrity of 304 austenitic stainless steel during turning, focusing on the cutting force, friction coefficient, cutting temperature, surface roughness, surface residual stress, work hardening capacity, and 3D surface topography. The results show that, compared to dry cutting, the addition of 3 wt.% h-BN nanofluid can reduce the friction coefficient on the rake face by 38.9%, lower the cutting temperature by 43.5%, decrease the surface roughness by 53.8%, decrease the surface residual stress by 61.6%, and reduce the work hardening degree by 27.5%. Two-dimensional profiles and the 3D surface topography display a more balanced peak–valley distribution. Furthermore, by studying the effect of different h-BN particle sizes in nanofluids on the surface integrity of the machined workpiece, it was found that nanoscale particles have a greater tendency to penetrate the tool–chip interface than submicron particles. Moreover, the h-BN particles in the nanofluid play a “rolling effect” and “microsphere” effect, and the sesame oil will also form a lubricating oil film in the knife-chip contact area, thereby reducing the friction coefficient, reducing the cutting force, and improving the machining surface quality. Full article

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year. Full article