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High-entropy alloys (HEAs) with significant magnetocaloric effects (MCEs) have attracted widespread attention due to their potential magnetic refrigeration applications over a much more comprehensive temperature range with large refrigerant capacity (RC). However, most of them are metallic glasses (MGs) with problems of limited size, resulting in the difficulty of further applications. Therefore, research on HEAs with crystalline structures and giant MCE is urgently needed. In this paper, GdErHoCoM (M = Cr and Mn) rare-earth HEA ingots with orthorhombic structures are developed, and their magnetic behavior and MCE are studied in detail. Phase investigations find that the main phase of GdErHoCoM ingots is probably (GdErHo)Co with an orthorhombic Ho3Co-type structure of a space group of Pnma. The secondary phases in GdErHoCoCr and GdErHoCoMn are body-center-cubic Cr and Mn-rich HoCo2-type phases, respectively. Magnetic investigations reveal that both ingots undergo a first-order magnetic phase transition below their respective Neel temperatures. Above their respective Neel temperatures, a second-order transition is observed. The Neel temperatures are 40 and 56 K for GdErHoCoCr and GdErHoCoMn, respectively. Additionally, the GdErHoCoCr and GdErHoCoMn ingots exhibit maximum magnetic entropy changes and RC values of 12.29 J/kg/K and 746 J/kg and 10.13 J/kg/K and 606 J/kg, respectively, under a magnetic field of 5 T. The ingots GdErHoCoM (M = Cr and Mn) show excellent MEC properties and can be manufactured easily, making them promising for magnetic refrigerant applications.

 

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.

In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13–35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.

Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%–25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.

This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

 

Prayer animal release (PAR)—a traditional “compassion‐based” religious practice of releasing captive animals into the wild to improve the karma of the releaser—has been regarded as a major anthropogenic pathway facilitating species invasions worldwide. However, comprehensive, quantitative assessments of PAR‐related invasion risks, crucial for the development of mitigation strategies, are lacking. To address this knowledge gap, we conducted a literature review of the prevalence of PAR events and examined the overlap between PAR intensity across China and habitat suitability for non‐native vertebrates released in these events. Our results revealed that 63% of the areas with high PAR intensity in China were also suitable for non‐native vertebrate establishment, a degree of overlap that was greater than expected by chance. In addition, field surveys in China detected higher richness of non‐native fishes at PAR sites than at non‐PAR sites. These findings imply an overall high risk of biological invasions associated with PARs. We recommend interdisciplinary cooperation among scientists, religious groups, and government agencies to effectively manage PARs and reduce the associated bioinvasion risk.

 

Interest in craft beers is increasing worldwide due to their flavor and variety. However, craft breweries have high water, energy, and carbon dioxide (CO2) demands and generate large quantities of high-strength waste and greenhouse gases. While many large breweries recover energy using anaerobic digestion (AD) and recapture CO2 from beer fermentation, little is known about the economic feasibility of applying these technologies at the scale of small craft breweries. In addition, compounds in hops (Humulus lupulus), which are commonly added to craft beer to provide a bitter or “hoppy” flavor, have been shown to adversely affect anaerobic microbes in ruminant studies. In this study, biochemical methane potential (BMP) assays and anaerobic sequencing batch reactor (ASBR) studies were used to investigate biomethane production from high-strength craft brewery waste, with and without hop addition. A spreadsheet tool was developed to evaluate the economic feasibility of bioenergy and CO2 recovery depending on the brewery’s location, production volume, waste management, CO2 requirement, energy costs, and hop waste addition. The results showed that co-digestion of yeast waste with 20% hops (based on chemical oxygen demand (COD)) resulted in slightly lower methane yields compared with mono-digestion of yeast; however, it did not significantly impact the economic feasibility of AD in craft breweries. The use of AD and CO2 recovery was found to be economically feasible if the brewery’s annual beer production is >50,000 barrels/year. 

ZigBee is a popular wireless communication standard for Internet of Things (IoT) networks. Since each ZigBee network uses hop-by-hop network-layer message authentication based Yanchao Zhang Arizona State University Star E E Tree E E R E Mesh E E R E E E on a common network key, it is highly vulnerable to packetC E injection attacks, in which the adversary exploits the compromised network key to inject arbitrary fake packets from any spoofed address to disrupt network operations and conCoordinator C R E sume the network/device resources. In this paper, we present PhyAuth, a PHY hop-by-hop message authentication frameE E C R R E E E R R C R E E Router E E E End Device Figure 1: ZigBee network topologies. work to defend against packet-injection attacks in ZigBee networks. The key idea of PhyAuth is to let each ZigBee E The coordinator acts as a central node responsible for mantransmitter embed into its PHY signals a PHY one-time password (called POTP) derived from a device-specific secret key and an efficient cryptographic hash function. An authentic POTP serves as the transmitter’s PHY transmission permission for the corresponding packet. PhyAuth provides three schemes to embed, detect, and verify POTPs based on different features of ZigBee PHY signals. In addition, PhyAuth involves lightweight PHY signal processing and no change to the ZigBee protocolstack. Comprehensive USRP experiments confirm that PhyAuth can efficiently detect fake packets with very low false-positive and false-negative rates while having a negligible negative impact on normal data transmissions. 

Commodity ultra-high-frequency (UHF) RFID authentication systems only provide weak user authentication, as RFID tags can be easily stolen, lost, or cloned by attackers. This paper presents the design and evaluation of SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. SmartRFID explores extremely popular smart devices and requires a legitimate user to enroll his smart device along with his RFID tag. Besides authenticating the RFID tag as usual, SmartRFID verifies whether the user simultaneously possesses the associated smart device with both feature-based machine learning and deep learning techniques. The user is considered authentic if and only if passing the dual verifications. Comprehensive user experiments on commodity smartwatches and RFID devices confirmed the high security and usability of SmartRFID. In particular, SmartRFID achieves a true acceptance rate of above 97.5% and a false acceptance rate of less than 0.7% based on deep learning. In addition, SmartRFID can achieve an average authentication latency of less than 2.21s, which is comparable to inputting a PIN on a door keypad or smartphone. 

Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods.